CIGRE

576

IT Strategies for Asset Management of Substations General Principles

Working Group B3.06 TF05

April 2014

IT STRATEGIES FOR ASSET MANAGEMENT OF SUBSTATIONS GENERAL PRINCIPLES WG B3.06 TF05 Members J. Smit, Convenor (NL), N. Barrera, Secretary (CH), Alan Wilson (UK), Nicolaie L. Fantana (DE), Jan Bednarik (IRL), Ravish Mehairjan(NL), Paul Leemans (BE), Iliana Portugues (UK), Paul Myrda (USA), Gerd Balzer (DE), Jan Bruinenberg (NL), Gilles Buffiére (FR), Paulino Aparicio (ES), Bente Bakka (NO), Hylco E. Hoekstra (NL), Serge Laederach, TF05 convener (CH), Thomas Melkersson (SE), Arthur Mackrell (UK), Tatsuru Kobayashi (JP), Qikai Zhuang(NL), Zhao Ma (UK), Ferenc Bodrogi (HU), Petr Spurný (CZ), Cawir Ginting (IDN), Dagmar +

Kopejtková (CZ), Ph. Wester (NL)

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Disclaimer notice “CIGRE gives no warranty or assurance about the contents of this publication, nor does it accept any responsibility, as to the accuracy or exhaustiveness of the information. All implied warranties and conditions are excluded to the maximum extent permitted by law”.

ISBN: 978-285-87327-15

IT STRATEGIES FOR ASSET MANAGEMENT OF SUBSTATIONS - GENERAL PRINCIPLES

ISBN: 978-285-87327-15

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Table of Contents EXECUTIVE SUMMARY ........................................................................................... 7 INTRODUCTION ................................................................................................. 12 IT STRATEGIES FOR ASSET MANAGEMENT OF SUBSTATIONS - GENERAL PRINCIPLES13 Information strategy to support utility asset management .............................. 13 Decision making with regard to stakeholder oriented risk management ......... 14 Data management – Intelligent hub ................................................................ 14 Survey on Practical Application of Asset Management Information Strategies . 15 Conclusions .................................................................................................... 16 INFORMATION STRATEGY TO SUPPORT UTILITY ASSET MANAGEMENT .............. 17 Asset Management Functional Model .............................................................. 18 Asset Management Processes ......................................................................... 19 Step 1: Determine/adjust vision and goals ................................................... 20 Step 2: Determine/adjust strategy and policy ............................................... 21 Step 3: Planning............................................................................................ 21 Step 4: Realisation ........................................................................................ 21 Decision Process ............................................................................................. 22 Risk/condition assessment ........................................................................... 23 Decision flow ................................................................................................ 23 Information Requirements .............................................................................. 25 Data Categories .............................................................................................. 26 Technical data category ................................................................................ 27 Economic data category ................................................................................ 27 Social data category ...................................................................................... 28 Information Pyramid ....................................................................................... 28 “Front” information systems – data input layer.............................................. 29 Data warehouse – data integration layer ....................................................... 29 Analysis tools – data analysis layer ............................................................... 30 Decision support – decision layer ................................................................. 30

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Conclusions .............................................................................................. 31 IMPLEMENTATION OF INFORMATION STRATEGY TO SUPPORT UTILITY ASSET MANAGEMENT ......................................................................................................................... 32 Decision making with regard to stakeholder oriented risk management ... 33 Stakeholder orientation .......................................................................... 33 Risk balancing using a scenario approach............................................... 34 The intelligent hub ................................................................................. 36 Asset oriented model .............................................................................. 37 Dimensional model ................................................................................. 39 Processing data ...................................................................................... 40 The architectural overview ...................................................................... 40 Conclusions and further outlook .............................................................. 42 IMPLEMENTATION OF INFORMATION STRATEGY TO SUPPORT UTILITY ASSET MANAGEMENT - DECISION MAKING WITH REGARD TO STAKEHOLDER ORIENTED RISK MANAGEMENT ......................................................................................................................... 44 Decision making with regard to stakeholder oriented risk management ... 45 The decision process considering various scenarios ............................... 45 Requirements ........................................................................................... 50 Stakeholder Requirements ...................................................................... 50 Information requirements ....................................................................... 52 Key performance indicators (KPI’s).......................................................... 55 Combining different KPI’s (indexing) ...................................................... 57 Conclusion ............................................................................................... 58 SURVEY ON PRACTICAL APPLICATION OF ASSET MANAGEMENT INFORMATION STRATEGIES ......................................................................................................................... 59 Experiences .............................................................................................. 59 Access to data and conversion into information ....................................... 60 Software tools to support decision making ............................................... 60 Data Sources............................................................................................. 61 Recording of primary and secondary equipment ..................................... 62

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Outage, performance and failure data .................................................... 63 Maintenance and repair data................................................................... 64 Diagnosis and Monitoring data ............................................................... 65 Data collection and systems used ........................................................... 66 Data processing and data warehousing .................................................. 68 Systems in use ........................................................................................ 69 Data processing from source to data warehouse .................................... 71 Synchronizing of data ............................................................................. 71 Data quality and validation ..................................................................... 72 Data requirements .................................................................................. 72 Information requirements ....................................................................... 74 Scenario approach and risk balancing ....................................................... 75 Strategy, depending on the type of equipment ....................................... 77 Applied philosophy for maintenance strategy, depending on the type of equipment ............................................................................................................... 79 Use of risk assessment ........................................................................... 81 Decision and control ................................................................................. 82 Decision Policy based on IT support systems .......................................... 83 Economic criteria .................................................................................... 84 Technical criteria .................................................................................... 84 Social criteria .......................................................................................... 85 Risk criteria ............................................................................................ 86 Conclusion ............................................................................................... 87 FINAL CONCLUSIONS .......................................................................................... 88 REFERENCES ....................................................................................................... 92

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EXECUTIVE SUMMARY Background of the Technical Brochure

The importance of establishing a well-considered information strategy within transmission and distribution utilities as a basis for decision making was recognized early by CIGRE. In the year 2000 the Joint Working Group (JWG) B3/C2-14/15 was requested to initiate the work regarding information strategy. During the period following the commencement of the JWG, utility organisational structures have changed immensely resulting from the liberalisation and separation of independent market and system operators (hence the coming into existence of the asset manager/ service provider business environment). Therefore, the work of the JWG, which continued and was finalized by WG B3-06, has concentrated on the information strategy as applicable in one of the separate functions of a utility. This Technical Brochure (TB) describes an information strategy pertinent to the asset management function with emphasis on substation asset management. Introduction

According to CIGRE WG C1-01, asset management involves the centralisation of key decision-making for the network business to maximise long term profits, whilst delivering high service levels to customers, with acceptable and manageable risk. In this context, risk management is seen as a mainstream regime to enable asset managers to translate corporate business values and requirements into a comparable, measurable and manageable dimension, namely Risk. Although the concepts of asset management have existed for over twenty years, utility asset managers still need to settle for a less than ideal condition regarding asset information to support risk-based decision making. This TB shows that this is mainly due to the on-going strong focus on technical data and less on economic and social data. Risk-based decision making, however, requires data in mixed strategies and matching technical, economic and social requirements from a holistic point of view. Succeeding in filling this gap is the first step in obtaining better decisions in the field

of

asset

management.

The work of JWG B3/C2-14 followed by WG B3-06 instigates in filling the gap between data management, through information strategies, and the link with risk based decision

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making. This TB establishes general principles for the information strategy for asset management and in particular, for substation asset management. Description of the TB

In chapter 1, a brief introduction is given of this TB describing the reason for initiating a joint working group between the substation and system committees to consider the topic “Information Strategies” from an asset management point of view. The contents of the remaining chapters are explained as well as the contributions which were derived from each subsequent chapter. Chapter 2 starts with the “hourglass model”. A model which is described in this TB is as an information strategy that, essentially, consists of two parts. These are: 

The risk management in a business relevant environment: this helps in utilizing and addressing the requirements on asset data



The data management: this supports the decision making process through constructing a system to acquire, warehouse and transmit data.

The linkage of the above mentioned two parts is the critical connection. This connection, named “intelligent hub” in this TB, aligns the translation of the information requirements from the “risk management side” to the requirements understandable by the “data management” side. In the remainder of the TB this conceptual “hourglass model” will be used to describe the information strategy process. With the goal to determine the status of current information strategy practices, an international survey was carried out. The survey comprises 19 utilities from Europe, North America, Middle East and Australia. The main results are highlighted in chapter 2, showing that within utilities the focus of data management is more on the technical level and less on economic and social data. While, a multi criteria decision making process needs matching technical, economic and social data. In chapter 3 the role of information strategy within asset management is defined. This asset management regime is based on the generic asset management functional model, which contains three domains, namely operation, maintenance and management. These domains are used to structure the asset management decision process. Moreover, decisions are supported by information in three categories, which are:

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

the technical category, which specify the condition for individual component and offers solution scenarios;



the economic category, which assesses the benefit of technical scenarios on reliability;



the societal category, which assesses the benefit of technical scenarios on risks and make the final decision.

The “information pyramid” is presented and describes a framework to realize the multilevel and categorized information model, which can be used to support decision processes in a stakeholder oriented risk management regime. In chapter 4, a practical approach of implementing an asset management information strategy was outlined using the “hourglass model” to explain the transition from risk management into data management. The value of the different stakeholder groups are used for the process of risk management, which is in line with stakeholders needs with regard to the three earlier mentioned information categories. Furthermore, the “intelligent hub” is utilized to explain methods and approaches to transform information requirements into data requirements. This information process can be regarded as one of the most important features for asset managers. In relation to the requirements of data, the processing methods (data extraction from existing data bases), usually referred to data warehousing, is described. The described information process has an iterative character with a lead line coming from the business relevance instead of, nowadays still often feasible, technical relevance. An information strategy focussed on stakeholder oriented risk management is described in chapter 3. In chapter 4 a data architecture design applicable for substation replacements is given. Chapter 5 makes the link by describing an implementation of an information strategy to support utility asset management. This chapter firstly expands the decision making process of chapter 3 into a complete information flow model. The model enumerates the available technical, economic and societal information source at component, network and corporate level, respectively. Based on a set of simplified scenarios of substation replacement, a detailed key performance indicator (KPI) dictionary is given as a general guideline for further design of database and software.

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The content explained through chapters 3 till 5 is considered to be in advance of the practice of today’s utility companies. Hence, the necessity is to reveal the implementation requirements of information strategies within utilities. For this purpose, as briefly highlighted in chapter 2, the WG distributed an international survey in the year 2006/2007 to get more information about the applied asset strategies and their related data management. The questionnaire, of which the detailed results are discussed in chapter 6, was focused on elucidating the current experiences, practices, support of information technology tools, and applied data management methodologies. In general, the results indicate that a majority of utilities own a large volume of digital records regarding daily operation and maintenance on a wide range of substations components. However, when it comes down to data processing the focus is mainly on technical aspects. Consequently, decisions on maintenance strategies are much better supported than those on replacement. In chapter 7 comprehensive final conclusions are drawn. Overall, the TB presents an overview of the methodology used for defining IT-strategies for AM and generic guidelines for initiating information strategies. In general, based on the study and survey of the WG, the following conclusions are drawn: 

The decision making process needs a holistic approach in the field of replacement, renewal and maintenance strategies. The results indicate that mixed strategies are common in most of the utilities, depending on the type of the components.



By following the “hourglass model”, which is described in this report, the “right information” should be extracted from the huge amount of available data with the focus to support asset management decisions.



The survey results indicate the remarkable need for enterprise resource planning (ERP) systems in the utilities, with the purpose of integrating the existing raw, or rather unprocessed data.



Accordingly, the “hourglass model” shows that there is a need to link information requirements with data requirements. In order to obtain such a link, it is necessary to have integrated data containing technical, economic, social information, and a well-defined enterprise-wide data model.

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Technical data is found to stand supreme in comparison to economic and social data in existing models at utilities. But then again, the decision making processes require data models that describe data needed in mixed strategies, and match technical, economic and social requirements from a holistic approach. Succeeding in closing this gap is a prerequisite for obtaining superior decisions in the field of asset management.

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INTRODUCTION The importance of asset management information strategies was recognized early in CIGRÉ. An initial working group was started as joint work between substation and systems committees in the year 2000 to consider “Information Strategies” from the asset management point of view. This work was then continued in WG B3-06 on Substation management. In a first step the asset management business, the decision process and the information requirements have been analysed. The results of this work are published in a scoping paper in Electra which is reproduced in Chapter 3. In a second step the study went more into details. Stakeholder orientation and business relevance were leading to a risk management approach which balances economic, technical and societal aspects. Also important issues of work were the data management to allocate and to share raw data and the intelligence to translate data into the necessary information. A summary of this work has been published during the A3 & B3 joint colloquium 2005 in Japan and it is reproduced in Chapter 4 [1]. Additional studies were done to consider in more detail the business relevance for risk based asset management decision making process as well as for information requirements and data modelling. The results of these studies are given in Chapter 5. During the process it became clear, that the work should be followed by investigations into the present status of application practice. These investigations focused on skills and tools as applied in utilities. A survey was initiated with the task to report about practical application of asset management information strategies. The survey results are presented in Chapter 6. The brochure closes with a conclusion and an outlook in Chapter 7.

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IT STRATEGIES FOR ASSET MANAGEMENT OF SUBSTATIONS GENERAL PRINCIPLES Information strategy to support utility asset management The information strategy is based upon a functional model. This model describes the different accountabilities of the asset management function. A generic data model illustrating the various items and categories of data is presented as well as information architecture to support asset management decision making. It was concluded that all asset management decisions should cover both technical and economic consequences of decisions as well as the potential societal and strategic impacts. Models for the decision processes, requirements and data categories show the complexity of the process. As asset management is a synonym for risk management a model “from risk to data management” (Fig. 1) is used. It recognizes the essential differences between: risk management in a business relevant environment and data management support for the decision making process. The model expresses the need to align the translation of data into information according to the risk based decision process. This process takes into account all relevant areas as social, technical and economic. Following this “hourglass” model, setting a business point of view will be described in Chapter 4.

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RISK MANAGEMENT Information based Business relevance

decision + control scenario approach + risk balancing informa on requirements

Data based Technical relevance

Intelligent hub data requirements data processing + warehousing data system PQ cost/ economic data asset performance data

data sources

DATA MANAGEMENT

Figure 1: From risk to data management

Decision making with regard to stakeholder oriented risk management The key success factor for electricity supply in general and for an asset manager is to satisfy all stakeholder requirements. As stakeholders' interests are different and possibly conflicting the asset management decisions are always the result of a trade-off. A sustainable asset management process has therefore to consider technical, economic as well as social and environmental aspects and must find a balance between all stakeholder values and requirements. Risk management is seen as one of the most important instruments of asset management as it enables the asset manager to translate those values and requirements into one comparable dimension: risk.

Data management – Intelligent hub In the data management part of the brochure the “intelligent hub” is described. It shows methods and approaches to transform information requirement into data requirements. This transformation process can be regarded as one of the most important features for asset managers. Related to the data requirements data processing methods (data warehousing) are described that provide data extraction from existing data bases. A

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description of the specific data sources for this data processing concludes the data management chapters. It is emphasized that the described process has an iterative character and it is mainly driven by the business relevance and not by the technical relevance.

Survey on Practical Application of Asset Management Information Strategies The goal of the survey was to determine the status of current practices, how and which ITtools can support asset management and which methodologies are being used. The survey comprises the results from 19 utilities from Europe, North America, Middle East and Australia. The main results are highlighted in the following: 

About integration: The decision making process in the field of replacement/renewal and maintenance strategies needs a holistic approach. The results show that the majority of the utilities use mixed strategies depending on the type of equipment. Decisions are mostly based on both technical and economic criteria.



About data: Primary equipment is mostly stored in enterprise-wide databases, but this is less common with secondary equipment. The information is mostly recorded on asset level. Due to its importance failure, outage, performance and maintenance data is recorded by most companies. In addition there is a high degree of recording diagnosis (offline) data. Most of this data is still being collected by hand.



About IT-systems: Utilities have a remarkable need for enterprise resource planning systems (ERP) and the integration of basic data. Integration of technical systems (e.g. SCADA, GeoIS, EAM) and non-technical systems (e.g. CRM, ERP) are not common in the branch. Asset management strategies identify the need for data integration and results show that many utilities are only at the beginning of this integration, starting to buy and to implement enterprise-wide systems and introducing solutions such as data warehousing as an attempt to integrate data.



About modelling: The survey shows that enterprise-wide data models do exist. Nevertheless the survey shows that data models from source systems are not sufficient in the asset managers' need of a holistic modelling approach.

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Summing up, results show that existing models within the utilities themselves remain strongly focus on technical data and less on economic data. Social data is only present in some models. The decision-making process on the other hand requires data models describing data in mixed strategies and matching technical, economic and social requirements from a holistic point of view. Succeeding in filling this gap is the first step in obtaining better asset management decisions.

Conclusions Based on analysis of the total asset management process, the requirements of the different stakeholders need to be considered. In this connection it makes sense to define various key performance indicators (KPI) depending on defined scenarios and to perform a risk assessment. This procedure requires a significant amount of data with suitable quality. This data needs to be up to date, an appropriate data model should be used and furthermore a master data set has to be maintained. Finally the survey provides an overview of currently used strategies and information systems in the area of power supply companies.

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INFORMATION STRATEGY TO SUPPORT UTILITY ASSET MANAGEMENT In 2000 the JWG B3/C2-14, covering the area of maintenance and reliability, was requested to begin work regarding Information Strategy for utilities. A task force was established based on a terms of reference document [2]. These terms mentioned the extreme strategic importance of setting up a well-considered Information Strategy within utilities as a basis for decision making in the application of Information Systems. The Information Strategy should focus on how utilities hold and manage data and information on their facilities. Decisions that may involve substantial investment in Information Technology must be based on an analysis of the business processes of the utility and its needs. During the period following the initiation of the work mentioned above, liberalisation of utility processes has continued and this has had a major influence on utilities’ organisational structures. As such, a separation between independent market and system operators and asset managers and service providers has been recognised. The separation between these functions, even to the level of complete independent corporations, has directed the work of the task force to concentrate on the Information Strategy as applicable in one of the separated utilities’ functions. This paper describes an information strategy applicable to the asset management function. Service provider functions will generally focus on satisfying the information needs of the asset management function. According to the Cigré Working Group C1-01 on Asset Management 3, with which agreement this work is published, the asset management responsibilities of a transmission or distribution business operating in an electricity market involves the centralisation of key decision making for the network business to maximise long term profits, whilst delivering high service levels to customers, with acceptable and manageable risks. The asset manager’s processes should guarantee the satisfaction of the stakeholders (e.g. customers, shareholders, employees, the assets owners, asset operators and the

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regulator), who will be benchmarking performance against other utilities 3, 4. The asset manager’s function can thus be placed in context and further details are explained in Chapter 3.

Asset Management Functional Model Organisational differences and differing asset management philosophies and priorities have resulted in significant disparity across different companies with respect to asset management strategy and implementation. This

diversity

makes

it

difficult

to

analyse

and

compare

asset

management

implementations and their relative efficiencies. Therefore, to support the understanding of asset management implementations, a generic functional model for asset management, illustrated in Fig. 2, as well as a description of the asset management accountabilities, shown in Fig. 3, is given.

Figure 2: Functional model for asset management The purpose of the functional model is to present a structured functional approach to the asset management process. The accountabilities are described to form a basis for the investigation of information requirements for particular units within a utility. Chapter 2.4

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explains the procedures and associated data/information requirements related to the main asset management process steps: 

Vision and goals,



Strategies, policies, standards,



Planning,



Implementation, realization.

Figure 3: Overview of accountabilities

Asset Management Processes A comprehensive understanding of the asset management process is important in defining data/information requirements and a consequent strategy. A model for asset management processes was developed that reflects responsibilities regarding optimisation of decisions.

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The asset management function must be flexible enough to respond to the changing demands and expectations of stakeholders, regulators/legislators and customers. Accordingly, an iterative and evolving process must be defined as illustrated in Fig. 4. This indicates where and how data and information relating to the process is used within each of the main process steps.

Figure 4: Asset management process - main functional steps and data/information requirements Step 1: Determine/adjust vision and goals Based on the requirements and objectives of the main stakeholders, regulators/legislators and clients, the organisation determines its vision and goals. This is normally initially achieved via a business plan, which is subsequently reviewed on an iterative basis.

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Adjustments are often made based on changing demands and feedback from the organisation’s performance. Step 2: Determine/adjust strategy and policy The next step in the process is to determine the organisation’s strategy and policy. This is based on environmental and regulatory scenarios. The use of scenarios will give the necessary flexibility. Per scenario the expectations which are related to the different aspects of the asset management function, are stated and answered by actions and reactions; thus stating policies. The policies (e.g. investment in LV, MV or HV networks, application of FACTS devices etc.) that facilitate the majority of scenarios (e.g. (de)centralised generation) should be prioritised. Following on from strategy definition, it is necessary to translate the strategy into planning and realisation directives. Step 3: Planning The planning step is normally concerned with the production of a detailed annual activity plans. To accomplish this, a long term planning exercise (typically 5-7 years) is needed. This plan is based upon forecasts (that take both load and non-load related issues into account) with regard to maintaining system functionality. When problem areas are identified, the asset management function must define one or more solutions (design, building and/or maintenance) for each issue while maintaining a system-wide perspective. The chosen resolutions form the basis of an annually-reviewed long-term plan (containing estimates on start/end dates and costs). The forthcoming year of this plan is annually extracted and forms the initial workload for the service provider(s) to the asset management function. Step 4: Realisation The plan is executed in an ongoing fashion by the service provider on behalf of the asset manager, who is responsible for monitoring and auditing the process. The results provide the asset manager with essential knowledge for the future and are input to the long term planning process.

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Decision Process The asset management process as described in the previous section necessitates decision support tools to choose the best option when presented with a number of alternative options in a decision-making environment. This can effectively be viewed as a continuously running decision process based upon technical, economic and social information. Upon further analysis, this decision process can be seen as being comprised of three separate levels, as illustrated in Fig. 5.

Figure 5: Asset management decision process The asset information level consists of technical asset data/information and condition assessment of components, bays, substations and networks. The financial information level combines the economic and technical and risk data/information for these assets and is mainly reliability-focused. The social information group applies the economic data/information relating to the business; combined with social information, to make decisions about risk which mainly have a corporate focus. As a general approach to data/information processing, the decision-making process proposed is directed at risk assessment:

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Risk/condition assessment Based on the inputs the S3 (“Safety”, “Social” and “System – oriented”) risks are evaluated. 

Safety risks represent impact on personal and environmental safety.



Social risks represent impact on external bodies directly dealing with the company, on the public and the environment, but also internal personal-related impacts (change of work procedures, personnel reduction etc.).



System risks represent impact on technical parameters of the assets (system or equipment reliability, operating conditions, risks of fatal failures etc.).

A framework for this S3 risk assessment is provided by external inputs, stating constraints and basic conditions of system operation. The data analysed during the risk assessment also includes financial data/information and asset data/information. Different scenarios are obtained from the S3 risk assessment. By combination of these scenarios with outputs from condition assessment and financial inputs the reliability of an asset can be controlled with respect to adequate costs and sufficient levels of asset reliability. System risks related to the particular level of asset reliability are also evaluated. In this step technically/economically optimal management of the asset is obtained as a result of reliability management. To make the process complete, the last two “S” (i.e. safety and social) risks must be taken into account. On this level (“Risk management”), the overall risk related to the final decision is evaluated and controlled. It concerns not only the technical and financial aspects, but respects also the personal and environmental safety, impact on external bodies, stakeholders’ demands and other “social risks” aspects as described above. Decision flow The decision process as illustrated in Fig. 5 can be further explained by presenting it as a decision flow diagram (Fig. 6). This details an overall view of the step by step decision process. A detailed description of the different levels can be found in chapter 5.1.1.

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Level 1: Component (asset) level The main task of the first level is the assessment of the condition of the equipment. Condition assessment is provided based on technical asset data/information. As an output of condition assessment the probabilities of assets’ expected behaviour modes are obtained (probability of failure, expected life, system constrains etc.).

Figure 6: Asset management decision flow

Level 2: Network level Combination of the scenarios’ technical data/information with relevant economic data from underlying economic systems and the information on the grid will result in a quantification of costs and benefits for each scenario. System risks related to the particular level of asset reliability are evaluated.

Level 3: Corporate level The costs and benefits of the scenarios are combined with the risks involved with each scenario to reach the optimum decision regarding the external requirements (social

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information) with the best-managed risk. The social and economic data/information, together with the reliability of the equipment inventory and network topology forms the ingredients of the risks involved.

Information Requirements Having analysed the asset management accountabilities and decision processes, the next step is to extract the information required to support asset management decisions (Fig. 7).

Figure 7: Asset management information requirements Asset management decisions applied in the processes described earlier employ different levels of data processing, often provided by a plethora of existing IT systems. During the last decade many commercially available IT tools have been developed. The available tools may be grouped into three categories:

Enterprise Asset Management (EAM): Such tools are directed at asset data storage and management, work flow management, monitoring & diagnostic systems, geographical information systems.

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Enterprise Resource Programmes (ERP): These are directed at the support of business process management, financial and spares control.

Asset Operation Systems: Directed at substation and network automation, customer information systems, energy management systems. High end and transactional information processing tools such as data warehouses and expert systems are also available. These can provide support in the area of gathering and processing data in order to provide decision support.

Data Categories The changing market environment demands effective whole-of-life management of the assets in order to optimise economic performance while ensuring that technical performance and any risks associated with the assets are kept within acceptable limits. Ultimately, asset managers will be judged on their ability to satisfy the needs of stakeholders by minimising the necessary capital and operational expenses while simultaneously managing environmental safety and quality (sustainability). This approach necessitates having access to data and information relating to technical, economic and social categories (Fig. 8).

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Figure 8: Data categories approach within asset management: event risk consequences (example) Technical data category Data and information for decision support within asset management is dependent on several asset-related parameters. Technical aspects cover condition assessment, aging models, failure probability and its impact on the system, etc. However, data and information relating to equipment inventory, new technology, network topology and development, generation and demand levels, available spare parts, current maintenance procedures, history of maintenance actions, history of failures, etc. are also covered by this category. Economic data category Each technical data component should have associated financial data. Costs of condition assessment

procedures

(and

tools)

and

life

cycle

costs

(investment,

operation,

maintenance and disposal) for equipment and spare parts are also contained within this class of data/information. The costs related to a failure are dependent on the outage-

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related expenditure. Failures may result in major damage to network components and their environment, which will lead to high maintenance and repair costs. The need for economic data/information finds its origin in the requirements of operating a business. For example, penalty costs from customer contracts, possible claims from customers, costs associated with undelivered energy, damage to reputation/share price, etc. Social data category Risks are not only determined by economics. There are also social and environmental aspects that must be considered, such as the impact on society of outages and failures. Failure acceptability can be reflected as the degree to which a failure is acceptable from a social perspective. The impact of failure depends on the criticality and number of connections affected by a failure and is also dependent on the time taken to restore supplies following a particular failure. The social impact of a utility’s policy is measurable using two factors: public image and the perception of safety.

Information Pyramid The practical implementation of IT tools to support the decision-making process is a challenging problem as the final architecture of information systems can strongly influence the processes and organisation of a utility. A generic information processing architecture to support decision processes can be identified. This is referred to as the “Information Pyramid” and is presented in Fig. 9. This architecture allows open use of data together with high stability and a high degree of integration. It reflects the descriptions of the decision-making processes (Chapter 2.3), data categories and information requirements as described in this paper. Different levels of the pyramid correspond to appropriate parts of the decision process.

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decision level

decision

decision input data analysis level

analysis analysis tools security consistency continuity

ERP

data integra on level

data models data warehouse

EAM

assets

EOS

Masterplan

front informa on system level

Figure 9: Information pyramid “Front” information systems – data input layer The base of the pyramid is populated by various “front” information systems covering the asset management accountabilities as presented in Fig. 3. Generally, this layer is a compound of several different information systems. The interoperability and operational quality of such systems strongly depend on the interfaces between particular systems. The layer provides input data and information for the decision processes as described previously. Data and information entering the asset related decision-making process can be looked upon as being grouped into the three basic categories – technical, economic and social. Data warehouse – data integration layer The next layer concerns data storage based upon tools for basic data integration. This integration level ensures that the data in the system are unique; it allows the basic verification of the data and also integrates multiple items of data and information to facilitate use by functions requiring data from a diverse range of sources. Such structured data-integration systems are commonly referred to as “data warehouse” systems.

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Characteristics of data warehouses include: 1. Enforcement of a consistent view of corporate information across all systems concerned which facilitates explicit decision making. 2. Provision of historical data (this is useful as many transactional information systems do not possess this functionality). 3. Presentation of a single transparent source for delivery of any available data with the user remaining unaware of the complexity involved in achieving this function. 4. Facilitation of data quality assurance and control as shown in the quality loop of Fig. 9. Referring to the decision process (Fig. 5), this layer allows organising, processing and integration of data belonging to the three data categories. The data warehouse supports the decision process level by ensuring quality, consistency and validity of data and information. Analysis tools – data analysis layer The decision process continues by using techniques such as condition assessment, S3 risk assessment etc. (Chapter 2.3). For such analyses, aggregated values and global technical, economic and social indicators are used. This use of data and information is the key tool for the asset management process, as it delivers a basis upon which decisions are made. Decision support – decision layer While commonly used analytical tools deliver general information about various business areas based upon aggregation of measured values, other highly sophisticated decision support tools are available and have been applied in a number of cases. Such tools may combine the results of different analyses and data to deliver a result in the form of decision recommendations. Therefore a specific category is assigned to this type of tool. They are not yet commonly applied within power utility businesses. Decision support should facilitate the final decisions to be made by the asset manager through the provision of a list of ranked recommendations with explanatory information.

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The responsibility for the decision should always lie with a suitably qualified person – the user of the information system. It is therefore essential for the information delivered by the system to be fully reliable, transparent and well understandable.

Conclusions Sustainable asset management processes are directed at maintaining the functionality of the system while respecting the (sometimes conflicting) expectations of the various stakeholders. The information strategy should focus on how utilities hold and manage data and information on their transmission and distribution plant and facilities. Based upon the approach described in the paper, information technology should focus on the provision of support for asset management decision-making processes. The task force recommends the application of a layered methodology in order to organize the decision process and application of specific IT tools to the different decision and control levels. This focussing is necessary in order to minimise the costs involved in the design or purchase of information systems.

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IMPLEMENTATION OF INFORMATION STRATEGY TO SUPPORT UTILITY ASSET MANAGEMENT As asset management is a synonym for risk management a model “from risk to data management” Fig. 1 is used to format this brochure. It recognizes the essential differences between: 

a risk management approach in a business relevant environment; and



data management support for the decision making process.

The model expresses the need to align the translation of data into information according to the risk based decision process and this process that takes into account all relevant areas including social, technical and economic [2]. This chapter follows this “hourglass” model; a business setting point of view will be described as well as further design of the decision processes and data/information. The values of the different stakeholder groups are used for the process of risk management. The process described will be in line with stakeholders needs with regard to social, economic and technical values. In the data management part a chapter “intelligent hub” will describe methods and approaches to transform

information

requirement

into

data

requirements.

On

one

hand,

this

transformation process can be regarded as one of the most important features for asset managers. On the other hand, it can assist data managers to construct the asset databases. Related to the data requirements are data processing methods (data warehousing) and these are described that realise data extraction from existing data bases. A description of the specific data sources for this data processing finalizes the data management chapters. It is emphasized that the described process has an iterative character with a “demand” or lead line coming from the business relevance instead of, nowadays still often feasible, the technical relevance.

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Decision making with regard to stakeholder oriented risk management Stakeholder orientation The context of an asset manager is quite complex. He has to satisfy requirements from many different stakeholders. Fig. 10 shows the asset management context and the different types of relevant stakeholders. To satisfy all stakeholder requirements is of the highest priority and stakeholder satisfaction is the key success factor for an optimal supply of electricity, which can be regarded as a public service and a basic need for all customer groups. The electricity market players – generation, trading and sales, consumer, public and environmental bodies as well as system operator, asset owner and service provider – should agree upon rules, quantification of service levels and payment and penalties. Therefore the identification and quantification of the key indicators is necessary, and this can be done by analysing the impact on the stakeholders. Most of the impact can be expressed in monetary terms, for example: Direct costs or profits by means of additional costs, losses, penalties, profits and indirect costs by means of costs of secondary defects, losses through delays etc. Social and ecological impact as environmental loss, loss of image, safety etc. can be difficult to quantify and to express in financial terms. They can be measured by stakeholder satisfaction, e.g. a score card system. Transparency, an open minded and honest attitude as well as respect and confidence are important factors to increase satisfaction.

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Figure 10: Asset management context Risk balancing using a scenario approach Disturbances and planned outages have an impact on both the upstream and downstream business processes of generation, transport, distribution and consumption. There may also be an impact on individuals and in some cases on the environment. A sustainable asset management process has therefore to consider technical, economic as well as social and environmental aspects and must find a balance between all stakeholder values and requirements, Fig.11. The responsibility is on a much higher level than just the substation level or the system operation level. The characteristics of high-end asset management and a good service level is the satisfaction of all stakeholders. However, the different stakeholders often have different points of view and different expectations. Nowadays, senior management faces a lack of well-prepared information, skills and decision supporting tools. In some instances this may lead to polarized strategies, which fail to satisfy the previously mentioned objectives of sustainability and overall stakeholder satisfaction. Corrective measures, e.g. from regulatory or other bodies, become necessary.

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Risk management is seen as one of the most important instruments for asset management. Considering the above mentioned aspects possible risks for all stakeholders have to be identified and the impact has to be analysed.

Technical

Economic

Asset Management Process

Societal

Environment al

Figure 11: Sustainable asset management process in which risk balancing of technical, societal, economic and environmental aspects is of importance Multiple scenarios can be found to demonstrate the influence performance at all three levels: e.g. increasing or decreasing maintenance and inspection intervals, replacement or refurbishment, changing the maintenance strategy. But also the combination of technical information with relevant economic data and future system performance will result in quantifiable benefits. Not exclusively to be expressed in economic terms but also in terms of reliability. On the corporate level, balancing the cost and benefits of the scenarios with the risk involved with each scenario will result in the strategic decision with the bestmanaged risk outcome. On this level, all stakeholders’ expectations can be taken into account when they are formulated as business values, resulting in a set of performance indicators to give expression to those expectations. The societal and economic

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information, together with the reliability of the equipment inventory and the system performance form the ingredients of the risks involved. A balancing of the risks will lead to the final decision. The intelligent hub From an information-oriented perspective, the risk management process can be thought of as propagating in two directions. The first direction is from the business risk/asset management strategy (i.e. high-level decision-making processes) towards the data that is required to provide information for decision making. In this “top-down“ path the overall business objectives are leading to a set of information requirements that can support decisions and can be considered as the foundations of the risk management process. From these information requirements the basic data requirements for the source systems can be derived. Those requirements also include “how” and “when” data should be presented to the users and other processes. All of these requirements influence data organization and architecture. The second direction comes from the data sources towards the business risk/asset management strategy. In this “bottom-up” path data is extracted from the operational systems, transformed and stored in a data warehouse and finally presented as valuable information, taking into account “how” and when” the data should be used to support the decision making in the risk management environment. The top-down path is the typical view when ‘designing’ the system and specifying the requirements. Sometimes there is a misunderstanding that the information systems can deliver strategic recommendations by themselves, which may lead to an underestimation of the necessary effort required in the “top-down” part of the process. In fact, it is the “top-down” path of specification of information requirements and key indicators resulting from the strategic goals, which form the core know-how of the whole risk management process. The “bottom-up” path is then used in day-to-day operations for translating data into information on the predefined basis. This two way transition needs intelligent support provided by humans, software or both. For further clarification this point is identified as the “intelligent hub” with the structure and mechanisms proposed further in this chapter.

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Asset oriented model An asset oriented data model forms the foundation of a system to support asset/risk management. To position a component in the IT structure while maintaining a link to its actual position within the system, such a model should take account of: 

the hierarchical structure of equipment: o enterprise; o network/system; o substation; o bay; o equipment/apparatus; or o further break down of the equipment to sub-parts (e.g. parts of a circuitbreaker).



the exchange of asset data between different equipment and different stakeholders within the enterprise having various objectives and data requirements.

The detail of hierarchical structure breakdown needs to be carefully defined with regard to the required level of information detail. Too much detail information can sometimes decrease effectiveness of its use. Essential to an asset oriented model is the separation between functional objects and physical objects (equipment). This concept allows connecting ERP, Geographic and SCADA type of information systems. An example of such an object approach and tree structure is shown in Fig. 12.

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Figure 12: Example: Object approach – tree structure As stated previously standards in definition are very important. As such a standard, which allows a differentiated approach, a dual asset description is proposed. In this model the asset is described from two points of view: Functional object approach involves the description of assets as non-material objects, i.e. its function and relative position in the system. Thus, an object can be seen as a part of the scheme with possibility to build a hierarchical structure of objects (see Fig. 12). Each object can have a “parent object” and a “successor object” (e.g. network of given voltage, substation, bay, generic position of a given instrument etc.). A functional object is system inherent and doesn’t change unless a reconstruction of a system or its part takes place.

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Specific component approach is a description of asset as a real piece of hardware, occupying a generic position in a network, providing the required function and having specific, well-defined properties. This approach does not permit a hierarchical description of the network; however its simplicity is beneficial. Phenomena, such as change of properties of component, which are independent on the component’s position, can be studied separately from the network phenomena. Both approaches are complementary and allow separate solution of complex problems. It is not only a method of representing and processing asset data, but a philosophical approach to the support of the asset management function. It also identifies the unique assets for the key register as a part of the data warehouse. Dimensional model Another way of structuring data is with regard to its value for the asset manager. A technique of dimensional modelling can be used to organize data in a manageable way. A simplified example is added in the Fig. 13.

Figure 13: Dimensional model This technique describes dimensions required and measures on a certain subject, each with its own level of detail. Using this technique, results in a single A4 format paper can

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represent many different kinds of reports. Explanation of the diagram: two possible reports or information needs are described. 

In red: A manager is interested in quarterly costs per substation per main process (e.g. maintenance) per department.



In blue: A business analyst is interested in a monthly report on hours on handling outages (= as a selection of one of the sub processes) for the whole enterprise.

Many other information needs could be described this way. Adding one dimension (e.g. “location”) or one measure (e.g. “number of outages”) reveals a whole new set of information needs. This dimensional diagram is one of the principles of “data warehousing”. Based on that concept, data is collected in the data warehouse from the source systems and presented in a way set by the model set-up so that an asset manager or asset analyst can easily navigate through the data. Processing data The asset manager is interested in the coherence between Technical, Economic and Social data on three levels: “Corporate”, “Network” and “Asset”. The integration of information therefore is essential in this point. The second issue of high importance is data quality in a means of complete, correct and actual data. Experience shows, that assuring the data quality can consume 10-50% of the total (information) project budget and is also the cause of trespassing many deadlines. The architectural overview The diagram in Fig. 14 shows as an example an architectural overview from both technical and organisational perspectives. Concerning data objects, we can consider any object or entity of a technical relevance, such as circuit breaker, transformer or connection of corporate relevance, such as a customer or contract etc. In the following figures a circuit breaker is used as a practical example.

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Figure 14: Data management (technical and organizational) In the figure above, the circle represents examples of present applications (ERP’s, Geographical Information System, Maintenance system etc.) that support the processes of the asset manager. Organisations or departments are free to choose their own applications as long as they refer to the key register. All systems then hold different information (technical, financial, environmental, etc.) about the same data object (e.g. a circuit breaker). The key part of the system placed in the centre is a key register. This register synchronizes key data concerning the main data objects as defined in the former chapters. The essential fact is that this register only contains keys, pointers to keys in connected systems and a very limited number of data elements that identify the object in the real world, so that data duplicity is avoided. As an example for a circuit breaker this can be serial-number and manufacturer. For analysis and reporting purposes relevant data of the circuit breaker [i.e. location (from GIS), last date (from the maintenance system), costs (from ERP) etc. are periodically extracted from the source systems and loaded into the data warehouse. This way data history can be built for trend analysis.

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A presentation layer provides the end user with data in a readily usable way. Based on both a pull and a push strategy one can analyse and report on data both from the warehouse and from the key register. This way of presenting data is often referred to as BI or Business Intelligence. Since data has the tendency to degrade with time, data needs to have the constant attention of the organization. Data monitoring and improvement related to the real world is therefore needed. Audits can contribute to maintaining data quality. The organization should also use the advantages of available techniques to maintain synchronization of the systems and to support new systems with required data obtained via the data warehouse from the source systems. Benefits This architecture offers the business the following benefits: 

It offers the business one holistic view. This reduces search-time and work (typing) for the user;



Better data quality by easily comparing data in different systems;



The key register offers a better “time to market” for data warehouses;



Systems can be disconnected without the loss of data;



Reduced “time to market” for new systems because they can be loaded with good quality data;



New service providers can be easily attached even if they use different systems. This principle also counts for decentralized business units; and



Service

providers

or

decentralized

business

units

can

choose

their

one

functionality/systems while central departments or the asset manager, as a spider in the web, can keep the necessary data. These benefits can be reached with minimal costs and without interrupting the operational process as the present applications stay in operation.

Conclusions and further outlook

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A practical approach to implementing an asset management information strategy was outlined using the “hourglass” model to illustrate the transition from risk management into data management. The process of risk management needs to ensure satisfaction of requirements of all stakeholder groups and relevant constraints with the final aim of social, economic and technical issues fulfilment. Nowadays different scenarios are often used to support a decision process. Each scenario should take social, economic and technical consequences into consideration. For the scenario evaluation process, data obtained from both system operations as other corporate processes need structured design. This structure is referred to as the “intelligent hub”, a principal description of transition from data into valuable information necessary for further processing. This “hub” is an important part of the utility’s information processing and may be a keystone for a successful information strategy and risk management philosophy. Data warehousing, finally, is necessary to link different key data elements from different information systems in order to support the data management process. This support must be given in a multidimensional and consistent approach.

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IMPLEMENTATION OF INFORMATION STRATEGY TO SUPPORT UTILITY ASSET MANAGEMENT - DECISION MAKING WITH REGARD TO STAKEHOLDER ORIENTED RISK MANAGEMENT The first information strategy publication [2] as per chapter 3 concluded that Information Strategy focuses on risk management, being the most important responsibility for the asset management function. Risk management identifies all credible risks; ascertaining and quantifying their potential impact and incorporating this information into all asset management decision processes. Risk management takes into account all relevant consequences from economic, technical and social perspectives. This risk-based decision process is complicated by the fact that both short-term, day-to-day decisions and longerterm strategic decisions must be supported. Furthermore, there are several, sometimes conflicting constraints and conflicting stakeholders’ requirements that must be taken into account. The different activities should be executed in an order that depends on the contribution to the quality of energy supply. It must, however, also satisfy the requirements regarding economic added value and safety aspects. The asset manager is thus facing a continuous process of decision-making based upon 5 

forecasting of needs and system development;



consequences on system performance;



influence on (regulated) price, costs and profit; as well as



impact to safety and society.

Risk assessment also necessitates the comparison of different scenarios of system development, maintenance and (re)investment strategies, major and minor failures (e.g. probabilistic or deterministic) etc. 6, 7, 8. Risk assessment and related information management form a very important part of the end-to-end process of asset management. In many utilities, data is collected and stored in line with financial responsibilities and give some basic information about the assets concerned. However, a common and structured approach to the assessment of the risk associated with these assets is not applied.

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Decision making with regard to stakeholder oriented risk management The decision process considering various scenarios As a structured approach that assesses data into asset decision information follows from the description of the three different decision levels as given in the publication [2] which is summarized in the following Fig. 15.

Equipment Inventory

1: Maintenance and diagnostic evaluation, scenario search and evaluation of effectiveness & usability

Technical information (on assets)

asset level

Component level

Maintenance Maintenance rules

1: Maintenance and diagnostic evaluation, scenario search, evaluation of their usability

Diagnostics Failure cases

Economical information (on assets) Technical information (on grid)

2: Economical evaluation of scenarios, creation of maintenance strategies, calculation of non-delivered of energy

System level

network level

Economical information (on business) Sociological information

3: Risk management, selection of optimal scenario, and optimal strategy

Corporate level

System performance

Reliability evaluation

Scenarios

Aging Models

Replace Run to failure

Decision

corporate level

Power qaulity System development

Refurbish

Spare parts

CBM/TBM/CM M/D modification

Investment costs

As Before Monitoring

Maintenance costs Diagnostics costs Outage costs

Selection of relevant economic data

2: Economical evaluation of different alternatives, Creation of maintenance strategies, life curves and life cost computation Business Values Customer contracts

Spare part costs

Regulator

Repair costs

Safety regulations

Figure 15: Information flow and processing for decisions

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Scenarios with quantified benefits or costs

Demanded reliability, availability and quality; Undelivered energy price, Claims, Fines

3: Risk Management “Decision”

Risk Assessment


Level 1: Asset / component level From a technical point of view, multiple scenarios can be used to illustrate influence on the performance of assets. For example, scenarios could be increasing or decreasing maintenance and inspection intervals, replacement or refurbishment, but also changing the maintenance strategy from corrective maintenance to time based or condition based maintenance. The scenarios are found by analysis and combination of the equipment inventory, the maintenance actions already performed on the equipment, the current rules that exist for maintenance, and condition assessment, that results from diagnostics, in combination with statistical evaluation of practical failure cases, reliability evaluation and ageing models result. All these scenarios will have a different effect on the technical performance (in terms of reliability and availability) of the asset. At this stage, scenarios should not be excluded based on their effect on technical performance, because in the next levels, the scenarios will be combined with other relevant data, which even may cause a scenario with a negative effect on technical performance to be the most economic while having the smallest risk. The stakeholders’ expectations for example will be taken into account at the third level. The decision to replace equipment by different scenarios may not only depend on the increased failure rate but also for example on the acceptance of the used technique, the know-how of the service department, availability of spare parts, or the current capability of the considered equipment.

Level 2: System level Technical information of the scenarios with relevant economic data is not exclusively expressed in economic terms, but could also be expressed in other terms such as reliability or expected lifetime. The main aspects of the system performance such as network topology and development, changing of the generation and demand and so on may influence the decision process regarding the asset. Possible consequences and impacts on the system with different

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scenarios are obtained from reliability calculations (system reliability, operating conditions, risk of fatal failures etc.). The reliability calculation of the non-delivered or supplied energy at certain system nodes is an important result to assess the performance of the system from the customer point of view (Delivery Point Performance or DPP). This will lead to a reliability centred approach to maintenance for the complete system. In this step of the decision-making process, modern methods of maintenance evaluation and planning can be used (e.g. Reliability Centred Maintenance, Risk Based Maintenance etc.). For example, enlarging an inspection interval may cause a benefit in decreased expenditures, but could have costs in terms of decreased reliability. On the other hand, shortening the interval might have costs in increased expenditures, but could lead to benefits in increased reliability.

Level 3: Corporate level On the corporate level, balancing the costs and benefits of the scenarios with the risk involved with each scenario will result in the strategic decision with the best-managed risk. On this level, all stakeholders’ expectations can be taken into account when they are formulated as business values, resulting in a set of performance indicators to give expression to those expectations. To make the process complete, the safety and social risks must also be taken into account. On this level (“Risk management”), the overall risk related to the final decision is evaluated and controlled. It concerns not only the technical and financial aspects, but respects also the personal and environmental safety, impact on external bodies, stakeholders’ demands and other “social risks” aspects as described above. Safety risks represent impact on personal and environmental safety. Social risks represent impact on external bodies directly dealing with the company, on the public and the environment, but also internal personnel-related impacts (change of work procedures, personnel reduction etc.). The societal and economic information, together with the reliability of the equipment inventory and the system performance form the ingredients of the risks involved. A balancing of the risks will lead to the final decision.

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The current used performance indicators were collected and summarised by the WG CIGRÉ 1.11 and published in Brochure 367 “Asset Management Performance Benchmarking” (2008) [9]. In this chapter the different Key Performance Indicators (KPI) are related to five classes: finance/business, safety, reliability, customer and employee, which can be allocated to the overall groups: technical (reliability); economic (finance/business) and social (safety, customer, and employee) as mentioned in chapter 3.2.3 and 3.4.1 of this brochure. Example scenario approach For every decision to be taken there are a certain number of possible scenarios [7]: 

status quo by means continuing as before (1)



replacement of all equipment (2)



refurbishing the most critical equipment (3)



re-design the substation concept (4)

Some generic questions need to be answered to define which scenarios have to be considered [10]: 

Are there planned or intended changes in the electric power system?



Are there planned or intended changes in the organizational structure?



What is the technical relevance and importance of the substation or bay etc. concerned?



What is the business relevance and importance of the substation, bay etc. concerned?

To assess the risk each of the scenarios has to be checked on the mentioned key aspects and the key performance indicators (KPI’s). Which of the indicators is used depends on the actual project and the corporate level’s interest to achieve the target of the company, for example in the area of social aspects. The information which should be used defining the key performance indicators are listed in 5.2.3 and Table 1 shows an example.

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Scenario

Key

KPI’s

aspects Scenario 1

technical

KPI 1 (e.g. remaining life duration)

(e.g. extending life time)

KPI 2 (e.g. failure probability) … economic

KPI 3 (e.g. revision costs) KPI 4 (e.g. life cycle costs) …

social

KPI 5 (e.g. environmental impact) …

Scenario 2

technical

KPI 6 (e.g. asset condition)

(e.g. renewal by replacement

KPI 7 (e.g. reliability)

of all equipment 1:1)

... economic

KPI 8 (e.g. re-investment costs) KPI 9 (e.g. business opportunity) …

social

KPI 10 (e.g. safety) KPI 11 (e.g. image) …

Scenario 3

technical

KPI 6 (e.g. asset condition)

(e.g. renewal by refurbishment

KPI 7 (e.g. reliability)

of the most critical

...

equipment)

economic

KPI 8 (e.g. re-investment costs) KPI 12 (e.g. following reinvestment) …

social

KPI 2 (e.g. failure probability) …

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Scenario

Key

KPI’s

aspects Scenario 4

technical

KPI 13 (e.g. power supply quality)

(e.g. redesign the

KPI 14 (e.g. availability)

configuration of a substation,

…

i.e. reduce the number of

economic

KPI 8 (e.g. re-investment costs)

busbars or eliminate the

KPI 4 (e.g. life cycle costs)

transfer bus)

KPI 9 (e.g. business opportunity) … social

KPI 15 (e.g. employee motivation) KPI 11 (e.g. image)

Table 1: Example of different scenarios, the key aspects and relevant KPI’s

Requirements Stakeholder Requirements The electricity market players – generation, trading and sales, consumer, public and environmental bodies as well as system operator, asset owner and service provider – should agree upon rules, quantification of service levels and payment and penalties. Therefore the identification and quantification of the key indicators is necessary, which can be done by analysing the impact on the stakeholders. Most of the impact can be expressed in monetary terms: 

Direct costs or profits by means of additional costs, losses, penalties, profits and



Indirect costs by means of costs of secondary defects, losses through delays etc.

Social and ecological impact as environmental loss, loss of image, safety etc. is difficult to quantify and to express in financial terms. They can be assessed by stakeholder satisfaction, e.g. using a score card system. Transparency, an open minded and honest attitude as well as respect and confidence are important factors to increase satisfaction. The following Table 2 shows the most important requirements of the particular stakeholders. Economic aspects are of most importance for all stakeholders which are

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running a business in a competitive, privatized environment. Public owned companies, probably with a monopoly, and public service providers (e.g. medical service, water & energy supply, transport, broadcasting and communications as newspaper, radio, TV, internet and telephone etc.) are more focused on technical requirements as quality and reliability. The environment (air, vegetation, animal life etc.), represented by environmental protection organisations, and the human being are practically purely social focused (existence, safety, convenience and comfort, motivation, fun etc.). Political organizations, legislator and regulator are balancing interests between market forces, public basic needs and power consumer needs. Stakeholder Trader, sales

Economic

Technical

Social

- Max. profit

Private generator, IPP - Max. profit

- Availability

Public generator

- Reliability

- Green power

- Low risk

- Safety

Asset owner

- Profit - Max. profit

- Availability - Asset condition

Supply industry

- Max. profit

- Product, quality/price

Service provider

- Max. profit

- Service level/price

System operator

- Profit

- Reliability

- Solidarity

- Power quality

- Cooperation - Safety

Employee

- Salary

- Existence - Motivation

Political organisations

- Market

- Market justice

structure

- Political

- Fair prices Regulator, legislator

- Controlled prices

balanced - As much as necessary - Benchmark

Private industry

- Max. profit

- Availability

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- Legal conformity


Stakeholder Public service

Economic - Profit

provider Private consumer

- Low prices

Technical

Social

- Reliability

- Safety

- Power quality

- Image

- Availability

- Safety - Comfort

Public

- Low prices

- Availability

Environmental

- Safety - Existence - Safety

Table 2: Stakeholders and their requirements according to their values The table shows the tendency from economic aspects of purely business driven organisations towards the societal aspects of more public, human and environmental oriented organisations. In any case, stakeholder satisfaction is directly proportional to the fulfilment of his particular requirements and inversely proportional to the risk of not fulfilling his particular requirements. Information requirements The aspects of information needed to support asset management decisions can be divided in three main categories according to the described aspects of Fig. 11: 

Technical aspects;



Economic aspects; and



Social aspects.

These different aspects are described in more detail below. TECHNICAL ASPECTS

The technical aspects consist of two different parts: information about the single equipment item as well as information about the system development. Relevant information needed for asset management decision support is dependent on several asset-related parameters, such as the insulation ageing characteristics of a component and the probability of an over-voltage across a component or to earth, e.g. as a result of switching activities. The component ageing is related to the operational age, the

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type, the history and the operating conditions of a network (sub) component. In order to decrease the probability to a failure, condition assessment can be used. A combination of diagnostic tools for condition assessment is chosen and applied, depending on the different types and locations of insulation defects induced degradation sites. The technical aspects cover amongst others, condition assessment, aging models, and failure probability, but also information on the equipment inventory, the system performance (quality of energy supply), the available spare parts, the current maintenance procedures, the history of maintenance actions, the history of failures and the manufacturer. Consideration of the equipment condition or the probability of outages is the basis to evaluate the system performance by utilization of reliability calculations. In principle there are two levels to perform reliability calculations: 

Equipment approach: This can be called as outage-oriented criteria. That means the probability of an equipment outage is evaluated on the basis for example on several condition information.



System approach: Complementary to this there are the customer-oriented criteria. In this case the frequency and duration of the interruption of supply at certain system nodes is calculated (non-delivered energy), which is caused due to the outage of equipment in the network. The result is that the non-delivered energy has to be considered to determine if this situation is acceptable for the customer or not. In the same way the non-supplied energy needs to be considered for example, that a power plant is not able to feed the system.

In principle customer-oriented criteria are only useful to fulfil the requirements of the customer as well as the utilities and therefore to apply certain maintenance measures. ECONOMIC ASPECTS

As every technical aspect will have its financial counterpart, economic aspects include the costs of maintenance, repairs and failures, but also the costs of condition assessment and the investment costs for equipment and spare parts (life-cycle costs). These costs will be called the economic information on assets.

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The costs related to a failure are dependent on the outage-related expenditure. Failures mostly result in major damage to network components and their environment, which will lead to high maintenance and repair costs. In case of major critical damages, professional solutions are needed to restore the energy supply as quickly as possible, eventuating in higher expenditures. In addition minor failures which are repaired during planned outages have also to be taken into consideration, because they will also influence the costs. Outages can result in customer compensation and responsibility claims in the form of penalties. The need for economic information finds its origin in the fact of driving a business, e.g. penalty costs from customer contracts, possible claims from customers and the costs of undelivered energy. These aspects will be called the economic information of the business. SOCIAL ASPECTS

However, within the asset management decision process, technical and economic aspects are not the only aspects to consider. As an example, risks are not only determined by the economics. There are also some societal aspects that have to be considered, such as the impact on society of outage and failures. Failure acceptability can be reflected as the degree in which a failure is acceptable from the social point of view. The failure impact is dependent on the criticality and number of connections, which is further affected by the time to restore the particular failure. Even so, frequent energy interruptions in a short period of time will not be acceptable from a social point of view. For example, power losses related to buildings with a high social standard, e.g. hospitals have a low acceptability level. Furthermore, the social impact of utility’s policy is determined among others by two factors: the image to the public and the feeling of safety. Power supply disturbances in buildings with high public exposure e.g. shopping malls, congress centres; hotel office towers, airports etc. have an impact on public opinion of safety. Some of the societal aspects will be guarded by the regulator, which will translate these aspects in proper regulation. This regulation will result in economic impacts such as penalty costs. Other societal aspects such as the image of the power utility or the personal

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safety of its employees cannot be easy translated into economic aspects, but should also be covered when investigating risk. This societal impact can have very strategic consequences for an organisation. Customers can show the tendency to change energy supplier so a successfully operating asset manager (from the perspective of lower maintenance costs) can cause a utility large damage (due to high failure rate). Key performance indicators (KPI’s) The information and key parameters needed by the utility for supporting risk management decisions and actions are related to the risk model presented in chapter 4.1.2. These key parameters are based on some generic data, derived from the overall usable and available data pool in the enterprise in a data processing and condensing process. The key parameters for risk management purposes are typically the results of a previous processing and combination of available information in a data condensing and information extraction process based on human experience, artificial intelligence and software tools. Also they may contain other decision relevant information, on the system, on the environment, or social/legal context and which should help towards decision making. This data or information is obtained from the real or virtual data warehouse of the enterprise. As an example, the following information regarding the aforementioned aspects should be taken into consideration when defining a key performance indicator, according to the company’s strategy 3, 11. Economic information: 

Life cycle costs



Costs (CAPEX, OPEX)



Liability costs due to third party damages



Prices



Direct loss of profit (business could not be made)



Indirect loss of profit due to delay of the business



Costs of non-delivered or non-supplied energy

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

Penalties



Costs of secondary defects (e.g. environmental or medical defects)



External additional costs or loss of profit because third parties could not make business



Business opportunities



etc.

Technical information: 

Reliability, availability of the equipment



Failure probability, MTBF, MTTR (on system level)



(n-1) security



Outages planed, unplanned



Power

supply

quality

(Energy

not

supplied,

System

Average

Interruption

Duration/Frequency Index, SAIDI/SAIFI) 

Service level



Asset condition



Remaining life duration



etc.

Social information: 

Stakeholder satisfaction and customer satisfaction



Safety level, failure rate, accident rate



Damages of the property in case of an outage



Social aspects of the customer (interruption of supply)



Employee motivation



Environmental load and impact (land use, pollution, electrical and magnetic fields)



Attitude: open minded, honest and respectful and confidence



Image of the company



etc.

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Combining different KPI’s (indexing) All types of information should be weighted and brought in relation with other information. E. g the condition index of an asset weighted with the importance index (Fig. 16a) gives the priority of investment or service activities. This example represents the procedure in case of high voltage circuit-breakers according 12 (Fig. 16b).

100

A

Erneuerung replacement

Zustand condition

80

service Instandhaltung

60 40 20 0 0

20

40

60

80

100

importance Wichtigkeit

80

ranking Dringlichkeit

B

60

40 priority 1

priority 2

20

0

A

B

C

D

E

F

G

H

I

J

K

L

M

N

circuit-breaker

Figure 16: Example of combining different KPI’s for a group of equipment (A: Condition and importance assessment. B: Prioritisation of the service activities).

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Conclusion This part has in general a stakeholder orientation. The top – down approach from all stakeholder requirements to information and data requirements as well as methods supporting risk assessment are discussed. Risk management has been concluded as core business for sustainable asset management.

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SURVEY ON PRACTICAL APPLICATION OF ASSET MANAGEMENT INFORMATION STRATEGIES The task force distributed a survey in the year 2006/2007 to obtain more information on the applied asset strategies and their related data management. The focus of the questions was on: 

Experiences, current practices,



How IT-tools can support?



Which methodologies are being used?

The questions are grouped as follows: 

Data sources,



Data processing and data warehouse,



Data requirements,



Information requirements,



Scenario approach and risk balancing,



Decision and control.

This brochure presents the survey results from 19 utilities from Europe, North America, Middle East and Australia. The answers in the diagrams are therefore always related to the total number of respondents (19). To keep the survey within a manageable size, not all fields of interest could be addressed in detail. The fields of interest covered and the results obtained are presented in chapters 6.4 to 6.9.

Experiences Experiences and usage of current software tools in a practical way are considered. Related fields of interest: 

Experiences with existing ERP’s and EAM’s,



Differences, strengths and weaknesses, pros and cons of the current systems used,

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59




What use do the suppliers claim to fulfil and how far do they actually fulfil the utility’s requirements,



Perceived deficiencies.

Access to data and conversion into information Different standalone systems are available nowadays which are more or less integrated in a proprietary IT-environment. It is a strong requirement from asset managers to get easy access to data and information of all systems, to avoid data redundancy and to have information on a common platform for further applications. The following type of data and information needs to be considered: 

Structured technical information about assets and the system/grid (e.g. asset condition, power quality, system availability etc.),



Structured economic (quantitative) information,



Unstructured social (qualitative) information (e.g. environmental impact, stakeholder satisfaction etc.).

Related fields of interest: 

Technologies to share information (e.g. data warehouse),



Technologies to process data and convert them into usable information,



Technologies to access data (e.g. browser, interfaces with other systems, presentation to the user).

Software tools to support decision making As mentioned above, the utilities’ requirement is to obtain a reasonable management information system (MIS) that allows the application of an asset and data management methodology as well as easy software tools to simulate different scenarios. Related fields of interest: 

What existing software tools can support in the different field of maintenance, refurbishment and investment decision making?



What is necessary to allocate and to get access to the necessary structured and unstructured information (e.g. data warehouse)?

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

What management information systems (MIS) are available which are in line with the requirements (focus on risk management and decision making)?



What is the reasonable functionality of software tools?

Data Sources The survey covered data sources with a total of 18 questions (Table 3) regarding: 

data recording of primary and secondary equipment (Q7- Q9)



outage and performance data (Q10 - Q13)



maintenance/repair and history (Q14 - Q16)



diagnosis and monitoring data (Q17, 18)



actual and planned data collection in IT systems (Q19, 20)



actual and planned front-end IT systems (Q21 - Q24)

Question Question number 7

How do you register installed power equipment?

8

How do you register installed automation, control and protection equipment?

9

How do you register details on location?

10

How do you record failures for power equipment?

11

How do you record failures for control and protection equipment?

12

How do you record operational data?

13

Do you use operational history for AM decisions?

14

Do you record maintenance and repair data?

15

Can you re-construct the maintenance/repair history from your records?

16

How long back can you re-construct the maintenance/repair history?

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17

Do you record diagnosis (off-line) data?

18

Do you record monitoring (on-line) data?

19

How do you collect data and enter it in your systems now?

20

How do you plan to collect data and enter it in your systems in the future?

21,22

What front end systems do you use now?

23,24

What front end systems do plan to add in the future?

Table 3: Questions on data sources Recording of primary and secondary equipment For AM, especially in the high voltage power equipment field, various data sources are still used (Fig. Q7 and Q8), in which basic as well as detailed information is registered. Different media such as paper (excel) files and databases are used at local, departmental and enterprise level. The enterprise-wide equipment database though is the most predominant in the branch (79% of the respondents). This is less distinct in the installed automation, control and protection equipment, for which 63% of the respondents use the enterprise-wide equipment database. Registration in other data sources is slightly higher than with high voltage power equipment. The highest information detail is recorded at the substation (68%) and bay levels (74%). Some utilities (32% of the respondents) register equipment location also using geographic coordinates – Fig. Q9.

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Q8: How do you regis ter ins talled automation, c ontrol and protec tion equipment

Q7: How do you regis ter ins talled power equipment

paper

0,26

files

paper

0,26

loc al databas es

files

0,11

department databas e

0,16

detailed informaiton

0,53

0,4

0,32

enterpris e databas e

0,63

detailed informaiton

0,32 0,2

0,26

department databas e

0,79

0

0,42

loc al databas es

enterpris e databas e

bas ic information

0,26

0,42

bas ic information

0,6

0,8

0,26

1

0

0,2

0,4

0,6

0,8

1

Q9: How do you regis ter details on loc ation?

0,32

c oordinates

bay

0,74

loc ation s ubs tation

0,68

0

0,2

0,4

0,6

0,8

1

Still, a substantial part of recording is done outside the enterprise-wide database. This raises questions on the data-integration, hence questions on data quality and decision quality. Outage, performance and failure data In the survey only questions related to failure data have been addressed. A pretty detailed failure recording in the high voltage power equipment and in the installed automation-, control- and protection is used (Fig. Q10 and Q11), though in about 40% of cases failure consequence is not recorded.

WG remark: Only 63% of respondents are reporting major failures according to CIGRÉ definitions. It is possible that companies use internal standards to define major failures, which are not in compliance to CIGRÉ’s definitions. Most respondents (75%) record this

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kind of operational history data and use it for decision-making Q13. The kind of data used is shown in Fig. Q12. Q10: How do you rec ord failures for power equipment

fault rec ording

Q11: How do you rec ord failures for c ontrol and protec tion equipment

0,63

faulted item

ac tions after failure

0,63

failure c ons equenc es

0,63

0,79

ac tions after failure

0,68

failure c ons equenc es

0,58

time of failure

0,84 0,89

c aus e of failure

time of failure

0,84

c aus e of failure

0,84

failure des c ription

1

1

failure des c ription minor failure (C IG R E )

0,63

major failure (C IG R E )

0,63 0

0,2

0,4

0,6

minor failure (C IG R E )

0,53

major failure (C IG R E ) 0,8

1

0,58 0

0,2

0,4

0,6

0,8

1

Q12 How do you record operational data? number of operations (where it applies) operational events time in operation load pattern peak values not recorded 0

0,2

0,4

0,6

0,8

1

WG conclusion: Although only failure data have been considered in the survey, mainly due to focus on asset issues, also system performance data, such as un-planed and planed outages, and load optimization/balancing, are important to the asset manager. It is therefore recommended to keep asset performance data and system performance data separately with mutual links between related events to enable cause – consequence analyses. Another important issue is that besides the existence of an enterprise-wide data source, departmental data sources for equipment also exists. This could lead to inconsistent data within the same enterprise. Maintenance and repair data Maintenance and repair data seems very important for power equipment as well as automation, control and protection equipment (Fig. Q14). An average of 76 % over all the equipment is established. Information regarding work performed can be well reconstructed

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but time and worker information is less well traceable. Also, to know something about the contractor is more important than knowing something about a "Worker" (Fig. Q15). Maintenance

is

well

recorded.

From

the

respondents,

most

can

reconstruct

maintenance/repair history for more than 10 year back depending on type of equipment (Fig. Q16). Respondent refer to digital and paper records. Q15: C an you re-c ons truc t the maintenanc e/repair his tory from your rec ords ?

Q14: Do you rec ord maintenanc e and repair data? c ontrol/protec tion

0,63 detailed information

ins t. trans formers

0,58

0,74

overhead lines

0,79

G IS s ubs tation

0,79

c ables

worker

0,21

s ervic e work done

0,58

part repaired

0,58

0,74

trans former

0,79

c irc uit breaker 0

0,2

0,4

0,6

0,8

0,26

time only

0,84 1

0

0,2

0,4

0,6

0,8

1

Q16: How long bac k c an you re-c ons truc t the maintenanc e/repair his tory?

more then 20

0,32

10-20 years

0,32

5-10 years

0,26

0-5 years

0,32

0

0,2

0,4

0,6

0,8

1

Diagnosis and Monitoring data Two questions dealt with recording diagnostic (offline data) and monitoring data (online data) as shown below in the combined Fig. Q17 and Q18. The answers show that the offline data recording for power equipment is more common than online data recording. There is a high degree of recording diagnostic (offline) data, especially in the case for circuit breakers (95%) and transformers (89%). Transformer online monitoring is also well represented (58%). Monitoring for cables and circuit breakers is less well represented (16%, respectively 21%).

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WG remark: Information on the present condition and the trend of an asset is very important. Both options diagnostics and monitoring are valuable and should be considered. Future developments on intelligent grids will probably provide much more valuable information about asset condition than obtained from present grids. Q17: Diagnosis (offline)… and Q18: Monitoring (online) data for various type of equipment

for inst. transformers

0,63

for GIS

0,68 0,16

for cables

0,68 0,58

for transformers

0,89 0,21

for circuit breakers

0,95 0

0,2

Q17: Diagnosis (offline)

0,4

0,6

0,8

1

Q18: Monitoring (online)

Data collection and systems used Many respondents (68%) collect data “by hand”. Some automatic data collection from an asset is used by 42% of respondents. Most of this data is stored in a digital format, although storage on paper is still used. This is definitely going to change according to the respondents. As can be expected, there will be an increased automatic collection and storage in digital format as on the other hand there will be a decrease on collection by hand and on storage on paper (Fig. Q19 and Q20).

According to the survey results, the respondents intend to increase their data collection actions in several ways: planned actions, time- and event triggered actions. However, the answers also indicate that data-collection is not rapidly changing since there are no significant differences between the “how do you collect data now” and “how do you collect data in the future” questions.

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Q19/20: How do you collect data and enter it in your systems?

event triggered c ollec tion

0,47

0,58

0,42 0,37

time triggered c ollec tion planned c ollec tion

0,47

0,58

s tore digital

0,79 0,21

s tore on paper

0,32

automatic

0,63

0,42 0,47

by-hand 0

0,2 Q19: Now

0,89

0,4

0,68 0,6

0,8

1

Q20: F uture

Questions were also asked on the usage of systems (Fig. Q21-Q24). The questions focused on which systems utilities use nowadays and which systems utilities plan to implement. Answers show that the whole range of (systems) functions commonly needed in the field of asset management are used or are planned for. Since some utilities indicate that they plan to add some of the systems it can be concluded that not all the utilities own the complete range. Answers also indicate that systems for Enterprise Asset Management, planning/scheduling systems (not ERP), MS office and specific systems and proprietary databases are most commonly (> 50% of the respondents) used. On Enterprise Resource Planning (ERP) a significant increase on adding those to the existing system landscape can be seen.

WG remark: Many utilities have needs to integrate basic data. The integration of technical and non-technical systems is not common in the branch. Evidence of this is also found in the widely used dedicated systems and proprietary databases. Also, presently there is a combination of ways how data is collected.

WG remark: Enterprise Asset Management highlights the need for data-integration. The questionnaire indicates that many utilities are at the beginning of this integration. The advice is not to focus on a point-to-point interface between systems, as one can end up

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with a “spaghetti” of interfaces. Best practice is to focus on aligning the data in the system landscape based on a common data model and based on good data management. One should focus for “one single version of the truth” for the most important business objects (e.g. customer, substation, transformer, outage etc.). The asset manager is the owner of the process and is in charge to set-up the IT system requirements. This calls for collaboration between business and IT departments. A variety of technical solutions can help to integrate on an automation level.

Q21-24: What front end systems do you use/plan? Dedic ated, s pec ific s ys tems

0,32

C ontent Management

0,16 0,11

C us tomer R elations hip

0,16

0,68

0,26

0,21

MS O ffic e P lanning/s c heduling s ys tems

0,63

0,26

E nergy Management

0,11

0,68

0,32

E nterpris e A s s et Management

0,26

G eographic Information S ys tem

0,58

0,21

E nterpris e R es ourc e P lanning

0,42 0,47

0,26 0

0,2

0,4 us e now

0,6 0,8 plan to add in the future

1

Data processing and data warehousing The survey covered a subset of questions directed towards finding the current status regarding processing and warehousing of data (Table 4). Items considered refer to: 

Use of decision support systems, analytical and business intelligence systems for processing of available data, (Q30),



Find if data is merged in data warehouse, and if yes what data is stored there, what systems do contribute with data and how this is extracted (Q31 – Q35),

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

Find the current practice regarding the synchronization of data warehouse data with the source systems like ERP, EAM, GeoIS, CRM etc. (Q36),



The current practices to ensure data quality and for validation of data (Q37).


Do you use business-intelligence, analytical or decision support systems to analyse your data?

31

Do you merge data in a data warehouse

32

What subjects are in the data warehouse

33

Is non structured data available for analyses purposes

34

What systems give input for the data warehouse

35

How is the data -needed for these data warehousing and decision support systems- retrieved from the source systems

36

Do you synchronize data between source systems (SCADA, EAM, ERP, GIS and CRM)

37

How often is data quality addressed or data validated

Table 0: Questions about data processing Systems in use The answers to Q30 to Q33 show that 37% of the respondents use business-intelligence, analytical or decision support systems to analyse data, whereas 42% have plans on using those systems in the future, some users have not answered this question. 58% of the respondents are merging data in data warehouse (DWH) and currently 25% are reported as being operational. The subjects that are met in those data warehouses are mostly technical (53%) and economic data (58%). With a 10% score the social data is not well represented. Data warehousing is mostly used for analysing on structured data. Non

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structured data is only partly available for analysis purposes (47%) and not many respondents are planning for it. According to the results above data warehousing and decision support systems are not commonly used in the field of asset management. Q30: Do you use business-intelligence, analytical or desicion support systems to analyze your data?

Q31: Do you merge data in a data warehouse

No

No

Yes

Yes

0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

Q33: Is non structured data available for analyzes purposes?

Q32: What subjects are in the data warehouse

For sociological data

No

For economical data

Partly

Yes

For technical data

0

0.2

0.4

0.6

0.8

0

1

Page

70

0.2

0.4

0.6

0.8

1


Data processing from source to data warehouse The sources used for data warehousing are ERP, EAM, SCADA, GeoIS and CRM (Fig. Q34). Q35: How is the data -needed for thes e datawarhous ing and dec is ion s upport s ys tems - retrieved from the s ourc e s ys tems ?

Q34: W hat s ys tems give input for the data warehous e? C us tomer R elation Management S ys tem (C R M)

0,21

G eographic al Information S ys tem (G IS )

A utomatic ally by E TL

0,26

E nterpris e R es ourc e P lanning (E R P )

0,21

E nterpris e A s s et Management (E A M)

0,42

Manualy trans ferred

0,21

0,32

S C A DA 0

0,2

0,11

B y hand

0,21 0,4

0,6

0,8

0

1

0,2

0,4

0,6

0,8

1

42% of the data coming from these sources is handled with ETL tooling (Automated Extraction, Transformation and Load into the data warehouse). 32% of the respondents indicate that this ETL process needs human interference or data is entered by hand (Fig. Q35). Synchronizing of data Respondents (42%) say that only part of the data is synchronized between source systems (Fig. 36). For the working group this is a key point for data quality. Although a key point for data quality, the problem is only partly recognized since only 20% plan to improve the situation. Q36: Do you s ync hroniz e data between s ourc e s ys tems (S C A DA , E A M, E R P , G IS and C R M)?

Q37: How often is data quality addres s ed or data validated

Never

0,05

0,37

No

L onger

P artly

0

Y early

0,42

0,26

Monthly Y es

0,42

0,05

Daily 0

0,2

0,4

0,6

0,8

0

1

Page

0,32

71

0,2

0,4

0,6

0,8

1


However, most respondents indicate that they address data quality or data validation aspects within a month (graph Q37). Results also show marginal synchronization of data between sources systems and only some plan for improvement.

WG remark: Beside the synchronization among data sources, in the case of time series data like from transient recorders, monitoring systems, protection, control system etc., the time synchronization is important to insure a proper analysis. Data quality and validation Since data quality in source systems is an important issue for asset managers the working group strongly advises to focus on improvement on the quality of data in the source systems to the appropriate and measurable level. Mastering the most important data is the key element here. By defining and maintaining “one single version of the truth” and synchronize this truth all over the source systems data quality will rapidly improve. This "single version of the truth" also serves as the most important source for data warehouse and decision support systems since it connects different data (technical and non-technical) in different source systems. Data requirements The survey covered decision and control with a total of 4 questions referring to data modelling (Q26-Q29) are presented in Table 5. Question Question number 26

Do you have a data model that translated information requirements into data requirements

27

Does the data model distinguish between the situation of today (AS-IS) and the future (TO-BE)?

28

Is non-structured data included or used in the data model

29

Do the installed IT systems and applications match with the data model

Table 7: Questions on data requirements

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72


The data requirements are derived from information requirements and are made visible in a data model. Both structured- and unstructured data are present in the utilities. Only in a few cases unstructured data is modelled in a data model. When it comes to data modelling of structured data 74% of the respondents answered that IT systems or applications match at least in part with the data model (Fig. Q29). Also 48% of the respondents say that unstructured data is part of their data model (Fig. Q28).

WG remark: Data model as used here is a reference model to translate information requirements from an asset management / enterprise point of view to data requirements at utility level and not to be understood as final data base model for an application. Results for the differentiation between technical, economic and social data match the expectations of the Working Group; there is a strong focus on technical data. The economic aspects are less represented and data models for social data are not established (Fig. Q26). Also around one fourth (26%) of the respondents say that they differentiate between AS-IS and TO-BE data models (Fig. Q27).

WG comment: Results show that enterprise-wide data models exist but that data models from source systems do not match. This is understandable since suppliers are developing for a broad range of customers and customer requirements. Data models, once designed, cannot be easily changed and are a well-kept secret among suppliers due to competition. Cost of changing data models is high and full of risk in perspective of future releases and this is not likely to change in the future. Q26: Do you have a data model that trans lated information requirements into data requirements ?

F or s oc iologic al data

Q27: Does the data model dis tinguis h between the s ituation of today (A S -IS ) and the future (TO -B E )?

0,05 No

F or ec onomic al data

0,47

0,26

Y es F or tec hnic al data

0,26

0,68

0

0,2

0,4

0,6

0,8

1

Page

0

73

0,2

0,4

0,6

0,8

1


Q28: Is non s truc tured data inc luded or us ed in the data model?

No

Q29: Do the ins talled IT s ys tems and applic ations matc h with the data model?

0,32 No

P artly

0,32

Y es

P artly

0,16

0

0,2

0

0,58

Y es

0,4

0,6

0,8

1

0,16

0

0,2

0,4

0,6

0,8

1

WG remark: The management should keep full attention to master data and focus on defining a basic “mean and lean” data model. This master data acts then as an integrator for your system landscape. The data should not be modelled from the beginning but only the part that is needed enterprise-wide. Standardization of business objects for “master data” for main primary and secondary equipment is recommended. This master data should be organized and managed in one database, as source for all other information systems. Information requirements The survey covered information requirements with one question “how issues of interest for the asset manager are translated into information specification”. The responses obtained are shown in the Fig. Q25. Q25 Are issues of assetmanagement translated into information specification. What is the status?

yes - On decision and Control

0,37

yes - On maintenance strategy

0,79

yes - On risk assessment/consequences

0,58

yes - On AM strategy

0,53

yes - Enterprise wide

0,11

0

0,2

Page

0,4

74

0,6

0,8

1


Half of the respondents have translated their asset management issues into information requirements; the other half has plans to implement this. However, for the ones that formulate their information requirements only about 11% are doing this on an enterprisewide basis. With a score of 79% positive answers, information requirements are formulated for maintenance strategies. This is followed by a score between 50 and 60% showing that information requirements are formulated also for AM-strategies and risk assessment.

WG remark: The specification of the information requirements, concerning the needs of asset management, requires stronger attention, since this is the first step in getting the ICT-department in the utility to understand the needs of the asset manager. Also, the absence of a holistic approach is an indication that this area, although important, is not as mature as recommended. This field of work needs serious attention. From a holistic point of view invest in this area! A major focus is required on getting and accepting enterprisewide basic data definitions for assets and asset events, and information requirements for the asset management needs. The overall effect will be that different data sources will match better; hence the overall data quality of your system landscape will rise and reduce access time and mistakes related to the data. Also, translating the issues of the asset manager into information specification helps in understanding goals and improving the necessary cooperation between AM and the IT-department.

Scenario approach and risk balancing The survey covered scenario approach and risk balancing with 24 questions (Table 6). This chapter presents the questionnaire results to the following question groups: 

Applied philosophy for replacement / renewal strategy, depending on the type of equipment (Q 38 – Q 46),



Applied philosophy for maintenance strategy, depending on the type of equipment (Q 47 – Q 54),



Usage of risk assessment in utilities (Q 55 – Q 62).

Page

75


Question

Question

number 38

What is the applied philosophy for a replacement/renewal strategy, depending on the type of equipment ... --> this question is

subdivided and continues on following lines 39

.. is it - risk assessment

40

.. is it - condition assessment

41

.. is it - technical life time

42

.. is it - financial life time

43

.. is it - importance (e.g. non delivered energy)

44

.. is it – corrective

45

.. is it – mixed

46

.. is it - nothing, i.e. no strategy

47

What is the applied philosophy for the maintenance strategy, depending on the type of equipment ... --> this question is

subdivided and continues on following lines 48

.. is it -risk assessment

49

.. is it -condition assessment

50

.. is it -fixed time (time based)

51

.. is it -importance (e.g. non delivered energy)

52

.. is it –corrective

53

.. is it –mixed

Page

76


54

.. is it -nothing/no strategy

55

Do you use the risk assessment? If yes, for which type of equipment?

56

Do you calculate the possible outage costs of equipment?

57

Do you calculate the possible maintenance/replacement costs?

58

Do you take into account for an outage consequences like ... --> this question is subdivided and continues on following lines

59

.. like economic

60

.. like environmental

61

.. like quality of supply (non-delivered energy)

62

.. like image, public relations

63

Do you use the "risk map" to classify the consequences of an outage in different types of groups, e.g. moderate, severe, catastrophic?

Table 8: Scenario and Risk Strategy, depending on the type of equipment Different strategies are used in utilities depending on the type of equipment, and in most cases these are mixed strategies. A combination of various basic strategies as can be seen in the graphs for question 39a/e-46a/e on the applied philosophy for replacement / renewal strategy by equipment.

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77


Q39-46a: W hat is the applied philos ophy for a replac ement/renewal s trategy on c irc uit breakers

nothing, i.e no s trategy

Q39-46b: W hat is the applied philos ophy for a replac ement/renewal s trategy on pwr. trans formers

0


mix ed

0,74

c orrec tive

0,26

importanc e (eg non delivered energy)


0,11

tec hnic al life time

0,42

c ondition as s es s ment

ris k as s es s ment

0,42


0,2

0,4

0,6

0,8


0,2

0,21 0,16

0,4

0,32


0,32 0

0,26


0,6

0,8

1

0,58


0,37


0,8

0,32

financ ial life time

0,16


0,6

0

importanc e (e.g. non delivered energy)

0,26 0,11


0,4

c orrec tive

0,21


0,37 0,2

mix ed

0,53

importanc e (e.g. non delivered energy)

0,37

Q39-46d: W hat is the applied philos ophy for a replac ement/renewal s trategy on ins tr. Trans formers ?

0,05

mix ed

0,21

0

1

Q39-46c : W hat is the applied philos ophy for a replac ement/renewal s trategy on c ables ?

c orrec tive

0,11


0,21


0,32



0

0,58 0,32

c orrec tive

0,32


0

mix ed

1

0,26 0

Q39-46e: W hat is the applied philos ophy for a replac ement/renewal s trategy for G IS


0

mix ed

0,53

c orrec tive

0,26 0,32

importanc e (e.g. non delivered energy) financ ial life time

0,11


0,16 0,32

c ondition as s es s ment ris k as s es s ment

0,37 0

Page

0,2

78

0,4

0,6

0,8

1

0,2

0,4

0,6

0,8

1


The most prominent philosophies applied to the equipment types are summarized as follows: Equipment Type

Decision Criteria

circuit-breakers:

risk / condition assessment

power transformer:

risk / condition assessment

cables:

condition assessment

instrument transformers:

technical lifetime (time-based) / corrective maintenance

gas insulated substation:

risk assessment

Table 9: Decision Criteria by Equipment Type The risk, as well as the condition assessment, is often applied in case of replacement or renewal strategies whereas the technical lifetime is off minor interest. The exceptions are the instrument transformers, for which the time-based or the corrective maintenance are the favourite strategies. The reason is that the condition based maintenance needs an assessment of the technical condition, which will lead to further financial expenses and these costs have to be compared to the investment costs of the considered piece of equipment. Applied philosophy for maintenance strategy, depending on the type of equipment In principle a mixed strategy is often applied by the companies, meaning that different types of strategies are used for the same asset group during the total useful lifetime of equipment (Fig. Q48-54). For example, during the first period the time-based maintenance can be applied and towards the end of the lifetime, a transition to a condition-based maintenance is useful.

Page

79


Q48-54a: W hat is the applied philos ophy for the maintenanc e s trategy on c irc uit breakers

Q39-46b: W hat is the applied philos ophy for a replac ement/renewal s trategy on pwr. trans formers

nothing/no s trategy

nothing, i.e no s trategy mix ed

mix ed

c orrec tive

c orrec tive


0 0,63 0,16

importanc e(eg non delivered energy)

0,21

financ ial life time fix ed time (time bas ed)

tec hnic al life time c ondition as s es s ment ris k as s es s ment 0

0,2

0,4

0,6

0,8

0,37


0,32


0,32

1

0

0,4

0,8

1

0


0

0,42

mix ed

mix ed

0,6

Q48-54c : W hat is the applied philos ophy for the maintenanc e s trategy on c ables

Q48-54b: W hat is the applied philos ophy for the maintenanc e s trategy on pwr trans formers


0,2

0,58

c orrec tive

0,16


0,16

fix ed time (time bas ed)

c orrec tive

0,16


0,16

0,37


0,26

ris k as s es s ment 0,2

0,4

0,6

0,8

1

0,26 0,2

0,32

0,32 0,32

0,21



0,21


0,6

0,8

1

Page

0,21



0,4

1

0,11


0,11

0,2

0,8

0,53

c orrec tive

0,05

0

0,6

mix ed

0,47


0,4

0


mix ed


0,26

Q48-54e: W hat is the applied philos ophy for the maintenanc e s trategy on G IS

0

c orrec tive


0

Q48-54d: W hat is the applied philos ophy for the maintenanc e s trategy on ins tr. trans formers nothing/no s trategy

0,26


0,32 0


0,26 0

80

0,2

0,4

0,6

0,8

1


Also in this case mixed strategies are used. The most prominent philosophies applied to the equipment depend on the type of equipment: Equipment

Philosophy

circuit-breakers:

time-based

power transformer:

time-based

cables:

risk / condition-based / time-based

instrument transformers:

time-based

gas insulated substation:

condition-based / time-based

Table 10: Equipment Maintenance Philosophies The answers show that the importance of the asset in the system influences the choice of the replacement strategy followed, whereas for the maintenance strategies it is of less interest. Use of risk assessment In general, the philosophy of risk assessment is widely used for the most expensive asset group, such as power transformer (58 %), GIS (53 %) and circuit-breaker (84 %). For other equipment risk assessment is not much applied, e.g. for instrument transformers (37 %) and cables (47 %). This answer is in line with the decision making process in case of replacement or renewal strategies. For the risk assessment the overall cost of an outage as well as the cost for maintenance / replacement to avoid the outage should be taken into consideration too. The responses show that except in case of power transformers, the costs due to an outage are not widely considered, whereas considerations of the maintenance/ replacement costs are often used for all asset groups, mainly for the power transformers, circuit-breakers and GIS (Fig. Q56 - 57).

Page

81


Q57: Do you c alc ulate the pos s ible maintenanc e/replac ement c os ts ?

Q56: Do you c alc ulate the pos s ible outage c os ts of an equipment?

G IS

0,21

G IS ins trument trans former

0,74

0,11

ins trument trans former

c ables

0,26

c ables

power trans formers

0,53

c irc uit breaker 0,2

0,53

power trans formers

0,89

c irc uit breaker

0,26 0

0,58

0,4

0,6

0,8

0,84 0

1

0,2

0,4

0,6

0,8

1

In many cases, the outage consequences (as per question Q58-62) are taken into account for the decision making process by the utilities and the following point of views are considered. In any case, the quality of supply plays an important role, followed by economic aspects: Economic

95 %

Environmental

74 %

Quality of supply (non-delivered energy)

100 %

Image, public relation

79 %

In theory it makes sense to apply a "risk map" to visualize the risk assessment, because the risk is defined as a product of probability of an outage and its consequence. But the answer on corresponding question (Q63 / no figure) show that this type of risk interpretation is not frequently used (20 %)

Decision and control The survey covered decision and control aspects with a total of 6 questions (Table 7).

Page

82



Is your decision policy based on an IT support system (in addition to written reports and demands)

65

Do you apply defined decision criteria

66

Have you defined measurable economic criteria and on what are they based

67

Have you defined measurable technical criteria and on what are they based

68

Have you defined measurable social criteria and on what are they based

69

Have you measurable risk criteria and on what are they based

Table 11: Decision and Control Questions Decision Policy based on IT support systems IT systems are at least partly used for decision support in addition to the commonly used written reports and demands. Over 60% of the respondents indicated that their decision policies are partly supported using IT systems, whereas only 11% of the respondents indicate that they do base their decision policy on IT systems (graph Q64). More wideranging is the different criteria used for making decisions, which are somewhat evenly spread throughout several considerations: 

Technical,



Economic,



Condition assessment,



Risk assessment,



Social.

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83


Q65: Do you apply defined dec is ion c riteria

Q64: Is your dec is ion polic y bas ed on an IT s upport s ys tem (in addition to written reports and demands )

partly


0,63


0,63

0,63

no

s oc iologic al

0,21

0,37

tec hnic al yes

0,84

0,11

ec onomic al 0

0,2

0,4

0,6

0,8

0,74 0

1

0,2

0,4

0,6

0,8

1

It is noted that the decision policy is most commonly based on technical (84%) and economic (74%) criteria than on other soft-skill considerations such as social (37%) criteria (Fig. Q65). Economic criteria Almost all respondents, who base their decision policy on economic considerations (79% Fig. Q65), use economic criteria such as the net present value (NPV) and life cycle cost (LCC) methodologies beyond the simple consideration of investment costs alone (Fig. Q66). Q66: Have you defined meas urable ec onimic al c riteria and on what are they bas ed minimum inves tment c os ts

0

minimum life c yc le c os ts (L C C )

0,37

net pres ent value (NP V )

0,42

ec onomic al value added (E V A )

0

0

0,2

0,4

0,6

0,8

1

Technical criteria Decision making has historically been based on technical criteria, reflected on the amount of responses obtained under this category. The respondents were asked to indicate which from the following specific technical criteria was commonly applied:

Page

84




Reliability information,



Condition information,



Age information,



Availability of spare parts,



Availability of know-how.

All of the above mentioned criteria were represented in the survey responses, with a slight lower percentage for the use of know-how availability (Fig. Q67). Q67: Have you defined meas urable tec hnic al c riteria and on what are they bas ed other

0,05

availability of knowhow

0,37

availability of s pare parts

0,63

age information

0,74

c ondition information

0,74

reliability information

0,68 0

0,2

0,4

0,6

0,8

1

Social criteria The respondents who consider social criteria in their decision policy mostly include personal safety and environmental aspects (Fig. Q68): however, it must also be considered that the measurability of these criteria is highly difficult and subjective and that the regulatory bodies do have a strong influence in their application.

Page

85


Q68: Have you defined meas urable s oc iologic al c riteria and on what are they bas ed other

0

environmental rec laim ris ks

0,74

power quality rec laim ris ks

0,26

equipment s afety ris ks

0,58

pers onal s afety ris ks

0,79 0

0,2

0,4

0,6

0,8

1

Risk criteria Another important criterion refers to the application of risk assessment regarding the economic, technical and social aspects considered, and the definition and application of corresponding measurable risk criteria to their own decision policy. From the total of respondents, who include risk assessment criteria (84% according to 8.1) in their decision policy, criteria including technical risks are predominantly used (79%), followed by the inclusion of economic (53%) and social (47%) risks. A consolidation of the risks to define measurable criteria is not commonly used, being adopted by only 21% of the respondents (Fig. Q69). Q69: Have you meas urable ris k c riteria and on what are they bas ed

c ons olidated ris ks

0,21

s oc ialogic al ris ks

0,47

tec hnic al ris ks

0,79

ec onomic al ris ks

0,53

0

0,2

0,4

Page

86

0,6

0,8

1


Conclusion Utilities have a huge amount of raw data available, which needs to be managed to support the decision making process. Quite a lot of front end data acquisition such as ERP’s (enterprise resource planning) and EAM’s (enterprise asset management) systems are available nowadays. From utility point of view to support decision making process all available IT Systems should be integrated in an overall methodology.

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FINAL CONCLUSIONS The report presents an overview of the methodology used for defining IT-strategies for asset management based on the WG work and results from a survey on this topic. The final goal of IT-strategies for AM is to obtain better decisions for asset management and for that it needs to have a holistic point of view including technical, economic and social aspects. The key aspects to be considered are grouped below:

About integration The decision-making process requires a holistic approach in the field of replacement, renewal and maintenance strategies. The results show that the majority of utilities use mixed strategies, depending on the type of equipment. Decisions are mostly based on both technical and economic criteria; social criteria such as safety and environmental aspects are somewhat behind in the IT. But despite the huge amounts of present data within the utilities and despite the broad range of functionality offered by vendors of (enterprise) systems, the asset manager still lacks an integrated solution.

About data To support asset management decisions a huge amount of data is available and the “right information” should be extracted from it as shown in the leading model, Fig. 1. Primary equipment is mostly stored in enterprise-wide databases, but this is less common with secondary equipment. The level of detail of this information is mostly recorded on asset level. It becomes clear that failure, outage, performance and maintenance data are very important in the decision making process; this data is well recorded. In addition there is a high degree of recording diagnosis (offline) data. Most of this data is collected by hand but this is, with the migration to intelligent grids, definitely going to change according to the respondents. Still a substantial amount of data is recorded outside the enterprise wide database. From a data management point of view the utilities should focus on integrating these existing datasets.

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About IT-systems This survey further shows a remarkable need for ERP systems in the utilities and the integration of basic data as described above. Integration of technical (e.g. SCADA GeoIS, EAM) and non-technical systems (e.g. CRM, ERP) are not common in the branch. Asset management strategies urge the need for data- integration and results show that many utilities are only at the beginning of this integration, starting to buy and to implement enterprise-wide systems and introducing data warehouse solutions in an attempt to integrate data. From a utility’s point of view these systems are not sufficient to support the decision making process since they are not integrated in an overall methodology. That’s probably the reason why decision support systems are not commonly used in the field of asset management.

About modelling According to the leading model (Fig. 1) there is a need to link information requirements with data requirements. To obtain that link it is necessary to have integrated data and a well-defined enterprise-wide data model. The survey shows that enterprise-wide data models do exist but that data models from source systems do not fully match. Source systems therefore often do not match the requirements of the asset manager. But this is only part of the gap. In addition, results also show that existing models, within the utilities themselves, strongly focus on technical data, less on economic data and social data is only present in some models. The decision-making process, on the other hand, requires data models that describe data needed in mixed strategies and matching technical, economic and social requirements from a holistic point of view. So existing models within the utilities itself often do not match the needed holistic approach. Succeeding in filling this gap is the first step in obtaining better decisions in the field of asset management. In the opinion of the Working Group and based on the responses of the survey it is important to consider the following items. From a business perspective:

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

A methodology to evaluate and handle risks should be implemented. o We hereby like to refer to the Cigré document “Transmission Asset Risk Management” from Cigré working group C1.16.



Adopt a common information model to be used in the utility. o Such a model is for example the Common Information Model for Utilities (CIM)

recently

adopted

by

the

UCTE.

Also

the

model

has

to

be

adapted/extended to the specific needs of the enterprise, based on needs in the decision making process (Fig. 15) and to cope with all relevant standards. 

Define enterprise wide goals in terms of data quality, data management and data integration. Information and Communication Technology (ICT) expertise on data modelling can help here.



Select asset master data based on the reference model. o Master data is defined as the key data attributes of the most important business objects.



Initiate Data-Quality processes. o Start a Data-Quality-Team on a (group of) business object(s). Within that group, create and maintain master data based on well-defined KPI’s. This team should implement a process that governs the activities of creating, updating and deleting master data during the whole lifecycle of a business object. Best practice indicates that only a limited and dedicated group of employees should change this master data. Business is responsible for the content of master data; ICT is responsible for the structure of the master data

From the Information and Communication Technology perspective: 

Support this concept of master data with needed functionalities.



Implement software tools to improve data quality and to support the Data-QualityTeam e.g. by dealing with issues like de-duplication, data cleaning, specialized input tools, handling of missing and wrong data etc.



As soon as master data functionalities are implemented, extend to other data which are not part of master data.



Synchronize master data all over the system-landscape.

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

Take the master data source as a reference point. Use this reference point in all related systems. System by system, and prioritized by the business relevance. This synchronization varies with the complexity of the system landscape. It is therefore also a natural incentive to “clean” the system landscape and to improve the quality of the data.



Organize that every ICT-project and every system-change respect the defined set of master data.



Its common practice that every ICT-project and every ICT-change or -interface has to

be

evaluated

before

it

is

implemented.

Re-use

those

processes

for

implementation of master data. By defining, maintaining and synchronizing this set of master data over the entire system landscape the data quality will rapidly improve. The master data also serves as the most important reference for use in data warehouse and decision support systems. An IT strategy for asset management requires a strong partnership between Business and ICT in a utility. In this partnership business is responsible for the data itself; the contents and the data quality. ICT is responsible for the structure, tools and availability of data through the IT-system. This partnership can be enhanced by creating an organisational unit that facilitates it and even further governance of information throughout the enterprise. This leads back to the middle of the previous presented leading model (Fig. 1); it suggests how utilities can form the creation of the intelligent hub.

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REFERENCES [1]

Cigré JW Group B3/C2 “Maintenance & Reliability”, Task Force 03: Implementation of information strategy to support utility asset management, Cigré A3&B3 joint colloquium Tokyo 2005, Paper no. P1-04

[2]

Cigré JW Group B3/C2 “Maintenance & Reliability”, Task Force 03: Information Strategy to support utility asset management, Electra No. 207, April 2003

[3]

Cigré Working Group C1.1: Asset Management of Transmission Systems and Associated Cigré Activities, Cigré Brochure 309, December 2006

[4]

Cigré JW Group 23/39.14: Maintenance Outsourcing Guidelines. Cigré Brochure 201, August 2001

[5]

Bartlett, S. (TF23.18): Asset Management in a deregulated environment, Cigré Paris 2002, report 23-303

[6]

Cigré Working Group C1.16: Transmission Asset Risk Management. Cigré Brochure 422, August 2008

[7]

Cigré Working Group 37-27: Ageing of the System – Impact on Planning, Cigré Brochure 176, December 2000

[8]

Balzer, G.; Bakic, K.; Haubrich, H.-J., Neumann, C.; Schorn, C.: Selection of an Optimal Maintenance and Replacement Strategy of H.V. Equipment by a Risk Assessment Process. Cigré 2006, Paris, rep. B3-103

[9]

Working Group C1.11: Asset Management Performance Benchmarking, Cigré Brochure 367, February 2008

[10] Balzer, G.; Schorn, C.: Risk assessment of high voltage equipment. CEPSI 2004, Shanghai, report 102 [11] Cigré Working Group B3.03: Guidelines to an Optimized Approach to the Renewal of Existing Air Insulated Substations. Cigré Brochure 300, August 2006 [12] Balzer, G.; Gößmann, T.; Schorn, C.; Benz, T.: The General Asset Management Process of Power Systems. CEPSI-2008, Macao; report 1052

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