083 Peter Tavner Reliability II

What we know about Onshore & Offshore Wind Turbine Reliability with w ith particular particular reference to Future Offshore Wind Farm Operational Performance Peter Tavner Emeritus Professor, Durham University, UK Past President, European Academy of Wind Energy

“The darkest regions of hell are reserved for those who remain neutral at times of moral crisis” D an an t e A l i g h i e r i

NTNU, EU FR7 MARE WINT Project

Keynotes •

Aim to reduce risk, raise turbine turbine Reliability Reliability and Availability, Availability, Reduce offshore wind Cost of Energy;

•

Wind Turbine Reliability from onshore experience;

•

What we know about WT W T reliability; reliability;

•

Wind Turbine Availability, Availability, what is happening Offshore?


Keynotes •

Aim to reduce risk, raise turbine turbine Reliability Reliability and Availability, Availability, Reduce offshore wind Cost of Energy;

•

Wind Turbine Reliability from onshore experience;

•

What we know about WT W T reliability; reliability;

•

Wind Turbine Availability, Availability, what is happening Offshore?


Wind Turbine Power Curves

Alstom 1.67 MW, MW, Variable-speed, Mitsubishi 1 MW, Variable-pitch Fixed-speed, Stall-regulated, Variable-pitch

NTNU, EU FR7 MARE W INT Project Project

Wind Turbine Operation18 days WT rating= 1.67MW Variable-speed, Variable-pitch Average output=490kW output=490kW Capacity factor =500/1670=29%

Actual Wind Power Production UK & Spain

http://www.bmreports.com/bsp/bsp_home.htm https://demanda.ree.es/eolicaEng.html

NTNU, EU FR7 MARE W INT Project

Trend in Turbine Failure Rates with Time


WT Reliability and Size, EU Small, group I

Medium, group II


Large, group III

Reliability, Downtime and Subassemblies, EU


Typical WT Generator Failure Intensities


More detail on WT Generator Failures


Typical WT Gearbox Failure Intensities


WT Reliability-Downtime per Assembly Stop Rate and Downtime from Egmond aan Zee Wind Farm, the Netherlands, over 3 Years -2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

Egmond aan Zee Failure Rate, 108 Turbine Years

Control System Yaw System

Egmond aan Zee Downtime, 108 Turbine Years

Scheduled Service Pitch System Gearbox Ambient Generator Converter Electrical Blade System Structure Grid Brake System 100

75

50

25

Annual Stop Frequency

0

0.5

1

1.5

2

Downtime per Stop (days)


2.5

3

Typical WT Converter Failure Intensities


Reliability & Subassemblies, EU, Reliawind

Data Source: Reliawind Deliverable D.1.3 – Reliability

Lighter larger background blocks show sub-systems; Darker smaller foreground blocks show assemblies;

Downtime & Subassemblies, EU, Reliawind

Data Source: Reliawind Deliverable D.1.3

–

Lighter large background blocks show sub-systems; Darker smaller foreground blocks show assemblies;

Summary of least reliable sub-assemblies & their failure modes Sub-system / Assembly Electrical (5 out of 13)

Failure Mode 1

Failure Mode 2

Failure Mode 3

Failure Mode 4

Failure Mode 5

Battery Failure

Pitch Motor Failure

Pitch Motor Converter Failure

Pitch Bearing Failure

Temperature or Humidity Sensor Failure

Internal leakage of proportional valve

Internal leakage of solenoid valve

Hydraulic cylinder leakage

Position sensor degraded or no signal

Pressure control valve sensor degraded signal

Frequency Converter (5 out of 18)

Generator-side or Grid-side Inverter Failure

Loss of Generator Speed Signal

Crowbar Failure

Converter Cooling Failure

Control Board Failure

Yaw System (5 out of 5)

Yaw gearbox & pinion lubrication out of specification

Degraded wind direction signal

Degraded guiding element function

Degraded hydraulic cylinder function

Brake operation valve does not operate

Temperature sensor modules malfunction

PLC analogue input malfunction

PLC analogue output malfunction

PLC digital input malfunction

PLC In Line Controller malfunction

Pitch System

Hydraulic (5 out of 5)

Control System (5 out of 5) Generator Assembly (5 out of 11)

Worn slip ring brushes

Stator winding temperature sensor failure

Encoder failure

Bearing failure

External fan failure

Gearbox Assembly

Planetary Gear

High Speed Shaft

Intermediate Shaft Bearing

Planetary

Lubrication System

Studies on Different WTs •

•

WT Make A –

153x1.5-2MW WTs with 3-blades, electric pitchregulation, geared-drive, DFIG generator and partially-rated converter

–

6 Wind Farms

–

Over 2 years

WT Make B –

366x2.5MW WTs with 3-blades, electric pitchregulation, geared-drive, synchronous generator and fully-rated converter

–

1 year NTNU, EU FR7 MARE WINT Project

SCADA Alarm System Performance Evaluation -KPIs *

•

Reference : Alarm systems, a guide to design, management and procurement No. 191 Engineering Equipment and Materials Users Association 1999 ISBN 0 8593 1076 0

KPIs: Key Performance Indices * – KPI 1, Average Alarm Rate: Long term average of the number of alarm triggers occurring within a 10 min SCADA interval. –

KPI 2, Maximum Alarm Rate: Maximum number of alarm triggers occurring within a 10 min SCADA interval.

•

Additional Definitions * – Alarm Shower: A single fault causing a large number of alarm triggers, in this work an Alarm Shower consists of > 10 alarm triggers


Alarm KPIs from 7 Wind Farms


WT Make A – Alarm System Performance 0 0 0 n 1 i m 0 1 r e 0 p 0 1 s r e g g i r 0 T 1 m r a l A e 1 Presentation g to Operators a r Alarm Rates e v must be pre A 1 processed , . 1 0 I P 10 1 K

Wind Farms 3&6

Reactive Alarms Wind Farm 4 Stable Alarms

Wind Farms 1&5

100

Recommendation for Alarm presentation to humans

Wind Farm 2

1000

10000

100000

KPI 2, Maximum AlarmTriggers per 10 min

•

Reactive- peak alarm rate during upset is unmanageable and alarm system will continue to present an unhelpful distraction to the operator for long period.

•

Stable- Alarms have been well defined for normal operation, but the system is less useful during plant upset. NTNU, EU FR7 MARE WINT Project

WT Pitch Mechanism Taxonomy


WT Pitch Alarm, Relationships over 2 years, Qiu et al


WT Converter Taxonomy


WT Converter Alarms, Statistical Relationships over 2 years, Qiu et al 337 Grid-side Inverter Overcurrent

338/343 Rotor-side Inverter Overcurrent/ Over- temperature 345 DC Overvoltage

349 Grid Voltage Dip

369 - Pitch 372-374 - Blade1-3 Emergency

322 - Inverter

263 Main Switch

WT Converter Alarm Showers, 2 years, Qiu et al

1st grid incident

2 years

2nd grid incident

Power Electronics Reliability, Measured, Wolfgang


WT Unreliability & Importance of Pre-Testing

Root Causes

Wind condition Weather Faulty design Faulty materials Poor maintenance

Condition Monitoring Signals

SCADA Signal Analysis

Failure Modes And Effects Analysis, FMEA

How? Pre-Testing during Prototype Development Or In-Service SCADA & CMS Analysis & Diagnosis

Why? Root Cause Analysis


Failure Location

Onshore Availability and Wind Speed Brazos, Texas, USA, 160 MW, 160 x Mitsubishi MWT1000, 1 MW 100.0

80.0

% , y t i l i b a l i a v A

60.0

40.0

20.0

0.0 0.0

2.0

4.0

6.0

8.0

10.0

Wind Speed, m/s


12.0

14.0

Offshore Availability and Wind Speed Barrow, UK, 90 MW, 30 x Vestas V90, 3 MW 100.0 80.0 % , y t i l i b a l i a v A

Scroby Sands, UK, 60 MW, 30 x Vestas V80, 2 MW 60.0

100.0

40.0

80.0

20.0 0.0 0.0

% , y t i l i b a l i a v A

Kentish Flats, UK,90 MW, 30 x Vestas V90, 3 MW 100.0

60.0

80.0

40.0 2.0

4.0

20.0 0.0 0.0

6.0 % ,

8.0

y 60.0 t i Wind l Speed, m/s i b a l i 40.0 a v A

2.0

4.0 20.0 0.0

6.0

10.0

12.0

Egmond aan Zee, Netherlands, 108 MW, 36 x Vestas V90, 14.0 3 MW

100.0 8.0

80.0 10.0

% , Wind Speed, m/s y t i 60.0 l i b a l i 40.0 a 4.0 0.0 2.0 v A

20.0

12.0

14.0

North Hoyle, UK, 60 MW, 30 x Vestas V80, 2 MW 6.0

8.0 100.0

10.0

12.0

14.0

Wind Speed, m/s 80.0

0.0 0.0

% ,

y 60.04.0 2.0 t i l i b a l i a v A

6.0

8.0

10.0

12.0

60

80

14.0

Wind Speed, m/s 40.0 20.0 0.0 00

20

40

10 0

12 0

2914 0

Onshore Availability and Wind Speed, World 40% energy produced at wind speeds >11m/s


North European Offshore Wind Farm Performance Three North European Offshore Wind Farms Scroby Sands North Hoyle Egmond aan Zee 100 90

Availability

% , y t i 80 l i b a l i a 70 v A ; % , r 60 o t c a F y 50 t i c a p a C 40 ; s / m , d 30 e e p S d 20 n i W

Capacity Factor

Wind Speed

10 0


Relationship between Failures & Weather


Relationship between Failures & Weather


Power Curves & Turbulence from SCADA 1101

1087

1114

Power curves Red – SCADA real power curve Blue – Manufacturer’s theoretical power curve NTNU, EU FR7 MARE WINT Project

Wind Turbulence from Fast Data

Distribution of wind velocity Is this difference driving failures? Gaussian Distribution

Probability density function of spatial transversal wind velocity increments over a distance of 10 m, for τ =4 s compared to a Gaussian distribution. NTNU, EU FR7 MARE WINT Project

Wind Turbulence from Slow Data WT SCADA

Distribution of wind velocity from SCADA Gaussian Distribution

Probability density function of wind velocity from 10 min SCADA NTNU, EU FR7 MARE WINT Project

Wind Turbulence from Slow Data WT SCADA


Turbulence in context But turbulence of this dimension could affect drive train Turbulence of this dimension will not affect drive train


SCADA Load Changes, 18 different WTs 2.5%

s e c2.0% n e r r u c c o e1.5% g n a h c d a1.0% o l e g a t n e c r 0.5% e P

1097 1209

• • •

Most based on 2.2 years data • Use bin size of 100 N High variety between 0-6000N WTs are from 5 different sites

1221 1222 1096 1217 1202 1219 1098 1103 1114 1120 1557 1558 1560 1339 1341 1342

0.0% 0

5000

10000

15000

20000

Radial load change on left HSS gearbox bearing every 10 mins (N) Courtesy: Dr Hui Long NTNU, EU FR7 MARE WINT Project

25000

Difference between sites, Site 1 2.5%

s e c n e 2.0% r r u c c o e g1.5% n a h c d a o1.0% l f o e g a t n0.5% e c r e P

Healthy WTs with relatively low load change WTs from same site have similar load change distributions

1097 1096 1098

0.0% 0

1000

2000

3000

4000

5000

Radial load change on left HSS gearbox bearing every 10 mins (N) NTNU, EU FR7 MARE WINT Project

6000

Difference between sites, Site 4 2.5%

s e c n e 2.0% r r u c c o e g1.5% n a h c d a o1.0% l f o e g a t n0.5% e c r e P

Two WTs on this site had serious failures

1209 1221 1222 1217 1202 1219

0.0% 0

1000

2000

3000

4000

5000

Radial load change on left HSS gearbox bearing every 10 mins (N) Courtesy Dr Hui Long NTNU, EU FR7 MARE WINT Project

6000

PDF of HSS Bearing Radial Load Increments from SCADA •

Compared with Gaussian distribution the evident different tail corresponds to large load increments;

•

Suggesting large load increments observed more frequently than expected; •

Courtesy: Dr Hui Long

Two WTs have different bearing load increment occurrence frequencies.

Conclusions •

For onshore WTs 75% faults cause 5% downtime, 25% faults cause 95% downtime

•

Pitch and Main Converters suffer from many faults but with low downtimes.

•

Pitch and Main Converters represent a significant part of the 75%.

•

This behaviour could be problematic offshore.

•

WT reliability is affecting offshore performance

•

Sub-assemblies with high failure rates are consistent

•

Turbulence seems to be causing failures

•

Analysis of SCADA wind speeds, torques & shaft speeds is showing ample evidence of turbulent effects

•

Link between turbulence and failures is difficult to prove

•

The mathematical tools to be used are not yet clear NTNU, EU FR7 MARE W INT Project

083 Peter Tavner Reliability II

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