what we know about onshore & offshore wind turbine ... · wind turbine reliability with...

What we know about Onshore & Offshore

Wind Turbine Reliability with 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”

Dante Alighieri

NTNU, EU FR7 MARE WINT Project

September 2013 1

Keynotes

• Aim to reduce risk, raise turbine Reliability and

Availability, Reduce offshore wind Cost of Energy;

• Wind Turbine Reliability from onshore experience;

• What we know about WT reliability;

• Wind Turbine Availability, what is happening

Offshore?


September 2013 2

Mitsubishi 1 MW,

Fixed-speed,

Stall-regulated,

Variable-pitch

Wind Turbine Power Curves

Alstom 1.67 MW,

Variable-speed,

Variable-pitch


September 2013 3

Wind

Turbine

Operation-

18 days

WT rating= 1.67MW

Variable-speed, Variable-pitch

Average 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


September 2013 5

http://www.bmreports.com/bsp/bsp_home.htm

https://demanda.ree.es/eolicaEng.html

Trend in Turbine Failure Rates with Time


September 2013 6

WT Reliability and Size, EU

Small, group I Medium, group II Large, group III


September 2013 7

Reliability, Downtime and Subassemblies, EU


September 2013 8

Typical WT Generator Failure Intensities


September 2013 9

More detail on WT Generator Failures


September 2013 10

Typical WT Gearbox Failure Intensities


September 2013 11

WT Reliability-Downtime per

Assembly

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

Brake System

Grid

Structure

Blade System

Electrical

Converter

Generator

Ambient

Gearbox

Pitch System

Scheduled Service

Yaw System

Control System Egmond aan Zee Failure Rate, 108Turbine Years

Egmond aan Zee Downtime, 108Turbine Years

100 75 50 25 0 0.5 1 1.5 2 2.5 3

Annual Stop Frequency Downtime per Stop (days)

Stop Rate and Downtime from Egmond aan Zee Wind Farm, the Netherlands, over 3 Years


September 2013 12

Typical WT Converter Failure Intensities


September 2013 13

Data Source:

Reliawind Deliverable D.1.3 – Reliability

Profiles Figures subject to update.

Lighter larger background blocks show sub-systems;

Darker smaller foreground blocks show assemblies;

The line shows the Pareto cumulative contribution.

Reliability & Subassemblies, EU,

Reliawind

Data Source:

Reliawind Deliverable D.1.3 –

Reliability Profiles

Lighter large background blocks show sub-systems;

Darker smaller foreground blocks show assemblies;

The line shows the Pareto cumulative contribution.

Downtime & Subassemblies, EU,

Reliawind

Sub-system / Assembly

Failure Mode 1

Failure Mode 2

Failure Mode 3

Failure Mode 4

Failure Mode 5

Pitch

System

Electrical

(5 out of

13)

Battery Failure Pitch Motor

Failure

Pitch Motor

Converter Failure

Pitch Bearing

Failure

Temperature or

Humidity Sensor

Failure

Hydraulic

(5 out of 5)

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

Control System

(5 out of 5)

Temperature

sensor modules

malfunction

PLC analogue

input malfunction

PLC analogue

output

malfunction

PLC digital input

malfunction

PLC In Line

Controller

malfunction

Generator Assembly

(5 out of 11) Worn slip ring

brushes

Stator winding

temperature

sensor failure

Encoder failure Bearing failure External fan

failure

Gearbox Assembly

(5 out of 5) Planetary Gear

Failure

High Speed Shaft

Bearing Failure

Intermediate

Shaft Bearing

Failure

Planetary

Bearing Failure

Lubrication

System

Malfunction

Summary of least reliable sub-assemblies

& their failure modes

Studies on Different WTs

• WT Make A

– 153x1.5-2MW WTs with 3-blades, electric pitch-

regulation, geared-drive, DFIG generator and

partially-rated converter

– 6 Wind Farms

– Over 2 years

• WT Make B

– 366x2.5MW WTs with 3-blades, electric pitch-

regulation, geared-drive, synchronous generator

and fully-rated converter

– 1 year


September 2013 17

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


September 2013 18

Alarm KPIs from 7 Wind Farms


September 2013 19

WT Make A – Alarm System Performance

• 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.

Wind Farms

3 & 6Wind Farm 4

Wind Farms

1 & 5Wind Farm 2

1 10000 10000010 100 1000

KPI 2, Maximum AlarmTriggers per 10 min

KP

I 1

, A

ve

ra

ge

Ala

rm

Trig

ge

rs

pe

r 1

0 m

in

10

01

00

00

.11

10

Reactive Alarms

Stable Alarms

Recommendation

for Alarm presentation

to humans Presentation

to Operators

Alarm Rates

must be pre-

processed


September 2013 20

WT Pitch Mechanism Taxonomy


September 2013 21

WT Pitch Alarm,

Relationships over 2 years,

Qiu et al


September 2013 22

WT Converter Taxonomy


September 2013 23

369 - Pitch

372-374 - Blade1-3 Emergency322 - Inverter

338/343

Rotor-side Inverter Over-

current/

Over- temperature

349

Grid Voltage Dip

337

Grid-side

Inverter Over-

current

263

Main Switch

345

DC Overvoltage

WT Converter Alarms,

Statistical Relationships over 2 years,

Qiu et al

WT Converter Alarm

Showers,

2 years,

Qiu et al 2 years

1st grid

incident 2nd grid

incident

Power Electronics Reliability,

Measured, Wolfgang


September 2013 26

WT Unreliability & Importance of Pre-Testing

Root Causes

Failure Modes

And Effects

Analysis,

FMEA

Condition

Monitoring

Signals

SCADA

Signal

Analysis

Wind condition

Weather

Faulty design

Faulty materials

Poor maintenance

How?

Pre-Testing during Prototype Development

Or In-Service SCADA & CMS Analysis & Diagnosis

Why?

Root Cause Analysis

Failure Location


September 2013 27

Onshore

Availability and Wind Speed

0.0

20.0

40.0

60.0

80.0

100.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Ava

ilab

ility

, %

Wind Speed, m/s

Brazos, Texas, USA, 160 MW, 160 x Mitsubishi MWT1000, 1 MW


September 2013 28

0.0

20.0

40.0

60.0

80.0

100.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Ava

ilab

ility

, %

Wind Speed, m/s

Barrow, UK, 90 MW, 30 x Vestas V90, 3 MW

Offshore

Availability and Wind Speed

29

0.0

20.0

40.0

60.0

80.0

100.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Ava

ilab

ility

, %

Wind Speed, m/s

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

0.0

20.0

40.0

60.0

80.0

100.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Ava

ilab

ility

, %

Wind Speed, m/s

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

0.0

20.0

40.0

60.0

80.0

100.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Ava

ilab

ility

, %

Wind Speed, m/s

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

0.0

20.0

40.0

60.0

80.0

100.0

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Ava

ilab

ility

,%

Wind Speed, m/s

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

Onshore Availability and Wind

Speed, World

40%

energy

produced

at wind

speeds

>11m/s


September 2013

North European Offshore Wind Farm Performance

0

10

20

30

40

50

60

70

80

90

100

Win

d S

pee

d, m

/s; C

apac

ity

Fact

or,

%; A

vaila

bili

ty, %

Availability

Capacity

Factor

Wind

Speed


September 2013 31

Three North European Offshore Wind Farms

Scroby Sands North Hoyle Egmond aan Zee

Relationship between Failures & Weather


September 2013 32

Relationship between Failures & Weather


September 2013 33

Power Curves & Turbulence from SCADA 1101 1087

1114

Power curves

Red – SCADA real power

curve

Blue – Manufacturer’s

theoretical power curve


September 2013 34

Wind Turbulence from Fast Data

Distribution of

wind velocity

Gaussian

Distribution

Is this difference

driving failures?

Probability density function of spatial transversal wind

velocity increments over a distance of 10 m, for τ=4 s

compared to a Gaussian distribution.


September 2013 35

Wind Turbulence from Slow Data

WT SCADA

Probability density function of wind velocity

from 10 min SCADA

Gaussian

Distribution

Distribution of

wind velocity

from SCADA


September 2013 36

Wind Turbulence from Slow Data

WT SCADA


September 2013 37

Turbulence in

context

But turbulence of this

dimension could affect

drive train

Turbulence of this

dimension will not

affect drive train


September 2013 38

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0 5000 10000 15000 20000 25000

Pe

rce

nta

ge lo

ad c

han

ge o

ccu

rre

nce

s

Radial load change on left HSS gearbox bearing every 10 mins (N)

1097

1209

1221

1222

1096

1217

1202

1219

1098

1103

1114

1120

1557

1558

1560

1339

1341

1342

• Most based on 2.2 years data

• Use bin size of 100 N

• High variety between 0-6000N

• WTs are from 5 different sites

SCADA Load Changes, 18 different WTs

Courtesy: Dr Hui Long NTNU, EU FR7 MARE WINT Project

September 2013 39

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0 1000 2000 3000 4000 5000 6000

Pe

rce

nta

ge o

f lo

ad c

han

ge o

ccu

rre

nce

s


1097

1096

1098

Difference between sites, Site 1

Healthy WTs with relatively low load change

WTs from same site have similar load change

distributions


September 2013 40

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

0 1000 2000 3000 4000 5000 6000

Pe

rce

nta

ge o

f lo

ad c

han

ge o

ccu

rre

nce

s


1209

1221

1222

1217

1202

1219

Difference between sites, Site 4

Two WTs on this site had

serious failures


September 2013 41

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;

• Two WTs have different

bearing load increment

occurrence frequencies.

Courtesy: Dr Hui Long

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


September 2013 43

References Tavner PJ, Xiang JP and Spinato F, Reliability analysis for wind

turbines. Wind Energy 10(1): 1-18, 2006.

Spinato F, Tavner PJ, van Bussel GJW and Koutoulakos E, Reliability

of wind turbine subassemblies. IET Renewable, Power Generation

3(4): 1–15, 2009.

Wilkinson M, Hendriks B, Spinato F, Gomez E, Bulacio H, Roca J,

Tavner P J, Feng Y, Long H, Methodology and results of the

ReliaWind reliability field study, Scientific Track, European Wind

Energy Conference, Warsaw, 2010.

Feng Y, Tavner PJ and Long H, Early experiences with UK round 1

offshore wind farms, Proc Institution of Civil Engineers – Energy,

163(EN4): 167–181, 2010.

Tavner, P J, Faulstich, S, Hahn, B, van Bussel, G J W, Reliability &

availability of wind turbine electrical & electronic components,

European Power Electronics Journal, 20(4): 1-6, 2010.

Faulstich, S., Hahn B., Tavner, P. J., Wind turbine downtime and its

importance for offshore deployment, Wind Energy,

DOI: 10.1002/we.421, published on early view.


September 2013 44

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