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Identifying risks by onshore experience

RELIABILITY OF OFFSHORE WIND TURBINES

S. Faulstich, B. Hahn, P. Lyding (Fraunhofer IWES)P.J. Tavner (Durham University)

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RELIABILITY OF OFFSHORE WIND TURBINES

Motivation

Sources of onshore experience

Identified risks

Conclusions

Outlook

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Motivation• Onshore:

• high availability

• a number of faults cause unplanned down times

• high maintenance efforts and costs

• Offshore: drop of availability expected

50%

60%

70%

80%

90%

100%

Jan 06 April 06 July 06 Oct 06 Jan 07 April 07 July 07 Oct 07

North Hoyle

Scroby Sands

Egmond aan zee

Barrow

Onshore-Average

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Motivation

12

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56

78

910

1

2

3

4

5

0,00,20,40,60,81,01,21,41,61,82,02,22,42,62,83,03,23,43,63,84,0

mea

n a

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fai

lure

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year of operation year of production

.

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Sources of onshore experience

Country Time span Number of turbines

Turbine-Years of experience

WMEP Germany 1989 – 2006 1468 ~15.000

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Sources of onshore experience

Country Time span Number of turbines

Turbine-Years of experience

WMEP Germany 1989 – 2006 1468 ~15.000

LWK Germany 1993 – 2006 241 5.719

Windstats Germany 1995 – 2004 4285 27.700

Windstats Denmark 1994 – 2003 904 18.700

VTT Finnland 2000 – 2004 92 356

Elforsk Sweden 1997 – 2004 723 4.378

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General results

Average failure rate [failures/turbine/year]over whole survey period

Annual downtime [hours/turbine/year] over whole survey period

WMEP 2,4 156

LWK 1,9 27

Windstats 1,8 93

Windstats 0,7 -

VTT 1,5 237

Elforsk 0,9 58

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General results

Highest failure rateLongest downtime per failure

WMEPElectricControlSensors

Gearbox Drive train Generator

LWKElectricBladesControl

Gearbox Blades Electric

WindstatsBladesElectricSensors

Gearbox Blades Drive Train

WindstatsControl Blades Yaw-System

–

VTTHydraulicBladesGearbox

Gearbox Blades Support & Housing

ElforskElectricHydraulicSensors

Drive train Yaw-System Gearbox

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Identified risks

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Identified risks

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Identified risks

Failure rate vs. turbine size (Durham University, LWK)

Failure rate vs. technical concept (IWES, WMEP)

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Identified risks

Wind speed dependancy of failure rate (IWES, WMEP)

Correlation between Wind energy index and failure rate (Durham University, LWK)

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Conclusions

Reliability of wind turbines has to get improved

Characteristics of failures differ strongly

Numerous small failures

Small number of large failures with long downtimes

Trends can be recognised

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Conclusions

Risk one: small number of large failures, complicated repair increasing duration of downtimes

Risk two: Numerous small failures, complicated access increasing duration of downtimes

Risk three: Large WTs with more complex technical concepts increasing number of failures

Risk four: Higher wind speeds increasing number of failures

Risk five: Additional stress through climatic conditions and wave loads increasing number of failures

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Outlook

-allows anonymous benchmarking and weak-point analyses

-gives the possibility to test and, if necessary, optimize the performance of offshore wind farms

The generation of a common database

-aims to help in answering essential questions concerning offshore wind energy

-contribute to political decision-making processes and facilitate further technological progress

Poster PO.71

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Thank you for your attention,

Visit us at stand B0311

Dipl.-Ing. M.Sc. Stefan Faulstich

Reliability and maintenance strategies

R&D Division Energy Economy and Grid Operation

Fraunhofer IWES