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Research Papers

Statistical Analysis of Component Failures: A 16 Year Survey on More Than 550 Wind Turbines

[+] Author and Article Information
Lorenzo Ferrari

Department of Energy, Systems, Territory and
Construction Engineering (DESTEC),
University of Pisa,
Largo Lucio Lazzarino, 1,
Pisa 56122, Italy
e-mail: lorenzo.ferrari@unipi.it

Guido Soldi

e2i Energie Speciali s.r.l,
Via Dante, 15,
Milano 20123, Italy
e-mail: guido.soldi@e2ienergiespeciali.it

Alessandro Bianchini

Department of Industrial Engineering,
University of Florence,
Via S. Marta, 3,
Firenze 50139, Italy
e-mail: alessandro.bianchini@unifi.it

Enzo Dalpane

e2i Energie Speciali s.r.l.,
Via Dante, 15,
Milano 20123, Italy
e-mail: enzo.dalpane@e2ienergiespeciali.it

Manuscript received June 28, 2018; final manuscript received July 19, 2018; published online October 24, 2018. Assoc. Editor: Liang Tang.

J. Eng. Gas Turbines Power 140(12), 121012 (Oct 24, 2018) (9 pages) Paper No: GTP-18-1398; doi: 10.1115/1.4041131 History: Received June 28, 2018; Revised July 19, 2018

A good prediction of the failure ratio of wind turbine (WT) components is pivotal to define a correct maintenance program and reduce the downtime periods. Even a small failure can lead to long downtime periods and high repairing costs. The installation sites, which generally have limited accessibility, and the necessity of special facilities to reach the components inside the nacelle, also play a key role in the correct management of WTs. In this study, a detailed survey on the failures occurred to the WTs managed by the Italian operator “e2i energie speciali” (more than 550 machines) over 16 years was performed and the results were analyzed in detail. Each failure was classified by considering the damaged component and the related downtime period. The analysis allowed the determination of several useful results such as the trend of failure occurrence with machine age and the identification of components and macrocomponents which are more critical in terms of both number of occurrences and downtime periods. The combination of component failure occurrences and related downtime periods was also computed to estimate which component is most critical for WT operation.

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References

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Figures

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Fig. 1

Regional distribution and location of selected wind farms

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Fig. 2

Number of machines (a) and installed capacity (b) by manufacturer

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Fig. 3

Number of failures per operation year

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Fig. 4

Failure rates per operation year and WT size

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Fig. 5

Failure rates per operation year and component

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Fig. 6

Failure rate (%) per main component

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Fig. 7

Gearbox damage distribution

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Fig. 8

Failure rate of DD and geared turbines per main component (same size 0.60 MW)

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Fig. 9

Turbulence standard deviation

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Fig. 10

Effects of installation site on failure rates (DD—0.60 MW power class)

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Fig. 11

Aggregated downtime per turbine subsystem

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Fig. 12

Downtime distribution per component

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Fig. 13

Reliability per a single wind turbine per year

Tables

Errata

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