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Failure Modes, Effects and Criticality Analysis for Wind Turbines Considering Climatic Regions and Comparing Geared and Direct Drive Wind Turbines

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  • Samet Ozturk

    (Earth and Environmental Engineering Department, Columbia University, 116th St & Broadway, New York, NY 10027, USA)

  • Vasilis Fthenakis

    (Earth and Environmental Engineering Department, Columbia University, 116th St & Broadway, New York, NY 10027, USA)

  • Stefan Faulstich

    (Fraunhofer Institute for Energy Economics and Energy System Technology—IEE, Königstor 59, 34119 Kassel, Germany)

Abstract

The wind industry is looking for ways to accurately predict reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique utilized to determine the critical subsystems of wind turbines. There are several studies in the literature which have applied FMECA to wind turbines, but no studies so far have used it considering different weather conditions or climatic regions. Furthermore, different wind turbine design types have been analyzed applying FMECA either distinctively or combined, but no study so far has compared the FMECA results for geared and direct-drive wind turbines. We propose to fill these gaps by using Koppen-Geiger climatic regions and two different turbine models of direct-drive and geared-drive concepts. A case study is applied on German wind farms utilizing the Wind Measurement & Evaluation Programme (WMEP) database which contains wind turbine failure data collected between 1989 and 2008. This proposed methodology increases the accuracy of reliability and availability predictions and compares different wind turbine design types and eliminates underestimation of impacts of different weather conditions.

Suggested Citation

  • Samet Ozturk & Vasilis Fthenakis & Stefan Faulstich, 2018. "Failure Modes, Effects and Criticality Analysis for Wind Turbines Considering Climatic Regions and Comparing Geared and Direct Drive Wind Turbines," Energies, MDPI, vol. 11(9), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2317-:d:167375
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    References listed on IDEAS

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    Cited by:

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    3. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
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    5. Luca Pinciroli & Piero Baraldi & Guido Ballabio & Michele Compare & Enrico Zio, 2021. "Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews," Energies, MDPI, vol. 14(20), pages 1-17, October.
    6. Liu, Min & Qin, Jianjun & Lu, Da-Gang & Zhang, Wei-Heng & Zhu, Jiang-Sheng & Faber, Michael Havbro, 2022. "Towards resilience of offshore wind farms: A framework and application to asset integrity management," Applied Energy, Elsevier, vol. 322(C).
    7. Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
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    9. Yuka Kikuchi & Takeshi Ishihara, 2021. "Availability and LCOE Analysis Considering Failure Rate and Downtime for Onshore Wind Turbines in Japan," Energies, MDPI, vol. 14(12), pages 1-17, June.

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