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Predicting Frequency, Time-To-Repair and Costs of Wind Turbine Failures

Author

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  • Samet Ozturk

    (Center for Life Cycle Analysis, Earth and Environmental Engineering, Columbia University, New York, NY 10027, USA
    Environmental Engineering, Bursa Technical University, 16310 Bursa, Turkey)

  • Vasilis Fthenakis

    (Center for Life Cycle Analysis, Earth and Environmental Engineering, Columbia University, New York, NY 10027, USA)

Abstract

Operation and maintenance (O&M) costs, and associated uncertainty, for wind turbines (WTs) is a significant burden for wind farm operators. Many wind turbine failures are unpredictable while causing loss of energy production, and may also cause loss of asset. This study utilized 753 O&M event data from 21 wind turbines operating in Germany, to improve the prediction of failure frequency and associated costs. We applied Bayesian updating to predict wind turbine failure frequency and time-to-repair (TTR), in conjunction to machine learning techniques for assessing costs associated with failures. We found that time-to-failure (TTF), time-to-repair and the cost of failures depend on operational and environmental conditions. High elevation (>100 m) of the wind turbine installation was found to increase both the probability of failures and probability of delayed repairs. Furthermore, it was determined that direct-drive turbines are more favorable at locations with high capacity factor (more than 40%) whereas geared-drive turbines show lower failure costs than direct-drive ones at temperate-coastal locations with medium capacity factors (between 20% and 40%). Based on these findings, we developed a decision support tool that can guide a site-specific selection of wind turbine types, while providing a thorough estimation of O&M budgets.

Suggested Citation

  • Samet Ozturk & Vasilis Fthenakis, 2020. "Predicting Frequency, Time-To-Repair and Costs of Wind Turbine Failures," Energies, MDPI, vol. 13(5), pages 1-25, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1149-:d:328026
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    References listed on IDEAS

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    1. Ahmed Raza & Vladimir Ulansky, 2019. "Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring," Energies, MDPI, vol. 12(19), pages 1-24, October.
    2. Kerres,, Bertrand & Fischer, Katharina & Madlener, Reinhard, 2014. "Economic Evaluation of Maintenance Strategies for Wind Turbines: A Stochastic Analysis," FCN Working Papers 3/2014, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    3. Reder, Maik & Yürüşen, Nurseda Y. & Melero, Julio J., 2018. "Data-driven learning framework for associating weather conditions and wind turbine failures," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 554-569.
    4. Samet Ozturk & Vasilis Fthenakis & Stefan Faulstich, 2018. "Assessing the Factors Impacting on the Reliability of Wind Turbines via Survival Analysis—A Case Study," Energies, MDPI, vol. 11(11), pages 1-20, November.
    5. Lijun Zhang & Kai Liu & Yufeng Wang & Zachary Bosire Omariba, 2018. "Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier," Energies, MDPI, vol. 11(10), pages 1-15, September.
    6. 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.
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    Cited by:

    1. Albara M. Mustafa & Abbas Barabadi & Tore Markeset & Masoud Naseri, 2021. "An overall performance index for wind farms: a case study in Norway Arctic region," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 938-950, October.
    2. Wang, Shixuan & Syntetos, Aris A. & Liu, Ying & Di Cairano-Gilfedder, Carla & Naim, Mohamed M., 2023. "Improving automotive garage operations by categorical forecasts using a large number of variables," European Journal of Operational Research, Elsevier, vol. 306(2), pages 893-908.

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