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Condition-Based Maintenance for Offshore Wind Turbines Based on Support Vector Machine

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  • Jichuan Kang

    (Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China
    HEU-UL International Joint Laboratory of Naval Architecture and Offshore Technology, Harbin 150001, China)

  • Zihao Wang

    (Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • C. Guedes Soares

    (Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
    HEU-UL International Joint Laboratory of Naval Architecture and Offshore Technology, Harbin 150001, China)

Abstract

A condition-based maintenance policy for offshore wind turbines is presented in consideration of the maintenance uncertainty and the weather effect. In this paper, the offshore wind turbine is divided into four main assemblies—namely, the rotor, gearbox, generator, and pitch system. The support vector machine classification technique is implemented to analyze the failure information, which was collected from field data in China. According to the results of fault diagnosis and prediction, the assembly that reaches the corresponding maintenance threshold will be repaired. At the same time, a maintenance opportunity occurs for the rest of the components, and an optimized plan can be determined by arranging the maintenance combination and time. The calculated results indicate that the proposed condition-based maintenance policy is beneficial to reduce the maintenance expenditure of offshore wind turbines.

Suggested Citation

  • Jichuan Kang & Zihao Wang & C. Guedes Soares, 2020. "Condition-Based Maintenance for Offshore Wind Turbines Based on Support Vector Machine," Energies, MDPI, vol. 13(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3518-:d:381917
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    References listed on IDEAS

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    Cited by:

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    5. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    6. Zhang, Shuo & Robinson, Emma & Basu, Malabika, 2022. "Hybrid Gaussian process regression and Fuzzy Inference System based approach for condition monitoring at the rotor side of a doubly fed induction generator," Renewable Energy, Elsevier, vol. 198(C), pages 936-946.
    7. Fausto Pedro García Márquez, 2022. "Special Issue on Advances in Maintenance Management," Energies, MDPI, vol. 15(7), pages 1-4, March.
    8. Yikai Ma & Wenjuan Zhang & Juergen Branke, 2024. "Genetic programming hyper-heuristic for evolving a maintenance policy for wind farms," Journal of Heuristics, Springer, vol. 30(5), pages 423-451, December.
    9. Nurullah Yildiz & Hassan Hemida & Charalampos Baniotopoulos, 2021. "Life Cycle Assessment of a Barge-Type Floating Wind Turbine and Comparison with Other Types of Wind Turbines," Energies, MDPI, vol. 14(18), pages 1-19, September.
    10. McMorland, J. & Collu, M. & McMillan, D. & Carroll, J. & Coraddu, A., 2023. "Opportunistic maintenance for offshore wind: A review and proposal of future framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).

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