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Wind farm predictive maintenance considering component level repairs and economic dependency

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  • Tian, Zhigang
  • Zhang, Han

Abstract

Existing studies on wind farm predictive maintenance mostly incorporated repairs on the level of wind turbine (WT) subassemblies, e.g. drive train, gearbox and generator, including major repairs and minor repairs. The economic dependency among subassemblies was also considered. But assumptions needed to be made on major and minor repairs regarding their costs and impact on system functions. Besides, existing studies considered only fixed costs on the WT and farm levels without differentiating fixed costs that apply to a subset of components, such as crane costs, resulting in over-simplification compared to wind farm practices. In this work, a wind farm predictive maintenance approach is developed considering component level repairs and economic dependency. There are multiple components in a subassembly, e.g. the generator consists of components like generator rotor, generator bearings, contactor, etc. These components' failures lead to major or minor repairs, downtime, and demand on repair resources, particularly cranes. With the proposed wind farm predictive maintenance approach, component level major and minor repairs and their costs can be modeled explicitly in a more realistic and accurate way. Economic dependency can also be modeled more accurately by incorporating the downtime, i.e. revenue loss, and repair resource requirement caused by different component failures. A predictive maintenance optimization model is developed to find the optimal maintenance policy. The number of WTs eligible for preventive maintenance is introduced as a new decision variable, which also captures the economic dependency among WTs. A simulation-based method is developed for maintenance cost evaluation. Examples are used to demonstrate the proposed approach.

Suggested Citation

  • Tian, Zhigang & Zhang, Han, 2022. "Wind farm predictive maintenance considering component level repairs and economic dependency," Renewable Energy, Elsevier, vol. 192(C), pages 495-506.
  • Handle: RePEc:eee:renene:v:192:y:2022:i:c:p:495-506
    DOI: 10.1016/j.renene.2022.04.060
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    References listed on IDEAS

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    1. Tian, Zhigang & Zuo, Ming J., 2006. "Redundancy allocation for multi-state systems using physical programming and genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1049-1056.
    2. Shafiee, Mahmood & Finkelstein, Maxim & Bérenguer, Christophe, 2015. "An opportunistic condition-based maintenance policy for offshore wind turbine blades subjected to degradation and environmental shocks," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 463-471.
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    4. Rommert Dekker & Ralph Wildeman & Frank Duyn Schouten, 1997. "A review of multi-component maintenance models with economic dependence," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 45(3), pages 411-435, October.
    5. Zhu, Wenjin & Fouladirad, Mitra & Bérenguer, Christophe, 2016. "A multi-level maintenance policy for a multi-component and multifailure mode system with two independent failure modes," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 50-63.
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    Cited by:

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    4. Kristjanpoller, Fredy & Cárdenas-Pantoja, Nicolás & Viveros, Pablo & Pascual, Rodrigo, 2023. "Wind farm life cycle cost modelling based on oversizing capacity under load sharing configuration," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
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    8. Zheng, Meimei & Su, Zhiyun & Wang, Dong & Pan, Ershun, 2024. "Joint maintenance and spare part ordering from multiple suppliers for multicomponent systems using a deep reinforcement learning algorithm," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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