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Analysis of a State Degradation Model and Preventive Maintenance Strategies for Wind Turbine Generators Based on Stochastic Differential Equations

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  • Hongsheng Su

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
    Rail Transit Electrical Automation Engineering Laboratory of Gansu Province, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Yifan Zhao

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Xueqian Wang

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

Preventive maintenance is widely used in wind turbine equipment to ensure their safe and reliable operation, and this mainly includes time-based maintenance (TBM) and condition-based maintenance (CBM). Most wind farms only use TBM as the main maintenance strategy in engineering practice. Although this can meet certain reliability requirements, it cannot fully utilize the characteristics of TBM and CBM. For this, a state model based on the stochastic differential equation (SDE) is established in this paper to describe the spatio-temporal evolution process of the degradation behavior of wind turbine generators, in which the components’ failure is represented by a proportional hazards model, the random fluctuation of the state is simulated by the Brownian motion, and the SDE model is solved by a function transformation method. Based on the model, the characteristics of TBM and CBM, and the asymptotic relationship between them, are discussed and analyzed, the necessity and feasibility of their combination are expounded, and a joint maintenance strategy is proposed and analyzed. The results show that the stochastic model can better reflect the real deterioration state of the generator. Moreover, TBM has a fixed maintenance interval, depending on global sample tracks and, only depending on the local sample track, CBM can follow the component state. Finally, the rationality and effectiveness of the proposed model and results are verified by a practical example.

Suggested Citation

  • Hongsheng Su & Yifan Zhao & Xueqian Wang, 2023. "Analysis of a State Degradation Model and Preventive Maintenance Strategies for Wind Turbine Generators Based on Stochastic Differential Equations," Mathematics, MDPI, vol. 11(12), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2608-:d:1165970
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    References listed on IDEAS

    as
    1. Hongsheng Su & Dantong Wang & Xuping Duan, 2020. "Condition Maintenance Decision of Wind Turbine Gearbox Based on Stochastic Differential Equation," Energies, MDPI, vol. 13(17), pages 1-13, August.
    2. Sarker, Bhaba R. & Faiz, Tasnim Ibn, 2016. "Minimizing maintenance cost for offshore wind turbines following multi-level opportunistic preventive strategy," Renewable Energy, Elsevier, vol. 85(C), pages 104-113.
    3. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    4. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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    Cited by:

    1. Jianxiong Gao & Yuanyuan Liu & Yiping Yuan & Fei Heng, 2023. "Residual Strength Modeling and Reliability Analysis of Wind Turbine Gear under Different Random Loadings," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    2. Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.

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