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Time-dependent reliability analysis of wind turbines considering load-sharing using fault tree analysis and Markov chains

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  • Yao Li
  • Frank PA Coolen

Abstract

Due to the high failure rates and the high cost of operation and maintenance of wind turbines, not only manufacturers but also service providers try many ways to improve the reliability of some critical components and subsystems. In reality, redundancy design is commonly used to improve the reliability of critical components and subsystems. The load dependencies and failure dependencies among redundancy components and subsystems are crucial to the reliability assessment of wind turbines. However, the redundancy components are treated as a parallel system, and the load correlations among them are ignored in much literature, which may lead to the wrong system’s reliability and much higher costs. For this reason, this article explores the influences of load-sharing on system reliability. The whole system’s reliability is quantitatively evaluated using fault tree analysis and the Markov-chain method. Following this, the optimisation of the redundancy allocation problem considering the load-sharing is conducted to maximise the system reliability and reduce the total cost of the system subjecting to the available system cost and space. The results produced by this methodology can show a realistic reliability assessment of the entire wind turbine from a quantitative point of view. The realistic reliability assessment can help to design a cost-effective and more reliable system and significantly reduce the cost of wind turbines.

Suggested Citation

  • Yao Li & Frank PA Coolen, 2019. "Time-dependent reliability analysis of wind turbines considering load-sharing using fault tree analysis and Markov chains," Journal of Risk and Reliability, , vol. 233(6), pages 1074-1085, December.
    • Handle: RePEc:sae:risrel:v:233:y:2019:i:6:p:1074-1085
    DOI: 10.1177/1748006X19859690
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    References listed on IDEAS

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    1. Huang, Xianzhen & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2019. "A heuristic survival signature based approach for reliability-redundancy allocation," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 511-517.
    2. Huang, Xianzhen & Jin, Sujun & He, Xuefeng & He, David, 2019. "Reliability analysis of coherent systems subject to internal failures and external shocks," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 75-83.
    3. Abouei Ardakan, Mostafa & Zeinal Hamadani, Ali, 2014. "Reliability optimization of series–parallel systems with mixed redundancy strategy in subsystems," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 132-139.
    4. Márquez, Fausto Pedro García & Pérez, Jesús María Pinar & Marugán, Alberto Pliego & Papaelias, Mayorkinos, 2016. "Identification of critical components of wind turbines using FTA over the time," Renewable Energy, Elsevier, vol. 87(P2), pages 869-883.
    5. Li, Zhaojun & Liao, Haitao & Coit, David W., 2009. "A two-stage approach for multi-objective decision making with applications to system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1585-1592.
    6. Zhiyu Jiang & Weifei Hu & Wenbin Dong & Zhen Gao & Zhengru Ren, 2017. "Structural Reliability Analysis of Wind Turbines: A Review," Energies, MDPI, vol. 10(12), pages 1-25, December.
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