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Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

Author

Listed:
  • Yun-Tao Shi

    (Key Lab of Field Bus and Automation of Beijing, North China University of Technology, Beijing 100144, China)

  • Yuan Zhang

    (Key Lab of Field Bus and Automation of Beijing, North China University of Technology, Beijing 100144, China)

  • Xiang Xiang

    (Key Lab of Field Bus and Automation of Beijing, North China University of Technology, Beijing 100144, China)

  • Li Wang

    (Key Lab of Field Bus and Automation of Beijing, North China University of Technology, Beijing 100144, China)

  • Zhen-Wu Lei

    (Key Lab of Field Bus and Automation of Beijing, North China University of Technology, Beijing 100144, China)

  • De-Hui Sun

    (Key Lab of Field Bus and Automation of Beijing, North China University of Technology, Beijing 100144, China)

Abstract

In recent years, the wind energy conversion system (WECS) has been becoming the vital system to acquire wind energy. However, the high failure rate of WECSs leads to expensive costs for the maintenance of WECSs. Therefore, how to detect and isolate the faults of WECSs with stochastic dynamics is the pressing issue in the literature. This paper proposes a novel comprehensive fault detection and isolation (FDI) method for WECSs. First, a stochastic model predictive control (SMPC) controller is studied to construct the closed-loop system of the WECS. This controller is based on the Markov-jump linear model, which could precisely establish the stochastic dynamics of the WECS. Meanwhile, the SMPC controller has satisfied control performance for the WECS. Second, based on the closed-loop system with SMPC, the stochastic hybrid estimator (SHE) is designed to estimate the continuous and discrete states of the WECS. Compared with the existing estimators for WECSs, the proposed estimator is more suitable for WECSs since it considers both the continuous and discrete states of WECSs. In addition, the proposed estimator is robust to the fault input. Finally, with the proposed estimator, the comprehensive FDI method is given to detect and isolate the actuators’ faults of the WECS. Both the system status and the actuators’ faults can be detected by the FDI method and it can effectively quantify the actuators’ fault by the fault residuals. The simulation results suggest that the SHE could effectively estimate the hybrid states of the WECS, and the proposed FDI method gives satisfied fault detection performance for the actuators of the WECS.

Suggested Citation

  • Yun-Tao Shi & Yuan Zhang & Xiang Xiang & Li Wang & Zhen-Wu Lei & De-Hui Sun, 2018. "Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs," Energies, MDPI, vol. 11(9), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2227-:d:165705
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    References listed on IDEAS

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    1. Yun-Tao Shi & Xiang Xiang & Li Wang & Yuan Zhang & De-Hui Sun, 2018. "Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System," Energies, MDPI, vol. 11(1), pages 1-20, January.
    2. Li, Lei & Qi, Wenhai & Chen, Xiaoming & Kao, Yonggui & Gao, Xianwen & Wei, Yunliang, 2018. "Stability analysis and control synthesis for positive semi-Markov jump systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 363-375.
    3. Nedaei, Mojtaba & Assareh, Ehsanolah & Walsh, Philip R., 2018. "A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods," Renewable Energy, Elsevier, vol. 128(PA), pages 362-374.
    4. de Bessa, Iury Valente & Palhares, Reinaldo Martinez & D'Angelo, Marcos Flávio Silveira Vasconcelos & Chaves Filho, João Edgar, 2016. "Data-driven fault detection and isolation scheme for a wind turbine benchmark," Renewable Energy, Elsevier, vol. 87(P1), pages 634-645.
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    Cited by:

    1. Maryam Khanbaghi & Aleksandar Zecevic, 2020. "Jump Linear Quadratic Control for Microgrids with Commercial Loads," Energies, MDPI, vol. 13(19), pages 1-21, September.

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