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

Yun-Tao Shi, Yuan Zhang, Xiang Xiang, Li Wang, Zhen-Wu Lei and Sun De-Hui
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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
Sun De-Hui: Key Lab of Field Bus and Automation of Beijing, North China University of Technology, Beijing 100144, China

Energies, 2018, vol. 11, issue 9, 1-22

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.

Keywords: wind energy conversion system; Markov jump linear system; stochastic model predictive control; stochastic hybrid estimation; fault detection and isolation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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