Abstract:Wind energy has been drawing considerable attention in recent years. However, due to the random nature of wind and high failure rate of wind energy conversion systems (WECSs), how to implement fault-tolerant WECS control is becoming a significant issue. This paper addresses the fault-tolerant control problem of a WECS with a probable actuator fault. A new stochastic model predictive control (SMPC) fault-tolerant controller with the Conditional Value at Risk (CVaR) objective function is proposed in this paper. First, the Markov jump linear model is used to describe the WECS dynamics, which are affected by many stochastic factors, like the wind. The Markov jump linear model can precisely model the random WECS properties. Second, the scenario-based SMPC is used as the controller to address the control problem of the WECS. With this controller, all the possible realizations of the disturbance in prediction horizon are enumerated by scenario trees so that an uncertain SMPC problem can be transformed into a deterministic model predictive control (MPC) problem. Finally, the CVaR object function is adopted to improve the fault-tolerant control performance of the SMPC controller. CVaR can provide a balance between the performance and random failure risks of the system. The Min-Max performance index is introduced to compare the fault-tolerant control performance with the proposed controller. The comparison results show that the proposed method has better fault-tolerant control performance.

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.