Concurrent fault diagnosis of modular multilevel converter with Kalman filter and optimized support vector machine

In this paper, concurrent fault diagnosis problem of modular multilevel converter (MMC) with Kalman filter and optimized support vector machine (SVM) is investigated. The state space model by synthesizing the circulating current and the output current is first established. Recurring to the Kalman filtering theory, the estimation on circulating and output current is realized, the residual is achieved by using the innovation which involved the predicted and measured current. Based on the obtained residual, the residual evaluation function and its threshold are constructed. Then, the fault can be detected according to the proposed fault detection strategy. Once the fault is detected, the fault localization unit is triggered and the residual data is adopted as data set. By employing the optimized SVM with genetic algorithm, the concurrent and intermittent fault localization of MMC can be accomplished. Finally, an 11-level MMC simulation systems with concurrent fault and intermittent fault are set up in MATLAB/Simulink, and the effectiveness of the proposed fault detection and localization method is verified