Short-Time Wavelet Entropy Integrating Improved LSTM for Fault Diagnosis of Modular Multilevel Converter
Yongming Han, Qi Wang, Ning Ding, Zhiqiang Geng
Abstract
The modular multilevel converter (MMC) is the main part of MMC-based high-voltage direct current (HVDC) system. The MMC bridge arm inductance fault and the submodule IGBT fault have the greatest influence on the transmission quality of transmission systems. Therefore, this article proposes a novel fault diagnosis method based on short-time wavelet entropy integrating the long short-term memory network (LSTM) and the support vector machine (SVM). The proposed short-time wavelet entropy calculation method is used to extract the fault information. First, the optimal short-term wavelet packet calculation period is determined. Moreover, the improved LSTM topology can process the wavelet entropy fault information in the time dimension. Then, the output of the LSTM is set as the input of the SVM to obtain the fault diagnosis result based on the adaptive classification. Finally, through the MMC fault diagnosis experiment of the double-ended MMC-HVDC transmission system, the effectiveness of the proposed method is verified. Compared with the traditional fault diagnosis method, the proposed method has better robustness, adaptability, and accuracy, which can greatly reduce the number of electrical signal samples and realize the fault diagnosis of multiple fault types by collecting a single signal.