Fault Detection Method Using a Convolution Neural Network for Hybrid Active Neutral-Point Clamped Inverters - Kim, Sang Hun; Yoo, Dong Yeon; An, Sang Won; Park, Ye Seul; Lee, Jung Won; Lee, Kyo Beum (IEEE Access, 2020)
This article presents an open-switch fault detection method for a hybrid active neutral-point clamped (HANPC) inverter based on deep learning technology. The HANPC inverter generates a three-level output voltage with four silicon switches and two silicon carbide switches per phase. The probability of open fault in switching devices increases because of the large number of switches of the entire power converter. The open-switch fault causes distortion of output currents. A convolution neural network (CNN) comprising several convolution layers and fully connected layers is used to extract features of distorted currents. A CNN network was trained using three-phase current information to determine the location of the open-switch fault. Our proposed CNN model can accurately detect approximately 99.6% of open-switch faults without requiring additional circuitry and regardless of the current level within an average time of 1.027ms. The feasibility and effectiveness of the proposed method are verified by experimental results.
Keywords: Active neutral point clamped; Convolution neural network; Detection methods; Distorted currents; Learning technology; Number of switches; Switching devices; Three-phase currents; convolution neural network; deep learning; hybrid active neutral-point inverter; Open-switch fault detection; silicon carbide