Sang-Hun Kim, Dong-Yeon Yoo, Sang-Won An, Ye-Seul Park, Jung-Won Lee, and Kyo-Beum Lee, "Fault Detection Method Using a Convolution Neural Network for Hybrid Active Neutral-Point Clamped Inverters," IEEE Access, vol. 8, pp. 140632–140642, Jul. 2020. -SCIE > Paper

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.