IGBT Open Circuit Fault Diagnosis Method for a Modular Multilevel Converter Based on PNN-MD

Modular multilevel converter (MMC) is an important part of high voltage direct current (HVDC) transmission system. With the increase of the number of submodules in series, the open circuit failure of IGBT is the most common fault type of MMC. Therefore, it is necessary to study IGBT open circuit fault diagnosis of MMC. The diagnosis method of IGBT open circuit faults for MMC is proposed based on probabilistic neural network (PNN) and Markovian distance (MD) algorithm to further improve the reliability of MMC. The fault diagnosis model based on PNN-MD theory is established by using Matlab/Simulink software. Then the MMC circuit model is established by using PSIM circuit model. The voltage and current characteristic values of each sub module circuit in normal working state and IGBT open circuit state are collected. Meanwhile, these values are used as training samples and input into pnn-md for training. Through simulation, compared with the application of BP neural network in fault diagnosis, the PNN-MD algorithm can diagnose IGBT open circuit fault of MMC faster and more accurately. The innovation of this paper is to combine machine learning with MMC fault diagnosis, and has a broad application prospect.