Survey of switch fault diagnosis for modular multilevel converter

Abstract

This study presents a survey on the existing fault diagnosis methods (FDMs) of the switch devices for the rapidly developing modular multilevel converters (MMCs). Three categories, namely mechanism‐based, signal processing‐based and artificial intelligence‐based FDMs, are evaluated and summarised depending on the operating principles. Mechanism‐based FDMs detect the faults by comparing the inner characteristics of MMC or their derived parameters with the expected values. Signal processing‐based FDMs detect the faults via comparing the processed output voltage or current with their expected values. Artificial intelligence‐based FDMs detect the faults in the way of employing a trained intelligent classifier. Methods belonging to each category are introduced in detail via comparing a lot of criteria of the FDMs. Then, a figure‐of‐merit is defined to evaluate various FDMs. Finally, the summary is given and the developing tendency is recommended for future work.