%0 Electronic Article %A Chuang Wang %A Lvchen Zhou %A Zunchao Li %K modular multilevel converter %K FDM %K MMC %K trained intelligent classifier %K switch fault diagnosis %K fault diagnosis methods %K switch devices %K signal processing %K artificial intelligence %X 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. %@ 1751-858X %T Survey of switch fault diagnosis for modular multilevel converter %B IET Circuits, Devices & Systems %D March 2019 %V 13 %N 2 %P 117-124 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=6mthn0a01veet.x-iet-live-01content/journals/10.1049/iet-cds.2018.5136 %G EN