Survey of switch fault diagnosis for modular multilevel converter

作者：Wang, Chuang; Zhou, Lvchen; Li, Zunchao^*

来源：IET Circuits Devices & Systems, 2019, 13(2): 117-124.

DOI：10.1049/iet-cds.2018.5136

• fault diagnosis
• artificial intelligence
• HVDC power convertors
• voltage-source convertors
• power engineering computing
• signal processing
• trained intelligent classifier
• switch fault diagnosis
• modular multilevel converter
• switch devices
• artificial intelligence
• fault diagnosis methods
• FDM
• signal processing
• MMC

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.

• 出版日期2019-3
• 单位西安交通大学