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.

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Due to the fact that wide area information is easily disturbed, lost and distorted, while it is not fully considered in current wide area protection principle, a wide area backup protection algorithm with high fault-tolerance performance is proposed to implement the CSBPS(Centralized Substation Backup Protection System). After the fault-tolerance judgment, The CSBPS can locate the fault equipment according to the fault direction information and fault distance information which is calculated by currents and voltages measured from the electric equipments of local substation and collected from the adjacent substations. The results of case study show that CSBPS can't make the uncorrected judgment even when any one of the protection element operates incorrectly, fails to operate or its fault information lost. The proposed algorithm is simple and reliable with good fault-tolerance capability.

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