Fault detection and classification in MMC-HVDC systems using learning methods

In this paper, we explore learning methods to improve the performance of the open-circuit
fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely,
convolutional neural networks (CNN) and auto encoder based deep neural networks (AE-based DNN),
as well as stand-alone SoftMax classifier are explored for the detection and classification of faults
of MMC-based high voltage direct current converter (MMC-HVDC). Only AC-side three-phase
current and the upper and lower bridges’ currents of the MMCs are used directly in our proposed
approaches without any explicit feature extraction or feature subset selection. The two-terminal
MMC-HVDC system is implemented in Power Systems Computer-Aided Design/Electromagnetic
Transients including DC (PSCAD/EMTDC) to verify and compare our methods. The simulation
results indicate CNN, AE-based DNN, and SoftMax classifier can detect and classify faults with
high detection accuracy and classification accuracy. Compared with CNN and AE-based DNN,
the SoftMax classifier performed better in detection and classification accuracy as well as testing
speed. The detection accuracy of AE-based DNN is a little better than CNN, while CNN needs less
training time than the AE-based DNN and SoftMax classifier.