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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-08, Vol.20 (16), p.4438
Main Authors: Wang, Qinghua, Yu, Yuexiao, Ahmed, Hosameldin O. A., Darwish, Mohamed, Nandi, Asoke K.
Format: Article
Language:English
Subjects:
Accuracy
AE-based DNN
Alternating current
Artificial intelligence
Bridges
Classification
CNN
Computer simulation
Converters
Current converters (AC to DC)
Deep learning
Direct current
fault classification
Fault detection
Fault diagnosis
Genetic algorithms
MMC-HVDC
Neural networks
Phase current
Principal components analysis
Sensors
Signal processing
SoftMax classifier
Support vector machines
Teaching methods
Wavelet transforms
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20164438