Abstract
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.
Highlights
With the increasing application of modular multilevel converter-based high-voltage direct current (MMC-high voltage direct current converter (HVDC)) systems, the reliability of modular multilevel converters (MMCs) is of major importance in ensuring power systems are safe and reliable
With the increasing application of modular multilevel converter-based high-voltage direct current (MMC-HVDC) systems, the reliability of MMC is of major importance in ensuring power systems are safe and reliable
Fault detection and classification are among the challenging tasks in MMC-HVDC systems in improving its reliability and, reducing potential dangers in the power systems, because there are a large number of power electronic sub-modules (SMs) in the MMC circuit, and each SM is a potential failure point [3,4]
Summary
With the increasing application of modular multilevel converter-based high-voltage direct current (MMC-HVDC) systems, the reliability of MMC is of major importance in ensuring power systems are safe and reliable. Zhu et al [25] proposed convolutional neural networks (CNN) for fault classification and fault location in AC transmission lines with back-to-back MMC-HVDC, in which two convolutional layers were used to extract the complex features of the voltage and the current signals of only one terminal of transmission lines. Excellent accuracies of fault detection and identification without data preprocessing or post-operations are achieved; Two deep learning methods and a stand-alone SoftMax classifier are used with raw data collected by current sensors, to achieve improved classification accuracy and reduced computation time.
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