Fault Detection and Identification in MMCs Based on DSCNNs

Guanyuan Cheng and Shaojian Song ()
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Guanyuan Cheng: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Shaojian Song: School of Electrical Engineering, Guangxi University, Nanning 530004, China

Energies, 2023, vol. 16, issue 8, 1-17

Abstract: Fault detection and location is one of the critical issues in engineering applications of modular multilevel converters (MMCs). At present, MMC fault diagnosis based on neural networks can only locate the open-circuit fault of a single submodule. To solve this problem, this paper proposes a fault detection and localization strategy based on a depthwise separable convolutional (DSC) neural network. By inputting the bridge arm circulating current and the submodule capacitor voltage into two serially connected neural networks, not only can this method achieve the classification of submodule open-circuit faults, submodule block short-circuit faults, and bridge arm inductance faults in MMCs, but it can also locate the switch where open-circuit faults occur. The simulation experimental results show that the proposed method achieves fault classification and locates multiple submodule open-circuit faults in the same bridge arm. This method achieves accuracies of ≥99% and 87.7% for the single-point and multi-point open-circuit fault localization in MMCs, respectively, which is better than some benchmark achievements in the current literature in terms of detection accuracy, and speed, and it has fewer model parameters and better real-time performance.

Keywords: MMC; fault detection; fault location; DSC (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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