Fault Diagnosis Method for MMC-HVDC Based on Bi-GRU Neural Network

Yanting Wang, Dingkun Zheng and Rong Jia
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Yanting Wang: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Dingkun Zheng: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Rong Jia: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China

Energies, 2022, vol. 15, issue 3, 1-17

Abstract: The Modular Multilevel Converter-High Voltage Direct Current (MMC-HVDC) system is recognized worldwide as a highly efficient strategy for transporting renewable energy across regions. As most of the MMC-HVDC system electronics are weak against overcurrent, protections of the MMC-HVDC system are the major focus of research. Because of the insufficiencies of the conventioned fault diagnosis method of MMC-HVDC system, such as hand-designed fault thresholds and complex data pre-processing, this paper proposes a new method for fault detection and location based on Bidirectional Gated Recurrent Unit (Bi-GRU). The proposed method has obvious advantages of feature extraction on the bi-directional structure, and it simplifies the pre-processing of fault data. The simplified pre-processing avoids the loss of valid information in the data and helps to extract detailed fault characteristics, thus improving the accuracy of the method. Extensive simulation experiments show that the proposed method meets the speed requirement of MMC-HVDC protections (2 ms) and the accuracy rate reaches 99.9994%. In addition, the method is not affected by noise and has a high potential for practical applications.

Keywords: MMC-HVDC; deep learning; bidirectional gated recurrent unit (Bi-GRU); fault diagnosis; feature extraction (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: 2022
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