Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems

Xianglun Nie, Jing Zhang (), Yu He (), Wenjian Luo, Tingyun Gu, Bowen Li and Xiangxie Hu
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Xianglun Nie: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Jing Zhang: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Yu He: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Wenjian Luo: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Tingyun Gu: Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Bowen Li: Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Xiangxie Hu: College of Electrical Engineering, Guizhou University, Guiyang 550025, China

Energies, 2023, vol. 16, issue 7, 1-19

Abstract: Fast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fault data stitching and image generation of resonant grounding distribution systems is proposed. Firstly, considering the correlation between the transient zero-sequence current (TZSC) of faulty and healthy feeders under the same operating conditions, a fault data stitching method is proposed, which splices the transient zero-sequence current signals of each feeder into system fault data, and then converts the system fault data into grayscale images by combining the signal-to-image conversion method. Then, an improved convolutional neural network (CNN) is used to train the grayscale images and then implement fault detection. The simulation results show that the proposed method has high accuracy and strong robustness compared with existing fault detection methods.

Keywords: fault data stitching; image generation; convolutional neural network; fault detection; feature extraction; feature characterization capability (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
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