Open Access

ARTICLE

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Penghui Liu^1,2,*, Yaning Zhang¹, Yuxing Dai², Yanzhou Sun^1,3
1 School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454003, China
2 Research and Development Center, Guangdong Zhicheng Champion Group Co., Ltd., Dongguan, 523000, China
3 Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Henan Polytechnic University, Jiaozuo, 454003, China
* Corresponding Author: Penghui Liu. Email: penghuiliu@hpu.edu.cn

Energy Engineering https://doi.org/10.32604/ee.2024.049318

Received 03 January 2024; Accepted 07 March 2024; Published online 16 April 2024

Abstract

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points. The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index, which leads to detection failure when the arc zero-off characteristic is short. To solve this problem, this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines. Firstly, the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied. After that, the convex hull, gradient product, and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria. Then, the logistic regression algorithm is employed to deal with the reference samples, establish the machine discrimination model, and realize the discrimination of fault types. Finally, simulation test results and experimental results verify the accuracy of the proposed method. The comparison analysis shows that the proposed method has higher recognition accuracy, especially when the arc dissipation power is smaller than 2 × 10³ W, the zero-off period is not obvious. In conclusion, the proposed method expands the arc fault identification theory.

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Keywords

Arc fault; convex hull algorithm; correlation coefficient; fault identification; gradient; logistic regression

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