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| Art fault detection method based on variational mode decomposition energy entropy hybrid time domain feature and random forest |
| DONG Zhiwen1,2, SU Jingjing1,3 |
1. College of Computer and Control Engineering, Minjiang University, Fuzhou 350118;
2. School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350108;
3. Zhejiang Institute of Mechanical & Electrical Engineering Co., Ltd, Hangzhou 310051 |
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Abstract In household appliances, nonlinear load devices continue to grow, which makes the limitations of traditional fault current detection methods based on "zero crossing" characteristics become evident. This paper introduces a novel approach for fault arc identification, combining signal time domain features with variational mode decomposition (VMD) intrinsic modal energy entropy. The analysis focuses on line current, initially extracting time-frequency features. Subsequently, the VMD algorithm decomposes the fault arc current into intrinsic mode function (IMF), and their energy entropy is computed. By leveraging both time-domain and energy entropy features, a multidimensional feature vector is employed for fault arc identification within a random forest model. Comparative experiments indicate that this method achieves an impressive fault arc identification accuracy of up to 99% and is suitable for diverse low-voltage distribution scenarios including various load types and nonlinear loads.
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Received: 31 October 2023
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| Cite this article: |
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DONG Zhiwen,SU Jingjing. Art fault detection method based on variational mode decomposition energy entropy hybrid time domain feature and random forest[J]. Electrical Engineering, 2024, 25(1): 1-7.
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| URL: |
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http://dqjs.cesmedia.cn/EN/Y2024/V25/I1/1
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2024, Vol. 25 
