Improving power cable partial discharge pattern recognition through gustafson-kessel fuzzy clustering techniques

被引:0
作者
Chen H. [1 ]
Zhang Z. [1 ]
Luo Q. [1 ]
Chen R. [1 ]
Zhao Y. [2 ,3 ]
机构
[1] Jiangmen Polytechnic, Chaolian Avenue, Pengjiang District, Guangdong Province, Jiangmen, Jiangmen City
[2] Guangdong University of Science and Technology, Dongguan
[3] Intelligent Manufacturing and Environmental Monitoring Engineering Technology Research Center of Dongguan City, Guangdong University of Science and Technology, Dongguan
关键词
Gustafson-Kessel(GK) fuzzy clustering; partial discharge; pattern recognition; power cable; wavelet packet transform;
D O I
10.3233/JIFS-235945
中图分类号
学科分类号
摘要
Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring. © 2024 – IOS Press. All rights reserved.
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页码:8943 / 8959
页数:16
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