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Article type: Research Article
Authors: Chen, Hongana; * | Zhang, Zongfua | Luo, Qingjiaa | Chen, Rongbina | Zhao, Yangb; c
Affiliations: [a] Jiangmen Polytechnic, Chaolian Avenue, Pengjiang District, Jiangmen City, Guangdong Province, Jiangmen, China | [b] Guangdong University of Science and Technology, Dongguan, China | [c] Intelligent Manufacturing and Environmental Monitoring Engineering Technology Research Center of Dongguan City, Guangdong University of Science and Technology, Dongguan, China
Correspondence: [*] Corresponding author. Hongan Chen, Jiangmen Polytechnic, No. 6 Chaolian Avenue, Pengjiang District, Jiangmen City, Guangdong Province, 529000, Jiangmen, China. E-mail: [email protected].
Abstract: 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.
Keywords: Gustafson-Kessel(GK) fuzzy clustering, power cable, partial discharge, pattern recognition, wavelet packet transform
DOI: 10.3233/JIFS-235945
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8943-8959, 2024
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