Acoustic Identification Method of Partial Discharge in GIS Based on Improved MFCC and DBO-RF

Xueqiong Zhu, Chengbo Hu, Jinggang Yang, Ziquan Liu, Zhen Wang, Zheng Liu () and Yiming Zang
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Xueqiong Zhu: State Grid Jiangsu Electric Power Company Limited Research Institute, Nanjing 211103, China
Chengbo Hu: State Grid Jiangsu Electric Power Company Limited Research Institute, Nanjing 211103, China
Jinggang Yang: State Grid Jiangsu Electric Power Company Limited Research Institute, Nanjing 211103, China
Ziquan Liu: State Grid Jiangsu Electric Power Company Limited Research Institute, Nanjing 211103, China
Zhen Wang: State Grid Jiangsu Electric Power Company Limited Research Institute, Nanjing 211103, China
Zheng Liu: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Yiming Zang: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2025, vol. 18, issue 7, 1-14

Abstract: Gas Insulated Switchgear (GIS) is a type of critical substation equipment in the power system, and its safe and stable operation is of great significance for ensuring the reliability of power system operation. To accurately identify partial discharge in GIS, this paper proposes an acoustic identification method based on improved mel frequency cepstral coefficients (MFCC) and dung beetle algorithm optimized random forest (DBO-RF) based on the ultrasonic detection method. Firstly, three types of typical GIS partial discharge defects, namely free metal particles, suspended potential, and surface discharge, were designed and constructed. Secondly, wavelet denoising was used to weaken the influence of noise on ultrasonic signals, and conventional, first-order, and second-order differential MFCC feature parameters were extracted, followed by principal component analysis for dimensionality reduction optimization. Finally, the feature parameters after dimensionality reduction optimization were input into the DBO-RF model for fault identification. The results show that this method can accurately identify partial discharge of typical GIS defects, with a recognition accuracy reaching 92.2%. The research results can provide a basis for GIS insulation fault detection and diagnosis.

Keywords: GIS; partial discharge; acoustic identification; mel cepstral coefficient; dung beetle optimization algorithm; random forest (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: 2025
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