Surface Discharge Audio Signal Recognition of 10 kV Solid Insulation Cabinet Based on Band Energy Feature Extraction

doi:10.16628/j.cnki.2095-8188.2020.03.006

Abstract

Abstract: In order to accurately monitor the insulation condition of 10 kV solid cabinets,a method based on wavelet packet frequency band energy identification for insulation state identification was proposed.An audio capture device is used to obtain audio signals from the surface discharge of the solid cabinet and the working environment.A wavelet packet base is established,and high frequency information and low frequency information are obtained by orthogonal decomposition.The energy of each frequency segment is calculated by analyzing the spectral characteristics of the audio signal for proper segmentation.Finally,the energy of each frequency segment is normalized to establish a feature vector as the basis for state recognition.The feature vectors of surface discharge sound and ambient noise are input into a support vector machine to classify and identify the insulation state.Through the analysis of experimental data,it is verified that the combination of wavelet packet analysis and support vector machine can accurately determine the surface discharge of solid insulated switchgear.

Key words: surface discharge, audio signal, wavelet packet analysis, support vector machine

CLC Number:

TM591

HOU Chunguang, SUN Xiaohan, ZHAO Changquan, CAO Yundong. Surface Discharge Audio Signal Recognition of 10 kV Solid Insulation Cabinet Based on Band Energy Feature Extraction[J]. DIANQI YU NENGXIAO GUANLI JISHU, 2020, 588(3): 38-42.

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