Power-Line Partial Discharge Recognition with Hilbert–Huang Transform Features

Yulu Wang, Hsiao-dong Chiang and Na Dong ()
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Yulu Wang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Hsiao-dong Chiang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Na Dong: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Energies, 2022, vol. 15, issue 18, 1-16

Abstract: Partial discharge (PD) has caused considerable challenges to the safety and stability of high voltage equipment. Therefore, highly accurate and effective PD detection has become the focus of research. Hilbert–Huang Transform (HHT) features have been proven to have great potential in the PD analysis of transformer, gas insulated switchgear and power cable. However, due to the insufficient research available on the PD features of power lines, its application in the PD recognition of power lines has not yet been systematically studied. In the present study, an enhanced light gradient boosting machine methodology for PD recognition is proposed; the HHT features are extracted from the signal and added to the feature pool to improve the performance of the classifier. A public power-line PD recognition contest dataset is introduced to evaluate the effectiveness of the proposed feature. Numerical studies along with comparison results demonstrate that the proposed method can achieve promising performances. This method which includes the HHT features contributes to the detection of PD in power lines.

Keywords: partial discharge; Hilbert–Huang Transform; LightGBM; feature extraction (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: 2022
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