Accurate Identification of Partial Discharge Signals in Cable Terminations of High-Speed Electric Multiple Unit Using Wavelet Transform and Deep Belief Network
Source abstract
Abstract
Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: High voltage insulation and dielectric phenomena · Power Transformer Diagnostics and Insulation · Image Enhancement Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
CRRC Changchun Railway Vehicles Co., Ltd., Changchun 130062, China
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Energy Department, UTBM, Université Bourgogne Franche-Comté, 90010 Belfort, France
CRRC Qingdao Sifang Co., Ltd., Qingdao 266000, China
Funding
National Natural Science Foundation of China · Excellent Young Scientists Fund of China · Southwest Jiaotong University New Interdisciplinary Cultivation Project · Fundamental Research Funds for the Central Universities
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