Partial Discharge Fault Diagnosis of Switchgear Based on APSO-BP Algorithm 2nd China International Youth Conference on Electrical Engineering CIYCEE 2021

报告详情

Partial Discharge Fault Diagnosis of Switchgear Based on APSO-BP Algorithm

编号：140 访问权限：仅限参会人更新：2021-12-03 10:42:04 浏览：625次口头报告

报告开始：2021年12月15日 15:30（Asia/Shanghai）

报告时间：15min

所在会场：[D] High voltage and insulation technology [D1] Session 4

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摘要

Diagnosis of partial discharge fault types of switchgear is the focus of the early warning of switchgear faults at this stage, which is of great significance to ensure the normal operation of the switchgear. Aiming at this problem, an adaptive particle swarm optimization (APSO) optimized BP neural network partial discharge fault diagnosis algorithm is proposed. By optimizing the inertia weight formula in the standard particle swarm, at the same time introducing genetic factors, mutation factors and time factors to accelerate the convergence speed of the particle swarm algorithm, so as to improve the performance of finding the optimal threshold and weight. First, the partial discharge signal is denoised, and then the signal features are extracted, and the dimensionality is reduced to 3-dimensional features through the principal component analysis algorithm, and finally the fault diagnosis is performed through the algorithm. Comparing the diagnosis results of different algorithms, it can be seen that the fault recognition rate of the APSO optimized BP neural network algorithm is about 5~15% higher than other algorithms, and the convergence speed and convergence accuracy are both improved, which proves the proposed APSO-BP algorithm Effectiveness.

关键词

switchgear faults diagnosis; partial discharge; particle swarm algorithm; BP neural network;

报告人

Zhuo Wang

Dalian Jiaotong University

I am currently studying for a master's degree at Dalian Jiaotong University. My main research field is high voltage detection technology.

稿件作者

Xiang Zheng Dalian Jiaotong University

Zhuo Wang Dalian Jiaotong University

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重要日期

会议日期

07月11日

2023

至

08月18日

2023
11月10日 2021

初稿截稿日期
12月10日 2021

注册截止日期
12月11日 2021

报告提交截止日期

主办单位

IEEE IAS

承办单位

IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST