Feature extraction of arc high impedance grounding fault of low‐voltage distribution lines based on Bayesian network optimisation algorithm

Author:

Sun Jing1ORCID

Affiliation:

1. Liaoning University of Technology Jinzhou Liaoning China

Abstract

AbstractIn order to accurately extract the fault features of arc high impedance grounding of low‐voltage distribution lines and judge the fault feature types of arc high impedance grounding of low‐voltage distribution lines, a fault feature extraction method for arc high impedance grounding of low‐voltage distribution lines based on Bayesian network optimisation algorithm is proposed. According to the model of arc high impedance grounding fault based on Thomson’s principle, the parameter information of each transmission signal in arc high impedance grounding fault is extracted. Through the denoising method of arc high impedance grounding signal based on combined filter, the noise information of transmission signal in case of arc high impedance grounding fault is removed and the signal purity is improved. The detection and recognition method for fault characteristics of arc high impedance grounding of low‐voltage distribution lines based on Bayesian network optimisation algorithm is used to detect and judge the fault characteristics of the abnormal characteristics of the denoised transmission signal, and complete the fault feature extraction. After testing, this method can accurately and real‐time extract the fault characteristics of arc high impedance grounding of low‐voltage distribution lines, and has application value.

Publisher

Institution of Engineering and Technology (IET)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Novel adaptive protection approach for optimal coordination of directional overcurrent relays;2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON);2024-06-25

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3