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文章摘要
基于最优特征量选取的开关柜故障判别方法研究
Research on Switchgear Fault Judgment Method Combined with Optimal Feature Selection
Received:May 18, 2021  Revised:July 08, 2021
DOI:10.19753/j.issn1001-1390.2023.08.015
中文关键词: 开关柜  状态评估  马氏距离法  故障判别
英文关键词: Electrical switchgear, Condition assessment, Mahalanobis distance method, Fault identification
基金项目:吉林省产业技术研究与开发专项计划(项目编号:2020C022-7)
Author NameAffiliationE-mail
Guo Zhiwei Zhaotong Power Supply Bureau of Yunnan Power Grid Co,Ltd 243609104@qq.com 
Xu Zitao Zhaotong Power Supply Bureau of Yunnan Power Grid Co,Ltd dubo0411@sina.com 
Liu Bo Zhaotong Power Supply Bureau of Yunnan Power Grid Co,Ltd jameslord@sina.com 
Luo Xin* Northeast Dianli University dubo0411@sina.com 
Zhang Wei tate Grid Zhejiang Electric Power Co., Ltd., Jiaxing Power Supply Company dubo0411@sina.com 
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中文摘要:
      为对开关柜运行状态评估进行快速准确评估,提出了基于多类型数据的最优特征量开关柜故障判别方法。基于实时监测的开关柜各类型电气量和非电气参数影响,采取最小冗余最大相关(Minimum Redundancy Maximun Relevance,MRMR)原则对采集的开关柜运行参数进行处理获得特征样本,并对其进行优化获得最优特征子集;采用马氏距离法对实时监测的运行状态特征量与标准设定样本进行比较,从而判别出开关柜的故障状态。实际测试和算例分析表明,所提出的方法能够有效提高故障识别的精度和效率。
英文摘要:
      In order to improve the accuracy of the evaluation of the operation status of the switchgear, a switchgear fault identification method based on the optimal characteristic quantity of multiple types of data is proposed. Based on the real-time monitoring of various types of switchgear electrical quantities and non-electrical parameters, the principle of minimum redundancy and maximum correlation is adopted to process the collected switchgear operating parameters to obtain feature samples, and optimize them to obtain the optimal feature subset; The distance method compares the real-time monitored operating state characteristic quantity with the standard setting sample to determine the fault state of the switchgear. Practical tests and analysis of calculation examples show that the proposed method can effectively improve the accuracy and efficiency of fault identification.#$NLKeywords:Electrical switchgear, Condition assessment, Mahalanobis distance method, Fault identification
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