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作 者:郭志伟 徐子涛 刘波 罗鑫 张炜 Guo Zhiwei;Xu Zitao;Liu Bo;Luo Xin;Zhang Wei(Zhaotong Power Supply Bureau,Yunnan Power Grid Co.,Ltd.,Zhaotong 657000,Yunnan,China;School of Electrical Engineering,Northeast Dianli University,Jilin 132000,Jilin,China;Pinghu Power Supply Company,State Grid Zhejiang Electric Power Co.,Ltd.,Pinghu 314200,Zhejiang,China)
机构地区:[1]云南电网有限责任公司昭通供电局,云南昭通657000 [2]东北电力大学电气工程学院,吉林吉林132000 [3]国网浙江省电力有限公司平湖供电公司,浙江平湖314200
出 处:《电测与仪表》2023年第8期85-91,共7页Electrical Measurement & Instrumentation
基 金:吉林省产业技术研究与开发专项计划项目(2020C 022-7);浙江省电力有限公司科技项目(2020-KJLH-PH-006)。
摘 要:为实现对开关柜运行状态进行快速准确评估,文章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以获取特征样本,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。In order to improve the fast and accurate evaluation of the operation status of the switchgear,a switchgear fault identification method based on the optimal feature 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 maximum relevance(MRMR) is adopted to process the collected switchgear operating parameters to obtain feature samples,and optimize them to obtain the optimal feature subset.The adopted Mahalanobis distance method compares the real-time monitored operating status feature 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.
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