摘要
为充分利用智能变电站的站端信息,帮助运维人员更加快速准确地定位智能变电站二次系统的故障,从而保证电力系统的安全稳定运行,文中提出一种基于多分类支持向量机(SVM)的智能变电站二次系统故障诊断方法,并采用粒子群优化(PSO)算法对参数进行自动寻优,根据智能变电站长期运行的历史状态数据和检修人员处理结果构建专家数据库。智能变电站二次系统的原始信号为大量0,1形式的状态量,采用开关量编码形式定义数据组,并使用主成分分析法对信号进行数据降维,最终构建了基于站端信息的智能变电站二次系统故障诊断模型。对比诊断模型与专家人工判断对实际故障的诊断结果,可知文中所提出的方法具有较好的准确性与适用性。
In order to make full use of the information of the intelligent substation,help the operation and maintenance personnel to locate the faults of the secondary system of the intelligent substation more quickly and accurately,so as to ensure the safe and stable operation of the power system,a fault diagnosis method for secondary system of intelligent substation based on multi classification support vector machine(SVM)is proposed,and particle swarm optimization(PSO)algorithm is used to optimize the parameters automatically.The expert database is built according to the historical state data of the long-term operation of the intelligent substation and the processing results of the maintenance personnel.The original signal of the secondary system of the intelligent substation is a large number of 0,1 state variables.The data group is defined in the form of switching value coding,and the data dimension of the signal is reduced by using the principal component analysis method.Finally,the fault diagnosis model of the secondary system of the intelligent substation based on the information of the terminal is constructed.The results of the comparison between the diagnosis model and the expert manual judgment show that the method proposed has excellent accuracy and applicability.
作者
袁明哲
邹经鑫
汪艮
李雪恺
卫佳奇
YUAN Mingzhe;ZOU Jinxin;WANG Gen;LI Xuekai;WEI Jiaqi(State Grid Sichuan Electric Power Company Chengdu Power Supply Company,Chengdu 610041,China;College of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China)
出处
《电力工程技术》
2020年第6期172-176,190,共6页
Electric Power Engineering Technology
基金
四川省科技计划资助项目(19YYJC0681)。
关键词
智能变电站
二次系统
故障诊断
专家数据库
主成分分析
支持向量机
smart substation
secondary system
fault diagnosis
expert database
principal component analysis
support vector machine(SVM)
