摘要
为了解决当前电力系统故障诊断方法存在的问题,提高故障诊断正确率,提出基于数据挖掘的电力系统故障自动诊断方法。采集电力系统故障数据,从中提取故障诊断特征,对故障诊断特征进行约简处理,得到最优的故障诊断特征。建立电力系统故障诊断的关联规则,根据关联规则实现故障状态诊断,与其他方法进行故障诊断仿真对比实验,结果表明:该方法改善了电力系统故障诊断,而且抗干扰性极强。
In order to solve some problems existing in current fault diagnosis methods of power system and improve the accuracy of fault diagnosis of power system, an automatic fault diagnosis method of power system based on data mining is proposed. The fault data of power system is collected, and the fault diagnosis features of power system are extracted from the data. The fault diagnosis features of power system are reduced to get the optimal fault diagnosis features of power system. The association rules of power system fault diagnosis are established, and the fault state diagnosis is realized according to the association rules, and the fault diagnosis of power system is carried out with other methods.The simulation results show that the proposed method improves the power system fault diagnosis and has strong anti-interference.
作者
励力帆
虞伟
桑清城
LI Lifan;YU Wei;SANG Qingcheng(State Grid Zhoushan Power Supply Company,Zhoushan 316000,China)
出处
《机械制造与自动化》
2022年第2期228-231,共4页
Machine Building & Automation
关键词
数据挖掘
电力系统
故障自动诊断
粗糙集
免疫算法
关联规则
data mining
power system
fault automatic diagnosis
rough set
immune algorithm
association rules
