电力故障深度挖掘方法的研究与仿真  

Research and Simulation of Electric Power Failure Depth Excavation Method

作　　者：侯燕[1,2] 王华东[1] 

机构地区：[1]周口师范学院计算机科学与技术学院,河南周口466000 [2]昆明理工大学信息工程与自动化学院,云南昆明650093

出　　处：《计算机仿真》2016年第1期404-407,共4页Computer Simulation

基　　金：云南省自然科学基金(2008CDZ088)

摘　　要：在电力系统故障优化检测的研究中,由于电力系统中的故障种类较多,无法用可识别的单独特征描述,这就导致不同电力故障的特征具备较大的相似性。传统的故障挖掘方法中,需要提取有区别性的电力故障特征,但是一旦特征具备较大的相似性,故障深度挖掘过程容易陷入无限分类求最优的过程,造成故障挖掘精度低的问题。提出基于改进遗传算法的电力故障深度挖掘方法。提取电力故障特征将其作为遗传算法中染色体的基因,将染色体作为故障原因,利用遗传算法进行迭代寻优;在故障挖掘的过程中对遗传算法中的编码方式、进化方式、交叉操作和变异操作进行了改进,并确定了故障深度挖掘过程中的适应度函数。实验结果表明,利用改进算法进行故障深度挖掘,能够提高挖掘的效率和挖掘的精度。In the power system fault sort is more, can＇t use identifiable individual character description, this leads to different power failure characteristics have great similarities. Traditional mining methods, there need to extract power failure characteristics of distinctiveness, but once characteristics have great similarity, classification of fault depth excavation process easy to fall into the infinite and the optimal process, the problem of low precision of break- downs and mining. Proposed power failure depth of mining method based on improved genetic algorithm. Power fail- ure feature extracting chromosome gene as a genetic algorithm, the chromosome as the cause of the problem, the iter- ative optimization using genetic algorithms （ga） ; In the process of fault mining mode of encoding of genetic algorithm （ga）, evolution, crossover operation and mutation operation is improved, and to determine the fault the fitness func- tion in the process of deep excavation. Experimental results show that the fault depth excavation using the improved algorithm, can improve the efficiency of mining and mining the accuracy.

关 键 词：改进遗传算法 故障挖掘 故障特征 

分 类 号：TM732[电气工程—电力系统及自动化]