摘要
随着电力系统迅速发展,电力系统中的数据也在急剧的增加。由于这些数据具有多样性和复杂性,决定了其具有多层次性和随机性,这给数据挖掘带来了极大的困扰。文中使用一种基于粗糙集理论的数据挖掘方法,通过粗糙集求取最小属性约简集,搜索决策表的约简形式,区分关键信号和非关键信号,从样本集中找出诊断规则,达到快速进行故障诊断的目的,最后由算例证明该算法在电力系统数据挖掘上的正确性。
With the development of power system,the data in power system is increasing rapidly. Because of the diversity and complexity of the data,it has Multi levels and randomness,which brings the problem of data mining. This paper presents a theoretical method for data mining based on rough set,by rough sets for take the minimum attribute reduction set,scanning the decision table reduction form,to distinguish the key signals and non critical,to find out the diagnostic rules from the sample set,achieve rapid fault diagnosis. Finally,examples prove correctness of the algorithm in power system data mining.
出处
《功能材料与器件学报》
CAS
2015年第6期209-213,共5页
Journal of Functional Materials and Devices
