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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 rapid development of power system, the data in power system is increasing rapidly too. Due to the diversity and complexity of these data, it determines the multi-level and randomness, which brings great troubles to data mining. In this paper, we use a data mining method based on rough set theory to get the minimum attribute reduction set, the reduction form of the search decision table, distinguish the critical signals and non-critical signals from the rough set, and find out the diagnostic rules from the sample set to achieve fast For the purpose of fault diagnosis, the example shows the correctness of the algorithm in power system data mining.