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首页> 外文会议>International Conference on Measurement, Instrumentation and Automation >Approach for Mining Fault Rules of Power Grid based on the Combination of Rough Set Theory and Association Rule
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Approach for Mining Fault Rules of Power Grid based on the Combination of Rough Set Theory and Association Rule

机译：基于粗糙集理论与关联规则组合的电网采矿故障规则的方法

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With the increasing of fault information transmission capacity in power grid, the volume of information which needs to be concerned by dispatchers has greatly increased, consequently making it difficult to identify the fault signal and analyze the cause of the accident quickly for dispatchers in massive fault information. To settle this problem, this paper uses a novel approach that combines rough set theory with association rule for mining fault rules in a large number of historical fault data of power grid. Firstly, it builds distributed original decision tables according to regions. And then it uses the information entropy algorithm in condition attribute reduction. Lastly, it applies the improved Apriori algorithm of association rule to fault rules mining based on the reduction decision table. In this way the problems of redundancy of massive fault information can be solved and complexity of rules extraction can be simplified effectively. It also improves the efficiency of fault rules mining.
机译：随着电网故障信息传输容量的增加，需要由调度员致力于发货人员的信息量大，因此难以识别故障信号并在大规模故障信息中快速分析事故的原因。为了解决这个问题，本文采用了一种新的方法，将粗糙集理论结合在大量历史故障数据中挖掘故障规则的关联规则。首先，它根据区域构建分布式原始决策表。然后它在条件属性中使用信息熵算法。最后，它适用于基于还原决策表的故障规则挖掘的改进的Apriori算法。以这种方式，可以解决大规模故障信息的冗余问题，并且可以有效地简化规则提取的复杂性。它还提高了故障规则挖掘的效率。

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