Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

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应用于智能电网故障检测的关联规则挖掘算法优化

来源:电工电气发布时间:2016-03-14 11:14 浏览次数:8

应用于智能电网故障检测的关联规则挖掘算法优化 

朱文灏1,2,郭其一1 
(1 同济大学 电子与信息工程学院,上海 201804;
2 上海施耐德低压终端电器有限公司,上海 201109)
 
 

摘 要: 针对于目前故障检测方法在智能电网应用中存在较大误差的问题,介绍了一种基于贝叶斯网络和关联规则数据挖掘的算法模型,通过Hash 技术优化Apriori 算法,对原数据挖掘,去除不期望的候选项集,并通过贝叶斯网络训练样本,减少检测误差,最终得到电网故障检测结果。仿真结果表明,这种基于贝叶斯网络和关联规则挖掘算法的故障检测模型,比传统算法在电网故障检测方面更有效率,且检测误差大幅降低。
关键词: 智能电网故障检测;关联规则挖掘;频繁项集优化;贝叶斯网络
中图分类号:TM743 文献标识码:A 文章编号:1007-3175(2015)03-0004-04


Optimization of Association Rules Mining Algorithm for Smart Grid Fault Detection 

ZHU Wen-hao1,2, GUO Qi-yi1 
(1 Department of Electrical Engineering, Tongji University, Shanghai 201804, China;
2 Schneider Shanghai Low Voltage Terminal Apparatus Co., Ltd, Shanghai 2011 09, China)
 
 

Abstract: Aiming at the problem that larger error always exists during the application of fault detection test method in smart grid, this paper introduced an algorithm model based on Bayesian network and association rule mining. With mining the original data and removing the undesired candidate, Apriori algorithm was optimized by Hash technology; also Bayesian network was introduced for sample training to decrease detection error, so as to finally obtain the result of power network fault detection. Simulation results show that compared with traditional algorithm, the proposed fault detection model, which is based on Bayesian network and association rules mining, is more efficient with lower detection error in power grid fault detection.
Key words: smart grid fault detection; association rules mining; frequent item set optimization; Bayesian network


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