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| Study on Association Rules Mining Algorithm for Micro-grid Fault Detection |
| Zhu Wenhao1, 2, Guo Qiyi1 |
1. Department of Electrical Engineering, Tongji University, Shanghai 201804;
2. Schneider Shanghai Low Voltage Terminal Apparatus Co., Ltd, Shanghai 201109 |
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Abstract In view of the problem that current fault detection methods exist large error in micro-grid fault detection, this paper presents a model based on Bayesian network and association rule mining. It firstly adopts Hash technology to optimize Apriori algorithm and remove the undesired candidate item set, conducts data mining of original data set, introduces Bayesian network for sample training to reduce detection error, and finally obtains power system detection result. Simulation results show that the proposed fault detection model based on Bayesian network and association rule mining is efficient in power system fault detection with detection error far less than that of traditional algorithm.
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Received: 18 August 2015
Published: 18 August 2015
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| Cite this article: |
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Zhu Wenhao,Guo Qiyi. Study on Association Rules Mining Algorithm for Micro-grid Fault Detection[J]. Electrical Engineering, 2015, 16(8): 7-10.
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| URL: |
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http://dqjs.cesmedia.cn/EN/Y2015/V16/I8/7
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2015, Vol. 16 
