A fault detection model for microgrid detection based on Bayesian network and association rule mining
Wenhao Zhu1, 2, Qiyi Guo1
COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(12A) 255-259
1Department of Electrical Engineering, Tongji University, Shanghai 200092, China
2Schneider Shanghai Low Voltage Terminal Apparatus Co., Ltd. Shanghai 201109, China
In view of the problem that current fault detection methods exist large error in microgrid 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 microgrid detection result. Simulation results show that the proposed fault detection model based on Bayesian network and association rule mining is efficient in microgrid fault detection with detection error far less than that of traditional algorithm.