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《Proceedings of the CSU-EPSA》 2017-01
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Grid Fault Diagnosis Based on Improved Genetic Optimization BP Neural Network

YUAN Pu;MAO Jianlin;XIANG Fenghong;LIU Lian;ZHANG Maoxing;Faculty of Information Engineering and Automation,Kunming University of Science and Technology;  
Despite the advantage of self-learning,self-adaptation and generalization ability,BP neural network is proneto be trapped into local minimums during the operation process,and the node numbers in the hidden layer also affectthe diagnosis effects. In this paper,according to the empirical formula,the range of node numbers in the hidden layeris narrowed,and the optimal number is searched in a small range. Considering its global optimization,genetic algorithm(GA)is adopted to optimize the initial weight and threshold value of the BP neural network training,which can avoidthe local minimums. Using the combination of the above two methods,the grid fault can be diagnosed accurately and ef-fectively,and the fault tolerance is improved.
【CateGory Index】: TP183;TM732
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【Citations】
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【Co-citations】
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1 YUAN Pu;MAO Jianlin;XIANG Fenghong;LIU Lian;ZHANG Maoxing;Faculty of Information Engineering and Automation,Kunming University of Science and Technology;;Grid Fault Diagnosis Based on Improved Genetic Optimization BP Neural Network[J];电力系统及其自动化学报;2017-01
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