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《Journal of Shandong University of Science and Technology(Natural Science)》 2017-03
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Fault Diagnosis of Power Transformer Based on Improved BP Neural Network Optimized by Bat Algorithm

GONG Maofa;LIU Yanni;WANG Laihe;SONG Jian;XIE Yunxing;College of Electrical Engineering and Automation,Shandong University of Science and Technology;College of Mining and Safety Engineering,Shandong University of Science and Technology;State Grid Shandong Dongying Electric Power Company;  
A fault diagnosis method for power transformer based on improved BP neural network is proposed.Using bat algorithm of BP neural network weights and threshold parameters optimization,and bat algorithm for later optimization easy to fall into local optimum defects,chaos algorithm using chaos optimization of population to reduce invalid iteration,and improve the speed of convergence.The parameter values obtained were applied to the construction of the BP neural network model,and the data were trained and tested.Through an example analysis,the optimization of the BP neural network for the fault diagnosis of the transformer is practical and effective.
【Fund】: 山东省自然科学基金项目(ZR2012EEM021)
【CateGory Index】: TM41;TP18
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