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《Electric Switchgear》 2012-05
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Simulation Research on Modified DE and Generalized Neural Network in Traustormer's Fault Diagnosis

YANG You-chan,HUANG Zhe,ZHOU Hui-long(College of Electrical Engineering,Guangxi University,Nanning 540004,China)  
Differential evolution algorithm exists in premature convergence easily to fall into the local optimal value shortage,the new methods of the transformer's fault diagnosis of generalized neural network combined medified DE algorithm and fuzzy clustering.The method adopts adaptive adjusting tactics to improve the differential evoluting parameters,thus optimizing fuzzy clustering objective function according to five characteristic gas content in transformer oil,meanuhile the generalized neural network can also give play to its fast training speed and fine appoximating effect,obtaining fault form.Seeing from the results of simulation experiment,take the mixed algorithm tomake the transformer's fault diagnosis,its accuracy and fast convergence speed have good imprrvement.Comparing withthe modified DE and FCM neural networks,its appoximating effect is also the best and beneficial to more diagnostic fault.Comparison by the sample result,the model is easy to be achieved and is of strong practicability and qenerulization
【CateGory Index】: TM407
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