Research on transformer fault classification method based on Bayes-CNN
The performance and root cause of power transformer failure have a certain degree of ambiguity and ran-domness,in complex situations,the traditional method is often difficult to accurately identify transformer faults,and there is certain amount of room for improvement in its accuracy.Therefore,a new method of transformer fault identification is proposed in this paper.In this method,a combination of Bayesian theory and convolutional neural network(CNN)algorithm is used to process characteristic gas data by convolutional neural network,and Bayesian algorithm is used to optimize model parameters,aiming at improving the accuracy of fault detection.By coding and preprocessing the fault types,the transformer fault classification model is constructed,and the Bayes-CNN model is applied to classify the transformer faults,which is verified by examples and compared with SVM,DBN and CNN models.The results show that the convergence speed and fitting accuracy of the model are significantly improved by using the Bayesian optimization CNN algorithm,which proves that the transformer fault classification method has better performance and provides a new method and idea for power transformer fault diagnosis.
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