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By analyzing the bottleneck facing the current power transformer fault diagnosis model, this paper puts forward a power transformer fault diagnosis model. The SOM model is introduced to optimize the training samples collected and to eliminate interference sample data existing in training samples of various types. At last, various excellent samples are extracted as the input information of the BP network to build the power transformer fault diagnosis model. The model analysis results suggest that various training samples extracted and optimized by the SOM network can efficiently improve the recognition and diagnosis performance of the BP network. Besides, its diagnosis accuracy is higher than that of traditional algorithms by more than 10%.

Power Transformer Fault Diagnosis Based on the SOM Network Combined with the BP Neural Network