Transformer Fault Diagnosis Method Based on Neural Network and D-S Evidence Theory

Abstract

The BP neural network is used to calculate the basic fault probability distribution of the dissolved gas in the transformer oil and the core grounding online monitoring data, and the Dempster-Shafer evidence theory is used to fuse the multi-source information of the basic probability of various types of faults to obtain a transformer fault diagnosis model. Transformer samples are used to verify the model, and the support vector machine and convolutional neural network fault diagnosis models are compared, and it is concluded that the proposed method is better in terms of fault diagnosis accuracy and stability.