Transformer Fault Diagnosis Method Based on Fuzzy Logic and D-S Evidence Theory

Firstly, extended fuzzy logic is used to calculate the basic fault probability distribution of dissolved gas in transformer oil and iron core grounding online monitoring data, and then use Dempster-Shafer evidence theory to fuse multi-source information on the basic probability of various types of faults to obtain a transformer fault diagnosis model. The model is verified by 10 transformer samples, and the support vector machine and convolutional neural network fault diagnosis models are compared at the same time. Finally, it is concluded that the proposed method is better in terms of fault diagnosis accuracy and stability.