Transformer Fault Diagnosis Method Based on Dynamic Weighted Combination Model

Abstract

The paper tried to integrate the DGA data with the gas production rate, which are the major indexes of transformer fault diagnosis. Duval’s triangle method, BP neural network and IEC three-ratio method were weighted. Firstly, the paper regarded the gas production rate as the independent variables, fitted the cubic curves of the gas production rate and variance of each diagnosis method, and then defined the weights of each algorithm through the data processing method of unequal precision. At last, the dynamic weighted combination diagnosis model was established. That is, the weight is different as the gas production rate changes although the method is identical. The results of diagnosis examples show that the accuracy rate of the weighted combination model is higher than any single algorithm, and it has certain stability as well.