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-ratiomethod 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.
Keywords: transformer fault,
weighted combination model, duval’s triangle, bp neural network, three-ratio method
Author: Hongli Yun
Journal Code: jptkomputergg160302
