For current fault diagnosis of power transformer

the method based on dissolved gas chromatographic dataanalysis is insufficient in fault information

and its diagnostic capability based on single algorithm is limited. This pa-per proposes a transformer fault diagnosis method based on multiple information fusion. The method firstly combinesthe data of electrical test with 17 groups of the concentrations of characteristic gas components in oil to achieve morefault information. The feature sensitivity evaluation and the kernel principal component analysis（KPCA）are used toreduce the feature dimension of the selected fault data to realize complementary of the multi-level fault information.Then

the nine-dimensional features after dimension reduction are taken as the input characteristic parameters of theBP neural network

the support vector machine（SVM）and the Bayesian method to determine fault type. Subsequent-ly

a conclusion is output if there is no disagreement on the preliminary diagnosis result

otherwise

the basic beliefassignment of each evidence is recalculated and the Dempster combination rule is used for decision fusion to makemore reasonable fault judgment. Example analysis shows that the proposed method can effectively deal with the prob-lems of insufficient information and single method

and improve diagnostic accuracy.