In order to effectively manage and monitor the running status of power transformer

on the basis of analyzing transformer data at running time

a transformer fault information pattern recognition and diagnosis model using the objective entropy weight method is developed. First of all

in conjunction with the information pattern recognition theory and information entropy and other related theories

the way of defining the fault mode matrix data of all the information is used to build the fault mode matrix. At the same time

the information entropy is used to represent the precise quantitative fault information. Next

fault pattern criterion functions are created on the principles of distance and projection. Fault training patterns are sequenced by the above criterion functions

and then the sequencing patterns are utilized to classify fault test patterns. Finally

by calculating the quasi-core vector using the classification results

the test pattern could be classified by Euclidean distance criterion. To validate the reliability and validity of the established model

one hundred and twenty samples of oil gas provided by an electric power company are adopted to test this model. Compared with the three ratios of the traditional fault diagnosis method

the proposed method has overcome the disadvantages of no coding and coding fuzzy boundary which would lead to misjudging problem in the traditional three-ratio method. This work is supported by National Natural Science Foundation of China(No. 61004118)and Scientific Research Foundation for the Returned Oversea Chinese Scholars

Ministry of Education of China(No. 2015-49).