In order to improve the accuracy of transformer fault diagnosis effectively

this paper combines particleswarm optimization（PSO）with multi-classification correlation vector machine to construct a method based on multi-classification correlation vector machine of particle swarm optimization for transformer fault diagnosis. The methodfirstly combines the dissolved gas in oil with the ratio of four characteristic gases as fault characteristic variables tofurther enrich the fault information. Secondly

the kernel parameters of the multi-classification correlation vector ma-chine are optimized by PSO algorithm and training sample data to obtain the parameters which are optimal and canimprove the efficiency of fault classification effectively. Finally

nine characteristic variables are used as feature in-puts

and the trained multi-classification correlation vector machines are used for fault diagnosis. The case analysisshows that this method can effectively supplement and improve the fault characteristic variables and fault classifica-tion model

and its fault diagnosis efficiency is more advantageous.