In order to improve the correct judgement rate in power transformer fault diagnosis

this paper investigates a dissolved gas analysis method of transformer via neural network based on particle swarm optimization with neighborhood operator.Based on correlation analysis and data pretreatment

some typical gases in transformer oil are selected as the input of neural network for training

then the fault diagnosis is accomplished via the trained and optimized neural network.The experimental results show that this method gains good classification result

and can identify faults under the difficult situation where transformer overheat and partial discharge coexist.Moreover

this method works with a higher correct judgement rate.