To improve the accuracy of fault diagnosis for transformer

a fault diagnosis model for oil immersed transformer based on one-dimensional all convolution neural network is proposed. Firstly

the data of gases dissolved in the transformer oil is standardized and coded. Then the network of the proposed diagnosis model is established. By using the characteristics of all convolutional network and convolution kernel

the network structure can effectively extract fault features from input data and then apply it for classification. Finally

the model is trained and tested. The test result and contrastive analysis show that superior performance is achieved when the model is applied to fault diagnosis of power transformer.