Dissolved gas analysis (DGA) occupies an extremely important position in transformer condition assessment

and many conventional methods have been proposed based on dissolved gas in oil analysis. In this paper

a combined deep neural network (CD NN) is proposed to combine the characteristics of dissolved gas in oil and conventional methods for transformer condition assessment. First

the sample data are normalized according to the characteristics of the conventional method parameters. Then

the normalized parameters are used as input parameters of the deep neural network. Multiple deep neural network models are built separately. Next

the prediction results of multiple deep neural network models are weighted according to the accuracy of different models. Finally

the one with the largest weight is selected as the final prediction result of the combined deep neural network. In this paper

we show the necessity of data normalization through data statistics and demonstrations. The comparison between setting up the normal state data and not considering the normal state data proves that the normal state is easy to misclassify with other states when predicting

which leads to a decrease in the prediction accuracy. By comparing with the composition method of this paper and the classification method commonly used in MATLAB software

it is confirmed that the method in this paper combines the advantages of other methods and corrects the prediction results

thus having higher accuracy.