In order to realize the accurate early warning and intelligent monitoring of transformer oil insulation state

by analyzing the dissolved gas analysis data in transformer oil sent for inspection by some power plants in Inner Mongolia over the years

a transformer oil insulation state prediction method based on feature evaluation and improved SOA(Seagull Optimization Algorithm) optimized DELM(Deep Extreme Learning Machine) model is proposed

so as to accurately predict the dissolved hydrogen and total hydrocarbon content in transformer oil during operation. In terms of feature extraction

the correlation degree between the features is evaluated by calculating the mutual information between the input vector and the predicted output

and the feature with the highest correlation degree constitutes the simplest input vector. In terms of predicted output

by adding additional variable and improving the selection of the SOA parameters

the algorithm can converge quickly and avoid falling into local optimal

so as to realize the network weight and hidden layer bias optimization of DELM prediction model. Finally

compared with other prediction models

the historical samples of the seven power plants are analyzed in sequence

and the applicability of the proposed method is verified comprehensively.