In order to effectively utilize the dielectric response characteristics of transformers to diagnose the insulation state

this paper proposes a two-level hybrid optimization method for analyzing time-domain dielectric response characteristics. The optimization algorithm is based on the combined statistical indicators (CSI) and random forest (RF) theory. The initial feature space set is formed with 23 time-domain characteristics. In the first-level stage

statistical indices correlation

distance

and information indicators are integrated to assess the synthesis score of the characteristics

while highly redundant and lowclass discrimination characteristics are eliminated from the initial space set. In the second-level stage

the Random Forest based outside bagging data theory is introduced to evaluate the least important characteristics

and the characteristics with low importance indices are excluded to obtain the final optimal feature space set. The proposed method is carried out on 82 sets of data from actual dielectric response tests on oil-paper insulation transformers. Finally

the final optimal feature space set

along with several other data sets

is tested via different diagnosis methods. The results show that the optimal feature space set obtained via the proposed method outperforms other feature space sets in terms of better adaptability and diagnosis accuracy.