Characteristic Optimization Based on Combined Statistical Indicators and Random Forest Theory

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.