Partial Discharge Pattern Recognition in GIS Based on Multiscale Dispersion Entropy and Stacking Ensemble Learning

Abstract

Partial discharge (PD) monitor in gas-insulated switchgear (GIS) is an important means to detect insulation defects of equipment. To solve the problem that the traditional PD extraction features are not obvious and recognition precision is limited. The paper presents a new pattern recognition algorithm by combining the multiscale dispersion entropy (MDE), locally linear embedding (LLE), and stacking ensemble learning, to effectively refine the recognition correct rate of PD types. First, the MDE values of PD signal were calculated as the feature value. Then, use LLE to reduce dimensions to refine the speed and precision of model recognition. Finally, use stacking ensemble learning to train and recognize the feature values after dimension reduction. Among them, K-nearest neighbor, random forest and Gaussian Bayes were selected for the first layer learners, and logical regression model was selected for the second layer learner. The validation results indicated that the recognition correct rate of the proposed algorithm for four typical PD types in GIS was more than 98%, and it has a strong anti-interference ability, which is significantly better than the traditional feature extraction methods.