Abstract
Optimal feature selection is one of the most significant challenges of Partial Discharge (PD) pattern recognition of cable system as the number of PD features is with large quantity, which reduces the efficiency of PD pattern recognition methods and restricts the effectiveness of PD based condition monitoring and diagnostics. To overcome the challenge a random forest method is presented in the paper to select the most effective PD features from 18 different kinds of features. Firstly, PD data from five types of artificial defects based on IEC 60270 system in High Voltage (HV) lab is introduced. Secondly, PD data pre-processing and feature extraction are carried out and 18 kinds of PD features are extracted from the raw data. Thirdly, the random forest method based optimal feature selection is presented in details and compared with Lasso method. Finally, the feature selection methods are evaluated with Random Forest and Logistic Regression based PD pattern recognition method. The top 6 features are recommended for PD pattern recognition based on the experimental data and the random forest method.
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