Pattern recognition for partial discharge using multi-feature combination adaptive boost classification model

Abstract

This paper proposes a multi-feature combination adaptive boost classification model, considering the difference and complementarity of the three different single feature sets for partial discharge pattern recognition. First, eight types of physical models are designed. Then, an UHF measurement system is used to collect partial discharge data. Second, three kinds of single feature sets extracted from the Phase Resolved Pulse Sequence (PRPS) data are combined with pairs and three to construct new feature sets. The final optimal feature set is selected from the single feature set and the combined feature set as the input of the classification model. Finally, using the boosting algorithm in combination learning to process the training data set, taking the support vector machine as the base classifier, and measuring the inconsistency between one base classifier and other base classifiers by using the ‘‘unpaired’’ diversity index based on information entropy. By this method, a series of various SVM-based classifiers with moderate accuracy are obtained, and finally an adaptive boost classification model based on the multi-feature combination method is obtained. For each defect, 25 samples were obtained at the same test voltage level, and a total of 150 samples were obtained at 6 voltage levels through multiple experiments. The proposed method was compared with the traditional methods using these data sets. The results revealed that the proposed method successfully identified the types of partial discharge insulation defects.