External insulation strength assessment of contaminated insulator based on acoustic emission

External insulation strength assessment of contaminated insulator is proposed in this paper using the acoustic emission signals generated when the polluted insulator flashover discharges. Systematic artificial contamination experiments were done. The acoustic emission signals generated from the polluted insulator were monitored by the sound monitoring device with high sensitivity. And the acoustic emission signals were analyzed. It shows that there is a relationship between the strength of filthy discharge and acoustic emission signals. The 14 features that can reflect acoustic emission signals of polluted insulator were extracted. Then a method of principal feature selection based on algorithm ReliefF is utilized. By using least squares support machine (LS-SVM), the classification model of is built. After analysis and comparison, LS-SVM model has a higher accuracy in t classification of different external insulation strength stages.