Inspection of partial discharge before contamination flashover is of great importance for preventing exterior insulation accidents.In this study,a new method for identification of discharge severities is proposed.Spec...Inspection of partial discharge before contamination flashover is of great importance for preventing exterior insulation accidents.In this study,a new method for identification of discharge severities is proposed.Specifically,a low‐cost ultraviolet(UV)sensor detection system was combined with time-frequency method,texture analysis and support vector machine(SVM)classifier to classify partial discharge severities for ceramic insulators.The visible images and the root‐mean‐square value of leakage currents detected simulta-neously are used to classify the UV signals into different discharge faults.The frequency and amplitude integration of UV pulses are minimum in corona discharge and larger in arc discharge.The images of UV signal spectrograms differ significantly at different discharge stages.The density and brightness of image textures are minimal in corona discharge and larger in arc discharge.Valid and reliable features selected by two texture feature extraction methods with SVM classifier have a reliable classification accuracy of 90.6%for ceramic insulators,and outperform a single time feature or other texture features.SVM outperforms k‐Nearest Neighbour,Naive Bayes and Decision Tree.Our new method with computational effectiveness and high practicality can solve the problem of high randomness and low accuracy of UV sensor detection.It can be further applied to the deterioration diagnosis of power facilities.展开更多
基金funded by the National Natural Science Foundation of China(U1966210)Science and Technology Project of State Grid Shanghai Electric Power Company of China(52,094,019,007A).
文摘Inspection of partial discharge before contamination flashover is of great importance for preventing exterior insulation accidents.In this study,a new method for identification of discharge severities is proposed.Specifically,a low‐cost ultraviolet(UV)sensor detection system was combined with time-frequency method,texture analysis and support vector machine(SVM)classifier to classify partial discharge severities for ceramic insulators.The visible images and the root‐mean‐square value of leakage currents detected simulta-neously are used to classify the UV signals into different discharge faults.The frequency and amplitude integration of UV pulses are minimum in corona discharge and larger in arc discharge.The images of UV signal spectrograms differ significantly at different discharge stages.The density and brightness of image textures are minimal in corona discharge and larger in arc discharge.Valid and reliable features selected by two texture feature extraction methods with SVM classifier have a reliable classification accuracy of 90.6%for ceramic insulators,and outperform a single time feature or other texture features.SVM outperforms k‐Nearest Neighbour,Naive Bayes and Decision Tree.Our new method with computational effectiveness and high practicality can solve the problem of high randomness and low accuracy of UV sensor detection.It can be further applied to the deterioration diagnosis of power facilities.
