Integrated Diagnosis of Abnormal Discharge Utilizing Solar-blind Ultraviolet and Pulse Current Signals
Abnormal discharge is the main cause of insulation degradation and equipment failure,and each independent de-tection method has specific pros and cons.This work studied an integrated method of diagnosing abnormal discharge by simultaneously utilizing signals of solar-blind ultraviolet and pulse current.First the actual models of typical insulation de-fects were prepared in the laboratory,and the abnormal discharge signals were collected through high-frequency current transformer and solar-blind ultraviolet sensor.Then the features of abnormal discharge were extracted through phase-re-solved partial discharge pattern,and the dataset containing phase information of discharge was obtained.Finally a dis-charge type identification model was established using a neural network,and a comparison of abnormal discharge diagnosis performance was conducted using different data sources.The results showed that the identification accuracy of the modified back propagation neural network model could reach 96.4%.Compared with single-source detection data,the use of optical-electrical integrated data achieved superior diagnosis performance.