A Intelligent Method for Insulation Defect Detection of Switchgear Based on Multi-source Sensing

Insulation fault accounts for a large proportion in electrical equipment faults, so it is an important strategy to detect and eliminate the faults in their latent phase. Insulation defects are usually accompanied by temperature rise or partial discharge, which can be used as an important basis for judging the insulation status of the equipment. The infrared photoelectric sensor can detect the temperature of the equipment, and the ultraviolet photoelectric sensor can detect the ultraviolet pulse signal generated by partial discharge of the equipment. In this paper, taking the cable terminal defects in the switch cabinet as an example, an infrared and ultraviolet photoelectric sensor synchronous acquisition device is constructed. Based on the adaptive fuzzy neural network, an intelligent detection method is proposed with combining two information sources of temperature rise and partial discharge. The experimental results show that compared to the information detection with single sensor, the diagnosis algorithm based on multi-source sensing significantly improves the accuracy of equipment defect diagnosis. The proposed method can provide a new research idea for identification and diagnosis of insulation defects of switchgear.