Insulation Defect Detection of Electrical Equipment Based on Infrared and Ultraviolet Photoelectric Sensing Technology

Abstract

Insulation faults account for a high proportion in the faults of electrical equipment. Insulation defects or faults of electrical equipment may cause excessive temperature rise or partial discharge, which can be used as the criteria of insulation state of electrical equipment. However, the existing detection methods can't meet the requirements of safe and stable operation of modern substations. The exploration of new methods for temperature rise and partial discharge detection has become important for online detection of electrical equipment. Photoelectric sensor is a device that converts optical signal into electrical signal. It can be used to detect the optical signal generated by the running electrical equipment. The infrared photoelectric sensor can detect the temperature of electrical equipment, and the ultraviolet photoelectric sensor can detect the ultraviolet pulse signal generated by partial discharge of electrical equipment. In this paper, the characteristics of infrared photoelectric sensor's temperature changing with the detection distance are studied, and then the optimum detection distance is obtained. The relationship between the output pulse signal of ultraviolet photoelectric sensor and discharge intensity is analyzed, and the attenuation characteristics of pulse signal with the increase of propagation distance are also analyzed. The optimum placement position is selected. An insulation defect detection system for electrical equipment based on infrared and ultraviolet photoelectric sensing technology is constructed. Based on the adaptive fuzzy neural network, the insulation state of electrical equipment is synthetically judged by the signals of the infrared and ultraviolet photoelectric sensors. Experimental results show that through the combined detection of infrared and ultraviolet photoelectric sensors, and then information fusion diagnosis, it can effectively reduce the single sensor's misjudgment caused by one-sided information, and the accuracy of fault diagnosis is significantly improved.