Intelligent Substation Equipment Condition Detection Method Based on Infrared Thermal Imaging Technology and Temperature Probability Density

Aiming at the problem that the existing substation equipment status detection methods are single and the detection effect is poor, a new intelligent substation equipment status detection method based on infrared thermal imaging technology and temperature probability density is proposed. Firstly, the kernel function estimation is used to extract the temperature probability density function of the infrared image. Secondly, the detection objects are preliminarily classified according to the temperature distribution in the infrared image; Finally, the K-means clustering method based on the improved artificial bee colony algorithm is used to extract the fault part in the infrared image. The research results show that the method proposed in this paper can improve the accuracy of fault location identification and reduce the number of iterations required for clustering.