Application of intelligent infrared thermal imager in thermal image feature recognition of power equipment in substation

The operation environment of power equipment in substation is complex, the surface temperature distribution of equipment is uneven, and it is easy to be affected by external interference. Traditional manual detection is difficult to accurately identify abnormal thermal image characteristics, which affects the efficiency of equipment fault warning. In order to solve this problem, this paper proposes a thermal image feature recognition method based on intelligent infrared thermal imager. Firstly, the thermal image is collected efficiently, and the image quality is improved by image denoising and enhancement technology. Then, threshold segmentation and edge detection techniques are used to extract key thermal image features. Finally, the feature pattern recognition algorithm is used to accurately classify the abnormal features of the equipment. Combined with the experimental design, the performance of the proposed method is verified in this paper. The experimental results show that the proposed method has significant advantages in feature recognition accuracy and computational efficiency, and provides a reliable and efficient solution for the condition monitoring of power equipment.