Fuzzy Clustering of Infrared Image Features Based on Lazy Snapping Algorithm

Abstract

Substation equipment is an important part of the power grid, which undertakes the function of power transmission and conversion and directly affects the operation status of the whole substation and power system. When the substation equipment is in an abnormal working state, the temperature will change. Therefore, the temperature information of the substation equipment is used as the judgment basis to complete the judgment of the working state of the equipment, which can realize the fault diagnosis of the substation equipment and ensure that the power system works in a safe and reliable environment. In this paper, according to the characteristics of the transformer equipment shape stability, the invariant moment is used to extract the infrared image feature of the transformer equipment. The support vector machine is used to complete the classification and recognition of the image. Hu invariant moments and Zernike invariant moments are used to extracting features respectively, and the results of feature extraction are used as training samples to train support vector machines for recognition. Using the Lazy Snapping algorithm to complete the infrared image segmentation processing of substation equipment, the target region is extracted from the background completely, and the segmentation image information is completed. In the experimental test, through the test of the recognition model constructed by invariant moments, it is proved that the recognition accuracy of Zernike invariant moments combined with support vector machine in this paper is higher, and the Lazy Snapping algorithm has obvious advantages in segmentation quality compared with other methods. Therefore, the method of intelligent identification and diagnosis of the thermal fault of substation equipment studied in this paper has important practical significance for the establishment of an online diagnosis system.