Hot Spot Recognition Method of Photovoltaic Infrared Thermal Image Based on Improved Selfish Herd Algorithm

Aiming at the problems of low accuracy and poor generalization ability of infrared thermal image recognition in photovoltaic power generation, a hot spot recognition method based on infrared thermal image and improved selfish sheep algorithm was proposed. By imitating the deep learning classification training process, datasets were made.. Based on the gaussian distribution, a hot spot recognition function was presented. The survival value after the selfish herd algorithm was improved by using datasets of hot spot recognition function of the location parameters optimization. Then, all kinds of test images were imported, after bilateral filtering using hot spot recognition function point by point calculation. Finally, the calculated results were segmented by threshold, and the hot spot detection results were obtained. The experimental results show that the hot spot recognition function trained by this model could effectively diagnose hot spots due to the concentration of Gaussian distribution, and at the same time suppress edge interference and highlight details. Because of its excellent optimization ability, the selfish sheep algorithm could greatly improve the convergence rate of the model, and provide a new idea and method for photovoltaic hot spot recognition based on the infrared thermal image.