Analysis on Solar Panel Crack Detection Using Optimization Techniques

Abstract

A Solar panel is considered as a proficient power hotspot for the creation of electrical energy for long years. Any deformity on the solar cell panel’s surface will prompt to decreased production of power and loss in the yield. Subsequently, the location of cracks on solar panel surfaces is the most essential stride during the inspection of solar panel, and it has important significance. In any case, these strategies cost lot of computation time and with low precision. Aiming for a few issues of the existing algorithm, a new framework is proposed to distinguish the cracks. Crack can be distinguished by utilizing optimization techniques based on segmentation. The optimization techniques are Particle Swarm Optimization (PSO), Differential Particle Swarm Optimization (DPSO) and Fractional Order Differential Particle Swarm Optimization (FODPSO). It is important to identify the crack in solar panel cells since they can directly diminish the execution of the panel and additionally the power yield. In view of the segmentation process, the potential regions which have cracks have been found, and then distinctive optimization algorithms were run on these areas to discover crack pixels. An extensive number of trials demonstrate that, this technique procures high accuracy and more complete crack contours with low computation costs.