A Robust Hybrid Method for Performance Analysis of Solar Tracking System

Abstract This study proposes an improved sun tracking system to increase the efficiency of solar energy collection by aligning solar panels with the sun's position. The proposed system applies image processing at the initial stage with the Guided Box Filter (GBF) for image smoothing, noise removal, and detail reduction. A continuous feedback process is implemented for dynamic adjustment with hesitant fuzzy linguistic bi-objective clustering for image segmentation, followed by the Modified Circle Hough Transform (MCHT) for precise circle detection and distance calculation. Numerical experiments demonstrate the system's capability to track the sun with high accuracy at different distances. At a distance of 2.39 m between the sun and the image center, the tracking error is negligible with a minimum altitude error of 0.4 and azimuth error of 0.5. Under normal circumstances, the system performs excellently with an altitude error of 0.15 and azimuth error of 0.25. At a distance of 0.06 m, which is close to zero, the altitude error is remarkably low at 0.15. The tracking accuracy and efficiency metrics which were reported in the study were obtained through image-based simulations that used processed sky images and controlled offsets instead of testing the system on actual solar panels in the field. The combination of high-quality image processing techniques and fuzzy control mechanisms provides a guaranteed way of maximizing the harvesting of solar energy, which plays a significant role in the development of renewable energy technologies.