Advanced image‑processing‑based single‑axis solar tracking for enhanced photovoltaicperformance
DOI:
https://doi.org/10.15282/isse.1.1.2026.13883Keywords:
Solar tracking system, Single-axis photovoltaic tracker , Image processing, Computer vision, Renewable energy systemsAbstract
Solar photovoltaic (PV) energy generation is strongly influenced by the angle at which solar irradiance strikes the PV module surface. Conventional fixed-tilt PV systems cannot track the sun’s apparent daily movement, resulting in reduced energy yield. Additionally, many sensor-based solar tracking systems experience performance degradation under non-uniform sky conditions. This study details the design, implementation, and experimental evaluation of an image-processing-based single-axis solar tracking system intended to improve PV performance while preserving mechanical simplicity and cost efficiency. The system that controls these panels should be perpendicular to the sun’s rays to obtain the maximum electricity using the image processing method. When the image is received, it will be processed through several image processing techniques to produce the sun’s exact location. While moving the PV panel, a vertical actuator is used with a stepper DC motor to control it, and a gyroscope sensor is used to detect the PV panel tilt angle. Raspberry Pi is used as the main processor. Several data have been taken to analyse and validate the system. By dividing into sunny day cases and cloudy day cases, the system is tested by two methods, which are the brightest region method and the contour finding method. The result shows that each method performs well on sunny day cases, with more than 71% of data detecting the actual angle with an error below 15%. The result for cloudy day cases shows both methods have a higher error number of data, which is only 27% error, below 15% for the brightest region, and 63% for contour finding.
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