EXPERIMENTAL INVESTIGATION OF SOLAR TRACKING SYSTEM FOR OPTIMIZING PHOTOVOLTAIC EFFICIENCY IN NIGERIA

Nigeria's vast solar energy potential remains underutilized due to inefficiencies in solar power generation. This study investigates the effectiveness of a microcontroller-based dual-axis solar tracker in optimizing photovoltaic efficiency. The system dynamically adjusts solar panel orientation to maximize sunlight exposure. Empirical tests at Enugu State University of Science and Technology (ESUT) showed a 2% improvement in energy generation compared to fixed panels. However, the power consumption of the tracking motors largely offset these gains, raising concerns about the overall efficiency and feasibility of such systems for large-scale use. The tracker employs servo motors controlled by a microcontroller, with light-dependent resistors (LDRs) enhancing sunlight detection and tracking accuracy. To improve efficiency, the study recommends low-power actuators and AI-based tracking algorithms to minimize unnecessary movements, reduce mechanical wear, and lower energy consumption. AI-driven predictive tracking could enhance system durability and cost-effectiveness, making solar tracking more viable for long-term deployment in Nigeria’s renewable energy sector. While dual-axis tracking offers potential benefits, its practical implementation requires further refinements to achieve an optimal balance of energy efficiency, reliability, and affordability for widespread adoption