Enhanced Maximum Power Point Tracking for Photovoltaic Systems Using Temperature-Compensated Resistance Prediction

Objectives Maximum power point tracking (MPPT) is essential for optimizing photovoltaic (PV) system efficiency under varying environmental conditions. This paper presents an enhanced MPPT algorithm that incorporates temperature-based load resistance prediction with dynamic irradiance compensation. Material and Methods The proposed system employs a cubic polynomial model (with temperature in °C) to predict optimal load resistance as a function of panel temperature, coupled with a real-time voltage-derived irradiance correction factor. Together, these two components allow the controller to position the operating point near the maximum power point predictively rather than solely through iterative search. Results Experimental validation using a 50 W polycrystalline silicon panel demonstrates tracking efficiency of 97.8% under standard test conditions, representing a 2.3% improvement over conventional Perturb and Observe (P&O) algorithms. The system achieves steady-state power extraction within 0.8 seconds and maintains stability across temperature ranges of 25°C to 65°C and irradiance levels from 200 W m - 2 to 1000 W m - 2 . Conclusion Hardware implementation costs are reduced by approximately 25% through the elimination of current sensors in favor of computational prediction.