RSM-Based Optimization and Validation of Maximum Power and Conversion Efficiency

The performance and cost-effectiveness of solar photovoltaic systems depend on accurate estimation and optimization of maximum power output and conversion efficiency under varying environmental conditions. This paper presents an integrated experimental-computational framework combining outdoor measurements, MATLAB/Simulink modeling, and statistical optimization through the response surface methodology (RSM). Key parameters, including open-circuit voltage, short-circuit current, and cell temperature, were experimentally recorded under different solar irradiance levels. Using these data and manufacturer specifications, maximum power, and conversion efficiency were computed through standard photovoltaic equations. A MATLAB/Simulink model incorporating open-circuit voltage (Voc), short-circuit current (Isc), and fill factor was developed to validate the experimental results. Further, RSM based on the Box–Behnken design was applied to analyze the combined effect of voltage, current, irradiance, and temperature on system performance. The results show close agreement between simulation and experimental findings, with deviations within ±1.2 W for maximum power and ±1.2% for efficiency. The RSM analysis showed that the two-factor interaction model for maximum power is highly significant (F = 49,328.02, p < 0.0001) with excellent fit (R-squared = 0.99999). Efficiency modeling indicated a significant linear model (F = 2570.09, p < 0.0001) with strong predictive accuracy (R-squared = 0.9983). Highly adequate precision values (716.77 for power and 162.17 for efficiency) confirm strong signal-to-noise ratios. The proposed research offers a simple and reliable approach for PV performance assessment and optimization, supporting improved system design and maximum power point tracking in small-scale solar applications.