Modeling and performance assessment of PVWPS considering various partial shading scenarios using multi-objective optimizers

This study presents a comprehensive comparative analysis of two photovoltaic water pumping system (PVWPS) configurations, Config A (parallel-connected) and Config B (series-connected), under varying irradiance, temperature, and partial shading conditions. MATLAB/Simulink simulations integrated with a hybrid Starfish Optimization Algorithm–Adaptive Neuro-Fuzzy Inference System (SFOA–ANFIS) maximum power point tracking (MPPT) controller are employed to evaluate the systems’ electrical, mechanical, and hydraulic performances with high accuracy. Config A, operating at lower voltage (≈27–61 V) and higher current (≈34–63 A), exhibits excellent performance stability, smoother dynamic response, and strong mismatch tolerance under nonuniform irradiance, resulting in higher daily water yield and enhanced shading resilience. Conversely, Config B, functioning at higher voltage (≈82–182 V) and lower current (≈11–20 A), achieves superior performance with rising temperature, delivering greater motor power (≈1600 W vs. 1500 W), torque (≈19 N·m vs. 18 N·m), speed (≈880 rpm vs. 850 rpm), flow rate (≈18 L/s vs. 17 L/s), and efficiency (up to 63 % vs. 58 %) under uniform and elevated temperature conditions. Config B benefits from reduced conduction losses and improved electrical efficiency in thermally stressed environments, while Config A maintains robustness and higher hydraulic output under fluctuating or shaded conditions. The SFOA–ANFIS MPPT achieves near-perfect tracking with <0.1 % deviation from reference values and reduces computational time by over 25 %, confirming its suitability for real-time applications. Overall, Config B performs best under stable, high-temperature conditions, whereas Config A ensures superior reliability and field viability, sustaining 5–10 % higher efficiency and 15–20 % greater water output.