A Hybrid Iot–Metaheuristic Approach For Accurate Parameter Estimation Of Solar Pv Cells Using Matlab

Abstract

Accurate parameter estimation of solar photovoltaic (PV) cells is essential for performance prediction, maximum power point evaluation, and real-time system optimization. This work presents the design and implementation of a single-diode PV model and an advanced parameter estimation framework based on metaheuristic optimization. The five key nonlinear parameters—photocurrent (Iph), saturation current (I0), ideality factor (n), series resistance (Rs), and shunt resistance (Rsh)—are estimated by minimizing the deviation between measured and modeled I–V characteristics. Population-based techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Wind-Driven Optimization (WDO) are implemented and compared in terms of convergence speed, computational efficiency, and estimation accuracy within a MATLAB simulation environment. Error metrics including RMSE, MAPE, and R² demonstrate that the metaheuristic approach significantly outperforms classical analytical estimation, providing robust parameter extraction under varying irradiance and temperature. The proposed framework offers a reliable foundation for intelligent monitoring, diagnostics, and performance forecasting in modern PV systems.