Parameter Identification of Photovoltaic Modules Using Newton-Raphson-Based Optimizer

Solar power is widely recognized as one of the most important sustainable energy sources because of its abundant availability, environmental friendliness, and potential to reduce dependence on fossil fuels. Accurate identification of photovoltaic (PV) module parameters is essential for reliable performance prediction, system design, control, and optimization. However, this task remains challenging due to the highly nonlinear, multimodal, and complex nature of PV mathematical models. Conventional analytical and numerical techniques often suffer from sensitivity to initial parameter values, slow convergence rates, and a tendency to become trapped in local optima, resulting in reduced estimation accuracy. To address these challenges, this paper presents a systematic investigation of the Newton-Raphson-Based Optimizer (NRBO) for PV parameter identification using both Single-Diode Model (SDM) and Double-Diode Model (DDM). Unlike previous studies that have mainly considered NRBO as one of several metaheuristic algorithms or incorporated it into hybrid analytical approaches, this work focuses on evaluating its standalone optimization capability for PV parameter extraction. The proposed approach is systematically evaluated under different irradiance and temperature operating conditions to assess its robustness. Furthermore, a focused comparative analysis is conducted against established metaheuristic algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Jaya Search (JS), in terms of estimation accuracy and convergence performance. The obtained results demonstrate the effectiveness of NRBO in accurately identifying PV model parameters and achieving competitive convergence and robust performance across the investigated operating conditions.