Improved gradient?based optimizer for parameters extraction of photovoltaic models

An efficient operation of the solar photovoltaic (PV) system relies on accurate and reliable equivalent models parameters. For different modular circuit models, parameter estimation PV cells is a key task that usually translated into optimization problems solved by metaheuristic algorithms. Gradient-based optimizer (GBO), which was proposed in 2020, method with swarm characteristics developed from Newton's method. Gradient search rule (GSR) local escape operator (LEO) main components GBO algorithm. This paper proposes an improved (IGBO) algorithm based original In IGBO, two strategies, adaptive weights chaotic behaviour, are introduced to adjust The purpose effectively approach optimal solution avoid falling optimum phases Chaotic behaviour aims replace randomness improve convergence speed accuracy To validate performance it applied extraction models. used this include single diode model (SDM), double (DDM) module model. Finally, comparing six well-established algorithms, experimental data show IGBO highly competitive, as reflected best results obtained terms root mean square error values. It can be concluded more competitive robustness, accuracy.