A novel fitness-distance balanced artificial ecosystem approach for accurate solar PV parameter estimation

Abstract Accurate parameter estimation of photovoltaic (PV) models is essential for enhancing the simulation and control of PV systems. This paper introduces a novel, robust stochastic algorithm that combines Fitness-Distance Balance (FDB) with Artificial Ecosystem Optimization (AEO) and incorporates the Newton-Raphson method for estimating optimal PV parameters such as Photocurrent (Iph), Saturation Current (Io), Diode Ideality Factor (n), Series Resistance (Rs), and Shunt Resistance (Rsh). The main objective function considered it to minimize the difference between the estimated and measured I–V data pairs, quantified by the root mean square error (RMSE). The study involves four cases aimed at reducing the RMSE of PV models, comprising both single-diode (SDM) and double-diode (DDM) models of R.T.C France, and two PV panels: Photowatt-PWP201 and STM6-120/36 models. The simulation results confirm that the FDB-AEO algorithm outperforms other competing algorithms, achieving the best RMSE values for various models, including SDM, DDM, Photowatt-PWP201, and STM6-120/36 PV models are 7.7299e-04, 7.4584e-04, 2.3564e-03, and 1.7315e-03, respectively. Furthermore, the statistical analysis demonstrates that the proposed method requires a lower standard deviation (SD) to estimate the unknown electrical parameters for the mentioned models, with values of 6.1761e-06, 9.2956e-06, 4.8872e-05, and 6.1959e-05, respectively. This indicates its superiority over other powerful metaheuristic algorithms in terms of precision, solution quality, and convergence speed.