Optimal identification of model parameters for PVs using equilibrium, coot bird and artificial ecosystem optimisation algorithms

Realising an accurate estimation of model parameters for solar cells and the Photovoltaic modules has serious importance for enhancing the performance of their control systems. Three neoteric metaheuristic methods of Artificial Ecosystem-based optimisation, Coot Bird-based optimisation, and Equilibrium optimiser have been applied and evaluated concerning the accurate estimation of various Photovoltaic models. The validation of the applied methods has occurred for valuing the model parameters of R.T.C. France solar cell, and Thin-film ST40 Photovoltaic module. The objective function has been formulated as the Root Mean Square Error between the actual and estimated data. Matlab/Simulink has been used for the verification of the optimisation methods. The outcomes demonstrate that: (1) The three optimisation algorithms can resolve the problem of the Photovoltaic parameter estimation; (2) There are small distinctions between the three algorithms concerning their best value of the impartial function; this distinction between the best and worst algorithm is 10-9 for R.T.C. France solar cell for SDM; (3) The best algorithm considering the best value of the objective function is Artificial Ecosystem-based optimisation for R.T.C. France solar cell; (4) Statistical results prove that the three algorithms have tracking efficiencies of 100%, 99.999%, and 98.285% for Artificial Ecosystem-based optimisation, Coot Bird-based optimisation, and Equilibrium optimiser, respectively, based on 10 individual runs for R.T.C. France solar cell for SDM. Moreover, the simulation results show that the I/V curves obtained employing Artificial Ecosystem-based optimisation, Coot Bird-based optimisation, and Equilibrium optimiser techniques were also matched with the corresponding datasheet curves with the Artificial Ecosystem-based optimisation and Coot Bird-based optimisation's predominance in standings the convergence speed, tracking efficiency, statistical indices, and solution accuracy.