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.

Building precise solar cells and PV modules models is indispensable for constructing I–V and P–V features. A crucial difficulty in the estimation process is time complexity. The application of optimization algorithms to solve the photovoltaics (PV) parameter estimation requires solving the mathematical equation through numerical analysis methods with an iterative process, leading to an increase in time complexity. This paper proposes a new objective function (OF) that can be solved using algebraic mathematics. The consequence of the proposed OF is to decrease the time and cost of implementation by reducing the complexity. The designed procedure has been tested and evaluated by estimating the PV parameters of two cases of study using Single Diode Model (SDM). The results have been validated with those obtained using the conventional OF. The paper also presented a new application of the recent optimization algorithm of the Dingo Optimization Algorithm (DOA) to reach the optimal solution for the OFs. With the proposed linear OF, the results demonstrate a 21.96% reduction in the simulation time over the nonlinear OF concerning a difference among them of 2.5460e‐06. The simulation results with statistical tests have proved the superiority of the new OF in the parameter estimation process.