Research Description
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.
