Abstract
Currently, the incorporation of solar panels in many applications is a booming trend, which necessitates accurate simulations and analysis of their performance under different operating conditions for further decision making. In this paper, various optimization algorithms are addressed comprehensively through a comparative study and further discussions for extracting the unknown parameters. Efficient use of the iterations within the optimization process may help meta-heuristic algorithms in accelerating convergence plus attaining better accuracy for the final outcome. In this paper, a method, namely, the premature convergence method (PCM), is proposed to boost the convergence of meta-heuristic algorithms with significant improvement in their accuracies. PCM is based on updating the current position around the best-so-far solution with two-step sizes: the first is based on the distance between two individuals selected randomly from the population to encourage the exploration capability, and the second is based on the distance between the current position and the best-so-far solution to promote exploitation. In addition, PCM uses a weight variable, known also as a controlling factor, as a trade-off between the two-step sizes. The proposed method is integrated with three well-known meta-heuristic algorithms to observe its efficacy for estimating efficiently and effectively the unknown parameters of the single diode model (SDM). In addition, an RTC France Si solar cell, and three PV modules, namely, Photowatt-PWP201, Ultra 85-P, and STM6-40/36, are investigated with the improved algorithms and selected standard approaches to compare their performances in estimating the unknown parameters for those different types of PV cells and modules. The experimental results point out the efficacy of the PCM in accelerating the convergence speed with improved final outcomes.
Highlights
Solar energy converted to electric power using a photovoltaic (PV) system offers considerable opportunities to overcome the drawbacks of the traditional energy sources in terms of unavailability, environmental pollution, and global warming [1,2,3,4,5]
The influence of the premature convergence method (PCM) is observed with three well-known optimization algorithms: equilibrium optimizer (EO), HHO, and moth–flame optimization algorithm (MFO)
This paper proposes a new strategy known as the premature convergence method (PCM) in order to accelerate the convergence speed of meta-heuristic algorithms while
Summary
Solar energy converted to electric power using a photovoltaic (PV) system offers considerable opportunities to overcome the drawbacks of the traditional energy sources in terms of unavailability, environmental pollution, and global warming [1,2,3,4,5]. It has difficulty in avoiding becoming trapped in local minima for DDM This algorithm used a number of improvements, namely, a crossover sorting mechanism for using the best individuals in the generation to reach better outcomes, and a dynamic population reduction strategy to increase the convergence speed. MWOA was proposed to overcome stagnation into local minima, and low convergence speed by employing a mutation operator based on the levy flight, and a local search strategy to promote the exploitation capability Thereafter, this algorithm was employed for tackling the parameter estimation of the PV models and could fulfill superior performance. Seven, and nine unknown parameters of SDM, DDM, and TDM, respectively, the gradient-based optimizer was recently proposed for tackling the global optimization problem, for which it was adapted due to having a high convergence speed with a highly local minima avoidance strategy.
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