Abstract
In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform the parameter estimation of photovoltaic systems (PV). The aim of the proposed ABC-Ls method was to improve the exploration capability of the standard ABC with a new local search procedure in addition to the exploitation and exploration balance of the standard ABC algorithm. The proposed ABC-Ls method was first tested on 15 well-known benchmark functions in the literature. In the results of the Friedman Mean Rank test used for statistical analysis, ABC-Ls method successfully ranked first with a value of 1.300 in benchmark functions. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and Photowatt-PWP-201 PV modules of PV systems for parameter estimations. In addition, the proposed ABC-Ls method has been applied to the KC200GT PV module for parameter estimation under different temperature and irradiance conditions of the PV modules. The success of ABC-Ls method was compared with genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ABC algorithm, tree seed algorithm (TSA), Jaya, Atom search optimization (ASO). The comparison results were presented in tables and graphics in detail. The RMSE values for the parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module of the proposed ABC-LS method were found as 9.8602E−04, 9.8257E−04 and 2.4251E−03, respectively. In this context, the proposed ABC-LS method has been compared with the literature for parameter estimation of single diode, double diode and Photowatt-PWP-201 PV module and it has been found that it provides a parameter estimation similar or better than other studies. The proposed ABC-Ls method for parameter estimation of the KC200GT PV module under different conditions is shown in convergence graphs and box plots, where it achieves more successful, effective and stable results than GA, PSO, ABC, TSA, Jaya and ASO algorithms.
Highlights
The demand for alternative, renewable and non-polluting energy sources has increased day by day due to the decreased fossil fuel sources and increased environmental concerns around the world [1, 2]
This study presents the mathematical models of three different photovoltaic systems (PV) systems: single diode module, double diode module and PV module models
The parameter values of single diode module, double diode module and PV module problems were estimated and benchmark functions were solved to determine the effectiveness of artificial bee colony (ABC)-Local Search (ABC-Local search (Ls)) compared to ABC, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms
Summary
The demand for alternative, renewable and non-polluting energy sources has increased day by day due to the decreased fossil fuel sources and increased environmental concerns around the world [1, 2]. As seen in the examination of some of the studies based on artificial intelligence optimization algorithms, Jervase et al (2001) proposed a technique based on genetic algorithms (GA) to increase the accuracy of solar cell parameters extracted using traditional methods. They designed the improved whale optimization algorithm (IWOA) to effectively balance local exploitation and global exploration They extracted the parameters of three different single diode, double diode and PV models using the IWOA [17]. After determining the success of the proposed method based on the results of benchmark functions, parameter estimation of the PV systems was performed and the results were found to be better than those in the literature.
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