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. After obtaining successful results on the benchmark tests, the proposed ABC-Ls method was applied to the single diode, double diode and PV modules of PV systems for parameter estimations. The success of the ABC-Ls method was compared with the algorithms commonly used in the literature such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm and standard ABC algorithm, and the comparison results were presented in tables and graphics in detail. It was found that the ABC-Ls method was more successful and achieved better results compared to GA, PSO and standard ABC. It was determined that the ABC-LS method performed similar or better PV parameter estimation compared to other studies in the literature.
Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems
