In order to improve the performance of solar energy systems, accurate modeling of current versus voltage (I-V) characteristics of solar cells has attracted the attention of various researches. The main drawback in accurate modeling is the lack of information about the precise parameter values which indeed characterize the solar cell. Since such parameters cannot be extracted from the datasheet specifications, an optimization technique is necessary to adjust experimental data to the solar cell model. Considering the I-V characteristics of solar cells, the optimization task involves the solution of complex non-linear and multi-modal objective functions. Several optimization approaches have been presented to identify the parameters of solar cells. However, most of them obtain sub-optimal solutions due to their premature convergence and their difficulty to overcome local minima in multi-modal problems. This chapter describes the use of the Artificial Bee Colony (ABC) algorithm to accurately identify the solar cells’ parameters. The ABC algorithm is an evolutionary method inspired by the intelligent foraging behavior of honeybees. In comparison with other evolutionary algorithms, ABC exhibits a better search capacity to face multi-modal objective functions. In order to illustrate the proficiency of the presented approach, it is compared to other well-known optimization methods. Experimental results demonstrate the high performance of the presented method in terms of robustness and accuracy.
ASJC Scopus subject areas
- Artificial Intelligence