Parameter Identification of Solar Cell Model Based on Improved Artificial Bee Colony Algorithm

Abstract

Artificial bee colony algorithm (ABC) is a swarm intelligence algorithm, which simulates the intelligent behavior of bee colony. ABC algorithm has achieved good performance in solving multivariable optimization problems. But ABC convergent slowly and is easy to fall into local extremum. These lead to the low accuracy of the optimal solution. In order to increase the accuracy of the parameters identification of solar cell model, an improved artificial bee colony algorithm (IABC) is proposed. In the stage of employed bee and onlooker bee, the bees have a 50% probability to update the position guided by global best honey source after neighborhood search. Meanwhile a full dimensional neighborhood search is employed to improve the search efficiency. The experimental results show that the convergence speed and the accuracy of the parameters are improved. It provides a new method for parameter identification of solar cell model.