Novel Cuckoo Search Algorithm with Quasi-Oppositional Population Initialization Strategy for Solar Cell Parameters Identification

The solar cell model parameters identification problem plays a key role in the modeling solar photovoltaic (PV) systems. This paper firstly proposed a new cuckoo search algorithm with quasi-oppositional population initialization strategy (QOPIS-CSA), by applying quasi-opposition based learning (QOBL) concept in the population initialization stage of the standard cuckoo search algorithm (CSA). And then, the proposed QOPIS-CSA is used to solve the problem of identifying the parameter of solar cell model based on measured voltage versus current (V-I) data of real solar cell. Finally, the proposed QOPIS-CSA has been validated on the different solar cell models, i.e., single diode model (SDM) and double diode model (DDM). Experimental results and comparisons imply that the excellent capability of the proposed QOPIS-CSA.