采用改进型SOS算法的光伏组件模型参数辨识

o solve the disadvantages of the most photovoltaic (PV) models parameter identification algorithms at present, which have low accuracy and poor reliability, this paper proposed an improved symbiotic organisms search (SOS) algorithm for parameter identification of PV module models. First, to enhance the performance of original SOS, a novel improved SOS algorithm, named as ImSOS, was proposed. In ImSOS, a quasi-reflection-based learning (QRBL) scheme was employed in the population initialization step of original SOS. Moreover, the strategy of the modifications of benefit factors was used in the mutualism phase of SOS. A strategy of narrowing the search range of randomly generated coefficients was adopted in the commensalism phase of SOS. And then, the procedures and flowchart of employing the proposed ImSOS for solving the PV module models parameter identification problem based on experimental current versus voltage (I-V) data of a real PV module was detailed. Finally, the proposed ImSOS was demonstrated on the parameter identification of different PV module models of the Sharp ND-R250A5 PV module. Experimental results and comparisons with original SOS and the other seven novel intelligent optimization algorithms implied the effectiveness and superiority of the proposed ImSOS. Therefore, the proposed ImSOS becomes a new effective method to accurately and reliably identify PV module models parameters.