Search

To solve the disadvantages of the most 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, this paper proposed a novel improved SOS algorithm, named as ImSOS. In ImSOS, it employed a quasi-reflection-based learning( QRBL) scheme in the population initialization step of original SOS. Moreover, it used the strategy of the modifications of benefit factors in the mutualism phase of SOS. It adopted a strategy of narrowing the search range of randomly generated coefficients in the commensalism phase of SOS. And then, it detailed the procedures and flowchart of employing the ImSOS for solving the PV module models parameter identification problem based on experimental current versus voltage(I-V) data of a real PV module. Finally,the 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 implies the effectiveness and superiority of the ImSOS. Therefore, the ImSOS becomes a new effective method to accurately and reliably identify PV module models parameters. [ABSTRACT FROM AUTHOR]