Parameter extraction of PV models using an enhanced shuffled complex evolution algorithm improved by opposition-based learning

Chen, Yixiang, Chen, Zhicong, Wu, Lijun, Long, Chao ORCID: https://orcid.org/0000-0002-5348-8404, Lin, Peijie and Cheng, Shuying 2019. Parameter extraction of PV models using an enhanced shuffled complex evolution algorithm improved by opposition-based learning. Energy Procedia 158 , pp. 991-997. 10.1016/j.egypro.2019.01.242

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Abstract

Accurate and efficient parameter extraction of PV models from I-V characteristic curves is significant for modeling, evaluation and fault diagnosis of PV modules/arrays. Recently, a large number of algorithms are proposed for this problem, but there are still some issues like premature convergence, low accurate and instability. In this paper, a new improved shuffled complex evolution algorithm enhanced by the opposition-based learning strategy (ESCE-OBL) is proposed. The proposed algorithm improves the quality of the candidate solution by the opposition-based learning strategy. Moreover, the basic SCE algorithm evolves with the traditional competition complex evolution (CCE) strategy, but it converges slowly and is prone to be trapped in local optima. In order to improve the exploration capability, the complex in the basic SCE is evolved by a new enhanced CCE. The ESCE-OBL algorithm is compared with some state-of-the-art algorithms on the single diode model (SDM) and double diode model (DDM) using benchmark I-V curves data. The comparison results demonstrate that the proposed ESCE-OBL algorithm can achieve faster convergence, stronger robustness and higher efficiency.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	1876-6102
Date of First Compliant Deposit:	26 April 2019
Last Modified:	04 May 2023 20:45
URI:	https://orca.cardiff.ac.uk/id/eprint/121886

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