A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models

Tong Kang, Jiangang Yao, Min Jin, Shengjie Yang and ThanhLong Duong
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Tong Kang: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Jiangang Yao: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Min Jin: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Shengjie Yang: College of Computer and Information Engineering, Hunan University of Commerce, Changsha 410205, China
ThanhLong Duong: Department of Electrical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam

Energies, 2018, vol. 11, issue 5, 1-31

Abstract: Parameter estimation of photovoltaic (PV) models from experimental current versus voltage (I-V) characteristic curves acts a pivotal part in the modeling a PV system and optimizing its performance. Although many methods have been proposed for solving this PV model parameter estimation problem, it is still challenging to determine highly accurate and reliable solutions. In this paper, this problem is firstly transformed into an optimization problem, and an objective function (OF) is formulated to quantify the overall difference between the experimental and simulated current data. And then, to enhance the performance of original cuckoo search algorithm (CSA), a novel improved cuckoo search algorithm (ImCSA) is proposed, by combining three strategies with CSA. In ImCSA, a quasi-opposition based learning (QOBL) scheme is employed in the population initialization step of CSA. Moreover, a dynamic adaptation strategy is developed and introduced for the step size without Lévy flight step in original CSA. A dynamic adjustment mechanism for the fraction probability ( P a ) is proposed to achieve better tradeoff between the exploration and exploitation to increase searching ability. Afterwards, the proposed ImCSA is used for solving the problem of estimating parameters of PV models based on experimental I-V data. Finally, the proposed ImCSA has been demonstrated on the parameter identification of various PV models, i.e., single diode model (SDM), double diode model (DDM) and PV module model (PMM). Experimental results indicate that the proposed ImCSA outperforms the original CSA and its superior performance in comparison with other state-of-the-art algorithms, and they also show that our proposed ImCSA is capable of finding the best values of parameters for the PV models in such effective way for giving the best possible approximation to the experimental I-V data of real PV cells and modules. Therefore, the proposed ImCSA can be considered as a promising alternative to accurately and reliably estimate parameters of PV models.

Keywords: photovoltaic modeling; parameter estimation; optimization problem; metaheuristic; opposition-based learning; quasi-opposition based learning; improved cuckoo search algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

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