A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models

•A hybrid algorithm, GWOCS, based on GWO and CS is proposed.•GWOCS is applied to extract the parameters of solar PV models.•A new OBL strategy aims to enhance the exploration ability of GWO.•Comprehensively experimental results demonstrate the competitive performance of GWOCS. Quickly, accurately an...

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Bibliographic Details
Published in:Energy conversion and management Vol. 203; p. 112243
Main Authors: Long, Wen, Cai, Shaohong, Jiao, Jianjun, Xu, Ming, Wu, Tiebin
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.01.2020
Elsevier Science Ltd
Subjects:
Algorithms
Cell culture
Computer simulation
Cuckoo search algorithm
Experimental data
Exploration
Grey wolf optimizer
Heuristic methods
Mathematical models
Parameter extraction
Parameters
Photovoltaic cells
Photovoltaics
Search algorithms
solar collectors
Solar photovoltaic model
Solar photovoltaic model
Cuckoo search algorithm
Grey wolf optimizer
Parameter extraction
ISSN:0196-8904, 1879-2227
Online Access:Get full text
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Summary:•A hybrid algorithm, GWOCS, based on GWO and CS is proposed.•GWOCS is applied to extract the parameters of solar PV models.•A new OBL strategy aims to enhance the exploration ability of GWO.•Comprehensively experimental results demonstrate the competitive performance of GWOCS. Quickly, accurately and reliably extract the parameters of solar photovoltaic (PV) model is very critical to simulate, evaluate and control the PV systems. During the past few years, many analytical, numerical and meta-heuristic algorithms have been suggested to extract the parameters of PV models based on the experimental data. However, extracting the parameters of PV models is still a great challenge. In this paper, a new hybrid algorithm based on grey wolf optimizer and cuckoo search (GWOCS) is developed to extract the parameters of different PV cell models with the experimental data under different operating conditions. In GWOCS, a new opposition learning strategy for the decision layer individuals (i.e., α, β, and δ) is proposed to enhance diversity of GWO. The main advantage of GWOCS is its ability to balance between exploration and exploitation. The performance of GWOCS is firstly tested on 10 complex benchmark functions. Then, the GWOCS is applied to extract the parameters of several solar PV cell models under different operating conditions. The comprehensively experimental results show the GWOCS is a promising candidate approach to extract the parameters of solar PV models.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2019.112243