Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm

Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the para...

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Published in:	Energy (Oxford) Vol. 249; p. 123760
Main Authors:	Long, Wen, Jiao, Jianjun, Liang, Ximing, Xu, Ming, Tang, Mingzhu, Cai, Shaohong
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 15.06.2022 Elsevier BV
Subjects:	Algorithms Benchmarks Differential evolution energy equations Function optimization Heuristic methods Laridae Mathematical models Model accuracy Mutation Optimization Optimization algorithms Optimization techniques Parameter estimation Photovoltaic cells Photovoltaic models Photovoltaics Reliability Reliability analysis Seagull optimization algorithm Trigonometric functions Seagull optimization algorithm Differential evolution Parameter estimation Function optimization Photovoltaic models
ISSN:	0360-5442, 1873-6785
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Abstract	Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the parameters of the solar PV models. However, it is still a challenging task to accurately, reliably, and quickly estimate the unknown parameters of PV models. This paper proposes a novel hybrid seagull optimization algorithm (HSOA) for estimating the unknown parameters of PV models effectively and accurately. In proposed HSOA, the personal historical best information is embedded into position search equation to improve the solution precision. A novel nonlinear escaping energy factor based on cosine function is presented for balancing global exploration and local exploitation. The differential mutation strategy is introduced to escape from the local optima. We firstly select twelve classical benchmark test functions to investigate the feasibility of HSOA, and experimental results show that HSOA is superior to most compared methods. Then, HSOA is used for solving parameters estimation problem of three benchmark solar PV models. The comparison results demonstrate that HSOA is superior to BOA, GWO, WOA, HHO, SOA, EEGWO, and ISCA on solution quality, convergence and reliability. •A hybrid algorithm called HSOA is proposed.•HSOA is applied to estimate the unknown parameters of PV models.•A modified position updating equation is presented.•A new nonlinear escaping energy strategy is designed.•Experimental results show the competitive performance of HSOA.
AbstractList	Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the parameters of the solar PV models. However, it is still a challenging task to accurately, reliably, and quickly estimate the unknown parameters of PV models. This paper proposes a novel hybrid seagull optimization algorithm (HSOA) for estimating the unknown parameters of PV models effectively and accurately. In proposed HSOA, the personal historical best information is embedded into position search equation to improve the solution precision. A novel nonlinear escaping energy factor based on cosine function is presented for balancing global exploration and local exploitation. The differential mutation strategy is introduced to escape from the local optima. We firstly select twelve classical benchmark test functions to investigate the feasibility of HSOA, and experimental results show that HSOA is superior to most compared methods. Then, HSOA is used for solving parameters estimation problem of three benchmark solar PV models. The comparison results demonstrate that HSOA is superior to BOA, GWO, WOA, HHO, SOA, EEGWO, and ISCA on solution quality, convergence and reliability. Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the parameters of the solar PV models. However, it is still a challenging task to accurately, reliably, and quickly estimate the unknown parameters of PV models. This paper proposes a novel hybrid seagull optimization algorithm (HSOA) for estimating the unknown parameters of PV models effectively and accurately. In proposed HSOA, the personal historical best information is embedded into position search equation to improve the solution precision. A novel nonlinear escaping energy factor based on cosine function is presented for balancing global exploration and local exploitation. The differential mutation strategy is introduced to escape from the local optima. We firstly select twelve classical benchmark test functions to investigate the feasibility of HSOA, and experimental results show that HSOA is superior to most compared methods. Then, HSOA is used for solving parameters estimation problem of three benchmark solar PV models. The comparison results demonstrate that HSOA is superior to BOA, GWO, WOA, HHO, SOA, EEGWO, and ISCA on solution quality, convergence and reliability. •A hybrid algorithm called HSOA is proposed.•HSOA is applied to estimate the unknown parameters of PV models.•A modified position updating equation is presented.•A new nonlinear escaping energy strategy is designed.•Experimental results show the competitive performance of HSOA.
ArticleNumber	123760
Author	Cai, Shaohong Xu, Ming Tang, Mingzhu Liang, Ximing Long, Wen Jiao, Jianjun
Author_xml	– sequence: 1 givenname: Wen surname: Long fullname: Long, Wen organization: Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang 550025, China – sequence: 2 givenname: Jianjun surname: Jiao fullname: Jiao, Jianjun organization: School of Mathematics and Statistics, Guizhou University of Finance & Economics, Guiyang 550025, China – sequence: 3 givenname: Ximing surname: Liang fullname: Liang, Ximing organization: School of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, China – sequence: 4 givenname: Ming surname: Xu fullname: Xu, Ming organization: School of Mathematics and Statistics, Guizhou University of Finance & Economics, Guiyang 550025, China – sequence: 5 givenname: Mingzhu surname: Tang fullname: Tang, Mingzhu organization: School of Energy Power and Engineering, Changsha University of Science & Technology, Changsha 410114, China – sequence: 6 givenname: Shaohong orcidid: 0000-0002-2843-974X surname: Cai fullname: Cai, Shaohong email: gzcd_csh58@126.com organization: Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang 550025, China
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Snippet	Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is...
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SubjectTerms	Algorithms Benchmarks Differential evolution energy equations Function optimization Heuristic methods Laridae Mathematical models Model accuracy Mutation Optimization Optimization algorithms Optimization techniques Parameter estimation Photovoltaic cells Photovoltaic models Photovoltaics Reliability Reliability analysis Seagull optimization algorithm Trigonometric functions
Title	Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm
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