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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Veröffentlicht in:Energy (Oxford) Jg. 249; S. 123760
Hauptverfasser: Long, Wen, Jiao, Jianjun, Liang, Ximing, Xu, Ming, Tang, Mingzhu, Cai, Shaohong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 15.06.2022
Elsevier BV
Schlagworte:
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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Zusammenfassung: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.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2022.123760