Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems
Deriving optimal photovoltaic (PV) models’ optimal parameters have tremendous significance in simulating, evaluating, and controlling the photovoltaic systems. Determining unknown parameters of these PV models is a multimodal, nonlinear, and complex optimization problem. Hence, developing a robust o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721004315 |
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| author | Iman Ahmadianfar Wenyin Gong Ali Asghar Heidari Noorbakhsh Amiri Golilarz Arvin Samadi-Koucheksaraee Huiling Chen |
| author_facet | Iman Ahmadianfar Wenyin Gong Ali Asghar Heidari Noorbakhsh Amiri Golilarz Arvin Samadi-Koucheksaraee Huiling Chen |
| author_sort | Iman Ahmadianfar |
| collection | DOAJ |
| description | Deriving optimal photovoltaic (PV) models’ optimal parameters have tremendous significance in simulating, evaluating, and controlling the photovoltaic systems. Determining unknown parameters of these PV models is a multimodal, nonlinear, and complex optimization problem. Hence, developing a robust optimization model to achieve optimal parameters of the PV models effectively is essential. This paper proposes an enhanced metaphor-free gradient-based optimizer (EGBO) for extracting PV parameters quickly, precisely, and reliably. In the EGBO, a rank-based mechanism is employed to update its parameters efficiently. Also, the logistic map (LC) is implemented to better use the local escaping operator (LEO) in the original GBO algorithm. The proposed EGBO optimally identifies various parameters in the PV model, such as single diodes, double diodes, and PV modules. The relevant results indicate that compared with most advanced optimization methods, the EGBO algorithm is competitive in reliability, accuracy, and convergence speed. Moreover, the relevant results from the experimental data drawn from the manufacturer’s datasheet demonstrate that the developed approach can offer highly accurate solutions at various irradiances and temperatures. Consequently, the achieved results confirm that the novel approach can be presented as a utility tool for deriving optimal PV models’ optimal parameters, and it can be helpful in modeling PV systems. |
| first_indexed | 2024-12-22T20:39:48Z |
| format | Article |
| id | doaj.art-621b098f549145099c556794092ec9bf |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-12-22T20:39:48Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-621b098f549145099c556794092ec9bf2022-12-21T18:13:21ZengElsevierEnergy Reports2352-48472021-11-01739793997Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systemsIman Ahmadianfar0Wenyin Gong1Ali Asghar Heidari2Noorbakhsh Amiri Golilarz3Arvin Samadi-Koucheksaraee4Huiling Chen5Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, IranSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, IranSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, IranCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China; Corresponding author.Deriving optimal photovoltaic (PV) models’ optimal parameters have tremendous significance in simulating, evaluating, and controlling the photovoltaic systems. Determining unknown parameters of these PV models is a multimodal, nonlinear, and complex optimization problem. Hence, developing a robust optimization model to achieve optimal parameters of the PV models effectively is essential. This paper proposes an enhanced metaphor-free gradient-based optimizer (EGBO) for extracting PV parameters quickly, precisely, and reliably. In the EGBO, a rank-based mechanism is employed to update its parameters efficiently. Also, the logistic map (LC) is implemented to better use the local escaping operator (LEO) in the original GBO algorithm. The proposed EGBO optimally identifies various parameters in the PV model, such as single diodes, double diodes, and PV modules. The relevant results indicate that compared with most advanced optimization methods, the EGBO algorithm is competitive in reliability, accuracy, and convergence speed. Moreover, the relevant results from the experimental data drawn from the manufacturer’s datasheet demonstrate that the developed approach can offer highly accurate solutions at various irradiances and temperatures. Consequently, the achieved results confirm that the novel approach can be presented as a utility tool for deriving optimal PV models’ optimal parameters, and it can be helpful in modeling PV systems.http://www.sciencedirect.com/science/article/pii/S2352484721004315Photovoltaic modelsParameter identificationGradient-based optimizerOptimizationSwarm intelligence |
| spellingShingle | Iman Ahmadianfar Wenyin Gong Ali Asghar Heidari Noorbakhsh Amiri Golilarz Arvin Samadi-Koucheksaraee Huiling Chen Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems Energy Reports Photovoltaic models Parameter identification Gradient-based optimizer Optimization Swarm intelligence |
| title | Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems |
| title_full | Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems |
| title_fullStr | Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems |
| title_full_unstemmed | Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems |
| title_short | Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems |
| title_sort | gradient based optimization with ranking mechanisms for parameter identification of photovoltaic systems |
| topic | Photovoltaic models Parameter identification Gradient-based optimizer Optimization Swarm intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721004315 |
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