A new method for parameter extraction of solar photovoltaic models using gaining–sharing knowledge based algorithm
For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (G...
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| Published in | Energy reports Vol. 7; pp. 3286 - 3301 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.11.2021
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2352-4847 2352-4847 |
| DOI | 10.1016/j.egyr.2021.05.030 |
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| Abstract | For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner–intermediate or junior acquisition and sharing stage, and the second phase is the intermediate–expert or senior acquisition and sharing stage. In order to verify the effectiveness of GSK, it is applied to five PV models including the single diode model, double diode model, and three PV modules. The influence of population size on the algorithm performance is empirically investigated. Besides, it is further compared with some other excellent metaheuristic algorithms including basic algorithms and advanced algorithms. Among the five PV models, the root mean square error values between the measured data and the calculated data of GSK are 9.8602E−04 ± 2.18E−17, 9.8280E−04 ± 8.72E−07, 2.4251E−03 ± 1.04E−09, 1.7298E−03 ± 6.25E−18, and 1.6601E−02 ± 1.44E−16, respectively. The results show that GSK has overall better robustness, convergence, and accuracy.
•A new method named gaining–sharing knowledge based algorithm (GSK) is employed.•GSK is applied to five solar PV models.•The influence of population size on GSK is empirically investigated.•GSK provides better results compared with seven basic algorithms and six advanced algorithms. |
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| AbstractList | For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner–intermediate or junior acquisition and sharing stage, and the second phase is the intermediate–expert or senior acquisition and sharing stage. In order to verify the effectiveness of GSK, it is applied to five PV models including the single diode model, double diode model, and three PV modules. The influence of population size on the algorithm performance is empirically investigated. Besides, it is further compared with some other excellent metaheuristic algorithms including basic algorithms and advanced algorithms. Among the five PV models, the root mean square error values between the measured data and the calculated data of GSK are 9.8602E−04 ± 2.18E−17, 9.8280E−04 ± 8.72E−07, 2.4251E−03 ± 1.04E−09, 1.7298E−03 ± 6.25E−18, and 1.6601E−02 ± 1.44E−16, respectively. The results show that GSK has overall better robustness, convergence, and accuracy.
•A new method named gaining–sharing knowledge based algorithm (GSK) is employed.•GSK is applied to five solar PV models.•The influence of population size on GSK is empirically investigated.•GSK provides better results compared with seven basic algorithms and six advanced algorithms. For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner–intermediate or junior acquisition and sharing stage, and the second phase is the intermediate–expert or senior acquisition and sharing stage. In order to verify the effectiveness of GSK, it is applied to five PV models including the single diode model, double diode model, and three PV modules. The influence of population size on the algorithm performance is empirically investigated. Besides, it is further compared with some other excellent metaheuristic algorithms including basic algorithms and advanced algorithms. Among the five PV models, the root mean square error values between the measured data and the calculated data of GSK are 9.8602E−04 ± 2.18E−17, 9.8280E−04 ± 8.72E−07, 2.4251E−03 ± 1.04E−09, 1.7298E−03 ± 6.25E−18, and 1.6601E−02 ± 1.44E−16, respectively. The results show that GSK has overall better robustness, convergence, and accuracy. |
| Author | Zhang, Jing Xiong, Guojiang Yuan, Xufeng Mohamed, Ali Wagdy Li, Lei |
| Author_xml | – sequence: 1 givenname: Guojiang surname: Xiong fullname: Xiong, Guojiang email: gjxiongee@foxmail.com organization: Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang, 550025, China – sequence: 2 givenname: Lei surname: Li fullname: Li, Lei organization: Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang, 550025, China – sequence: 3 givenname: Ali Wagdy orcidid: 0000-0002-5895-2632 surname: Mohamed fullname: Mohamed, Ali Wagdy organization: Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt – sequence: 4 givenname: Xufeng surname: Yuan fullname: Yuan, Xufeng organization: Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang, 550025, China – sequence: 5 givenname: Jing surname: Zhang fullname: Zhang, Jing organization: Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang, 550025, China |
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| Keywords | Gaining–sharing knowledge-based algorithm Solar photovoltaic Parameter extraction |
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