Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm
Integrating solar photovoltaic (PV) systems into the modern power grid introduces a variety of new problems. The accurate modelling of PV is required to strengthen the system characteristics in simulation environments. Modelling such PV systems is reflected by a nonlinear I–V characteristic curve be...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S235248472301538X |
| _version_ | 1797378618400178176 |
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| author | Pankaj Sharma Saravanakumar Raju Rohit Salgotra Amir H. Gandomi |
| author_facet | Pankaj Sharma Saravanakumar Raju Rohit Salgotra Amir H. Gandomi |
| author_sort | Pankaj Sharma |
| collection | DOAJ |
| description | Integrating solar photovoltaic (PV) systems into the modern power grid introduces a variety of new problems. The accurate modelling of PV is required to strengthen the system characteristics in simulation environments. Modelling such PV systems is reflected by a nonlinear I–V characteristic curve behaviour with numerous unknown parameters because there is insufficient data in the cells’ datasheet. As a result, it is always a priority to identify these unknown parameters. To extract features of solar modules and build high-accuracy models for modelling, control, and optimization of PV systems, current–voltage data is required. A hybrid evolutionary algorithm is proposed in this paper for precise and effective parameter estimation from experimental data of various PV models. The proposed algorithm is named as hybrid flower grey differential (HFGD) algorithm and is based on the hybridization of flower pollination algorithm (FPA), grey wolf optimizer (GWO), and differential evolution (DE) algorithm. For performance evaluation, CEC 2019 benchmark data set is used. To increase the accuracy of the output solutions, we also combined the Newton–Raphson approach with the proposed algorithm. Four PV cells/modules with diverse characteristics, including RTC France Single Diode Model (SDM), RTC France Double DM (DDM), Amorphous Silicon aSi:H, and PVM 752 GaAs Thin-Film, are used to validate the effectiveness as well as the feasibility of the proposed algorithm. The parameter results obtained through the utilization of HFGD algorithm have been compared with other evolutionary algorithms through aspects of precision, reliability, and convergence. Based on the outcomes of the comparison, it has been seen that the HFGD algorithm obtained the lowest root-mean-square error (RMSE) value. Friedman’s rank and Wilcoxon test are carried out for the statistical analysis of the proposed work. The I–V and P–V characteristics are drawn along with the box plot for different PV cells/modules. Statistical and experimental results show the superiority of the proposed algorithm with respect to its counterpart. |
| first_indexed | 2024-03-08T20:10:17Z |
| format | Article |
| id | doaj.art-9c263a57c25a4edfbff034acef091a33 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T20:10:17Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-9c263a57c25a4edfbff034acef091a332023-12-23T05:22:11ZengElsevierEnergy Reports2352-48472023-11-011044474464Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithmPankaj Sharma0Saravanakumar Raju1Rohit Salgotra2Amir H. Gandomi3School of Electrical Engineering, Vellore Institute of Technology, IndiaSchool of Electrical Engineering, Vellore Institute of Technology, IndiaFaculty of Physics and Applied Computer Science, AGH University of Kraków, Poland; Faculty of Information Technology, Middle East University, Amman 11813, Jordan; Corresponding author at: Faculty of Physics and Applied Computer Science, AGH University of Kraków, Poland.Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, Australia; University Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, Hungary; Corresponding author at: University Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, Hungary.Integrating solar photovoltaic (PV) systems into the modern power grid introduces a variety of new problems. The accurate modelling of PV is required to strengthen the system characteristics in simulation environments. Modelling such PV systems is reflected by a nonlinear I–V characteristic curve behaviour with numerous unknown parameters because there is insufficient data in the cells’ datasheet. As a result, it is always a priority to identify these unknown parameters. To extract features of solar modules and build high-accuracy models for modelling, control, and optimization of PV systems, current–voltage data is required. A hybrid evolutionary algorithm is proposed in this paper for precise and effective parameter estimation from experimental data of various PV models. The proposed algorithm is named as hybrid flower grey differential (HFGD) algorithm and is based on the hybridization of flower pollination algorithm (FPA), grey wolf optimizer (GWO), and differential evolution (DE) algorithm. For performance evaluation, CEC 2019 benchmark data set is used. To increase the accuracy of the output solutions, we also combined the Newton–Raphson approach with the proposed algorithm. Four PV cells/modules with diverse characteristics, including RTC France Single Diode Model (SDM), RTC France Double DM (DDM), Amorphous Silicon aSi:H, and PVM 752 GaAs Thin-Film, are used to validate the effectiveness as well as the feasibility of the proposed algorithm. The parameter results obtained through the utilization of HFGD algorithm have been compared with other evolutionary algorithms through aspects of precision, reliability, and convergence. Based on the outcomes of the comparison, it has been seen that the HFGD algorithm obtained the lowest root-mean-square error (RMSE) value. Friedman’s rank and Wilcoxon test are carried out for the statistical analysis of the proposed work. The I–V and P–V characteristics are drawn along with the box plot for different PV cells/modules. Statistical and experimental results show the superiority of the proposed algorithm with respect to its counterpart.http://www.sciencedirect.com/science/article/pii/S235248472301538XMeta-heuristic optimization techniquesSolar PVCEC 2019 benchmarkHFGD algorithmParameter estimation |
| spellingShingle | Pankaj Sharma Saravanakumar Raju Rohit Salgotra Amir H. Gandomi Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm Energy Reports Meta-heuristic optimization techniques Solar PV CEC 2019 benchmark HFGD algorithm Parameter estimation |
| title | Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm |
| title_full | Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm |
| title_fullStr | Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm |
| title_full_unstemmed | Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm |
| title_short | Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm |
| title_sort | parametric estimation of photovoltaic systems using a new multi hybrid evolutionary algorithm |
| topic | Meta-heuristic optimization techniques Solar PV CEC 2019 benchmark HFGD algorithm Parameter estimation |
| url | http://www.sciencedirect.com/science/article/pii/S235248472301538X |
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