Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search
This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO...
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ftmdpi:oai:mdpi.com:/2071-1050/15/6/5027/ 2023-08-20T04:08:44+02:00 Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search Habib Satria Rahmad B. Y. Syah Moncef L. Nehdi Monjee K. Almustafa Abdelrahman Omer Idris Adam agris 2023-03-12 application/pdf https://doi.org/10.3390/su15065027 EN eng Multidisciplinary Digital Publishing Institute Energy Sustainability https://dx.doi.org/10.3390/su15065027 https://creativecommons.org/licenses/by/4.0/ Sustainability; Volume 15; Issue 6; Pages: 5027 northern goshawk optimization PV solar energy parameter estimation Text 2023 ftmdpi https://doi.org/10.3390/su15065027 2023-08-01T09:13:55Z This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO algorithm technique while evading premature convergence. The suggested hybrid algorithm, chaotic northern goshawk, and pattern search (CNGPS), takes advantage of the chaotic NGO algorithm’s effective global search capability as well as the pattern search method’s powerful local search capability. The effectiveness of the recommended CNGPS algorithm is verified through the use of mathematical test functions, and its results are contrasted with those of a conventional NGO and other effective optimization methods. The CNGPS is then used to extract the PV parameters, and the parameter identification is defined as an objective function to be minimized based on the difference between the estimated and experimental data. The usefulness of the CNGPS for extraction parameters is evaluated using three distinct PV models: SDM, DDM, and TDM. The numerical investigates illustrate that the new algorithm may produce better optimum solutions and outperform previous approaches in the literature. The simulation results display that the novel optimization method achieves the lowest root mean square error and obtains better optima than existing methods in various solar cells. Text Northern Goshawk MDPI Open Access Publishing Sustainability 15 6 5027 |
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northern goshawk optimization PV solar energy parameter estimation |
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northern goshawk optimization PV solar energy parameter estimation Habib Satria Rahmad B. Y. Syah Moncef L. Nehdi Monjee K. Almustafa Abdelrahman Omer Idris Adam Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search |
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northern goshawk optimization PV solar energy parameter estimation |
description |
This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO algorithm technique while evading premature convergence. The suggested hybrid algorithm, chaotic northern goshawk, and pattern search (CNGPS), takes advantage of the chaotic NGO algorithm’s effective global search capability as well as the pattern search method’s powerful local search capability. The effectiveness of the recommended CNGPS algorithm is verified through the use of mathematical test functions, and its results are contrasted with those of a conventional NGO and other effective optimization methods. The CNGPS is then used to extract the PV parameters, and the parameter identification is defined as an objective function to be minimized based on the difference between the estimated and experimental data. The usefulness of the CNGPS for extraction parameters is evaluated using three distinct PV models: SDM, DDM, and TDM. The numerical investigates illustrate that the new algorithm may produce better optimum solutions and outperform previous approaches in the literature. The simulation results display that the novel optimization method achieves the lowest root mean square error and obtains better optima than existing methods in various solar cells. |
format |
Text |
author |
Habib Satria Rahmad B. Y. Syah Moncef L. Nehdi Monjee K. Almustafa Abdelrahman Omer Idris Adam |
author_facet |
Habib Satria Rahmad B. Y. Syah Moncef L. Nehdi Monjee K. Almustafa Abdelrahman Omer Idris Adam |
author_sort |
Habib Satria |
title |
Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search |
title_short |
Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search |
title_full |
Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search |
title_fullStr |
Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search |
title_full_unstemmed |
Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search |
title_sort |
parameters identification of solar pv using hybrid chaotic northern goshawk and pattern search |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/su15065027 |
op_coverage |
agris |
genre |
Northern Goshawk |
genre_facet |
Northern Goshawk |
op_source |
Sustainability; Volume 15; Issue 6; Pages: 5027 |
op_relation |
Energy Sustainability https://dx.doi.org/10.3390/su15065027 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/su15065027 |
container_title |
Sustainability |
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15 |
container_issue |
6 |
container_start_page |
5027 |
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1774721198406500352 |