An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation
Identification of photovoltaic (PV) module characteristics in solar systems is a vital task, nowadays, for optimal PV power estimation. In this paper, this challenge task has been studied using a novel advanced Kepler optimization algorithm (KOA). The standard version of KOA is adopted and assessed...
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| Language: | English |
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Elsevier
2024-11-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024159336 |
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| author | Ghareeb Moustafa Hashim Alnami Ahmed R. Ginidi Abdullah M. Shaheen |
| author_facet | Ghareeb Moustafa Hashim Alnami Ahmed R. Ginidi Abdullah M. Shaheen |
| author_sort | Ghareeb Moustafa |
| collection | DOAJ |
| description | Identification of photovoltaic (PV) module characteristics in solar systems is a vital task, nowadays, for optimal PV power estimation. In this paper, this challenge task has been studied using a novel advanced Kepler optimization algorithm (KOA). The standard version of KOA is adopted and assessed for getting the nine parameters of the PV triple diode model (3DM) considering three different practical PV modules. Kepler's principles of planetary motion are used by KOA to forecast the location and velocity of planets at any particular moment. However, the success rate of the KOA is not compatible, and its efficiency needs to be enhanced. As a result, an Improved KOA (IKOA) is created by incorporating an advanced mechanism of Local Escaping Operator (LEO), resulting in improved process of searching with evading local optima. This mechanism means that the exploitation approach will activate with around half of the solutions for every iteration starting at the initial phase of the iteration journey. The suggested IKOA besides the standard KOA are developed for predicting PV parameters for three distinct PV modules which are Photowatt PWP201, R.T.C France and STM6-40/36. The results corresponding to the latest algorithms are also compared with the proposed IKOA about different published works. The simulation findings reveal that the suggested IKOA exhibits notable average improvement rates for the three modules of 62.27 %, 55.1 %, and 32.12 %, respectively. Furthermore, the suggested IKOA asserts significant superiority and robustness over previously reported results. |
| format | Article |
| id | doaj-art-0727c3f0c20c46bb85434a636b0305f5 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-0727c3f0c20c46bb85434a636b0305f52024-11-15T06:13:56ZengElsevierHeliyon2405-84402024-11-011021e39902An improved Kepler optimization algorithm for module parameter identification supporting PV power estimationGhareeb Moustafa0Hashim Alnami1Ahmed R. Ginidi2Abdullah M. Shaheen3Department of Electrical and Electronic Engineering, College of Engineering and Computer Science, Jazan University, P.O. Box114, Jazan, 45142, Saudi ArabiaDepartment of Electrical and Electronic Engineering, College of Engineering and Computer Science, Jazan University, P.O. Box114, Jazan, 45142, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering, Suez University, P.O. Box: 43221, Suez, Egypt; Corresponding author.Department of Electrical Engineering, Faculty of Engineering, Suez University, P.O. Box: 43221, Suez, EgyptIdentification of photovoltaic (PV) module characteristics in solar systems is a vital task, nowadays, for optimal PV power estimation. In this paper, this challenge task has been studied using a novel advanced Kepler optimization algorithm (KOA). The standard version of KOA is adopted and assessed for getting the nine parameters of the PV triple diode model (3DM) considering three different practical PV modules. Kepler's principles of planetary motion are used by KOA to forecast the location and velocity of planets at any particular moment. However, the success rate of the KOA is not compatible, and its efficiency needs to be enhanced. As a result, an Improved KOA (IKOA) is created by incorporating an advanced mechanism of Local Escaping Operator (LEO), resulting in improved process of searching with evading local optima. This mechanism means that the exploitation approach will activate with around half of the solutions for every iteration starting at the initial phase of the iteration journey. The suggested IKOA besides the standard KOA are developed for predicting PV parameters for three distinct PV modules which are Photowatt PWP201, R.T.C France and STM6-40/36. The results corresponding to the latest algorithms are also compared with the proposed IKOA about different published works. The simulation findings reveal that the suggested IKOA exhibits notable average improvement rates for the three modules of 62.27 %, 55.1 %, and 32.12 %, respectively. Furthermore, the suggested IKOA asserts significant superiority and robustness over previously reported results.http://www.sciencedirect.com/science/article/pii/S2405844024159336Kepler optimization algorithmLocal Escaping OperatorPV parameters extractionPractical solar modules |
| spellingShingle | Ghareeb Moustafa Hashim Alnami Ahmed R. Ginidi Abdullah M. Shaheen An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation Heliyon Kepler optimization algorithm Local Escaping Operator PV parameters extraction Practical solar modules |
| title | An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation |
| title_full | An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation |
| title_fullStr | An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation |
| title_full_unstemmed | An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation |
| title_short | An improved Kepler optimization algorithm for module parameter identification supporting PV power estimation |
| title_sort | improved kepler optimization algorithm for module parameter identification supporting pv power estimation |
| topic | Kepler optimization algorithm Local Escaping Operator PV parameters extraction Practical solar modules |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024159336 |
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