Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems
Abstract The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper prese...
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| Format: | Article |
| Language: | English |
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Wiley
2024-03-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.13121 |
| _version_ | 1797278817895579648 |
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| author | Mohamed H. Hassan Salah Kamel Abd‐ElHady Ramadan José Luís Domínguez‐García Hamed Zeinoddini‐Meymand |
| author_facet | Mohamed H. Hassan Salah Kamel Abd‐ElHady Ramadan José Luís Domínguez‐García Hamed Zeinoddini‐Meymand |
| author_sort | Mohamed H. Hassan |
| collection | DOAJ |
| description | Abstract The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem‐based optimization (LAEO), which improves upon the original artificial ecosystem‐based optimization (AEO). The proposed LAEO algorithm integrates the adaptive probability (AP) and leader‐based mutation‐selection strategies to enhance the search capability, improve the balance between exploration and exploitation, and overcome local optima. To evaluate the effectiveness of LAEO, it was tested on 23 different benchmark functions. Additionally, LAEO was applied to estimate the parameters of static three‐diode PV models, as well as integral‐order and fractional‐order dynamic models. This paper showcases practical implementations of photovoltaic (PV) parameter estimation in various scenarios, including the static three‐diode model, dynamic integral order model (IOM), and fractional order model (FOM). The results were assessed from various angles to examine the precision, performance, and stability of the LAEO algorithm. |
| first_indexed | 2024-03-07T16:14:40Z |
| format | Article |
| id | doaj.art-1e61ca651fbf43a28fb1ea10be986acc |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-07T16:14:40Z |
| publishDate | 2024-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-1e61ca651fbf43a28fb1ea10be986acc2024-03-04T11:27:49ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952024-03-011851026105810.1049/gtd2.13121Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systemsMohamed H. Hassan0Salah Kamel1Abd‐ElHady Ramadan2José Luís Domínguez‐García3Hamed Zeinoddini‐Meymand4Ministry of Electricity and Renewable Energy Cairo EgyptDepartment of Electrical Engineering, Faculty of EngineeringAswan UniversityAswan EgyptDepartment of Electrical Engineering, Faculty of EngineeringAswan UniversityAswan EgyptIREC Catalonia Institute for Energy Research Barcelona SpainDepartment of Electrical and Computer EngineeringGraduate University of Advanced TechnologyKermanIranAbstract The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem‐based optimization (LAEO), which improves upon the original artificial ecosystem‐based optimization (AEO). The proposed LAEO algorithm integrates the adaptive probability (AP) and leader‐based mutation‐selection strategies to enhance the search capability, improve the balance between exploration and exploitation, and overcome local optima. To evaluate the effectiveness of LAEO, it was tested on 23 different benchmark functions. Additionally, LAEO was applied to estimate the parameters of static three‐diode PV models, as well as integral‐order and fractional‐order dynamic models. This paper showcases practical implementations of photovoltaic (PV) parameter estimation in various scenarios, including the static three‐diode model, dynamic integral order model (IOM), and fractional order model (FOM). The results were assessed from various angles to examine the precision, performance, and stability of the LAEO algorithm.https://doi.org/10.1049/gtd2.13121diodesoptimisationparameter estimationphotovoltaic power systemsrenewable energy sources |
| spellingShingle | Mohamed H. Hassan Salah Kamel Abd‐ElHady Ramadan José Luís Domínguez‐García Hamed Zeinoddini‐Meymand Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems IET Generation, Transmission & Distribution diodes optimisation parameter estimation photovoltaic power systems renewable energy sources |
| title | Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems |
| title_full | Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems |
| title_fullStr | Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems |
| title_full_unstemmed | Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems |
| title_short | Optimizing photovoltaic models: A leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems |
| title_sort | optimizing photovoltaic models a leader artificial ecosystem approach for accurate parameter estimation of dynamic and static three diode systems |
| topic | diodes optimisation parameter estimation photovoltaic power systems renewable energy sources |
| url | https://doi.org/10.1049/gtd2.13121 |
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