Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential Evolution
Estimating the optimal parameter values for photovoltaic (PV) models is inherently challenging due to the complex and nonlinear nature of their current–voltage (I–V) characteristic curves. Precise parameter estimation is critical for ensuring the efficient operation of PV systems, as it directly inf...
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Elsevier
2025-08-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825005034 |
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| author | Khalid M. Hosny Amr A. Abd El-Mageed Amr A. Abohany Reda M. Hussein Mona Gaffar |
| author_facet | Khalid M. Hosny Amr A. Abd El-Mageed Amr A. Abohany Reda M. Hussein Mona Gaffar |
| author_sort | Khalid M. Hosny |
| collection | DOAJ |
| description | Estimating the optimal parameter values for photovoltaic (PV) models is inherently challenging due to the complex and nonlinear nature of their current–voltage (I–V) characteristic curves. Precise parameter estimation is critical for ensuring the efficient operation of PV systems, as it directly influences energy output and current generation. Traditional methods for addressing this problem often suffer from convergence to local optima and require substantial computational resources, particularly concerning the count of fitness evaluations. To overcome these challenges, this paper presents an enhanced optimization method: the Brown Bear Optimization Algorithm (BBOA) hybridized with Diagonal Linear Uniform Initialization (DLUI) and the Differential Evolution (DE) algorithm, termed BBOA-DLUI-DE. This hybrid approach’s innovative design lies in integrating the DE algorithm to enhance solution diversity, ensuring better exploration and preventing premature convergence. DLUI contributes to a uniformly diverse initial population that supports rapid and robust optimization. This synergy between BBOA, DLUI, and DE addresses the limitations of existing methods by combining efficient global search capabilities with effective local refinement. The proposed BBOA-DLUI-DE method has been rigorously evaluated against state-of-the-art techniques, demonstrating superior performance in finding optimal parameter values for various PV models. Comparative statistical and practical analyses highlight that BBOA-DLUI-DE outperforms traditional methods regarding accuracy and computational efficiency. Furthermore, validation using manufacturing data sheets (MCSM55 and TFST40) confirms the practical applicability and robustness of the proposed method, making it a highly effective tool for estimating PV parameters. |
| format | Article |
| id | doaj-art-d4da4cfc79f54e3e9dfca2e927362034 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-d4da4cfc79f54e3e9dfca2e9273620342025-08-22T04:55:12ZengElsevierAlexandria Engineering Journal1110-01682025-08-0112716419910.1016/j.aej.2025.04.020Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential EvolutionKhalid M. Hosny0Amr A. Abd El-Mageed1Amr A. Abohany2Reda M. Hussein3Mona Gaffar4Department of Information Technology, Faculty of Computers and Information, Zagazig University, Zagazig, Egypt; Corresponding author.Department of Information Systems, Sohag University, Sohag, EgyptFaculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt; Faculty of Computers and Information, Damanhour University, Damanhour, EgyptFaculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, EgyptDepartment of Computer Engineering and Information, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia; Machine Learning and Information Retrieval Department, Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, EgyptEstimating the optimal parameter values for photovoltaic (PV) models is inherently challenging due to the complex and nonlinear nature of their current–voltage (I–V) characteristic curves. Precise parameter estimation is critical for ensuring the efficient operation of PV systems, as it directly influences energy output and current generation. Traditional methods for addressing this problem often suffer from convergence to local optima and require substantial computational resources, particularly concerning the count of fitness evaluations. To overcome these challenges, this paper presents an enhanced optimization method: the Brown Bear Optimization Algorithm (BBOA) hybridized with Diagonal Linear Uniform Initialization (DLUI) and the Differential Evolution (DE) algorithm, termed BBOA-DLUI-DE. This hybrid approach’s innovative design lies in integrating the DE algorithm to enhance solution diversity, ensuring better exploration and preventing premature convergence. DLUI contributes to a uniformly diverse initial population that supports rapid and robust optimization. This synergy between BBOA, DLUI, and DE addresses the limitations of existing methods by combining efficient global search capabilities with effective local refinement. The proposed BBOA-DLUI-DE method has been rigorously evaluated against state-of-the-art techniques, demonstrating superior performance in finding optimal parameter values for various PV models. Comparative statistical and practical analyses highlight that BBOA-DLUI-DE outperforms traditional methods regarding accuracy and computational efficiency. Furthermore, validation using manufacturing data sheets (MCSM55 and TFST40) confirms the practical applicability and robustness of the proposed method, making it a highly effective tool for estimating PV parameters.http://www.sciencedirect.com/science/article/pii/S1110016825005034Parameter estimationPhotovoltaic modelsSolar systemBrown bear optimization algorithmDifferential evolution algorithm |
| spellingShingle | Khalid M. Hosny Amr A. Abd El-Mageed Amr A. Abohany Reda M. Hussein Mona Gaffar Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential Evolution Alexandria Engineering Journal Parameter estimation Photovoltaic models Solar system Brown bear optimization algorithm Differential evolution algorithm |
| title | Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential Evolution |
| title_full | Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential Evolution |
| title_fullStr | Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential Evolution |
| title_full_unstemmed | Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential Evolution |
| title_short | Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential Evolution |
| title_sort | precise estimation of solar photovoltaic parameters via brown bear optimization and differential evolution |
| topic | Parameter estimation Photovoltaic models Solar system Brown bear optimization algorithm Differential evolution algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825005034 |
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