Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation
This study introduces a new approach for parameter optimization in the four-diode photovoltaic (PV) model, employing a Dynamic Fitness-Guided Particle Swarm Optimization (DFGPSO) algorithm and Enhanced Newton-Raphson (ENR) method. The new DFGPSO algorithm is specifically designed to address the intr...
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Elsevier Ltd
2025
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| author | Premkumar M. Ravichandran S. Hashim T.J.T. Sin T.C. Abbassi R. |
| author2 | 57191413142 |
| author_facet | 57191413142 Premkumar M. Ravichandran S. Hashim T.J.T. Sin T.C. Abbassi R. |
| author_sort | Premkumar M. |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This study introduces a new approach for parameter optimization in the four-diode photovoltaic (PV) model, employing a Dynamic Fitness-Guided Particle Swarm Optimization (DFGPSO) algorithm and Enhanced Newton-Raphson (ENR) method. The new DFGPSO algorithm is specifically designed to address the intrinsic challenges in PV modelling, such as local optima entrapment and slow convergence rates that typically hinder traditional optimization methods. By integrating a dynamically evolving fitness function derived from advanced swarm intelligence, the proposed approach significantly enhances global search capabilities. This new fitness function adapts continuously to the search landscape, facilitating rapid convergence towards optimal solutions and effectively navigating the complex, non-linear, and multi-modal parameter space of the PV model. Moreover, the robustness of the DFGPSO algorithm is substantially improved through the strategic incorporation of the ENR method. This integration not only provides accurate initial guesses for the particle positions, thus expediting the convergence process, but also minimizes computational burden, making the method more efficient. Comprehensive simulation studies across various case scenarios demonstrate that the proposed method markedly outperforms existing state-of-the-art optimization algorithms. It delivers faster convergence, enhanced accuracy, and robust performance under diverse environmental conditions, establishing a reliable and precise tool for optimizing PV system performance. This advancement promises significant improvements in energy yield and system reliability for the PV industry. ? 2024 Elsevier B.V. |
| format | Article |
| id | my.uniten.dspace-36137 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | my.uniten.dspace-361372025-03-03T15:41:26Z Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation Premkumar M. Ravichandran S. Hashim T.J.T. Sin T.C. Abbassi R. 57191413142 57219263030 57217828276 57212007867 27567490600 Particle swarm optimization (PSO) Energy Fitness functions Guided particle swarm optimization Model parameter estimation Newton-Raphson's method Parameters estimation Particle swarm optimization algorithm Particle swarm optimizers Photovoltaic model Photovoltaics Optimization algorithms This study introduces a new approach for parameter optimization in the four-diode photovoltaic (PV) model, employing a Dynamic Fitness-Guided Particle Swarm Optimization (DFGPSO) algorithm and Enhanced Newton-Raphson (ENR) method. The new DFGPSO algorithm is specifically designed to address the intrinsic challenges in PV modelling, such as local optima entrapment and slow convergence rates that typically hinder traditional optimization methods. By integrating a dynamically evolving fitness function derived from advanced swarm intelligence, the proposed approach significantly enhances global search capabilities. This new fitness function adapts continuously to the search landscape, facilitating rapid convergence towards optimal solutions and effectively navigating the complex, non-linear, and multi-modal parameter space of the PV model. Moreover, the robustness of the DFGPSO algorithm is substantially improved through the strategic incorporation of the ENR method. This integration not only provides accurate initial guesses for the particle positions, thus expediting the convergence process, but also minimizes computational burden, making the method more efficient. Comprehensive simulation studies across various case scenarios demonstrate that the proposed method markedly outperforms existing state-of-the-art optimization algorithms. It delivers faster convergence, enhanced accuracy, and robust performance under diverse environmental conditions, establishing a reliable and precise tool for optimizing PV system performance. This advancement promises significant improvements in energy yield and system reliability for the PV industry. ? 2024 Elsevier B.V. Final 2025-03-03T07:41:26Z 2025-03-03T07:41:26Z 2024 Article 10.1016/j.asoc.2024.112295 2-s2.0-85205902332 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205902332&doi=10.1016%2fj.asoc.2024.112295&partnerID=40&md5=28ddf716d169d7c48b07fce9019c79e1 https://irepository.uniten.edu.my/handle/123456789/36137 167 112295 Elsevier Ltd Scopus |
| spellingShingle | Particle swarm optimization (PSO) Energy Fitness functions Guided particle swarm optimization Model parameter estimation Newton-Raphson's method Parameters estimation Particle swarm optimization algorithm Particle swarm optimizers Photovoltaic model Photovoltaics Optimization algorithms Premkumar M. Ravichandran S. Hashim T.J.T. Sin T.C. Abbassi R. Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation |
| title | Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation |
| title_full | Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation |
| title_fullStr | Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation |
| title_full_unstemmed | Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation |
| title_short | Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation |
| title_sort | fitness-guided particle swarm optimization with adaptive newton-raphson for photovoltaic model parameter estimation |
| topic | Particle swarm optimization (PSO) Energy Fitness functions Guided particle swarm optimization Model parameter estimation Newton-Raphson's method Parameters estimation Particle swarm optimization algorithm Particle swarm optimizers Photovoltaic model Photovoltaics Optimization algorithms |
| url_provider | http://dspace.uniten.edu.my/ |