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Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation

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dc.citedby2
dc.contributor.authorPremkumar M.en_US
dc.contributor.authorRavichandran S.en_US
dc.contributor.authorHashim T.J.T.en_US
dc.contributor.authorSin T.C.en_US
dc.contributor.authorAbbassi R.en_US
dc.contributor.authorid57191413142en_US
dc.contributor.authorid57219263030en_US
dc.contributor.authorid57217828276en_US
dc.contributor.authorid57212007867en_US
dc.contributor.authorid27567490600en_US
dc.date.accessioned2025-03-03T07:41:26Z
dc.date.available2025-03-03T07:41:26Z
dc.date.issued2024
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo112295
dc.identifier.doi10.1016/j.asoc.2024.112295
dc.identifier.scopus2-s2.0-85205902332
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85205902332&doi=10.1016%2fj.asoc.2024.112295&partnerID=40&md5=28ddf716d169d7c48b07fce9019c79e1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36137
dc.identifier.volume167
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleApplied Soft Computing
dc.subjectParticle swarm optimization (PSO)
dc.subjectEnergy
dc.subjectFitness functions
dc.subjectGuided particle swarm optimization
dc.subjectModel parameter estimation
dc.subjectNewton-Raphson's method
dc.subjectParameters estimation
dc.subjectParticle swarm optimization algorithm
dc.subjectParticle swarm optimizers
dc.subjectPhotovoltaic model
dc.subjectPhotovoltaics
dc.subjectOptimization algorithms
dc.titleFitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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