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Article

An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters

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Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan
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Clean and Resilient Energy Systems (CARES) Research Laboratory, Texas A&M University, Galveston, TX 77553, USA
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Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 44000, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editors: Alessandro Cannavale, Filippo Spertino and Paolo Di Leo
Energies 2021, 14(11), 2980; https://doi.org/10.3390/en14112980
Received: 1 April 2021 / Revised: 9 May 2021 / Accepted: 13 May 2021 / Published: 21 May 2021
(This article belongs to the Special Issue Photovoltaic Modules 2021)
The efficiency of PV systems can be improved by accurate estimation of PV parameters. Parameter estimation of PV cells and modules is a challenging task as it requires accurate operation of PV cells and modules followed by an optimization tool that estimates their associated parameters. Mostly, population-based optimization tools are utilized for PV parameter estimation problems due to their computational intelligent behavior. However, most of them suffer from premature convergence problems, high computational burden, and often fall into local optimum solution. To mitigate these limitations, this paper presents an improved variant of particle swarm optimization (PSO) aiming to reduce shortcomings offered by conventional PSO for estimation of PV parameters. PSO is improved by introducing two strategies to control inertia weight and acceleration coefficients. At first, a sine chaotic inertia weight strategy is employed to attain an appropriate balance between local and global search. Afterward, a tangent chaotic strategy is utilized to guide acceleration coefficients in search of an optimal solution. The proposed algorithm is utilized to estimate the parameters of the PWP201 PV module, RTC France solar cell, and a JKM330P-72 PV module-based practical system. The obtained results indicate that the proposed technique avoids premature convergence and local optima stagnation of conventional PSO. Moreover, a comparison of obtained results with techniques available in the literature proves that the proposed methodology is an efficient, effective, and optimal tool to estimate PV modules and cells’ parameters. View Full-Text
Keywords: acceleration coefficients; chaotic and tangent; inertia weight; parameter estimation; solar cell and modules acceleration coefficients; chaotic and tangent; inertia weight; parameter estimation; solar cell and modules
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MDPI and ACS Style

Kiani, A.T.; Nadeem, M.F.; Ahmed, A.; Khan, I.A.; Alkhammash, H.I.; Sajjad, I.A.; Hussain, B. An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters. Energies 2021, 14, 2980. https://doi.org/10.3390/en14112980

AMA Style

Kiani AT, Nadeem MF, Ahmed A, Khan IA, Alkhammash HI, Sajjad IA, Hussain B. An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters. Energies. 2021; 14(11):2980. https://doi.org/10.3390/en14112980

Chicago/Turabian Style

Kiani, Arooj Tariq, Muhammad Faisal Nadeem, Ali Ahmed, Irfan A. Khan, Hend I. Alkhammash, Intisar Ali Sajjad, and Babar Hussain. 2021. "An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters" Energies 14, no. 11: 2980. https://doi.org/10.3390/en14112980

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