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

Author

Listed:
  • Arooj Tariq Kiani

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan)

  • Muhammad Faisal Nadeem

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan
    Clean and Resilient Energy Systems (CARES) Research Laboratory, Texas A&M University, Galveston, TX 77553, USA)

  • Ali Ahmed

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan)

  • Irfan A. Khan

    (Clean and Resilient Energy Systems (CARES) Research Laboratory, Texas A&M University, Galveston, TX 77553, USA)

  • Hend I. Alkhammash

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Intisar Ali Sajjad

    (Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan)

  • Babar Hussain

    (Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 44000, Pakistan)

Abstract

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.

Suggested Citation

  • Arooj Tariq Kiani & Muhammad Faisal Nadeem & Ali Ahmed & Irfan A. Khan & Hend I. Alkhammash & Intisar Ali Sajjad & Babar Hussain, 2021. "An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters," Energies, MDPI, vol. 14(11), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:2980-:d:559257
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