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Implementation of a Novel Tabu Search Optimization Algorithm to Extract Parasitic Parameters of Solar Panel

Author

Listed:
  • Naveena Bhargavi Repalle

    (Electrical and Electronics Engineering, CVR College of Engineering, Hyderabad 501510, India)

  • Pullacheri Sarala

    (Electrical and Electronics Engineering, Malla Reddy Engineering College, Maisammaguda, Secunderabad 500100, India)

  • Lucian Mihet-Popa

    (Faculty of Information Technology, Engineering and Economics, Oestfold University College, 1757 Halden, Norway)

  • Shashidhar Reddy Kotha

    (Electrical and Electronics Engineering, CVR College of Engineering, Hyderabad 501510, India)

  • Nagalingam Rajeswaran

    (Electrical and Electronics Engineering, Malla Reddy Institute of Engineering and Technology, Maisammaguda, Secunderabad 500100, India)

Abstract

The aging of PV cells reduces their electrical performance i.e., the parasitic parameters are introduced in the solar panel. The shunt resistance (R Sh ), series resistance (R S ), photo current (I Ph ), diode current (I d ), and diffusion constant (a 1 ) are known as parasitic or extraction parameters. Cracks and hotspots reduce the performance of PV cells and result in poor V–I characteristics. Certain tests are carried out over a long period of time to determine the quality of solar cells; for example, 1000 h of testing is comparable to 20 years of operation. The extraction of solar parameters is important for PV modules. The Tabu Search Optimization (TSO) algorithm is a robust meta-heuristic algorithm that was employed in this study for the extraction of parasitic parameters. Particle Swarm Optimization (PSO) and a Genetic lgorithm (GA), as well as other well-known optimization methods, were used to test the proposed method’s correctness. The other approaches included the lightning search algorithm (LSA), gravitational search algorithm (GSA), and pattern search (PS). It can be concluded that the TSO approach extracts all six parameters in a reasonably short period of time. The work presented in this paper was developed and analyzed using a MATLAB-Simulink software environment.

Suggested Citation

  • Naveena Bhargavi Repalle & Pullacheri Sarala & Lucian Mihet-Popa & Shashidhar Reddy Kotha & Nagalingam Rajeswaran, 2022. "Implementation of a Novel Tabu Search Optimization Algorithm to Extract Parasitic Parameters of Solar Panel," Energies, MDPI, vol. 15(13), pages 1-12, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4515-:d:843906
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    Citations

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    Cited by:

    1. Diego Peña & Paul Arevalo & Yadyra Ortiz & Franciso Jurado, 2024. "Survey of Optimization Techniques for Microgrids Using High-Efficiency Converters," Energies, MDPI, vol. 17(15), pages 1-24, July.
    2. Hossam Hassan Ali & Mohamed Ebeed & Ahmed Fathy & Francisco Jurado & Thanikanti Sudhakar Babu & Alaa A. Mahmoud, 2023. "A New Hybrid Multi-Population GTO-BWO Approach for Parameter Estimation of Photovoltaic Cells and Modules," Sustainability, MDPI, vol. 15(14), pages 1-33, July.
    3. Ram Ishwar Vais & Kuldeep Sahay & Tirumalasetty Chiranjeevi & Ramesh Devarapalli & Łukasz Knypiński, 2023. "Parameter Extraction of Solar Photovoltaic Modules Using a Novel Bio-Inspired Swarm Intelligence Optimisation Algorithm," Sustainability, MDPI, vol. 15(10), pages 1-27, May.

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