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Parameter Extraction of Solar Photovoltaic Modules Using a Novel Bio-Inspired Swarm Intelligence Optimisation Algorithm

Author

Listed:
  • Ram Ishwar Vais

    (Department of Electrical Engineering, Rajkiya Engineering College, Sonbhadra 231206, Uttar Pradesh, India)

  • Kuldeep Sahay

    (Department of Electrical Engineering, Institute of Engineering and Technology, Lucknow 226021, Uttar Pradesh, India)

  • Tirumalasetty Chiranjeevi

    (Department of Electrical Engineering, Rajkiya Engineering College, Sonbhadra 231206, Uttar Pradesh, India)

  • Ramesh Devarapalli

    (Department of Electrical/Electronics and Instrumentation Engineering, Institute of Chemical Technology, Indianoil Odisha Campus, Bhubaneswar 751013, India)

  • Łukasz Knypiński

    (Faculty of Automatic Control, Robotic and Electrical Engineering, Poznan University of Technology, 60-965 Poznan, Poland)

Abstract

For extracting the equivalent circuit parameters of solar photovoltaic (PV) panels, a unique bio-inspired swarm intelligence optimisation algorithm (OA) called the dandelion optimisation algorithm (DOA) is proposed in this study. The suggested approach has been used to analyse well-known single-diode (SD) and double-diode (DD) PV models for several PV module types, including monocrystalline SF430M, polycrystalline SG350P, and thin-film Shell ST40. The DOA is adopted by minimizing the sum of the squares of the errors at three locations (short-circuit, open-circuit, and maximum power points). Different runs are conducted to analyse the nature of the extracted parameters and the V – I characteristics of the PV panels under consideration. Obtained results show that for Mono SF430M, the error in the SD model is 2.5118e-19, and the error in the DD model is 2.0463e-22; for Poly SG350P, the error in the SD model is 9.4824e-21, and the error in the DD model is 2.1134e-20; for thin-film Shell ST40, the error in the SD model is 1.7621e-20, and the error in DD model is 7.9361e-22. The parameters produced from the suggested method yield the least amount of error across several executions, which suggests its better implementation in the current situation. Furthermore, statistical analysis of the SD and DD models using DOA is also carried out and compared with two hybrid OAs in the literature. Statistical results show that the standard deviation, sum, mean, and variance of various PV panels using DOA are lower compared to those of the other two hybrid OAs.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8407-:d:1152828
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    References listed on IDEAS

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    1. Shayan, Mostafa Esmaeili & Najafi, Gholamhassan & Ghobadian, Barat & Gorjian, Shiva & Mamat, Rizalman & Ghazali, Mohd Fairusham, 2022. "Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm," Renewable Energy, Elsevier, vol. 201(P2), pages 179-189.
    2. Biswas, Partha P. & Suganthan, P.N. & Wu, Guohua & Amaratunga, Gehan A.J., 2019. "Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 132(C), pages 425-438.
    3. 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.
    4. Mostafa Esmaeili Shayan & Gholamhassan Najafi & Barat Ghobadian & Shiva Gorjian & Mohamed Mazlan & Mehdi Samami & Alireza Shabanzadeh, 2022. "Flexible Photovoltaic System on Non-Conventional Surfaces: A Techno-Economic Analysis," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    5. Oliva, Diego & Cuevas, Erik & Pajares, Gonzalo, 2014. "Parameter identification of solar cells using artificial bee colony optimization," Energy, Elsevier, vol. 72(C), pages 93-102.
    6. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
    7. AlHajri, M.F. & El-Naggar, K.M. & AlRashidi, M.R. & Al-Othman, A.K., 2012. "Optimal extraction of solar cell parameters using pattern search," Renewable Energy, Elsevier, vol. 44(C), pages 238-245.
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