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Parameter Estimation of Three-Diode Photovoltaic Model Using Reinforced Learning-Based Parrot Optimizer with an Adaptive Secant Method

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  • Nandhini Kullampalayam Murugaiyan

    (Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode 638401, India)

  • Kumar Chandrasekaran

    (Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore 641032, India)

  • Magdalin Mary Devapitchai

    (Electrical and Electronics Engineering, Sri Krishna College of Technology, Coimbatore 641042, India)

  • Tomonobu Senjyu

    (Faculty of Engineering, University of the Ryukyus, Senbaru, Nishihara, Nakagami, Okinawa 903-0213, Japan)

Abstract

In the developing landscape of photovoltaic (PV) technology, accuracy in simulating PV cell behaviour is dominant for enhancing energy conversion efficiency. This study introduces a new approach for parameter estimation in the three-diode PV model, a basis in the representation of PV cell characteristics. The methodology combines a reinforced learning-based parrot optimizer (RLPO) with an adaptive secant method (ASM) to fine-tune the parameters governing the PV model. The RLPO algorithm is inspired by the mimetic ability of parrots, i.e., foraging, staying, communicating, and fear noticed in trained Pyrrhura Molinae parrots, as it influences reinforced learning mechanisms to adaptively explore and exploit the search space for optimal parameter sets. Simultaneously, the ASM enhances the convergence rate through an iterative adjustment mechanism, responding to the curvature of the objective function, thereby ensuring accuracy in parameter estimation. The combination of the RLPO and ASM addresses the complexities and non-linearities inherent in the PV model, offering a robust framework for parameter estimation. Through extensive simulations, the proposed method demonstrated superior performance in terms of accuracy, convergence speed, and reliability when compared to existing algorithms. The empirical results emphasize the effectiveness of integrating a reinforced learning strategy with an adaptive method in handling the details of PV model parameterization. These outcomes show that the algorithm can handle issues related to optimization in PV systems, opening the door to progress in sustainable energy technologies.

Suggested Citation

  • Nandhini Kullampalayam Murugaiyan & Kumar Chandrasekaran & Magdalin Mary Devapitchai & Tomonobu Senjyu, 2024. "Parameter Estimation of Three-Diode Photovoltaic Model Using Reinforced Learning-Based Parrot Optimizer with an Adaptive Secant Method," Sustainability, MDPI, vol. 16(23), pages 1-34, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10603-:d:1535963
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    References listed on IDEAS

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    1. Chen, Xu & Xu, Bin & Mei, Congli & Ding, Yuhan & Li, Kangji, 2018. "Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation," Applied Energy, Elsevier, vol. 212(C), pages 1578-1588.
    2. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2020. "Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization," Energy, Elsevier, vol. 195(C).
    3. de Mars, Patrick & O’Sullivan, Aidan, 2021. "Applying reinforcement learning and tree search to the unit commitment problem," Applied Energy, Elsevier, vol. 302(C).
    4. Jiao, Shan & Chong, Guoshuang & Huang, Changcheng & Hu, Hanqing & Wang, Mingjing & Heidari, Ali Asghar & Chen, Huiling & Zhao, Xuehua, 2020. "Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models," Energy, Elsevier, vol. 203(C).
    5. Dizqah, Arash M. & Maheri, Alireza & Busawon, Krishna, 2014. "An accurate method for the PV model identification based on a genetic algorithm and the interior-point method," Renewable Energy, Elsevier, vol. 72(C), pages 212-222.
    6. Ishaque, Kashif & Salam, Zainal & Mekhilef, Saad & Shamsudin, Amir, 2012. "Parameter extraction of solar photovoltaic modules using penalty-based differential evolution," Applied Energy, Elsevier, vol. 99(C), pages 297-308.
    7. Sandrolini, L. & Artioli, M. & Reggiani, U., 2010. "Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis," Applied Energy, Elsevier, vol. 87(2), pages 442-451, February.
    8. Jieming Ma & T. O. Ting & Ka Lok Man & Nan Zhang & Sheng-Uei Guan & Prudence W. H. Wong, 2013. "Parameter Estimation of Photovoltaic Models via Cuckoo Search," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-8, August.
    9. 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.
    10. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2019. "Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm," Applied Energy, Elsevier, vol. 250(C), pages 109-117.
    11. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    12. Ridha, Hussein Mohammed & Hizam, Hashim & Gomes, Chandima & Heidari, Ali Asghar & Chen, Huiling & Ahmadipour, Masoud & Muhsen, Dhiaa Halboot & Alghrairi, Mokhalad, 2021. "Parameters extraction of three diode photovoltaic models using boosted LSHADE algorithm and Newton Raphson method," Energy, Elsevier, vol. 224(C).
    13. Ahmad, Lujean & Khordehgah, Navid & Malinauskaite, Jurgita & Jouhara, Hussam, 2020. "Recent advances and applications of solar photovoltaics and thermal technologies," Energy, Elsevier, vol. 207(C).
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