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A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system

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  • Sheik Mohammed, S.
  • Devaraj, D.
  • Imthias Ahamed, T.P.

Abstract

This paper presents a novel hybrid algorithm to search the maximum power point (MPP) for the solar PV system. The proposed algorithm is a combination of two techniques i.e., the conventional Perturb & Observe (P&O) algorithm and Learning Automata (LA) optimization. To evaluate the proposed algorithm, a unique PV system model is designed for a number of different scenarios with various weather conditions. For each scenario, an exhaustive simulation is carried out and the results are compared with the conventional P&O MPPT algorithm. The results demonstrate that the proposed MPPT method has significantly improved the tracking performance, response to the fast changing weather conditions and also has less oscillation around MPP as compared to the conventional P&O MPPT and Modified P&O MPPT. The performance of proposed hybrid MPP algorithm is demonstrated experimentally. The results show that overall dynamic response of the proposed algorithm is remarkably better than conventional P&O MPPT and the Modified P&O MPPT algorithm.

Suggested Citation

  • Sheik Mohammed, S. & Devaraj, D. & Imthias Ahamed, T.P., 2016. "A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system," Energy, Elsevier, vol. 112(C), pages 1096-1106.
    • Handle: RePEc:eee:energy:v:112:y:2016:i:c:p:1096-1106
    DOI: 10.1016/j.energy.2016.07.024
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    4. Han, Youhua & Li, Ming & Wang, Yunfeng & Li, Guoliang & Ma, Xun & Wang, Rui & Wang, Liang, 2019. "Impedance matching control strategy for a solar cooling system directly driven by distributed photovoltaics," Energy, Elsevier, vol. 168(C), pages 953-965.
    5. Boukenoui, R. & Ghanes, M. & Barbot, J.-P. & Bradai, R. & Mellit, A. & Salhi, H., 2017. "Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems," Energy, Elsevier, vol. 132(C), pages 324-340.
    6. García-Triviño, Pablo & Sarrias-Mena, Raúl & García-Vázquez, Carlos A. & Leva, Sonia & Fernández-Ramírez, Luis M., 2023. "Optimal online battery power control of grid-connected energy-stored quasi-impedance source inverter with PV system," Applied Energy, Elsevier, vol. 329(C).
    7. Maissa Farhat & Oscar Barambones & Lassaâd Sbita, 2020. "A Real-Time Implementation of Novel and Stable Variable Step Size MPPT," Energies, MDPI, vol. 13(18), pages 1-18, September.
    8. Julio López Seguel & Seleme I. Seleme, 2021. "Robust Digital Control Strategy Based on Fuzzy Logic for a Solar Charger of VRLA Batteries," Energies, MDPI, vol. 14(4), pages 1-27, February.
    9. Julio López Seguel & Seleme I. Seleme & Lenin M. F. Morais, 2022. "Comparative Study of Buck-Boost, SEPIC, Cuk and Zeta DC-DC Converters Using Different MPPT Methods for Photovoltaic Applications," Energies, MDPI, vol. 15(21), pages 1-26, October.
    10. Abdelkafi, Achraf & Masmoudi, Abdelkarim & Krichen, Lotfi, 2018. "Assisted power management of a stand-alone renewable multi-source system," Energy, Elsevier, vol. 145(C), pages 195-205.
    11. Mao, Mingxuan & Zhang, Li & Duan, Pan & Duan, Qichang & Yang, Ming, 2018. "Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller," Energy, Elsevier, vol. 143(C), pages 181-190.

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