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Development of self-adaptive P&O MPPT algorithm for wind generation systems with concentrated search area

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  • Youssef, Abdel-Raheem
  • Mousa, Hossam H.H.
  • Mohamed, Essam E.M.

Abstract

To eradicate downsides of current perturb and observe(P&O) maximum power point tracking (MPPT) algorithms as well improvements on dynamic performances, this article proposes fast-hybrid P&O (FH-PO) and intelligent self-adaptive P&O (SA-PO) algorithms for wind generation systems. Both proposed algorithms concentrate the search area for the maximum power point (MPP) to 10% of optimal P-ω curve without dividing it into modular operating sectors and prior-knowledge of perturbation step-sizes. Below 90% of optimal power, the FH-PO algorithm perturbs the rotor speed with fixed step-sizes to enhance convergence speed without redundant calculations of step-sizes at each point. At the remaining 10%, an adaptive step-size is employed to ensure low oscillations around the MPP. However, FH-PO algorithm doesn’t reflect the real required step-size on each point. The SA-PO algorithm utilizes the self-adaptive step-size routine which adeptly estimates the required step-size by applying the idea of optimal hypothetical circle. Although both proposed algorithms have smallest oscillations, the SA-PO algorithm yields smallest settling time and a 4.34% increase in system efficiency. A fair comparison among proposed algorithms and other current P&O algorithms is deliberated to confirm the SA-PO algorithm superiority. The performances of proposed algorithms are tested by real wind data (Hokkaido-Island, Japan) using MATLAB/SIMULINK.

Suggested Citation

  • Youssef, Abdel-Raheem & Mousa, Hossam H.H. & Mohamed, Essam E.M., 2020. "Development of self-adaptive P&O MPPT algorithm for wind generation systems with concentrated search area," Renewable Energy, Elsevier, vol. 154(C), pages 875-893.
    • Handle: RePEc:eee:renene:v:154:y:2020:i:c:p:875-893
    DOI: 10.1016/j.renene.2020.03.050
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    4. Dali, Ali & Abdelmalek, Samir & Bakdi, Azzeddine & Bettayeb, Maamar, 2021. "A new robust control scheme: Application for MPP tracking of a PMSG-based variable-speed wind turbine," Renewable Energy, Elsevier, vol. 172(C), pages 1021-1034.
    5. José Genaro González-Hernández & Rubén Salas-Cabrera, 2021. "Wind Power Extraction Optimization by Dynamic Gain Scheduling Approximation Based on Non-Linear Functions for a WECS Based on a PMSG," Mathematics, MDPI, vol. 9(17), pages 1-19, August.
    6. Chen, Peng & Han, Dezhi, 2023. "Reward adaptive wind power tracking control based on deep deterministic policy gradient," Applied Energy, Elsevier, vol. 348(C).
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