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

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

Renewable Energy, 2020, vol. 154, issue C, 875-893

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.

Keywords: Self-adaptive P&O; Fast-hybrid P&O; WECS; MPPT; Search area; Five-phase PMSG (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:154:y:2020:i:c:p:875-893

DOI: 10.1016/j.renene.2020.03.050

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