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Author: search.filters.author.Kaitwanidvilai, SomyotSubject: search.filters.subject.MPPT

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    Maximum power point tracking using neural network in flyback MPPT inverter for PV systems
    (2012-12-01)
    Konghuayrob, Poom
    ;
    Kaitwanidvilai, Somyot
    Generally, perturb and observe (P&O) technique is widely adopted in photovoltaic (PV) system to maximize the output power. In flyback inverter, the modulation index needs to be adjusted based on the P&O algorithm. However if the changing step size of modulation index (Δma) is too large, the fast MPP (Maximum Power Point) tracking can be achieved but the power oscillation around the MPP will be large. In contrary, the small changing step size results in long tracking time and small oscillation. Consequently, this paper proposes a technique to adjust the changing step size (Δma) of Flyback inverter to achieve both acceptable tracking time and low power oscillation. In the proposed technique, irradiance is adopted as the input of a neural network which is used to estimate the appropriate modulation step size. Simulation results confirm that the proposed neural network based inverter can find the appropriate changing step size (Δma) which is adequate for any irradiance conditions. © 2012 IEEE.
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    Maximum power point tracking using fuzzy logic control for photovoltaic systems
    (2011-07-26)
    Takun, Pongsakor
    ;
    Kaitwanidvilai, Somyot
    ;
    Jettanasen, Chaiyan
    In this paper, a fuzzy logic control (FLC) is proposed to control the maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed technique uses the fuzzy logic control to specify the size of incremental current in the current command of MPPT. As results indicated, the convergence time of maximum power point (MPP) of the proposed algorithm is better than that of the conventional Perturb and Observation (P&O) technique.