The perturb and observe (P&O) best operation conditions are investigated in order to identify the edge efficiency performances of this most popular maximum power point tracking (MPPT) technique for photovoltaic (PV) applications. It is shown that P&O may guarantee top-level efficiency, provided that a proper predictive (by means of a parabolic interpolation of the last three operating points) and adaptive (based on the measure of the actual power) hill climbing strategy is adopted. The approach proposed is aimed at realizing, in addition to absolute best tracking performances, high robustness and promptness both in sunny and cloudy weather conditions. The power gain with respect to standard P&O technique is proved by means of simulation results and experimental measurements performed on a low power system. Besides the performance improvements, it is shown that the proposed approach allows possible reduction of hardware costs of analog-to-digital (A/D) converters used in the MPPT control circuitry.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Predictive & Adaptive MPPT Perturb and Observe Method


    Beteiligte:
    Fermia, N. (Autor:in) / Granozio, D. (Autor:in) / Petrone, G. (Autor:in) / Vitelli, M. (Autor:in)


    Erscheinungsdatum :

    01.07.2007


    Format / Umfang :

    4624102 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Perturb and Observe Digital Maximum Power Point Tracker for Satellite Applications

    Brambilla, A. / Gambarara, M. / Torrente, G. et al. | British Library Conference Proceedings | 2002


    Optimization of Braking-Torque in Anti-lock Braking System using Perturb & Observe Technique

    Patil, Anoopkumar / Nissimagoudar, Prabha / Naik, Abhishek et al. | IEEE | 2021



    Multicuts and Perturb & MAP for Probabilistic Graph Clustering

    Kappes, J. r. | British Library Online Contents | 2016