Maximum power point tracking is a key asset to ensure an efficient energy conversion when a photovoltaic power source is involved. In this work, a novel approach combining a Neural-Network based tracking technique with an highly efficient algorithm for non-inverting buck-boost DC-DC converter (NIBB) control is proposed. The approach is validated through comparison against the well-known P&O algorithm, resulting superior both in terms of identifying the correct operating point for the PV device, and in terms of dynamic stability of the converter.
A Neural Adaptive Assisted Backstepping Controller for MPPT in Photovoltaic Applications / Boutebba, Okba; Laudani, Antonino; Lozito, Gabriele Maria; Corti, Fabio; Reatti, Alberto; Semcheddine, Samia. - ELETTRONICO. - (2020), pp. 1-6. (Intervento presentato al convegno IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)) [10.1109/EEEIC/ICPSEurope49358.2020.9160518].
A Neural Adaptive Assisted Backstepping Controller for MPPT in Photovoltaic Applications
Lozito, Gabriele Maria;Corti, Fabio;Reatti, Alberto;
2020
Abstract
Maximum power point tracking is a key asset to ensure an efficient energy conversion when a photovoltaic power source is involved. In this work, a novel approach combining a Neural-Network based tracking technique with an highly efficient algorithm for non-inverting buck-boost DC-DC converter (NIBB) control is proposed. The approach is validated through comparison against the well-known P&O algorithm, resulting superior both in terms of identifying the correct operating point for the PV device, and in terms of dynamic stability of the converter.
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