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.
Proceedings 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Goal 9: Industry, Innovation, and Infrastructure
Boutebba, Okba; Laudani, Antonino; Lozito, Gabriele Maria; Corti, Fabio; Reatti, Alberto; Semcheddine, Samia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1205768
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