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 PO 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.
Boutebba, O., Laudani, A., Lozito, G.M., Corti, F., Reatti, A., Semcheddine, S. (2020). A Neural Adaptive Assisted Backstepping Controller for MPPT in Photovoltaic Applications. In Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020 (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/EEEIC/ICPSEurope49358.2020.9160518].
A Neural Adaptive Assisted Backstepping Controller for MPPT in Photovoltaic Applications
Laudani A.;Lozito G. M.;
2020-01-01
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 PO 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
