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.
A Neural Adaptive Assisted Backstepping Controller for MPPT in Photovoltaic Applications
Laudani A.;
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.File in questo prodotto:
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