Affiliations: [a] Department of Electrical Engineering, University of
Colorado Denver, Denver, CO, USA | [b] Department of Engineering, University of Roma Tre, Roma, Italy
Correspondence:
[*]
Corresponding author: Alessandro Salvini, Dipartimento di
Ingegneria, University of Roma Tre, Via Vito Volterra n. 62, Roma, I-00146,
Italy. Tel.: +39 06 5733 7337; Fax: +39 06 5733 7026; E-mail: asalvini@uniroma3.it
Abstract: This paper proposes a maximum power point (MPP) tracking algorithm
based on neural networks to correctly track the MPP even under abrupt changes
in solar irradiance and to improve the dynamic performance across the dc
capacitor utilized in the power converter that serves as an interphase to
connect photovoltaic power plants into the ac grid. Traditional maximum power
point tracking algorithms such as "perturb and observe" (P&O) and
"incremental conductance" (IC) are able to track the point of maximum power
in most cases. However, they can fail under rapid changing atmospheric
conditions. Furthermore, in architectures with a power converter operated at
variable dc-link, P&O and IC-based algorithms provide a step-like
voltage reference which translates into a repetitive overshoot across the dc
capacitor. This may negatively affect the lifespan of the capacitor and affect
the overall dynamic of the system. This paper develops a neural network (NN)
algorithm that overcome the aforementioned issues, including thorough modelling
and control of the various components involved in the realization of a
grid-connected photovoltaic power plant. The approach is validated via detailed
computer simulations on experimental data.
Keywords: DC-Link dynamics, maximum power point tracker, neural networks, photovoltaic panels
