A neural network MPP tracker using a Buck-Boost DC/DC converter for photovoltaic systems

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference bet...

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Bibliographic Details
Main Authors: Makhloufi, M. T., Abdessemed, Y., Khireddine, M. S.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Batteries
Computer simulation
Control systems
Controllers
Load modeling
Matlab
Maximum power
Maximum power point trackers
Neural networks
Photovoltaic cells
Solar cells
Solar radiation
Switches
Voltage control
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Description
Summary:This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.
ISSN:2379-0067
DOI:10.1109/ICoSC.2016.7507039