The paper presents a maximum power point tracking algorithm based on a neural network implementation and aimed to search the optimal working point in two different photovoltaic technologies coupled to a buck-boost DC-DC converter. The neural network generates the current reference for the control algorithm that manages the DC-DC converter switches. The paper analyzes the neural network training and reports the results obtained considering the two different PV technologies.
Neural Networks for Maximum Power Point Tracking Application to Silicon and CIGS Photovoltaic Modules / Bertoluzzo M.; Sieni E.; Zordan M.; Forato M.; Mognaschi M.E.; Lozito G.M.; Riganti Fulginei F.. - ELETTRONICO. - (2018), pp. 1-6. (Intervento presentato al convegno 4th IEEE International Forum on Research and Technologies for Society and Industry, RTSI 2018 tenutosi a ita nel 2018) [10.1109/RTSI.2018.8548445].
Neural Networks for Maximum Power Point Tracking Application to Silicon and CIGS Photovoltaic Modules
Sieni E.;Lozito G. M.;
2018
Abstract
The paper presents a maximum power point tracking algorithm based on a neural network implementation and aimed to search the optimal working point in two different photovoltaic technologies coupled to a buck-boost DC-DC converter. The neural network generates the current reference for the control algorithm that manages the DC-DC converter switches. The paper analyzes the neural network training and reports the results obtained considering the two different PV technologies.
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