This paper covers the study towards the implementation of a Neural Network based approach for the efficiency control of Photovoltaic systems. The algorithm aims to track the maximum power point for the PV device whenever abrupt changes in climatic conditions occur. The core of the algorithm is a Neural Network (NN) trained by using a suitable mathematical model of the photovoltaic device. Different NN architectures and several optimization solutions were studied to investigate the best approach in terms of computational efficiency, memory footprint and prediction accuracy. The best found architecture has been implemented and tested on the microcontroller unit LM4F120H5QR by Texas Instruments by using a prototype board to drive the operating point of a low-power solar cell.
Microcontroller based maximum power point tracking through FCC and MLP neural networks / Lozito G.M.; Bozzoli L.; Salvini A.. - ELETTRONICO. - (2014), pp. 207-211. (Intervento presentato al convegno 6th European Embedded Design in Education and Research Conference, EDERC 2014 tenutosi a ita nel 2014) [10.1109/EDERC.2014.6924389].
Microcontroller based maximum power point tracking through FCC and MLP neural networks
Lozito G. M.;
2014
Abstract
This paper covers the study towards the implementation of a Neural Network based approach for the efficiency control of Photovoltaic systems. The algorithm aims to track the maximum power point for the PV device whenever abrupt changes in climatic conditions occur. The core of the algorithm is a Neural Network (NN) trained by using a suitable mathematical model of the photovoltaic device. Different NN architectures and several optimization solutions were studied to investigate the best approach in terms of computational efficiency, memory footprint and prediction accuracy. The best found architecture has been implemented and tested on the microcontroller unit LM4F120H5QR by Texas Instruments by using a prototype board to drive the operating point of a low-power solar cell.
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