Implementation of a MPPT Neural Controller for Photovoltaic Systems on FPGA Circuit

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Abstract: The maximum power point tracking (MPPT) system controls the voltage and the current output of the photovoltaic (PV) system to deliver maximum power to the load. Parameters values were extracted using Newton Raphson method from characteristics of Shell SP75 module. This paper presents a comparative analysis of incremental conductance (IC), and neural network based MPPT techniques. The Artificial Neural Network (ANN) method is used to deliver the appropriate duty cycle signal used to drive boost converter to track the MPP even with variations of the input values using Matlab/Simulink for the simulation and Hardware Description Language (VHDL) for the implementation on kit Field Programming Gate Array (FPGA) Spartan-3E of Xilinx.

WSEAS Transactions on Power Systems, ISSN / E-ISSN: 1790-5060 / 2224-350X, Volume 9, 2014, Art. #48

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Naoufel Khaldi, Hassan Mahmoudi, Malika Zazi, Youssef Barradi, "Implementation of a MPPT Neural Controller for Photovoltaic Systems on FPGA Circuit," WSEAS Transactions on Power Systems, vol. 9, pp. 471-478, 2014, DOI:
Naoufel Khaldi, Hassan Mahmoudi, Malika Zazi, Youssef Barradi. Implementation of a MPPT Neural Controller for Photovoltaic Systems on FPGA Circuit. WSEAS Transactions on Power Systems. 2014;9:471-478.
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