A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter

Mohamed Tahar Makhloufi, Yassine Abdessemed, Mohamed Salah Khireddine


This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of the Cuk converter due to the switching technique is difficult to be handled by conventional controller. To overcome this problem, a neural network controller with online learning back propagation algorithm is developed. The NNC designed tracked the converter voltage output and improve the dynamic performance regardless load disturbances and supply variations. The proposed controller effectiveness during dynamic transient response is then analyze and verified using MATLAB-Simulink. Simulation results confirm the excellent performance of the proposed NNC.


Modified Cuk Converter Photovoltaic System Maximum Power PointTracker Artificial neural network Controller Battery

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DOI: http://doi.org/10.11591/ijece.v6i4.pp1421-1433
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ISSN 2088-8708, e-ISSN 2722-2578