Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/7555
Title: Differential Flatness Using the Predictive Neural Network Control Law for Hybrid power System
Authors: Ilyes Tegani
Abdenacer Aboubou
Ramzi Saadi
Mohamed yacine Ayad
Mohamed Becheri
Keywords: conhol; renewable energy; hybrid system; photovoltaic; wind turbine; fuel cell; battery; flatness systems; neural network; energy management.
Issue Date: 18-Mar-2016
Abstract: In this paper, a control desigr for a renewable energy hybrid power system that is fed by a photovoltaic (pv), wind turbine (wr) and fuel cell (FC) sources with a battery (Batt) storage device is presented. The energy generated is managed through a nonlinear approach based on the differential flatness property. The control technique used in this work permits entire description the of the state's trajectories, and so to improve the dynamic response, stability and robustness of the proposed hybrid system by decreasing the static error in the output regulated voltage. The control law ofthis approach is improved using the predictive neural network (PNN) to ensure a better tracking for the reference trajectory signals. The obtained results show that the proposed flatness-PNN is able to manage well the power flow in a hybrid system with multi-renewable sources, providing more stabiliÿ by decreasing the perturbation in the controlled DC bus voltage.
URI: http://dspace.univ-biskra.dz:8080/jspui/handle/123456789/7555
Appears in Collections:Publications Internationales

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