Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System

In this paper, a control design for a renewable energy hybrid power system that is fed by a photovoltaic (PV), Wind turbine (WT) and fuel cell (FC) sources with a batteries (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 the entire description 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 of this  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 multirenewable sources, providing more stability by decreasing the perturbation in the controlled DC bus voltage.

[1]  B. Nahid-Mobarakeh,et al.  A nonlinear control algorithm of Li-ion battery substation for DC distributed system , 2014, 2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion.

[2]  B. Nahid-Mobarakeh,et al.  Differential flatness control approach for fuel cell/solar cell power plant with Li-ion battery storage device for grid-independent applications , 2014, 2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion.

[3]  Phatiphat Thounthong,et al.  Energy management of fuel cell/battery/supercapacitor hybrid power source for vehicle applications , 2009 .

[4]  Amal Karray,et al.  Adaptive and sliding mode control of a mobile manipulator actuated by DC motors , 2014, Int. J. Autom. Control..

[5]  Alireza Payman,et al.  Contribution à la gestion d'énergie dans les systèmes hybrides multi-sources multi-charges , 2009 .

[6]  John Cortés-Romero,et al.  Trajectory Tracking Control of a Mobile Robot Through a Flatness-Based Exact Feedforward Linearization Scheme , 2015 .

[7]  Kimmo Kauhaniemi,et al.  Hierarchical control structure in microgrids with distributed generation: Island and grid-connected mode , 2015 .

[8]  Serge Pierfederici,et al.  Energy control of supercapacitor/fuel cell hybrid power source , 2008 .

[9]  Riccardo Marino,et al.  ADAPTIVE CONTROL OF STEPPER MOTORS VIA NONLINEAR EXTENDED MATCHING , 1992 .

[10]  O. Kraa,et al.  Optimal Sizing Design and Energy Management of Stand-alone Photovoltaic/Wind Generator Systems , 2014 .

[11]  S.Angelin.  Optimal Operation of Power in RenewableEnergy by Using prediction in RecurrentNeural Network , 2015 .

[12]  M. Padma Lalitha,et al.  The future of fuzzy logic based wind energy conversion system with solid oxide fuel cell and the passions of radical pedagogy , 2014, 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH).

[13]  Phatiphat Thounthong,et al.  Current-fed DC-DC converter with Flatness based control for renewable energy , 2014, 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

[14]  Zhang Yong,et al.  Longitudinal brake control of hybrid electric bus using adaptive fuzzy sliding mode control , 2012 .

[15]  Michio Sugeno,et al.  Model Following Control of a Unicycle Mobile Robot via Dynamic Feedback Linearization Based on Piecewise Bilinear Models , 2014, IPMU.

[16]  Reza Ghaderi,et al.  Control of PEM fuel cell system via higher order sliding mode control , 2012, Int. J. Autom. Control..

[17]  P. Thounthong,et al.  Analysis of Supercapacitor as Second Source Based on Fuel Cell Power Generation , 2009, IEEE Transactions on Energy Conversion.

[18]  Josep M. Guerrero,et al.  A Survey on Control of Electric Power Distributed Generation Systems for Microgrid Applications , 2015 .

[19]  M. Sedighizadeh,et al.  A Predictive control based on Neural Network for Proton Exchange Membrane Fuel Cell , 2011 .

[20]  Jean Lévine,et al.  Flatness Characterization: Two Approaches , 2010 .

[21]  Nicolas Petit,et al.  Commande par platitude. Equations différentielles ordinaires et aux dérivées partielles , 2008 .

[23]  Caisheng Wang,et al.  Power Management of a Stand-Alone Wind/Photovoltaic/Fuel Cell Energy System , 2008, IEEE Transactions on Energy Conversion.

[24]  Matias Halinen,et al.  Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data , 2015 .

[25]  M. Fliess,et al.  Flatness and defect of non-linear systems: introductory theory and examples , 1995 .

[26]  James Larminie,et al.  Fuel Cell Systems Explained , 2000 .

[27]  Brayima Dakyo,et al.  Management of the wind turbine energy delivered to the grid based on the flatness control method , 2014, 2014 IEEE Energy Conversion Congress and Exposition (ECCE).

[28]  P. Thounthong.  Model Based-Energy Control of a Solar Power Plant With a Supercapacitor for Grid-Independent Applications , 2011, IEEE Transactions on Energy Conversion.

[29]  Olivier Gergaud,et al.  Modélisation énergétique et optimisation économique d'un système de production éolien et photovoltaïque couplé au réseau et associé à un accumulateur , 2002 .

[30]  E. Sulaiman,et al.  DC-DC interleaved boost converter using FPGA , 2013, 2013 IEEE Conference on Clean Energy and Technology (CEAT).

[31]  L.Y. Pao,et al.  Control of variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture , 2006, IEEE Control Systems.