BibTex RIS Cite

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

Year 2015, Volume: 5 Issue: 2, 635 - 647, 01.06.2015

Ilyes Tegani Abdenacer Aboubou Ramzi Saadi Mohamed Yacine Ayad Mohamed Becherif

Abstract

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.

Keywords

Control; design; renewable energy; hybrid system; photovoltaic; Wind turbine; fuel cell; battery; stand-alone; flatness systems; neural network; energy management.

References

  • P. Thounthong, S. Rael, and B. Davat, "Energy management of fuel cell/battery/supercapacitor hybrid power source for vehicle applications". Journal of Power Sources, 2009. 193(1): p. 376-385.
  • O. Palizban and K. Kauhaniemi, "Hierarchical control structure in microgrids with distributed generation: Island and grid-connected mode". Renewable and Sustainable Energy Reviews, 2015. 44: p. 797-813.
  • A. Pohjoranta, et al., "Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data". Journal of Power Sources, 2015. : p. 239-250.
  • A.M. Bouzid, et al., "A survey on control of electric power distributed generation systems for microgrid applications". Renewable and Sustainable Energy Reviews, 2015. 44: p. 751-766.
  • A. Karray and M. Feki, "Adaptive and sliding mode control of a mobile manipulator actuated by DC motors". International Journal of Automation and Control, 2014. (2): p. 173-190.
  • S.M. Rakhtala Rostami, A. Ranjbar Noei, and R. Gaderi, "Control of PEM fuel cell system via higher order sliding mode control". International Journal of Automation and Control, 2012. 6(3): p. 310-329.
  • K.E. Johnson, et al., "Control of variable-speed wind turbines: maximizing energy capture". Control Systems, IEEE, 26(3): p. 70-81. adaptive techniques for R. Marino and P. Tomei. Adaptive Control of Stepper motors via nonlinear extended matching. in IF AC Workshop on Motion Control for Intelligent Automation. A. Luviano-Juárez, J. Cortés-Romero, and H. Sira- Ramírez, "Trajectory Tracking Control of a Mobile Robot Through Linearization Scheme". Journal of Dynamic Systems, Measurement, and Control, 2015. 137(5): p. 051001.
  • T. Taniguchi, L. Eciolaza, and M. Sugeno. Model Following Control of a Unicycle Mobile Robot via Dynamic Feedback Linearization Based on Piecewise Bilinear Models. in Information Processing and Management of Uncertainty in Knowledge-Based Systems. 2014. Springer.
  • M. Aimene, A. Payman, and B. Dakyo. Management of the wind turbine energy delivered to the grid based on the flatness control method. in Energy Conversion Congress and Exposition (ECCE), 2014 IEEE. 2014. IEEE.
  • I. Tégani, et al., "Optimal sizing design and energy management of stand-alone photovoltaic/wind generator systems". Energy Procedia, 2014. 50: p. 163-170.
  • M.P. Lalitha, T. Janardhan, and R.M. Mohan. The future of fuzzy logic based wind energy conversion system with solid oxide fuel cell and the passions of radical pedagogy. in Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH), 2014 Innovative Applications of. 2014. IEEE.
  • S. Jie, Z. Yong, and Y. Chengliang, "Longitudinal brake control of hybrid electric bus using adaptive fuzzy sliding mode control". International Journal of Modelling, Identification and Control, 2012. 15(3): p. 147-155.
  • M. Fliess, et al., "Flatness and defect of non-linear systems: introductory theory and examples". International journal of control, 1995. 61(6): p. 1327-1361.
  • M. Phattanasak, et al. Current-fed DC-DC converter with Flatness based control for renewable energy. in Electrical Telecommunications and Information Technology (ECTI- CON), 2014 11th International Conference on. 2014. IEEE. Computer,
  • S. Sikkabut, et al. A nonlinear control algorithm of Li- ion battery substation for DC distributed system. in Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2014 International Symposium on. IEEE.
  • P. Thounthong, et al. Differential flatness control approach for fuel cell/solar cell power plant with Li-ion battery storage device for grid-independent applications. in Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2014 International Symposium on. IEEE.
  • P. Thounthong, "Model based-energy control of a solar power plant with a supercapacitor for grid-independent applications". Energy conversion, ieee transactions on, 26(4): p. 1210-1218.
  • P. Thounthong, S. Rael, and B. Davat, "Analysis of supercapacitor as second source based on fuel cell power generation". Energy conversion, ieee transactions on, 24(1): p. 247-255. A.A. Bakar, et al. DC-DC interleaved boost converter using FPGA. in Clean Energy and Technology (CEAT), IEEE Conference on. 2013. IEEE.
  • O. Gergaud, Modélisation énergétique et optimisation économique d'un système de production éolien et photovoltaïque couplé au réseau et associé à un accumulateur, 2002, École normale supérieure de Cachan-ENS Cachan.
  • J. Larminie, A. Dicks, and M.S. McDonald, Fuel cell systems explained. Vol. 2. 2003: Wiley New York.
  • B. Laroche, P. Martin, and N. Petit, "Commande par platitude. Equations différentielles ordinaires et aux dérivées partielles". 2008.
  • F. Antritter and J. Lévine, Flatness characterization: Two approaches, in Advances in the Theory of Control, Signals and Systems with Physical Modeling2011, Springer. p. 127-139.
  • A. Payman, "Contribution à la Gestion de l’Energie dans les Systèmes Hybrides Multi-sources Multi-charges". PhDthesis, Polytechnic Institute of Lorraine, Nancy, France, 2009.
  • A. Payman, S. Pierfederici, and F. Meibody-Tabar, "Energy control of supercapacitor/fuel cell hybrid power source". Energy Conversion and Management, 2008. (6): p. 1637-1644.
  • D.V.M. S.Angelin. Optimal Operation of Power in Renewable Energy by Using prediction in Recurrent Neural Network, International Journal of Innovative Research in Computer and Communication Engineering. cited http://www.mathworks.com/help/nnet/ug/design-neural- networkpredictive-controller-in-simulink.html. from: M. Sedighizadeh, M. Rezaei, and V. Najmi, "A Predictive Control based on neural network for Proton Exchange Membrane Fuel Cell". World Academy of Science, Engineering and Technology,

Year 2015, Volume: 5 Issue: 2, 635 - 647, 01.06.2015

Ilyes Tegani Abdenacer Aboubou Ramzi Saadi Mohamed Yacine Ayad Mohamed Becherif

Abstract

References

  • P. Thounthong, S. Rael, and B. Davat, "Energy management of fuel cell/battery/supercapacitor hybrid power source for vehicle applications". Journal of Power Sources, 2009. 193(1): p. 376-385.
  • O. Palizban and K. Kauhaniemi, "Hierarchical control structure in microgrids with distributed generation: Island and grid-connected mode". Renewable and Sustainable Energy Reviews, 2015. 44: p. 797-813.
  • A. Pohjoranta, et al., "Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data". Journal of Power Sources, 2015. : p. 239-250.
  • A.M. Bouzid, et al., "A survey on control of electric power distributed generation systems for microgrid applications". Renewable and Sustainable Energy Reviews, 2015. 44: p. 751-766.
  • A. Karray and M. Feki, "Adaptive and sliding mode control of a mobile manipulator actuated by DC motors". International Journal of Automation and Control, 2014. (2): p. 173-190.
  • S.M. Rakhtala Rostami, A. Ranjbar Noei, and R. Gaderi, "Control of PEM fuel cell system via higher order sliding mode control". International Journal of Automation and Control, 2012. 6(3): p. 310-329.
  • K.E. Johnson, et al., "Control of variable-speed wind turbines: maximizing energy capture". Control Systems, IEEE, 26(3): p. 70-81. adaptive techniques for R. Marino and P. Tomei. Adaptive Control of Stepper motors via nonlinear extended matching. in IF AC Workshop on Motion Control for Intelligent Automation. A. Luviano-Juárez, J. Cortés-Romero, and H. Sira- Ramírez, "Trajectory Tracking Control of a Mobile Robot Through Linearization Scheme". Journal of Dynamic Systems, Measurement, and Control, 2015. 137(5): p. 051001.
  • T. Taniguchi, L. Eciolaza, and M. Sugeno. Model Following Control of a Unicycle Mobile Robot via Dynamic Feedback Linearization Based on Piecewise Bilinear Models. in Information Processing and Management of Uncertainty in Knowledge-Based Systems. 2014. Springer.
  • M. Aimene, A. Payman, and B. Dakyo. Management of the wind turbine energy delivered to the grid based on the flatness control method. in Energy Conversion Congress and Exposition (ECCE), 2014 IEEE. 2014. IEEE.
  • I. Tégani, et al., "Optimal sizing design and energy management of stand-alone photovoltaic/wind generator systems". Energy Procedia, 2014. 50: p. 163-170.
  • M.P. Lalitha, T. Janardhan, and R.M. Mohan. The future of fuzzy logic based wind energy conversion system with solid oxide fuel cell and the passions of radical pedagogy. in Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH), 2014 Innovative Applications of. 2014. IEEE.
  • S. Jie, Z. Yong, and Y. Chengliang, "Longitudinal brake control of hybrid electric bus using adaptive fuzzy sliding mode control". International Journal of Modelling, Identification and Control, 2012. 15(3): p. 147-155.
  • M. Fliess, et al., "Flatness and defect of non-linear systems: introductory theory and examples". International journal of control, 1995. 61(6): p. 1327-1361.
  • M. Phattanasak, et al. Current-fed DC-DC converter with Flatness based control for renewable energy. in Electrical Telecommunications and Information Technology (ECTI- CON), 2014 11th International Conference on. 2014. IEEE. Computer,
  • S. Sikkabut, et al. A nonlinear control algorithm of Li- ion battery substation for DC distributed system. in Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2014 International Symposium on. IEEE.
  • P. Thounthong, et al. Differential flatness control approach for fuel cell/solar cell power plant with Li-ion battery storage device for grid-independent applications. in Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2014 International Symposium on. IEEE.
  • P. Thounthong, "Model based-energy control of a solar power plant with a supercapacitor for grid-independent applications". Energy conversion, ieee transactions on, 26(4): p. 1210-1218.
  • P. Thounthong, S. Rael, and B. Davat, "Analysis of supercapacitor as second source based on fuel cell power generation". Energy conversion, ieee transactions on, 24(1): p. 247-255. A.A. Bakar, et al. DC-DC interleaved boost converter using FPGA. in Clean Energy and Technology (CEAT), IEEE Conference on. 2013. IEEE.
  • O. Gergaud, Modélisation énergétique et optimisation économique d'un système de production éolien et photovoltaïque couplé au réseau et associé à un accumulateur, 2002, École normale supérieure de Cachan-ENS Cachan.
  • J. Larminie, A. Dicks, and M.S. McDonald, Fuel cell systems explained. Vol. 2. 2003: Wiley New York.
  • B. Laroche, P. Martin, and N. Petit, "Commande par platitude. Equations différentielles ordinaires et aux dérivées partielles". 2008.
  • F. Antritter and J. Lévine, Flatness characterization: Two approaches, in Advances in the Theory of Control, Signals and Systems with Physical Modeling2011, Springer. p. 127-139.
  • A. Payman, "Contribution à la Gestion de l’Energie dans les Systèmes Hybrides Multi-sources Multi-charges". PhDthesis, Polytechnic Institute of Lorraine, Nancy, France, 2009.
  • A. Payman, S. Pierfederici, and F. Meibody-Tabar, "Energy control of supercapacitor/fuel cell hybrid power source". Energy Conversion and Management, 2008. (6): p. 1637-1644.
  • D.V.M. S.Angelin. Optimal Operation of Power in Renewable Energy by Using prediction in Recurrent Neural Network, International Journal of Innovative Research in Computer and Communication Engineering. cited http://www.mathworks.com/help/nnet/ug/design-neural- networkpredictive-controller-in-simulink.html. from: M. Sedighizadeh, M. Rezaei, and V. Najmi, "A Predictive Control based on neural network for Proton Exchange Membrane Fuel Cell". World Academy of Science, Engineering and Technology,
There are 25 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ilyes Tegani This is me

Abdenacer Aboubou This is me

Ramzi Saadi This is me

Mohamed Yacine Ayad This is me

Mohamed Becherif This is me

Publication Date June 1, 2015
Published in Issue Year 2015 Volume: 5 Issue: 2

Cite

APA Tegani, I., Aboubou, A., Saadi, R., Ayad, M. Y., et al. (2015). Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System. International Journal Of Renewable Energy Research, 5(2), 635-647.
AMA Tegani I, Aboubou A, Saadi R, Ayad MY, Becherif M. Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System. International Journal Of Renewable Energy Research. June 2015;5(2):635-647.
Chicago Tegani, Ilyes, Abdenacer Aboubou, Ramzi Saadi, Mohamed Yacine Ayad, and Mohamed Becherif. “Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System”. International Journal Of Renewable Energy Research 5, no. 2 (June 2015): 635-47.
EndNote Tegani I, Aboubou A, Saadi R, Ayad MY, Becherif M (June 1, 2015) Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System. International Journal Of Renewable Energy Research 5 2 635–647.
IEEE I. Tegani, A. Aboubou, R. Saadi, M. Y. Ayad, and M. Becherif, “Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System”, International Journal Of Renewable Energy Research, vol. 5, no. 2, pp. 635–647, 2015.
ISNAD Tegani, Ilyes et al. “Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System”. International Journal Of Renewable Energy Research 5/2 (June 2015), 635-647.
JAMA Tegani I, Aboubou A, Saadi R, Ayad MY, Becherif M. Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System. International Journal Of Renewable Energy Research. 2015;5:635–647.
MLA Tegani, Ilyes et al. “Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System”. International Journal Of Renewable Energy Research, vol. 5, no. 2, 2015, pp. 635-47.
Vancouver Tegani I, Aboubou A, Saadi R, Ayad MY, Becherif M. Differential Flatness Using the Predictive Neural Network Control Law for Hybrid Power System. International Journal Of Renewable Energy Research. 2015;5(2):635-47.