Abstract

The control of variable speed wind turbines is a complex problem since they are considered as nonlinear and time varying systems. In general, classical control techniques do not take into consideration the stochastic and dynamical aspect of the wind and they are not very robust. In order to address these weaknesses, neural approaches are proposed: a direct neural model DNM of the wind turbine is elaborated and then an inverse neural controller INC is developed. The other objective of this study is to optimize the power generated by the wind turbine. To achieve this aim, we have elaborated a neural controller which takes into account the optimal speed of the turbine. Finally, some modifications of the neural control strategy are used to improve the results. The neural controllers were tested with a wind turbine simple mathematical model. The obtained results have shown better performance in comparison with classical control techniques.

References

• T. Burton, D. Sharpe, N. Jenkins and E. Bossanyi, Wind Energy Handbook. Wiley, Chichester, UK. 2001.
• A. Wright, Modern Control Design for Flexible Wind Turbines. Ph. D. thesis. Boulder, CO: University of Colorado, USA. 2003.
• A. Boukhezzar, Nonlinear control of variable-speed wind turbines for generator torque limiting and power optimization. Journal Solar Energy Engineering, Trans. ASME 128(4): (pp. 516 – 530). 2006.
• F. - D. Bianchi, H. De Battista and R. J. Mantz, Wind Turbine Control Systems: Principles, Modeling and Gain Scheduling Design. Springer-Verlag London Limited. 2007.
• B. Boukhezzar and H. Siguerdidjane, Robust Multiobjective Control of a Variable Speed Wind Turbine, European Wind Energy Conference Proceedings, EWEA, London 2004.
• N. -K . Kasabov, Foundation of neural networks, fuzzy systems and knowledge engineering, Second edition. A Bradford Book, The MIT Press. 1998.
• F. -L. Luo, and R. Unbehaben, Applied neural networks for signal processing. Cambridge University Press. 1998.
• E. S. Abdin and W. Xu , Control Design and Dynamic Performance Analysis of a Wind Turbine-Induction Generator Unit, IEEE Trans. Energy Convers. , 15(1), (pp. 91–96) 2000.
• T. Ekelund, Modeling and Linear Quadratic Optimal Control of Wind Turbines, Ph. D. thesis, Chalmers University of Technology, Sweden 1997.
• B. Connor, W. E. Leithead and M. Grimble, LQG Control of a Constant Speed Horizontal Axis Wind Turbine, Proceedings of the Third IEEE Conference on Control Applications, Glasgow, Scotland, August (24–26), Vol. 1, (pp. 251–252) 1994.
• P. Bongers, Modeling and Identification of Flexible Wind Turbines and a Factorizational Approach to Robust Control, Ph. D. thesis, Delft University of Technology, Netherlands 1994.
• D. Connor, S. N. Iyer, W. E. Leithead and M. J. Grimble, Control of Horizontal Axis Wind Turbine Using H? Control, Proceedings of the First IEEE Conference on Control Applications, Dayton, OH, September (13–16) 1992.
• H. D. Battista, R. J. Mantz and C. F. Christiansen, Dynamical Sliding Mode Power Control of Wind Driven Induction Generators, IEEE Trans. Energy Convers. , 15(14), (pp. 451–457) 2000.
• Y. D. Song, B. Dhinakaran and X. Y. Bao, Variable Speed Control of Wind Turbines Using Nonlinear and Adaptive Algorithms, J. Wind. Eng. Ind. Aerodyn. , 85, (pp. 293–308) 2000.
• D. -E. Rumelhart, G. Hinton and R. Willams. Learning internal representation by error propagation in Parallel distributed processing: exploration in the microstructure of cognition. Foundations (Vol. 1). Cambridge, Mass, MIT Press. 1986.
• H. Camblong, Minimisation de l'impact des perturbations d'origine éolienne dans la génération d'électricité par des aéroturbines à vitesse variable. PhD thesis ENSAM. Bordeaux. 2003.
• L. Fausett, Fundamentals of Neural Networks: architectures, algorithms and applications. Prentice Hall, NY, USA. 1994.
• C. -M. Bishop, Neural Networks for pattern recognition. Clarendon Press. Oxford. UK. 1995.
• M. -A. Arbib, Handbook of brain theory and neural networks. Second edition. A Bradford Book. The MIT Press. Cambridge, Massachusetts, London, England. 2003.
• G. Bloch and T. Denoeux, Neural networks for process control and optimization, ISA Transactions, volume 42, Issue 1, (pp 39-51), January 2003.
• S. Rajasekaran and G. A. VijayalaksmiPai, neural network, fuzzy logic and genetic algorithms-synthesis and applications, Prentice Hall, chapter 3, (pp 34-86), 2005.
• S. Mangrulkar, artificial neural systems, ISA Transactions, volume 29, Issue 1, (pp 5-7) , 1990.
• D. A. Rehbein, S. M. Maze and J. P. Havener, the application of neural networks in the process industry, ISA Transactions, volume 31, Issue 4, (pp7-13), 1992.
• K. J Jihen Prakash, Dipesh S. Pattle and Amiya K. J, Neuro- estimator based GMS control of a batch reactive distillation, ISA Transactions, volume 50, Issue3, (pp357-363), July2011,
• B. Kusumoputro, H. Budiatro and W. Jatmiko, Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial order discrimination system, ISA Transactions, volume 41, Issue 4,(pp395-407), October 2002.
• C. Brahmi, M. Chtourou, M. Djemel and A. Khamlichi, Neural Control Strategies for Variable Wind Speed Wind Turbine, Proceedings of the Euro Mediterranean Scientific Congress on Engineering, Algeciras 2011.