Output Electrical Power Control of Horizontal Axis Wind Turbine Using Indirect Model Reference Adaptive Neuro Controller

Azimi, Vahid; Menhaj, Mohammad Bagher

Document Type : Review Article

Authors

¹ Department of Electrical and Computer Engineering, Cleveland State University, Cleveland, OH 44115, USA

² Amirkabir University of Technology, Tehran, Iran

Abstract

In this paper, we investigate on Indirect Model Reference Adaptive Neuro Control (IMRANC), for output electrical power tracking of a nonlinear non-affine Horizontal Axis Wind Turbine (HAWT). The nonlinear system is first identified by the Nonlinear Autoregressive network with Exogenous inputs (NARX) model that is a recurrent dynamic network. Afterward an IMRANC is designed based on NARX identified model to reach the close loop system to desired reference model. The MLP networks are applied for both of model and controller subsystems and are then trained by the Marquardt-Levenberg Back-Propagation (LMBP) algorithm while the Tapped Delay Lines (TDL) components are considered over input and feedback paths. Simulation results are final presented to validate the effectiveness of the proposed method like robustness and good load disturbance attenuation and accurate tracking, even in the presence of parameter variations due to changing of hydraulic pressure in hydraulic pitch system and also disturbances on the system.

Keywords

References

[1] A. S. Yilmaz, Z. Ozer, “Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks”, Expert Systems with Applications, Vol. 36, No. 6, ,pp. 9767–9775, 2009.

[2] C. Sloth, T. Esbensen, and J. Stoustrup, “Robust and fault-tolerant linear parameter-varying control of wind turbines”, Mechatronics, Vol. 21, No. 4, 2011.

[3] H. Camblong, “Digital robust control of a variable speed pitch regulated wind turbine for above rated wind speeds”, Control Engineering Practice, Vol. 16, No. 8, pp. 946–958, 2008.

[4] P. F. Odgaard, J. Stoustrup, and M. Kinnaert, “Fault Tolerant Control of Wind Turbines – a benchmark model”, 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, 2009.

[5] O. Ognyanova, P. Petkov, E. Haralanova, and T. Puleva, “Robust control of a wind turbine”, 7th Mediterranean Conference and Exhibition on Power Generation, Transmission, Distribution and Energy Conversion, 2010.

[6] K. E. Johnson, L. Y. Pao, M. J. Balas, and L. J. Fingersh, “Control of variable-speed wind turbines”, IEEE Control Systems Magazine, pp. 70-81, 2006.

[7] T. Esbensen, C. Sloth, “Fault diagnosis and fault-tolerant control of wind turbines”, Thesis, 2009.

[8] A. Perdana, “Dynamic models of wind turbines”, PHD Thesis, 2008.

[9] C. Dobrila, R. Stefansen, “Fault tolerant wind turbine control”, MSc Thesis, 2007.

[10] K. Hammerum, “A fatigue approach to wind turbine control”, PHD Thesis, 2006.

[11] V. Azimi, M. A. Nekoui, and A. Fakharian., “Robust multi-objective H2/ H∞ tracking control based on T-S fuzzy model for a class of nonlinear uncertain drive systems”, Proceeding of the Institution of Mech. Eng. Part I-Journal of Systems and Control Engineering, Vol. 226, No. 8, , pp. 1107-1118, 2012.

[12] V. Azimi, A. Fakharian, and M. B. Menhaj “Position and current control of an Permanent-Magnet Synchronous Motor by using loop-shaping methodology: blending of H∞ mixed-sensitivity problem and T-S fuzzy model scheme”, Journal of Dynamic Systems Measurement and Control-Transactions of the ASME, Vol.135, No. 5, pp. 051006-1–051006-11, 2013.

[13] V. Azimi, M. B. Menhaj, and A. Fakharian, “Robust H2/ H∞ Control for a Robot Manipulator Fuzzy System”, 13th Iranian Conference on Fuzzy Systems (IFSC), Iran, 2013.

[14] V. Azimi, M. B. Menhaj, and A. Fakharian, “Fuzzy Robust Control of MIMO Nonlinear Uncertain systems”, 13th Iranian Conference on Fuzzy Systems (IFSC), Iran, 2013.

[15] V. Azimi, A. Fakharian, and M. B. Menhaj, “Robust Mixed-Sensitivity Gain-Scheduled H∞ tracking control of a nonlinear Time-Varying IPMSM via a T-S fuzzy model”, 9th France-Japan & 7th Europe-Asia Congress on and Research and Education in Mechatronics (REM), France, 2012.

[16] A. Fakharian, V. Azimi, “Robust mixed-sensitivity H∞ control for a class of MIMO uncertain nonlinear IPM synchronous motor via T-S fuzzy model”, Methods and Models in Automation and Robotics (MMAR), Poland, 2012.

[17] V. Azimi, M. A. Nekoui, and A. Fakharian, “Speed and torque control of induction motor by using robust H∞ mixed-sensitivity problem via T-S fuzzy model”, Iranian Conference on Electrical Engineering (ICEE), Iran, 2012.

[18] V. Azimi, M. B. Menhaj, and A. Fakharian, “Fuzzy Mixed-Sensitivity Control of Uncertain Nonlinear Induction Motor”, Majlesi Journal of Electrical Engineering, Vol. 8, No. 2, pp. 45-53, 2014.

[19] V. Azimi, M. B. Menhaj, and A. Fakharian, “Tool position tracking control of a nonlinear uncertain flexible robot manipulator by using robust H2 /H∞ controller via T–S fuzzy model”, Sadhana, Vol. 40, No. 2, pp. 307-333, 2015.

[20] A. Dzielinski, “Neural network based NARX models in nonlinear adaptive control”, Int. J. Appl. Math. Comput. Sci., Vol.12, No.2, pp. 235-240, 2002.

[21] H. T. Siegelmann, B. G. Horne, and C. L. Giles, “Computational capabilities of recurrent NARX neural networks”, IEEE Transactions on systems, man, and cybernetics-Part B: Cybernetics, Vol. 27, No. 2, 1997.

[22] E. Diaconescu, “The use of NARX Neural Networks to predict Chaotic Time Series”, Wseas Transactions on computer research, Vol. 3, No. 3, pp. 182-191, 2008.

[23] M. T. Hagan, M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm”, IEEE Transactions on neural networks, Vol. 5, No. 6, 1994.

[24] S. Suresh, S. N. Omkar, and V. Mani, “Parallel implementation of back-propagation algorithm in networks of workstations”, IEEE Transactions on Parallel and Distributed Systems, Vol. 16, No. 1, pp. 24-34, 2005.

[25] J. J. Rubio, “Modified optimal control with a back propagation network for robotic arms”, IET control theory and applications , Vol. 6, No. 14, pp. 2216-2225, 2012.

[26] W. Yu, A. S. Poznyak, “Indirect adaptive control via parallel dynamic neural networks”, IEE Proc.-Control Theory Appl., Vol. 146, No. 1, 1999.

[27] Q. I. Ruiyun, A. B. Mietek, “Indirect adaptive controller based on a self-structuring fuzzy system for nonlinear modeling and control”, Int. J. Appl. Math. Comput. Sci., Vol. 19, No. 4, pp. 619-630, 2009.

Output Electrical Power Control of Horizontal Axis Wind Turbine Using Indirect Model Reference Adaptive Neuro Controller

References

References

Volume 9, Issue 2 - Serial Number 2June 2015Pages 11-26

Volume 9, Issue 2 - Serial Number 2
June 2015
Pages 11-26