Cover
Vol. 18 No. 2 (2022)

Published: December 31, 2022

Pages: 43-52

Original Article

Variable Speed Controller of Wind Generation System using Model predictive Control and NARMA Controller

Raheel Jawad 1 Majda Ahmed Hussein M. Salih Yasser Ahmed Mahmood
1Electro mechanical. Eng. Dept., University of Technology, Baghdad, Iraq
History
  • Received: March 12, 2022
  • Revised: June 11, 2022
  • Accepted: June 11, 2022
  • Available Online: June 29, 2022
DOI

https://doi.org/10.37917/ijeee.18.2.6

Abstract

This paper applied an artificial intelligence technique to control Variable Speed in a wind generator system. One of these techniques is an offline Artificial Neural Network (ANN-based system identification methodology, and applied conventional proportional-integral-derivative (PID) controller). ANN-based model predictive (MPC) and remarks linearization (NARMA-L2) controllers are designed, and employed to manipulate Variable Speed in the wind technological knowledge system. All parameters of controllers are set up by the necessities of the controller's design. The effects show a neural local (NARMA-L2) can attribute even higher than PID. The settling time, upward jab time, and most overshoot of the response of NARMA-L2 is a notable deal an awful lot less than the corresponding factors for the accepted PID controller. The conclusion from this paper can be to utilize synthetic neural networks of industrial elements and sturdy manageable to be viewed as a dependable desire to normal modeling, simulation, and manipulation methodologies. The model developed in this paper can be used offline to structure and manufacturing points of conditions monitoring, faults detection, and troubles shooting for wind generation systems.

Keywords

References

  1. BoroumandJazi G., Saidur R., Rismanchi B. and Mekhilef S., "A Review on the Relation Between the Energy and Exergy Efficiency Analysis and The Technical Characteristic of the Renewable Energy Systems", Renewable and Sustainable Energy Reviews, Vol. 16, PP. 3131-3135, 2012.
  2. Sarkar M. R., Julai S., Tong C. W., Chao O. Z. and Rahman M., "Mathematical Modelling and Simulation of Induction Generator Based Wind Turbine in Matlab/Simulink", ARPN, Vol. 10, No. 22, 2015.
  3. Yaramasu V. and Wu B., "Model predictive control of wind energy conversion systems", J. Wiley and Sons, USA, 2017.
  4. Silpa B., "Pitch Control of Wind Turbine through PID, Fuzzy and adaptive Fuzzy-PID controllers", Thesis, Rochester Institute of Technology, 2017.
  5. Y. Qi and Q. Meng, "The Application of Fuzzy PID Control in Pitch Wind Turbine", ELSVIER. Energy Procedia, vol. 16, pp. 1635-1641, 2012.
  6. C., Zafer, E. Çam, M. Lüy, and H. Mamur, "Proportional – Integral – derivative Parameter Optimisation of Blade Pitch Controller in Wind Turbines by a New Intelligent Genetic Algorithm”, IEEE, vol. 10, pp.1220-1228, 2016.
  7. L. Suganthi, S. Iniyan, Anand Samuel A, “Applications of fuzzy logic in renewable energy systems,” A review, vol.48, pp.585-607, 2015.
  8. Paulo J. Costa, Adriano S. Carvalho, António J. Martins, “Fuzzy Logic as a Method to Optimize Wind Systems Interconnected with the Smart Grid,” Wind Power Systems pp 383–405, 2010.
  9. Edgar N. Sanchez, and Riemann Ruiz-Cruz, “Doubly Fed Induction Generators: Control for Wind Energy”, CRC Press, USA, 2016.
  10. Gonzalo Abad, Jesu´s Lo´pez, Miguel A. Rodrı´guez, Luis Marroyo and Grzegorz Iwanski "Doubly Fed Indication Machine Modeling and Control for Wind Energy Generation”, Wiley-IEEE Press, 2011.
  11. D. Pallares, F. Johnsson, “Macroscopic modelling of fluid dynamics in large-scale circulating fluidized beds”, Progress in energy and combustion science, Vol. 32, No. 5, pp. 539–569, 2006.
  12. C. Zhao, L. Dun, W. Zhou, X. Chen, D. Zeng, T. Flynn, D. Kraft, “Coal combustion characteristics on an oxy-cfb combustor with warm flue gas recycle”, in: Proceedings of the 21st International Conference on Fluidized Bed Combustion, 2012, pp. 3–6.
  13. U. Kesgin, “Genetic algorithm and artificial neural network for engine optimisation of efficiency and no emission”, Fuel, Vol. 83, No. 7, pp. 885–895, 2004.
  14. M. Caudill, "Neural Network Primer: Part I,” AI Expert, vol. 2, no. 12, pp. 46-52, 1989.
  15. M. T. Hagan, H. B. Demuth and M. H. Beale, “Neural Network Design, Boulder, Colorado: Campus Publishing Service”, Colorado University Bookstore, University of Colorado at Boulder, 2002, 736 pp.
  16. P. Orukpe, “Basics of Model Predictive Control”, Imperial College, London, 2005, 27 pp.
  17. M. H. Beale, M. T. Hagan and H. B. Demuth, “Neural Network Toolbox™ User’s Guide”, R2011b ed., Natick, MA: MathWorks, 2011, 404 pp.
  18. K. S. Narendra and S. Mukhopadhyay, "Adaptive Control Using Neural Networks and Approximate Models," IEEE Transactions on Neural Networks, vol. 8, no. 3, pp. 475-485, 1997.
  19. [19]M. Araki, "PID Control," in Control, Systems, Robotics, and Automation, vol. 2, Luiz Carlos da Silva, 1984.
  20. Mohd S. Saad, Hishamuddin Jamaluddin, Intan Z. M. Darus, "Pid Controller Tuning Using Evolutionary Algorithms," School Of Manufacturing Engineering, University Malaysia Perlis, Vol. 7, Issue 4, October 2012.
  21. "Wikimedia Commons 2," 2013. [Online]. Available: http://commons.wikimedia.org. [Accessed 2013].
  22. Salim, H. Kh. F. Sultan, R. Jawad, "Comparison between PID and Artificial Neural Networks to Control of Boiler for Steam Power Plant", Sumy State University, 2019. DOI:10.21272/jes.2019.6(1).e2