Abstract
This paper presents an optimal tracking and robust controller for a variable-speed wind turbine (VSWT). The main objective of the controller is to optimize the energy captured from the wind at below rated power, and minimize the mechanical stress in the system. In order to guarantee the wind power capture optimization without any chattering behavior, this study proposes to combine the H∞ control with particle swarm optimization (PSO) algorithm. The PSO technique with efficient global search is used to optimize the H∞ controller parameters simultaneously to control the system trajectories, which determines the system performance. The stability of the system using this controller is analyzed by Lyapunov theory. In present work, the simulation results of the proposed method (PSO-H∞) are compared with the conventional sliding mode control (SMC). The comparison results reveal that the proposed controller is more effective in reducing the tracking error and chattering.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdeddaim, S., & Betka, A. (2013). Optimal tracking and robust power control of the DFIG wind turbine. International Journal of Electrical Power and Energy Systems, 49, 234–242.
Asl, H. J., & Yoon, J. (2016). Power capture optimization of variable-speed wind turbines using an output feedback controller. Renewable Energy, 86, 517–525.
Boufounas, E., Boumhidi, J., Farhane, N., & Boumhidi, I. (2013). Neural network sliding mode controller for a variable speed wind turbine. Control and Intelligent Systems, 41, 251–258.
Boukhezzar, B., & Siguerdidjane, H. (2010). Comparison between linear and nonlinear control strategies for variable speed wind turbines. Control Engineering Practice, 18, 1357–1368.
Herbert, G. M. J., Iniyan, S., Sreevalsan, E., & Rajapandian, S. (2007). A review of wind energy technologies. Renewable and Sustainable Energy Reviews, 11, 1117–1145.
Jena, D., & Rajendran, S. (2015). A review of estimation of effective wind speed based on control of wind turbines. Renewable and Sustainable Energy Reviews, 43, 1046–1052.
Jiang, H., Wang, J., Wu, J., & Geng, W. (2017). Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions. Renewable and Sustainable Energy Reviews, 69, 1199–1217.
JinXing, C. (2013). Support vector regression based on optimal training subset and adaptive particle swarm optimization algorithm. Applied Soft Computing, 13, 3473–3481.
Kassem, A. M., Hasaneen, K. M., & Yousef, A. M. (2013). Dynamic modeling and robust power control of DFIG driven by wind turbine at finite grid. Electric Power Systems Research, 44, 375–382.
Nguang, S. K., & Shi, P. (2003). H∞ fuzzy output feedback control design for nonlinear systems: An LMI approach. IEEE Transactions on Fuzzy Systems, 11, 331–340.
Ofualagba, G., & Ubeku, E. U. (2008). Wind energy conversion system-wind turbine modelling. In IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century.
Rajendran, S., & Jena, D. (2015). Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine. Electrical Power and Energy Systems, 69, 421–429.
Slotine, J. J. (1984). Sliding controller design for non-linear systems. International Journal of Control, 40, 421–434.
Wang, H. P., Pintea, A., Christov, N., Borne, P., & Popescu, D. (2012). Modelling and recursive power control of horizontal variable speed wind turbines. Journal of Control Engineering and Applied Informatics, 14, 33–41.
Yanga, Y. S., & Wang, X. F. (2007). Adaptive H∞ tracking control for a class of uncertain nonlinear systems using radial-basis-function neural networks. Neurocomputing, 70, 932–941.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lamzouri, F.Ez., Boufounas, EM., Amrani, A.E. (2019). Optimal H∞ Control for a Variable-Speed Wind Turbine Using PSO Evolutionary Algorithm. In: El Hani, S., Essaaidi, M. (eds) Recent Advances in Electrical and Information Technologies for Sustainable Development. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-05276-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-05276-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05275-1
Online ISBN: 978-3-030-05276-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)