Modeling and Lyapunov-Designed based on Adaptive Gain Sliding Mode Control for Wind Turbines

Abstract

In this paper, modeling and the Lyapunov-designed control approach are studied for the Wind Energy Conversion Systems(WECS). The objective of this study is to ensure the maximum energy production of a WECS while reducing the mechanicalstress on the shafts (turbine and generator). Furthermore, the proposed control strategy aims to optimize the wind energycaptured by the wind turbine operating under rating wind speed, using an Adaptive Gain Sliding Mode Control (AG-SMC). Theadaptation for the sliding gain and the torque estimation are carried out using the sliding surface as an improved solution thathandles the conventional sliding mode control. Furthermore, the resultant WECS control policy is relatively simple, meaningthe online computational cost and time are considerably reduced. Time-domain simulation studies are performed to discussthe effectiveness of the proposed control strategy.

References

[1] M. H. Baloch, J. Wang, G. S Kaloi, Modeling and controller design for wind energy conversion system based on a cage induction generator using turbulence speed, Journal of Power Technologies 43 (2016).
[2] T. Ackermann, Wind power in power systems, in JG. Slootweg, H. Polinder, W L. Kling, Reduced Order Modeling of Wind Turbines, New York, NY, USA: Wiley (2005) 555–585.
[3] J.G. Slootweg, Reduced order modeling of wind turbines, Wind Power in Power Systems, Wiley (2005) 555–585.
[4] K. D. E. Kerrouche, A. Mezouar, L. Boumediene: The Suitable Power Control of Wind Energy Conversion based Doubly Fed Induction Generator, IJCA 87 (2014) 35 – 44.
[5] O. Publishing, I. E. Agency, World energy out look. Paris: Organisation for Economic Cooperation and Development; 2010.
[6] E. W. E. Association, Wind directions-the European wind industry magazine 1 (1) (2012).
[7] N. Ahmad, T Hiyama, Maximum Power Point Tracking Based Optimal Control Wind energy Conversion System, IEEE international symposium, in : Conf. Advances in computing Control and Tele Tech, 2010.
[8] K. Ghedamsi, D. Aouzellag, E.M. Berkouk, Control of wind generator associated to flywheel energy storage system, Renewable Energy 33 (9) (2008) 2145-2156.
[9] F. Amrane, A. Chaiba, S. Mekhilef, High performances of Grid-connected DFIG based on Direct Power Control with Fixed Switching Frequency via MPPT Strategy using MRAC and Neuro-Fuzzy Control, Journal of Power Technologies 96 (1) 27-39.
[10] V. Calderaro, V. Galdi, A. Piccolo, P. Siano, A fuzzy controller for maximum energy extraction from variable Speed wind power generation systems, Electric Power Systems Research 78 (2008) 1109–1118.
[11] V. Galdi , A. Piccolo , P. Siano, Exploiting maximum energy from variable speed wind power generation systems by using an adaptive Takagi–Sugeno–Kang fuzzy model, Energy Conversion and Management 50 (2) (2009) 413–421.
[12] F. Poitiers, T. Bouaouiche, M. Machmoum, Advanced control of a doubly-fed induction generator for wind energy conversion, Electric Power Systems Research 79 (2009) 1085–1096.
[13] A. Kerboua, M. Abid, Hybrid fuzzy sliding mode control of a doubly-fed induction generator speed in wind turbines, Journal of Power Technologies 95 (2) (2015) 126.
[14] A. Junyent-Ferre, O. Gomis-Bellmunt, A. Sumper, M. Sala, M. Mata, Modelling and control of the doubly fed induction generator wind turbine, Simulation Modelling Practice and Theory, 18 (2010) 1365–1381.
[15] M. Mohseni, M. A. S. Masoum, S. M. Islam, Low and high voltage ride-through of DFIG wind turbines using hybrid current controlled converters, Electric Power Systems Research 81 (2011) 1456–1465.
[16] K. D. E. Kerrouche, A. Mezouar, L. Boumediene, Kh. Belgacem, Modeling and Optimum Power Control based DFIG Wind Energy Conversion System, IREE 9 (1) (2014) 174–185.
[17] M. Stiebler, Wind Energy Systems for Electric Power Generation. Berlin Heidelberg: Verlag, Springer; 2008.
[18] I. Munteanu, A. I. Bratcu, N. A. Cutululis, E. Ceanga, Optimal control of wind energy systems towards a global approach. London: Verlag, Springer; 2008.
[19] A. Petersson, T. Thiringer, L. Harnefors, and T. Petru, Modeling and Experimental Verification of Grid Interaction of a DFIG Wind
Turbine, IEEE Trans. Energy Conversion 4 (4) (2005) 878–886.
[20] S. Abdeddaim, A. Betka, Optimal tracking and robust power control of the DFIG wind turbine, International Journal of Electrical Power & Energy Systems 49 (2013) 234–242.
[21] K. Kerrouche, A. Mezouar, L. Boumedien, A simple and efficient
maximized power control of DFIG variable speed wind turbine, IEEE 3rd International Conference Systems and Control (ICSC), 2013.
[22] V. I. Utkin, Sliding Modes in Optimization and Control. New York: Springer-Verlag, 1992.
[23] K. Ouaria, T. Rekioua, M. Ouhrouche, Real time simulation of
nonlinear generalized predictive control for wind energy conversion system with nonlinear observer, ISA Transactions 53 (2014) 76–84.
[24] A. Manjock, Design Codes FAST and ADAMS for Load Calculations of Onshore Wind Turbines, 2005, National Renewable Energy Laboratory (NREL): Golden, Colorado, USA, 2005.
[25] F. Plestan, Y. Shtessel, V. Bregeault, A. Poznyak, New methodologies for adaptive sliding mode control, IJC (9) 83 (2010) 1907-1919.
Published
2016-07-07
How to Cite
KERROUCHE, Kamel Djamel Eddine et al. Modeling and Lyapunov-Designed based on Adaptive Gain Sliding Mode Control for Wind Turbines. Journal of Power Technologies, [S.l.], v. 96, n. 2, p. 124--136, july 2016. ISSN 2083-4195. Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/847>. Date accessed: 29 july 2021.
Section
Renewable and Sustainable Energy

Keywords

Wind turbine, Maximum power point tracking, Proportional integral controller, Adaptive sliding mode control,

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.