El-mahjoub Boufounas, Jaouad Boumhidi, Nabil Farhane, and Ismail Boumhidi
[1] J.F. Manwell, J.G. McGowan, and A.L. Rogers, Wind energyexplained: Theory, design and applications (New York: JohnWiley & Sons, 2002). [2] G. Ofualagba and E.U. Ubeku, Wind energy conversion system– Wind turbine modelling, Power and Energy Society GeneralMeeting – Conversion and Delivery of Electrical Energy in the21st Century, IEEE, 2008, 1–8. [3] T. Burton, D. Sharpe, N. Jenkins, and E. Bossanyi, Windenergy handbook (New York: John Wiley & Sons, 2001). [4] S. Wang, S. Habibi, and R. Burton, The smooth sliding modecontroller and filter, Control and Intelligent Systems, 38(3),2010, 130–139. [5] V.I. Utkin, Sliding modes in control optimization (Germany,Berlin: Springer-Verlag, 1992). [6] J.J. Slotine, Sliding controller design for non-linear systems,International Journal of Control, 40(2), 1984, 421–434. [7] V. Utkin, J. Guldner, and J. Shi, Sliding mode control inelectromechanical system (London: Taylor & Francis, 1999). [8] P.F. Puleston and F. Valenciaga, Chattering reduction in ageometric sliding mode method. A robust low-chattering con-troller for an autonomous wind system, Control and IntelligentSystems, 37(1), 2009, 39–45. [9] H. Amimeur, D. Aouzellag, R. Abdessemed, and K. Ghedamsi,Sliding mode control of a dual-stator induction generatorfor wind energy conversion systems, International Journal ofElectrical Power and Energy Systems, 42(1), 2012, 60–70. [10] H.M. Hasanien and S.M. Muyeen, Speed control of grid-connected switched reluctance generator driven by variablespeed wind turbine using adaptive neural network controller,Electric Power Systems Research, 84(1), 2012, 206–213. [11] S.S. Ge, C.C. Hang, T.H. Lee, and T. Zhang, Stable adaptiveneural network control (Norwell, MA: Kluwer, 2001). [12] K. Hornik, M. Stinchombe, and H. White, Universal Approx-imation of an unknown mapping and its derivatives usingmultilayer feedforward networks, Neural Networks, 3(5), 1990,551–560. [13] A.S. Yilmaz and Z. ¨Ozer, Pitch angle control in wind turbinesabove the rated wind speed by multi-layer perceptron andradial basis function neural networks, Expert Systems withApplications, 36(6), 2009, 9767–9775. [14] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, Learninginternal representations by error propagation, in D.E. Rumel-hart, J.L. McClelland (eds.), Parallel distributed processing,Vol. 1 (Cambridge: MIT Press, 1986). [15] J.M. Rubio and L.T. Aguilar, Maximizing the performance ofvariable speed wind turbine with nonlinear output feedbackcontrol, Procedia Engineering, 35, 2012, 31–40. [16] L. Munteanu, A.L. Bratcu, N.-A. Cutululis, and E. Ceanga,Optimal control of wind energy systems, advances in industrialcontrol (London: Springer, 2008). [17] J.F. Adri`a, G.B. Oriol, S. Andreas, S. Marc, and M. Montserrat,Modeling and control of the doubly fed induction generatorwind turbine, Simulation Modelling Practice and Theory, 18(9),2010, 1365–1381. [18] Ph. Delarue, A. Bouscayrol, A. Tounzi, X. Guillaud, and G.Lancigu, Modelling, control and simulation of an overall windenergy conversion system, Renewable Energy, 28(8), 2003,1169–1185. [19] A. Gaillard, P. Poure, S. Saadate, and M. Machmoum, Variablespeed DFIG wind energy system for power generation andharmonic current mitigation, Renewable Energy, 34(6), 2009,1545–1553. [20] K.A Stol, Geometry and structural properties for the controlsadvanced research turbine (CART) from model tuning, Sub-contractor Report SR-500-32087, National Renewable EnergyLaboratory, Golden, CO, Sept. 2004.257
Important Links:
Go Back