Robust fault tolerant control of DFIG wind energy systems with unknown inputs

被引:32
作者
Kamal, E. [1 ]
Aitouche, A. [2 ]
机构
[1] Lille Univ Nord France, LAGIS, PolytechLille, F-59655 Villeneuve Dascq, France
[2] Lille Univ Nord France, LAGIS Lab, HEI, Lille, France
关键词
DFIG; FTC; Parameters uncertainties; Sensor faults; TS fuzzy model; Unknown inputs; H-INFINITY CONTROL; POWER-GENERATION SYSTEM; FED INDUCTION GENERATOR; NONLINEAR-SYSTEMS; LMI TECHNIQUES; FUZZY CONTROL; DESIGN; TURBINE; SIMULATION; WECS;
D O I
10.1016/j.renene.2012.10.024
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper proposes a Fuzzy Dedicated Observers (FDOS) method using a Nonlinear Unknown Input Fuzzy Observer (UIFO) with a Fuzzy Scheduler Fault Tolerant Control (FSFTC) algorithm for fuzzy Takagi-Sugeno (TS) systems subject to sensor faults, parametric uncertainties, and time varying unknown inputs. FDOS provide residuals for detection and isolation of sensor faults which can affect a TS model. The TS fuzzy model is adopted for fuzzy modeling of the uncertain nonlinear system and establishing fuzzy state observers. The concept of Parallel Distributed Compensation (PDC) is employed to design FSFTC and fuzzy observers from the TS fuzzy models. TS fuzzy systems are classified into three families based on the input matrices and a FSFTC synthesis procedure is given for each family. In each family, sufficient conditions are derived for robust stabilization, in the sense of Taylor series stability and Lyapunov method, for the TS fuzzy system with parametric uncertainties, sensor faults, and unknown inputs. The sufficient conditions are formulated in the format of Linear Matrix Inequalities (LMIs). The effectiveness of the proposed controller design methodology is finally demonstrated through a wind energy system with Doubly Fed Induction Generators (DFIG) to illustrate the effectiveness of the proposed method. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2 / 15
页数:14
相关论文
共 45 条
[31]   Multisensor switching control strategy with fault tolerance guarantees [J].
Seron, Maria M. ;
Zhuo, Xiang W. ;
De Dona, Jose A. ;
Martinez, John J. .
AUTOMATICA, 2008, 44 (01) :88-97
[32]   Robust LMI-Based Control of Wind Turbines with Parametric Uncertainties [J].
Sloth, Christoffer ;
Esbensen, Thomas ;
Niss, Michael O. K. ;
Stoustrup, Jakob ;
Odgaard, Peter F. .
2009 IEEE CONTROL APPLICATIONS CCA & INTELLIGENT CONTROL (ISIC), VOLS 1-3, 2009, :776-+
[33]   Multi-sensor optimal information fusion Kalman filters with applications [J].
Sun, SL .
AEROSPACE SCIENCE AND TECHNOLOGY, 2004, 8 (01) :57-62
[34]   Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: Quadratic stabilizability, H-infinity control theory, and linear matrix inequalities [J].
Tanaka, K ;
Ikeda, T ;
Wang, HO .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1996, 4 (01) :1-13
[35]   Observer-based robust fuzzy control of nonlinear systems with parametric uncertainties [J].
Tong, SC ;
Li, HX .
FUZZY SETS AND SYSTEMS, 2002, 131 (02) :165-184
[36]   Dynamic simulation of hybrid wind-diesel power generation system with superconducting magnetic energy storage [J].
Tripathy, SC .
ENERGY CONVERSION AND MANAGEMENT, 1997, 38 (09) :919-930
[37]  
Uhlen K, 1997, IEEE T ENERG CONVERS, V9, P701
[38]  
Van der Hooft EL, 2007, RENEW ENERG, V32, P1273
[39]  
Wang YQ, 2008, INT J CONTROL AUTOM, V6, P339
[40]  
Wei X., 2008, P 17 WORLD C INT FED, P3222