Intelligent Control of Wind Energy Conversion Systems

Wind turbines form complex nonlinear mechanical systems exposed to uncontrolled wind profiles. This makes turbine controller design a challenging task (Athanasius & Zhu, 2009). As such, control of wind energy conversion systems (WECS) is difficult due to the lack of systematic methods to identify requisite robust and sufficiently stable conditions, to guarantee performance. The problem becomes more complex when plant parameters become uncertain. Fuzzy control is one of the techniques which deal with this class of systems. The stability of fuzzy systems formed by a fuzzy plant model and a fuzzy controller has recently been investigated. Various stability conditions have been obtained through the employment of Lyapunov stability theory (Schegner & La Seta, 2004; Tripathy, 1997), fuzzy gainscheduling controllers (Billy, 2011a, 2011b; Iescher et al., 2005), switching controllers (Lescher et al., 2006) and by other methods (Chen & Hu, 2003; Kamal et al., 2008; Muljadi & Edward, 2002). Nonlinear controllers (Boukhezzar & Siguerdidjane, 2009; Chedid et al., 2000; Hee-Sang et al., 2008) have also been proposed for the control of WECS represented by fuzzy models. In addition to stability, robustness is also an important requirement to be considered in the study of uncertain nonlinear WECS control systems. Robustness in fuzzy-model-based control has been extensively studied, such as stability robustness versus modelling errors and other various control techniques for Takagi–Sugeno (TS) fuzzy models (Kamal et al., 2010; Uhlen et al., 1994). In order to overcome nonlinearity and uncertainties, various schemes have been developed in the past two decades (Battista & Mantz, 2004; Boukhezzar & Siguerdidjane, 2010; Prats et al., 2000; Sloth et al., 2009). (Battista & Mantz, 2004) addressing problems of output power regulation in fixed-pitch variable-speed wind energy conversion systems with parameter uncertainties. The design of LMI-based robust controllers to control variable-speed, variable-pitch wind turbines, while taking into account parametric uncertainties in the aerodynamic model has been presented (Sloth et al., 2009). (Boukhezzar & Siguerdidjane, 2010) comparing several linear and nonlinear control strategies, with the aim of improving wind energy conversion systems. (Prats et al., 2000) have also investigated fuzzy logic controls to reduce uncertainties faced by classical control methods. Furthermore, although the problem of control in the maximization of power generation in variable-speed wind energy conversion systems (VS-WECS) has been greatly studied, such