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Over last 15 years the problem of rotor angle stability of electric power systems (EPS) has received a great attention. A fundamental problem in the design of feedback controllers for EPS is that of robust stabilizing both rotor angle and voltage magnitude, and achieving a specified transient behavior. Robustness implies operation with adequate stability margins and admissible performance level in spite of plant parameters variations and in the presence of external disturbances. The EPS have nonlinearities and are subject to variations as a result of a change in the systems loading and/or configuration. Then, the EPS are modeled as complex large-scale nonlinear systems and the generators may be interconnected over several kilometers in very large power systems. Thus, the controller design is a challenging problem. A complete centralized control scheme could be difficult to implement in EPS, due to the reliability and distortion in information transfer. On the other hand, accurate prediction of system responses and system robustness to disturbances under different operation conditions are guarantee by robust decentralized control schemes. The decentralized controllers are locally implemented, so do not need system information communication among subsystems. In each subsystem, the effects of the other subsystems are considered as a disturbance. To design decentralized control schemes for EPS, a controller is designed for each generator connected to the system. The control schemes of power systems are commonly based on reduced order linearized model and classical control algorithms that ensure asymptotic stability of the equilibrium point under small perturbations (Anderson & Fouad, 1994, DeMello & Concordia, 1969). Improvements on linear techniques have been analyzed in (Wang et al., 1998, Djukanovic et at., 1998a, Djukanovic et al., 1998b). Nevertheless, these controllers have been designed by using linear models. To analyze the EPS entire operation region, nonlinear control design techniques are more appropriate. Various nonlinear techniques have been implemented, e.g., control based on direct Lyapunov method (Machowsky et al., 1999), feedback linearization (FL) technique (Akhkrif, et al, 1999, Wu & Malik, 2006, ) including backstepping (Jung et al., 2005 King et al., 1994), intelligent neural networks (Venayagamoorthy et al., 2003, Mohagheghi et al., 2007), fuzzy logic (Yousef & Mohamed, 2004) and normal form analysis (Kshatriya, et al., 2005, Liu et al., 2006). |
