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This paper proposes a novel decentralized adaptive excitation control scheme to globally stabilize large-scale power systems and enhance the transient stability. Two smooth functions are introduced to counteract the effect of unknown time-varying interactions, then a completely decentralized adaptive controller is constructed on the basis of the adaptive backstepping approach. The proposed controller utilizes only local measurements and has no requirement for the bounds of interconnections in the power system. All signals of the overall closed-loop large-scale power system are proved to be globally uniformly bounded. The proposed control scheme is tested on a two-area benchmark power system in the face of a symmetrical three-phase short circuit fault. Simulation results have showed better transient performances in comparison with existing controllers. [ABSTRACT FROM AUTHOR]

Abstract: Designing a state estimator for a linear state-space model requires knowledge of the characteristics of the disturbances entering the states and the measurements. In [Odelson, B. J., Rajamani, M. R., & Rawlings, J. B. (2006). A new autocovariance least squares method for estimating noise covariances. Automatica, 42(2), 303–308], the correlations between the innovations data were used to form a least-squares problem to determine the covariances for the disturbances. In this paper we present new and simpler necessary and sufficient conditions for the uniqueness of the covariance estimates. We also formulate the optimal weighting to be used in the least-squares objective in the covariance estimation problem to ensure minimum variance in the estimates. A modification to the above technique is then presented to estimate the number of independent stochastic disturbances affecting the states. This minimum number of disturbances is usually unknown and must be determined from data. A semidefinite optimization problem is solved to estimate the number of independent disturbances entering the system and their covariances. [Copyright &y& Elsevier]