Globally stabilizing robust adaptive voltage and speed regulator for large-scale power systems

The paper presents a decentralized robust adaptive multivariable controller, with a guarantee of global stability, for large-scale uncertain power systems. The design is based on the reformulation of the conventional multi-machine power systems model into a new one with generators terminal voltages as state variables. This model while suitable for the application of modern control method, introduces a difficulty with regards to current design methods for large-scale systems. The interactions between generators treated as perturbations do not meet the common assumption on matching perturbations. A new robust adaptive method for a certain class of large-scale systems is therefore introduced. It does not require the matching condition, and can be seen as an extension of Shi and Sing's design method [1]. The main advantage of the proposed controller is that the asymptotic stability of the entire power system involves the mechanical and the electrical variables contrary to most methods encountered in the literature. Our solution consists of a nonlinear excitation and a turbine valve input canceling some nonlinearities of the model. An auxiliary control with linear and nonlinear components is used to stabilize the system. It compensates the unknown parameters in the model by continuously updating both the gain of the nonlinear components and parameters of the excitation. The adaptation algorithm for the gain uses sigma-modification whereas the one for the excitation parameters involves the projection method. Both algorithms are robust and prevent estimation drift. The computation of the linear part's matrix-gain involves the resolution of an algebraic Riccati equation and helps to solve the perturbation mismatching problem. A power system with nonlinear and dynamic loads is used to assess the proposed controller performance compared to the conventional AVR/PSS/GOVERNOR. The results show that transient performances are considerably improved after a severe contingency.