A probabilistic approach to model-based adaptive control for damping of interarea oscillations

This work demonstrates an adaptive control strategy for damping interarea oscillations in a large power system model, employing a probabilistic approach to model-based control. The scheme accounts for the uncertain nature of the post-disturbance dynamics of the system for computing the control moves. A number of linearized plant models are considered to represent the system dynamics following the probable contingencies. Conventional observer-based state feedback controllers are designed to achieve the desired performance for each of these models. This strategy has been used to design and test a damping controller for a thyristor controlled series compensator (TCSC) device installed in a 16-machine, 68-bus system model. The control scheme worked satisfactorily following possible disturbances without any prior knowledge about the specific post-disturbance dynamics.

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