Reduced-order Models of Power Systems based on Controllability and Observability

Abstract

Reduced-order models for dynamic control of power systems are formulated using a modal analysis technique, based on the notion of controllability and observability. In this technique, an input/ouutput index is used to identify and rank the strongly controllable and observable modes of the system given a particular input/output pair. The system state variables that are strongly related to the retained modes are then determined by analysis of a participation factor martrix. Davison's method of reducing linear systems is then applied to formulate the desired reduced order dynamic equivalent. This technique of forming dynamic equivalents is investigated on a single machine infinite bus system. Several reduced order model equivalents are formed and evaluated on their performance and accuracy.