Reduced Model Based Feedback Stabilization of Large-scale Sparse Power System Model

Abstract

Linearizing a power system model around the equilibrium point we may obtain unstable large-scale sparse differential-algebraic equations (DAEs) with index 1 form. Riccati-based feedback stabilization of such large-scale unstable system is a challenging task. This paper shows that the Riccati-based feedback stabilization matrix for the original unstable system can be computed efficiently from the reduced order state space system. For this purpose we apply the balanced truncation (BT) to the large-scale unstable index 1 DAEs for reduced-order state space model. To implement the BT, we efficiently solve two Lyapunov equations with respect to the Bernoulli stabilized system. The efficiency of the proposed technique is tested by applying to a data set of Brazilian power system model.