Computationally Efficient Optimal Control for Unstable Power System Models

In this article, the focus is mainly on gaining optimal control for unstable power system models and stabilizing them through Riccati-based feedback stabilization process with sparsity-preserving techniques. We are to find solution of Continuous-time Algebraic Riccati Equations (CAREs) governed from derived Brazilian Inter-Connected Power System (BIPS) models, which large-scale sparse index-1 descriptor systems. propose projection-based Rational Krylov Subspace Method (RKSM) iterative computation CAREs. The novelties RKSM computations implementation time-convenient adaptive shift parameters. modify Low-Rank Cholesky-Factor integrated Alternating Direction Implicit (LRCF-ADI) technique-based nested Kleinman–Newton (KN) method a form adjust solve desired compare results achieved by that using RKSM. applicability adaptability proposed techniques justified numerically MATLAB simulations. Transient behaviors target investigated comparative analysis tabular graphical approaches.