Simplifying Large-Scale Power System Models for System Level Performance Predictions on SPICE

1992 ◽  
Author(s):  
Robert J. Spier ◽  
Mark E. Liffring
Keyword(s):  
Power System ◽  
Large Scale ◽  
System Level ◽  
System Models ◽  
Performance Predictions ◽  
Level Performance

Identifying Power Flow Control Infeasibilities in Large-Scale Power System Models

2009 ◽  
Vol 24 (1) ◽  
pp. 86-95 ◽  
Author(s):  
JoÃo A. Passos Filho ◽  
Nelson Martins ◽  
Djalma M. Falcao
Keyword(s):  
Power System ◽  
Flow Control ◽  
Power Flow ◽  
Large Scale ◽  
Power Flow Control ◽  
System Models

Fourier-series approach to model order reduction and controller interaction analysis of large-scale power system models

2018 ◽  
Vol 12 (6) ◽  
pp. 1247-1255 ◽  
Author(s):  
Carlos Morales Rergis ◽  
Ramón Jiménez Betancourt ◽  
Emilio Barocio Espejo ◽  
Arturo Román Messina
Keyword(s):  
Fourier Series ◽  
Power System ◽  
Model Order Reduction ◽  
Large Scale ◽  
Interaction Analysis ◽  
Order Reduction ◽  
Model Order ◽  
System Models

scholarly journals Computationally Efficient Optimal Control for Unstable Power System Models

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Mahtab Uddin ◽  
M. Monir Uddin ◽  
Md. Abdul Hakim Khan
Keyword(s):  
Optimal Control ◽  
Power System ◽  
Large Scale ◽  
Krylov Subspace ◽  
Feedback Stabilization ◽  
Descriptor Systems ◽  
Low Rank ◽  
Computationally Efficient ◽  
System Models ◽  
Alternating Direction

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

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