Gramian-Based Reduction Method Applied to Large Sparse Power System Descriptor Models
By Francisco Damasceno Freitas, Joost Rommes, Nelson Martins. IEEE Transactions on Power Systems, 2008
187 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Numerical Analysis, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/4e62efba821180a722b2d0a2f52ed0ef3d5bece9
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In Electrical and Electronic Engineering
- Quanyuan Jiang
- Guangchao Geng
- Peter Benner
- Zexiang Zhu
- Andrija T. Sari
- Domenico Fabio Savo
- Jens Saak
- Uki
In Numerical Analysis
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- Peter Benner
- Quanyuan Jiang
- Guangchao Geng
- Zexiang Zhu
- Andrija T. Sari
- Domenico Fabio Savo
- Uki
In Statistical and Nonlinear Physics
- Jens Saak
- Peter Benner
- Guangchao Geng
- Zexiang Zhu
- Andrija T. Sari
- Domenico Fabio Savo
- Quanyuan Jiang
- Uki
Citations in the corpus
- Power System Dynamic Model Reduction Based on Extended Krylov Subspace Method (2016, methodology): “This tolerance depends on the characteristics of each problem, since the eigenvalues of the Gramians and related Hankel singular values may change with each system and its associated inputs and outputs [10].”
- Numerical solution of large and sparse continuous time algebraic matrix Riccati and Lyapunov equations: a state of the art survey (2013, background): “For simple index-1 systems the authors of [22] show that the index reduction can be performed implicitly.”
- Dynamic model reduction: An overview of available techniques with application to power systems (2012, methodology): “1 Balanced Truncation (BT) BT consists of three main steps [141].”