Krylov Subspace and Balanced Truncation Methods for Power System Model Reduction

2013 ◽  
pp. 119-142 ◽  
Author(s):  
Shanshan Liu ◽  
Peter W. Sauer ◽  
Dimitrios Chaniotis ◽  
M. A. Pai
Keyword(s):  
Power System ◽  
Model Reduction ◽  
Krylov Subspace ◽  
System Model ◽  
Balanced Truncation ◽  
Power System Model

Nonlinear Dynamic Power System Model Reduction Analysis Using Balanced Empirical Gramian

2013 ◽  
Vol 448-453 ◽  
pp. 2368-2374 ◽  
Author(s):  
Hong Shan Zhao ◽  
Ning Xue ◽  
Ning Shi
Keyword(s):  
Nonlinear System ◽  
Power System ◽  
Model Reduction ◽  
Reduction Method ◽  
System Model ◽  
System Dynamic Model ◽  
Lower Dimension ◽  
Input And Output ◽  
System Input ◽  
Power System Model

This paper presents an empirical Gramian balanced reduction method which efficiently solves nonlinear power system model reduction problems. This method projects the nonlinear power system dynamic model to a lower dimension subspace and the reduced model can retain the original nonlinear system input and output dynamic behaviors. A four-generator nonlinear power system, combining different factors which form empirical controllable and observable Gramian matrices, is used to analyze how external excitations and outputs affect balanced empirical Gramian reduction. The case verifies the feasibility and effectiveness of balanced empirical Gramian reduction method.

Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

2014 ◽  
Vol 29 (5) ◽  
pp. 2049-2059 ◽  
Author(s):  
Shaobu Wang ◽  
Shuai Lu ◽  
Ning Zhou ◽  
Guang Lin ◽  
Marcelo Elizondo ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Power System ◽  
Model Reduction ◽  
System Model ◽  
Dynamic Feature ◽  
Power System Model

scholarly journals Nonlinear power system model reduction based on empirical Gramians

2017 ◽  
Vol 68 (6) ◽  
pp. 425-434 ◽  
Author(s):  
Hongshan Zhao ◽  
Xiaoming Lan ◽  
Hui Ren
Keyword(s):  
Power System ◽  
Model Reduction ◽  
Simulation Analysis ◽  
Original Model ◽  
System Model ◽  
Singular Matrix ◽  
Computation Complexity ◽  
Nonlinear Model Reduction ◽  
Power System Model ◽  
Reduction Approach

AbstractAn effective nonlinear model reduction approach, empirical Gramians balanced reduction approach, is studied, to reduce the computation complexity in nonlinear power system model application. The realization procedure is: firstly, computing the empirical controllable and observable Gramians matrices of nonlinear power system model, secondly, by these two matrices, computing the balance transformation matrix to obtain the balanced system model of the original model, then, computing the controllable and observable matrices of the balanced system to obtain the diagonal Hankel singular matrix. Finally, deciding the lower-order subspace to obtain the reduced power system model. A 15-machine power system model is taken as an example to perform the reduction simulation analysis.

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