scholarly journals Nonlinear model reduction of dynamical power grid models using quadratization and balanced truncation

2020 ◽  
Vol 68 (12) ◽  
pp. 1022-1034
Author(s):  
Tobias K. S. Ritschel ◽  
Frances Weiß ◽  
Manuel Baumann ◽  
Sara Grundel

AbstractIn this work, we present a nonlinear model reduction approach for reducing two commonly used nonlinear dynamical models of power grids: the effective network (EN) model and the synchronous motor (SM) model. Such models are essential in real-time security assessments of power grids. However, as power grids are often large-scale, it is necessary to reduce the models in order to utilize them in real-time. We reformulate the nonlinear power grid models as quadratic systems and reduce them using balanced truncation based on approximations of the reachability and observability Gramians. Finally, we present examples involving numerical simulation of reduced EN and SM models of the IEEE 57 bus and IEEE 118 bus systems.

Automatica ◽  
2021 ◽  
Vol 127 ◽  
pp. 109534
Author(s):  
Yu Kawano ◽  
Jacquelien M.A. Scherpen

2021 ◽  
Vol 40 (4) ◽  
pp. 1-15
Author(s):  
Siyuan Shen ◽  
Yin Yang ◽  
Tianjia Shao ◽  
He Wang ◽  
Chenfanfu Jiang ◽  
...  

2008 ◽  
Vol 17 (03) ◽  
pp. 439-446
Author(s):  
HAOHANG SU ◽  
YIMEN ZHANG ◽  
YUMING ZHANG ◽  
JINCAI MAN

An improved method is proposed based on compressed and Krylov-subspace iterative approaches to perform efficient static and transient simulations for large-scale power grid circuits. It is implemented with CG and BiCGStab algorithms and an excellent result has been obtained. Extensive experimental results on large-scale power grid circuits show that the present method is over 200 times faster than SPICE3 and around 10–20 times faster than ICCG method in transient simulations. Furthermore, the presented algorithm saves the memory usage over 95% of SPICE3 and 75% of ICCG method, respectively while the accuracy is not compromised.


2001 ◽  
Author(s):  
Zeyu Liu ◽  
John Wagner

Abstract The mathematical modeling of dynamic systems is an important task in the design, analysis, and implementation of advanced automotive control systems. Although most vehicle control algorithms tend to use model-free calibration architectures, a need exists to migrate to model-based control algorithms which offer greater operating performance. However, in many instances, the analytical descriptions are too complex for real-time powertrain and chassis model-based control algorithms. Therefore, model reduction strategies may be applied to transform the original model into a simplified lower-order form while preserving the dynamic characteristics of the original high-order system. In this paper, an empirical gramian balanced nonlinear model reduction strategy is examined for the simplification process of dynamic system descriptions. The empirical gramians may be computed using either experimental or simulation data. These gramians are then balanced and unimportant system dynamics truncated. For comparison purposes, a Taylor Series linearization will also be introduced to linearize the original nonlinear system about an equilibrium operating point and then a balanced realization linear reduction strategy will be applied. To demonstrate the functionality of each model reduction strategy, two nonlinear dynamic system models are investigated and respective transient performances compared.


Author(s):  
Nicolas Faedo ◽  
Francisco Javier Dores Piuma ◽  
Giuseppe Giorgi ◽  
Giovanni Bracco ◽  
John V. Ringwood ◽  
...  

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