Abstract—We describe a method for simultaneously identifying
and reducing dynamic power systems models in the form of
differential-algebraic equations. Often, these models are large
and complex, containing more parameters than can be identified
from the available system measurements. We demonstrate our
method on transient stability models, using the IEEE 14-bus test
system. Our approach uses techniques of information geometry to
remove unidentifiable parameters from the model. We examine
the case of a networked system with 58 parameters using full
observations throughout the network. We show that greater
reduction can be achieved when only partial observations are
available, including reduction of the network itself.
Index Terms—Parameter Estimation, Reduced Order Systems,
System Identification.
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Simultaneous Global Identification of Dynamic and Network Parameters in Transient Stability Studies
The paper describes a global identification procedure for dynamic power system models in the form of differential and algebraic equations. Power system models have a number of features that makes their improvement challenging - they are multi-level, multi-user and multi-physics. Not surprisingly, they are nonlinear and time varying, both in terms of states (memory variables) and parameters, and discrete structures, such as graphs, are strongly blended with continuous dynamics, resulting in network dynamics. The transient stability models are used as a prototypical example. Our method is based on information geometry, and uses advances in computational differential geometry to characterize high-dimensional manifolds in the space of measurements. In the case of network parameters, a comparison is presented with circuit-theoretic techniques. The results are illustrated on the case of IEEE 14-bus test system with 58 parameters in our realization.
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- NSF-PAR ID:
- 10101133
- Date Published:
- Journal Name:
- 2018 IEEE Power & Energy Society General Meeting (PESGM)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PESGM.2018.8586586
