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Applied unsupervised learning in model reduction of linear dynamic systems

โœ Scribed by D. Kukolj; D. Popovic; M. Borota

Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
581 KB
Volume
33
Category
Article
ISSN
0898-1221

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โœฆ Synopsis

In this paper, a method of unsupervised learning is proposed for the purposes of reducing large-scale complex dynamic systems. Reduction of a system is carried out through the division of state variables into groups and through the selection of the characteristic representatives of each group. The proposed methodology is tested on an electric power system. The obtained results indicate that the model of the dynamic system can be significantly simplified while retaining its basic dynamic characteristics.

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## In this paper, a model reduction technique to remedy the singularity of reduced- order models is proposed. The approach adopted is based on the least-square fitting of timemoments of the system. The proposed method is also auailable to stabilize unstable reduced models. This method is superior t