Transient Stability Assessment of Power System Based on Support Vector Machine

DOI

10.2991/iske.2007.143How to use a DOI?

Keywords

Transient stability assessment, Support vector machines, Single machine attributes.

Abstract

Machine learning methods are promising tools to transient stability assessment (TSA) of power system. Support vector machine (SVM) is used to assess the transient stability of power system after faults occur on transmission lines. Single machine attributes were studied as inputs of the SVM classifier. Experimental results in IEEE 50-generator test system showed that, attributes of single machine with small inertia coefficient are effective in TSA, and the SVM classifier with RBF kernel using these single machine attributes achieved satisfying classification accuracy.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shengyong Ye
AU  - Yongkang Zheng
AU  - Qingquan Qian
PY  - 2007/10
DA  - 2007/10
TI  - Transient Stability Assessment of Power System Based on Support Vector Machine
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 837
EP  - 841
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.143
DO  - 10.2991/iske.2007.143
ID  - Ye2007/10
ER  -