Transient Stability Assessment of Power System Based on Support Vector Machine
- https://doi.org/10.2991/iske.2007.143How to use a DOI?
- Transient stability assessment, Support vector machines, Single machine attributes.
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/iske.2007.143 ID - Ye2007/10 ER -