Power System Transient Stability Assessment Based on PCA and Support Vector Machine

DOI

10.2991/meees-18.2018.63How to use a DOI?

Keywords

transient stability; principal component analysis; feature reduction dimension; support vector machine.

Abstract

Combining the synchronized phasor measurement unit (PMU), a power system transient stability assessment method based on principal component analysis and support vector machine is proposed. Firstly, the PMU data is obtained through simulation and the original feature set is constructed. Then the principal feature analysis (PCA) is used to compress the original feature set and reduce the feature size. The obtained main components contain sufficient information of the initial sample and are used as input to Support Vector Machine (SVM) to train and test the sample. The classification effect of New England 10-machine 39-bus system is analyzed. The results show that the proposed model is accurate and effective for power system transient stability analysis.

Copyright

© 2018, 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  - Jingxuan Tang
AU  - Huibin Sui
PY  - 2018/05
DA  - 2018/05
TI  - Power System Transient Stability Assessment Based on PCA and Support Vector Machine
BT  - Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering & Science (MEEES 2018)
PB  - Atlantis Press
SP  - 361
EP  - 365
SN  - 2352-5401
UR  - https://doi.org/10.2991/meees-18.2018.63
DO  - 10.2991/meees-18.2018.63
ID  - Tang2018/05
ER  -