Research on Power System Transient Stability Assessment Based on Statistical Learning Theory

DOI

10.2991/nceece-15.2016.103How to use a DOI?

Keywords

Transient stability assessment; Bagging; Support vector machine; Data set reconstruction

Abstract

This paper presents a method of model construction for the power system transient stability assessment based on statistical learning theory integrated with the bagging and the approximate reasoning. Support vector machines operate on the principle of structure risk minimization. This paper takes full advantage of its ability to solve the problem with small sample, nonlinear and high dimension. Hence better generalization ability is guaranteed. The multi-class identification for power system transient stability assessment is solved by the data set reconstruction. The assessment model uses the data set regulation, bagging and approximate reasoning to improve the training speed, the accuracy and stability of the estimation result. The IEEE 39-Bus test system is employed to demonstrate the validity of the proposed approach.

Copyright

© 2016, 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  - Wanyu Xu
PY  - 2015/12
DA  - 2015/12
TI  - Research on Power System Transient Stability Assessment Based on Statistical Learning Theory
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 552
EP  - 557
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.103
DO  - 10.2991/nceece-15.2016.103
ID  - Xu2015/12
ER  -