Application of the XGBOOST on the Assessment of Transient Stability of Power System

DOI

10.2991/ice2me-19.2019.2How to use a DOI?

Keywords

machine learning; power system; XGBOOST algorithm; transient stability

Abstract

In the context of big data, machine learning plays an important role in many fields. With the increasing scale of power system and capacity of power grid, it becomes more and more complicated to accurately evaluate the transient stability of power system. In this paper, a power system transient stability assessment method based on XGBOOST algorithm is proposed. The XGBOOST algorithm is introduced to train the decision tree model and evaluate the transient stability of power system by converting the simulated power system operating data into the characteristic variables of power system. The results show that the training model of the algorithm can solve this kind of problem accurately and quickly.

Copyright

© 2019, 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  - Sen Shen
AU  - Qunying Liu
AU  - Xinchen Tao
AU  - Shaojian Ni
PY  - 2019/03
DA  - 2019/03
TI  - Application of the XGBOOST on the Assessment of Transient Stability of Power System
BT  - Proceedings of the 2019 International Conference on Electronical, Mechanical and Materials Engineering (ICE2ME 2019)
PB  - Atlantis Press
SP  - 6
EP  - 10
SN  - 2352-5401
UR  - https://doi.org/10.2991/ice2me-19.2019.2
DO  - 10.2991/ice2me-19.2019.2
ID  - Shen2019/03
ER  -