Application of the XGBOOST on the Assessment of Transient Stability of Power System
Authors
Sen Shen, Qunying Liu, Xinchen Tao, Shaojian Ni
Corresponding Author
Qunying Liu
Available Online March 2019.
- 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/).
Cite this article
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 -