Transient stability assessment of power system in combination with update mechanism

Authors

Songkai LIU, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
Xiao GONG, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
Chao YANG, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, ChinaFollow
Longcheng LIU, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
Yanzhang LI, Wuhan Power Supply Company, State Grid Hubei Electric Power Co., Ltd., Wuhan 430013, China
Lei ZHANG, College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, Yichang 443002, China
Yating ZHANG, Taiyuan Power Supply Company, State Grid Shanxi Electric Power Co., Ltd., Taiyuan 030000, China

Keywords

data-driven; machine learning; transient stability assessment; electric power system; model update

Abstract

Power system is a time-varying complex system. In recent years, data-driven machine learning method has been widely used in the field of transient stability assessment of power system. However, when the power system is subjected to a large disturbance and the working condition changes, the machine learning model needs to be trained according to the new operating data. Thus, it is difficult to timely respond to transient stability assessment of the system under the new topology structure. To solve this problem, a model update mechanism is proposed in this paper, which updates the model according to different conditions. In addition, an oblique double random forest with multisurface proximal support vector machine (MPSVM) (MPDRF) model is introduced as a classifier to assess the stable state of power system. The simulation test on the New England 10-machine 39-bus system verifies the effectiveness of the proposed method. The results show that the method combined with update mechanism has high assessment performance, compared with the traditional method.

DOI

10.19781/j.issn.1673-9140.2025.02.001

First Page

1

Last Page

9

Recommended Citation

LIU, Songkai; GONG, Xiao; YANG, Chao; LIU, Longcheng; LI, Yanzhang; ZHANG, Lei; and ZHANG, Yating (2025) "Transient stability assessment of power system in combination with update mechanism," Journal of Electric Power Science and Technology: Vol. 40: Iss. 2, Article 1.
DOI: 10.19781/j.issn.1673-9140.2025.02.001
Available at: https://jepst.researchcommons.org/journal/vol40/iss2/1