Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning

Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning

Transient stability assessment (TSA) is of great importance in power system operation and control. One of the usual tasks in TSA is to estimate the critical clearing time (CCT) of a given fault under the given network topology and pre-fault power flow. Data-driven methods try to obtain models descri...

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Bibliographic Details
Main Authors: Xianzhuang Liu, Xiaohua Zhang, Lei Chen, Fei Xu, Changyou Feng
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:Journal of Modern Power Systems and Clean Energy
Subjects:
Transient stability assessment
critical clearing time
network topology change
Mahalanobis kernel regression
ensemble learning
data-driven
Online Access:https://ieeexplore.ieee.org/document/9275596/

Internet

https://ieeexplore.ieee.org/document/9275596/

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