A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm

A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm

Aiming at the difficulty of unstable pattern recognition after power system fault, a novel identification framework for transient instability mode identification based on knowledge discovery by accuracy maximization (KODAMA) and extreme gradient boosting (XGBoost) algorithm is proposed. In this meth...

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Bibliographic Details
Main Authors: Neng Zhang, Huimin Qian, Yuchao He, Lirong Li, Chaoyun Sun
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Transient stability
KODAMA algorithm
XGBoost algorithm
machine learning
Online Access:https://ieeexplore.ieee.org/document/9592755/

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