Power System Dominant Instability Mode Identification Based on Improved Multi-Head Attention and CNN-Reference-Cited by-同舟云学术

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Power System Dominant Instability Mode Identification Based on Improved Multi-Head Attention and CNN

Published:2024-05-10 Issue: Volume:47 Page:2218-2225
ISSN:
Container-title:2024 IEEE 7th International Electrical and Energy Conference (CIEEC)
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Author:

Wang Changjiang¹,Zhang Qianlong¹,Zhou Qinyong²,Zhang Jian²,Sun Yujiao²,Wei Mingkui³,Zhong Shicheng¹

Affiliation:

1. Northeast Electric Power University,Department of Electrical Engineering,Jilin,China

2. China Electric Power Research Institute,State Key Laboratory of Power Grid Safety and Energy Conservation,Beijing,China

3. State Grid Corporation of China,Southwest Branch,Chengdu,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10582916/10582978/10583215.pdf?arnumber=10583215

Reference7 articles.

1. Primary Exploration of Six Essential Factors in New Power System;Kang;Power System Technology,2023

2. Method identifying voltage instability and angle instability based on tracking thevenin equivalent parameters;Tang,2009

3. Coupling mechanism analysis and coupling strength evaluation index of transient power angle stability;Li,2021

4. A graph attention networks-based model to distinguish the transient rotor angle instability and short-term voltage instability in power systems

5. Semi-supervised learning framework of dominant instability mode identification via fusion of virtual adversarial training and mean teacher model;Zhang,2023

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