A graph attention networks-based model to distinguish the transient rotor angle instability and short-term voltage instability in power systems-Reference-Cited by-同舟云学术

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

Published:2022-05 Issue: Volume:137 Page:107783
ISSN:0142-0615
Container-title:International Journal of Electrical Power & Energy Systems
language:en
Short-container-title:International Journal of Electrical Power & Energy Systems

Author:

Zhang Runfeng^ORCID,Yao Wei^ORCID,Shi Zhongtuo^ORCID,Zeng Lingkang,Tang Yong,Wen Jinyu

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference46 articles.

1. Definition and classification of power system stability;Kundur;IEEE Trans Power Syst,2004

2. Framework for artificial intelligence analysis in large-scale power grids based on digital simulation;Tang;CSEE J Power Energy Syst,2018

3. Relationships between voltage and angle stability of power systems;Vournas;Int J Electr Power Energy Syst,1996

4. Overbye TJ, Pai MA, Sauer PW. Some aspects of the energy function approach to angle and voltage stability analysis in power systems. In Proceedings of the 31st ieee conference of decision and control 1992. p. 2941–2946.

5. Theoretical analysis of the interaction between power system stability modes with the normal forms of vector fields;Zhang,2006

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