ST-AGNet: Dynamic power system state prediction with spatial–temporal attention graph-based network-Reference-Cited by-同舟云学术

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ST-AGNet: Dynamic power system state prediction with spatial–temporal attention graph-based network

Published:2024-07 Issue: Volume:365 Page:123252
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Zhang Shiyao,Zhang Shuyu^ORCID,Yu James J.Q.^ORCID,Wei Xuetao

Publisher

Elsevier BV

Reference35 articles.

1. Power system dynamic state estimation: Motivations, definitions, methodologies, and future work;Zhao;IEEE Trans Power Syst,2019

2. Roles of dynamic state estimation in power system modeling, monitoring and operation;Zhao;IEEE Trans Power Syst,2021

3. A robust iterated extended Kalman filter for power system dynamic state estimation;Zhao;IEEE Trans Power Syst,2017

4. Switching Markov Gaussian models for dynamic power system inertia estimation;Cao;IEEE Trans Power Syst,2016

5. Prediction of the transient stability boundary based on nonparametric additive modeling;Lv;IEEE Trans Power Syst,2017

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