Adaptive Identification of Power System Operational States Based on Spatio-Temporal Dynamic Graph Neural Networks-Reference-Cited by-同舟云学术

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Adaptive Identification of Power System Operational States Based on Spatio-Temporal Dynamic Graph Neural Networks

Published:2024-05-24 Issue: Volume: Page:910-916
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Container-title:2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI)
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Author:

Zhang Qingqing¹,Ma Qinfeng¹,Liu Mingshun¹,Zhang Jie²,Zhu Yihua³,Liang Zhuohang⁴,An Su¹,Pu Qingxin¹,Dai Jiang¹

Affiliation:

1. Power Dispatching and Control Center of Guizhou Power Grid Co., Ltd.,Guizhou,China

2. China Southern Power Grid,State Key Laboratory of HVDC, Electric Power Research Institute,Guangzhou,China,510663

3. National Energy Power Grid Technology R&D Centre,Guangzhou,China,510663

4. Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System,Guangzhou,China,510663

Funder

China Southern Power Grid

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10593799/10594004/10594213.pdf?arnumber=10594213

Reference13 articles.

1. Multilevel Fuzzy Comprehensive Assessment for Forewarning Grade of Voltage State and Tendency in Power Systems[J];Bao-Zhu;Power System Technology,2005

2. Trends Identification of Power System Operating States[J];Wang;Transactions of China Electrotechnical Society,2015

3. A Deep End-to-End Model for Transient Stability Assessment With PMU Data

4. Loss-of-load state identification using self-organizing map

5. Convolutional neural network-based power system transient stability assessment and instability mode prediction

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