@inproceedings{85a27580e4954869aa2f51dce9cf72bb,
title = "ResNet-GRU Based Transient Voltage Stability Margin Evaluation of Power System",
abstract = "The safe and stable operation of power system faces serious challenges such as the variability of operation mode and the lack of disturbance attenuation ability. To achieve the accurate voltage stability evaluation and provide reliable reference for emergency control, this paper proposes a transient voltage stability margin evaluation method based on deep residual network (ResNet) and gated recurrent unit (GRU). Firstly, a practical transient voltage stability margin index based on two-element table is presented to construct sample label in the case of large disturbances. Secondly, a ResNet-GRU hybrid model is established to quantitatively evaluate voltage stability margin. The complementary advantages of ResNet in spatial coupling feature extraction and GRU in temporal compliance relationship learning are fully exploited. Finally, case studies are performed in Northwest China local region power grid. A series of experiment and simulation results verify the effectiveness and accuracy of the proposed method for voltage stability margin evaluation.",
keywords = "ResNet-GRU model, neural network, transient voltage stability margin, voltage instability",
author = "Dong Liu and Fan Li and Jishuo Qin and Taikun Tao and Xiaofan Su and Xin Gao and Boyu Qin",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023 ; Conference date: 06-06-2023 Through 09-06-2023",
year = "2023",
doi = "10.1109/CEEGE58447.2023.10246653",
language = "英语",
series = "6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "74--79",
booktitle = "6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023",
}
