ResNet-GRU Based Transient Voltage Stability Margin Evaluation of Power System

Dong Liu; Fan Li; Jishuo Qin; Taikun Tao; Xiaofan Su; Xin Gao; Boyu Qin

doi:10.1109/CEEGE58447.2023.10246653

ResNet-GRU Based Transient Voltage Stability Margin Evaluation of Power System

Dong Liu
, Fan Li
, Jishuo Qin
, Taikun Tao
, Xiaofan Su
, Xin Gao
, Boyu Qin

School of Electrical Engineering

State Grid Corporation of China
Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	74-79
Number of pages	6
ISBN (Electronic)	9798350312669
DOIs	https://doi.org/10.1109/CEEGE58447.2023.10246653
State	Published - 2023
Event	6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023 - Grimstad, Norway Duration: 6 Jun 2023 → 9 Jun 2023

Publication series

Name	6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023

Conference

Conference	6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023
Country/Territory	Norway
City	Grimstad
Period	6/06/23 → 9/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

ResNet-GRU model
neural network
transient voltage stability margin
voltage instability

Access to Document

10.1109/CEEGE58447.2023.10246653

Cite this

Liu, D., Li, F., Qin, J., Tao, T., Su, X., Gao, X., & Qin, B. (2023). ResNet-GRU Based Transient Voltage Stability Margin Evaluation of Power System. In 6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023 (pp. 74-79). (6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEEGE58447.2023.10246653

@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",

}

Liu, D, Li, F, Qin, J, Tao, T, Su, X, Gao, X & Qin, B 2023, ResNet-GRU Based Transient Voltage Stability Margin Evaluation of Power System. in 6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023. 6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 74-79, 6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023, Grimstad, Norway, 6/06/23. https://doi.org/10.1109/CEEGE58447.2023.10246653

ResNet-GRU Based Transient Voltage Stability Margin Evaluation of Power System. / Liu, Dong; Li, Fan; Qin, Jishuo et al.
6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 74-79 (6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - ResNet-GRU Based Transient Voltage Stability Margin Evaluation of Power System

AU - Liu, Dong

AU - Li, Fan

AU - Qin, Jishuo

AU - Tao, Taikun

AU - Su, Xiaofan

AU - Gao, Xin

AU - Qin, Boyu

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - ResNet-GRU model

KW - neural network

KW - transient voltage stability margin

KW - voltage instability

UR - https://www.scopus.com/pages/publications/85173657345

U2 - 10.1109/CEEGE58447.2023.10246653

DO - 10.1109/CEEGE58447.2023.10246653

M3 - 会议稿件

AN - SCOPUS:85173657345

T3 - 6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023

SP - 74

EP - 79

BT - 6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023

Y2 - 6 June 2023 through 9 June 2023

ER -

Liu D, Li F, Qin J, Tao T, Su X, Gao X et al. ResNet-GRU Based Transient Voltage Stability Margin Evaluation of Power System. In 6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 74-79. (6th International Conference on Electrical Engineering and Green Energy, CEEGE 2023). doi: 10.1109/CEEGE58447.2023.10246653

ResNet-GRU Based Transient Voltage Stability Margin Evaluation of Power System

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Keywords

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