Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework

Yucheng Chen; Haipeng Xie

doi:10.1109/CIEEC54735.2022.9846359

Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework

Yucheng Chen
, Haipeng Xie

School of Electrical Engineering

Xi'an Jiaotong University

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

5 Scopus citations

Abstract

In order to further improve the accuracy of power system transient voltage stability assessment, a power system transient stability assessment method based on improved TCN-BiLSTM is proposed in this paper. Taking the time series of power system bottom measurement data as the input, TCN network with attention enhancement module and BiLSTM network are used to extract timing features in parallel, Then, the transient voltage stability of the system is judged by feature fusion, and the cost-sensitivity coefficient is added to the loss function to improve the correct judgment ability of unstable samples.

Original language	English
Title of host publication	Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4420-4426
Number of pages	7
ISBN (Electronic)	9781665411042
DOIs	https://doi.org/10.1109/CIEEC54735.2022.9846359
State	Published - 2022
Event	5th IEEE International Electrical and Energy Conference, CIEEC 2022 - Nanjing, China Duration: 27 May 2022 → 29 May 2022

Publication series

Name	Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022

Conference

Conference	5th IEEE International Electrical and Energy Conference, CIEEC 2022
Country/Territory	China
City	Nanjing
Period	27/05/22 → 29/05/22

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Bi directional long short memory network
Deep learning
Loss function
Temporal convolutional network
Transient stability assessment

Access to Document

10.1109/CIEEC54735.2022.9846359

Cite this

Chen, Y., & Xie, H. (2022). Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework. In Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022 (pp. 4420-4426). (Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIEEC54735.2022.9846359

Chen, Yucheng ; Xie, Haipeng. / Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework. Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 4420-4426 (Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022).

@inproceedings{5e7196d050684cdaa7eaee1fe8056b0a,

title = "Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework",

abstract = "In order to further improve the accuracy of power system transient voltage stability assessment, a power system transient stability assessment method based on improved TCN-BiLSTM is proposed in this paper. Taking the time series of power system bottom measurement data as the input, TCN network with attention enhancement module and BiLSTM network are used to extract timing features in parallel, Then, the transient voltage stability of the system is judged by feature fusion, and the cost-sensitivity coefficient is added to the loss function to improve the correct judgment ability of unstable samples.",

keywords = "Bi directional long short memory network, Deep learning, Loss function, Temporal convolutional network, Transient stability assessment",

author = "Yucheng Chen and Haipeng Xie",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 5th IEEE International Electrical and Energy Conference, CIEEC 2022 ; Conference date: 27-05-2022 Through 29-05-2022",

year = "2022",

doi = "10.1109/CIEEC54735.2022.9846359",

language = "英语",

series = "Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4420--4426",

booktitle = "Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022",

}

Chen, Y & Xie, H 2022, Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework. in Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022. Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022, Institute of Electrical and Electronics Engineers Inc., pp. 4420-4426, 5th IEEE International Electrical and Energy Conference, CIEEC 2022, Nanjing, China, 27/05/22. https://doi.org/10.1109/CIEEC54735.2022.9846359

Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework. / Chen, Yucheng; Xie, Haipeng.
Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 4420-4426 (Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022).

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

TY - GEN

T1 - Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework

AU - Chen, Yucheng

AU - Xie, Haipeng

PY - 2022

Y1 - 2022

N2 - In order to further improve the accuracy of power system transient voltage stability assessment, a power system transient stability assessment method based on improved TCN-BiLSTM is proposed in this paper. Taking the time series of power system bottom measurement data as the input, TCN network with attention enhancement module and BiLSTM network are used to extract timing features in parallel, Then, the transient voltage stability of the system is judged by feature fusion, and the cost-sensitivity coefficient is added to the loss function to improve the correct judgment ability of unstable samples.

AB - In order to further improve the accuracy of power system transient voltage stability assessment, a power system transient stability assessment method based on improved TCN-BiLSTM is proposed in this paper. Taking the time series of power system bottom measurement data as the input, TCN network with attention enhancement module and BiLSTM network are used to extract timing features in parallel, Then, the transient voltage stability of the system is judged by feature fusion, and the cost-sensitivity coefficient is added to the loss function to improve the correct judgment ability of unstable samples.

KW - Bi directional long short memory network

KW - Deep learning

KW - Loss function

KW - Temporal convolutional network

KW - Transient stability assessment

UR - https://www.scopus.com/pages/publications/85137370404

U2 - 10.1109/CIEEC54735.2022.9846359

DO - 10.1109/CIEEC54735.2022.9846359

M3 - 会议稿件

AN - SCOPUS:85137370404

T3 - Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022

SP - 4420

EP - 4426

BT - Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Electrical and Energy Conference, CIEEC 2022

Y2 - 27 May 2022 through 29 May 2022

ER -

Chen Y, Xie H. Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework. In Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 4420-4426. (Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022). doi: 10.1109/CIEEC54735.2022.9846359

Transient Voltage Stability Assessment Based on an Improved TCN-BiLSTM Framework

Abstract

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Keywords

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