Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method

Wanjun Huang; Xinran Zhang; David J. Hill

doi:10.1109/ISGT-Asia.2019.8881311

Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method

Wanjun Huang
, Xinran Zhang
, David J. Hill

The University of Hong Kong

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

6 引用（Scopus）

摘要

Short-term voltage stability (STVS) is a growing concern with the increasing penetration of induction motors, power electronic loads and renewables in power systems. In this study, we investigate the impact of network structures on STVS using data-driven methods. An integrated graph metric set (IGMS) is proposed to characterize the network structures. The transient voltage severity index (TVSI) is used to quantify the STVS performance. Then based on artificial neural networks (ANNs), a two-stage ANN-based probabilistic prediction (TSAPP) method is proposed to establish the mapping between the system STVS and network structures. The simulation results on the Guangdong power grid verify that this method has very high reliability in comparing the system STVS with different network structures. The proposed TSAPP method in this paper can provide guidance for network planning.

源语言	英语
主期刊名	2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019
出版商	Institute of Electrical and Electronics Engineers Inc.
页	970-975
页数	6
ISBN（电子版）	9781728135205
DOI	https://doi.org/10.1109/ISGT-Asia.2019.8881311
出版状态	已出版 - 5月 2019
已对外发布	是
活动	2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019 - Chengdu, 中国期限: 21 5月 2019 → 24 5月 2019

出版系列

姓名	2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

会议

会议	2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019
国家/地区	中国
市	Chengdu
时期	21/05/19 → 24/05/19

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1109/ISGT-Asia.2019.8881311

其它文件与链接

链接到 Scopus 的出版物

引用此

Huang, W., Zhang, X., & Hill, D. J. (2019). Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method. 在 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019 (页码 970-975). 文章 8881311 (2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISGT-Asia.2019.8881311

@inproceedings{f3b810dfe18a4271a5845bea0567c40a,

title = "Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method",

abstract = "Short-term voltage stability (STVS) is a growing concern with the increasing penetration of induction motors, power electronic loads and renewables in power systems. In this study, we investigate the impact of network structures on STVS using data-driven methods. An integrated graph metric set (IGMS) is proposed to characterize the network structures. The transient voltage severity index (TVSI) is used to quantify the STVS performance. Then based on artificial neural networks (ANNs), a two-stage ANN-based probabilistic prediction (TSAPP) method is proposed to establish the mapping between the system STVS and network structures. The simulation results on the Guangdong power grid verify that this method has very high reliability in comparing the system STVS with different network structures. The proposed TSAPP method in this paper can provide guidance for network planning.",

keywords = "Short-term voltage stability, artificial neural network, integrated graph metric set, network structure",

author = "Wanjun Huang and Xinran Zhang and Hill, \{David J.\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019 ; Conference date: 21-05-2019 Through 24-05-2019",

year = "2019",

month = may,

doi = "10.1109/ISGT-Asia.2019.8881311",

language = "英语",

series = "2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019",

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

pages = "970--975",

booktitle = "2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019",

address = "美国",

}

Huang, W , Zhang, X & Hill, DJ 2019, Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method. 在 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019., 8881311, 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019, Institute of Electrical and Electronics Engineers Inc., 页码 970-975, 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019, Chengdu, 中国, 21/05/19. https://doi.org/10.1109/ISGT-Asia.2019.8881311

Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method. / Huang, Wanjun ; Zhang, Xinran; Hill, David J.
2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 页码 970-975 8881311 (2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method

AU - Huang, Wanjun

AU - Zhang, Xinran

AU - Hill, David J.

PY - 2019/5

Y1 - 2019/5

N2 - Short-term voltage stability (STVS) is a growing concern with the increasing penetration of induction motors, power electronic loads and renewables in power systems. In this study, we investigate the impact of network structures on STVS using data-driven methods. An integrated graph metric set (IGMS) is proposed to characterize the network structures. The transient voltage severity index (TVSI) is used to quantify the STVS performance. Then based on artificial neural networks (ANNs), a two-stage ANN-based probabilistic prediction (TSAPP) method is proposed to establish the mapping between the system STVS and network structures. The simulation results on the Guangdong power grid verify that this method has very high reliability in comparing the system STVS with different network structures. The proposed TSAPP method in this paper can provide guidance for network planning.

AB - Short-term voltage stability (STVS) is a growing concern with the increasing penetration of induction motors, power electronic loads and renewables in power systems. In this study, we investigate the impact of network structures on STVS using data-driven methods. An integrated graph metric set (IGMS) is proposed to characterize the network structures. The transient voltage severity index (TVSI) is used to quantify the STVS performance. Then based on artificial neural networks (ANNs), a two-stage ANN-based probabilistic prediction (TSAPP) method is proposed to establish the mapping between the system STVS and network structures. The simulation results on the Guangdong power grid verify that this method has very high reliability in comparing the system STVS with different network structures. The proposed TSAPP method in this paper can provide guidance for network planning.

KW - Short-term voltage stability

KW - artificial neural network

KW - integrated graph metric set

KW - network structure

UR - https://www.scopus.com/pages/publications/85074910453

U2 - 10.1109/ISGT-Asia.2019.8881311

DO - 10.1109/ISGT-Asia.2019.8881311

M3 - 会议稿件

AN - SCOPUS:85074910453

T3 - 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

SP - 970

EP - 975

BT - 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

Y2 - 21 May 2019 through 24 May 2019

ER -

Huang W , Zhang X, Hill DJ. Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method. 在 2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 页码 970-975. 8881311. (2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019). doi: 10.1109/ISGT-Asia.2019.8881311

Impact of Network Structure on Short-Term Voltage Stability Using Data-Driven Method

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