Power System Inertia Estimation Based on Long Short Term Memory Network

Zhen Tang; Lihua Hao; Jiapeng Li; Qiuyi Chen; Yujun Li; Zhao Xu

doi:10.1109/iSPEC53008.2021.9736068

Power System Inertia Estimation Based on Long Short Term Memory Network

Zhen Tang
, Lihua Hao
, Jiapeng Li
, Qiuyi Chen
, Yujun Li
, Zhao Xu

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

5 Citations (Scopus)

Abstract

With the increasingly high penetration of renewable energy into the power system, traditional synchronous generators are gradually substituted by power-electronic-converter-based sources that commonly lack inertia support capability. Since deficient system inertia could cause larger frequency deviation under the same disturbance, commanding the inertia level of the system is beneficial and necessary for the operators to take precautions and ensure stable power supply. This paper puts forward a long short-term memory (LSTM) based inertia estimation method. Through massive training, the LSTM network successfully constructs the relationship between the system inertia and the system frequency sequence as well as the unbalanced power. The hyperparameters of the proposed deep learning model can be determined by the classic grid search. Case studies on the IEEE-39 system verify that the LSTM network can well handle the time-varying sequence and the proposed method can accurately estimate the system inertia.

Original language	English
Title of host publication	Proceedings - 2021 IEEE Sustainable Power and Energy Conference
Subtitle of host publication	Energy Transition for Carbon Neutrality, iSPEC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2542-2547
Number of pages	6
ISBN (Electronic)	9781665414395
DOIs	https://doi.org/10.1109/iSPEC53008.2021.9736068
Publication status	Published - 2021
Event	2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021 - Nanjing, China Duration: 22 Dec 2021 → 24 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021

Conference

Conference	2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021
Country/Territory	China
City	Nanjing
Period	22/12/21 → 24/12/21

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

deep learning
Frequency response
inertia estimation
LSTM

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Energy Engineering and Power Technology

More information

10.1109/iSPEC53008.2021.9736068

Cite this

Tang, Z., Hao, L., Li, J., Chen, Q., Li, Y., & Xu, Z. (2021). Power System Inertia Estimation Based on Long Short Term Memory Network. In Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021 (pp. 2542-2547). (Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iSPEC53008.2021.9736068

Tang, Zhen ; Hao, Lihua ; Li, Jiapeng et al. / Power System Inertia Estimation Based on Long Short Term Memory Network. Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 2542-2547 (Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021).

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title = "Power System Inertia Estimation Based on Long Short Term Memory Network",

abstract = "With the increasingly high penetration of renewable energy into the power system, traditional synchronous generators are gradually substituted by power-electronic-converter-based sources that commonly lack inertia support capability. Since deficient system inertia could cause larger frequency deviation under the same disturbance, commanding the inertia level of the system is beneficial and necessary for the operators to take precautions and ensure stable power supply. This paper puts forward a long short-term memory (LSTM) based inertia estimation method. Through massive training, the LSTM network successfully constructs the relationship between the system inertia and the system frequency sequence as well as the unbalanced power. The hyperparameters of the proposed deep learning model can be determined by the classic grid search. Case studies on the IEEE-39 system verify that the LSTM network can well handle the time-varying sequence and the proposed method can accurately estimate the system inertia.",

keywords = "deep learning, Frequency response, inertia estimation, LSTM",

author = "Zhen Tang and Lihua Hao and Jiapeng Li and Qiuyi Chen and Yujun Li and Zhao Xu",

note = "Funding Information: This paper is supported by the Electric Power Research Institute, State Grid Shanxi Electric Power Company under Grand SGTYHT/19-JS-215. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021 ; Conference date: 22-12-2021 Through 24-12-2021",

year = "2021",

doi = "10.1109/iSPEC53008.2021.9736068",

language = "English",

series = "Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021",

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

pages = "2542--2547",

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}

Tang, Z, Hao, L, Li, J, Chen, Q, Li, Y & Xu, Z 2021, Power System Inertia Estimation Based on Long Short Term Memory Network. in Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021. Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 2542-2547, 2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021, Nanjing, China, 22/12/21. https://doi.org/10.1109/iSPEC53008.2021.9736068

Power System Inertia Estimation Based on Long Short Term Memory Network. / Tang, Zhen; Hao, Lihua; Li, Jiapeng et al.
Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2542-2547 (Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Power System Inertia Estimation Based on Long Short Term Memory Network

AU - Tang, Zhen

AU - Hao, Lihua

AU - Li, Jiapeng

AU - Chen, Qiuyi

AU - Li, Yujun

AU - Xu, Zhao

PY - 2021

Y1 - 2021

N2 - With the increasingly high penetration of renewable energy into the power system, traditional synchronous generators are gradually substituted by power-electronic-converter-based sources that commonly lack inertia support capability. Since deficient system inertia could cause larger frequency deviation under the same disturbance, commanding the inertia level of the system is beneficial and necessary for the operators to take precautions and ensure stable power supply. This paper puts forward a long short-term memory (LSTM) based inertia estimation method. Through massive training, the LSTM network successfully constructs the relationship between the system inertia and the system frequency sequence as well as the unbalanced power. The hyperparameters of the proposed deep learning model can be determined by the classic grid search. Case studies on the IEEE-39 system verify that the LSTM network can well handle the time-varying sequence and the proposed method can accurately estimate the system inertia.

AB - With the increasingly high penetration of renewable energy into the power system, traditional synchronous generators are gradually substituted by power-electronic-converter-based sources that commonly lack inertia support capability. Since deficient system inertia could cause larger frequency deviation under the same disturbance, commanding the inertia level of the system is beneficial and necessary for the operators to take precautions and ensure stable power supply. This paper puts forward a long short-term memory (LSTM) based inertia estimation method. Through massive training, the LSTM network successfully constructs the relationship between the system inertia and the system frequency sequence as well as the unbalanced power. The hyperparameters of the proposed deep learning model can be determined by the classic grid search. Case studies on the IEEE-39 system verify that the LSTM network can well handle the time-varying sequence and the proposed method can accurately estimate the system inertia.

KW - deep learning

KW - Frequency response

KW - inertia estimation

KW - LSTM

UR - https://www.scopus.com/pages/publications/85128087644

U2 - 10.1109/iSPEC53008.2021.9736068

DO - 10.1109/iSPEC53008.2021.9736068

M3 - Conference article published in proceeding or book

AN - SCOPUS:85128087644

T3 - Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021

SP - 2542

EP - 2547

BT - Proceedings - 2021 IEEE Sustainable Power and Energy Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

Y2 - 22 December 2021 through 24 December 2021

ER -

Tang Z, Hao L, Li J, Chen Q, Li Y, Xu Z. Power System Inertia Estimation Based on Long Short Term Memory Network. In Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2542-2547. (Proceedings - 2021 IEEE Sustainable Power and Energy Conference: Energy Transition for Carbon Neutrality, iSPEC 2021). doi: 10.1109/iSPEC53008.2021.9736068

Power System Inertia Estimation Based on Long Short Term Memory Network

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

More information

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