Power System Inertia Estimation Based on Long Short Term Memory Network

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (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 languageEnglish
Title of host publicationProceedings - 2021 IEEE Sustainable Power and Energy Conference
Subtitle of host publicationEnergy Transition for Carbon Neutrality, iSPEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2542-2547
Number of pages6
ISBN (Electronic)9781665414395
DOIs
Publication statusPublished - 2021
Event2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021 - Nanjing, China
Duration: 22 Dec 202124 Dec 2021

Publication series

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

Conference

Conference2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021
Country/TerritoryChina
CityNanjing
Period22/12/2124/12/21

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

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