A machine learning based approach for frequency response prediction in low inertia power system

Akhilesh Panwar, Zakir Hussain Rather, Suryanarayana Doolla, Ariel Liebman, Roger Dargaville

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

The uncertainty in securely operating the power system has increased with large-scale integration of inverter based resources (IBRs). IBR-driven displacement of conventional sources results in diminishing inherent frequency support capability of such systems as, unlike conventional synchronous generators, IBRs do not inherently support the grid frequency. Therefore, with increasing vulnerability, there is a need for an accurate frequency prediction model that could help a grid operator better plan system resources and securely operate the power system. Against this backdrop, this paper initially provides a critical insight into the frequency response of IBR-dominated systems to highlight the limitation of using a linear prediction model. Following the critical analysis, the paper proposes a data extraction framework and XGBoost algorithm-based regression model to predict RoCoF, frequency nadir, and quassi steady-state frequency of the IBR-dominated power system. The proposed approach has been implemented on a modified IEEE-39 bus system, and its comparative analysis with other state-of-art algorithms supports the superiority of the proposed method.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Innovative Smart Grid Technologies (Asia) (IEEE ISGT-Asia 2022)
EditorsDaisuke Mashima
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages175-179
Number of pages5
ISBN (Electronic)9798350399660
ISBN (Print)9798350399677
DOIs
Publication statusPublished - 2022
EventIEEE Innovative Smart Grid Technologies - Asia 2022 - Singapore, Singapore
Duration: 1 Nov 20225 Nov 2022
Conference number: 11th
https://ieeexplore.ieee.org/xpl/conhome/10003265/proceeding (Proceedings)
https://ieee-isgt-asia.org/ (Website)

Publication series

NameProceedings of the 11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)2378-8534
ISSN (Electronic)2378-8542

Conference

ConferenceIEEE Innovative Smart Grid Technologies - Asia 2022
Abbreviated titleISGT-Asia 2022
Country/TerritorySingapore
CitySingapore
Period1/11/225/11/22
Internet address

Keywords

  • frequency stability
  • inertia
  • machine learning
  • power system
  • prediction
  • renewable energy

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