Spatio-temporal Data-Driven and Machine Learning based Applications for Transmission Systems

F. R.Segundo Sevilla*, Y. Liu, E. Barocio, P. Korba, A. Zamora, D. Dotta, F. Bellizio, J. Cremer, J. Zhao, More Authors

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

This paper summarizes recent advancements on spatio-temporal data-driven and machine learning methods for static and dynamic security assessment, and their particular use cases. It is a collective effort of different research groups members of the IEEE Working Group on Big Data Analytics for Transmission Systems, to provide transmission system operators (TSOs) with innovative tools and ideas for their potential implementation. The algorithms presented here are classified as non-training and training approaches, namely spatio-temporal and machine learning based, considering as input time series from time domain simulations, and or synchrophasor data from wide-area monitoring systems. The efficacy of these algorithms is then evaluated in different IEEE benchmark models and using real system measurements from different countries.

Original languageEnglish
Title of host publication2024 IEEE Power and Energy Society General Meeting, PESGM 2024
PublisherIEEE
Number of pages5
ISBN (Electronic)9798350381832
DOIs
Publication statusPublished - 2024
Event2024 IEEE Power and Energy Society General Meeting, PESGM 2024 - Seattle, United States
Duration: 21 Jul 202425 Jul 2024

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2024 IEEE Power and Energy Society General Meeting, PESGM 2024
Country/TerritoryUnited States
CitySeattle
Period21/07/2425/07/24

Keywords

  • coherency
  • data-driven
  • event detection
  • machine learning
  • modal analysis
  • parameter identification
  • preventive control
  • static and dynamic security assessment
  • transient stability

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