Learning from power system data stream

Mauro Escobar, Daniel Bienstock, Michael Chertkov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Assuming access to synchronized stream of Phasor Measurement Unit (PMU) data over a significant portion of a power system interconnect, say controlled by an Independent System Operator (ISO), what can you extract about past, current and future state of the system? We have focused on answering these practical questions pragmatically ∗ empowered with nothing but standard tools of data analysis, such as PCA, filtering and cross-correlation analysis. Quite surprisingly we have found that even during quiet 'no significant events' periods this standard set of statistical tools allows the 'phasor-detective' to extract from the data important hidden anomalies, such as problematic control loops at loads and wind farms, and mildly malfunctioning assets, such as transformers and generators.

Original languageEnglish (US)
Title of host publication2019 IEEE Milan PowerTech, PowerTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538647226
DOIs
StatePublished - Jun 2019
Event2019 IEEE Milan PowerTech, PowerTech 2019 - Milan, Italy
Duration: Jun 23 2019Jun 27 2019

Publication series

Name2019 IEEE Milan PowerTech, PowerTech 2019

Conference

Conference2019 IEEE Milan PowerTech, PowerTech 2019
Country/TerritoryItaly
CityMilan
Period6/23/196/27/19

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality

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