Learning from power system data stream

Mauro Escobar, Daniel Bienstock, Michael Chertkov

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

1 Citation (Scopus)

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
Title of host publication2019 IEEE Milan PowerTech, PowerTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538647226
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes
Event2019 IEEE Milan PowerTech, PowerTech 2019 - Milan, Italy
Duration: 23 Jun 201927 Jun 2019

Publication series

Name2019 IEEE Milan PowerTech, PowerTech 2019

Conference

Conference2019 IEEE Milan PowerTech, PowerTech 2019
Country/TerritoryItaly
CityMilan
Period23/06/1927/06/19

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