Document Type

Post-Print

Publication Date

5-2016

Subjects

Electric power systems -- Load dispatching -- Data processing

Abstract

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30x speedup for queries with high selectivity. With the development of these two components, we have developed a system that allows efficient analysis of multiple time-aligned PMU data streams.

Description

This is the author’s version of a work that was accepted for publication in Electric Power Systems Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Electric Power Systems Research, [VOL 140 (2016)] and can be found online at: http://dx.doi.org/10.1016/j.epsr.2016.05.003

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

DOI

10.1016/j.epsr.2016.05.003

Persistent Identifier

http://archives.pdx.edu/ds/psu/19376

Available for download on Saturday, November 24, 2018

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