PMUVis: A Large-Scale Platform to Assist Power System Operators in a Smart Grid

Anjana Arunkumar; Nitin Gupta; Andrea Pinceti; Lalitha Sankar; Chris Bryan

doi:10.1109/MCG.2022.3171506

PMUVis: A Large-Scale Platform to Assist Power System Operators in a Smart Grid

Anjana Arunkumar, Nitin Gupta, Andrea Pinceti, Lalitha Sankar, Chris Bryan

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Electric transmission power grids are being revamped with the widespread deployment of GPS-enabled phasor measurement units (PMUs) for real-time wide-area monitoring and control via precise, time-synchronized measurements of voltage and current. Large, concurrently produced volumes of noisy data hinder PMU usability, particularly for the analysis of power oscillation and load fluctuation events in the grid. We examine visualization challenges for events in the electric power grid and develop PMUVis, a visualization platform that supports scalable analysis of grid network topology and anomalous events in near time. PMUVis incorporates a novel FFT-based approach over raw and temporally aggregated data to examine oscillation event propagation through the grid network. We validate PMUVis with expert reviews and a case study and discuss how visualization can be leveraged to enhance real-time spatiotemporal grid analysis by advancing operator capabilities.

Original language	English (US)
Pages (from-to)	84-95
Number of pages	12
Journal	IEEE Computer Graphics and Applications
Volume	42
Issue number	6
DOIs	https://doi.org/10.1109/MCG.2022.3171506
State	Published - Nov 1 2022

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

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10.1109/MCG.2022.3171506

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abstract = "Electric transmission power grids are being revamped with the widespread deployment of GPS-enabled phasor measurement units (PMUs) for real-time wide-area monitoring and control via precise, time-synchronized measurements of voltage and current. Large, concurrently produced volumes of noisy data hinder PMU usability, particularly for the analysis of power oscillation and load fluctuation events in the grid. We examine visualization challenges for events in the electric power grid and develop PMUVis, a visualization platform that supports scalable analysis of grid network topology and anomalous events in near time. PMUVis incorporates a novel FFT-based approach over raw and temporally aggregated data to examine oscillation event propagation through the grid network. We validate PMUVis with expert reviews and a case study and discuss how visualization can be leveraged to enhance real-time spatiotemporal grid analysis by advancing operator capabilities.",

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PMUVis: A Large-Scale Platform to Assist Power System Operators in a Smart Grid

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