## Lessons on Combining Topology and Geography : Visual Analytics for Electrical Outage Management

2016
Sander, Sonja
Platz, Axel
Bouwman, Gies
##### Publication type
Contribution to a conference collection
Published
##### Published in
EuroVis Workshop on Visual Analytics / Andrienko, Lessons; Sedlmair, Michael (ed.). - The Eurographics Association, 2016. - 1116. - ISBN 978-3-03868-016-1
##### Abstract
Outage management in electrical networks is a complex task for operators and requires comprehensive overviews of the topology. At the same time valuable information for detecting the root cause may have geographical context such as digging activities or falling trees. Consequently, vendors of state-of-the-art SCADA systems started to integrate this valuable information source as well. However, in todays systems both views are separated, requiring operators to mentally connect the geographical and topological information. The wish of operators is to provide a comprehensive combination of both spaces in a single view. However, how to project geographical elements into the topology to support the workflow of real operators is yet unclear. In this paper, we present a design study for an interactive visualization system that provides a comprehensive overview for power grid operators. It provides full coverage of both spaces in order to measure how real operators make use of the geographical information. It bypasses the projection problem by interactive brushing-and-linking to support associative analysis. We extracted the mental-model of domain experts in real use cases and found a general bias source in sequential analysis of two spaces. We contribute our problem and task abstraction, lessons learned, and implications for future research.
##### Subject (DDC)
004 Computer Science
##### Conference
EuroVA16, Jun 6, 2016 - Jun 10, 2016, Groningen, the Netherlands
##### Cite This
ISO 690JÄGER, Alexander, Sebastian MITTELSTÄDT, Daniela OELKE, Sonja SANDER, Axel PLATZ, Gies BOUWMAN, Daniel A. KEIM, 2016. Lessons on Combining Topology and Geography : Visual Analytics for Electrical Outage Management. EuroVA16. Groningen, the Netherlands, Jun 6, 2016 - Jun 10, 2016. In: ANDRIENKO, Lessons, ed., Michael SEDLMAIR, ed.. EuroVis Workshop on Visual Analytics. The Eurographics Association, 1116. ISBN 978-3-03868-016-1. Available under: doi: 10.2312/eurova.20161116
BibTex
@inproceedings{Jager2016Lesso-38109,
year={2016},
doi={10.2312/eurova.20161116},
title={Lessons on Combining Topology and Geography : Visual Analytics for Electrical Outage Management},
isbn={978-3-03868-016-1},
publisher={The Eurographics Association},
booktitle={EuroVis Workshop on Visual Analytics},
editor={Andrienko, Lessons and Sedlmair, Michael},
author={Jäger, Alexander and Mittelstädt, Sebastian and Oelke, Daniela and Sander, Sonja and Platz, Axel and Bouwman, Gies and Keim, Daniel A.},
note={Article Number: 1116}
}

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<dcterms:abstract xml:lang="eng">Outage management in electrical networks is a complex task for operators and requires comprehensive overviews of the topology. At the same time valuable information for detecting the root cause may have geographical context such as digging activities or falling trees. Consequently, vendors of state-of-the-art SCADA systems started to integrate this valuable information source as well. However, in todays systems both views are separated, requiring operators to mentally connect the geographical and topological information. The wish of operators is to provide a comprehensive combination of both spaces in a single view. However, how to project geographical elements into the topology to support the workflow of real operators is yet unclear. In this paper, we present a design study for an interactive visualization system that provides a comprehensive overview for power grid operators. It provides full coverage of both spaces in order to measure how real operators make use of the geographical information. It bypasses the projection problem by interactive brushing-and-linking to support associative analysis. We extracted the mental-model of domain experts in real use cases and found a general bias source in sequential analysis of two spaces. We contribute our problem and task abstraction, lessons learned, and implications for future research.</dcterms:abstract>
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Yes