A Novel Visualization Technique for Electric Power Grid Analytics

The application of information visualization holds tremendous promise for the electric power industry, but its potential has so far not been sufficiently exploited by the visualization community. Prior work on visualizing electric power systems has been limited to depicting raw or processed information on top of a geographic layout. Little effort has been devoted to visualizing the physics of the power grids, which ultimately determines the condition and stability of the electricity infrastructure. Based on this assessment, we developed a novel visualization system prototype, GreenGrid, to explore the planning and monitoring of the North American Electricity Infrastructure. The paper discusses the rationale underlying the GreenGrid design, describes its implementation and performance details, and assesses its strengths and weaknesses against the current geographic-based power grid visualization. We also present a case study using GreenGrid to analyze the information collected moments before the last major electric blackout in the Western United States and Canada, and a usability study to evaluate the practical significance of our design in simulated real-life situations. Our result indicates that many of the disturbance characteristics can be readily identified with the proper form of visualization.

[1]  Armand Navabi,et al.  Graphael: A System for Generalized Force-Directed Layouts , 2004, GD.

[2]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[3]  C. W. Taylor,et al.  Model validation for the August 10, 1996 WSCC system outage , 1999 .

[4]  Ivan Herman,et al.  Graph Visualization and Navigation in Information Visualization: A Survey , 2000, IEEE Trans. Vis. Comput. Graph..

[5]  Thomas J. Overbye,et al.  Visualization of power system data , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[6]  Thomas J. Overbye,et al.  New methods for the visualization of electric power system information , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[7]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[8]  Tina Eliassi-Rad,et al.  Data Sciences Technology for Homeland Security Information Management and Knowledge Discovery , 2005 .

[9]  Pak Chung Wong,et al.  A Dynamic Multiscale Magnifying Tool for Exploring Large Sparse Graphs , 2008, Inf. Vis..

[10]  Chaomei Chen,et al.  Information Visualization: Beyond the Horizon , 2006 .

[11]  Armand Navabi,et al.  Journal of Graph Algorithms and Applications Simultaneous Graph Drawing: Layout Algorithms and Visualization Schemes , 2022 .

[12]  F. Maghsoodlou,et al.  Energy management systems , 2004, IEEE Power and Energy Magazine.

[13]  L. Pereira,et al.  Cascade to black [system blackouts] , 2004, IEEE Power and Energy Magazine.

[14]  J. Skilling,et al.  Algorithms and Applications , 1985 .

[15]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .

[16]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[17]  Pak Chung Wong,et al.  Have Green - A Visual Analytics Framework for Large Semantic Graphs , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.

[18]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[19]  M. Friedman The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[20]  Harvey J. Miller,et al.  Time-space transformations of geographic space for exploring, analyzing and visualizing transportation systems , 2007 .