A Data-Driven Approach to Interactive Visualization of Power Grids

Period of Performance: 01/01/2011 - 12/31/2011

$150K

Phase 1 SBIR

Recipient Firm

Power Info Llc
12600 SE 38th St. Suite 230
Bellevue, WA 98006
Principal Investigator

Abstract

The emerging field of visual analytics, which integrates analytical reasoning with interactive visualization, appears to be a promising technique for improving the business practices in todays electric power industry. The conducted investigation has revealed that the existing commercial power grid visualization tools focus on displaying the data rather than providing users an interactive ability to facilitate information exploration. These tools, typically designed to support a particular type of applications, restrict the information exploration process to follow a limited number of pre-defined visualization patterns created by human designers, thus hindering users ability to discover. This project proposes a data-driven approach to interactive visualization of power grids. A data-driven visualization uses empirically or mathematically derived data to dynamically formulate the visualization. The approach relies on developing powerful data manipulation algorithms to create visualizations based on the characteristics of data. It will result in an interactive and user-driven visualization tool that fosters scientific understanding and insight, therefore unleashing the power of visualization. The proposed data-driven visualization approach, as demonstrated in the conducted preliminary investigation, has proven to be promising for building the next-generation power grid visualization tools. Application of this approach has resulted in a query-driven model exploratory tool currently being leveraged by utilities and vendors in the power industry. This project will focus on prototyping an event driven visualization tool designed to enhance situational awareness in a power grid control center environment. The goal is to assist grid operators to perceptually monitor a large number of events and timely present the analytical information that reduces cognitive demands on operators.