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Graphical Contingency Analysis Tool

Stage: Production

The Graphical Contingency Analysis (GCA) Tool is a visual analytic software tool that aids power grid operators and planners to effectively manage complex outage predictions and helps operators and planners predict potential network failures and take effective actions in response to adverse situations.

Utilities are required by regulatory agencies to operate their grids so that instability, uncontrolled separation, or cascading outages will not occur as a result of the most severe single contingency. Today, power grid operation is largely based on operators’ experience with very limited real-time decision support, resulting in inefficient operations.

Researchers at PNNL have developed a visualization analytics technology (referred as Graphical Contingency Analysis Tool or GCA Tool) to provide effective decision supports to utility operators and planners. The human constraint of trying to analyze too much data and make decisions in a reasonable time can be released by the GCA Tool that provides quick visualization of potential problem areas to improve wide-area situational awareness. Further, additional features of the GCA Tool allow the user to rank order contingencies from worst to best, and to assess alterative actions to help them understand what will happen once specific actions are taken. Overall, the GCA Tool can provide effective decision making support to operators and planners.

Applications and Industries

  • electric utility operators and planners
  • consultants providing planning services to utilities


  • Provides intuitive graphical representation, allowing faster and more accurate identification of potential operational problems
  • Ranks contingencies to focus the operator/planner’s attention on the most severe one and to prioritize preventative actions, adding meanings to the results of contingency analysis for an operator or analyst
  • Interactively assess alterative actions in terms of what will happen if each action is taken, providing more-informed decision making in an actual event