This technology is an integrated decision support control center system that assists operators during system maintenance and supports situation resolution during emergencies in a utility network.
Unmet Need: Efficient, integrated reporting of system status at control centers
Situational awareness of emergencies in a utility network are often done by defining a problem through multiple detection of anomalies. This information is often contained in multiple applications and is displayed in different formats, each of which must be searched and analyzed by the operator. There is a need for a system that can facilitate the identification of problems in a timely manner and that can support the operator’s analysis and problem resolution.
The Technology: Integrated decision support center for displaying and analyzing events in a utility grid
This technology is a decision support cockpit that gathers and provides prioritized information coming from multiple sources to the operator in a concise and integrated manner. The graphic display of the condensed data facilitates real-time analysis and response to various events within a system. The information is displayed emphasizing crucial events, supporting quick action plan development. Machine learning aspects can also be incorporated to generate predictive risk models for components and systems.
Applications:
- Integrated event monitoring and decision support system for utility networks, power plants
- Situational awareness of emergencies
- Intelligent utility grids
Advantages:
- Data integration and concise display facilitates overall situation assessment
- Prioritized display of information
- Links are provided for quick access to further information
- Dynamic information acquisition via automated or manual updates
- Integration of legacy information for easy retrieval
- Shortens response time
Lead Inventor:
Roger N. Anderson, Ph.D.
Patent Information:
Patent Status
Related Publications:
Rudin C, Waltz D, Anderson R, Boulanger A, Salleb-Aouissi A, Chow M, Dutta H, Gross P, Huang B, Ierome S, Isaac DF, Kressner A, Passonneau RJ, Radeva A, Wu L. “Machine Learning for the New York City Power Grid” IEEE Trans Pattern Anal Mach Intell. 2011 May 19; 34(2): 328-345.
Gross P, Salleb-Aouissi A, Dutta H, Boulanger A. “Ranking Electrical Feeders of the New York Power Grid” International Conference on Machine Learning and Application, 2009 Dec 13-15:1-7.
Gross P, Boulanger A, Arias M, Waltz D, Long P, Lawson C, Anderson R. “Predicting electricity distribution feeder failures using machine learning susceptibility analysis” IAAI’06 Proceedings of the 18th conference on Innovative applications of artificial intelligence. 2006 July 16-20; 2: 1705-1711.
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