Decision Support Systems and Methods for Complex Networks

• US 20100241608A1
• Filed: 03/23/2009
• Published: 09/23/2010
• Est. Priority Date: 03/23/2009
• Status: Active Grant

• Current Assignee: Battelle Memorial Institute
• Original Assignee: Battelle Memorial Institute
• Inventors: Ma, Jian, Schneider, Kevin P., Wong, Pak Chung, Chen, Yousu, Huang, Zhenyu, Mackey, Patrick S.
• Granted Patent: US 8,126,804 B2
• Time in Patent Office: Days
• US Class Current: 706/52
• CPC Class Codes: G06N 7/08 using chaos models or non-linear system models
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

Claims

1. A method of analyzing operation data in a decision support system for a complex network, the method comprising:

Characterizing network conditions from operation data using a state estimator;

Generating contingency scenarios based on the network conditions;

Determining risk indices for at least a portion of the contingency scenarios;

Visually presenting the contingency scenarios with risk indices as graphical representations in the context of a visual representation of the network; and

Trending historical risk indices based on their graphical representations to predict future network conditions.

2. The method of claim 1, wherein the complex network comprises an electric power grid.

3. The method of claim 1, wherein said generating contingency scenarios comprises using a processor-implemented contingency analysis algorithm.

4. The method of claim 1, wherein risk indices are generated for contingency scenarios that fail an “

N-1”

reliability standard, where “

N”

is the total number of applicable elements in the network.

5. The method of claim 1, wherein each risk index quantifies the risk for failure of a particular network element in a particular contingency scenario.

6. The method of claim 1, further comprising visually presenting an impact area on the representation of the network, the impact area corresponding to a potentially affected region in the network.

7. The method of claim 6, wherein the impact area is represented by contours on the visual representation of the network.

8. The method of claim 1, wherein the representation of the network comprises a map.

9. The method of claim 1, wherein said visually presenting comprises presenting a plurality of contingency scenarios superimposed over one another.

10. A decision support system that analyzes operation data from a complex network, the system comprising:

Communications hardware operative to communicate operation data between the support system and the network;

One or more processors each, or collectively, programmed to characterize network conditions from operation data using a state estimator, to generate contingency scenarios based on the network conditions, to determine risk indices for at least a portion of the contingency scenarios, to generate a visualization comprising the risk indices as graphical representations in the context of a visual representation of the network, and to trend historical risk indices based on their graphical representations to predict future network conditions.

11. The system of claim 10, comprising a plurality of processors configured for parallel computing.

12. The system of claim 10, wherein the complex network comprises an electric power grid.

13. The system of claim 10, wherein each risk index quantifies the risk for failure of a particular network element in a particular contingency scenario.

14. The system of claim 10, wherein the one or more processors are each further programmed to generate a visual representation of an impact area on the visual representation of the network, the impact area corresponding to a potentially affected region in the network.

15. The system of claim 14, wherein the visual representation of the impact area comprises contours on the visual representation of the network.

16. The system of claim 10, wherein the visual representation of the network comprises a map.

17. The system of claim 10, wherein the visualization comprises a plurality of contingency scenarios superimposed over one another.