REAL-TIME SIMULATION OF POWER GRID DISRUPTION

• US 20130191052A1
• Filed: 01/23/2013
• Published: 07/25/2013
• Est. Priority Date: 01/23/2012
• Status: Abandoned Application

• Current Assignee: UT-Battelle LLC
• Original Assignee: UT-Battelle LLC
• Inventors: Fernandez, Steven J., Shankar, Mallikarjun, Liu, Yilu, Omitaomu, Olufemi A., Nutaro, James J., Dimitrovski, Aleksandar D., Groer, Christopher S., Spafford, Kyle L., Vatsavai, Ranga R.
• Application Number:
  US13/747,779
• Publication Number:
  US 20130191052A1
• Time in Patent Office: Days
• US Class Current: 702/60
• CPC Class Codes: G01R 21/00 Arrangements for measuring electric power or power factor G01R7/12 takes precedence
• CPC Class Codes: G01R 31/088 Aspects of digital computing
• CPC Class Codes: G06F 17/00 Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

A method and system for monitoring the electric grid and predicting failures and/or other issues. Streams of data about a power grid are received from a plurality of remote power grid sensors and converted into a univariate time sequence. Anomaly patterns are identified in the univariate time sequence and analyzed or simulated to predict the power grid disruption. The anomaly patterns are compared to power disruption contingencies stored in a database to simulate and/or predict the present or future power disruption represented by the anomaly pattern.

Claims

1. A method of detecting a power grid disruption, the method comprising:

receiving a stream of data about a power grid;

converting the stream of data into a univariate time sequence;

extracting a power event from the univariate time sequence; and

predicting the power grid disruption from the extracted power event.

2. The method of claim 1, wherein a data processor receives the stream of data and includes a recordable medium comprising executable coded instructions that when executed causes the data processor to perform the converting, detecting, and predicting steps.

3. The method of claim 1, wherein the univariate time sequence includes changes between successive windows extracted from an original sequence.

4. The method of claim 1, wherein the stream of data is received from a plurality of remote sensors in sensing combination with the power grid.

5. The method of claim 1, wherein extracting power events comprises anomaly detection.

6. The method of claim 1, wherein the extracted power event is selected from a predetermined change in one of frequency, voltage, or phase angle within the univariate time sequence.

7. The method of claim 1, wherein the prediction includes a size and location of the power grid disruption.

8. The method of claim 1, further comprising:

a data processor comparing the extracted power event to a database of recorded disruption events; and

the data processor matching the extracted power event to one recorded event of the database to predict the power grid disruption.

9. The method of claim 1, further comprising comparing anomaly patterns of the extracted power event to patterns of the recorded disruption events.

10. The method of claim 1, further comprising automatically sending an alert with information on the predicted power grid disruption.

11. A method of detecting a power grid disruption, the method comprising:

receiving with a data processor a stream of data about a power grid;

automatically detecting an anomaly in the data;

automatically comparing the anomaly to a database of power disruption contingencies; and

predicting the power grid disruption upon matching the anomaly to one of the power disruption contingencies.

12. The method of claim 11, wherein the anomaly is selected from a predetermined change in one of frequency, voltage, or phase angle within the data.

13. The method of claim 11, wherein the prediction includes a size and location of the power grid disruption.

14. The method of claim 11, further comprising automatically sending an alert with information on the predicted power grid disruption.

15. The method of claim 11, further comprising:

receiving with the data processor a plurality of continuous streams of data about the power grid from a plurality of remote power grid sensors; and

the data processor automatically converting the plurality of continuous streams into a univariate time sequence.

16. The method of claim 15, wherein the anomaly is selected from a predetermined change in one of frequency, voltage, or phase angle within the univariate time sequence.

17. A series of preprogrammed instructions on a non-transitory recordable medium that, when executed by a computing machine, cause the computing machine to predict a power grid disruption, the steps comprising:

receiving streams of data about a power grid from a plurality of remote power grid sensors;

converting the streams of data into a univariate time sequence;

identifying an anomaly in the univariate time sequence; and

predicting the power grid disruption from the identified anomaly.

18. The instructions of claim 17, further comprising detecting a predetermined change in one of frequency, voltage, or phase angle within the univariate time sequence.

19. The instructions of claim 18, further comprising comparing the predetermined change to a plurality of change patterns in a database of power disruption contingencies.

20. The instructions of claim 19, wherein the predetermined change comprises an anomaly pattern.