Situational awareness / situational intelligence system and method for analyzing, monitoring, predicting and controlling electric power systems

Patent metadata

Field	Value
Publication number	US-9778629-B2
Application number	US-201314088984-A
Country	US
Kind code	B2
Filing date	Nov 25, 2013
Priority date	Nov 28, 2012
Publication date	Oct 3, 2017
Grant date	Oct 3, 2017

Abstract

A system and method for modeling, controlling and analyzing electrical grids for use by control room operators and automatic control provides a multi-dimensional, multi-layer cellular computational network (CCN) comprising an information layer; a knowledge layer; a decision-making layer; and an action layer; wherein each said layer of said CCN represents one of a variable in an electric power system. Situational awareness/situational intelligence is provided therefrom so that the operators and grid control systems can make the correct decision and take informed actions under difficult circumstances to maintain a high degree of grid integrity and reliability by analyzing multiple variables within a volume of time and space to provide an understanding of their meaning and predict their states in the near future where these multiple variables can have different timescales.

First claim

What is claimed is:

1. An electrical grid monitoring, predictive monitoring, and control system comprising:
a controller in electrical communication with a multiplicity of electrical devices in an electric grid, wherein said controller receives control state data from each electrical device of said multiplicity of electrical devices indicating a current state of each said electrical device;
a multi-dimensional, multi-layer cellular computational network (CCN) disposed within said controller comprising:
an information layer;
a knowledge layer;
a decision-making layer; and
an action layer;
wherein each said layer of said CCN represents one of a multiplicity of control state variables in the electric grid; and
wherein each said layer is further comprised of a multiplicity of cells each containing computational algorithms capable of cognitive learning to create a control state model by receiving cellular control state information from one or more cells; and

wherein said controller analyzes said multiplicity of control state data then determines a current control state, an interim predicted control state, and a final predicted control state for one or more of said multiplicity of electrical devices;
wherein said interim predicted control state is derived from predicted measurements associated with said multiplicity of control state variables in the electric grid;
wherein said final predicted control state is derived from a combination of said current control state and one or more interim predicted control states; and
wherein said controller automatically changes said current state of said one or more of said multiplicity of electrical devices based on the final predicted control state.

2. The electrical grid control system of claim 1 wherein said controller creates a current control state model and a final predicted control state model to indicate the current state and final predicted future state of said one or more of said multiplicity of electrical devices.

3. The electrical grid control system of claim 1 wherein said controller creates a current control state model and a final predicted control state model to indicate the current state and final predicted future state of said electrical grid.

4. The electrical grid control system of claim 1 wherein said controller indicates a recommended action defining a course of action for future control of said electrical grid.

5. The electrical grid control system of claim 4 wherein said controller transmits control information based upon said recommended action to one or more of said multiplicity of electrical devices thereby causing a change in the state of said one or more of said multiplicity of electrical devices.

6. The electrical grid control system of claim 1 wherein said controller calculates a stress of said electrical grid.

7. The electrical grid control system of claim 6 comprising calculation of a final predicted state model from said stress to indicate a final predicted future state of said electrical grid.

8. The electrical grid control system of claim 6 wherein said system provides a dynamic predictive state estimation model to allow for improved detection, identification and removal of bad measurements.

9. The electrical grid control system of claim 6 wherein said controller creates real-time indicators and predictive security indicators.

10. The electrical grid control system of claim 1 wherein said controller creates an optimal predicted control state model from the situational intelligence derived from a multiplicity of possible predicted future states of said one or more of said multiplicity of electrical devices.

11. A method of controlling an electrical grid in a situational awareness/situational intelligence framework comprising the steps of:
receiving control state information from at least one of a multiplicity of electrical devices disposed within an electrical grid;
analyzing said control state information in said controller using a multi-dimensional, multi-layer cellular computational network (CCN) disposed within said controller comprising:
an information layer;
a knowledge layer;
a decision-making layer; and
an action layer;
wherein each said layer of said CCN represents one of a control state variable of a multiplicity of control state variables in said electric grid; and
wherein each said layer is further comprised of a multiplicity of cells each containing computational algorithms capable of cognitive learning to create a control state model by receiving cellular control state information from one or more cells; and

creating an interim predicted control state for at least one of said multiplicity of electrical devices;
creating a final predicted control state for at least one of said multiplicity of electrical devices; and
wherein said controller automatically changes said current state of said one or more of said multiplicity of electrical devices based on the final predicted control state.

12. The method of claim 11 further comprising the step of creating a final predicted state model to indicate the future state of said one or more of said multiplicity of electrical devices.

13. The method of claim 11 further comprising the step of creating a final predicted state model to indicate the future state of the electrical grid.

14. The method of claim 11 further comprising the step of creating a recommended action for use in selecting a course of action for future control of the electrical grid.

15. The method of claim 11 further comprising the step of sending updated control state information to one or more of said multiplicity of electrical devices for the purpose of causing a change in the state of said one or more of said multiplicity of electrical devices.

16. The method of claim 11 further comprising the step of lowering the number of phasor measurement units disposed within the electrical grid without degrading said controller's ability to provide full observability of the electrical grid.

17. The method of claim 11 further comprising the step of creating virtual phasor measurement units for use within said CCN.

18. The electrical grid control system of claim 11 wherein said controller creates an optimal predicted control state model from the situational intelligence derived from a multiplicity of possible predicted future states of said one or more of said multiplicity of electrical devices.

19. An electrical grid monitoring and control system containing a situational awareness/situational intelligence framework comprising:
a controller in electrical communication with a multiplicity of electrical devices in an electrical grid, wherein said controller receives control state information from at least one electrical device in said multiplicity of electrical devices indicating a current state of said at least one electrical device;
a multi-dimensional, multi-layer cellular computational network (CCN) disposed within said controller comprising:
an information layer;
a knowledge layer;
a decision-making layer; and
an action layer;
wherein each said layer of said CCN represents one of a control state variable of a multiplicity of control state variables in said electric grid; and
wherein each said layer is further comprised of a multiplicity of cells each containing computational algorithms capable of cognitive learning to create a control state model by receiving cellular control state information from another cell;

wherein said controller analyzes said control state information and said cognitive learning within

Assignees

• Univ Clemson

Inventors

• Venayagamoorthy Ganesh Kumar

Classifications

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• H02J13/12

  Monitoring network conditions, e.g. electrical magnitudes or operational status · CPC title

• Y04S10/30

  State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge · CPC title

• G05B13/048 Primary

  using a predictor · CPC title

• H02J3/00

  Circuit arrangements for AC mains or AC distribution networks · CPC title