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

• US 9,778,629 B2
• Filed: 11/25/2013
• Issued: 10/03/2017
• Est. Priority Date: 11/28/2012
• Status: Active Grant

• Current Assignee: Clemson University
• Original Assignee: Clemson University
• Inventors: Venayagamoorthy, Ganesh Kumar
• Primary Examiner(s): Shechtman, Sean
• Assistant Examiner(s): Wynne, David
• Application Number:
  US14/088,984
• Publication Number:
  US 20140148962A1
• Time in Patent Office: 1,408 Days
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/00 Systems supporting electrical power generation, transmission or distribution
• CPC Class Codes: Y04S 10/22 Flexible AC transmission systems [FACTS] or power factor or reactive power compensating or correcting units
• CPC Class Codes: Y04S 10/30 State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 20/00 Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

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.

Claims

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 one or more of said multiplicity of cells, 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; 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.

20. The electrical grid control system of claim 19 wherein said CCN is capable of coupling one or more of said layers to create a final predicted state model to indicate said final predicted control state of said one or more of said multiplicity of electrical devices.

21. The electrical grid control system of claim 19 wherein said CCN is capable of coupling one or more of said layers to create a final predicted state model to indicate said final predicted control state of said electrical grid.

22. The electrical grid control system of claim 19 wherein said controller calculates the stress of the electrical grid to create a current state model and a final predicted state model to indicate said current control state and said final predicted control state of said electrical grid, wherein said electrical grid comprises renewable generation devices.

23. The electrical grid control system of claim 19 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.