Self-healing power grid and method thereof

• US 8,504,214 B2
• Filed: 06/18/2010
• Issued: 08/06/2013
• Est. Priority Date: 06/18/2010
• Status: Expired due to Fees

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Genc, Sahika, Gokcen, Ibrahim
• Primary Examiner(s): Padmanabhan, Kavita
• Assistant Examiner(s): STEVENS, THOMAS H
• Application Number:
  US12/818,206
• Publication Number:
  US 20110313581A1
• Time in Patent Office: 1,145 Days
• Field of Search: 700/291, 700/292, 700/295, 700/286
• US Class Current: 700/292
• CPC Class Codes: G05B 23/0248 Causal models, e.g. fault tree; digraphs; qualitative physics

Abstract

A method for determining a self-healing power grid status is presented. The method includes receiving respective real-time monitoring data corresponding to one or more power grid components, wherein one or more agents are coupled to the power grid components. The method includes the steps of determining a respective current infectiousness state based upon the received respective real-time monitoring data, determining respective output data based upon the respective current infectiousness state, exchanging the respective output data with one or more neighboring agents, and generating a respective new infectiousness state based upon the respective exchanged output data and a state transition diagram.

Claims

1. A method for determining a self-healing power grid status, comprising:

receiving respective real-time monitoring data corresponding to one or more power grid components, wherein one or more agents are coupled to said power grid components;

determining a respective current infectiousness state based upon the received respective real-time monitoring data;

determining respective output data based upon the respective current infectiousness state;

exchanging the respective output data with one or more neighboring agents;

generating one or more state transition probabilities based upon one or more parameters and a state transition diagram; and

generating a respective new infectiousness state based upon the one or more state transition probabilities.

2. The method of claim 1, wherein receiving the respective real-time monitoring data comprises:

generating the respective real-time monitoring data by observing real-time characteristics of the one or more power grid components; and

sending the respective real-time monitoring data to respective agent.

3. The method of claim 1, wherein determining the respective output data comprises verifying whether the respective current infectiousness state is a dead state.

4. The method of claim 1, wherein exchanging the respective output data with the one or more neighboring agents comprises:

transmitting the respective output data to the one or more neighboring agents; and

receiving output data corresponding to the one or more neighboring agents.

5. The method of claim 1, wherein the one or more parameters comprises a fault spreading rate, a power grid component removal rate, an observation probability, or combinations thereof.

6. The method of claim 1, further comprising:

determining one or more cascading faults based upon the respective new infectiousness state and the state transition diagram; and

generating a cascading fault flag based upon the determination of the one or more cascading faults.

7. The method of claim 6, further comprising determining one or more preventive measures based upon the cascading fault flag.

8. The method of claim 1, further comprising:

updating the respective current infectiousness state by equating the respective new infectiousness state to the respective current infectiousness state; and

generating respective updated output data based upon the updated respective current infectiousness state.

9. A self-healing power grid, comprising:

a plurality of power grid components;

a plurality of monitoring layers coupled to the power grid components, wherein the plurality of monitoring layers observe one or more real-time characteristics of the plurality of power grid components to generate respective real-time monitoring data;

a plurality of agents communicatively coupled to one or more of the plurality of monitoring layers, wherein one or more of the plurality of agents;

receive the respective real-time monitoring data corresponding to one or more power grid components;

determine a respective current infectiousness state based upon the received respective real-time monitoring data;

determine respective output data based upon the respective current infectiousness state;

exchange the respective output data with one or more neighboring agents;

generate respective new infectiousness state based upon the respective exchanged output data;

generate one or more state transition probabilities based upon one or more parameters and a state transition diagram; and

generate the respective new infectiousness state based upon the one or more state transition probabilities.

10. The power grid of claim 9, wherein the respective real-time monitoring data comprises status alarms, limit violation alarms, power outages, power outage distribution factors, network topology, operational state, or combinations thereof.

11. The power grid of claim 9, wherein the one or more real-time characteristics comprise voltage, temperature, current, or combinations thereof.

12. The power grid of claim 9, wherein the plurality of monitoring layers comprise an energy management system (EMS), a system data exchange (SDX), a system control and data acquisition system (SCADA), a topology processor, or combinations thereof.

13. The power grid of claim 9, wherein the plurality of power grid components comprises regional transmission operators, transmission substations, distribution substations, distributed energy sources, load serving entities, generators, distribution control systems, transmission lines, distribution lines, extra high voltage system, high voltage system, medium voltage system, transformers, regional transmission organization, softwares, independent system operator, power grid organizations, energy management systems (EMS), system control and data acquisition systems (SCADA), field remote terminals (RTU), master stations, control areas, a topology processor, interchange distribution calculator, or combinations thereof.

14. The power grid of claim 9, wherein the respective current infectiousness state or the new infectiousness state comprises a normal state, a dead state, a faulted state, a hidden faulted state, a recovered state, or combinations thereof.

15. The power grid of claim 9, further comprising a diagnoser layer that:

determines one or more cascading faults in the self-healing power grid based upon the at least one respective new infectiousness state of the plurality of agents and a state transition diagram; and

generates a cascading fault flag based upon the determination of the one or more cascading faults.

16. The power grid of claim 15, further comprising establishing equilibrium points and comparing the at least one respective new infectiousness state to the equilibrium points.

17. The power grid of claim 15, further comprising a prevention layer that determines one or more preventive measures based upon the cascading fault flag.

18. The power grid of claim 9, wherein the plurality of agents further:

update the respective current infectiousness state by equating the respective new infectiousness state to the respective current infectiousness state; and

generate respective updated output data based upon the updated respective current infectiousness state.