SYSTEMS AND METHODS FOR ASSET CONDITION MONITORING IN ELECTRIC POWER SUBSTATION EQUIPMENT

• US 20110213744A1
• Filed: 02/26/2010
• Published: 09/01/2011
• Est. Priority Date: 02/26/2010
• Status: Active Grant

• Current Assignee: GE Digital Holdings LLC (GE Vernova, Inc.)
• Original Assignee: General Electric Company
• Inventors: Beauchemin, Claude, Aubin, Jacques, Sparling, Brian David
• Granted Patent: US 8,484,150 B2
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G05B 23/0227 Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions

Abstract

Certain embodiments of the invention may include systems and methods for asset condition monitoring in electrical power substation equipment. According to an example embodiment of the invention, a method is provided for detecting behavior in electrical substation equipment. The method may include receiving realtime data associated with a plurality of parameters associated with the substation equipment, storing at least a portion of the realtime data over a period of time, wherein the stored data comprises historical data, comparing the realtime and historical data, and generating a report based at least in part on a comparison of the realtime data and historical data.

Claims

1. A method for detecting behavior in electrical substation equipment comprising:

receiving realtime data associated with a plurality of parameters associated with the substation equipment;

storing at least a portion of the realtime data over a period of time, wherein the stored data comprises historical data;

comparing the realtime and historical data; and

generating a report based at least in part on a comparison of the realtime data and historical data.

2. The method of claim 1, wherein receiving realtime data associated with a plurality of parameters associated with the substation equipment comprises receiving realtime data associated with one or more of transformers, transformer components, switches, bushings, or tap changers.

3. The method of claim 1, wherein monitoring a plurality of parameters comprises monitoring the electrical substation equipment over a predefined duration.

4. The method of claim 1, wherein the plurality of parameters comprise one or more of:

dissolved gasses in oil, oil quality, dielectric strength, acidity, color, interfacial tension, oil dissipation factor, oil inhibitor content, oil furanic compound content, partial discharge activity, dissolved metals in oil, particle count in oil, dissipation factor, capacitance, infrared images frequency response, recovery voltage, impedance, motor torque index, temperature differential index, number of days since the last operation of the reversing switch, or cooling system status.

5. The method of claim 1, wherein generating the report comprises generating an alert when the behavior associated with one or more of the plurality of parameters changes.

6. The method of claim 1, wherein comparison of the realtime data and historical data comprises analyzing a condition associated with the electrical substation equipment.

7. The method of claim 1, wherein generating the report comprises identifying a probable cause of behavior.

8. The method of claim 1, wherein generating the report comprises generating a recommendation for corrective action.

9. The method of claim 1, wherein comparison of realtime data with historical data comprises analyzing at least a portion of the data with a learned normal behavior model, wherein normal behavior is based at least in part on the historical data.

10. The method of claim 1, wherein the method further comprises analyzing maintenance data associated with the electrical substation equipment.

11. A system for detecting behavior in electrical substation equipment comprising:

one or more transformers;

a plurality of monitors operable to measure data associated with one or more transformers; and

at least one processor configured to;

receive, from the plurality of monitors, realtime data associated with the one or more transformers;

store at least a portion of the realtime data over a period of time, wherein the stored data comprises historical data;

compare the realtime and historical data; and

generate a report based at least in part on a comparison of the realtime data and the historical data.

12. The system of claim 11, wherein the at least one processor is further configured to monitor a plurality of parameters associated with the one or more of transformers, transformer components, switches, bushings, or tap changers over a predefined duration.

13. The system of claim 11, wherein the parameters comprise one or more of:

dissolved gasses in oil, oil quality, dielectric strength, acidity, color, interfacial tension, oil dissipation factor, oil inhibitor content, oil furanic compound content, partial discharge activity, dissolved metals in oil, particle count in oil, dissipation factor, capacitance, infrared images frequency response, recovery voltage, impedance, motor torque index, temperature differential index, number of days since the last operation of the reversing switch, or cooling system status.

14. The system of claim 11, wherein outputting the report comprises generating an alert when the behavior associated with one or more of the plurality of parameters changes.

15. The system of claim 11, wherein comparison of the realtime data and historical data comprises analyzing a condition associated with the electrical substation equipment.

16. The system of claim 11, wherein the report comprises a probable cause of behavior.

17. The system of claim 11, wherein the report comprises a recommendation for corrective action.

18. The system of claim 11, wherein comparison of realtime data with historical data comprises analyzing at least a portion of the data with a learned normal behavior model, wherein normal behavior is based at least in part on the historical data.

19. The system of claim 11, wherein comparison of realtime data with historical data comprises analyzing at least a portion of the data with a learned normal behavior model, wherein normal behavior is based at least in part on the historical data, and wherein the model is agnostic with respect to the parameters associated with the data.

20. The system of claim 11, wherein the one or more processors are further configured to analyze maintenance data associated with electrical substation equipment.