SYSTEMS AND METHODS FOR ALARM FILTERING AND MANAGEMENT WITHIN A REAL-TIME DATA ACQUISITION AND MONITORING ENVIRONMENT

• US 20080120080A1
• Filed: 07/20/2007
• Published: 05/22/2008
• Est. Priority Date: 04/12/2006
• Status: Active Grant

• Current Assignee: Power Analytics Corporation (PAC Global, Inc.)
• Original Assignee: EDSA Micro Corporation (PAC Global, Inc.)
• Inventors: Nasle, Adib
• Granted Patent: US 7,693,608 B2
• Time in Patent Office: Days
• US Class Current: 703/13
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05B 23/0272 Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user
• CPC Class Codes: G06F 2111/02 CAD in a network environment, e.g. collaborative CAD or distributed simulation
• CPC Class Codes: G06F 2119/06 Power analysis or power optimisation
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

A system for filtering and interpreting real-time sensory data from an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics sewer is comprised of a virtual system modeling engine, an analytics engine, and a decision engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-lime data output and predicted data output of the electrical system. The decision engine compares the real-time data output against the predicted data output to filter out and interpret indicia of electrical system health and performance. The client terminal is communicatively connected to the power analytics server and configured to display the filtered and interpreted indicia

Claims

1. A system for filtering and interpreting real-time sensory data from an electrical system, comprising:

a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the electrical system;

a power analytics server communicatively connected to the data acquisition components, comprising,

a virtual system modeling engine configured to generate predicted data output for the electrical system utilizing a virtual system model of the electrical system,

an analytics engine configured to monitor the real-time data output and the predicted data output of the electrical system, the analytics engine further configured to initiate a calibration and synchronization operation to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold, and

a decision engine configured to compare the real-time data output against the predicted data output to filter out and interpret indicia of electrical system health and performance; and

a client terminal communicatively connected to the power analytics server and configured to display the filtered and interpreted indicia.

2. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein the displayed indicia specified an alarm condition on the electrical system.

3. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 2, wherein the displayed indicia is color coded to communicate a severity of the alarm condition.

4. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 2, wherein the alarm condition is related to a power flow parameter of the electrical system.

5. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 4, wherein the power flow parameter is a kilowatt (kW) value.

6. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 4, wherein the power flow parameter is a kVAR value.

7. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, wherein the power flow parameter is a power factor value.

8. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 2, wherein the alarm condition relates to a short circuit fault current value that is indicative of stress and withstand capabilities of protective devices on the electrical system.

9. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 2, wherein the alarm condition relates to power quality on the electrical system.

10. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 9, wherein the power quality is indicated by voltage value distortions on the electrical system.

11. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 9, wherein the quality of power is indicated by current value distortions on the electrical system.

12. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 2, wherein alarm condition relates to the electrical system loading.

13. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 2, wherein the alarm condition relates to electrical system capacity.

14. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 2, wherein the alarm condition relates to electrical system frequency.

15. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 2, wherein the alarm condition relates to electrical system power harmonics waveform.

16. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 1, further including a machine learning engine configured to store and process patterns observed from the real-time data output and the predicted data output, the machine learning engine further configured to forecast an aspect of the electrical system.

17. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 16, wherein, the machine learning engine includes,

an associative memory layer,

a sensory layer, and

a neocortical model.

18. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 16, wherein the forecasted aspect is a predicted ability of the electrical system to resist system output deviations from defined tolerance limits of the electrical system.

19. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 16, wherein the forecasted aspect is a predicted reliability and availability of the electrical system.

20. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 16, wherein the forecasted aspect is a predicted total power capacity of the electrical system.

21. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 16, wherein the forecasted aspect is a predicted ability of the electrical system to maintain availability of total power capacity.

22. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 16, wherein the forecasted aspect is a predicted utilization of the total power capacity of the electrical system.

23. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 16, wherein the forecasted aspect is a predicted ability of the electrical system to withstand a contingency event that results in stress to the electrical system.

24. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 23, wherein the contingency event relates to load shedding.

25. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 23, wherein the contingency event relates to load adding.

26. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 23, wherein the contingency event relates to loss of utility power supply to the electrical system.

27. The system for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 23, wherein the contingency event relates to a loss of distribution infrastructure associated with the electrical system.

28. The method for filtering and interpreting real-time sensory data from an electrical system, comprising:

updating a virtual system model of the electrical system in response to real-time data;

generating predicted data output from the electrical system using the updated virtual system model;

comparing the real-time data against the predicted data;

identifying an alarm condition based on deviations detected during the comparison; and

communicating the alarm condition to a display.

29. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 28, wherein the displayed alarm condition is color coded to indicate severity.

30. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 28, wherein the alarm condition is related to a power flow parameter of the electrical system.

31. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 30, wherein the power flow parameter is a kilowatt (kW) value.

32. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 30, wherein the power flow parameter is a kVAR value.

33. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 30, wherein the power flow parameter is a power factor value.

34. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 30, wherein the alarm condition relates to a short circuit fault current value that is indicative of stress and withstand capabilities of protective devices on the electrical system.

35. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 28, wherein the alarm condition relates to power quality on the electrical system.

36. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 35, wherein the power quality is indicated by voltage value distortions on the electrical system.

37. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 35, wherein the quality of power is indicated by current value distortions on the electrical system.

38. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 28, wherein alarm condition relates to the electrical system loading.

39. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 28, wherein the alarm condition relates to electrical system power capacity.

40. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 28, wherein the alarm condition relates to electrical system frequency.

41. The method for filtering and interpreting real-time sensory data from an electrical system, as recited in claim 28, wherein the alarm condition relates to electrical system harmonics waveform.

42. The method for filtering and interpreting real-time sensory data from an electrical system, comprising:

updating a virtual system model of the electrical system in response to real-time data;

generating predicted data output from the electrical system using the updated virtual system model;

comparing the real-time data against the predicted data;

identifying an alarm condition based on the comparison; and

communicating the alarm condition to a display.