Systems and methods for alarm filtering and management within a real-time data acquisition and monitoring environment

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

receiving real-time data;

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

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

generating a real-time model based on the real-time data;

comparing the real-time data against the predicted data;

monitoring the real-time data output and the predicted data output of the electrical system;

initiating 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;

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 28 , 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. A 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;

updating a real-time 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;

initiating 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;

identifying an alarm condition based on the comparison; and

communicating the alarm condition to a display.