Real-time predictive systems for intelligent energy monitoring and management of electrical power networks
View Patent ↗A system for intelligent monitoring and management of 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 server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.
1. A system for intelligent monitoring and management of an electrical system, the system comprising:
a data acquisition component communicatively connected to one or more sensors which acquire real-time data output from the electrical system, wherein the electrical system comprises a plurality of components;
a power analytics server communicatively connected to the data acquisition component, wherein the power analytics sever comprises
a real-time energy pricing engine which generates real-time utility power pricing data using real-time dynamic utility power pricing data,
a virtual system modeling engine which generates predicted data output for the electrical system utilizing a virtual system model of the electrical system, and generates predicted utility power pricing data using the virtual system model of the electrical system and the real-time dynamic utility power pricing data, wherein the virtual system model comprises virtual component data corresponding to the plurality of components of the electrical system and relationships between the plurality of components of the electrical system,
an analytics engine configured to
monitor the real-time data output and the predicted data output of the electrical system,
determine a difference between the real-time data output and the predicted data output,
if the difference between the real-time data output and the predicted data output exceeds a first threshold but not a second threshold that is higher than the first threshold, initiate a calibration and synchronization operation to update the virtual system model,
if the difference between the real-time data output and the predicted data output does not exceed the first threshold, not initiate the calibration and synchronization operation, and,
if the difference between the real-time data output and the predicted data output exceeds the second threshold, generate an alarm;
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, and
an energy management system engine configured to process the real-time data output, the predicted data output, and the forecasted aspect to generate a user interface that conveys an operational state of the electrical system; and
a client terminal communicatively connected to the power analytics server and configured to display the user interface.
2. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein, the machine learning engine includes, an associative memory layer, a sensory layer, and a neocortical model.
3. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the virtual system model includes current system components and operational parameters comprising the electrical system.
4. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the real-time dynamic utility power pricing data is received from a utility power provider supplying electrical power to the electrical system.
5. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the operational state is the real-time operational performance of the electrical system.
6. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the operational state is the predicted operational performance of the electrical system.
7. The system for intelligent monitoring and management of an electrical system, as recited in claim 5 , wherein the real-time operational performance of the electrical system includes the real-time cost of energy utilized by the electrical system.
8. The system for intelligent monitoring and management of an electrical system, as recited in claim 5 , wherein the real-time operational performance of the electrical system includes the real-time cost of intrinsic power losses within the electrical system.
9. The system for intelligent monitoring and management of an electrical system, as recited in claim 5 , wherein the real-time operational performance of the electrical system includes the real-time cost of power losses based on a power factor value for the electrical system.
10. The system for intelligent monitoring and management of an electrical system, as recited in claim 6 , wherein the predicted operational performance of the electrical system includes the predicted cost of energy utilized by the electrical system.
11. The system for intelligent monitoring and management of an electrical system, as recited in claim 6 , wherein the predicted operational performance of the electrical system includes the predicted cost of intrinsic power losses within the electrical system.
12. The system for intelligent monitoring and management of an electrical system, as recited in claim 6 , wherein the predicted operational performance of the electrical system includes the predicted cost of power losses based on a power factor value for the electrical system.
13. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , further including a historical data trending database connected to the energy management system engine and configured to store the real-time data output, the predicted data output and the forecasted aspects output from the power analytics server.
14. The system for intelligent monitoring and management of an electrical system, as recited in claim 13 , wherein the energy management system engine is further configured to apply a historical trending algorithm to the stored real-time data output, the predicted data output, and the forecasted aspect to provide a historical data trending display.
15. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system active power data.
16. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system reactive power data.
17. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system power factor data.
18. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system humidity data.
19. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system temperature data.
20. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system data for total cost of power.
21. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system data for total penalty cost based on a power factor value for the electrical system.
22. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system power frequency data.
23. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system voltage data.
24. The system for intelligent monitoring and management of an electrical system, as recited in claim 14 , wherein the historical data trending display includes historical electrical system cost of losses data.
25. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , 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.
26. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the forecasted aspect is a predicted reliability and availability of the electrical system.
27. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the forecasted aspect is a predicted total power capacity of the electrical system.
28. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the forecasted aspect is a predicted ability of the electrical system to maintain availability of total power capacity.
29. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the forecasted aspect is a predicted utilization of the total power capacity of the electrical system.
30. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the virtual system model is stored on a virtual system model database communicatively connected with the power analytics server.
31. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , 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.
32. The system for intelligent monitoring and management of an electrical system, as recited in claim 31 , wherein the contingency event relates to load shedding.
33. The system for intelligent monitoring and management of an electrical system, as recited in claim 31 , wherein the contingency event relates to load adding.
34. The system for intelligent monitoring and management of an electrical system, as recited in claim 31 , wherein the contingency event relates to loss of utility power supply to the electrical system.
35. The system for intelligent monitoring and management of an electrical system, as recited in claim 31 , wherein the contingency event relates to a loss of distribution infrastructure associated with the electrical system.
36. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the client terminal is a thin client computing device.
37. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the client terminal is a wide area network capable computing device.
38. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the client terminal is a mobile computing device.
39. The system for intelligent monitoring and management of an electrical system, as recited in claim 1 , wherein the analytics engine is configured to, if the difference between the real-time data output and the predicted data output exceeds the second threshold, not initiate the calibration and synchronization operation.