SYSTEMS AND METHODS FOR REAL-TIME FORECASTING AND PREDICTING OF ELECTRICAL PEAKS AND MANAGING THE ENERGY, HEALTH, RELIABILITY, AND PERFORMANCE OF ELECTRICAL POWER SYSTEMS BASED ON AN ARTIFICIAL ADAPTIVE NEURAL NETWORK

• US 20090113049A1
• Filed: 11/07/2008
• Published: 04/30/2009
• Est. Priority Date: 04/12/2006
• Status: Abandoned Application

• Current Assignee: Power Analytics Corporation (PAC Global, Inc.)
• Original Assignee: EDSA Micro Corporation (PAC Global, Inc.)
• Inventors: Nasle, Adib, Nasle, Ali
• Application Number:
  US12/267,346
• Publication Number:
  US 20090113049A1
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G05B 13/026 using a predictor
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 19/0428 Safety, monitoring G05B19/0423 takes precedence
• CPC Class Codes: G05B 2219/2639 Energy management, use maximum of cheap power, keep peak load low
• 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 30/13 Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/10 Interfaces, programming languages or software development kits, e.g. for simulating neural networks
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/06 Simulation on general purpose computers

Abstract

A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are 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 virtual system modeling engine, an analytics engine, an adaptive prediction engine. 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 adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm.

Claims

1. A system for making real-time predictions about the health, reliability, and performance of a monitored system, comprising:

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

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

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

an analytics engine configured to monitor the real-time data output and the predicted data output of the monitored 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

an adaptive prediction engine configured to forecast an aspect of the monitored system using a neural network algorithm, the adaptive prediction engine further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm; and

a client terminal communicatively connected to the power analytics server, the client terminal configured to display the forecasted aspect.

2. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 1, wherein the neural network algorithm is a back propagation algorithm.

3. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 2, wherein the measure of error is a sum squared error.

4. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 1, wherein the adaptive prediction engine is further configured to forecast the aspect of the monitored system when subjected to a simulated contingency event.

5. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 1, wherein the monitored system is an electrical system.

6. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 1, wherein the virtual system model includes current system components and operational parameters comprising the monitored system.

7. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 6, wherein the current system components are comprised of static components and rotating components.

8. The system for making real-time predictions about the health, reliability, and performance of a monitored 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.

9. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 1, wherein the forecasted aspect is a predicted reliability and availability of the electrical system.

10. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 1, wherein the forecasted aspect is a predicted total power capacity of the electrical system.

11. The system for making real-time predictions about the health, reliability, and performance of a monitored 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.

12. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 1, wherein the forecasted aspect is a predicted utilization of the total power capacity of the electrical system.

13. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 4, wherein the forecasted aspect is a predicted ability of the electrical system to withstand the simulated contingency event that results in stress to the electrical system.

14. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 4, wherein the contingency event relates to load shedding.

15. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 4, wherein the contingency event relates to load adding.

16. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 4, wherein the contingency event relates to loss of utility power supply to the electrical system.

17. The system for making real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 4, wherein the contingency event relates to a loss of distribution infrastructure associated with the electrical system.

18. A computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, comprising:

receiving real-time data output from one or more sensors interfaced to the monitored system;

generating predicted data output for the one or more sensors interfaced to the monitored system utilizing a virtual system model of the monitored system;

calibrating the virtual system model of the monitored system when a difference between the real-time data output and the predicted data output exceeds a threshold;

processing the real-time data output using a neural network algorithm;

optimizing the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm; and

forecasting an aspect of the monitored system using the neural network algorithm.

19. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 18, wherein the monitored system is an electrical system.

20. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 19, further including:

choosing a simulated contingency event to subject the monitored system to; and

forecasting the aspect of the monitored system by running an analysis of the calibrated virtual system model operating under conditions simulating the contingency event chosen.

21. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the contingency event relates to execution of a start-up sequence for a component of the electrical system.

22. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the contingency event relates to load shedding.

23. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the contingency event relates to critical clearing time of a tripped circuit breaker within the electrical system.

24. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the contingency event relates to a change in protective device operations and interactions.

25. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the contingency event relates to loss of utility power supply to the monitored system.

26. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the contingency event relates to loss of a generator in the electrical system.

27. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the contingency event relates to a loss of distribution infrastructure associated with the electrical system.

28. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the forecasted aspect is the electrical system'"'"'s ability to maintain sufficient active and reactive power reserves to cope with the contingency event.

29. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the forecasted aspect is the electrical system'"'"'s ability to operate safely after the contingency event.

30. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the forecasted aspect is the electrical system'"'"'s ability to operate reliably after the contingency event.

31. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 30, wherein the electrical system'"'"'s operational reliability is measured as a system reliability index rating.

32. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the forecasted aspect is the electrical system'"'"'s ability to continue to operate with minimum operating cost after the contingency event.

33. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the forecasted aspect is the electrical system'"'"'s ability to provide an acceptably high level of power quality after the contingency event.

34. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 33, wherein the level of power quality is measured by the electrical system'"'"'s ability to maintain voltage and frequency within a tolerance.

35. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 18, wherein the virtual system model is updated to reflect real-time weather conditions impacting the electrical system.

36. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the forecasted aspect is the electrical system'"'"'s operational stability under real-time weather conditions.

37. The computer-implemented method for utilizing a neural network algorithm utilized to make real-time predictions about the health, reliability, and performance of a monitored system, as recited in claim 20, wherein the forecasted aspect is the electrical system'"'"'s ability to recover from a contingency event without violating operational constraints of the electrical system.