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 20160247065A1
• Filed: 04/05/2016
• Published: 08/25/2016
• Est. Priority Date: 02/14/2006
• Status: Abandoned Application

• Current Assignee: Power Analytics Corporation (PAC Global, Inc.)
• Original Assignee: Power Analytics Corporation (PAC Global, Inc.)
• Inventors: Nasle, Adib, Nasle, Ali
• Application Number:
  US15/090,657
• Publication Number:
  US 20160247065A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

Systems and methods for making real-time predictions about the health, reliability, and performance of a monitored system are disclosed. A data acquisition component acquires real-time data output from the monitored system. A power analytics server comprises a virtual system modeling engine, an analytics engine, and an adaptive prediction engine. The virtual system modeling engine is operable to generate predicted data output for the monitored system utilizing a virtual system model of the monitored system. An analytics engine is operable to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold. The adaptive prediction engine is operable to forecast an aspect of the monitored system based on an adaptive neural network algorithm and automatically minimize a measure of error between the real-time data output and a corresponding forecasted data output by the adaptive prediction engine.

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 based on an adaptive neural network algorithm, the adaptive prediction engine further configured to automatically minimize a measure of error between the real-time data output and a corresponding forecasted data output by the adaptive prediction engine.

2. The system of 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.

3. The system of claim 2, wherein the contingency event relates to load shedding, load adding, loss of a power supply, and/or loss of distribution infrastructure to an electrical system.

4. The system of claim 1, wherein the monitored system is selected from the group consisting of an electric power grid, a microgrid, a data center and any other electrical systems.

5. The system of claim 1, wherein the virtual system model includes current system components and operational parameters comprising the monitored system.

6. The system for of claim 1, wherein the forecasted aspect is a predicted ability of the monitored system to resist system output deviations from defined tolerance limits of the monitored system.

7. The system of claim 1, wherein the forecasted aspect is a predicted reliability and availability of an electrical system.

8. The system of claim 1, wherein the forecasted aspect is a predicted total power capacity of an electrical system.

9. The system of claim 1, wherein the forecasted aspect is a predicted ability of an electrical system to maintain availability of a total power capacity and/or a predicted ability of the electrical system to withstand the simulated contingency event that results in stress to the electrical system.

10. The system of claim 1, wherein the forecasted aspect is a predicted utilization of the total power capacity of an electrical system.

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

receiving real-time data output from the monitored system;

generating predicted data output for 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;

forecasting an aspect of the monitored system based on an adaptive neural network algorithm; and

minimizing a measure of error between the real-time data output and a corresponding forecasted data output.

12. The method of claim 11, further comprising 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.

13. The method of claim 12, wherein the contingency event relates to execution of a start-up sequence for a component of an electrical system, load shedding, load adding, loss of a power supply, loss of distribution infrastructure to the electrical system, critical clearing time of a tripped circuit breaker within the electrical system, and/or a change in protective device operations.

14. The method of claim 11, wherein the monitored system is selected from the group consisting of an electric power grid, a microgrid, a data center and any other electrical systems.

15. The method of claim 11, wherein the virtual system model includes current system components and operational parameters comprising the monitored system.

16. The method for of claim 11, wherein the forecasted aspect is a predicted ability of the monitored system to resist system output deviations from defined tolerance limits of the monitored system.

17. The method of claim 11, wherein the forecasted aspect is a predicted reliability and availability of an electrical system.

18. The method of claim 11, wherein the forecasted aspect is a predicted total power capacity of an electrical system.

19. The method of claim 11, wherein the forecasted aspect is a predicted ability of an electrical system to maintain availability of a total power capacity and/or a predicted ability of the electrical system to withstand the simulated contingency event that results in stress to the electrical system.

20. The method of claim 11, wherein the forecasted aspect is a predicted utilization of the total power capacity of an electrical system.