Method for predicting symmetric, automated, real-time ARC flash energy within a real-time monitoring system

• US 9,886,535 B2
• Filed: 04/04/2016
• Issued: 02/06/2018
• Est. Priority Date: 06/29/2007
• Status: Active Grant

• Current Assignee: Bentley Systems Incorporated
• Original Assignee: Power Analytics Corporation (PAC Global, Inc.)
• Inventors: Radibratovic, Branislav, Meagher, Kevin
• Primary Examiner(s): Louis, Andre Pierre
• Application Number:
  US15/089,922
• Publication Number:
  US 20160246906A1
• Time in Patent Office: 673 Days
• Field of Search: 703 13, 703 18
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

Systems and methods for making real-time predictions about an arc flash event on an electrical system are disclosed. An analytics server is communicatively connected to a data acquisition component. The analytics server comprises a virtual system modeling engine configured to create a virtual system model reflecting current network topology of the electrical system; an analytics engine configured to monitor predicted output data from the virtual system model and real-time output data from the electrical system acquired by the data acquisition component and update the virtual system model when a difference between the real-time output data and the predicted output data exceeds a threshold; and an arc flash simulation engine configured to forecast at least one aspect of and arc flash event based on the virtual system model.

Claims

1. A system for making real-time predictions about an arc flash event on an electrical system, comprising:

a computer processor and at least one memory;

an analytics server communicatively connected to a data acquisition component, wherein the analytics server comprises;

a virtual system modeling engine configured to create a virtual system model reflecting current network topology of the electrical system, wherein the electrical system comprises at least one unobservable motor and at least one uninterrupted power supply (UPS);

an analytics engine configured to monitor predicted output data from the virtual system model and real-time output data from the electrical system acquired by the data acquisition component and update the virtual system model when a difference between the real-time output data and the predicted output data exceeds a threshold;

an arc flash simulation engine configured to forecast at least one aspect of an arc flash event based on the updated virtual system model, wherein the at least one UPS is transferred to a by-pass position in the virtual system model and wherein the arc flash simulation engine is configured to calculate and compare arcing energy for each significant location in the virtual system model when the at least one unobservable motor in the virtual system model is set to an ON position, and calculate and comparing arcing energy for each significant location in the virtual system model when the at least one unobservable motor in the virtual system model is set to an OFF position, respectively, to determine a highest arcing energy for each significant location, wherein a virtual no load is generated and used as second critical input to determine the highest arcing energy for each significant location in the virtual system model; and

a central analytics server configured to control an aspect of a monitored system, when its corresponding analytics server is out of operation, wherein the central analytics server is further configured to take over functionality of the analytics server and to modify operating parameters of one or more sensors that are interfaced with the monitored system.

2. The system of claim 1, further comprising a client terminal communicatively connected to the analytics server;

and wherein the analytics server is configured to communicate the at least one forecasted aspect to the client terminal.

3. The system of claim 1, wherein the threshold is a Defined Difference Tolerance (DDT) value for at least one of a frequency deviation, a voltage deviation, a power factor deviation, and other deviations between the real-time output data and predicted output data.

4. The system of claim 1, wherein the arc flash simulation engine applies a standard for forecasting the at least one aspect;

wherein the standard is selected from the group consisting of Institute of Electrical and Electronics Engineers (IEEE) 1584 standard and National Fire Protection Association (NFPA) 70E standard.

5. The system of claim 1, wherein the at least one aspect comprises a worst case arc flash event for a component within the electrical system over a specified time period.

6. The system of claim 5, wherein the component is a protective device selecting from the group consisting of a switch, a circuit breaker, a fuse, a replay and any other protective device used in the electrical system.

7. The system of claim 1, wherein the analytics server is further configured to transfer the at least one UPS in the virtual system model back to a correct position, wait for a predetermined delay time, and repeat the forecasting.

8. The system of claim 1, wherein the analytics server is further configured to present a worst case arcing energy over the last 30 days when requested.

9. A method for making real-time predictions about an arc flash event on an electrical system, comprising:

creating, using a computer processor, a virtual system model reflecting current network topology of the electrical system, wherein the electrical system comprises at least one unobservable motor and at least one uninterrupted power supply (UPS);

monitoring predicted output data from the virtual system model and real-time output data from the electrical system;

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

transferring the at least one UPS to a by-pass position in the virtual system model;

setting the at least one unobservable motor in an ON position in the virtual system model and calculating arcing energy for each significant location in the virtual system model when the at least one unobservable motor is in the ON position;

setting the at least one unobservable motor in an OFF position in the virtual system model and calculating arcing energy for each significant location in the virtual system model when the at least one unobservable motor is in the OFF position;

obtaining a highest arcing energy by comparing the two calculations for each significant location in the virtual system model, wherein a virtual no load scenario is generated and used as second critical input to determine the highest arcing energy for each significant location in the virtual system model; and

controlling an aspect of a monitored system, using a central analytics server, when its corresponding analytics server is out of operation, wherein the central analytics server takes over functionality of analytics server and modifies operating parameters of one or more sensors that are interfaced with the monitored system.

10. The method of claim 9, wherein the threshold is a Defined Difference Tolerance (DDT) value for at least one of a frequency deviation, a voltage deviation, a power factor deviation, and other deviations between the real-time output data and predicted output data.

11. The method of claim 9, further comprising applying a standard for calculating the arcing energy for each significant location when the at least one unobservable motor is in the ON position and in the OFF position;

wherein the standard is selected from the group consisting of Institute of Electrical and Electronics Engineers (IEEE) 1584 standard and National Fire Protection Association (NFPA) 70E standard.

12. The method of claim 9, further comprising transferring the at least one UPS in the virtual system model back to a correct position, waiting for a predetermined delay time, and repeating the real-time predictions.

13. The method of claim 9, further comprising presenting worst case arcing energy over the last 30 days when requested.

14. The method of claim 9, further comprising storing the highest arcing energy in a memory.

15. The method of claim 9, wherein the electrical system further comprises at least one observable motor.

16. The method of claim 15, further comprising switching the at least one observable motor ON or OFF in the virtual system model when making the real-time predictions.