UTILITY SERVICE COMPONENT RELIABILITY AND MANAGEMENT

• US 20100306014A1
• Filed: 06/01/2010
• Published: 12/02/2010
• Est. Priority Date: 06/01/2009
• Status: Abandoned Application

• Current Assignee: Consolidated Edison Company of New York Incorporated (Consolidated Edison Incorporated)
• Original Assignee: Consolidated Edison Incorporated
• Inventors: Chow, Maggie
• Application Number:
  US12/791,363
• Publication Number:
  US 20100306014A1
• Time in Patent Office: Days
• US Class Current: 705/8
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

A computer-implemented method and system performing allocating capital assets for managing a plurality of utility service components. The method includes ranking each of the utility service components based on data retrieved corresponding to the utility service components, calculating a base failure metric for each of the utility service components, receiving a selection of at least one utility service component of the plurality of utility service components inputted by a user, analyzing the selected utility service component under a plurality of improvement scenarios, calculating an estimated failure metric of the selected utility service component based on each of the improvement scenarios, and displaying comparison information between the base failure metric and the estimated failure metric.

Claims

1. A computer-implemented method of allocating capital assets for managing a plurality of utility service components, the method comprising:

ranking each of the utility service components based on data retrieved corresponding to the utility service components;

calculating a base failure metric for each of the utility service components;

receiving a selection of at least one utility service component of the plurality of utility service components inputted by a user;

analyzing the selected utility service component under a plurality of improvement scenarios;

calculating an estimated failure metric of the selected utility service component based on each of the improvement scenarios; and

displaying comparison information between the base failure metric and the estimated failure metric.

2. The computer-implemented method of claim 1, wherein the base failure metric is a mean-time-between-failure (MTBF) and the estimated failure metric is a cost per MTBF.

3. The computer-implemented method of claim 2, wherein ranking each of the utility service components and calculating the base failure metric are performed via a machine learning model.

4. The computer-implemented method of claim 3, wherein the data retrieved comprises at least one of past outage history, component characteristics, network configuration, electrical characteristics or environmental characteristics.

5. The computer-implemented method of claim 4, wherein the component characteristics comprises at least one of cable length, installation information, voltage information or electrical phase information.

6. The computer-implemented method of claim 2, wherein receiving a selection of at least one utility service component of the plurality of utility service components inputted by a user comprises:

displaying the plurality of utility service components in a graphical representation to be viewed by the user.

7. The computer-implemented method of claim 6, wherein a plurality of segments of each of the plurality of utility service components are represented by different colors on the graphical representation.

8. The computer-implemented method of claim 7, wherein a risk level of each of the segments of each of the plurality of utility service components are graphically displayed.

9. The computer-implemented method of claim 7, wherein the improvement scenarios comprise at least one of load relief, segment replacement and segment reliability.

10. The computer-implemented method of claim 9, wherein the utility service components are electrical feeder circuits and the plurality of segments are different types of cables and joints between each of the cables.

11. The computer-implemented method of claim 1, wherein analyzing the selected utility service component under a plurality of improvement scenarios comprises:

displaying the improvement scenarios for the selected utility service component, to the user;

receiving a selection of an improvement scenario from the user; and

calculating cost of improvement based on cost information input by the user.

12. The computer-implemented method of claim 11, further comprising:

receiving a selection of segments of the selected utility service component to be improved through an input by the user;

13. The computer-implemented method of claim 12, wherein the user selects the segments to be improved based on at least one of a target cost, percentage of segments to be improved, rank of the segment, load of the segment, or rank x load.

14. The computer-implemented method of claim 1, further comprising:

displaying capital asset allocation information corresponding to the selected utility service component and the comparison information.

15. A computer readable storage medium storing program instructions executable by a computer to perform a method of allocating capital assets for managing a plurality of utility service components, the method comprising:

ranking each of the utility service components based on data retrieved corresponding to the utility service components;

calculating a base failure metric for each of the utility service components;

receiving a selection of at least one utility service component of the plurality of utility service components inputted by a user;

analyzing the selected utility service component under a plurality of improvement scenarios;

calculating an estimated failure metric of the selected utility service component based on each of the improvement scenarios; and

displaying comparison information between the base failure metric and the estimated failure metric.

16. The computer readable storage medium of claim 15, wherein the base failure metric is a mean-time-between-failure (MTBF) and the estimated failure metric is a cost per MTBF.

17. The computer readable storage medium of claim 16, wherein ranking each of the utility service components and calculating the base failure metric are performed via a machine learning model.

18. The computer readable storage medium of claim 17, wherein the data retrieved comprises at least one of past outage history, component characteristics, network configuration, electrical characteristics or environmental characteristics.

19. The computer readable storage medium of claim 18, wherein the component characteristics comprises at least one of cable length, installation information, voltage information or electrical phase information.

20. The computer readable storage medium of claim 16, wherein receiving a selection of at least one utility service component of the plurality of utility service components inputted by a user comprises:

displaying the plurality of utility service components in a graphical representation to be viewed by the user.

21. The computer readable storage medium of claim 20, wherein a plurality of segments of each of the plurality of utility service components are represented by different colors on the graphical representation.

22. The computer readable storage medium of claim 21, wherein a risk level of each of the segments of each of the plurality of utility service components are graphically displayed.

23. The computer readable storage medium of claim 21, wherein the improvement scenarios comprise at least one of load relief, segment replacement and segment reliability.

24. The computer readable storage medium of claim 23, wherein the utility service components are electrical feeder circuits and the plurality of segments are different types of cables and joints between each of the cables.

25. The computer readable storage medium of claim 15, wherein analyzing the selected utility service component under a plurality of improvement scenarios comprises:

displaying the improvement scenarios for the selected utility service component, to the user;

receiving a selection of an improvement scenario from the user; and

calculating cost of improvement based on cost information input by the user.

26. The computer readable storage medium of claim 25, further comprising:

receiving a selection of segments of the selected utility service component to be improved through an input by the user.

27. The computer readable storage medium of claim 26, wherein the user selects the segments to be improved based on at least one of a target cost, percentage of segments to be improved, rank of the segment, load of the segment, or rank x load.

28. The computer readable storage medium of claim 15, further comprising:

displaying capital asset allocation information corresponding to the selected utility service component and the comparison information.

29. A system comprising:

a user interface configured to receive and transmit data to and from a user and a processing unit configured to;

receive ranking information corresponding to a plurality of utility service components based on data retrieved corresponding to the utility service components and a base failure metric for each of the utility service components,

receive a selection of at least one utility service component of the plurality of utility service components inputted by a user via the user interface,

analyze the selected utility service component under a plurality of improvement scenarios as selected by the user,

calculate an estimated failure metric of the selected utility service component based on each of the improvement scenarios, and

display via the user interface, comparison information between the base failure metric and the estimated failure metric to the user.

30. The system of claim 29, further comprising a visualization module configured to provide graphical mapping information of the utility service components to be displayed to the user via the user interface.

31. The system of claim 30, further comprising a re-ranking module configured to re-rank the utility service components based on an improvement scenario as selected by the user.

32. The system of claim 29, wherein a machine learning tool calculates ranking information corresponding to a plurality of utility service components based on data retrieved corresponding to the utility service components and the base failure metric for each of the utility service components and supplies the ranking information and the base failure metrics to the processing unit.