MATCHING POSITIVE TRANSITIONS IN A TIME TRACE DISAGGREGATION PROCESS

• US 20140067292A1
• Filed: 08/31/2012
• Published: 03/06/2014
• Est. Priority Date: 08/31/2012
• Status: Active Grant

• Current Assignee: Verlitics LLC
• Original Assignee: Verlitics Incorporated
• Inventors: Alles, Harold Gene
• Granted Patent: US 9,194,892 B2
• Time in Patent Office: Days
• US Class Current: 702/60
• CPC Class Codes: G01R 19/2513 Arrangements for monitoring electric power systems, e.g. power lines or loads; Logging
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/30 State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving

Abstract

A method can include detecting a positive transition in a time trace disaggregation process (TTDP), creating a new instance j, capturing a positive transition data block for the instance j, creating a time trace feature description for power, reactive power, and current, and matching the instance j to a device type by performing a quick match based on at least one quick match parameters. Responsive to a positive identification based on the quick match, the instance j may be assigned to the device type.

Claims

1. A method, comprising:

detecting a positive transition in a time trace disaggregation process (TTDP);

creating a new instance j;

capturing a positive transition data block for the instance j;

creating a time trace feature description for at least one of the following;

power, reactive power, and current;

matching the instance j to a device type by performing a quick match based on at least one of a plurality of quick match parameters; and

responsive to a positive identification based on the quick match, assigning the instance j to the device type.

2. The method of claim 1, wherein the positive transition data block comprises samples for at least one of the following:

power, reactive power, and current.

3. The method of claim 1, further comprising determining values of at least one of power, reactive power, and current for samples immediately before the positive transition.

4. The method of claim 1, wherein the matching further comprises:

performing a CalculateMatchError(j,k) function to determine an error value TERR that is a measure of similarity between the instance j and a device k; and

based on a result of the CalculateMatchError(j,k) function, creating a new device using the instance j or assigning the instance j to the device k.

5. The method of claim 4, wherein performing the CalculateMatchError(j,k) function comprises determining whether the value of TERR is less than a threshold match error value DEVNIM[j] for the device k.

6. The method of claim 5, further comprising normalizing TERR by dividing TERR by DEVMIN[k] and then comparing the result to ERRMIN.

7. The method of claim 1, wherein the plurality of quick match parameters comprises at least one of the following:

average power during a predefined number of samples, average reactive power during a predefined number of samples, and average current during a predefined number of samples.

8. The method of claim 7, wherein the plurality of quick match parameters further comprises at least one of the following:

generic device type, service leg, and instance code.

9. The method of claim 1, wherein the matching further comprises performing a MatchInstanceToGenericDevice (j,k) function, the MatchInstanceToGenericDevice (j,k) comprising testing a MAXPOW parameter to determine whether the MAXPOW parameter is within a range of a MAXPOW maximum limit and a MAXPOW minimum limit.

10. The method of claim 9, wherein performing the MatchInstanceToGenericDevice (j,k) function further comprises testing a MINPOW parameter to determine whether the MINPOW parameter is within a range of a MINPOW maximum limit and a MINPOW minimum limit.

11. The method of claim 10, wherein performing the MatchInstanceToGenericDevice (j,k) function further comprises testing a POWERFACTOR1 parameter to determine whether the POWERFACTOR1 parameter is within a range of a POWERFACTOR1 maximum limit and a POWERFACTOR1 minimum limit.

12. The method of claim 11, wherein performing the MatchInstanceToGenericDevice (j,k) function further comprises testing a PEAKCNT parameter to determine whether the PEAKCNT parameter is within a range of a PEAKCNT maximum limit and a PEAKCNT minimum limit.

13. The method of claim 12, wherein performing the MatchInstanceToGenericDevice (j,k) function further comprises testing a STEPCNT parameter to determine whether the STEPCNT parameter is within a range of a STEPCNT maximum limit and a STEPCNT minimum limit.

14. The method of claim 1, wherein the matching further comprises performing a CalulateMatchError(j, k) function, the CalulateMatchError(j, k) function comprising:

sequentially selecting all feature triples jj for instance j; and

determining whether each of the feature triples jj are marked as used.

15. The method of claim 14, wherein performing the CalulateMatchError(j, k) function further comprises:

sequentially selecting all features kk for device k; and

determining whether each of the features kk are marked as used.

16. The method of claim 15, further comprising performing a FeatureMatch(jj, kk) function to compare the feature triples jj to the features kk.

17. The method of claim 16, further comprising performing a MisFeatureMatch(jj) function to determine a difference in values at a sample number of each feature triple jj.

18. The method of claim 1, further comprising performing a CLUSTER ANALYSIS function to determine whether a match threshold is too large such that two or more different devices may be grouped into a single device.

19. The method of claim 18, further comprising:

creating new devices so that there is a separate device for each cluster based on a result of the CLUSTER ANALYSIS function; and

selecting a value of the match threshold for each device to provide sufficient selectivity so that new instances may be properly matched.

20. The method of claim 1, further comprising performing a CONSOLIDATION ANALYSIS function to determine whether there are multiple devices that should be consolidated.

21. The method of claim 20, further comprising:

associating multiple instances with a single device based on a result of the CONSOLIDATION ANALYSIS function; and

adjusting the match threshold to ensure that new instances are correctly matched.