Electrical phase identification

• US 9,568,522 B2
• Filed: 11/03/2014
• Issued: 02/14/2017
• Est. Priority Date: 10/20/2014
• Status: Active Grant

• Current Assignee: Itron, Inc.
• Original Assignee: Itron, Inc.
• Inventors: Aiello, Roberto, Amihood, Patrick
• Primary Examiner(s): NGUYEN, HOAI AN D
• Application Number:
  US14/531,845
• Publication Number:
  US 20160109497A1
• Time in Patent Office: 834 Days
• Field of Search: 324/76.11, 324/76.77, 324/522, 324/713, 324/66, 324/103.R, 324/764.01, 702/1, 702/57, 702/64, 702/60, 702/61
• US Class Current: 1/1
• CPC Class Codes: G01R 11/40 for polyphase operation
• CPC Class Codes: G01R 19/2513 Arrangements for monitoring electric power systems, e.g. power lines or loads; Logging
• CPC Class Codes: G01R 25/00 Arrangements for measuring phase angle between a voltage and a current or between voltages or currents
• CPC Class Codes: G01R 25/005 Circuits for comparing several input signals and for indicating the result of this comparison, e.g. equal, different, greater, smaller, or for passing one of the input signals as output signal
• CPC Class Codes: G01R 31/44 Testing lamps
• CPC Class Codes: G01R 31/60 Identification of wires in a multicore cable
• CPC Class Codes: G01R 31/67 Testing the correctness of wire connections in electric apparatus or circuits
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 13/00034 the elements or equipment being or involving an electric power substation
• CPC Class Codes: H02J 3/26 Arrangements for eliminating or reducing asymmetry in polyphase networks
• CPC Class Codes: Y02E 40/50 Arrangements for eliminating or reducing asymmetry in polyphase networks
• CPC Class Codes: Y04S 10/30 State monitoring, e.g. fault, temperature monitoring, insulator monitoring, corona discharge

Abstract

An electrical phase (e.g., a phase from among three-phase power) connected to an electrical meter may be determined. In one example of the techniques, changes in energy or power (e.g., a derivative or differences) may be determined based at least in part on measurements from each of a plurality of meters. Changes in energy or power may be determined based at least in part on electrical transmissions measured at each of the phases of a feeder. A meter may be selected from among the plurality of meters. For each of the plurality of electrical phases, the changes in energy or power measured by the meter may be compared or correlated to the changes in energy or power measured at the feeder. A phase that is connected to the meter may be determined, from among the plurality of electrical phases, based at least in part on the comparisons or correlations.

Claims

1. A method, comprising:

determining changes in energy or power in data measured by each of a plurality of meters;

determining changes in energy or power in data measured for each of a plurality of electrical phases of a feeder;

selecting a meter from among the plurality of meters;

comparing the determined changes in the energy or power data of the selected meter to the determined changes in the energy or power data of each of the plurality of electrical phases of the feeder; and

determining a phase from among the plurality of electrical phases that is connected to the meter based at least in part on the comparisons.

2. The method of claim 1, wherein determining the changes in energy or power in the data measured at each of the plurality of meters comprises taking a derivative of the data obtained by each meter.

3. The method of claim 1, wherein determining the changes in energy or power in the data measured at each of the plurality of meters comprises taking differences in data values.

4. The method of claim 1, wherein selecting the meter is based at least in part on a count of peaks in the determined changes of energy or power of each of the plurality of meters.

5. The method of claim 1, wherein selecting the meter is based at least in part on a quantity of kilowatt hours measured by each of the plurality of meters.

6. The method of claim 1, additionally comprising:

subtracting data measured by the selected meter from data measured for the determined phase of the feeder;

determining changes in energy or power for the data measured for the determined phase of the feeder after the subtraction;

selecting a second meter from among the plurality of meters;

comparing the determined changes in the energy or power data of the second meter to the determined changes in the energy or power data of each of the plurality of electrical phases of the feeder; and

determining a phase from among the plurality of electrical phases that is connected to the second meter based at least in part on the comparisons.

7. The method of claim 1, wherein phases are determined for meters from among the plurality of meters in an iterative manner until determined changes in energy or power in data measured at each meter not yet selected has less than a threshold number of peaks.

8. The method of claim 1, wherein phases are determined for meters from among the plurality of meters in an iterative manner until determined changes in energy or power measured at each meter not yet selected from among the plurality of meters has less than a threshold value of measured power usage.

9. The method of claim 1, additionally comprising:

identifying known-phase meters, whose phase is known with certainty above a confidence level threshold;

subtracting values related to energy or power measured by the known-phase meters from data associated with phases of the feeder; and

removing data associated with the known-phase meters from the plurality of meters before selecting the meter.

10. A smart grid network, comprising:

a feeder configured to provide three-phase power;

a plurality of meters to measure power received from the feeder; and

a computing device configured for;

obtaining electrical transmission values for each phase of the feeder;

determining electrical change values for each of phase of the feeder, based at least in part on the electrical transmission values of each phase of the feeder;

obtaining electrical consumption values measured at each of the plurality of meters; and

determining electrical change values for each of the plurality of meters, based at least in part on the electrical consumption values measured at each of the plurality of meters;

wherein for each meter within at least a subset of the plurality of meters, the computing device is configured for;

comparing electrical change values at each phase of the feeder to electrical change values at the meter; and

determining to which phase the meter is associated based at least in part on the comparing.

11. The smart grid network of claim 10, wherein the computing device is additionally configured for:

iteratively subtracting electrical consumption values measured at the meter from electrical transmission data measured at the phase of the feeder to which the meter is associated; and

iteratively updating the electrical change values of the phase of the feeder to which the meter was associated.

12. The smart grid network of claim 10, wherein the computing device is additionally configured for:

removing data from the electrical transmission values of the feeder and removing data from the electrical consumption values measured at a meter, if the data are below a threshold confidence level; and

avoiding boundaries created by the removal when determining electrical change values.

13. The smart grid network of claim 10, additionally comprising:

selecting meters from among the plurality of meters based at least in part on a number and a magnitude of electrical changes in data measured by each meter, and based at least in part on a quantity of energy measured by each meter.

14. The smart grid network of claim 10, wherein comparing the electrical change values of data measured at each phase of the feeder to electrical change values of data measured by the meter comprises:

comparing the electrical change values of data measured at of each phase of the feeder to electrical change values of data measured by the meter using data of less than a threshold age; and

comparing the electrical change values of data measured at each phase of the feeder to electrical change values of data measured by the meter without regard to the threshold age if data of less than the threshold age failed to provide a predetermined confidence level of correlation.

15. The smart grid network of claim 10, wherein obtaining electrical transmission values measured for each of phase of the feeder and obtaining consumption values measured by each of the plurality of meters is performed over more than one day and using intervals of thirty minutes or less.

16. A computing device, comprising:

a processing unit;

a memory, in communication with the processing unit;

a data module, defined in the memory, to gather data comprising power or energy transmitted by each of a plurality of electrical phases of a feeder and power or energy measured by each of a plurality of meters;

a derivative module, defined in the memory, to determine changes in power or changes in energy transmitted by each of the plurality of electrical phases of the feeder and to determine changes in power or changes in energy in data measured by each of the plurality of meters; and

a correlation module, defined in the memory, to determine a phase to which a meter from among the plurality of meters is associated based at least in part on changes determined by the derivative module.

17. The computing device of claim 16, wherein:

a data cleaning module is configured to remove incorrect data from the gathered data; and

the derivative module is configured to determine changes in a manner that avoids locations of the removed data.

18. The computing device of claim 16, additionally comprising:

a prior phase estimate module to subtract values indicating energy measured by meters of known phase from the data associated with the feeder.

19. The computing device of claim 16, additionally comprising a grouping module configured for:

counting derivative peaks that exceed a threshold for each meter; and

grouping meters based at least in part on numbers of times the threshold was exceeded;

wherein the grouped meters are utilized by the correlation module and correlated with appropriate phases.

20. The computing device of claim 16, additionally comprising:

a strongest transformer module to select a transformer based at least in part on data measured by at least some of the plurality of meters.

21. The computing device of claim 16, additionally comprising:

a phase-switch-detection module configured to detect a phase-switch of a meter during data collection by comparing a plurality of different confidence measures within a period of data collection.