Automated hierarchy classification in utility monitoring systems

• US 7,272,518 B2
• Filed: 07/01/2005
• Issued: 09/18/2007
• Est. Priority Date: 07/01/2005
• Status: Active Grant

• Current Assignee: Square D Company (Schneider Electric SE)
• Original Assignee: Square D Company (Schneider Electric SE)
• Inventors: Bickel, Jon A., Carter, Ronald W.
• Primary Examiner(s): WACHSMAN, HAL D
• Application Number:
  US11/174,100
• Publication Number:
  US 20070005277A1
• Time in Patent Office: 809 Days
• Field of Search: 702 57- 62, 702/64, 702/65, 702/115, 702/182, 702/183, 702/185, 702187-189, 702/196
• US Class Current: 702/61
• CPC Class Codes: G01D 2204/24 Identification of individual loads, e.g. by analysing current/voltage waveforms
• CPC Class Codes: G01D 2204/47 Methods for determining the topology or arrangement of meters in a network
• CPC Class Codes: G01D 4/004 Remote reading of utility meters to a fixed location
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y04S 20/30 Smart metering, e.g. specially adapted for remote reading

Abstract

An auto-learned hierarchy algorithm that learns the hierarchical layout of a power monitoring system. Historical power data from each meter is received and placed into a data table. The main is assumed to be at the top of the hierarchy and is designated as the reference. A check matrix is developed indicating whether a possible connection exists between each meter pair combination. A correlation coefficient matrix (CCM) is calculated based on the data table, and entries in which no connection is possible are zeroed. The column for the reference meter from the CCM is copied to a correlation reference array (CRA), and the meter having the highest correlation with the reference meter in the CRA is marked as connected to the reference meter in a connection table. That meter'"'"'s power is subtracted from the data table and the procedure is repeated until all meters have been analyzed.

Claims

1. A method of automatically learning how a number, N, of devices, including a reference device, are directly or indirectly linked in a distribution system, comprising:

receiving at time intervals device data measured by each of said devices;

calculating from said device data a first correlation coefficient between said reference device and every other device in said distribution system to produce N-1 correlation coefficients;

determining the highest correlation coefficient among said N-1 correlation coefficients;

responsive to said determining, automatically determining whether the device associated with said highest correlation coefficient and said reference device are linked; and

if said automatically determining determines that said device associated with said highest correlation coefficient is linked to said reference device, storing, on a computer-readable medium, second data representing that said device associated with said highest correlation coefficient and said reference device are linked.

2. The method of claim 1, further comprising:

calculating a second correlation coefficient between said reference device and every other device in said distribution system except for said device associated with said highest correlation coefficient among said N-1 correlation coefficients to produce N-2 correlation coefficients;

determining the highest correlation coefficient among said N-2 correlation coefficients, said highest correlation coefficient being associated with one of said other devices; and

responsive to said determining said highest correlation coefficient among said N-2 correlation coefficients, automatically determining whether said one of said other devices and said reference device are linked; and

if said automatically determining whether said one of said other devices and said reference device are linked determines that said one of said other devices associated with said highest correlation coefficient among said N-2 correlation coefficients is linked to said reference device, storing, on the computer-readable medium, second data representing that said one of said other devices associated with said highest correlation coefficient among said N-2 correlation coefficients and said reference device are linked.

3. The method of claim 1, wherein said distribution system is a utility system, wherein said utility is selected from the group consisting of water, air, gas, electricity, and steam.

4. The method of claim 1, wherein said distribution system is a power system.

5. The method of claim 1, wherein said device data is indicative of at least one of power, voltage, current, voltage distortion, BTU per hour, MBTU per hour, energy, gallons per minute, or cubic feet per minute.

6. The method of claim 1, wherein said device data is indicative of power.

7. The method of claim 1, wherein N is at least two.

8. The method of claim 1, wherein said devices include power meters.

9. The method of claim 1, wherein said devices are monitoring devices that monitor at least one of power, energy, volume of water per minute, volume of air per minute, volume of gas per minute, or volume of steam per minute.

10. The method of claim 1, wherein said devices include at least one of a main, a feeder, a header, or a load.

11. The method of claim 1, further comprising receiving the time that said device data was received from each of said devices.

12. The method of claim 1, wherein said devices include a device that characterizes the quantity, quality, or flow of a utility, said utility being water, air, gas, electricity, or steam.

13. A computer readable medium encoded with computer-executable instructions for performing the method of claim 1.

14. A method of automatically determining the hierarchy of devices arranged within a power system, wherein devices at the top of the hierarchy generally measure more power than devices lower on the hierarchy, comprising:

receiving electrical parameter data from each device in said power system at periodic time intervals for a predetermined time period;

arranging said electrical parameter data into a data table that tabulates said electrical parameter data for each device at each of said periodic time intervals;

forming at least a portion of a correlation matrix that includes correlation coefficients between at least some combination pairs of said devices;

determining the highest correlation coefficient among said correlation coefficients, said highest correlation coefficient being associated with an interrelated combination pair of said combination pairs of said devices; and

storing, on a computer-readable medium, data representing that the two devices of said interrelated combination pair are connected to one another.

15. The method of claim 14, further comprising:

removing

from said data table a device for which an interrelationship was identified;

recalculating said correlation coefficients; and

analyzing said recalculated correlation coefficients to identify another interrelationship between a second combination pair of said devices.

16. The method of claim 14, further comprising forming at least a portion of a matrix that indicates for each pair combination of said devices in said data table whether an interrelationship exists between a given pair of devices.

17. The method of claim 14, wherein said electrical parameter data includes power, voltage, current, voltage distortion, or current distortion.

18. The method of claim 14, wherein said interrelationship is a direct physical link.

19. The method of claim 14, wherein said power system is radially distributed.

20. The method of claim 14, further comprising:

identifying the device having the highest electrical parameter value; and

responsive to said identifying, identifying said device having the highest electrical parameter value as a reference device,

wherein said interrelated combination pair includes said reference device and another device.

21. The method of claim 14, further comprising determining whether at least one of said correlation coefficients exceeds a correlation threshold for any given combination pair of devices, and if so, identifying that combination pair as being mutually connected.

22. The method of claim 14, further comprising determining a maximum correlation coefficient in said correlation matrix, and identifying the combination pair associated with said maximum correlation coefficient as being mutually connected.

23. The method of claim 14, wherein said receiving is carried out after a new device is added to said power system.

24. The method of claim 14, further comprising:

forming a data representation of said hierarchy having a plurality of levels, at least some of said devices being linked together directly in said hierarchy among said plurality of levels, a device on a first level being interrelated to another device on a second level when they are directly linked together, said forming said data representation including determining which of said at least some of said devices are directly linked together in said hierarchy and on which level of said plurality of levels each of said at least some of said devices is located in said hierarchy; and

responsive to said forming said data representation, displaying said hierarchy to an operator for viewing a spatial position of at least one of said devices relative to other devices of said devices in said power system.

25. The method of claim 14, wherein said devices arranged within said power system include power meters.

26. The method of claim 14, wherein said power system includes loads coupled to said devices, each of said periodic time intervals being shorter than the shortest duty cycle of any load in said power system.

27. A method of automatically determining how devices in a multi-level hierarchy are linked to one another, comprising:

receiving data from at least some of said devices in said hierarchy, said data representing a unidirectional flow of a utility measured by said at least some of said devices;

producing a correlation coefficient based on respective data received from a first device of said devices and from a second device of said devices;

responsive to the producing, automatically determining whether at least a first device and a second device are mutually linked in the multi-level hierarchy by at least comparing said correlation coefficient against at least one coefficient; and

responsive to the automatically determining, storing data representing how said first device and said second device are linked.

28. The method of claim 27, wherein said utility is at least one utility selected from the group consisting of water, air, gas, electricity, and steam.

29. The method of claim 27, wherein said first and second devices are meters.

30. The method of claim 27, wherein said at least one coefficient is a threshold, said comparing including determining whether said correlation coefficient between said first device and said second device exceeds said threshold, the method further comprising identifying said first device and second device as being directly linked to one another when said correlation coefficient exceeds said threshold.

31. The method of claim 27, wherein said at least one coefficient includes a plurality of other correlation coefficients between said first device or said second device and every other device in said multi-level hierarchy, the method further comprising identifying said first device and said second device as being directly linked to one another when said correlation coefficient between said first device and said second device exceeds all of said other correlation coefficients.

32. The method of claim 27, wherein said hierarchy is a radial distribution hierarchy.

33. The method of claim 27, wherein said data is representative of electrical power.

34. A method of automatically determining how devices in a multi-level hierarchy are linked to one another, comprising:

receiving data from at least some of said devices in said hierarchy, said data representing a unidirectional flow of a utility measured by said at least some of said devices;

determining whether at least a first device and a second device are mutually linked responsive to producing a correlation coefficient based on respective data received from said first device and from said second device; and

storing data representing how said first device and said second device are linked, wherein said determining includes identifying said first device and said second device as being directly linked to one another when said correlation coefficient between said first device and said second device exceeds a threshold.