Automated integration of data in utility monitoring systems

• US 20070005275A1
• Filed: 07/01/2005
• Published: 01/04/2007
• 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: Carter, Ronald, Bickel, Jon, Curtis, Larry
• Granted Patent: US 7,349,815 B2
• Time in Patent Office: Days
• US Class Current: 702/60
• CPC Class Codes: G01D 4/002 Remote reading of utility meters
• CPC Class Codes: G01R 22/063 related to remote communication
• 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 automated integrated monitoring (IM) algorithm that automatically puts data from a utility monitoring system into context by temporally aligning the data to a common reference point and by identifying the location of each monitoring device in a hierarchy relative to other devices. Frequency variation data is received from all meters. The data is automatically aligned to a common reference point, such as a precise zero crossing, using a cross-correlation algorithm to determine the time delay at which the data is most correlated. Once the data is aligned, power data is received from all meters in a hierarchy, and the monitoring system layout is auto-learned using a correlation algorithm to determine which two meters are most likely correlated with one another based upon their historical power readings. Once the layout is complete, additional decisions regarding hardware and software configuration can automatically be made by the IM algorithm.

Claims

1. A method of automated integrated monitoring, comprising:

instructing a plurality of monitoring devices in a radial-fed hierarchy to buffer data indicative of a first parameter for a predetermined period of time;

receiving said data indicative of said first parameter for each of said plurality of monitoring devices;

automatically aligning data associated with a reference one of said plurality of monitoring devices and a second one of said plurality of monitoring devices by cross-correlating said data associated with said reference monitoring device and said data associated with said second monitoring device such that said data associated with said second monitoring device is shifted relative to said data associated with said reference monitoring device at the point at which a cross-correlation coefficient produced by said cross-correlating exceeds a threshold;

instructing said plurality of monitoring devices to buffer data indicative of a second parameter at periodic time intervals;

receiving said data indicative of said second parameter for each of said plurality of monitoring devices; and

automatically determining a hierarchy including said plurality of monitoring devices, their location relative to one another, and their interrelationships relative to one another.

2. A computer readable medium encoded with instructions for directing a controller to perform the method of claim 1.

3. The method of claim 1, wherein said first parameter is at least one parameter selected from the group consisting of fundamental frequency variations, variations in harmonic frequencies, and amplitude variations.

4. The method of claim 1, wherein said second parameter is at least one parameter selected from the group consisting of power, voltage, current, and distortion.

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

6. The method of claim 1, wherein said automatically determining includes calculating a correlation coefficient.

7. A method of automatically contextualizing data, comprising:

receiving cycle-by-cycle frequency variation data from a plurality of meters in a radial-fed power monitoring system for a predetermined period of time;

automatically aligning said frequency variation data by cross-correlating frequency variation data associated with a reference meter and a second meter and shifting the data associated with said second meter relative to said reference meter at the point of maximum correlation;

receiving power data from said plurality of meters at periodic time intervals; and

automatically learning the hierarchy of said plurality of meters in said radial-fed power monitoring system by calculating correlation coefficients between respective power data associated with pairs of said plurality of meters.

8. The method of claim 7, further comprising displaying said hierarchy.

9. The method of claim 7, further comprising automatically setting the under-voltage threshold of one of said plurality of meters based on said automatically aligning and said automatically learning.

10. The method of claim 7, further comprising synchronizing said reference meter and said second meter at said point of maximum correlation.

11. A method of automatically integrating data in a utility monitoring system, comprising:

communicating an instruction to at least two monitoring devices of said power monitoring system to store signal data representing frequency variations on a cycle-by-cycle basis for a predetermined number of cycles;

responsive to said communicating, receiving from a reference one of said at least two monitoring devices reference signal data corresponding to said signal data stored by said reference monitoring device;

responsive to said communicating, receiving from a second of said at least two monitoring devices second signal data corresponding to said signal data stored by said second monitoring device;

aligning said first signal data with said second signal data by shifting in cycle increments said second signal data relative to said reference signal data until a maximum cross-correlation coefficient is computed by a cross-correlation function that computes a cross-correlation coefficient at each of said cycle increments;

receiving parameter data from said at least two monitoring devices at periodic time intervals for a predetermined time period;

arranging said parameter data into a data table that tabulates said parameter data for said at least two monitoring devices at each of said periodic time intervals;

forming at least a portion of a correlation matrix that includes correlation coefficients between said at least two monitoring devices, said correlation coefficients being calculated according to a correlation algorithm; and

analyzing said correlation matrix to identify an interrelationship between said at least two monitoring devices.

12. The method of claim 11, further comprising communicating an instruction to said at least two monitoring devices to mark their respective cycle counts.

13. The method of claim 11, further comprising:

responsive to said aligning, synchronizing said at least two monitoring devices such that at the point of alignment said at least two monitoring devices are temporally aligned.

14. The method of claim 11, further comprising:

providing a reference clock; and

responsive to said aligning, resetting a clock in said reference monitoring device or said second monitoring device to said reference clock.

15. The method of claim 11, further comprising indicating for said at least two monitoring devices whether an interrelationship exists between said at least two monitoring devices.

16. The method of claim 11, wherein said analyzing includes determining whether a correlation coefficient exceeds a correlation threshold for said at least two monitoring devices, and if so, identifying said at least two monitoring devices as interrelated.

17. The method of claim 11, further comprising presenting a hierarchy that displays said interrelationship between said at least two monitoring devices.