SYSTEM AND METHOD EMPLOYING A HIERARCHICAL LOAD FEATURE DATABASE TO IDENTIFY ELECTRIC LOAD TYPES OF DIFFERENT ELECTRIC LOADS

• US 20130138669A1
• Filed: 11/28/2011
• Published: 05/30/2013
• Est. Priority Date: 11/28/2011
• Status: Abandoned Application

• Current Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Original Assignee: Bin Lu
• Inventors: LU, BIN, Yang, Yi, Sharma, Santosh K., Zambare, Prachi, Madane, Mayura A.
• Application Number:
  US13/304,783
• Publication Number:
  US 20130138669A1
• Time in Patent Office: Days
• US Class Current: 707/758
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning

Abstract

A method identifies electric load types of a plurality of different electric loads. The method includes providing a hierarchical load feature database having a plurality of layers; including with each of a plurality of the layers a corresponding load feature set, the corresponding load feature set of at least one of the layers being different from the corresponding load feature set of at least another one of the layers; including with one of the layers a plurality of different electric load types; sensing a voltage signal and a current signal for each of the different electric loads; determining at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by relating the different load features to the hierarchical load feature database.

Claims

1. A method of identifying electric load types of a plurality of different electric loads, said method comprising:

providing a hierarchical load feature database comprising a plurality of layers;

including with each of a plurality of said layers a corresponding load feature set, the corresponding load feature set of at least one of said layers being different from the corresponding load feature set of at least another one of said layers;

including with one of said layers a plurality of different electric load types;

sensing a voltage signal and a current signal for each of said different electric loads;

determining at least four different load features from said sensed voltage signal and said sensed current signal for a corresponding one of said different electric loads; and

identifying by a processor one of said different electric load types by relating the different load features to the hierarchical load feature database.

2. The method of claim 1 further comprising:

employing a first one of said layers having a plurality of load features selected from the group consisting of true power factor, displacement power factor, current total harmonic distortion, admittance, and a voltage-current trajectory graphical representation.

3. The method of claim 2 further comprising:

employing a second one of said layers having a plurality of load features selected from the group consisting of nominal power, distortion power factor, current total harmonic distortion, a voltage-current trajectory graphical representation, normalized third and fifth harmonics of voltage and current, and high-frequency components of voltage and current.

4. The method of claim 3 further comprising:

employing a third one of said layers having a plurality of load features selected from the group consisting of transient on/off behavior, event detection, and long-term operating mode patterns.

5. The method of claim 1 further comprising:

selecting a plurality of load features for each of said layers; and

defining a plurality of load categories or load sub-categories employing the load features for some of said layers.

6. The method of claim 1 further comprising:

employing said providing the hierarchical load feature database as an offline process; and

employing said determining and said identifying by said processor as a real-time process.

7. The method of claim 1 further comprising:

employing said sensing the voltage signal and the current signal for each of said different electric loads in real-time;

selecting a plurality of load features for each of said layers;

selecting a first load feature set for a first one of said layers, and identifying one of a plurality of different first load categories for a corresponding one of said different electric loads for the first one of said layers;

selecting a second load feature set for a second one of said layers, and identifying one of a plurality of different second load sub-categories for the corresponding one of said different electric loads for the second one of said layers; and

selecting a third load feature set for a third one of said layers, and identifying one of said different electric load types for the corresponding one of said different electric loads for the third one of said layers.

8. The method of claim 1 further comprising:

including with said processor a power calculator to calculate power related quantities for a plurality of said different electric loads.

9. The method of claim 1 further comprising:

employing a first load feature set of a first one of said layers; and

employing a second different load feature set of a second one and a third one of said layers.

10. The method of claim 9 further comprising:

employing a plurality of load categories for the first one of said layers;

employing a plurality of load sub-categories for the second one of said layers; and

employing said plurality of different electric load types for the third one of said layers.

11. The method of claim 9 further comprising:

including with the first load feature set a plurality of polynomial coefficients of a voltage-current trajectory, and admittance; and

including with the second different load feature set thinness of a voltage-current trajectory, and admittance.

12. The method of claim 1 further comprising:

employing said determining and said identifying by said processor as a machine learning and pattern recognition process selected from the group consisting of an artificial neural network process, a support vector machine process, and a proximity analysis process.

13. The method of claim 1 further comprising:

providing offline training of said hierarchical load feature database by a processor.

14. The method of claim 1 further comprising:

providing said identifying by said processor in real-time.

15. The method of claim 1 further comprising:

identifying by said processor a load operating mode of said one of said different electric load types.

16. The method of claim 15 further comprising:

selecting the load operating mode from the list consisting of on, off, and standby.

17. The method of claim 1 further comprising:

employing the hierarchical load feature database as a hierarchical and scalable load feature database.

18. The method of claim 1 further comprising:

including with said corresponding load feature set a plurality of load features; and

including with each of the load features of said corresponding load feature set a range of values for a corresponding one of a plurality of load categories, a plurality of load sub-categories, and said plurality of different electric load types.

19. A system comprising:

a hierarchical load feature database comprising a plurality of layers, one of said layers including a plurality of different electric load types;

a plurality of sensors structured to sense a voltage signal and a current signal for each of a plurality of different electric loads; and

a processor structured to;

determine at least four different load features from said sensed voltage signal and said sensed current signal for a corresponding one of said different electric loads, and

identify one of said different electric load types by relating the different load features to the hierarchical load feature database,

wherein each of a plurality of said layers includes a corresponding load feature set, and

wherein the corresponding load feature set of at least one of said layers is different from the corresponding load feature set of at least another one of said layers.

20. The system of claim 19 wherein the corresponding load feature set includes a plurality of load features;

and wherein each of the load features of said corresponding load feature set includes a range of values for a corresponding one of a plurality of load categories, a plurality of load sub-categories, and said plurality of different electric load types.