Methods and systems for detecting series arcs in electrical systems

• US 20080157781A1
• Filed: 12/27/2006
• Published: 07/03/2008
• Est. Priority Date: 12/27/2006
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Younsi, Karim, Yan, Weizhong, Zhou, Yingneng, Hu, Xiao, Mason, Henry
• Application Number:
  US11/646,033
• Publication Number:
  US 20080157781A1
• Time in Patent Office: Days
• US Class Current: 324/536
• CPC Class Codes: H02H 1/0015 Using arc detectors
• CPC Class Codes: H02H 1/0092 concerning the data processing means, e.g. expert systems, neural networks

Abstract

A method of detecting a series arc in an alternating current electrical system is provided. The method is a pattern-recognition based approach and includes passing raw current signals from a conductor in the electrical system through one or more filters to provide a filtered signal; extracting one or more features from the filtered signal; and classifying the features to a known state representative of the series arc.

Claims

1. A method for detecting a series arc in an alternating current electrical system, comprising:

passing raw current signals from a conductor in the electrical system through one or more filters to provide a filtered signal;

extracting one or more features from said filtered signal; and

classifying said one or more features to a known state of the series arc.

2. The method as in claim 1, wherein said one or more features comprises a feature selected from the group consisting of a mean of said filtered signal, a standard deviation of said filtered signal, a mean filtered signal, a mean of a full cycle, a standard deviation of a full cycle, a maximum standard deviation of a window of signal in a cycle, a minimum standard deviation of a window of signal in a cycle, a ratio of the maximum and minimum standard deviations, an absolute sum of each sample in a cycle, a relation of the standard deviations for each adjacent window in a cycle, a root mean square value, a maximum difference between two adjacent samples, a minimum difference between two adjacent samples, a ratio of the maximum difference to the minimum difference, a range of the difference signal, a sum of the differences between adjacent points, and any combinations thereof.

3. The method as in claim 1, wherein said one or more features comprises a peak feature selected from the group consisting of a minimum of peak amplitude, a maximum of peak amplitude, a difference of minimum and maximum amplitudes, a mean of peak amplitude, a standard deviation of amplitude, a kurtosis of peak amplitude, a skewness of peak amplitude, a root mean square (RMS) of peak amplitude, a crest factor of peak amplitude, number of peaks per unit time, and any combinations thereof.

4. The method as in claim 1, wherein said one or more features comprises a peak feature selected from the group consisting of a second order moment of a peak shape, a third order moment of a peak shape, a fourth order moment of a peak shape, a time distance from maximum peak to centroids of peaks, and any combinations thereof.

5. The method as in claim 1, wherein said one or more filters is selected from the group consisting of a high-pass filter, a low-pass filter, a band-pass filter, a signal processing algorithm, and any combinations thereof.

6. The method as in claim 1, further comprising measuring said raw signals across a bimetal in series with said conductor.

7. The method as in claim 1, further comprising inputting said raw signals to said one or more filters from a database of raw signals.

8. The method as in claim 1, further comprising performing classifying features using one or more different classifiers, said one or more classifiers being selected from the group consisting of a decision tree, a neural network, a support vector machine, a random forest, and any combinations thereof.

9. A method for detecting a series arc in an alternating current electrical system, comprising:

inputting a raw current signal from a conductor in the electrical system into one or more filters to provide a filtered signal;

extracting a plurality of features from said filtered signal; and

comparing said plurality of features to a known feature representative of the series arc.

10. The method as in claim 9, wherein said known feature comprises a shape signature of said plurality of features.

11. The method as in claim 9, wherein said plurality of features comprises more than one feature selected from the group consisting of a mean of said filtered signal, a standard deviation of said filtered signal, a mean filtered signal, a mean of a full cycle, a standard deviation of a full cycle, a maximum standard deviation of a window of signal in a cycle, a minimum standard deviation of a window of signal in a cycle, a ratio of the maximum and minimum standard deviations, an absolute sum of each sample in a cycle, a relation of the standard deviations for each adjacent window in a cycle, a root mean square value, a maximum difference between two adjacent samples, a minimum difference between two adjacent samples, a ratio of the maximum difference to the minimum difference, a range of the difference signal, a sum of the differences between adjacent points, and any combinations thereof.

12. The method as in claim 9, wherein said known feature comprises a peak feature signature of said plurality of features.

13. The method as in claim 9, wherein said plurality of features comprises more than one feature selected from the group consisting of a minimum of peak amplitude, a maximum of peak amplitude, a difference of minimum and maximum amplitudes, a mean of peak amplitude, a standard deviation of amplitude, a kurtosis of peak amplitude, a skewness of peak amplitude, a root mean square (RMS) of peak amplitude, a crest factor of peak amplitude, number of peaks per unit time, and any combinations thereof.

14. The method as in claim 9, wherein said plurality of features comprises more than one feature selected from the group consisting of a second order moment of a peak shape, a third order moment of a peak shape, a fourth order moment of a peak shape, a time distance from maximum peak to centroids of peaks, and any combinations thereof.

15. The method as in claim 9, wherein said one or more filters comprises a filter selected from the group consisting of a low-pass filter, a high-pass filters, a band-pass filter, a signal processing algorithm, and any combinations thereof.

16. The method as in claim 9, further comprising measuring said raw signals across a bimetal in series with said conductor.

17. A system for detecting a series arc in an alternating current electrical system, comprising:

a source of a raw current waveform signal;

a filter configured to generate a filtered signal from said raw current waveform signal; and

a microprocessor in electrical communication with said filter, said microprocessor being configured to generate one or more features from said filtered signal and compare said one or more features to one or more known features representative of the series arc in said raw current waveform signal.

18. The system as in claim 17, wherein said source comprises a bimetal of a current interrupter.

19. The system as in claim 17, wherein said filter is selected from the group consisting of a high-pass filter, a low-pass filter, a band-pass filter, a signal processing algorithm, and any combinations thereof.

20. The system as in claim 17, wherein said microprocessor is configured to provide a trip signal to an arc fault circuit interrupter when the series arc is detected.