Automated fuse identification within an operational smart power grid

Aaron J. Wilson; Donald R. Reising; Robert W. Hay; Ray C. Johnson

doi:10.1109/APPEEC.2018.8566414

Automated fuse identification within an operational smart power grid

Aaron J. Wilson, Donald R. Reising, Robert W. Hay, Ray C. Johnson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

During normal operation for power utilities an average of 2,100 events are recorded each month. Currently, utility personnel employ an archaic by-hand approach in the analysis of these events. This limits the analysis to roughly 2% of the total recorded events. This work applies pattern recognition and machine learning to perform automated identification of faults cleared by fuses. A critical step in the automation is the accurate detection of the waveform samples associated with the fault itself. Poor detection of these samples negatively impacts the identification performance. This work investigated three detection methods: Variance trajectory, binary vectors, and the analytic signal. A naïve Bayes classifier was employed to identify seven fuse sizes using let-through energy. Naïve Bayes identification performance of 94.3%, 93.6%, and 94.7% was achieved for the variance trajectory, binary vectors, and analytic signal detection approaches, respectively.

Original language	English
Title of host publication	IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018
Publisher	IEEE Computer Society
Pages	719-723
Number of pages	5
ISBN (Electronic)	9781538656860
DOIs	https://doi.org/10.1109/APPEEC.2018.8566414
State	Published - Dec 6 2018
Externally published	Yes
Event	10th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018 - Kota Kinabalu, Malaysia Duration: Oct 7 2018 → Oct 10 2018

Publication series

Name	Asia-Pacific Power and Energy Engineering Conference, APPEEC
Volume	2018-October
ISSN (Print)	2157-4839
ISSN (Electronic)	2157-4847

Conference

Conference	10th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018
Country/Territory	Malaysia
City	Kota Kinabalu
Period	10/7/18 → 10/10/18

Funding

This work is supported by funding through the Electric Power Research Institute’s (EPRI) Distribution Modernization Demonstration (DMD) Data Mining Initiative.

Access to Document

10.1109/APPEEC.2018.8566414

Cite this

Wilson, A. J., Reising, D. R., Hay, R. W., & Johnson, R. C. (2018). Automated fuse identification within an operational smart power grid. In IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018 (pp. 719-723). Article 8566414 (Asia-Pacific Power and Energy Engineering Conference, APPEEC; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/APPEEC.2018.8566414

@inproceedings{6bcd3eec07c54a1bb62982e1585e7972,

title = "Automated fuse identification within an operational smart power grid",

abstract = "During normal operation for power utilities an average of 2,100 events are recorded each month. Currently, utility personnel employ an archaic by-hand approach in the analysis of these events. This limits the analysis to roughly 2% of the total recorded events. This work applies pattern recognition and machine learning to perform automated identification of faults cleared by fuses. A critical step in the automation is the accurate detection of the waveform samples associated with the fault itself. Poor detection of these samples negatively impacts the identification performance. This work investigated three detection methods: Variance trajectory, binary vectors, and the analytic signal. A na{\"i}ve Bayes classifier was employed to identify seven fuse sizes using let-through energy. Na{\"i}ve Bayes identification performance of 94.3%, 93.6%, and 94.7% was achieved for the variance trajectory, binary vectors, and analytic signal detection approaches, respectively.",

author = "Wilson, {Aaron J.} and Reising, {Donald R.} and Hay, {Robert W.} and Johnson, {Ray C.}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

year = "2018",

month = dec,

day = "6",

doi = "10.1109/APPEEC.2018.8566414",

language = "English",

series = "Asia-Pacific Power and Energy Engineering Conference, APPEEC",

publisher = "IEEE Computer Society",

pages = "719--723",

booktitle = "IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018",

}

Wilson, AJ, Reising, DR, Hay, RW & Johnson, RC 2018, Automated fuse identification within an operational smart power grid. in IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018., 8566414, Asia-Pacific Power and Energy Engineering Conference, APPEEC, vol. 2018-October, IEEE Computer Society, pp. 719-723, 10th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018, Kota Kinabalu, Malaysia, 10/7/18. https://doi.org/10.1109/APPEEC.2018.8566414

Automated fuse identification within an operational smart power grid. / Wilson, Aaron J.; Reising, Donald R.; Hay, Robert W. et al.
IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018. IEEE Computer Society, 2018. p. 719-723 8566414 (Asia-Pacific Power and Energy Engineering Conference, APPEEC; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automated fuse identification within an operational smart power grid

AU - Wilson, Aaron J.

AU - Reising, Donald R.

AU - Hay, Robert W.

AU - Johnson, Ray C.

PY - 2018/12/6

Y1 - 2018/12/6

N2 - During normal operation for power utilities an average of 2,100 events are recorded each month. Currently, utility personnel employ an archaic by-hand approach in the analysis of these events. This limits the analysis to roughly 2% of the total recorded events. This work applies pattern recognition and machine learning to perform automated identification of faults cleared by fuses. A critical step in the automation is the accurate detection of the waveform samples associated with the fault itself. Poor detection of these samples negatively impacts the identification performance. This work investigated three detection methods: Variance trajectory, binary vectors, and the analytic signal. A naïve Bayes classifier was employed to identify seven fuse sizes using let-through energy. Naïve Bayes identification performance of 94.3%, 93.6%, and 94.7% was achieved for the variance trajectory, binary vectors, and analytic signal detection approaches, respectively.

AB - During normal operation for power utilities an average of 2,100 events are recorded each month. Currently, utility personnel employ an archaic by-hand approach in the analysis of these events. This limits the analysis to roughly 2% of the total recorded events. This work applies pattern recognition and machine learning to perform automated identification of faults cleared by fuses. A critical step in the automation is the accurate detection of the waveform samples associated with the fault itself. Poor detection of these samples negatively impacts the identification performance. This work investigated three detection methods: Variance trajectory, binary vectors, and the analytic signal. A naïve Bayes classifier was employed to identify seven fuse sizes using let-through energy. Naïve Bayes identification performance of 94.3%, 93.6%, and 94.7% was achieved for the variance trajectory, binary vectors, and analytic signal detection approaches, respectively.

UR - http://www.scopus.com/inward/record.url?scp=85060389637&partnerID=8YFLogxK

U2 - 10.1109/APPEEC.2018.8566414

DO - 10.1109/APPEEC.2018.8566414

M3 - Conference contribution

AN - SCOPUS:85060389637

T3 - Asia-Pacific Power and Energy Engineering Conference, APPEEC

SP - 719

EP - 723

BT - IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018

PB - IEEE Computer Society

T2 - 10th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2018

Y2 - 7 October 2018 through 10 October 2018

ER -

Automated fuse identification within an operational smart power grid

Abstract

Publication series

Conference

Funding

Access to Document

Other files and links

Fingerprint

Cite this