Automated Identification of Electrical Disturbance Waveforms within an Operational Smart Power Grid

Aaron J. Wilson; Donald R. Reising; Robert W. Hay; Ray C. Johnson; Abdelrahman A. Karrar; T. Daniel Loveless

doi:10.1109/TSG.2020.2990079

Automated Identification of Electrical Disturbance Waveforms within an Operational Smart Power Grid

Aaron J. Wilson, Donald R. Reising, Robert W. Hay, Ray C. Johnson, Abdelrahman A. Karrar, T. Daniel Loveless

Research output: Contribution to journal › Article › peer-review

33 Scopus citations

Abstract

Electric power utilities employ 'smart' re-closing devices capable of capturing electrical disturbance (ED) waveforms in real-time. These waveforms are digitally sampled, transmitted back to the utility, and stored in text files that comply with the IEEE COMmon format for TRAnsient Data Exchange (COMTRADE) standard. An average of 2,100 ED events are recorded, by 850 devices, on a monthly basis within Electric Power Board's distribution network. Due to operational restraints such as: time, money, and person power, only 2% of these recorded events are currently analyzed by power utility personnel. The remaining 98% of recorded events remain a significant loss in information, which could be vital in fully understanding the operating condition within the distribution network. This work presents a hierarchical automated classification process that assigns ED events into one of three 'categories': valid data, switching events, and faults/power quality (PQ) disturbances. This process correctly assigned 92.9% of the tested 140 COMTRADE files into one of these categories. In addition to this categorization, this work presents an automated approach for the classification of magnetic inrush events. A classification rate of 90.63% is demonstrated using 32 transformer inrush COMTRADE files.

Original language	English
Article number	9076714
Pages (from-to)	4380-4389
Number of pages	10
Journal	IEEE Transactions on Smart Grid
Volume	11
Issue number	5
DOIs	https://doi.org/10.1109/TSG.2020.2990079
State	Published - Sep 2020
Externally published	Yes

Funding

Manuscript received September 11, 2019; revised February 24, 2020 and April 8, 2020; accepted April 21, 2020. Date of publication April 23, 2020; date of current version August 21, 2020. This work was supported in part by the Electric Power Research Institute Distribution Modernization Demonstrations Data Mining Initiative under Grant 00-10006873, and in part by the University of Chattanooga Foundation Incorporated. Paper no. TSG-01340-2019. (Corresponding author: Donald R. Reising.) Aaron J. Wilson is with the Department of Electrical Engineering, University of Tennessee at Knoxville, Knoxville, TN 37996 USA.

Funders	Funder number
Electric Power Research Institute Distribution Modernization Demonstrations Data Mining Initiative	00-10006873
University of Chattanooga Foundation	TSG-01340-2019

Keywords

COMTRADE
Smart power grid
automated identification
electrical disturbance
fault
fuse
harmonics
machine learning
power quality
switching

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10.1109/TSG.2020.2990079

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title = "Automated Identification of Electrical Disturbance Waveforms within an Operational Smart Power Grid",

abstract = "Electric power utilities employ 'smart' re-closing devices capable of capturing electrical disturbance (ED) waveforms in real-time. These waveforms are digitally sampled, transmitted back to the utility, and stored in text files that comply with the IEEE COMmon format for TRAnsient Data Exchange (COMTRADE) standard. An average of 2,100 ED events are recorded, by 850 devices, on a monthly basis within Electric Power Board's distribution network. Due to operational restraints such as: time, money, and person power, only 2% of these recorded events are currently analyzed by power utility personnel. The remaining 98% of recorded events remain a significant loss in information, which could be vital in fully understanding the operating condition within the distribution network. This work presents a hierarchical automated classification process that assigns ED events into one of three 'categories': valid data, switching events, and faults/power quality (PQ) disturbances. This process correctly assigned 92.9% of the tested 140 COMTRADE files into one of these categories. In addition to this categorization, this work presents an automated approach for the classification of magnetic inrush events. A classification rate of 90.63% is demonstrated using 32 transformer inrush COMTRADE files.",

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author = "Wilson, {Aaron J.} and Reising, {Donald R.} and Hay, {Robert W.} and Johnson, {Ray C.} and Karrar, {Abdelrahman A.} and Loveless, {T. Daniel}",

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Automated Identification of Electrical Disturbance Waveforms within an Operational Smart Power Grid

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