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Permanent link (DOI): https://doi.org/10.7939/R3P26QF0J

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Abnormality Detection Methods for Utility Equipment Condition Monitoring Open Access

Descriptions

Other title
Subject/Keyword
abnormality detection
signatures of equipment failures
soft detection
equipment condition monitoring
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Li, Benzhe
Supervisor and department
Xu, Wilsun (Department of Electrical and Computer Engineering)
Jing, Yindi (Department of Electrical and Computer Engineering)
Examining committee member and department
Xu, Wilsun (Department of Electrical and Computer Engineering)
Jing, Yindi (Department of Electrical and Computer Engineering)
Zhao, Qing (Department of Electrical and Computer Engineering)
Liang, Hao (Department of Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Energy Systems
Date accepted
2016-07-25T09:18:18Z
Graduation date
2016-06:Fall 2016
Degree
Master of Science
Degree level
Master's
Abstract
The wide spread use of power quality monitoring tools in recent years has enabled utility companies to extract non-power-quality information from the power quality monitoring data. A high potential use of such data is the equipment condition monitoring. General purpose detection method for waveform abnormality is considered as an important step for data analytics based equipment condition monitoring. Two general purpose detection methods are proposed in this thesis. The first one is a modified detection method based on existing scheme, where segment RMS values of differential waveform and half-cycle refreshed RMS values of original waveform are used as features for the detection. The second method is based on statistical characteristics of current signals, where abnormalities are detected by comparing the statistical distributions of waveform variations with and without disturbances. Current waveform is used for the detection since they are more sensitive to equipment conditions than voltage waveform. An automatic threshold selection scheme is adopted in the detection method. In addition, as hard binary detection can sometimes lead to large error especially for boundary situations, a soft detection scheme is proposed which returns soft detection results. The soft detection scheme can reduce the number of missing events compared with binary detection methods. Moreover, the detection value provides reference for severity of a certain abnormal event.
Language
English
DOI
doi:10.7939/R3P26QF0J
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Benzhe Li, Yindi Jing, and Wilsun Xu, “A Generic Waveform Abnormality Detection Method for Utility Equipment Condition Monitoring,” IEEE Transactions on Power Delivery, DOI: 10.1109/TPWRD.2016.2580663, 2016.Wilsun Xu, Benzhe Li. Draft report: Electric signatures of power equipment failures. [Online]. Available: http://grouper.ieee.org/groups/td/pq/data/

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File title: UofAMastersThesis.pdf
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