Journal
IEEE TRANSACTIONS ON POWER DELIVERY
Volume 29, Issue 3, Pages 1390-1397Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRD.2013.2285097
Keywords
Feature extraction; phasor measurement unit (PMU); power system disturbance; wide-area monitoring
Categories
Funding
- Interdisciplinary Research Grant from VP Research's office at New Mexico State University
- Division Of Human Resource Development
- Direct For Education and Human Resources [1345232] Funding Source: National Science Foundation
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Identifying disturbance events recorded by phasor measurement units (PMUs) has drawn researchers' attention in recent times. Some approaches to identify typical disturbance events frequently occurring in power systems have been documented. However, in order to comprehensively identify all disturbance events recorded by PMUs, it is required to know how many types of events are detected and recorded by PMUs monitoring a certain power system. In other words, for classification purposes, one must know the number of classes to begin with. This paper uses actual disturbance files stored in the database of a North American utility from 2007 through 2010 to determine how many classes (types of disturbances) the disturbance files contain. After analyzing various clustering techniques, a suitable unsupervised learning technique has been chosen and successfully implemented for this purpose. The results show that this process should be underpinned to any comprehensive event detection tool for PMU data.
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