NDLI: Automatic classification of Power Quality disturbances via higher-order cumulants and self-organizing networks

Content Provider	IEEE Xplore Digital Library
Author	González de la Rosa, J.J. Moreno-Muñoz, A. Palomares, J.C. Agüera, A.
Copyright Year	2010
Description	Author affiliation: Univ. of Córdoba. Electronics, Research Group PAIDI-TIC-168, Albert Einstein building C-2, SPAIN, E-14071-Spain (Moreno-Muñoz, A.) \|\| Univ. of Cádiz. Electronics, Research Group PAIDI-TIC-168, EPSA-Av. Ramón Puyol S/N, E-11202-Algeciras-Spain (González de la Rosa, J.J.; Palomares, J.C.; Agüera, A.)
Abstract	This work accomplishes the classification of Power Quality (PQ) disturbances using fourth-order sliding cumulants' maxima as the key feature. These statistics estimators are calculated over high-pass filtered real-life signals, to avoid the low-frequency 50-Hz sinusoid. Four types of electrical AC supply anomalies constitute the starting grid of a competitive layer performance, which manages to classify 90 signals within a 2D-space (whose coordinates are the minima and the maxima of the sliding cumulants calculated over each register). Four clusters have been clearly identified via the competitive network, each of which corresponds to a type of anomaly. Then, a Self-Organizing Network is conceived in order to guess additional classes in the feature space. Results suggest the idea of two additional sets of signals, which are more related to the degree of signals' degeneration than to real new groups of anomalies. We collaterally conclude the need of additional features to face the problem of subclass division. The experience sets the foundations of an automatic procedure for PQ event classification.
Starting Page	1579
Ending Page	1584
File Size	329091
Page Count	6
File Format	PDF
ISBN	9781424463909
e-ISBN	9781424463923
DOI	10.1109/ISIE.2010.5636337
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Transient analysis Power quality Time domain analysis Voltage fluctuations Support vector machine classification
Content Type	Text
Resource Type	Article

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