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Authors: Juan José González de la Rosa 1 ; José Carlos Palomares 1 ; Agustín Agüera 1 and Antonio Moreno Muñoz 2

Affiliations: 1 Univ. Cádiz, Spain ; 2 Univ. Córdoba, Spain

ISBN: 978-989-8425-02-7

Keyword(s): Higher-Order Statistics (HOS), Neural classifiers, Power-quality.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Neural Networks Based Control Systems ; Nonlinear Signals and Systems ; Signal Processing, Sensors, Systems Modeling and Control

Abstract: This work renders the classification of Power Quality (PQ) disturbances using fourth-order sliding cumulants’ maxima as the key feature. These 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. (More)

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Paper citation in several formats:
José González de la Rosa J.; Carlos Palomares J.; Agüera A.; Moreno Muñoz A. and (2010). CLASSIFICATION OF POWER QUALITY DISTURBANCES VIA HIGHER-ORDER STATISTICS AND SELF-ORGANIZING NEURAL NETWORKS.In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-989-8425-02-7, pages 183-190. DOI: 10.5220/0002915101830190

@conference{icinco10,
author={Juan {José González de la Rosa} and José {Carlos Palomares} and Agustín Agüera and Antonio {Moreno Muñoz}},
title={CLASSIFICATION OF POWER QUALITY DISTURBANCES VIA HIGHER-ORDER STATISTICS AND SELF-ORGANIZING NEURAL NETWORKS},
booktitle={Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2010},
pages={183-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002915101830190},
isbn={978-989-8425-02-7},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - CLASSIFICATION OF POWER QUALITY DISTURBANCES VIA HIGHER-ORDER STATISTICS AND SELF-ORGANIZING NEURAL NETWORKS
SN - 978-989-8425-02-7
AU - José González de la Rosa, J.
AU - Carlos Palomares, J.
AU - Agüera, A.
AU - Moreno Muñoz, A.
PY - 2010
SP - 183
EP - 190
DO - 10.5220/0002915101830190

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