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Multiple Chaos Synchronization System for Power Quality Classification in a Power System

This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, volta...

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Detalles Bibliográficos
Autores principales: Huang, Cong-Hui, Lin, Chia-Hung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934410/
https://www.ncbi.nlm.nih.gov/pubmed/24764771
http://dx.doi.org/10.1155/2014/902167
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author Huang, Cong-Hui
Lin, Chia-Hung
author_facet Huang, Cong-Hui
Lin, Chia-Hung
author_sort Huang, Cong-Hui
collection PubMed
description This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, voltage fluctuation phenomena, and voltage interruptions. Multiple detectors are used to monitor the dynamic errors between the master system and the slave system and are used to construct the feature patterns from time-domain signals. The maximum likelihood method (MLM), as a classifier, performs a comparison of the patterns of the features in the database. The proposed method can adapt itself without the need for adjustment of parameters or iterative computation. For a sample power system, the test results showed accurate discrimination, good robustness, and faster processing time for the detection of PQ disturbances.
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spelling pubmed-39344102014-04-24 Multiple Chaos Synchronization System for Power Quality Classification in a Power System Huang, Cong-Hui Lin, Chia-Hung ScientificWorldJournal Research Article This document proposes multiple chaos synchronization (CS) systems for power quality (PQ) disturbances classification in a power system. Chen-Lee based CS systems use multiple detectors to track the dynamic errors between the normal signal and the disturbance signal, including power harmonics, voltage fluctuation phenomena, and voltage interruptions. Multiple detectors are used to monitor the dynamic errors between the master system and the slave system and are used to construct the feature patterns from time-domain signals. The maximum likelihood method (MLM), as a classifier, performs a comparison of the patterns of the features in the database. The proposed method can adapt itself without the need for adjustment of parameters or iterative computation. For a sample power system, the test results showed accurate discrimination, good robustness, and faster processing time for the detection of PQ disturbances. Hindawi Publishing Corporation 2014-02-10 /pmc/articles/PMC3934410/ /pubmed/24764771 http://dx.doi.org/10.1155/2014/902167 Text en Copyright © 2014 C.-H. Huang and C.-H. Lin. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Huang, Cong-Hui
Lin, Chia-Hung
Multiple Chaos Synchronization System for Power Quality Classification in a Power System
title Multiple Chaos Synchronization System for Power Quality Classification in a Power System
title_full Multiple Chaos Synchronization System for Power Quality Classification in a Power System
title_fullStr Multiple Chaos Synchronization System for Power Quality Classification in a Power System
title_full_unstemmed Multiple Chaos Synchronization System for Power Quality Classification in a Power System
title_short Multiple Chaos Synchronization System for Power Quality Classification in a Power System
title_sort multiple chaos synchronization system for power quality classification in a power system
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3934410/
https://www.ncbi.nlm.nih.gov/pubmed/24764771
http://dx.doi.org/10.1155/2014/902167
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