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Multiple Chaos Synchronization System for Power Quality Classification in a Power System
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-09
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Huang, Cong-Hui& Lin, Chia-Hung. 2014. Multiple Chaos Synchronization System for Power Quality Classification in a Power System. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1051519
Modern Language Association (MLA)
Huang, Cong-Hui& Lin, Chia-Hung. Multiple Chaos Synchronization System for Power Quality Classification in a Power System. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1051519
American Medical Association (AMA)
Huang, Cong-Hui& Lin, Chia-Hung. Multiple Chaos Synchronization System for Power Quality Classification in a Power System. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1051519
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051519