Specifications

propagation
Uncontrolled terms: Dielectric permittivities - Functionally graded piezoelectric material -
Governing differential equations - Inhomogeneous plate - Piezoelectric coefficient -
Resonance frequencies - Variable coefficients - Wave propagation direction
Classification code: 701 Electricity and Magnetism - 701.1 Electricity: Basic Concepts and
Phenomena - 711 Electromagnetic Waves - 921.2 Calculus - 933.1 Crystalline Solids
DOI: 10.1088/0964-1726/22/9/095021
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
9.
Accession number: 20133916790283
Title: Fast S transform-based classification of power quality disturbance
Authors: Man, Weishi1 ; Zhang, Zhiyu1 ; Kang, Qing2 ; Miao, Yongkang1 ; Xi, Xiaoli1/;张志禹;;;
席晓莉
Author affiliation: 1 School of Automation and Information, Xi'an University of Technology,
Xi'an 710048, China
2 Logistical Engineering University, Chongqing 401311, China
Corresponding author: Man, W.
Source title: Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University
Abbreviated source title: Hsi An Chiao Tung Ta Hsueh
Volume: 47
Issue: 8
Issue date: August 2013
Publication year: 2013
Pages: 133-140
Language: Chinese
ISSN: 0253987X
CODEN: HCTPDW
Document type: Journal article (JA)
Publisher: Xi'an Jiaotong University, West Xian Ning Road 28, Xi'an, 710049, China
Abstract: Focusing on higher computation cost and lack of real-time detection for all techniques
based on traditional S-transform to identify power quality disturbances, a real-time approach
combining fast S-transform with least squares support vector machine is proposed. The standard
deviation of module coefficients, maximum module coefficient of each frequency band, and
module coefficient corresponding to the rated frequency are extracted from the one-dimensional
vector of the fast S-transform of the original power quality signals as features, and the least
squares support vector machine based on optimized parameters and the minimum output coding
is used to classify and identify the voltage swell, voltage sag, voltage interruption, spike, transient
oscillation and harmonic waves. Compared with the traditional approach based on S-transform,
the proposed approach reduces the tasks in both extracting features and training of the support
vector machine classifier due to fewer training samples. The longer the duration of the voltage
disturbance signal, the higher the saving efficiency. To the same accuracy, for the disturbance
signal with a length of 1024 points, processing time can be saved by 99%. The classification