Journal ArticleDOI
Wavelet based on-line disturbance detection for power quality applications
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TLDR
In this paper, a wavelet transform based online voltage disturbance detection approach is proposed to identify voltage disturbances and discriminates the type of event which has resulted in the voltage disturbance, e.g. either a fault or a capacitor switching incident.Abstract:
This paper introduces a new online voltage disturbance detection approach based on the wavelet transform. The proposed approach: (1) identifies voltage disturbances; and (2) discriminates the type of event which has resulted in the voltage disturbance, e.g. either a fault or a capacitor-switching incident. The proposed approach is: (1) significantly faster; and (2) more precise in discriminating the type of transient event than conventional voltage-based disturbance detection approaches. The feasibility of the proposed disturbance detection approach is demonstrated based on digital time-domain simulation of a power distribution system using the PSCAD/EMTDC software package.read more
Citations
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Journal ArticleDOI
A critical review of detection and classification of power quality events
TL;DR: A comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances is presented in this paper.
Journal ArticleDOI
A self-organizing learning array system for power quality classification based on wavelet transform
Haibo He,Janusz A. Starzyk +1 more
TL;DR: It is shown that there is no statistically significant difference in performance of the proposed method for PQ classification when different wavelets are chosen, which means one can choose the wavelet with short wavelet filter length to achieve good classification results as well as small computational cost.
Journal ArticleDOI
Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis
Ming Dong,David He +1 more
TL;DR: An integrated platform for multi-sensor equipment diagnosis and prognosis based on hidden semi-Markov model (HSMM), which shows that the increase of correct diagnostic rate is indeed very promising and the equipment prognosis can be implemented in the same integrated framework.
Journal ArticleDOI
Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks
TL;DR: In this article, a novel approach for detection and classification of power quality (PQ) disturbances is proposed, where distorted waveforms are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with higher signal-to-noise ratio.
Journal ArticleDOI
Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review
David Granados-Lieberman,Rene de Jesus Romero-Troncoso,Roque Alfredo Osornio-Rios,Arturo Garcia-Perez,Eduardo Cabal-Yepez +4 more
TL;DR: A review of techniques and methodologies developed for power quality analysis and power disturbance classification is presented in this article, in order to show their major characteristics, such as harmonics, sags, swells etc.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI
Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.