Journal ArticleDOI
Power quality disturbance data compression using wavelet transform methods
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TLDR
In this paper, a wavelet compression technique for power quality disturbance data is presented, which is performed through signal decomposition, thresholding of wavelet transform coefficients and signal reconstruction.Abstract:
In this paper, the authors present a wavelet compression technique for power quality disturbance data. The compression technique is performed through signal decomposition, thresholding of wavelet transform coefficients and signal reconstruction. Threshold values are determined by weighting the absolute maximum value at each scale. Wavelet transform coefficients whose values are below the threshold are discarded, while those that are above the threshold are kept along with their temporal locations. The authors show the efficacy of the technique by compressing actual disturbance data. The file size of the compressed data is only one-sixth to one-third that of the original data. Therefore, the cost related to storing and transmitting the data is significantly reduced.read more
Citations
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Journal ArticleDOI
Power quality detection and classification using wavelet-multiresolution signal decomposition
TL;DR: In this paper, a multiresolution signal decomposition technique is used to detect and localize transient events and furthermore classify different power quality disturbances, which can also be used to distinguish among similar disturbances.
Journal ArticleDOI
Power quality analysis using S-transform
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Time-frequency and time-scale domain analysis of voltage disturbances
Y.H. Gu,Math Bollen +1 more
TL;DR: In this paper, the analysis of voltage disturbance recordings in the time-frequency domain and in time-scale domain is discussed, where the discrete short-time Fourier transform (STFT) is used for the timefrequency domain; dyadic and binary-tree wavelet filters for the temporal domain; and the discrete STFT is also able to detect and analyze transients in a voltage disturbance.
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
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.
References
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Journal ArticleDOI
Power quality assessment via wavelet transform analysis
TL;DR: In this article, the authors present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances using dyadic-orthonormal wavelet transform analysis.
Journal ArticleDOI
Image compression using wavelet transform and multiresolution decomposition
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Proceedings ArticleDOI
Electric power quality disturbance detection using wavelet transform analysis
TL;DR: This approach is robust in detecting and localizing a wide range of power disturbances such as fast voltage fluctuations, short and long duration voltage variations, and harmonic distortion.
Wavelet Shrinkage and W.V.D.: A 10-minute tour
TL;DR: In this article, the authors presented at the International Conference on Wavelets and Applications, Toulouse, France, June, 1992, based on presentation at the NSF DMS 92-09130.
Journal Article
Solving problems of power quality
TL;DR: In this paper, EPRI is developing equipment and approaches for application on both sides of the meter to provide appropriate solutions to the many types of problems encountered, including power quality problems, from industrial production lines to personal computers.