NDLI: Parallel computing for efficient time-frequency feature extraction of power quality disturbances

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Parallel computing for efficient time-frequency feature extraction of power quality disturbances

Content Provider	IET Digital Library
Author	Krishna, Brahmadesam V. Baskaran, Kaliaperumal
Abstract	Fast signal processing implementation techniques for detection and classification of power quality (PQ) disturbances are the need of the hour. Hence in this work, a parallel computing approach has been proposed to speed up the feature extraction of PQ signals to facilitate rapid building of classifier models. Considering that the Fourier, the one-dimensional discrete wavelet, the time-time and the Stockwell transforms have been used extensively to extract pertinent time-frequency features from non-stationary and multi-frequency PQ signals, acceleration approaches using data and task parallelism have been employed for parallel implementation of the above time-frequency transforms. In the first approach, data parallelism was applied to the Stockwell transform and the time-time transform-based feature extraction methods separately to alleviate capability problems. Also, data parallelism was applied to Fourier and wavelet-based feature extraction methods independently to alleviate capacity problems. Secondly, a combination of task and data parallelism was applied to speed up S-transform based three-phase sag feature extraction. Experiments were conducted using shared-memory and distributed memory architectures to try out the effectiveness of the proposed parallel approaches. The performances of these parallel implementations were analysed in terms of computational speed and efficiency in comparison with the sequential approach.
Starting Page	312
Ending Page	326
Page Count	15
ISSN	17519675
Volume Number	7
e-ISSN	17519683
Issue Number	Issue 4, Jun (2013)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/7/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2012.0262
Journal	IET Signal Processing
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	1D Discrete Wavelet Transform Acceleration Approach Classifier Model Data Parallelism Discrete Wavelet Transform Distributed Memory Architecture Distributed Memory System Feature Extraction Fourier Transform Fourier Transforms Harmonics Integral Transforms Multifrequency PQ Signal Multiprocessing System Nonstationary PQ Signal Parallel Architecture Parallel Computing Parallel Memories Parallel Processing Parallel Software Power Engineering Computing Power Quality Disturbance Classification Power Quality Disturbance Detection Power Supply Quality Power System Fault S-transform Sag Feature Extraction Shared Memory Architecture Shared Memory System Signal Classification Signal Detection Signal Processing Stockwell Transform Task Parallelism Time-frequency Analysis Time-frequency Feature Extraction Time-frequency Transform Time-time Transform
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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