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POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION

Power quality has become a great concern to all electricity consumers. Poor quality can cause equipment failure, data and economical. An automated monitoring system is needed to ensure signal quality, reduces diagnostic time and rectifies failures. This paper presents the detection and clas...

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Main Authors: ahmad, Nur Hafizatul Tul Huda, Abdullah, Abdul Rahim, JOPRI, MOHD HATTA
Format: Conference or Workshop Item
Language:English
Published: 2012
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utem.edu.my/id/eprint/9357/1/2012_Paper_POWER_QUALITY_SIGNALS_DETECTION_AND_CLASSIFICATION_USING.pdf
http://eprints.utem.edu.my/id/eprint/9357/
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spelling my.utem.eprints.93572015-05-28T04:03:31Z http://eprints.utem.edu.my/id/eprint/9357/ POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION ahmad, Nur Hafizatul Tul Huda Abdullah, Abdul Rahim JOPRI, MOHD HATTA TK Electrical engineering. Electronics Nuclear engineering Power quality has become a great concern to all electricity consumers. Poor quality can cause equipment failure, data and economical. An automated monitoring system is needed to ensure signal quality, reduces diagnostic time and rectifies failures. This paper presents the detection and classification of power quality signals using linear timefrequency distributions (TFD). The power quality signals focus on swell, sag, interruption, transient, harmonic, interharmonic and normal voltage based on IEEE Std. 1159-2009. The time-frequency analysis techniques selected are spectrogram and Gabor transform to represent the signals in time-frequency representation (TFR). From the time frequency representation (TFR) obtained, the signal parameters are estimated to identify the signal characteristics. The signal characteristics are the average of root means square voltage (Vave,rms), total waveform distortion (TWD), total harmonic distortion (THD) and total non harmonic distortion (TnHD) and duration of swell, sag, interruption and transient signals will be used as input for signals classification. The results show that spectrogram with the half window shift (HWS) provides better performance in term of accuracy, memory size, and computation complexity 2012-12-17 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/9357/1/2012_Paper_POWER_QUALITY_SIGNALS_DETECTION_AND_CLASSIFICATION_USING.pdf ahmad, Nur Hafizatul Tul Huda and Abdullah, Abdul Rahim and JOPRI, MOHD HATTA (2012) POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION. In: The Power and Energy Conversion Symposium (PECS 2012), 17/12/2012, UTEM.
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
ahmad, Nur Hafizatul Tul Huda
Abdullah, Abdul Rahim
JOPRI, MOHD HATTA
POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION
description Power quality has become a great concern to all electricity consumers. Poor quality can cause equipment failure, data and economical. An automated monitoring system is needed to ensure signal quality, reduces diagnostic time and rectifies failures. This paper presents the detection and classification of power quality signals using linear timefrequency distributions (TFD). The power quality signals focus on swell, sag, interruption, transient, harmonic, interharmonic and normal voltage based on IEEE Std. 1159-2009. The time-frequency analysis techniques selected are spectrogram and Gabor transform to represent the signals in time-frequency representation (TFR). From the time frequency representation (TFR) obtained, the signal parameters are estimated to identify the signal characteristics. The signal characteristics are the average of root means square voltage (Vave,rms), total waveform distortion (TWD), total harmonic distortion (THD) and total non harmonic distortion (TnHD) and duration of swell, sag, interruption and transient signals will be used as input for signals classification. The results show that spectrogram with the half window shift (HWS) provides better performance in term of accuracy, memory size, and computation complexity
format Conference or Workshop Item
author ahmad, Nur Hafizatul Tul Huda
Abdullah, Abdul Rahim
JOPRI, MOHD HATTA
author_facet ahmad, Nur Hafizatul Tul Huda
Abdullah, Abdul Rahim
JOPRI, MOHD HATTA
author_sort ahmad, Nur Hafizatul Tul Huda
title POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION
title_short POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION
title_full POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION
title_fullStr POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION
title_full_unstemmed POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION
title_sort power quality signals detection and classification using linear time frequency distribution
publishDate 2012
url http://eprints.utem.edu.my/id/eprint/9357/1/2012_Paper_POWER_QUALITY_SIGNALS_DETECTION_AND_CLASSIFICATION_USING.pdf
http://eprints.utem.edu.my/id/eprint/9357/
_version_ 1665905399031660544
score 13.211869

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