Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements

Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements

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A number of 387 discharge portraying waveforms recorded on 18 different 150 kV post insulators installed at two different Substations in Crete, Greece are considered in this paper. Twenty different features are extracted from each waveform and two feature selection algorithms (t-test and mRMR) are e...

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Bibliographic Details
Main Authors: D. Pylarinos, K. Theofilatos, K. Siderakis, E. Thalassinakis
Format: Article
Language:English
Published: D. G. Pylarinos 2013-12-01
Series:Engineering, Technology & Applied Science Research
Subjects:
insulators
leakage current
discharges
classification
frequency
genetic algorithms
Online Access:https://etasr.com/index.php/ETASR/article/view/418
id doaj-a154105a7843473cbb350b49a3b4c883
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spelling doaj-a154105a7843473cbb350b49a3b4c8832020-12-02T17:24:21ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362013-12-0136Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current MeasurementsD. Pylarinos0K. Theofilatos1K. Siderakis2E. Thalassinakis3Dr-Ing Electrical & Computer Engineer, Researcher/Consultant, GreecePattern Recognition Laboratory, Department of Computer Engineering & Informatics, University of Patras, GreeceElectrical Engineering Department,Technological Educational Institute of Crete, GreeceIslands Network Operation Department, Hellenic Electricity Distribution Network Operator S.A., GreeceA number of 387 discharge portraying waveforms recorded on 18 different 150 kV post insulators installed at two different Substations in Crete, Greece are considered in this paper. Twenty different features are extracted from each waveform and two feature selection algorithms (t-test and mRMR) are employed. Genetic algorithms are used to classify waveforms in two different classes related to the portrayed discharges. Five different data sets are employed (1. the original feature vector, 2. time domain features, 3. frequency domain features, 4. t-test selected features 5. mRMR selected features). Results are discussed and compared with previous classification implementations on this particular data group. https://etasr.com/index.php/ETASR/article/view/418insulatorsleakage currentdischargesclassificationfrequencygenetic algorithms
collection DOAJ
language English
format Article
sources DOAJ
author D. Pylarinos
K. Theofilatos
K. Siderakis
E. Thalassinakis
spellingShingle D. Pylarinos
K. Theofilatos
K. Siderakis
E. Thalassinakis
Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements
Engineering, Technology & Applied Science Research
insulators
leakage current
discharges
classification
frequency
genetic algorithms
author_facet D. Pylarinos
K. Theofilatos
K. Siderakis
E. Thalassinakis
author_sort D. Pylarinos
title Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements
title_short Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements
title_full Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements
title_fullStr Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements
title_full_unstemmed Discharges Classification using Genetic Algorithms and Feature Selection Algorithms on Time and Frequency Domain Data Extracted from Leakage Current Measurements
title_sort discharges classification using genetic algorithms and feature selection algorithms on time and frequency domain data extracted from leakage current measurements
publisher D. G. Pylarinos
series Engineering, Technology & Applied Science Research
issn 2241-4487
1792-8036
publishDate 2013-12-01
description A number of 387 discharge portraying waveforms recorded on 18 different 150 kV post insulators installed at two different Substations in Crete, Greece are considered in this paper. Twenty different features are extracted from each waveform and two feature selection algorithms (t-test and mRMR) are employed. Genetic algorithms are used to classify waveforms in two different classes related to the portrayed discharges. Five different data sets are employed (1. the original feature vector, 2. time domain features, 3. frequency domain features, 4. t-test selected features 5. mRMR selected features). Results are discussed and compared with previous classification implementations on this particular data group.
topic insulators
leakage current
discharges
classification
frequency
genetic algorithms
url https://etasr.com/index.php/ETASR/article/view/418
work_keys_str_mv AT dpylarinos dischargesclassificationusinggeneticalgorithmsandfeatureselectionalgorithmsontimeandfrequencydomaindataextractedfromleakagecurrentmeasurements
AT ktheofilatos dischargesclassificationusinggeneticalgorithmsandfeatureselectionalgorithmsontimeandfrequencydomaindataextractedfromleakagecurrentmeasurements
AT ksiderakis dischargesclassificationusinggeneticalgorithmsandfeatureselectionalgorithmsontimeandfrequencydomaindataextractedfromleakagecurrentmeasurements
AT ethalassinakis dischargesclassificationusinggeneticalgorithmsandfeatureselectionalgorithmsontimeandfrequencydomaindataextractedfromleakagecurrentmeasurements
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