Power quality detection and classification using wavelet and support vector machine
This work presents the identification and classification of various disturbances that affect the quality of energy, seen as the quality of the voltage wave (harmonics, sag, swell and flicker). For this, the wavelet transform is used, which allows to have characteristic patterns as input signals of t...
- Autores:
-
Garrido Arévalo, Víctor Manuel
Gil-González, Walter
Holguín, M.
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9530
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9530
https://iopscience.iop.org/article/10.1088/1742-6596/1448/1/012002/meta
- Palabra clave:
- Energía eléctrica
Calidad de la energía
Voltaje
Redes eléctricas
Distribución de energía eléctrica
Electric power
Quality of energy
Voltage
Electrical networks
Electric power distribution
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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| dc.title.spa.fl_str_mv |
Power quality detection and classification using wavelet and support vector machine |
| title |
Power quality detection and classification using wavelet and support vector machine |
| spellingShingle |
Power quality detection and classification using wavelet and support vector machine Energía eléctrica Calidad de la energía Voltaje Redes eléctricas Distribución de energía eléctrica Electric power Quality of energy Voltage Electrical networks Electric power distribution |
| title_short |
Power quality detection and classification using wavelet and support vector machine |
| title_full |
Power quality detection and classification using wavelet and support vector machine |
| title_fullStr |
Power quality detection and classification using wavelet and support vector machine |
| title_full_unstemmed |
Power quality detection and classification using wavelet and support vector machine |
| title_sort |
Power quality detection and classification using wavelet and support vector machine |
| dc.creator.fl_str_mv |
Garrido Arévalo, Víctor Manuel Gil-González, Walter Holguín, M. |
| dc.contributor.author.none.fl_str_mv |
Garrido Arévalo, Víctor Manuel Gil-González, Walter Holguín, M. |
| dc.subject.keywords.spa.fl_str_mv |
Energía eléctrica Calidad de la energía Voltaje Redes eléctricas Distribución de energía eléctrica Electric power Quality of energy Voltage Electrical networks Electric power distribution |
| topic |
Energía eléctrica Calidad de la energía Voltaje Redes eléctricas Distribución de energía eléctrica Electric power Quality of energy Voltage Electrical networks Electric power distribution |
| description |
This work presents the identification and classification of various disturbances that affect the quality of energy, seen as the quality of the voltage wave (harmonics, sag, swell and flicker). For this, the wavelet transform is used, which allows to have characteristic patterns as input signals of the support vector machine, these are evaluated in their different configurations, bi-class, minimum output coding, error correcting output and one versus all. For all of them, in the first instance they were trained with 200 samples, then the results were validated with 100 samples and finally the evaluation was made with 500 different samples, obtaining that the best result is presented with the minimum output coding configuration. |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019-09-24 |
| dc.date.accessioned.none.fl_str_mv |
2020-11-04T20:17:52Z |
| dc.date.available.none.fl_str_mv |
2020-11-04T20:17:52Z |
| dc.date.submitted.none.fl_str_mv |
2020-10-30 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_8544 |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_c94f |
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publishedVersion |
| dc.identifier.citation.spa.fl_str_mv |
Garrido-Arévalo, V., Gil-González, W. and Holguin, M., 2020. Power quality detection and classification using wavelet and support vector machine. Journal of Physics: Conference Series, 1448, p.012002. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9530 |
| dc.identifier.url.none.fl_str_mv |
https://iopscience.iop.org/article/10.1088/1742-6596/1448/1/012002/meta |
| dc.identifier.doi.none.fl_str_mv |
448 (2020) 10.1088/1742-6596/1448/1/012002 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Garrido-Arévalo, V., Gil-González, W. and Holguin, M., 2020. Power quality detection and classification using wavelet and support vector machine. Journal of Physics: Conference Series, 1448, p.012002. 448 (2020) 10.1088/1742-6596/1448/1/012002 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/9530 https://iopscience.iop.org/article/10.1088/1742-6596/1448/1/012002/meta |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
| dc.rights.cc.*.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
| dc.format.extent.none.fl_str_mv |
7 páginas |
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application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
| dc.source.spa.fl_str_mv |
Journal of Physics: Conference Series, Volume 1448 |
| institution |
Universidad Tecnológica de Bolívar |
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Garrido Arévalo, Víctor Manuel879a8aea-14af-4c41-88e8-b64d35f8e1e6Gil-González, Walter59bfddb4-d5c7-4bd3-8cbe-49b131a07e1cHolguín, M.efd2587b-8fd5-40b7-92c1-5af7a1a4983c2020-11-04T20:17:52Z2020-11-04T20:17:52Z2019-09-242020-10-30Garrido-Arévalo, V., Gil-González, W. and Holguin, M., 2020. Power quality detection and classification using wavelet and support vector machine. Journal of Physics: Conference Series, 1448, p.012002.https://hdl.handle.net/20.500.12585/9530https://iopscience.iop.org/article/10.1088/1742-6596/1448/1/012002/meta448 (2020) 10.1088/1742-6596/1448/1/012002Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis work presents the identification and classification of various disturbances that affect the quality of energy, seen as the quality of the voltage wave (harmonics, sag, swell and flicker). For this, the wavelet transform is used, which allows to have characteristic patterns as input signals of the support vector machine, these are evaluated in their different configurations, bi-class, minimum output coding, error correcting output and one versus all. For all of them, in the first instance they were trained with 200 samples, then the results were validated with 100 samples and finally the evaluation was made with 500 different samples, obtaining that the best result is presented with the minimum output coding configuration.7 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference Series, Volume 1448Power quality detection and classification using wavelet and support vector machineinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_c94fhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_8544Energía eléctricaCalidad de la energíaVoltajeRedes eléctricasDistribución de energía eléctricaElectric powerQuality of energyVoltageElectrical networksElectric power distributionCartagena de IndiasInvestigadoresW Morsi 2012 A wavelet-based approach for reactive power metering in modern three-phase grids considering time-varying power quality disturbances Electric Power Systems Research 87 31Barros J, Diego R and De Apráiz M 2012 Applications of wavelets in electric power quality: Voltage events Electric Power Systems Research 88 130Saini M and Kapoor R 2012 Classification of power quality events – A review Int. Journal of Electrical Power & Energy Systems 43(1) 11Chirag N and Prasanta K 2013 Power quality index based on discrete wavelet transform Int. Journal of Electrical Power & Energy Systems 53 994Ferreira D, Seixas J, Cerqueira A and Duque C 2015 A new power quality deviation index based on principal curves Electric Power Systems Research 125 8Morsi W and El-Hawary M 2010 Novel power quality indices based on wavelet packet transform for nonstationary sinusoidal and non-sinusoidal disturbances Electric Power Systems Research 80(7) 753Zhang Y, Dong Z, Liu A, Wang S, Ji G, Zhang Z and Yang J 2015 Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine Journal of Medical Imaging and Health Informatics 5(7) 1395Devi Arockia Vanithaa C, Devarajb D and Venkatesuluc M 2015 Gene expression data classification using support vector machine and mutual information-based gene selection Procedia Computer Science 47 13Wu L et al. 2017 Detection of american football head impacts using biomechanical features and support vector machine classification Scientific Reports 8(855) 1Ramesh N and Jagan B 2017 Fault classification in power systems using EMD and SVM Ain Shams Engineering Journal 8(2) 103Jan S, Lee Y, Shin J and Koo I 2017 Sensor fault classification based on support vector machine and statistical time-domain features IEEE Access 5 8682Raya P and Mishrab D 2016 Support vector machine-based fault classification and location of a long transmission line Engineering Science and Technology, an International Journal 19(3) 1368Abid A, Khan M, Ullah A, Alam M and Sohail M 2017 Real time health monitoring of industrial machine using multiclass support vector machine 2nd International Conference on Control and Robotics Engineering (Bangkok: IEEE) p 77De Yong D, Bhowmik S and Magnago F 2015 An effective power quality classifier using wavelet transform and support vector machines Expert Systems with Applications 42(15-16) 6075Liu Z, Cui Y and Li W 2015 A classification method for complex power quality disturbances using EEMD and rank wavelet SVM IEEE Transactions on Smart Grid 6(4) 1678Li J, Teng Z, Tang Q and Song J 2015 Detection and classification of power quality disturbances using double resolution S-Transform and DAG-SVMs IEEE Transactions on Instrumentation and Measurement 65(10) 2302Abdoos A, Mianaei P and Ghadikolaei M 2016 Combined VMD-SVM based feature selection method for classification of power quality events Applied Soft Computing 38 637Garrido-Arévalo V, Díaz-Rodríguez J and Pardo-García A 2015 Analysis of the power quality using intelligent techniques WSEAS Transactions on Power Systems 10 15Garrido-Arévalo V, Díaz-Rodríguez J and Pardo-García A 2014 Classification of power quality phenomena using intelligent techniques Transmission & Distribution Conference and Exposition - Latin America (Medellín: IEEE) p 10Suthaharan S 2016 Machine learning models and algorithms for big data classification Integrated Series in Information Systems 36 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