Performance analysis of machine Learning algorithms on power quality disturbances classification

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dc.authorid0000-0001-9528-7187
dc.authorid0000-0001-6846-8222
dc.authorscopusid59231848100
dc.authorscopusid57188849283
dc.authorscopusid15754585300
dc.contributor.authorGümüş, Birsen
dc.contributor.authorÇoban, Melih
dc.contributor.authorTezcan, Süleyman Sungur
dc.date.accessioned2024-09-25T19:42:51Z
dc.date.available2024-09-25T19:42:51Z
dc.date.issued2024
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionPower Electronics in Everything (PEiE); TMEiCen_US
dc.description12th International Conference on Smart Grid, icSmartGrid 2024 -- 27 May 2024 through 29 May 2024 -- Hybrid, Setubal -- 200813en_US
dc.description.abstractThe importance of the concept of power quality in electrical power systems is increasing. This situation causes the acceleration of work on detecting and eliminating power quality events. It is possible to automatically detect and classify these events using signal processing techniques and machine learning systems. In this study, the performances of Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naive Bayes (NB) classification algorithms were compared among themselves and with the literature. Signals containing 15 single and multiple power quality events were used for classification. Discrete Wavelet Transform (DWT) technique was preferred to extract the features of the signals. The data sets obtained by feature extraction were divided into test data and training data with a ratio of 20%, 30%, 40%. Thus, the classifiers were trained and tested with data sets of different sizes and their performances were evaluated. The highest accuracy rate was obtained as 99.75% with DT when 20% of the data set of noiseless signals was used as test data. The lowest accuracy rate was obtained as 30.6% with KNN when 30% of the data set of signals with 10dB noise was used as test data. In addition, the performances of the classifiers were examined by performing a 5-fold cross validation test and the average accuracy rates obtained from this were compared with other studies in the literature. © 2024 IEEE.en_US
dc.identifier.doi10.1109/icSmartGrid61824.2024.10578195
dc.identifier.endpage254en_US
dc.identifier.isbn979-835036161-2
dc.identifier.scopus2-s2.0-85199484352en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage249en_US
dc.identifier.urihttps://doi.org/10.1109/icSmartGrid61824.2024.10578195
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12304
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇoban, Melih
dc.institutionauthorid0000-0001-9528-7187
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof12th International Conference on Smart Grid, icSmartGrid 2024en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectClassificationen_US
dc.subjectDiscrete Wavelet Transformen_US
dc.subjectMachine Learningen_US
dc.subjectPower Quality Disturbancesen_US
dc.titlePerformance analysis of machine Learning algorithms on power quality disturbances classificationen_US
dc.typeConference Objecten_US

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