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dc.contributor.authorSameer Alghazi, Omnia
dc.contributor.authorMian Qaisar, Saeed
dc.date.accessioned2023-04-29T12:14:37Z
dc.date.available2023-04-29T12:14:37Z
dc.date.issued2023-03-15
dc.identifier.doihttps://doi.org/10.1007/978-3-031-19560-0_13en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/748
dc.description.abstractThis paper presents an approach for classification of the power quality disturbances (PQDs). The classification of real-time power quality disturbances (PQDs) is proposed in this work. The PQD signals are modelled based on the IEEE 1159–2019 standard. The outcome of the used PQD model is employed for analyzing the performance of suggested classification method. Firstly, the PQD signals are segmented and then each segment is further processed by machine learning based classifiers for identification of PQDs. The study is conducted for six major classes of the PQDs. The highest identification precision is secured by the Support Vector Machine classifier. It respectively attains the Accuracy = 94.32%, Precision =  84.55%, Recall = 84.33%, Specificity = 96.52%, F-measure = 84.19%, Kappa = 92.59%, and Area Under the Curve (AUC) = 97.83%.en_US
dc.description.sponsorshipEffat Universityen_US
dc.publisherSpringeren_US
dc.subjectPower Quality Disturbancesen_US
dc.subjectMachine learningen_US
dc.subjectClassificationen_US
dc.subjectEvaluation measuresen_US
dc.titlePower Quality Disturbances Classification Based on the Machine Learning Algorithmsen_US
dc.contributor.researcherDepartment Collaborationen_US
dc.subject.KSAENERGYen_US
dc.source.indexScopusen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.pgstudent1en_US
dc.contributor.firstauthorSameer Alghazi, Omnia
dc.conference.nameResearch and Innovation Forum 2022. RIIFORUM 2022en_US


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