ENHANCED FAULT DETECTION AND CLASSIFICATION IN TRANSMISSION LINES USING FINE-TUNED LSTM MODEL AND DBN TRANSFORM-BASED FEATURE SELECTION

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dc.authoridAVCI, Dr. Isa/0000-0001-7032-8018
dc.contributor.authorAl Sultan, Muhamed
dc.contributor.authorAvci, Isa
dc.contributor.authorTalab, Odia
dc.date.accessioned2024-09-29T16:12:25Z
dc.date.available2024-09-29T16:12:25Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractFault detection and classification in transmission lines is an important problem in power system protection. This paper proposes a novel fault detection and classification approach based on the fine-tuned LSTM model and dbN wavelet transform. Specifically, the selection of the optimal decomposition scale is proposed. An improved Arithmetic Optimisation Algorithm (IAOA) to enhance the accuracy of the LSTM model by optimizing its hyperparameters and reducing model (RMSE) error is implemented. The proposed method makes a significant advancement in the field of fault detection and classification. A simulated version of the model is run through MATLAB using the Three-Phase Series compensation network (735kV, 60 Hz, and 300 km of fault distance) to classify faults. Features are extracted to a depth of three using the dbN, which is modelled as a wavelet function in this investigation. Finally, the IAOA-LSTM model achieves 99.99% accuracy and 0.0010 loss when testing 2545 simulated samples with five different fault types. Maintaining the stability and reliability of power systems relies heavily on fault detection and classification, which is aided greatly by the proposed method. Implementing the IAOA algorithm for hyperparameter optimization and model error reduction has also been shown to enhance the accuracy of the LSTM model further. Therefore, the proposed approach can significantly contribute to developing more advanced and efficient protection systems for power transmission lines.en_US
dc.identifier.endpage94en_US
dc.identifier.issn1823-4690
dc.identifier.startpage76en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8740
dc.identifier.wosWOS:001148672800006en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherTaylors Univ Sdn Bhden_US
dc.relation.ispartofJournal of Engineering Science and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdbN wavelet transformen_US
dc.subjectGround Faultsen_US
dc.subjectImproved AOAen_US
dc.subjectLSTM modelen_US
dc.subjectModulus maximum matrix Short-circuit faultsen_US
dc.titleENHANCED FAULT DETECTION AND CLASSIFICATION IN TRANSMISSION LINES USING FINE-TUNED LSTM MODEL AND DBN TRANSFORM-BASED FEATURE SELECTIONen_US
dc.typeArticleen_US

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