An analysis system of fault diagnosis and classification in electrical energy system distribution networks based on convolutional neural network

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dc.contributor.authorKadhum, Saja Jawad Kadhum
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.date.accessioned2023-12-21T09:10:32Z
dc.date.available2023-12-21T09:10:32Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractOne of the primary causes contributing to the disruption of dependability and the termination of energy supply is the frequency of faults in distribution networks, making solar energy one of the most dependable renewable energy sources. Consequently, effective and rapid problem detection and prediction in distribution networks are critical for enhancing overall dependability, boosting customer happiness, and optimizing electrical energy efficiency. Rapid improvements in communication and automation technologies for distribution power networks allow a distributed generation (D.G.) protection coordination and reclosing strategy based on information exchange. Several artificial intelligence (A.I.) methodologies are used in modern classification, defect detection, and optimization for solar photovoltaic (P. V.) panels. Many algorithms are used to convert solar energy into electricity. During our investigation, we began searching for possible faults in the power system. The grid is a hybrid system that uses fossil fuels and solar energy to create electricity. The CNN approach was utilized and trained using a dataset comprising the highest and lowest voltage values that could be detected in a particular electrical network.en_US
dc.identifier.citationKadhum, S. J. K., & Ibrahim, A. A. (2023, October). An analysis system of fault diagnosis and classification in electrical energy system distribution networks based on convolutional neural network. In 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-6). IEEE.en_US
dc.identifier.isbn9798350342154
dc.identifier.scopus2-s2.0-85179132417
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4401
dc.indekslendigikaynakScopus
dc.institutionauthorKadhum, Saja Jawad Kadhum
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023 - Proceedings
dc.relation.isversionof10.1109/ISMSIT58785.2023.10304997en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networken_US
dc.subjectFossil fuel electricityen_US
dc.subjectSolar energyen_US
dc.subjectSolar photovoltaic panelsen_US
dc.titleAn analysis system of fault diagnosis and classification in electrical energy system distribution networks based on convolutional neural network
dc.typeConference Object

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