Transmission line loss determination of electricity by using convolutional neural network

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dc.contributor.authorSadeq Al-Samkri, Elaf Hayder
dc.contributor.authorAl-Jumaili, Saif
dc.contributor.authorNoori, Harith Muthanna
dc.contributor.authorDuru, Adil Deniz
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2022-12-27T16:47:12Z
dc.date.available2022-12-27T16:47:12Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractBusinesses are concerned about energy losses. Electronic gadgets have become more prevalent as a result of their adoption. The frequency with which home electricity usage data is collected has grown, allowing for sophisticated data analysis that was previously unavailable. Indeed, adopting Smart Grid (SG) networks, which are freshly improved networks of linked devices, may considerably enhance the existing energy infrastructure's dependability, economy, and durability. The SG involves sharing a lot of data, including information on specific users' power use. And using this information, machine learning and deep learning algorithms may be able to detect power theft users reliably. This paper presented a Convolutional Neural Network (CNN)-based model for automated network-based vulnerability scanning that has excellent classification performance in many categories. Testing from iteration two to four iterations, this study examines research to discover the ideal configuration of the sequential model (SM) for categorization. The method is validated using a two-layer design, including an efficiency of 0.92, the whole first layer is composed of 128 nodes while the second level consists of 64 nodes. This allows for the development of a higher-level classifier for electrical signals, which may be employed in a number of applications. CNN was used to create electrical signal detectors, and SM was used to extract data from an electricity usage dataset. Furthermore, the number of features in the data set can be reduced using the Blue Monkey (BM) approach, and these results are then used to develop high-performance models. In this regard, the focus of this study has been on lowering the amount of needed features in the dataset in order to establish a rising classification algorithm for electrical signals. Experiments have applied the proposed systems' fantastic performance, with just 666 characteristics required to combine the CNN and BM methods. Comparative to 1035 traits when CNN was used alone. This shows that the CNN and BM models are better than the CNN model in terms of lowering sufficient know while maintaining the same reliability.en_US
dc.identifier.citationSadeq Al-Samkri, E. H. ,Al–jumaili, S., Noori, H. M., Duru, A. D., Uçan, O. N. (2022). Transmission line loss determination of electricity by using convolutional neural network. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 812-817). IEEE.en_US
dc.identifier.endpage817en_US
dc.identifier.isbn9781665470131
dc.identifier.scopus2-s2.0-85142790463
dc.identifier.scopusqualityN/A
dc.identifier.startpage812en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3163
dc.indekslendigikaynakScopus
dc.institutionauthorSadeq Al-Samkri, Elaf Hayder
dc.institutionauthorAl-Jumaili, Saif
dc.institutionauthorNoori, Harith Muthanna
dc.institutionauthorUçan, Osman Nuri
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings
dc.relation.isversionof10.1109/ISMSIT56059.2022.9932753en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.subjectDeep Learning (DL)en_US
dc.subjectElectricity Consumption Dataseten_US
dc.titleTransmission line loss determination of electricity by using convolutional neural network
dc.typeConference Object

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