The thermal modeling for underground cable based on ANN prediction

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dc.authoridAlaa Ali Hameed / 0000-0002-8514-9255en_US
dc.authorscopusidAlaa Ali Hameed / 56338374100en_US
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorAl-Dulaimi, Abdullah Ahmed
dc.contributor.authorGuneser, Muhammet Tahir
dc.contributor.authorHameed, Alaa Ali
dc.date.accessioned2022-06-11T07:50:47Z
dc.date.available2022-06-11T07:50:47Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractMany factors affect the ampacity of the underground cable (UC) to carry current, such as the backfill material (classical, thermal, or a combination thereof) and the depth at which it is buried. Moreover, the thermal of the UC is an effective element in the performance and effectiveness of the UC. However, it is difficult to find thermal modeling and prediction in the UC under the influence of many parameters such as soil resistivity (?soil), insulator resistivity (?insulator), and ambient temperature. In this paper, the calculation of the UC steady-state rating current is the most important part of the cable installation design. This paper also applied an artificial neural network (ANN) to develop and predict for 33 kV UC rating models. The proposed system was built by using the MATLAB package. The ANN-based UC rating is achieves the best performance and prediction for the UC rating current. The performance of the proposed model is superior to other models. The experiment was conducted with 200 epochs. The proposed model achieved high performance with low MSE (0.137) and the regression curve gives an excellent performance (0.99). © 2022, Springer Nature Switzerland AG.en_US
dc.identifier.citationAl-Dulaimi, A. A., Guneser, M. T., & Hameed, A. A. (2022). The thermal modeling for underground cable based on ANN prediction doi:10.1007/978-3-031-04112-9_23 Retrieved from www.scopus.comen_US
dc.identifier.doi10.1007/978-3-031-04112-9_23en_US
dc.identifier.endpage314en_US
dc.identifier.issn1865-0929en_US
dc.identifier.scopus2-s2.0-85128955518en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage301en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-04112-9_23
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2872
dc.identifier.volume1543en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorHameed, Alaa Ali
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofCommunications in Computer and Information Scienceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectCable Ampacityen_US
dc.subjectHeat Transferen_US
dc.subjectThermal Backfillen_US
dc.subjectThermal Modelingen_US
dc.subjectUnderground Cables Performanceen_US
dc.titleThe thermal modeling for underground cable based on ANN predictionen_US
dc.typeConference Objecten_US

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