The Thermal Modeling for Underground Cable Based on ANN Prediction

Küçük Resim Yok

Tarih

2022

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Springer Science and Business Media Deutschland GmbH

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Many 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.

Açıklama

5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021 -- 17 December 2021 through 18 December 2021 -- Instanbul -- 276749

Anahtar Kelimeler

Artificial neural network (ANN), Cable ampacity, Heat transfer, Thermal backfill, Thermal modeling, Underground cables performance

Kaynak

Communications in Computer and Information Science

WoS Q Değeri

Scopus Q Değeri

Q4

Cilt

1543 CCIS

Sayı

Künye