The Thermal Modeling for Underground Cable Based on ANN Prediction

Abdullah Ahmed Al-Dulaimi; Muhammet Tahir Guneser; Alaa Ali Hameed

doi:10.1007/978-3-031-04112-9_23

The Thermal Modeling for Underground Cable Based on ANN Prediction

Abdullah Ahmed Al-Dulaimi^*, Muhammet Tahir Guneser, Alaa Ali Hameed

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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 (ρ_soil), insulator resistivity (ρ_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).

Original language	English
Title of host publication	Pattern Recognition and Artificial Intelligence - 5th Mediterranean Conference, MedPRAI 2021, Proceedings
Editors	Chawki Djeddi, Akhtar Jamil, Imran Siddiqi, Alaa Ali Hameed, Ismail Kucuk
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	301-314
Number of pages	14
ISBN (Print)	9783031041112
DOIs	https://doi.org/10.1007/978-3-031-04112-9_23
Publication status	Published - 2022
Externally published	Yes
Event	5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021 - Instanbul, Turkey Duration: 17 Dec 2021 → 18 Dec 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1543 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021
Country/Territory	Turkey
City	Instanbul
Period	17/12/21 → 18/12/21

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

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

Access to Document

10.1007/978-3-031-04112-9_23

Cite this

Al-Dulaimi, A. A., Guneser, M. T., & Hameed, A. A. (2022). The Thermal Modeling for Underground Cable Based on ANN Prediction. In C. Djeddi, A. Jamil, I. Siddiqi, A. A. Hameed, & I. Kucuk (Eds.), Pattern Recognition and Artificial Intelligence - 5th Mediterranean Conference, MedPRAI 2021, Proceedings (pp. 301-314). (Communications in Computer and Information Science; Vol. 1543 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-04112-9_23

Al-Dulaimi, Abdullah Ahmed ; Guneser, Muhammet Tahir ; Hameed, Alaa Ali. / The Thermal Modeling for Underground Cable Based on ANN Prediction. Pattern Recognition and Artificial Intelligence - 5th Mediterranean Conference, MedPRAI 2021, Proceedings. editor / Chawki Djeddi ; Akhtar Jamil ; Imran Siddiqi ; Alaa Ali Hameed ; Ismail Kucuk. Springer Science and Business Media Deutschland GmbH, 2022. pp. 301-314 (Communications in Computer and Information Science).

@inproceedings{9d064b9eddb1454fa13219b7141aa1af,

title = "The Thermal Modeling for Underground Cable Based on ANN Prediction",

abstract = "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 (ρsoil), insulator resistivity (ρinsulator), 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).",

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

author = "Al-Dulaimi, {Abdullah Ahmed} and Guneser, {Muhammet Tahir} and Hameed, {Alaa Ali}",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021 ; Conference date: 17-12-2021 Through 18-12-2021",

year = "2022",

doi = "10.1007/978-3-031-04112-9_23",

language = "English",

isbn = "9783031041112",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "301--314",

editor = "Chawki Djeddi and Akhtar Jamil and Imran Siddiqi and Hameed, {Alaa Ali} and Ismail Kucuk",

booktitle = "Pattern Recognition and Artificial Intelligence - 5th Mediterranean Conference, MedPRAI 2021, Proceedings",

address = "Germany",

}

Al-Dulaimi, AA, Guneser, MT & Hameed, AA 2022, The Thermal Modeling for Underground Cable Based on ANN Prediction. in C Djeddi, A Jamil, I Siddiqi, AA Hameed & I Kucuk (eds), Pattern Recognition and Artificial Intelligence - 5th Mediterranean Conference, MedPRAI 2021, Proceedings. Communications in Computer and Information Science, vol. 1543 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 301-314, 5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021, Instanbul, Turkey, 17/12/21. https://doi.org/10.1007/978-3-031-04112-9_23

The Thermal Modeling for Underground Cable Based on ANN Prediction. / Al-Dulaimi, Abdullah Ahmed; Guneser, Muhammet Tahir; Hameed, Alaa Ali.
Pattern Recognition and Artificial Intelligence - 5th Mediterranean Conference, MedPRAI 2021, Proceedings. ed. / Chawki Djeddi; Akhtar Jamil; Imran Siddiqi; Alaa Ali Hameed; Ismail Kucuk. Springer Science and Business Media Deutschland GmbH, 2022. p. 301-314 (Communications in Computer and Information Science; Vol. 1543 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - The Thermal Modeling for Underground Cable Based on ANN Prediction

AU - Al-Dulaimi, Abdullah Ahmed

AU - Guneser, Muhammet Tahir

AU - Hameed, Alaa Ali

PY - 2022

Y1 - 2022

N2 - 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 (ρsoil), insulator resistivity (ρinsulator), 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).

AB - 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 (ρsoil), insulator resistivity (ρinsulator), 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).

KW - Artificial neural network (ANN)

KW - Cable ampacity

KW - Heat transfer

KW - Thermal backfill

KW - Thermal modeling

KW - Underground cables performance

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U2 - 10.1007/978-3-031-04112-9_23

DO - 10.1007/978-3-031-04112-9_23

M3 - Conference contribution

AN - SCOPUS:85128955518

SN - 9783031041112

T3 - Communications in Computer and Information Science

SP - 301

EP - 314

BT - Pattern Recognition and Artificial Intelligence - 5th Mediterranean Conference, MedPRAI 2021, Proceedings

A2 - Djeddi, Chawki

A2 - Jamil, Akhtar

A2 - Siddiqi, Imran

A2 - Hameed, Alaa Ali

A2 - Kucuk, Ismail

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021

Y2 - 17 December 2021 through 18 December 2021

ER -

Al-Dulaimi AA, Guneser MT, Hameed AA. The Thermal Modeling for Underground Cable Based on ANN Prediction. In Djeddi C, Jamil A, Siddiqi I, Hameed AA, Kucuk I, editors, Pattern Recognition and Artificial Intelligence - 5th Mediterranean Conference, MedPRAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 301-314. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-04112-9_23

The Thermal Modeling for Underground Cable Based on ANN Prediction

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Keywords

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