Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators

Ali A. Salem; R. Abd-Rahman; Samir A. Al-Gailani; M. S. Kamarudin; N. A. Othman; N. A.M. Jamail

doi:10.1007/978-3-030-33582-3_35

Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators

Ali A. Salem
, R. Abd-Rahman^*
, Samir A. Al-Gailani
, M. S. Kamarudin
, N. A. Othman
, N. A.M. Jamail

^*Corresponding author for this work

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

7 Scopus citations

Abstract

. In this paper, flashover characteristics of polluted cup-pin insulators have investigated by means of laboratory test and a mathematical model. Data from experimental works combined with the model results from a new mathematical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining the critical Flashover characteristics (current and voltage). Series of laboratory testing and measurement are carried for 1:1, 1:5, 1/10 and 1:15 ratios of top to bottom surface salt deposit density on cup and pin polluted insulators (T/B). The new model was derived based on dimensional analysis approach of the parameters which commonly effect the phenomenon of pollution flashover of insulators. This model was developed by establishment the relationship between flashover current and voltage, arc constant, arc length and layer pollution conductivity of insulator. The constant of arc A and n is determined using genetic algorithm. Comparative studies have evidently shown that the proposed AI-based technique gives the satisfactory results compared to the analytical model and test data with the coefficient of determination R-Square value more than 96%.

Original language	English
Title of host publication	Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing
Editors	Faisal Saeed, Fathey Mohammed, Nadhmi Gazem
Publisher	Springer
Pages	362-372
Number of pages	11
ISBN (Print)	9783030335816
DOIs	https://doi.org/10.1007/978-3-030-33582-3_35
State	Published - 2020
Externally published	Yes
Event	4th International Conference of Reliable Information and Communication Technology, IRICT 2019 - Johor Bahru, Malaysia Duration: 22 Sep 2019 → 23 Sep 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1073
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	4th International Conference of Reliable Information and Communication Technology, IRICT 2019
Country/Territory	Malaysia
City	Johor Bahru
Period	22/09/19 → 23/09/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Artificial Neural Network
Flashover
Mathematical model
Outdoor insulators

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-030-33582-3_35

Cite this

Salem, A. A., Abd-Rahman, R., Al-Gailani, S. A., Kamarudin, M. S., Othman, N. A., & Jamail, N. A. M. (2020). Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators. In F. Saeed, F. Mohammed, & N. Gazem (Eds.), Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing (pp. 362-372). (Advances in Intelligent Systems and Computing; Vol. 1073). Springer. https://doi.org/10.1007/978-3-030-33582-3_35

Salem, Ali A. ; Abd-Rahman, R. ; Al-Gailani, Samir A. et al. / Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators. Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing. editor / Faisal Saeed ; Fathey Mohammed ; Nadhmi Gazem. Springer, 2020. pp. 362-372 (Advances in Intelligent Systems and Computing).

@inproceedings{171ca9484d3349eea53d80496209a407,

title = "Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators",

abstract = ". In this paper, flashover characteristics of polluted cup-pin insulators have investigated by means of laboratory test and a mathematical model. Data from experimental works combined with the model results from a new mathematical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining the critical Flashover characteristics (current and voltage). Series of laboratory testing and measurement are carried for 1:1, 1:5, 1/10 and 1:15 ratios of top to bottom surface salt deposit density on cup and pin polluted insulators (T/B). The new model was derived based on dimensional analysis approach of the parameters which commonly effect the phenomenon of pollution flashover of insulators. This model was developed by establishment the relationship between flashover current and voltage, arc constant, arc length and layer pollution conductivity of insulator. The constant of arc A and n is determined using genetic algorithm. Comparative studies have evidently shown that the proposed AI-based technique gives the satisfactory results compared to the analytical model and test data with the coefficient of determination R-Square value more than 96\%.",

keywords = "Artificial Neural Network, Flashover, Mathematical model, Outdoor insulators",

author = "Salem, \{Ali A.\} and R. Abd-Rahman and Al-Gailani, \{Samir A.\} and Kamarudin, \{M. S.\} and Othman, \{N. A.\} and Jamail, \{N. A.M.\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 4th International Conference of Reliable Information and Communication Technology, IRICT 2019 ; Conference date: 22-09-2019 Through 23-09-2019",

year = "2020",

doi = "10.1007/978-3-030-33582-3\_35",

language = "English",

isbn = "9783030335816",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "362--372",

editor = "Faisal Saeed and Fathey Mohammed and Nadhmi Gazem",

booktitle = "Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing",

address = "Germany",

}

Salem, AA, Abd-Rahman, R, Al-Gailani, SA, Kamarudin, MS, Othman, NA & Jamail, NAM 2020, Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators. in F Saeed, F Mohammed & N Gazem (eds), Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing. Advances in Intelligent Systems and Computing, vol. 1073, Springer, pp. 362-372, 4th International Conference of Reliable Information and Communication Technology, IRICT 2019, Johor Bahru, Malaysia, 22/09/19. https://doi.org/10.1007/978-3-030-33582-3_35

Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators. / Salem, Ali A.; Abd-Rahman, R.; Al-Gailani, Samir A. et al.
Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing. ed. / Faisal Saeed; Fathey Mohammed; Nadhmi Gazem. Springer, 2020. p. 362-372 (Advances in Intelligent Systems and Computing; Vol. 1073).

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

TY - GEN

T1 - Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators

AU - Salem, Ali A.

AU - Abd-Rahman, R.

AU - Al-Gailani, Samir A.

AU - Kamarudin, M. S.

AU - Othman, N. A.

AU - Jamail, N. A.M.

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - . In this paper, flashover characteristics of polluted cup-pin insulators have investigated by means of laboratory test and a mathematical model. Data from experimental works combined with the model results from a new mathematical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining the critical Flashover characteristics (current and voltage). Series of laboratory testing and measurement are carried for 1:1, 1:5, 1/10 and 1:15 ratios of top to bottom surface salt deposit density on cup and pin polluted insulators (T/B). The new model was derived based on dimensional analysis approach of the parameters which commonly effect the phenomenon of pollution flashover of insulators. This model was developed by establishment the relationship between flashover current and voltage, arc constant, arc length and layer pollution conductivity of insulator. The constant of arc A and n is determined using genetic algorithm. Comparative studies have evidently shown that the proposed AI-based technique gives the satisfactory results compared to the analytical model and test data with the coefficient of determination R-Square value more than 96%.

AB - . In this paper, flashover characteristics of polluted cup-pin insulators have investigated by means of laboratory test and a mathematical model. Data from experimental works combined with the model results from a new mathematical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining the critical Flashover characteristics (current and voltage). Series of laboratory testing and measurement are carried for 1:1, 1:5, 1/10 and 1:15 ratios of top to bottom surface salt deposit density on cup and pin polluted insulators (T/B). The new model was derived based on dimensional analysis approach of the parameters which commonly effect the phenomenon of pollution flashover of insulators. This model was developed by establishment the relationship between flashover current and voltage, arc constant, arc length and layer pollution conductivity of insulator. The constant of arc A and n is determined using genetic algorithm. Comparative studies have evidently shown that the proposed AI-based technique gives the satisfactory results compared to the analytical model and test data with the coefficient of determination R-Square value more than 96%.

KW - Artificial Neural Network

KW - Flashover

KW - Mathematical model

KW - Outdoor insulators

UR - https://www.scopus.com/pages/publications/85077775644

U2 - 10.1007/978-3-030-33582-3_35

DO - 10.1007/978-3-030-33582-3_35

M3 - Conference contribution

AN - SCOPUS:85077775644

SN - 9783030335816

T3 - Advances in Intelligent Systems and Computing

SP - 362

EP - 372

BT - Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing

A2 - Saeed, Faisal

A2 - Mohammed, Fathey

A2 - Gazem, Nadhmi

PB - Springer

T2 - 4th International Conference of Reliable Information and Communication Technology, IRICT 2019

Y2 - 22 September 2019 through 23 September 2019

ER -

Salem AA, Abd-Rahman R, Al-Gailani SA, Kamarudin MS, Othman NA, Jamail NAM. Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators. In Saeed F, Mohammed F, Gazem N, editors, Emerging Trends in Intelligent Computing and Informatics - Data Science, Intelligent Information Systems and Smart Computing. Springer. 2020. p. 362-372. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-33582-3_35

Artificial intelligence techniques for predicting the flashover voltage on polluted cup-pin insulators

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this