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Article

Prediction of Critical Flashover Voltage of High Voltage Insulators Leveraging Bootstrap Neural Network

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Department of Electrical Engineering, HITEC University Taxila, Punjab 47080, Pakistan
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Institute for Energy and Environment, University of Strathclyde, Glasgow G1 1XQ, UK
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School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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Department of Computer Science, King Fahad Naval Academy, Al Jubail 35512, Saudi Arabia
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Faculty of Computing and Information Technology, King Abdul Aziz University, Jeddah 21431, Saudi Arabia
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Computer & Information Science Department, Higher Colleges of Technology, Abu Dhabi 25026, UAE
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(10), 1620; https://doi.org/10.3390/electronics9101620
Received: 19 August 2020 / Revised: 18 September 2020 / Accepted: 24 September 2020 / Published: 2 October 2020
(This article belongs to the Special Issue Theory and Applications of Fuzzy Systems and Neural Networks)
Understanding the flashover performance of the outdoor high voltage insulator has been in the interest of many researchers recently. Various studies have been performed to investigate the critical flashover voltage of outdoor high voltage insulators analytically and in the laboratory. However, laboratory experiments are expensive and time-consuming. On the other hand, mathematical models are based on certain assumptions which compromise on the accuracy of results. This paper presents an intelligent system based on Artificial Neural Networks (ANN) to predict the critical flashover voltage of High-Temperature Vulcanized (HTV) silicone rubber in polluted and humid conditions. Various types of learning algorithms are used, such as Gradient Descent (GD), Levenberg-Marquardt (LM), Conjugate Gradient (CG), Quasi-Newton (QN), Resilient Backpropagation (RBP), and Bayesian Regularization Backpropagation (BRBP) to train the ANN. The number of neurons in the hidden layers along with the learning rate was varied to understand the effect of these parameters on the performance of ANN. The proposed ANN was trained using experimental data obtained from extensive experimentation in the laboratory under controlled environmental conditions. The proposed model demonstrates promising results and can be used to monitor outdoor high voltage insulators. It was observed from obtained results that changing of the number of neurons, learning rates, and learning algorithms of ANN significantly change the performance of the proposed algorithm. View Full-Text
Keywords: critical flashover voltage; Artificial Neural Networks (ANN); Gradient Descent (GD); Levenberg-Marquardt (LM); Conjugate Gradient (CG); Quasi-Newton (QN); Resilient Backpropagation (RBP); Bayesian Regularization Backpropagation (BRBP) critical flashover voltage; Artificial Neural Networks (ANN); Gradient Descent (GD); Levenberg-Marquardt (LM); Conjugate Gradient (CG); Quasi-Newton (QN); Resilient Backpropagation (RBP); Bayesian Regularization Backpropagation (BRBP)
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MDPI and ACS Style

Niazi, M.T.K.; Arshad; Ahmad, J.; Alqahtani, F.; Baotham, F.A.; Abu-Amara, F. Prediction of Critical Flashover Voltage of High Voltage Insulators Leveraging Bootstrap Neural Network. Electronics 2020, 9, 1620. https://doi.org/10.3390/electronics9101620

AMA Style

Niazi MTK, Arshad, Ahmad J, Alqahtani F, Baotham FA, Abu-Amara F. Prediction of Critical Flashover Voltage of High Voltage Insulators Leveraging Bootstrap Neural Network. Electronics. 2020; 9(10):1620. https://doi.org/10.3390/electronics9101620

Chicago/Turabian Style

Niazi, M. Tahir Khan, Arshad, Jawad Ahmad, Fehaid Alqahtani, Fatmah AB Baotham, and Fadi Abu-Amara. 2020. "Prediction of Critical Flashover Voltage of High Voltage Insulators Leveraging Bootstrap Neural Network" Electronics 9, no. 10: 1620. https://doi.org/10.3390/electronics9101620

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