Prediction of critical flashover voltage of high voltage insulators leveraging bootstrap neural network

M. Tahir Khan Niazi, Arshad*, Jawad Ahmad, Fehaid Alqahtani, Fatmah A.B. Baotham, Fadi Abu-Amara

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
6 Downloads (Pure)


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.

Original languageEnglish
Article number1620
Number of pages21
Issue number10
Publication statusPublished - 2 Oct 2020


  • 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)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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