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

Niazi, M. Tahir Khan and Arshad and Ahmad, Jawad and Alqatani, Fahad and Baotham, Fatmah AB and Abu-Amara, Fadi (2020) Prediction of critical flashover voltage of high voltage insulators leveraging bootstrap neural network. Electronics, 9 (10). 1620. ISSN 2079-9292 (

[thumbnail of Niazi-etal-Electronics-2020-Prediction-of-critical-flashover-voltage-of-high-voltage]
Text. Filename: Niazi_etal_Electronics_2020_Prediction_of_critical_flashover_voltage_of_high_voltage.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (2MB)| Preview


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.