ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2021.016988 Article Prediction Flashover Voltage on Polluted Porcelain Insulator Using ANN Ali Salem 1 , Rahisham Abd-Rahman 1 , Waheed Ghanem 2, * , Samir Al-Gailani 3,4 and Salem Al-Ameri 1 1 Faculty of Electrical and Electronic Engineering, University Tun Hussein Onn Malaysia, Johor, 86400, Malaysia 2 Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Terengganu, 21030, Malaysia 3 School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, 14300, Malaysia 4 Department of Electrical and Electronics Engineering, Faculty of Engineering, Al-Madinah International University, Kuala Lumpur, 57100, Malaysia * Corresponding Author: Waheed Ghanem. Email: waheedghanem@umt.edu.my Received: 15 January 2021; Accepted: 17 February 2021 Abstract: This paper aims to assess the effect of dry band location of contam- inated porcelain insulators under various fashover voltages due to humidity. Four locations of dry bands are proposed to be tested under different severity of contamination artifcially produce using salt deposit density (SDD) sprayed on an insulator. Laboratory tests of polluted insulators under proposed sce- narios have been conducted. The fashover voltage of clean insulators has been identifed as a reference value to analyze the effect of contamination distribution and its severity. The dry band dimension has been taken into consideration in experimental tests. The fashover voltage has been predicted using an artifcial neural network (ANN) technique based on the laboratory test data. The ANN approach is constructed with fve input data (geometry the insulator and parameters of contamination) and fashover voltage as the output of the model. Results indicated that the pollution distribution based on the proposed scenario has a signifcant infuence on the fashover voltage performances. Validation of the ANN model reveals that the relative error values between the experimental results and the prediction appeared to be within 5%. This indicates the signifcant effciency of the ANN technique in predicting the fashover voltage insulator under test. Keywords: Insulator; pollution distribution; artifcial neural network; dry band 1 Introduction The fashover phenomena on polluted insulators is a major problem that seriously threatens the health and reliability of operation regarding power transmission. Much consideration has recently been given to cup and pin insulators which are both used in distribution and transmission systems [1]. The high voltage outdoor insulators are enveloped by a layer of pollutants that fy through the air, relative humidity, rain, or fog, and settles on the body of the insulator. This contamination layer becomes conducting and allows the fow of leakage current (LC) to the This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.