Open Access

ARTICLE

Prediction Flashover Voltage on Polluted Porcelain Insulator Using ANN

Ali Salem¹, Rahisham Abd-Rahman¹, Waheed Ghanem^2,*, Samir Al-Gailani^3,4, Salem Al-Ameri¹

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:

Computers, Materials & Continua 2021, 68(3), 3755-3771. https://doi.org/10.32604/cmc.2021.016988

Received 15 January 2021; Accepted 17 February 2021; Issue published 06 May 2021

Abstract

This paper aims to assess the effect of dry band location of contaminated porcelain insulators under various flashover voltages due to humidity. Four locations of dry bands are proposed to be tested under different severity of contamination artificially produce using salt deposit density (SDD) sprayed on an insulator. Laboratory tests of polluted insulators under proposed scenarios have been conducted. The flashover voltage of clean insulators has been identified 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 flashover voltage has been predicted using an artificial neural network (ANN) technique based on the laboratory test data. The ANN approach is constructed with five input data (geometry the insulator and parameters of contamination) and flashover voltage as the output of the model. Results indicated that the pollution distribution based on the proposed scenario has a significant influence on the flashover 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 significant efficiency of the ANN technique in predicting the flashover voltage insulator under test.

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