Arshad
Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique
Arshad; Ahmad, Jawad; Tahir, Ahsen; Stewart, Brian G.; Nekahi, Azam
Abstract
There is a vital need to understand the flashover process of polymeric insulators for safe and reliable power system operation. This paper provides a rigorous investigation of forecasting the flashover parameters of High Temperature Vulcanized (HTV) silicone rubber based on environmental and polluted conditions using machine learning. The modified solid layer method based on the IEC 60507 standard was utilised to prepare samples in the laboratory. The effect of various factors including Equivalent Salt Deposit Density (ESDD), Non-soluble Salt Deposit Density (NSDD), relative humidity and ambient temperature, were investigated on arc inception voltage, flashover voltage and surface resistance. The experimental results were utilised to engineer a machine learning based intelligent system for predicting the aforementioned flashover parameters. A number of machine learning algorithms such as Artificial Neural Network (ANN), Polynomial Support Vector Machine (PSVM), Gaussian SVM (GSVM), Decision Tree (DT) and Least-Squares Boosting Ensemble (LSBE) were explored in forecasting of the flashover parameters. The prediction accuracy of the model was validated with a number of error cost functions, such as Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Mean Absolute Percentage Error (MAPE) and R. For improved prediction accuracy, bootstrapping was used to increase the sample space. The proposed PSVM technique demonstrated the best performance accuracy compared to other machine learning models. The presented machine learning model provides promising results and demonstrates highly accurate prediction of the arc inception voltage, flashover voltage and surface resistance of silicone rubber insulators in various contaminated and humid conditions.
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 27, 2020 |
Online Publication Date | Jul 30, 2020 |
Publication Date | Jul 30, 2020 |
Deposit Date | Sep 24, 2020 |
Publicly Available Date | Sep 24, 2020 |
Journal | Energies |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 13 |
Issue | 15 |
Article Number | 3889 |
DOI | https://doi.org/10.3390/en13153889 |
Keywords | silicone rubber; NSDD; ESDD; surface resistance; flashover; machine learning; bootstrapping |
Public URL | http://researchrepository.napier.ac.uk/Output/2688147 |
Files
Forecasting Flashover Parameters Of Polymeric Insulators Under Contaminated Conditions Using The Machine Learning Technique
(538 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
You might also like
A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids
(2019)
Journal Article
A secure image encryption scheme based on chaotic maps and affine transformation
(2015)
Journal Article
Chaos-based diffusion for highly autocorrelated data in encryption algorithms
(2015)
Journal Article
A New Image Encryption Scheme Based on Dynamic S-Boxes and Chaotic Maps
(2016)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search