Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique

Arshad, Jawad Ahmad, Ahsen Tahir, Brian G. Stewart and Azam Nekahi
Additional contact information
Arshad: Institute for Energy and Environment, University of Strathclyde, Glasgow G1 1XQ, UK
Jawad Ahmad: School of Computing, Edinburgh Napier University, Edinburgh EH11 4DY, UK
Ahsen Tahir: Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
Brian G. Stewart: Institute for Energy and Environment, University of Strathclyde, Glasgow G1 1XQ, UK
Azam Nekahi: School of Computing Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.

Energies, 2020, vol. 13, issue 15, 1-16

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.

Keywords: silicone rubber; NSDD; ESDD; surface resistance; flashover; machine learning; bootstrapping (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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