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Mohd Fadli Rahmat
Universiti Teknologi Malaysia, Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Sensoring Leakage Current to Predict Pollution Levels to Improve Transmission Line Model via ANN Saraa I. Khalel; Mohd Fadli Rahmat; Mohd Wazir Bin Mustafa
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 1: February 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (849.531 KB) | DOI: 10.11591/ijece.v7i1.pp68-76

Abstract

Pollution insulator is a serious threat to the safety operations of electric power systems. Leakage current detection is widely employed in transmission line insulators to assess pollution levels. This paper presents the prediction of pollution levels on insulators based on simulated leakage current and voltage in a transmission tower.The simulation parameters are based on improved transmission line model with leakage current resistance insertion between buses. Artificial neural network (ANN) is employed to predict the level of pollution with different locations of simulated leakage current and voltage between two buses. With a sufficient number of training, the test results showed a significant potential for pollution level prediction with more than 95% Correct Classification Rate (CCR) and output of the ANN showed high agreement with Simulink results.
Co-Authors Mohd Wazir Bin Mustafa Saraa I. Khalel