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Mohd Abdul Talib Mat Yusoh
Universiti Teknologi MARA (UiTM

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Classification neutral to ground (NG) voltage using levenberg-marquardt neural network (LMNN) Suzaryfazli Kamaruddin; Ahmad Farid Abidin; Mohd Abdul Talib Mat Yusoh
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1189-1195

Abstract

In electrical systems nowadays, power quality issues have become a major concern for customers and electrical utilities. The high Neutral to ground (NG) voltage are one of the power quality issues which could cause adverse effect such as malfunction the devices, neutral overheating and electricity shock. Thus, the high NG voltage should be classified in order to perform the mitigation work accurately. This paper presents the classification of neutral to ground voltage using Levenberg-Marquardt Neural Network (LMNN) technique. The Discrete Wavelet Transform (DWT) is applied in this method to extract the features of NG voltage which needed in classification process.  The result shows the LMNN perform accurately in classify the types of NG voltage, where its accuracy result is reach more than 90% accuracy.
Co-Authors Ahmad Farid Abidin Suzaryfazli Kamaruddin