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All Journal Green Engineering: Journal of Engineering and Applied Science
Khoirudin, Irfan
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering

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Feature Extraction Using Discrete Wavelet Transform and Zero Sequence Current for Multi-Layer Perceptron Based Fault Classification Khoirudin, Irfan; Sri Arttini Dwi Prasetyowati
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 4 (2025): October: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i4.239

Abstract

Application of Multi-Layer Perceptron neural network to fault classification in high-voltage transmission lines is demonstrated in this paper. Different fault types on protected transmission line should be detected and classified rapidly and correctly. This paper presents the use of Discrete Wavelet Transform energy features combined with zero sequence current magnitude as input features for neural network classifier. The proposed method uses eight extracted features to learn hidden relationship in fault signal patterns. Using proposed approach, fault detection and classification of all 11 fault types could be achieved with high accuracy. Improved performance is experienced once the neural network is trained sufficiently with 1188 fault samples, thus performing correctly when faced with different system conditions. Results of performance studies show that proposed neural network-based classifier achieves 96.18% average accuracy, which demonstrates that it can improve the performance of conventional fault classification algorithms, which in turn can provide more efficient solutions in the management and protection of high voltage electrical systems.
Co-Authors Sri Arttini Dwi Prasetyawati