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Progam Studi Teknik Elektro, Fakultas Teknik Universitas Muhammadiyah Surakarta Jl. Ahmad Yani, Pabelan, Kartasura, Surakarta 57162 Telp: 0271-717417 Ext.: 3223
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Emitor: Jurnal Teknik Elektro
Published by Universitas Muhammadiyah Surakarta
ISSN : 14118890     EISSN : 25414518     DOI : https://doi.org/10.23917/emitor
Core Subject : Engineering,
Engineering
Emitor: Jurnal Teknik Elektro merupakan jurnal ilmiah yang diterbitkan oleh Jurusan Teknik Elektro Fakultas Teknik Universitas Muhammadiyah Surakarta dengan tujuan sebagai media publikasi ilmiah di bidang ke-teknik elektro-an yang meliputi bidang Sistem Tenaga Listrik (STL), Sistem Isyarat dan Elektronika (SIE) yang meliputi Elektronika, Telekomunikasi, Komputasi, Kontrol, Instrumentasi, Elektronika Medis (biomedika) dan Sistem Komputer dan Informatika (SKI).
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 81 Documents
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Impact of Noise on Fault Classification in High-Voltage Transmission Lines Using LVQ Neural Networks Hardiyanti Mursat, Marta; Novizon, Novizon; Sulthanah, Hana
Emitor: Jurnal Teknik Elektro Vol 25, No 3: November 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v25i3.13620

Abstract

Accurate fault detection and classification in high-voltage transmission lines are essential to ensure system reliability and operational safety. However, the presence of noise and transient disturbances often degrades the accuracy of conventional protection schemes. This study investigates the impact of Gaussian noise on fault classification performance using a neural network-based framework combined with Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) feature extraction. Four types of faults, single line to ground, line to line, double line to ground, and three phase to ground were simulated on a 150 kV transmission system using ATPDraw under various noise levels 40 dB. Linear Discriminant Analysis (LDA) and Learning Vector Quantization (LVQ3) were employed for feature reduction and classification, respectively. The proposed model achieved a test accuracy of 98.84% under free noise conditions and 96.80% under noisy conditions. This is outperforming traditional classifiers such as Support Vector Machine (SVM) and Decision Tree (DT). Results indicate that incorporating time-frequency domain features with noise-resilient neural architectures significantly enhances classification robustness and reliability. This research contributes a novel approach for noise-tolerant fault classification, offering practical potential for real-world implementation in intelligent protection systems and smart grid applications.

Page 9 of 9 | Total Record : 81

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