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All Journal International Journal of Applied Power Engineering (IJAPE) Elektron Jurnal Ilmiah TELKOMNIKA (Telecommunication Computing Electronics and Control) JURNAL NASIONAL TEKNIK ELEKTRO Proceeding of the Electrical Engineering Computer Science and Informatics PROtek : Jurnal Ilmiah Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Jurnal Amplifier: Jurnal Ilmiah Bidang Teknik Elektro dan Komputer Andalasian International Journal of Applied Science, Engineering, and Technology Andalas Journal of Electrical and Electronic Engineering Technology Jurnal Andalas: Rekayasa dan Penerapan Teknologi Emitor: Jurnal Teknik Elektro
Novizon, Novizon
Jurusan Teknik Elektro Fakultas Teknik Universitas Andalas
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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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.
Enhancing security in portable solar power supply design for alternative energy applications Syafii, Syafii; Leonanda, Benny Dwika; Novizon, Novizon; Armysa, Rindina
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp120-131

Abstract

Access to reliable electricity remains a challenge in remote and off-grid areas, where conventional power sources are often unreliable or unavailable. This paper presents the design and development of an internet of things (IoT) system for monitoring and securing a portable solar power station tailored for alternative energy applications. The system, which can be recharged using photovoltaic energy sources, employs a coulomb counting method to accurately estimate the battery's state of charge (SoC) and prevent overcharging and overdischarging. The portable power supply provides stable direct current (DC) outputs (5 V, 12 V, 24 V) and an alternating current (AC) output for various remote area applications, including telecommunications and household use. A dual-relay mechanism is used for battery protection: one relay disconnects charging at 100% SoC and reactivates at 70%, while the other disconnects the load at 20% SoC to avoid deep discharge. IoT connectivity enables real-time monitoring and remote control via smartphone. This development promotes efficient energy management, battery longevity, and improved access to sustainable electricity in underserved regions.
Co-Authors . Darwison Abdul Rajab Abdul-Malek, Zulkarnain Akbar Abadi, Akbar Alfajri, Roan Amirul Luthfi Angraini, Tuti Anton Anton Ari Fitra, A. Ariadi Hazmi Armysa, Rindina Astri Astia Ningsih Aulia Aulia Aulia Aulia Aulia Aulia Aulia Aulia, Aulia Aulia, Aulia Baharuddin Baharuddin Chatarina Umbul Wahyuni Dano Yustasia Daud, Darwison Dona Juliandri Dwi Gustiono Eka Putra Waldi Fahreza, Rahmad Firdaus Firdaus Gunawan, Eko Amri Hanalde Andre Hardiyanti Mursat, Marta Hasanah, Mardini Ikhsan, Intan Aprillia Juliandri, Dona Laksono, Heru Dibyo Leonanda, Benny Dwika M. A. Hafizi M. Heru Setiawan Maemonah, Maemonah Mardini Hasanah Mardini Hasanah Melda Latif Mondrizal Mondrizal Mubarok, Fatih Mumuh Muharam Mumuh Muharram N. Riska Nofriadi Nofriadi Nofriadi, Nofriadi Novandri, Athalla Novendra, Rico Nurfi Syahri Pratama, Rizki Wahyu Rahadian Asneli Putri Rahmad Fahreza Reri Afrianita Reri Afrianita Riska, N. Riza Widia Roan Alfajri Roza Susanti Rudy Fernandez Rusvaira Qatrunnada Rusydi, M Ilhamdi Seftiani, Yudia Meka Silvia Wulandari Sitti Amalia Sulthanah, Hana Syafii . Syukri Yunus Tesya Uldira Septiyeni Wati Wati Wati, Wati Widi Adriana , Anisa Yamashika, Herris Yudia Meka Seftiani Yusreni Warmi Yusreni Warmi Yustasia, Dano Zaini Zulkarnain Abdul-Malek Zulkurnain Abdul-Malek Zurnawita Zurnawita