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All Journal Mechatronics, Electrical Power, and Vehicular Technology Jurnal INKOM Jurnal Teknologi dan Sistem Komputer Jurnal Elektronika dan Telekomunikasi JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) Indonesian Journal of Laboratory
Ade Ramdan
Research Center for Informatics, Indonesian Institute of Sciences, Kompleks LIPI Gd. 20, Lt. 3, Jl. Sangkuriang, Bandung
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences

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Journal : Mechatronics, Electrical Power, and Vehicular Technology

 1 
Modeling of Electric Field Around 100 MVA 150/20 kV Power Transformator using Charge Simulation Method Rachman, Noviadi Arief; Risdiyanto, Agus; Ramdan, Ade
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 1 (2013)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2013.v4.33-40

Abstract

Charge Simulation Method is one of the field theory that can be used as an approach to calculate the electromagnetic distribution on the electrical conductor. This paper discussed electric field modeling around power transformator by using Matlab to find the safety distance. The safe distance threshold of the electric field to human health refers to WHO and SNI was 5 kV/m. The specification of the power transformator was three phases, 150/20 kV, and 100 MVA. The basic concept is to change the distribution charge on the conductor or dielectric polarization charge with a set of discrete fictitious charge. The value of discrete fictitious charge was equivalent to the potential value of the conductor, and became a reference to calculate the electric field around the surface contour of the selected power transformator. The measurement distance was 5 meter on each side of the transformator surface. The results showed that the magnitude of the electric field at the front side was 5541 V/m, exceeding the safety limits.
Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder Yuliani, Asri Rizki; Pardede, Hilman Ferdinandus; Ramdan, Ade; Zilvan, Vicky; Yuwana, Raden Sandra; Amri, M Faizal; Kusumo, R. Budiarianto Suryo; Pramanik, Subrata
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.905

Abstract

Using lithium-ion (Li-ion) batteries exceeding their useful lifetime may be dangerous for users, and hence, developing an accurate prediction system for batteries that remain useful for life is necessary. Many deep learning models, such as gated recurrent units and long short-term memory (LSTM), have been proposed for that purpose and have shown good results. However, their performance when dealing with noisy data degrades significantly. This may hamper their implementations for the real world since battery data are prone to noise. In this paper, we develop a robust prediction model in a noisy environment for predicting the remaining useful life (RUL) of Li-ion batteries. We propose a denoising autoencoder (DAE) utilized to remove noise from the data. The DAE is built with convolutional layers instead of traditional feed-forward networks here. We combine DAE with LSTM as the predictor. The proposed framework is evaluated using artificially corrupted battery data provided by National Aeronautics and Space Administration (NASA). The results reveal that our proposed method improves robustness when data contain various types of noise. A comparative study using the traditional approach has also been conducted. Our evaluation shows that convolutional layers are more effective than the traditional approach and that the original composition of the DAE was built using traditional feed-forward networks. DAE with convolutional layers has the best average performance with MSE of 0.61 and is the most consistent model.
Co-Authors Agus Risdiyanto Ahmad Afif Supianto Amri, M Faizal Andria Arisal, Andria Anjani, Rifania Dahlan, Rico Dewi, Arnetta Listiana Fani Fauziah, Fani Gojali, Elli A. Gojali, Elli Ahmad Heri Syahrian, Heri Herlan, Herlan Heryana, Ana Kadar, Jimmy Abdel Krisnandi, Dikdik Kusumo, R. Budiarianto Suryo Noviadi Arief Rachman Oka Mahendra Pardede, Hilman F. Pardede, Hilman F. Pardede, Hilman Ferdinandus Prajitno, Dicky Rianto Pramanik, Subrata pratiwi, hasih Prayoga, Reka Ardi Ramadhan, Nur Jamiludin S, Nanang Subekti, Irham Suryawati, Endang Suryo Kusumo, Raden Budiarianto Sutarlan, Elan Vicky Zilvan, Vicky Yuawana, Raden Sandra Yuliani, Asri R Yuliani, Asri Rizki Yuwana, Raden Sandra