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INDONESIA
Mechatronics, Electrical Power, and Vehicular Technology
Published by Lembaga Ilmu Pengetahuan Indonesia
ISSN : 20873379     EISSN : 20886985     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Mechatronics, Electrical Power, and Vehicular Technology (hence MEV) is a journal aims to be a leading peer-reviewed platform and an authoritative source of information. We publish original research papers, review articles and case studies focused on mechatronics, electrical power, and vehicular technology as well as related topics. All papers are peer-reviewed by at least two referees. MEV is published and imprinted by Research Center for Electrical Power and Mechatronics - Indonesian Institute of Sciences and managed to be issued twice in every volume. For every edition, the online edition is published earlier than the print edition.
Arjuna Subject : -
Articles 596 Documents
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Stability analysis of a hybrid DC-DC buck converter model using dissipation inequality and convex optimization Tua A. Tamba; Jonathan Chandra; Bin Hu
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 1 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.47-54

Abstract

The stability analysis of a DC-DC buck converter is a challenging problem due to the hybrid systems characteristic of its dynamics. Such a challenge arises from the buck converter operation which depends upon the ON/OFF logical transitions of its electronic switch component to correspondingly activate different continuous vector fields of the converter’s temporal dynamics. This paper presents a sum of squares (SOS) polynomial optimization approach for stability analysis of a hybrid model of buck converter which explicitly takes into account the converter’s electronic switching behavior. The proposed method first transforms the converter’s hybrid dynamics model into an equivalent polynomial differential algebraic equation (DAE) model. An SOS programming algorithm is then proposed to computationally prove the stability of the obtained DAE model using Lyapunov’s stability concept. Based on simulation results, it was found that the proposed method requires only 8.5 seconds for proving the stability of a buck converter model. In contrast, exhaustive simulations based on numerical integration scheme require 15.6 seconds to evaluate the stability of the same model. These results thus show the effectiveness of the proposed method as it can prove the converter stability in shorter computational times without requiring exhaustive simulations using numerical integration.
Quasi-dynamic hosting capacity in radial distribution feeder Riki Khomarudin; Kevin Marojahan Banjar-Nahor; Nanang Hariyanto
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 1 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.62-71

Abstract

The target of massive installation of renewable energy is the focus of this research. Several industrial sectors continue to install photovoltaic rooftop to support green energy. One of the main objectives of this research is to see the maximum impact of installing a photovoltaic rooftop at 1 point of customer and spread capacity for each customer. This research uses a radial distribution network system that closely resembles the distribution network in Indonesia, where the load profile considers the load characteristics of industrial, commercial, and residential loads. This study uses the line equation theorem method to calculate the voltage rises by considering two current measurement points: the current at the end and the current at the base. The obtained voltage rise is then accumulated to be summed up with the customer afterward. The results are obtained by considering three scenarios: 1) voltage limits, 2) voltage limits and line loading, and 3) voltage limits, thermal, and harmonics in accordance with regulations. The obtained results are closely aligned with the simulations performed on the hosting capacity software such as DIgSILENT.
Front Cover MEV Vol 14 Iss 1 Ghalya Pikra
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 1 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Front Cover MEV Vol 14 Iss 1
Five-axis parallel mechanism system (PMS) CNC partial link control system based on modified inverse kinematic of 6-DOF UPS parallel manipulator Nur Jamiludin Ramadhan; Indrawanto Indrawanto; Hoe Dinh Nguyen
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 1 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.1-10

Abstract

This paper presents a control system algorithm for a five-axis parallel mechanism system (PMS) CNC milling machine based on a 6-DOF Stewart platform parallel manipulator with a universal-prismatic-spherical (UPS) configuration. The control system reads the G-Code commands as standard CNC machine language, then extract data points and interpolates them to generate the robot trajectory patterns as motion references. Then, the control system uses the modified inverse kinematic equation to determine the length of each link to move the end effector to track the trajectory patterns from the previous G-code extraction process. The inverse kinematic equation is modified especially for the five-axis PMS CNC milling machine by including machine-offset and tools-offset parameters so it will be easier for the control system to implement the kinematic equation. As expected, the system simulation results successfully followed the G-Code program moving commands. The average error of the length control system is 0,1 mm, while the average error of the length change rate control system is 1,8 mm/s. The maximum error is 26.9 mm was caused by the system's inability to follow the motion profile in transient. It can be concluded that 6-DOF Stewart platform parallel structures,which provide better performance than serial structures, can be implemented as a new concept for the motion mechanism of five-axis CNC milling machines. The five-axis PMS CNC milling machine also promises better performance than conventional five-axis gantry structures CNC.
Water quality assessment monitoring system using fuzzy logic and the internet of things Fakhrurroja, Hanif; Nuryatno, Edi Triono; Munandar, Aris; Fahmi, Muhammad; Mahardiono, Novan Agung
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.198-207

Abstract

Water utilization has recently been at its highest level of demand. The water needed to be clean, healthy, and determined to be suitable for consumption. Therefore, it is necessary to have a system that can monitor the water quality so thatinformation related to wate r suitability can be received regularly and in real-time. This paper addresses the critical need for real-time water quality monitoring systems. This study proposed a novel approach integrating the Tsukamoto fuzzy algorithm into an internet of things (IoT)-based framework, forming part of the Fuzzy Inference System. Our system serves as a decision support tool, enabling continuous assessment of water quality. The method categorizes water quality into three levels: good, moderate, and unhealthy, providing timely and precise suitability information. The results demonstrate the effectiveness of the fuzzy logic method in delivering accurate output. Through remotely deployed IoT devices, water suitability and status can be monitored and analyzed in real-time over the internet. This research bridges the gap between traditional water quality assessment methods and the demands of our modern, technology-driven society, ensuring a reliable supply of safe and consumable water.
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.
Back Cover MEV Vol 14 Iss 2 Pikra, Ghalya
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.%p

Abstract

Characteristics of common code conducted emission of multi-boost converters Sudrajat, Muhammad Imam; Nuvus, Afiva Riyatun; Mandaris, Dwi
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.150-157

Abstract

One of the primary challenges faced when utilizing power converters such as a DC boost converter is electromagnetic interference (EMI) issues, one of which is common-mode (CM) noise. In order to mitigate the unwanted EMI from converters and design proper EMI filters, it is imperative to possess comprehensive insight into the characteristics of CM noise generated from the converters. This study presents the investigation regarding the characteristic of CM noise emitted by multi-boost converters when operated under varying duty cycle conditions. The research was conducted by measuring and analyzing the CM noise generated by three identical boost converters arranged in a parallel configuration. The result shows that the amplitude of each harmonic of CM noise generated by the multi-boost converters is 5 dB to 10 dB higher than CM noise from a single-boost converter. This is due to each converter being configured in the same conditions, producing a constructive interaction of the generated CM noise. Moreover, the duty cycle of pulse-width modulation (PWM) has a strong influence on the characteristic of the amplitude of each harmonic frequency. It is proven by the amplitude pattern of each harmonic of CM noise. Under duty cycle variations, the converters generate similar peaks and valley amplitude patterns as the Fourier transformation of the trapezoidal waveform used in the PWM setting.
Front Cover MEV Vol 15 Iss 1 Pikra, Ghalya
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.2024.1023

Abstract

Photovoltaic energy harvesting booster under partially shaded conditions using MPPT based sand cat swarm optimizer Abdilla, Moch Rafi Damas; Windarko, Novie Ayub; Sumantri, Bambang
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.2024.857

Abstract

Photovoltaic (PV) systems perform a vital role in addressing the worldwide energy crisis and fulfilling the escalating energy demand. The variability in irradiance, temperature, and unpredictable weather conditions possess a direct impact on the productivity of PV systems. Furthermore, the existence of partially shaded conditions intensifies the complexity of PV systems, resulting in significant power degradation. These conditions present significant challenges for PV systems to achieve maximum power output and produce optimal energy. To address the prevailing challenges, this study introduces a maximum power point tracking (MPPT) control methodology utilizing a sand cat swarm optimizer (SCSO). This ingenious strategy adapts the sand cat hunting style. The investigation centers on optimizing energy harvesting in PV systems, with a specific emphasis on enhancing precision, rapid convergence, and minimizing oscillations. The suggested SCSO performance is evaluated under a variety of weather situations, including both instances of partially shaded and uniform irradiance. The SCSO results are juxtaposed with other existing bio-inspired algorithms, such as grey wolf optimization (GWO), particle swarm optimization (PSO), and tunicate swarm algorithm (TSA). The proposed SCSO technique achieves 99.94 % tracking accuracy on average and shows superior performance, with faster tracking response and less power oscillation. Moreover, the proposed SCSO generates significantly more energy than the rest compared algorithms. The performance of the suggested method is further validated through a hardware-based experimental assessment, demonstrating an optimal level of tracking performance.

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