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Elimensi Journal of Electrical Engineering
Published by Cattleya Darmaya Fortuna
ISSN : -     EISSN : 29872928     DOI : https://doi.org/10.54209/elimensi.v3i03
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering
Articles published in cover key areas in electrical engineering such as : Electrical power and energy: Transmission and distribution, high voltage, electrical energy conversion, power electronics and drive. Telecomunication and Signal Processing: Antenna and wave propagation, network and systems, Modulation and signal processing, Radar and sonar, Radar imaging; Radio, multimedia content, Routing protocols, Wireless communications, Signal Processing, Image Processing, Voice Processing. Control automation and Robotic: Robotics, Automation, Pattern Recognition, Biosignal Engineering, Control Theory, Applied Control, System Design, Optimization, Process Control, Sensor, Machine Learning.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 30 Documents
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Risk-Based Arrester Placement Optimization in Substations Using NSGA-II and EM Simulation Gunawan, Ipan
Journal of Electrical Engineering Vol. 2 No. 02 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i02.421

Abstract

This study proposes a risk-based arrester placement optimization method to improve the protection of substation systems against lightning and switching surges, using the NSGA-II (Non-Dominated Sorting Genetic Algorithm II) algorithm and Electromagnetic Transients (EMT) simulation. The main objective of this study is to reduce the Expected Annual Risk (EAR) related to equipment damage and operational costs through optimal arrester placement, while minimizing the Life-Cycle Cost (LCC) of the substation protection system. EMT simulation is used to model the system response to lightning and switching surge events, while NSGA-II is used to solve a multi-objective optimization problem, considering various potential locations and different arrester ratings. The optimization results show a clear trade-off between cost and risk reduction, with the best solution providing the optimal balance between risk reduction and lifecycle cost. Several critical locations, such as transformer terminals and incomer lines, were identified as priorities for arrester installation, as they offer significant risk reduction at relatively low cost. The reduction in peak equipment-damaging voltage (BIL) after mitigation with arresters also showed substantial improvement. Sensitivity analysis showed that the protection design remained effective despite variations in external parameters such as lightning density and soil resistivity. With this approach, utilities can make more informed decisions about selecting arrester locations and types that meet their budgets and protection needs, significantly reducing the risk of system failure. The results of this study can guide electricity companies in implementing more efficient and economical substation protection policies, while extending equipment life and improving distribution network reliability.
Risk and Reliability Analysis of Substation Protection Systems Using a Probabilistic Approach Putri, Adinda
Journal of Electrical Engineering Vol. 2 No. 03 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i03.422

Abstract

The reliability of a substation protection system is crucial for maintaining the continuity of power supply and reducing the risk of equipment damage due to external disturbances, such as lightning or operational errors. However, in practice, protection systems are often affected by uncertainties that can affect system performance. This study aims to analyze the risk and reliability of a substation protection system using a probabilistic approach to assess the impact of uncertain variables on system performance. This approach combines Monte Carlo simulation to generate event probability distributions and a multi-objective optimization algorithm to determine the optimal arrester location and capacity. Simulation results indicate that the probabilistic approach provides a more realistic picture of protection system performance under uncertain conditions compared to traditional deterministic methods. Furthermore, this study identifies the trade-offs between risk reduction and investment costs required for protection optimization, and provides recommendations for more efficient and accountable protection investment policies. These findings can serve as a reference for electric utilities in designing more reliable and cost-effective substation protection systems.
Aplikasi Deep Learning dalam Pengolahan Sinyal EMG untuk Pengenalan Gesture Tangan pada Prostesis Adaptif Putri, Adira
Journal of Electrical Engineering Vol. 2 No. 02 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i02.423

Abstract

Penelitian ini mengembangkan sistem pengenalan gesture tangan berbasis sinyal elektromiografi (EMG) menggunakan teknik Deep Learning untuk aplikasi prostesis adaptif. Dalam penelitian ini, kami menggabungkan Jaringan Saraf Konvolusional (CNN) untuk ekstraksi fitur spasial dan Long Short-Term Memory (LSTM) untuk pemodelan urutan temporal sinyal EMG, dengan tujuan meningkatkan akurasi dan responsivitas sistem pengenalan gesture tangan. Model ini dilatih dengan data sinyal EMG yang dikumpulkan dari berbagai individu dengan berbagai tingkat pengalaman dan kondisi fisik, kemudian diuji menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil evaluasi menunjukkan bahwa kombinasi CNN dan LSTM mampu meningkatkan akurasi pengenalan gesture tangan, mencapai akurasi 92,3%, dengan F1-score 92,5%, dibandingkan dengan hanya menggunakan CNN. Selain itu, sistem yang dikembangkan menunjukkan latensi yang rendah (±50 ms) sehingga mampu merespons input secara real-time, yang esensial untuk pengendalian prostesis adaptif. Uji coba implementasi pada prototipe prostesis menunjukkan bahwa pengguna dapat mengendalikan prostesis dengan presisi yang tinggi dalam berbagai gesture tangan. Penelitian ini memberikan kontribusi signifikan dalam pengembangan prostesis adaptif yang lebih responsif dan alami, serta membuka peluang untuk pengembangan lebih lanjut pada sistem kontrol berbasis sinyal EMG dengan pendekatan Deep Learning.
Optimization of TCSC Placement and Capacity to Improve Transient Stability of Transmission Systems Srimadewi, Putri
Journal of Electrical Engineering Vol. 2 No. 01 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i01.424

Abstract

This study proposes an integrated optimization framework for the location and capacity determination of Thyristor Controlled Series Capacitor (TCSC) to improve the transient stability of a high-penetration renewable energy transmission system. The system dynamics model is evaluated in the electromagnetic-transient domain for various representative disturbances, while load and power injection uncertainties are modeled using Latin Hypercube Sampling. The objective function is formulated in a multi-objective manner to balance stability margin enhancement, rotor oscillation damping, ROCOF reduction, voltage recovery, and investment cost efficiency. The NSGA-II algorithm is used to obtain the Pareto set under the operational constraints of voltage, power flow, and reactance modulation limits of the TCSC. In a case study of a modified 150 kV network, the Pareto solution shows an increase in Critical Clearing Time in the order of tens of milliseconds, a decrease in peak ROCOF, an increase in minimum post-fault voltage, and a reduction in network losses compared to the condition without TCSC. Sensitivity analysis shows that the performance remains robust to variations in load and plant input power, thus the proposed framework provides practical guidelines for transmission operators to select the most effective TCSC configuration within budget constraints and reliability targets.
UKF-Based IMU–LiDAR Sensor Fusion Method for Robot Navigation in Feature-Minimal Indoor Environments Sianipar, Revly
Journal of Electrical Engineering Vol. 2 No. 01 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i01.425

Abstract

This paper proposes an Unscented Kalman Filter (UKF)-based IMU–LiDAR sensor fusion method for autonomous robot navigation in feature-poor enclosed spaces (homogeneous corridors, plain walls, changing lighting), conditions that often weaken LiDAR scan matching and cause large drifts in pure IMU integration. The proposed architecture models motion dynamics in error-state with online-estimated gyroscope/accelerometer biases, while LiDAR measurements are extracted as scan-to-submap constraints that are condensed into adaptive uncertainty relative pose observations. Time synchronization and extrinsic IMU–LiDAR calibration are performed on-the-fly using weak priors to maintain system stability despite time offsets. Evaluation on three indoor scenarios (a 40 m corridor, a 60 m L-aisle, and a 30 × 20 m warehouse with minimal texture) shows a 42–58% reduction in positional RMSE compared to LiDAR-only ICP and 73–81% compared to IMU-only, with translational drift < 0.6% of the distance traveled and heading drift < 0.35°/min. The system runs in real-time at 20–30 Hz on a mid-range CPU, maintaining a 100% localization success rate with no tracking failures at velocities of 0.4–1.2 m/s. These results confirm that UKF with adaptive uncertainty modeling and bias estimation is capable of integrating LiDAR inertial and geometric forces to produce accurate and robust state estimation in feature-poor indoor environments, while providing an efficient foundation for advanced trajectory planning and motion control.
Comparison of Reflectarray vs. Transmitarray Reconfigurable Architectures for 6G Sub-THz Hia, Hendra
Journal of Electrical Engineering Vol. 2 No. 01 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i01.426

Abstract

This paper compares reconfigurable reflectarray (RA) and transmitarray (TA) architectures for 6G sub-THz (D/F-band) applications, with emphasis on high-speed beam steering, aperture efficiency, and system integrability. We present an evaluation framework that integrates full EM co-simulation and link-level modeling to assess key figures of merit: η_ap, scan loss, fractional bandwidth, SLL, polarimetry, beam squint, and EIRP/G/T in backhaul and directional access scenarios. Three tuning mechanisms—2-bit MEMS, continuous liquid-crystal (LC), and varactor/CMOS—are discussed in terms of insertion loss, linearity, switching latency, bias requirements, and fabrication tolerances. The analysis shows that RA offers a simple feed but is susceptible to blockage and mutual coupling over wide scans, while TA minimizes blockage and facilitates multilevel true-time-delay for controlling squint, at the cost of multilevel assembly complexity. Case studies of sub-arrays at 130–170 GHz illustrate practical trade-offs between phase quantization (1–2 bit vs. continuous), dielectric loss, and bias routing versus wide-scan performance. This framework yields target-based architecture selection guidelines: RA for low cost and profile at moderate scan rates, TA for wide scan rates/high EIRP and advanced radio integration, and scalable unit-cell and control network design recommendations toward 6G reconfigurable metasurface antennas.
EMG-Based Hand Gesture Recognition Using Interpretable Deep Learning for Prostheses Dermawan, Rizki
Journal of Electrical Engineering Vol. 2 No. 01 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i01.427

Abstract

This study proposes an EMG-based hand gesture recognition method for adaptive prosthesis applications using interpretable deep learning. The proposed method utilizes EMG (electromyography) signals obtained from arm muscles to identify various hand movements performed by prosthesis users. By using a deep learning architecture, the developed model can classify hand movements with high accuracy. This approach also integrates model interpretability through saliency map visualization techniques, which allows understanding of the key features used by the network to make decisions. EMG datasets collected from several subjects were trained to recognize hand gestures such as gripping, grasping, and waving, and were complemented with signal processing to reduce noise and improve data quality. Evaluation results show that the proposed deep learning model achieves classification accuracy of up to 95%, with a relatively low time-to-decision, making it suitable for prosthesis applications that require fast and accurate responses. The results of this study have the potential to improve prosthesis performance with smoother and more responsive control, as well as provide new insights for the development of biomedical signal-based prosthetic devices.
Deep Learning-Based Disturbance Detection in Smart Distribution Networks Using PMU Data Kusuma, Halim
Journal of Electrical Engineering Vol. 2 No. 01 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i01.428

Abstract

This study proposes a deep learning-based fault detection method for intelligent distribution networks using Phasor Measurement Unit (PMU) data. With the increasing development of intelligent distribution systems, the need for fast and accurate fault detection systems is crucial to improve the reliability and resilience of the power grid. Utilizing PMU data, which provides real-time information on voltage, current, and frequency, enables more precise and rapid fault detection. In this study, we developed a deep learning model that uses Long Short-Term Memory (LSTM) to sequentially process PMU data and detect faults such as short circuits, phase faults, and line outages. The model was trained on a PMU dataset covering a wide range of normal and fault conditions in the distribution network. Evaluation results show that the proposed model is capable of detecting faults with >98% accuracy and has a faster detection time compared to traditional detection methods. This approach also demonstrates the ability to identify fault types with a high degree of reliability and reduces the risk of system failure due to detection delays. By using deep learning methods, this study contributes to improving the reliability of intelligent distribution systems and provides a basis for the application of PMU technology in more efficient and automated distribution network monitoring and maintenance.
High-Resolution SAR Image Reconstruction Using Deep Unfolding for Disaster Monitoring Sihombing, Anju
Journal of Electrical Engineering Vol. 2 No. 03 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i03.429

Abstract

This study proposes a deep unfolding approach for high-resolution SAR (Synthetic Aperture Radar) image reconstruction, aiming to improve image quality in disaster monitoring applications. SAR images often suffer from deterioration and resolution degradation due to atmospheric conditions and signal interference, which can reduce the accuracy of disaster analysis. The proposed deep unfolding technique combines the advantages of conventional optimization methods with the capabilities of deep learning to learn better and more accurate image representations. The approach consists of iterative unfolding that adapts data-driven learning with an optimization model to address noise, distortion, and resolution deficiencies in SAR images. The developed deep unfolding model is trained using SAR data from various disaster events, such as floods, earthquakes, and tsunamis, to learn distinctive patterns and structures in SAR images. Experimental results show that this approach successfully improves image quality with significant noise reduction and up to 30% resolution increase compared to conventional reconstruction techniques. Evaluation using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics demonstrated substantial improvements in the quality of recovered imagery, enabling more effective and accurate disaster monitoring. With the ability to recover lost details in SAR imagery, this deep unfolding approach opens up opportunities for broader applications in satellite imagery-based disaster monitoring and emergency response.
The Effect of Multilevel Rectifiers on Power Quality in Urban Electric Traction Systems Rahmad, Supri
Journal of Electrical Engineering Vol. 2 No. 03 (2024): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v2i03.430

Abstract

This study examines the effect of multilevel rectifiers on power quality in urban electric traction systems. Electric traction systems used for public transportation, such as trains and trams, require high power quality to ensure efficient operation and reduce interference on the power grid. One key component in these systems is the rectifier, which converts alternating current (AC) to direct current (DC) for traction motors. Multilevel rectifiers offer several advantages over conventional rectifiers, including harmonic reduction, increased efficiency, and reduced voltage distortion that can affect motor performance and overall power quality. This study explores how the use of multilevel rectifiers can reduce current and voltage harmonics in electric traction systems, as well as their impact on power factor and operational efficiency. Using simulations and experimental analysis across various traction system operating scenarios, the results show that multilevel rectifiers can significantly improve power quality, reduce harmonic distortion, and increase the system power factor. Furthermore, the implementation of multilevel rectifiers has been shown to reduce heat generation by power electronic components, which in turn increases equipment reliability and operational life. Thus, this study demonstrates that the use of multilevel rectifiers in urban electric traction systems not only improves power quality but also offers a solution to increase energy efficiency and reduce the environmental impact of electric transportation systems. The implementation of this technology could be a crucial step in the development of intelligent and sustainable transportation systems.

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