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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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Design of Reconfigurable Metasurface Antenna for 6G in sub-THz band Wijaya, Harly
Journal of Electrical Engineering Vol. 3 No. 03 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i03.394

Abstract

We propose and evaluate a reconfigurable metasurface antenna at D band (f₀≈140 GHz) for short-range 6G applications. The main problem is in sub-THz.—High FSPL, component losses, and beam steering limitations are addressed through a loss-aware co-design that integrates unit-cells (2-bit MEMS and continuous LC), feed/illumination, and radiation-friendly bias networks, along with multi-objective beam codebook optimization (gain maximization, SLL minimization, and scan loss). Full EM simulation results on a transmitarray (TA) architecture show dB at 134–146 GHz (≈8.6%), realized peak gain of 31.8 dBi (aperture efficiency ~38–43%), best SLL of −14.1 dB, beam pointing error ≤1.1°, and beam steering up to ±40° with worst-case scan loss of 3.4 dB; the LC variant provides more precise pointing (≈0.4°). Compared to the reflectarray baseline, TA excels in wide scans due to minimal feed blockage. Link-level estimations show support for 64-QAM (10–30 m) and 256-QAM (10–20 m) under LOS conditions. This approach validates the feasibility of an efficient and realizable sub-THz reconfigurable antenna for 6G extreme
Implementation of Smart Grid Technology to Increase The Efficiency of Electrical Energy Distribution In Urban Areas Nainggolan, Jekly Boy
Journal of Electrical Engineering Vol. 3 No. 03 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i03.395

Abstract

The rapid growth of urban populations and economic activities has significantly increased electricity demand, placing heavy pressure on conventional distribution systems that are prone to high power losses, voltage instability, and limited fault detection capabilities. This research investigates the application of smart grid technology as a solution to enhance the efficiency, reliability, and sustainability of electricity distribution in urban areas. A comparative simulation was conducted using MATLAB/Simulink and ETAP to evaluate two distribution models: a conventional system and a smart grid-enabled system incorporating advanced metering infrastructure (AMI), real-time monitoring sensors, adaptive load management, and renewable energy integration. The results indicate that smart grid implementation reduces average power losses by nearly 40%, improves voltage stability with deviations reduced from ±7% to ±3%, and enhances system reliability as reflected in reduced SAIDI and SAIFI indices. Furthermore, the integration of rooftop photovoltaic (PV) generation contributes to peak load reduction while supporting the transition towards sustainable energy systems. These findings highlight the contextual relevance of smart grid adoption in Indonesia, where infrastructure and regulatory challenges remain, and provide practical recommendations for utilities and policymakers. The novelty of this study lies in its focus on urban distribution networks in developing countries, offering a localized model that integrates efficiency improvements, renewable energy penetration, and adaptive control strategies to support sustainable urban energy development.
Statcom Performance Analysis In Improving Voltage Stability In Distribution Networks With High Renewable Energy Penetration Siregar, Andi
Journal of Electrical Engineering Vol. 3 No. 01 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i01.396

Abstract

The increasing penetration of inverter-based renewable energy generators (PV/wind) in distribution networks raises new challenges related to voltage stability due to active power variability, phase imbalance, and local reactive capacity limitations. This paper analyzes the performance of Static Synchronous Compensator (STATCOM) in improving voltage stability in distribution networks with high renewable energy penetration through detailed modeling, design/comparison of control methods, and evaluation of their practical implications. The system model includes a VSC-based STATCOM representation in a dq framework with a phasor follower (PLL), DC-link dynamics, and Volt/VAR coordination with other voltage control devices (OLTC, capacitor bank, and DER inverter). Three control schemes are studied and compared—(i) set-point voltage regulation with PI anti-windup, (ii) adaptive Q–V droop characteristic based on load voltage sensitivity, and (iii) robust control with fault current feed-forward—focusing on the response to fast disturbances (irradiance/wind ramps, large motor startups, and single-phase-to-ground sags) and continuous operating conditions (daytime reverse power flow, load imbalance). Case studies are conducted on a representative medium-sized distribution feeder, with a multistage renewable penetration scenario (30–70% of peak load) and STATCOM locations selected based on voltage sensitivity index and reactive capacity constraints. Time-based simulation results show that STATCOM integration improves the voltage profile along the feeder, accelerates post-disturbance voltage recovery (shorter settling time and smaller overshoot), suppresses pu deviation at critical buses, reduces voltage equipment switching operations (more stable VVO coordination), and reduces network losses under several peak load and rapidly changing weather scenarios. In addition, STATCOM helps maintain the LVRT/HVRT limits of DER inverters so that the dynamic interactions of the system remain under control during sag/swell events. The contributions of this paper are (1) an integrated framework for modeling and evaluating STATCOM performance in high DER environments, (2) a systematic comparison of several control strategies with uniform performance indicators (voltage profile, recovery time, voltage stability index, and losses), and (3) practical recommendations for placement and set-point adjustments to be compatible with utility operating practices and grid codes. These findings provide a technical foundation for distribution planners and operators in formulating STATCOM-based flexible reinforcement strategies to support large-scale renewable integration without compromising voltage quality.
Scheduling Optimization of Hybrid Microgrid Generators Based on Deep Reinforcement Learning Sianipar, Santi Rama
Journal of Electrical Engineering Vol. 3 No. 01 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i01.397

Abstract

Unit scheduling in hybrid microgrids (PV/wind–generator–battery) is nonlinear, multi-constraint, and affected by uncertainties in load and renewable energy forecasts. Conventional rule-based or deterministic optimization methods often require accurate models and are less robust to forecast errors, while large-dimensional exact solutions are not always feasible for real-time operations. This study proposes a Deep Reinforcement Learning (DRL)-based generator scheduling optimization framework that formulates the problem as a Markov Decision Process. The state vector includes multi-horizontal load/renewable energy forecasts, battery state of charge, fuel price, and unit operating limits; actions are the genset power setpoint and battery charge/access rate. A reward function internalizes fuel costs, battery degradation, emissions, curtailment, and unsupplied energy penalties, while also encouraging reserve provision. To ensure operational safety, we add a safety layer that projects policy actions onto the feasible set (SOC limits, ramp rate, minimum on/off, and converter capacity). Training is performed offline with domain randomization over weather and load profiles, and then evaluated in a rolling horizon scheme with minute resolution. Simulation results demonstrate operating cost savings and curtailment reduction compared to the MILP/MPC baseline, with high constraint compliance and sub-second inference times, making it suitable for implementation in edge controllers. This approach demonstrates scalability across a wide range of microgrid configurations and remains robust to uncertainties, offering a practical path to low-cost and low-emission operation.
Scheduling Optimization of Hybrid Microgrid Generators Based on Deep Reinforcement Learning Panggabean, Rido Sanjaya
Journal of Electrical Engineering Vol. 3 No. 01 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i01.398

Abstract

The high penetration of Distributed Energy Resources (DER) changes the direction of power flow, reduces fault currents, and makes the grid configuration more dynamic, making conventional static setting-based protection schemes vulnerable to miscoordination, misoperation, and zone isolation failure. This paper proposes a graph-based adaptive protection framework for smart grids that models the power system as a weighted multigraph, where nodes represent buses/transformer secondaries and edges represent lines, switches, and DER elements. The graph topology and weights are updated in near-real-time from SCADA/PMU/AMI, and then analyzed through graph metrics (e.g., cut-set, community detection, and betweenness) to: (i) identify the most stable protection zone boundaries against configuration changes, (ii) estimate the direction and “footprint” of relevant fault currents under grid-following and grid-forming conditions, and (iii) select a pre-computed set of protection equipment settings (OCR/ROC, directional, distance, DFR, adaptive recloser). The policy engine mechanism executes transitions between settings based on trigger events (topology changes, islanding, or voltage oscillations) with safety guards to prevent chattering. Scenario evaluations show that this approach reduces miscoordination events under inverter-limited fault current conditions, maintains selectivity during reverse power flow, and accelerates the recovery of healthy areas after fault isolation. These results emphasize the potential of the graph-based method as a scalable, adaptive protection foundation ready to be integrated into smart grid control centers with high DER.
High-Resolution SAR Image Reconstruction Using Deep Unfolding Boas, Asher
Journal of Electrical Engineering Vol. 3 No. 01 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i01.399

Abstract

This paper proposes a deep unfolding framework for high-resolution Synthetic Aperture Radar (SAR) image reconstruction under non-ideal acquisition conditions (undersampling, phase/motion mismatch, and multiplicative speckle noise). The proposed method (DU-SAR) decomposes the optimization algorithm into a series of steps with two main components: (i) a differentiable SAR physics operator-based data consistency algorithm, and (ii) a speckle-aware proximal/learned denoiser to preserve edges and textures. To address defocus due to phase errors, we embed an in-the-loop joint autofocus that updates the phase map at each unrolling step. The training scheme is two-stage—pretraining on synthetic data with varying undersampling/SNR levels and self-supervised fine-tuning on real data based on measurement domain consistency—with GPU acceleration, mixed precision, and multi-resolution unrolling for efficiency. Experimental results show consistent improvements over classical, model-based, and deep baselines end-to-end: at 50% undersampling, DU-SAR achieves a PSNR of 30.9 dB and an SSIM of 0.87, and 28.9 dB/0.83 at 25%; robustness to phase errors is maintained with an SSIM of 0.71 at an RMS error of 1.00 rad. Performance-wise, an inference latency of approximately 85 ms per 512×512 patch makes the method feasible for near real-time on mid-range GPUs. These findings confirm that physics-consistent and speckle-aware deep unfolding effectively recovers high-frequency details while maintaining focus and computational efficiency.
Risk-Based Arrester Placement in Substations: A Multi-Objective Probabilistic Approach Tumanggor, Andrian
Journal of Electrical Engineering Vol. 3 No. 01 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i01.400

Abstract

This study proposes a risk-based arrester placement framework in substations using a multi-objective probabilistic approach that combines electromagnetic transient (EMT) modeling, Latin Hypercube Sampling (LHS) for uncertainty propagation, and NSGA-II to generate a set of cost–risk Pareto solutions. The model incorporates lightning and switching surge sources, equipment characteristics (BIL/LIWV, arrester V–I curves, energy duty limits), and technical–economic consequences (EENS, interruption costs). A case study on a double busbar substation with eight candidate points shows three representative solutions: minimum-cost (3 arresters), knee-point (5 arresters), and minimum-risk (7 arresters). The knee-point solution—arresters at incomers L1–L2, the main bus, and HV & MV transformer terminals—reduces Expected Risk by ≈ 58% and SAIDI by ≈ 57% compared to the deterministic baseline (arresters only at incomers), with improved insulation coordination margins (e.g., p95 of the transformer HV terminals drops from ~712 kV to ~635 kV) and energy reserves of ≥30% over manufacturer specifications. Sensitivity analysis identifies ground grid resistance (Rg), lightning peak current, and strike position as the primary risk drivers, indicating that co-optimization of arresters and grounding has the potential to further improve performance. The results confirm that this approach is robust and economical, and ready to be adopted as a basis for protection investment decisions in modern substations.
Conceptual Framework for Lightning Risk Management in Substations Based on Multi-Layered Protection Sing, Krisna
Journal of Electrical Engineering Vol. 3 No. 02 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i02.401

Abstract

This article presents a conceptual framework for lightning risk management at substations that emphasizes a defense-in-depth approach as the primary strategy for technical and operational risk reduction. The proposed framework integrates an understanding of lightning hazard (strike density, local climate, topography), asset vulnerability (isolation coordination, clearances, and BIL per equipment), and failure consequences (service reliability, safety, CAPEX/OPEX costs) into a standards-based risk management cycle. The protection layers are conceptualized from external mitigation (shielding/air-termination, down-conductors, and low-impedance grounding systems), internal protection (surge arresters and insulation coordination), to detection and control layers (lightning current monitoring, event recording, condition-based maintenance). The framework also maps the process of establishing target protection levels using the ALARP risk matrix, conceptually determining arrester locations/powers, and the governance of technical inspections and audits throughout the asset lifecycle. Integration of substation digital data (IEC 61850) to improve event observability and maintenance policy feedback is also proposed. While remaining theoretical (without calculations), this framework provides systematic guidance for utilities in developing layered protection policies that are adaptive to tropical climate variability, budget constraints, and reliability demands, while also serving as a basis for further quantitative studies
Fault-Tolerant Sensor Fusion for Autonomous Mobile Robots Using Graph Neural Networks and Uncertainty-Aware SLAM Ridwan, Hartono
Journal of Electrical Engineering Vol. 3 No. 02 (2025): Elimensi : Journal of Electrical Engineering
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/elimensi.v3i02.402

Abstract

This research proposes GNN-FT-SLAM, a disturbance-tolerant sensor fusion framework for autonomous robots that combines Graph Neural Networks (GNN) at the perception layer with an uncertainty-aware graph-factor SLAM backend. GNN constructs a multicenter graph (camera, LiDAR, IMU, odometry) to contextually model measurement reliability and predict adaptive covariances that are then used as factor weights in SLAM optimization. The pipeline includes multicenter synchronization, dynamic graph construction, reliability-focused message passing, probabilistic (aleatoric/epistemic) heads, as well as fault detection–isolation and modality reconfiguration (fallback and dynamic factor activation) modules. Evaluations on nominal, synthetic stress (motion blur, glare/low-light, LiDAR sparsity, IMU bias), and real-world fault scenarios demonstrate performance improvements over robust baselines (ORB-SLAM3, LIO-SAM, VINS-Mono): 32–55% reduction in ATE, improved RPE, fault detection AUROC up to 0.92, and improved uncertainty calibration (NLL and ECE decreased). The system runs in real-time (~27 Hz) on an edge GPU with an average latency of 37 ms. These findings confirm that combining deep learning graph representations and probabilistic inference results in adaptive, uncertainty-aware, and fault-tolerant sensor fusion, relevant for autonomous robot operations in dynamic and cluttered environments.
Wide-Bandgap GaN Based Modular Multilevel Converter for High Power Motor Drives: Loss Modeling and Thermal Optimization Sudirman, Ridwan
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.v2i03.403

Abstract

This research addresses the development of a Wide-Bandgap Gallium Nitride (GaN)-based Modular Multilevel Converter (MMC) for high-power motor drives, with a focus on loss modeling and thermal optimization. The use of GaN devices enables operation at high switching frequencies and high voltages, thereby improving system efficiency and reducing the size of passive components. However, operation at high power also poses challenges related to thermal management and loss distribution in the MMC modules. This research models conduction losses, switching losses, and losses in passive components, then integrates them into a thermal analysis to identify hotspots and optimize cooling. This approach involves simulating a GaN MMC under various high-power motor operating conditions to assess loss distribution and temperature profiles. Simulation results show that an integrated thermal design strategy can reduce the peak device temperature by 15–20% compared to conventional designs, while maintaining high energy conversion efficiency (>98%). This thermal optimization also allows the use of higher switching frequencies, which contributes to the reduction of inductor and capacitor sizes in the system. This research makes an important contribution to the design of GaN-based modular converters for high-power motor applications, with a combination of high efficiency, effective heat management, and system size optimization.

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