JAREE (Journal on Advanced Research in Electrical Engineering)

Navigation System for Wheeled Robots Using SLAM GMapping and LiDAR

2025-02-06T08:59:31+00:00

One of the primary challenges in developing autonomous robots is enabling them to operate in dynamic, unstructured environments, where traditional navigation methods often fail. For instance, self-driving cars must continuously adapt to changing road conditions, traffic, and unexpected obstacles. In this context, efficient sensors and computing systems play a vital role. LiDAR (Light Detection and Ranging) technology has proven highly effective for autonomous robots, particularly for environmental mapping and obstacle avoidance. This project utilizes the LiDAR A1 sensor to support the SLAM (Simultaneous Localization and Mapping) algorithm, allowing the robot to navigate and map its surroundings autonomously.
The Raspberry Pi 4B was chosen as the central processing unit for its capacity to handle complex algorithms in real-time and to facilitate communication between sensors and actuators. Precise motor control is achieved using an Arduino Mega, enhancing the robot's ability to respond dynamically to its environment. By integrating SLAM with LiDAR and robust motor control, this research aims to create a robot that can not only perceive its surroundings but also navigate them autonomously. This project contributes to the field of autonomous robotics, particularly in navigation and environmental mapping, offering a practical, efficient approach for robots to operate independently in complex settings.

High Voltage Insulation Assessment Based on a Mathematical Model of Transmission Line with Lightning Strike Simulation

2025-01-31T13:21:17+00:00

Lightning is a natural source that causes a major failure on power system especially in transmission line. It could decrease the reliability of the power system due to generating the overvoltage phenomenon which lead to insulator broken. When lightning strokes the tower or the ground wire, the effect of different lightning will have different consequences for voltage rise and affect to discharge process. During the discharge process, the voltage in each arm of tower will rise and lead to insulator being broken due to insulator being unable to withstand the current surge. Hence, the number of footing resistance took a crucial part during this process. Higher footing resistance will hinder the discharge process and affect the magnitude of voltage raises throughout the transmission lines. In PT. Vale Indonesia, the magnitude of footing resistance is varied and no standardized value to maintain the footing resistance value. The standardization of footing resistance is required to prevent large-scale failure from occurring in the system. This paper aimed to obtain the effect of direct strike in transmission tower and the optimum value of footing resistance to prevent large scale of failure that affect to production for PT. Vale Indonesia transmission line system considered by simulation from ATPDraw.

Nonlinear Model Predictive Control for Autonomous Vehicle Overtaking with Bezier Curve Path

2025-06-17T08:55:10+00:00

Rapid advancements in autonomous vehicle technology have significantly improved driving safety, yet formulating safe and efficient overtaking strategies remains a key challenge. This study proposes an algorithmic framework for autonomous vehicles to perform overtaking maneuvers, validated through comprehensive simulations. The framework utilizes Nonlinear Model Predictive Control (NMPC) to manage complex vehicle dynamics, processing real-time velocity and steering angle inputs. Real-time feedback from LiDAR sensors enables dynamic trajectory generation using Bezier curve formulations, which provide reference inputs for the pure pursuit algorithm to guide the NMPC during overtaking. Once the vehicle aligns with the obstacle, the Bezier curve is adjusted to smoothly return the vehicle to its original path. The approach was thoroughly evaluated using the Robot Operating System (ROS) and Gazebo, ensuring realistic visualization and robust performance under dynamic conditions.

Cable Tension Estimation System for Cable-Stayed Structures Using FPGA and Accelerometer Based on the Flat Taut String Method

2025-07-16T15:36:27+00:00

Abstract—The One of critical aspects of Structural Health Monitoring (SHM) systems is cable tension estimation, particularly in cable-stayed structures. This study compares two tension estimation approaches: a direct method using a load cell and an indirect method based on vibration response analysis through an FPGA–accelerometer system. The developed system utilizes an Altera DE0-Nano FPGA board to acquire acceleration signals from the ADXL345 sensor via SPI, and simultaneously transmits the data to a host computer via UART. The data is collected in real time and externally analyzed using the Welch Fast Fourier Transform (FFT) algorithm. The extracted fundamental frequency is then applied to the flat taut string theory to estimate cable tension. The experiment was conducted on an isolated test rig under various tension conditions, with ground truth measurements obtained from a calibrated load cell. The results indicate that the system offers reliable estimation performance, especially in the high-tension group, achieving a mean relative error of 6.18% and a coefficient of determination (R²) of 0.963 compared to load cell readings. It also demonstrates good measurement consistency, with an average repeatability error of 4.8%. Despite using the low-cost ADXL345 sensor with a measured average Signal-to-Noise Ratio (SNR) of approximately 25.9 dB, the system exhibits sufficiently high accuracy and reliability. Therefore, the proposed FPGA–accelerometer-based approach is considered a viable, non-invasive, and efficient alternative for cable tension estimation in SHM applications.

A Design of Wideband Circular Polarization Microstrip Antenna for RFEH Implementation

2025-01-20T10:38:56+00:00

The development of microstrip antenna technology is rapid. A microstrip antenna can support RFEH (Radio Frequency Energy Harvesting) implementation. A wideband circular polarization (CP) microstrip antenna at 2.45 GHz is presented. This research offers an antenna with circular patch structure with right and left parts cut off. The ground plane of the antenna is rectangular at the bottom to widen the bandwidth. The simulation and measurement results for return loss are -24.892 dB and -22.975 dB respectively. For axial ratio are 2.209 dB and 0.080 dB respectively. This value close to the ideal value for a wideband CP microstrip antenna

Analyzing Neural Responses to Video Advertisements: From Visual Attention to Cognitive Load

2025-02-02T10:13:37+00:00

Understanding consumer engagement is essential for optimizing marketing strategies. This study analyzes neural responses using electroencephalography (EEG) to distinguish interesting and ordinary video advertisements. EEG data from 30 participants (aged 17–40) were recorded across eight channels (F8, F7, T8, T7, P8, P7, O2, O1) and decomposed into five subbands (Delta, Theta, Alpha, Beta, Gamma). Key features, including Power Spectral Density (PSD), Mean Absolute Value (MAV), Hjorth Activity, Zero Crossing Rate (ZCR), and Wavelet Transform, were extracted and analyzed. Results showed that Gamma activity in occipital regions (O1, O2) was higher during interesting advertisements, indicating heightened visual attention, and reduced Beta activity in frontal regions (F7, F8) indicating reduced cognitive load. These findings offer valuable insights for neuromarketing by identifying neural markers of engagement and providing actionable insights for designing effective advertisements.

Microgrid Protection Enhancement Using Voltage Restrained Overcurrent Relay: Case Study of Semau Island

2025-07-16T02:22:22+00:00

The adoption of renewable energy sources in microgrids has increased rapidly in recent years; some microgrids operate in a hybrid operation, full supply from PV during the day and using diesel at night. The increase in inverter-based renewable energy sources has an impact on the character of the network when a short circuit occurs. In synchronous generator-based power plants, the short-circuit contribution current generated is very large. Meanwhile, in inverter-based power plants, the short-circuit current generated is not much different from the nominal current. Under these conditions, conventional overcurrent protection relays are no longer effective in protecting the network, so development is needed so that the network remains safe and disturbances can be isolated properly and quickly. The proposed relay is a voltage-limited overcurrent relay. It should detect disturbances and quickly isolate them in both synchronous generator-based and inverter-based sources. Simulation of the resulting settings is carried out using ETAP software. The results of this study indicate that when generating only from PV, the overcurrent relay does not operate when there is a short circuit. After VR-OCR is activated, the relay can operate with a time of 857 ms.

Power Allocation Optimization In Hybrid Energy Storage System Based On Low Pass Filter And Artificial Neural Network

2025-06-23T07:11:42+00:00

This paper presents optimizing power allocation in hybrid energy storage systems (HESS) by combining Low Pass Filter (LPF) and Artificial Neural Network (ANN) techniques. Conventional LPF-based strategies employ fixed cut-off frequencies that cannot adapt to dynamic operating conditions, resulting in suboptimal performance and reduced battery lifespan. This research proposes optimization framework that determines optimal cut-off frequencies through three approaches: cost-based, stress-based, and hybrid optimization. Spectral analysis of requested power confirmed that energy content is predominantly concentrated in low-frequency ranges (10^-6 to 10^-3 Hz), justifying the frequency-based power allocation strategy. The hybrid optimization method yielded an optimal cut-off frequency of 0.0013 Hz, balancing economic and technical considerations with a 15% reduction in battery stress compared to cost-based approaches while increasing system costs by only 8%. The implemented ANN model achieved predictive capability (R² = 0.9728) in determining optimal cut-off frequencies across various operating conditions. Performance evaluations demonstrated that the ANN-based approach produces nearly identical power allocation patterns compared to conventional LPF methods while executing approximately 120 times faster, enabling real-time adaptation to changing system conditions. The combined methodology effectively separates low-frequency power components (87%) to batteries and high-frequency components (13%) to supercapacitors, optimizing component utilization based on their intrinsic characteristics while maintaining system efficiency above 88%.

Design of Power System Stabilizer Using Harris Hawk Optimizer for 150 kV Sulselrabar Electricity System

2025-06-30T01:48:57+00:00

In this paper, design of the Power System Stabilizer (PSS) using Harris Hawk Optimizer (HHO) is proposed to improve stability of the 150 kV interconnected electricity system in South, Southeast, and West Sulawesi (Sulawesi Selatan, Tenggara, dan Barat – Sulselrabar), as one of the large-scaled electricity of Indonesia. The simulation and optimization are performed in Simulink/MATLAB software. HHO is used to find the optimal parameter of PSS based on the Critical Damping Index (CDI) calculation. For investigating the performance, load changes are subjected to the power system. Based on the results, HHO achieved the best stability improvement compared to the Cuckoo Search Algorithm (CSA). Based on the time domain simulation analyses, the HHO-based PSS design reduced overall oscillation by 83.81%, and reduced settling time by 57.9%. Based on the Integral Time Absolute Error (ITAE) calculation, the HHO-based PSS design also reduced the average error by 67.34%.

Short-Term Reservoir Inflow Prediction Using Weather Forecast Data and ExtraTrees Regressor for Hydroelectric Power Plant Operations

2025-07-25T12:08:58+00:00

Reliable short-term inflow forecasting is critical for optimizing hydroelectric power plant (HPP) operations, where water availability is directly influenced by dynamic and often extreme weather patterns. Inaccurate inflow estimations can result in either uncontrolled water spills or reservoir levels dropping below operational thresholds, both leading to reduced energy efficiency and potential operational risks. This study presents a data-driven inflow prediction framework utilizing the ExtraTrees Regressor algorithm, trained on four years of combined historical hydrological and meteorological data, and validated using independent weather forecast datasets. The model achieves a coefficient of determination (R²) of up to 82%, demonstrating strong predictive accuracy. By incorporating forecasted weather parameters into the predictive model, the proposed approach enables proactive reservoir management and more effective turbine scheduling, significantly reducing the incidence of emergency spill events. Findings from this research highlight the advantages of transitioning from conventional intuition-based methods to systematic data-driven strategies, offering enhanced reliability, operational resilience, and efficiency in hydropower generation planning.

ANFIS-Based Energy Management for a Grid-Connected PV-Battery Hybrid System at Renewable Energy Integration Demonstrator of Indonesia

2026-04-02T11:55:22+00:00

This paper presents the design and performance evaluation of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Energy Management System (EMS) for a grid-connected PV–battery hybrid system. The proposed EMS aims to coordinate the power exchange among the photovoltaic (PV) array, battery storage, and the utility grid to ensure reliable operation and optimal renewable utilization. The ANFIS controller receives three inputs PV power, load power, and battery state of charge (SOC) and generates the battery reference power () to regulate the charging and discharging processes. The system was trained using approximately 1600 data sets, with 30% used for testing and checking, achieving a final root-mean-square (RMS) error of 0.0589. The proposed control strategy was validated through three test scenarios: (1) constant load with variable irradiance, (2) variable load with constant irradiance, and (3) simultaneous variations of load and irradiance. Simulation results show that the ANFIS-based EMS effectively maintains power balance, prevents battery overcharge or deep discharge, and stabilizes SOC within the 30–80% range. In addition, the controller efficiently manages power sharing between the PV system, battery, and grid, ensuring optimal energy utilization and reduced grid dependency. Overall, the results confirm that the proposed EMS demonstrates adaptive, stable, and intelligent performance suitable for smart microgrid applications.

Pressure Control based Hybrid MPC and PID Control for Parallel Centrifugal Well Injection Pumps

2025-07-23T03:11:16+00:00

Well Injection pumps is essential equipment in the upstream oil and gas industry. These pumps are primarily employed to inject water into the reservoir to maintain reservoir pressure, or to waste the water excess after exploration. The most common use Pressure control is only PID control, which has limitations such as Complex and Time-Consuming Parameter Tuning and Poor Adaptability to Parameter Changes. To address this challenge, this study develops hybrid MPC and PID control for pressure control to increase response time and reduce overshoot due to parameter changes. To get the plant model, ARMAX (Auto Regressive Exogenous Moving Average with Exogenous Inputs) is used by analyzing historical measurement data for pressure and throttle valve. The proposed strategy effectively combines the fast response of the PID controller with the foresight and optimization capabilities of the MPC. Load change simulations show that the Hybrid MPC-PID controller exhibits faster response time and more precise overshoot reduction capability compared to the single PID and pure MPC strategies. The final optimized model achieved substantial improvements in the IAE index, where the hybrid MPC-PID achieved 0.37% compared to 4.71% for the pure MPC and 7.24% for the PID controller. The implementation of this Control enables increased efficiency in the Water Injection Pump.