Jurnal Nasional Teknik Elektro

Comparative Analysis of Two-Stage and Single-Stage Models in Batteryless PV Systems for Motor Power Supply

2024-03-31T18:26:09+07:00

Implementing photovoltaic (PV) systems as direct power sources for motors without batteries is a complex process that requires a sophisticated control mechanism. The crucial aspect of PV systems is the Maximum Power Point Tracking (MPPT) process, which ensures that the installed PV system generates optimal energy output. A recent study has analyzed research related to PV systems supplying power to pump motors, and the results have successfully classified these systems into two main models: the two-stage and the single-stage. The two-stage model involves separate power tracking and load consumption control processes, while the single-stage model integrates power tracking and load consumption control into a single process. A comparative analysis of these two models has revealed that the two-stage model exhibits higher stability due to the separate power tracking and load consumption control processes. Aspects such as the MPPT process, motor power consumption, and the utilization of DC-link capacitors were examined in this study. The findings of this comparative study contribute valuable insights into the effectiveness and stability of two-stage and single-stage models in PV systems supplying power to motors without batteries. The results will significantly interest researchers and practitioners working in Photovoltaic systems and motor control, providing helpful information for designing and implementing more efficient and reliable PV systems.

A Techno-Economic Analysis for Raja Ampat Off-Grid System

2024-03-31T22:52:27+07:00

Indonesia, an expansive archipelagic nation with over 17,000 islands, encounters significant challenges in ensuring a sustainable and dependable electricity supply, particularly in its West Papua region. The reliance on diesel fuel for electricity generation in this area poses substantial environmental risks and incurs high costs. A comprehensive research study addressing the environmental and economic challenges associated with diesel dependence in West Papua proposed a shift towards sustainable and cost-effective solutions by advocating for adopting off-grid hybrid power systems. This study targeted Yensawai Village in the Raja Ampat Islands, employing a detailed techno-economic analysis through HOMER Pro to identify the most cost-effective system configurations. The findings indicated that the optimal setup consists of a 160 kW diesel generator, complemented by a 70.1 kW solar photovoltaic (PV) system, a 30 kW inverter, and an 80 kWh battery storage unit. This configuration not only proved to be economically viable by reducing the levelized cost of electricity (CoE) by 15.7%—achieving a CoE of $0.236/kWh compared to the base scenario's $0.280/kWh—but also highlighted the potential for similar benefits across regional systems. By focusing on the economic advantages of hybrid energy configurations, this research contributes significantly to the broader discourse on sustainability and the urgent need to reduce diesel dependence, offering a practical approach to cutting electricity generation costs in remote island communities and advancing sustainability initiatives.

Image Processing-Based Application for Determining Wound Types in Forensic Medical Cases

2024-03-31T22:51:14+07:00

Wounds result from physical violence that damages the continuity of body tissues and are frequently observed in forensic medicine and medicolegal science. In forensic medicine and medicolegal science, wounds play a significant role in creating a medicolegal examination and report (VeR) for deceased individuals and living victims. However, research findings indicate that the quality of clinical forensic descriptive results in VeR needs to improve in several hospitals in Indonesia. Meanwhile, high-quality VeR results are crucial in determining penalties for perpetrators in court, and poor VeR results can hinder the legal process. The application of information technology in medicine has yielded numerous tools that can assist experts in carrying out their duties. Likewise, clinical forensics, a generally conservative forensic pathology practice, can be enhanced through image-processing techniques and machine learning. Digital technology support for forensic cases has been available previously, such as in forensic photography; however, its application still needs improvement, and further development is required. This study applied a Yolo V4-based machine learning and image processing algorithm to classify and detect types of wounds. This algorithm was chosen for its high speed and accuracy in classification and detection tasks. The research results showed that the learning model's performance, measured in accuracy, precision, recall, and average F1 score, reached 92%. Usability testing showed that the system performed well and could be helpful with minor improvements.

Enhanced Identification of Valvular Heart Diseases through Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN)

2024-03-31T18:25:32+07:00

Valvular Heart Disease (VHD) is a significant cause of mortality worldwide. Although extensive research has been conducted to address this issue, practical implementation of existing VHD detection results in medicine still falls short of optimal performance. Recent investigations into machine learning for VHD detection have achieved commendable accuracy, sensitivity, and robustness. To address this limitation, our research proposes utilizing Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN) to enhance VHD detection. Notably, SFD-CNN operates on phonocardiogram (PCG) signals, distinguishing itself from existing methods based on electrocardiogram (ECG) signals. We present two experimental scenarios to assess the performance of SFD-CNN: one under default parameter conditions and another with hyperparameter tuning. The experimental results demonstrate that SFD-CNN surpasses other existing models, achieving outstanding accuracy (96.80%), precision (93.25%), sensitivity (91.99%), specificity (98.00%), and F1-score (92.09%). The outstanding performance of SFD-CNN in VHD detection suggests that it holds great promise for practical use in various medical applications. Its potential lies in its ability to accurately identify and classify VHD, enabling early detection and timely intervention. SFD-CNN could significantly improve patient outcomes and reduce the burden on healthcare systems. With further development and refinement, SFD-CNN has the potential to revolutionize the field of VHD detection and become an indispensable tool for healthcare professionals.

Development of DC Motor Speed Control Using PID Based on Arduino and Matlab For Laboratory Trainer

2024-03-31T22:51:02+07:00

DC motors are widely used as propulsions, including in electric bicycles. The problem faced by students in the DC motor control laboratory working using software simulation is that they do not have practical learning experience using digital instruments. This article aims to develop a DC motor speed control that can be used to learn practical Proportional Integral Derivative (PID) control in the laboratory. The DC motor speed control was developed using a combination of Arduino UNO microcontroller and Matlab software. The PID method was used because it is still broadly studied and applied in industries. The test results showed that the developed trainer can work well with PID variable values that can be entered via the keypad, and DC motor transient responses can be displayed in Matlab. From the experimental results, it was found that the optimal PID variable values were Kp=0.04, Ki=0.05, and Kd=0.004, where the controller produced a low overshoot value, i.e., 0.73% of its set point and a settling time of 10.66 seconds. The test results of using the developed trainer in the Fundamental of Control Engineering laboratory work showed that the developed trainer gave students practical learning experience in designing PID control for DC motor speed by using digital equipment, i.e., microcontroller and actual DC motor as well as analyzing its corresponding transient response in Matlab software environment

IoT-Based Disaster Response Robot for Victim Identification in Building Collapses

2024-03-31T22:53:02+07:00

Natural disasters like earthquakes frequently cause building collapses, trapping many victims under dense rubble. The first 72 hours are crucial for locating survivors, but the dangers of secondary collapse hinder direct access. Teleoperated robots can provide vital visual data to aid rescue efforts, though many prototypes remain constrained by high complexity, cost, and minimal customizability. This work investigates developing an Internet of Things (IoT) integrated disaster response robot that delivers accessible and remotely controllable capabilities for victim identification in hazardous collapse sites. Requirements analysis was conducted through a literature review and first responder interviews to determine the critical capabilities needed. The robot was designed using 3D modeling software and assembled using 3D printed and off-the-shelf components. It features remote-controllable movement, real-time video feed, geopositioning, and remote lighting toggling. Rigorous lab tests validated core functionalities, including camera image acquisition, Bluetooth communication ranges up to 10 meters, and comparable GPS coordinate accuracy to a smartphone. Further field experiments showcased the robot's ability to transmit smooth video signals over distances up to 12 meters and its adeptness at navigating complex terrains, evidenced by its proficient left/right panning and ability to surmount obstacles. An affordable Internet-of-Things integrated disaster robot tailored to victim identification was successfully designed, prototyped, and tested. This robot aids search and rescue operations by delivering visual and spatial data about hard-to-reach victims during the critical hours after disaster strikes. This confirms strong potential, accessibility, and customizability for professional and volunteer urban search and rescue teams across environments and economic constraints.