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JURNAL NASIONAL TEKNIK ELEKTRO
Published by Universitas Andalas
ISSN : 23022949     EISSN : 24077267     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Jurnal Nasional Teknik Elektro (JNTE) adalah jurnal ilmiah peer-reviewed yang diterbitkan oleh Jurusan Teknik Elektro Universitas Andalas dengan versi cetak (p-ISSN:2302-2949) dan versi elektronik (e-ISSN:2407-7267). JNTE terbit dua kali dalam setahun untuk naskah hasil/bagian penelitian yang berkaitan dengan elektrik, elektronik, telekomunikasi dan informatika.
Arjuna Subject : -
Articles 15 Documents
 1   2 
Search results for , issue "Vol 12, No 2: July 2023" : 15 Documents clear
A GSM-Based Fault Detection on Overhead Distribution Lines Charles Ofori; Joseph Cudjoe Attachie; Felix Obeng-Adjapong
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.986.2023

Abstract

Power distribution in Ghana is managed by the Electricity Company of Ghana (ECG) which is responsible for ensuring accessibility of electricity to consumers. One of the challenges that affect the effective operation of ECG is the slow response to faults on the overhead distribution lines. Fault detection on the distribution lines is a very tedious activity but a necessary procedure to ensure efficient power distribution to consumers. This paper seeks to design a system that can detect faults, the type of faults and their location before they cause any casualties to transformers and other power system equipment. This would replace the primitive method of patrolling and manual inspection of faults currently done by the Electricity Company of Ghana (ECG). This objective was achieved using a GSM-based system on an Arduino platform and ATmega 328P microcontroller to locate the occurrence of faults efficiently. Faults are introduced into the system by triggering the type of fault on the Arduino platform which opens the corresponding relay of the line fault. The opening of this relay sends a signal to the microcontroller and a corresponding LED which switches to display the type of fault. The microcontroller then communicates to the GSM module which displays the said fault and location on a display screen with the help of a virtual terminal. This system was tested under the various unsymmetrical faults to show the efficiency of the system using C++ programming. The simulation shows that the system offers a fast fault response time.
Audible Obstacle Warning System for Visually Impaired Person Based on Image Processing Andik Yulianto; Ni'matul Ma'muriyah; Lina Lina
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1008.2023

Abstract

To be able to do their daily activities, a visually impaired person needs a guidance device to help him/her walk including to avoid obstacles on their way to the destination. The quick and clear instruction is given to the user is the most challenging problem to be solved. The visually impaired person should have simple guidance about the obstruction in front of him/her. Most guidance devices use simple sounds to give the warning without information about which direction the user should go. In this paper, an obstacle warning system based on image processing methods was developed. A guidance device for visually impaired persons using a single-board computer based on an image-processing algorithm has been designed. The main sensor of the guidance device is a NoIR camera. The distance measurement approximation model was developed with errors up to 4.3%. The test found that the proposed system can detect obstruction in the form of a person, the device also detects the stairs. The best detection obtains when the object position is less than 300 cm in front of the user.  The stair detection was carried out by using the Hough line transform method. The output of the system is the sound of direction that can be heard through the headset.
Perbandingan Performa Metode Maximum Power Point Tracking Human Psychology Optimization (HPO), Artificial Bee Colony (ABC) dan Fuzzy Logic Controller (FLC) pada Flyback Converter Kondisi Parsial Shading Moh. Zaenal Efendi; Mochammad Rody Dwirantono; Suhariningsih Suhariningsih; Lucky Raharja
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1022.2023

Abstract

Maximum Power Point Tracking (MPPT) is a method to track the power point of an energy source with the intention to generate maximum power. The surface of the Solar Panel has the possibility of being blocked when it receives sunlight. The barrier can be in the shape of shadows of objects that are nearby solar panels. The problem causes the power generated to be not optimal and makes more than one MPPT peak on the characteristics of P-V. This paper compares several methods of MPPT such as Human Psychology Optimization (HPO), Artificial Bee Colony (ABC), and Fuzzy logic Controller (FLC) under partial shading conditions, the comparison of three method by simulation. This algorithm hooks up to a flyback converter to provide MPP. From the results of MPPT accuracy in partial shading situations, the ABC and HPO approach methods can achieve GMPP with more than 82.22 % accuracy. For convergence, ABC needs extra time to discover GMPP. From the results, the Fuzzy approach can track however nevertheless trapped on LMPP.
External Leakage Current Separation to Determine Arrester Condition Due to Contamination Novizon Novizon; Mondrizal Mondrizal; Darwison Darwison; Aulia Aulia; Tesya Uldira Septiyeni
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1026.2023

Abstract

Leakage current measurements can be used to determine the aging condition of the ZnO arrester. The leakage current that occurs in the arrester is divided into two, namely external and internal leakage currents. The external leakage current is affected by contamination and the internal leakage current is affected by the aging of the varistor in the arrester. The external and internal leakage currents are measured separately to determine their contribution to the arrester condition. In this study, the effect of salt contamination on the arrester was studied further. The level of contamination used consisted of low, medium and heavy. The obtained leakage current is analyzed using wavelet energy. The results of this study indicate that the wavelet energy of each leakage current is different and can be used as an indicator in further analysis. The conclusion obtained is that the external leakage current is affected by contamination and has a different energy with the internal leakage current due to aging of the varistor arrester components.
“Tec-House” WEBCAM-BASED REMOTE SENSING SYSTEM FOR HOME AND BUILDING SECURITY USING THE HAAR CASCADE METHOD Nyoman Santiyadnya; Kadek Reda Setiawan Suda
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1059.2023

Abstract

A home security system is something that every home owner must pay attention crimes such as burglary often put homeowners at risk. Therefore we need a tool that can bring together automatically remotely to protect the house. The system worked on in this article is a remote sensing system based on webcam. The method used in this sensing system uses the haar cascade classifier method. The results obtained from this remote sensing system are for the implementation of the system on homeowner data sets with 98% results, while for non-home owner image data sets with 96% results. From the results of using a webcam-based remote sensing system using the Haar Cascade Classifier method it can be implemented properly and the average error is 97%. The existence of this Tec-House tool can reduce the crime of theft in a house or building.
The Study of Plant Microbial Fuel Cell for Alternative Energy Source Melda Latif; Paskalina Aprila Tiy; Mumuh Muharam; Aulia; Amirul Luthfi
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1061.2023

Abstract

Plant Microbial Fuel Cell (P-MFC) is one of Microbial Fuel Cell type. It can produce electricity and source for plant living. By using the humus soil in the anode chamber, the electron can flow to the cathode chamber. The principle of Plant Microbial Fuel Cell is same with the battery. It flows the direct current. This research makes dual chamber of P-MFC prototype. The salt bridge is used as connection between anode chamber to cathode chamber. The humus soil comes from burning organic waste. Its color is black and contains a lot of microbes. The plant selected in this research was Water Spinach. The number of water spinach were 20 and 25 stems. P-MFC which has more Water Spinach will produce more voltage and current than the others. For 25 Water Spinach, P-MFC produced 762.4 mV no-load average voltage and 125.8 mV, 085 mA for load condition. The result was bigger caused by for more plants will be more microbes resulted in the humus soil.
Enabling Guardian Angels: Designing and Constructing a Wireless Nurse CallSystem with IMU-Based Fall Detection for Enhanced Patient Safety Ardiansyah Al Farouq; Berryl Cholif Arrohman Nurriduwan
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1084.2023

Abstract

Falling poses a significant health concern across all age groups, with particular severityamong the elderly. Hospitalized patients, in particular, are vulnerable to injuries andevendeath due to falls. While patient supervision is essential for fall prevention, constant proximitybetween patients and healthcare staff is not always feasible. To tackle this challenge, thisstudy aimed to develop a solution that enables immediate assistance for patients who aredistant from the nurse call button when a fall occurs.The study employed the IMU sensor,which combines an accelerometer and a gyroscope. This sensor served as a transmitter todetect gravity acceleration and magnitude when afall event takes place. Thedata obtainedfrom the IMU sensor were further processed using an Arduino Uno microcontroller. Thesensor was integrated into a belt worn around the waist of the participants, who performedvarious movements such as falling facing down, falling up, falling to the right, falling to theleft, standing then sitting, and sitting then standing.The experimental tests yielded compellingresults, with all trials achieving an accuracy rate of 81.7%. The accuracy was determined byanalyzing the confusion matrix, which enabled accurate calculations.The utilization of thisinnovative tool significantly reduces the risk of patients experiencing detrimental outcomesfollowing falls by promptly notifying medical personnel, even when they aredistant from thenurse call button. Moreover, the implementation of this tool enhances overall safety forhospitalized patients, especially those at a high risk of falling. Future research can explore theintegration of additional sensors or the development of more sophisticated algorithms tofurther enhancethe accuracy and efficacy of this tool.
An Embedded Convolutional Neural Network for Maze Classification and Navigation Gunawan Dewantoro; Dinar Rahmat Hadiyanto; Andreas Ardian Febrianto
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1091.2023

Abstract

Traditionally, the maze solving robots employ ultrasonic sensors to detect the maze walls around the robot. The robot is able to transverse along the maze omnidirectionally measured depth. However, this approach only perceives the presence of the objects without recognizing the type of these objects. Therefore, computer vision has become more popular for classification purpose in robot applications. In this study, a maze solving robot is equipped with a camera to recognize the types of obstacles in a maze. The types of obstacles are classified as: intersection, dead end, T junction, finish zone, start zone, straight path, T–junction, left turn, and right turn. Convolutional neural network, consisting of four convolution layers, three pooling layers, and three fully-connected layers, is employed to train the robot using a total of 24,000 images to recognize the obstacles. Jetson Nano development kit is used to implement the trained model and navigate the robot. The results show an average training accuracy of 82% with a training time of 30 minutes 15 seconds. As for the testing, the lowest accuracy is 90% for the T-junction with the computational time being 500 milliseconds for each frame. Therefore, the convolutional neural network is adequate to serve as classifier and navigate a maze solving robot.
Desain dan Implementasi Sistem Sensor untuk Lokalisasi pada Autonomous Robot IVANA di Area Gedung Moch. Iskandar Riansyah Riansyah; Ardiansyah Al Farouq; Putu Duta Hasta Putra
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1093.2023

Abstract

One of the popular studies recently is about social robots that have been implemented in several public areas such as offices. The  robot is an employee or worker assistant robot in the Telkom Surabaya Institute of Technology building to help carry out the work of delivering packages to the destination according to the tasks given. The problem that often occurs is an error in the robot's localization system causing the robot's movement to the target point to experience a position error. This research contributes to the comparative evaluation of 2 localization methods on mobile robots, namely the first is the use of a rotary encoder sensor and the second is the use of sensor fusion based on the extended Kalman filter implemented on the robot prototype. This study aims to develop a sensor system that is adapted to the design of the robot and the environment in which the robot is tested and to find out the comparison of the two methods. The use of extended Kalman filter-based sensor fusion can provide more accurate results in robot localization, especially when moving on complex paths. Sensor fusion enables the combination of several sensors such as rotary encoders and IMU (Inertial Measurement Unit) sensors to provide more complete and accurate information about the position and orientation of the robot. In this study, sensor fusion successfully reduced the localization error of the  robot to 0.63 m when moving straight and 0.29 m when moving on a complex path, compared to the use of a single sensor which resulted in a larger error of 0.89 m. Based on the study that has been conducted, it can be considered as a potential solution in the development of other social robots to improve the accuracy and performance of the robots when performing certain tasks in the future.
Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network Francis Boafo Effah; Daniel Kwegyir; Daniel Opoku; Peter Asigri; Emmanuel Asuming Frimpong
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 2: July 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n2.1094.2023

Abstract

The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.

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