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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 783 Documents
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A Comprehensive Study of Capacitive Loaded Resonant Converter Topologies for Charging Applications Geethanjali Pandeswara; Naresh Pasula
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3521

Abstract

Resonant converters (RCs) are perceiving global interests of the research community for its eminent contribution in design of many industrial and commercial applications. Rich literature and well-established technology is available to define the role of RCs in such applications where the load is predominantly passive and resistive. However in applications like charging, the nature of load is often interpreted as capacitive and the knowledge on how a RC reciprocates to such variable, non linear load is limited. Motivated by this, the paper investigates about 25 capacitive loaded resonant structures and each of them is thoroughly analyzed to evaluate various key parameters like the output current, peak input current,  and current gain. A comparative study is done to categorize and organize these topologies in regard to each of the said parameters.  This provides a quick overview of various resonant converter topologies and helps designers to choose a structure that may fit their application. To this base knowledge, the study is further narrowed down to find suitable topology for charging application and accordingly proposed a novel fourth-order RC topology called LA7. A hardware prototype was built to compare and validate the simulated and measured performances.
Low Voltage Capability of Generator for Frequency Regulation of Wind Energy System Smrutiranjan Nayak; Sanjeeb kumar Kar; Subhransu Sekhar Dash
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3426

Abstract

For the extraction of wind energy through a doubly fed induction generator (DFIG), low voltage is major particular essential controlled by the transmission structure executive. Under a structure issue condition, DFIG should remain with respect to the lattice for a particular least period and deal open power support on a case-by-case basis by the Transmission framework administrator. A pleasant control plot involving gear course of action through a superconducting resistance type issue current limiter (R-SFCL) and programming plan based on the rotor reference current direction control system (RRCOCS) with transient voltage control (TVC), is proposed in this paper to address the Low voltage essential. The results got by the proposed procedure are differentiated and RRCOCS and RRCOCS-TVC.
Classification of EEG Signal by Using Optimized Quantum Neural Network Dalael Saad Abdul-Zahra; Ali Talib Jawad; Hassan Muwafaq Gheni; Ali Najim Abdullah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3486

Abstract

In recent years the algorithms of machine learning was used for brain signals identifing which is a useful technique for diagnosing diseases like Alzheimer's and epilepsy. In this paper, the Electroencephalogram (EEG) signals are classified using an optimized Quantum neural network (QNN) after normalizing these signals, wavelet transform (WT) and the independent component analysis (ICA), were utilized for feature extraction.  These algorithms used to reduces the dimensions of the data, which is an input to the optimized QNN for the purpose of performing the classification process after the feature extraction process. This research uses an optimized QNN, a form of feedforward neural network (FFNN), to recognize (EEG) signals. The Particle swarm optimization (PSO) algorithm was used to optimize the quantum neural network, which improved the training process of the system's performance. The optimized (QNN) provided us with somewhat faster and more realistic results. According to simulation results, the total classification for (ICA) is 82.4 percent, while the total classification for (WT) is 78.43 percent; from these results, using the ICA for feature extraction is better than using WT.
Hybrid Deep Neural Network for Facial Expressions Recognition Wijdan Rashid Abdulhussien; Nidhal K. El Abbadi; Abdul M. Gaber
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 4: December 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i4.3425

Abstract

Facial expressions are critical indicators of human emotions where recognizing facial expressions has captured the attention of many academics, and recognition of expressions in natural situations remains a challenge due to differences in head position, occlusion, and illumination. Several studies have focused on recognizing emotions from frontal images only, while in this paper wild images from the FER2013 dataset have been used to make a more generalizing model with the existence of its challenges, it is among the most difficult datasets that only got 65.5 % accuracy human-level. This paper proposed a model for recognizing facial expressions using pre-trained deep convolutional neural networks and the technique of transfer learning. this hybrid model used a combination of two pre-trained deep convolutional neural networks, training the model in multiple cases for more efficiency to categorize the facial expressions into seven classes. The results show that the best accuracy of the suggested models is 74.39%  for the hybrid model, and 73.33% for Fine-tuned the single EfficientNetB0 model, while the highest accuracy for previous methods was 73.28%. Thus, the hybrid and single models outperform other state of art classification methods without using any additional, the hybrid and single models ranked in the first and second position among these methods. Also, The hybrid model has even outperformed the second-highest in accuracy method which used extra data. The incorrectly labeled images in the dataset unfairly reduce accuracy but our best model recognized their actual classes correctly.
Transformer-Less Cascaded Voltage Source Converter Based STATCOM Rashmi Sharma; Vijay H Makwana
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3483

Abstract

In this work, a transformer-less voltage source converter (VSC) based STATCOM is proposed with a combination of cascaded conventional three-phase voltage source inverters. This modular structure provides multilevel operation with reduced switch count and independent DC-link capacitors. The actual contribution of this paper is the transformer-less configuration of a conventional cascaded voltage source converter which provides reduced cost and volume as compared to other transformer-less converter configurations. The system provides reactive power compensation with better power quality when connected to the nonlinear power electronics load also. A simple control system is provided for balancing the Dc link capacitor voltage and reactive power compensation. The validation of the proposed model is analyzed with simulation using MATLAB/SIMULINK software and the results are obtained with different linear and nonlinear load configurations.
Deep Learning Model for Sentiment Analysis on Short Informal Texts Sam Farisa Chaerul Haviana; Bagus Satrio Waluyo Poetro
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3181

Abstract

This paper proposes a classification model to classify short informal texts. Those short informal texts were texts that were noisy, typos, irregular, and could consist of a very small number of words or even only a single word. The proposed model was trained using a dataset collected from student comments from an application called Evaluasi Dosen Oleh Mahasiswa (EDOM). This application assesses the lecturers using questionnaires filled out by students. It also records the student's comments but is not part of the evaluation calculation, therefore this work makes the data possible to be part of the assessment through sentiment analysis. This work focuses on building suitable preprocessing algorithm and building a simple deep learning network. The preprocessing algorithm was based on multiple word n-gram and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, and the network was built with a relatively shallow network. To evaluate the model in real usage, an application was built. The results were very convincing, reaching 0.979 in accuracy and 0.63 in F1-Score. Nonetheless, the imbalanced dataset was the only factor that needed to be investigated further for better overall performance.
Optimized Weight Point ADF using SOS Algorithm Gayatri Mohapatra; Manoj Kumar Debnath
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3368

Abstract

Active dc filter (ADF) has become the most viable alternatives for the compensation of the harmonics in the power system analysis. These filters are capable enough to minimize the total harmonic distortion (THD) and provide compensation towards the power quality issues appearing in the transmission system. A simulated model of a HVDC system is designed in MATLAB and the disturbance is injected in the form of load change and the controller efficacy is checked. This paper basically deals with the operational characteristics of the active filter for specific voltage rating irrespective of load and used to reduce harmonics present in the output voltage of the HVDC converter when cascaded with the inverter. The gains of the ADF are optimized with Symbiotic Organism Search Optimization (SOS) with THD as a constraint.
Voice controlled Camera Assisted Pick and Place Robot Using Raspberry Pi Muneera Altayeb; Amani Al-Ghraibah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3636

Abstract

Modern monitoring systems or manufacturing machines have a major drawback as they depend on human operators who can easily get distracted or make mistakes, so a system is needed that can constantly monitor the desired area and make decisions while identifying a pre-trained object. Tracking objects with a camera is critical in any automated monitoring and tracking system. The main goal of this paper is to design and implement a robot that can distinguish objects based on their features, such as color and size, and based on artificial intelligence and image processing algorithms.  The robot will analyze the video stream to detect the colored object, and specify its location inside the video frame. Using the detected position, the raspberry pi will decide the rotation direction whether it is to the right to the left, or forward until it reaches the object, grabs it and puts it in the robot's pocket.  The main controlling unit of the system is the Raspberry Pi, the robot is equipped with a Wi-Fi modem to communicate with the mobile application, which is used to control the robot in two modes: manual mode, where the user can point the robot in any direction either by pressing function button or through voice commands. The second mode is the Automatic mode, where the user can ask the robot to detect an object according to a set of characteristics and grab it without any human intervention and based on a novel digital image processing object-tracking algorithm, the accuracy in voice command mode has reached 95%.   
An algorithm using YOLOv4 and DeepSORT for tracking vehicle speed on highway Phat Huu Nguyen; Manh Bui Duy
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3448

Abstract

Currently, expressways are increasingly developed and expanded. Several highways of Vietnam allow vehicles to travel up to 120 kilometers per hour helping to transport goods quickly and bring a lot of socio-economic benefits. Vehicle monitoring plays an important role in reducing traffic accidents helping to handle violations.The paper proposes a model to identify and monitor car speed on highways. The proposal method uses YOLOv4 combining with DeepSORT for vehicle identification and tracking. We then calculate the speed of car based on video recording and sending back from highway. The execution context is highway where vehicles move very fast. The results show that system meets set requirements with over 90% accuracy and execution times for up to 70 frames per second that is suitable for real systems.
The Problems of Renewable Power Plant Construction Affecting the Energy Security of Thailand Songkrit Trerutpicharn; Waranon Kongsong; Kijbodi Kongbenjapuch; Seree Tuprakay; Boonchuay Srithammasak; Sumate Roykulcharoen
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3247

Abstract

The objectives of this research were to study the process of submitting an application for every license that affects the success of renewable power plant construction and the energy security of Thailand in accordance with the Energy Industry Act 2007, the engineering factors used in the selection of all types of renewable power plant construction, and the Key Performance Indicators of all types of renewable power plant construction. The data analysis was divided into two sections. For the first section, the quantitative data was collected from the questionnaire conducted by the purposive sampling that included those related to renewable power plant projects, which asked the questions about the rules and regulations and power purchase agreement under the Energy Industry Act 2007. As for the factors influencing the project success, the private sector, combined in the sample group, included the design engineers, consulting and control engineers, and contractors. The 400 engineers were randomly selected from the registration of the Council of Engineers, including senior professional engineers, professional engineers, associate engineers, and adjunct engineers. In the second section, the qualitative data came from the in-depth interviews with five specialists and experts in the renewable power plant industry and in legal knowledge about the rules and regulations and power purchase agreements according to the Energy Industry Act 2007, who work in the Metropolitan Electricity Authority, a renewable power plant construction company, a renewable energy consulting company, in the field of renewable power plant investment, and as a renewable power plant specialist (Office of the Energy Regulatory Commission). The data was analyzed by using the following statistics: percentage, frequency, mean, standard deviation, and the Enter method of multiple regression. According to the results, the overall success of using the engineering factors in selecting a renewable power plant establishment has the mean at a high level. With regard to the types of power plants, the solar power plant is ranked at the top, followed by the second, the biomass power plant; then, the waste-to-energy power plant, the biogas power plant, and the wind power plant, respectively. The type of power plant with a moderately high mean is the hydroelectric power plant. The findings show the engineering factors related to the success of all types of renewable power plants. Moreover, regarding the problem of energy policy, deciding which type of energy to use is highly complicated because there are many dimensional reasons and no form of energy is the best or the worst option. However, it is not too difficult for specialists to make a decision.

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