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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 111 Documents
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Search results for , issue "Vol 14, No 2: April 2024" : 111 Documents clear
Integrated application of synergetic approach for enhancing intelligent steam generator control systems Isamiddin, Siddikov; Dilnoza, Umurzakova
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1508-1518

Abstract

This article focuses on the integrated application of the synergetic approach to enhance the quality of intelligent steam generator control systems.By combining various techniques such as model-based control, adaptive control, and artificial intelligence, an efficient and flexible control system can be developed. Model-based control utilizes mathematical models of steam generators to formulate control algorithms and predict system behavior. Adaptive control enables the system to adapt to changing conditions by adjusting control parameters based on real-time measurements. Artificial intelligence techniques, including neural networks and genetic algorithms, facilitate learning, optimization, and data-driven decision-making processes. The objectives of this research are to investigate the benefits of the synergetic approach in steam generator control, including improved steam generation efficiency, optimized energy consumption, enhanced system stability and reliability, and adaptability to varying operating conditions and disturbances. The findings and conclusions of this study are expected to provide valuable insights for engineers, researchers, and professionals involved in the design and implementation of intelligent steam generator control systems. By integrating the synergetic approach, substantial enhancements in control quality can be achieved, leading to optimal operation and maximum efficiency of power plants.
Maximum power point tracking and space vector modulation control of quasi-z-source inverter for grid-connected photovoltaic systems Jaoide, Essaid; El Aamri, Faicel; Outazkrit, Mbarek; Radouane, Abdelhadi; Mouhsen, Azeddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1424-1436

Abstract

The quasi-Z-source inverter (qZSI) become one of the most promising power electronic converters for photovoltaic (PV) applications, due to its capability to perform a buck-boost conversion of the input voltage in a single stage. The control strategy based maximum power point tracking (MPPT) and proportional integral (PI) controller are well known in grid-connected with traditional configuration but not in qZSI. This paper presents a control strategy for qZSI grid-connected based on the MPPT algorithm and the linear control by PI controllers. This is complemented by the capability to efficiently transfer the harvested power to the grid, ensuring a unity power factor. The proposed control strategy effectively separates the control mechanisms for the direct current (DC) and alternating current (AC) sides by utilizing the two control variables, the shoot-through duty ratio and the modulation index. An adapted space vector modulation technique is then utilized to generate the switching pulse width modulation (PWM) signals, using these two control variables as inputs. The proposed approach was tested and validated under MATLAB/Simulink and PLECS software.
An overlapping conscious relief-based feature subset selection method Mim, Nishat Tasnim; Kadir, Md. Eusha; Akhter, Suravi; Khan, Muhammad Asif Hossain
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2068-2075

Abstract

Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based methods are gaining popularity because of their eligibility in capturing feature interdependency and relevancy with the endpoints. However, most of the distance-based methods only rank the features and ignore the class overlapping issues. Features with class overlapping data work as an obstacle during classification. Therefore, the objective of this research work is to propose a method named overlapping conscious MultiSURF (OMsurf) to handle data overlapping and select a subset of informative features discarding the noisy ones. Experimental results over 20 benchmark dataset demonstrates the superiority of OMsurf over six existing state-of-the-art methods
A deep dive into enhancing frequency stability in integrated photovoltaic power grids Abderrazak Tadjeddine, Ali; Arbaoui, Iliace; Hichem, Hamiani; Nour, Mohamed; Alami, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1203-1214

Abstract

Voltage control strategies (VCS) and frequency stability analysis (FSA) are essential for power system reliability, particularly during high-load periods. Stable voltage and frequency levels prevent malfunction, power quality deterioration, and supply interruptions. Grid operators must skillfully manage VCS and FSA control to ensure system stability. Nonlinear loads, especially under transient conditions, significantly affect voltage stability (VS), introducing harmonics, waveform distortion, and stability complexities. Accurate modeling of these nonlinear loads is vital when traditional static load models fall short. Frequency fluctuations from power generation-demand imbalances require vigilant monitoring and regulation. Effective frequency control mechanisms are indispensable for preserving desired frequencies. Using a Western Algeria case study, this paper underscores FSA's significance in integrating photovoltaic (PV) systems into power grids. It addresses challenges from frequency fluctuations due to dynamic ZIP load profiles, emphasizing the importance of FSA for reliable grid operation. The study offers insights and practical approaches to enhance VS, FSA control, and energy management (EM), improving grid reliability and ensuring uninterrupted power supply. We must look into FSA's benefits in integrating PV systems to improve performance and lower grid interruptions. This includes looking into its control mechanisms and feedback systems.
Control energy management system for photovoltaic with bidirectional converter using deep neural network Widjonarko, Widjonarko; Utomo, Wahyu Mulyo; Omar, Saodah; Baskara, Fatah Ridha; Rosyadi, Marwan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1437-1447

Abstract

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS.
Predicting and detecting fires on multispectral images using machine learning methods Aitimov, Murat; Kaldarova, Mira; Kassymova, Akmaral; Makulov, Kaiyrbek; Muratkhan, Raikhan; Nurakynov, Serik; Sydyk, Nurmakhambet; Bapiyev, Ideyat
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1842-1850

Abstract

In today's world, fire forecasting and early detection play a critical role in preventing disasters and minimizing damage to the environment and human settlements. The main goal of the study is the development and testing of machine learning algorithms for automated detection of the initial stages of fires based on the analysis of multispectral images. Within the framework of this study, the capabilities of three popular machine learning methods: extreme gradient boosting, logistic regression, and vanilla convolutional neural network (vanilla CNN), are considered in the task of processing and interpreting multispectral images to predict and detect fires. XGBoost, as a gradient-boosted decision tree algorithm, provides high processing speed and accuracy, logistic regression stands out for its simplicity and interpretability, while vanilla CNN uses the power of deep learning to analyze spatial and spectral data. The results of the study show that the integration of these methods into monitoring systems can significantly improve the efficiency of early fire detection, as well as help in predicting potential fires.
Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms Dani, Yasi; Artanta Ginting, Maria
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1959-1968

Abstract

In this study, we apply two classification algorithm methods, namely the Gaussian naïve Bayes (GNB) and the decision tree (DT) classifiers. The Gaussian naïve Bayes classifier is a probability-based classification model that predicts future probabilities based on past experiences. Whereas the decision tree classifier is based on a decision tree, a series of tests that are performed adaptively where the previous test affects the next test. Both of these methods are simulated on the Iris dataset where the dataset consists of three types of Iris: setosa, virginica, and versicolor. The data is divided into two parts, namely training and testing data, in which there are several features as information on flower characteristics. Furthermore, to evaluate the performance of the algorithms on both methods and determine the best algorithm for the dataset, we evaluate it using several metrics on the training and testing data for each method. Some of these metrics are recall, precision, F1-score, and accuracy where the higher the value, the better the algorithm's performance. The results show that the performance of the decision tree classifier algorithm is the most outperformed on the Iris dataset.
Performance enhancement of machine learning algorithm for breast cancer diagnosis using hyperparameter optimization Hridoy, Rashidul Hasan; Arni, Arindra Dey; Ghosh, Shomitro Kumar; Chakraborty, Narayan Ranjan; Mahmud, Imran
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2181-2190

Abstract

Breast cancer is the most fatal women’s cancer, and accurate diagnosis of this disease in the initial phase is crucial to abate death rates worldwide. The demand for computer-aided disease diagnosis technologies in healthcare is growing significantly to assist physicians in ensuring the effectual treatment of critical diseases. The vital purpose of this study is to analyze and evaluate the classification efficiency of several machine learning algorithms with hyperparameter optimization techniques using grid search and random search to reveal an efficient breast cancer diagnosis approach. Choosing the optimal combination of hyperparameters using hyperparameter optimization for machine learning models has a straight influence on the performance of models. According to the findings of several evaluation studies, the k-nearest neighbor is addressed in this study for effective diagnosis of breast cancer, which got a 100.00% recall value with hyperparameters found utilizing grid search. k-nearest neighbor, logistic regression, and multilayer perceptron obtained 99.42% accuracy after utilizing hyperparameter optimization. All machine learning models showed higher efficiency in breast cancer diagnosis with grid search-based hyperparameter optimization except for XGBoost. Therefore, the evaluation outcomes strongly validate the effectiveness and reliability of the proposed technique for breast cancer diagnosis.
Design and implementation of a low-cost circuit for medium-speed flash analog to digital conversions Hussain Hassan, Nashaat M.; Esmaeel Salama, Mohamed Adel; Hussein, Aziza I.; Mabrook, Mohamed Mourad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2361-2368

Abstract

Despite the considerable advancements in analog-to-digital conversion (ADC) circuits, many papers neglect several crucial considerations: Firstly, it does not ensure that ADCs work well in the software or hardware. Secondly, it is not certain that ADCs have a wide range of amplitude responses for the input voltages to be convenient in many applications, especially in electronics, communications, computer vision, CubeSat circuits, and subsystems. Finally, many of these ADCs need to look at the suitability of the proposed circuit to the most extensive range of frequencies. In this paper, a design of a low-cost circuit is proposed for medium-speed flash ADCs. The proposed circuit is simulated based on a set of electronic components with specific values to achieve high stability operation for a wide range of frequencies and voltages, whether in software or hardware. This circuit is practically implemented and experimentally tested. The proposed design aims to achieve high efficiency in the sampling process over a range of amplitudes from 10 mV to 10 V. The proposed circuit operates at a bandwidth of frequencies from 0 Hz to greater than 10 kHz in the simulation and hardware implementation.
Field oriented control driver development based on BTS7960 for physiotherapy robot implementation Halisyah, Andi Nur; Adiputra, Dimas; Al Farouq, Ardiansyah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1486-1495

Abstract

In conjunction with sustainable development goal 3 (SDG 3), it is important to develop a national electrical component for physiotherapy robot development. This study aimed to develop an open-loop field-oriented control (FOC) driver utilizing BTS7960. The driver utilized three BTS7960s that produce sinewave with variable angular frequency (ω). The research then compared the open-loop FOC driver with electronics speed controller (ESC) performance to drive a brushless DC (BLDC) motor with an initial rotation per minute (RPM) of 400, 500, and 600. The main observation was RPM reduction when the BLDC motor was subjected to loads of 20, 35, 50, 65, and 80 gr. The result showed that the open-loop FOC driver performed better, especially on an 80 gr load. For an initial RPM of 600, the RPM reduced to 100 when controlled with an open-loop FOC driver, but lesser when controlled using ESC. The open-loop FOC driver produces higher torque on the BLDC motor so it could rotate with less reduction compared to ESC, which is evident. The open-loop FOC driver can be easily developed using BTS7960 with a settling time of 4 seconds. However future studies should still consider close-loop FOC drivers to achieve higher torque performance and faster transient response for physiotherapy robot applications.

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