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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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Impact of optical current transformer on protection scheme of hybrid transmission line Zainal Arifin; Muhammad Zulham; Eko Prasetyo
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp1-11

Abstract

Continuity of power transmission is important to ensure the reliability of the electricity supply. As most system faults are temporary, the auto reclose (AR) scheme has been used extensively to minimise the outage duration, prevent widespread outages, thus increase system stability. Meanwhile, the hybrid transmission line (HTL) combining overhead line (OHL) and high voltage cable has been introduced to provide an inexpensive solution for an urban power grid. Protecting HTL with a conventional protection system would forbid the operation of the AR scheme due to difficulty to ensure whether the fault occurred on the OHL or cable section. Therefore, the circulating current protection (CCP) scheme is used in the cable section to ensure the fault location and block the AR scheme. The technology of an optical current transformer (OCT) as one of the non-conventional instrument transformers (NCIT) has emerged to provide a solution to drawbacks on the conventional current transformer (CCT). Consequently, this paper investigated the impact of using OCT over the CCT for CCP of the HTL. The result shows that OCT could be used for CCP on much longer cable sections thus increase its reliability as the AR scheme can be used on longer or multiple cable section.
Study of designing regulator for temperature electrical resistance furnace using Kalman stochastic reconstructor Benyekhlef Kada; Abdelkader El Kebir; Mohammed Berka; Hafida Belhadj; Djamel Eddine Chaouch
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp134-143

Abstract

Electric resistance furnaces are the most popular and widely used industrial electro thermal equipment which continues to be the subject of many improvements. The aim of this paper is to control the temperature of electrical furnace for noisy thermocouple sensors. It can be assessed by observing some variables, which are very difficult to observe. Due to limitations, mainly the location of thermal sensors and their noises. In this case, the temperature measurement is trained with centered Gaussian white noise. The problem of accurate temperatures estimation for such sensors is solved using Kalman filter, which is an optimized estimator that provides a computationally efficient way to estimate system state. Thus, variables that are not directly measurable can be reconstructed from the algorithm. Kalman stochastic reconstructor (KSR). We cannot use with fixed parameters to control the temperature. For this reason, this paper comes up with a KSR approach based pole placement (PL) hybrid controller to realize an algorithm for the temperature control electrical furnace. Results based on Matlab simulation show that the improved algorithm has well produced an optimal estimate of the temperature. Evolving over time from noisy measurements. Hybrid algorithm KSR approach based PL give good performance compared to PL controllers.
Content-based image retrieval for fabric images: A survey Silvester Tena; Rudy Hartanto; Igi Ardiyanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1861-1872

Abstract

In recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. Generally, CBIR includes two main components, namely feature extraction and similarity measurement. Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into traditional methods and convolutional neural networks (CNN). Traditional descriptors deal with low-level features, while CNN addresses the high-level, called semantic features. Traditional descriptors have the advantage of shorter computation time and reduced system requirements. Meanwhile, CNN descriptors, which handle high-level features tailored to human perceptions, deal with large amounts of data and require a great deal of computation time. In general, the features of a CNN's fully connected layers are used for matching query and database images. In several studies, the extracted features of the CNN's convolutional layer were used for image retrieval. At the end of the CNN layer, hash codes are added to reduce  search time.
Optimal integration of photovoltaic distributed generation in electrical distribution network using hybrid modified PSO algorithms Nasreddine Belbachir; Mohamed Zellagui; Adel Lasmari; Claude Ziad El-Bayeh; Benaissa Bekkouche
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp50-60

Abstract

The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies making them more essential due to their advantages as reducing power losses and ameliorating system’s voltage profiles and reliability. This article addresses the optimal location and size of multiple distributed generations (DGs) based on solar photovoltaic panels (PV) connected to electrical distribution network (EDN) using the various proposed hybrid particle swarm optimization (PSO) algorithms based on chaotic maps and adaptive acceleration coefficients. These algorithms are implemented to optimally allocate the DGs based PV (PV-DG) into EDN by minimizing the multi-objective function (MOF), which is represented as the sum of three technical parameters of the total active power loss (TAPL), total voltage deviation (TVD), and total operation time (TOT) of overcurrent relays (OCRs). The effectiveness of the proposed PSO algorithms were validated on both standards IEEE 33-bus, and 69-bus. The optimal integrating of PV-DGs into EDNs reduced the TAPL percentage by 56.94 % for the IEEE 33-bus and by 61.17 % for the IEEE 69-bus test system, enhanced the voltage profiles while minimizing the TVD by 37.35 % and by 32.27 % for two EDNs, respectively.
Design of fractional order PID controller for AVR system using whale optimization algorithm Layla H. Abood; Bashra Kadhim Oleiwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1410-1418

Abstract

In this paper a robust fractional order PID (FOPID) controller is proposed to control the automatic voltage regulator (AVR) system, the tuning of the controller gains are done using whale optimization algorithm (WOA) and integral time absolute error (ITAE) cost function is adopted to achieve an efficient performance. The transient analysis was done and compared with conventional PID in terms of overshoot, settling time, rise time, and peak time to explain the superiority of the proposed controller. Finally, a robustness analysis is done by adding external disturbances to the system and changing the system parameters by ±20% from its original value, the controller overcomes the disturbances signals with less than 0.25 s and faces the changes of the system values and returning the response within (0.7-1) sec and led the system to the desired response efficiently. The numerical simulations showed that the smart WOA offers satisfying results and faster response reflected clearly on the robust and stable performance of the proposed controller in improving the transient analysis of AVR system response.
Development of ontology-based model to support learning process in LMS Hussein Ali Ahmed Ghanim; László Kovács
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp507-518

Abstract

E-Learning is an important support mechanism for educational systems to increase the efficiency of the education process including students and teachers. The current e-learning systems typically lack the level of metacognitive awareness, adaptive tutoring, and time management skills and have not always met the expectations of the learners as required. In this study, we introduce a novel ontological model for the learning process in the e-learning domain. In the framework, we have built a domain ontology that represents knowledge of the learning, the outcome domain ontology covers the whole learning process. We focused on the learning process ontology model conceptualizing knowledge constructions, such as learning courses, and we present the created course and learning process ontology in detail. In this work, we considered three layers of learning process. The top layer defines a general framework of learning process, conceptual model layer, defines the framework of the actual process of the learning process and course ontology model contains the knowledge unit of the learning process. The prototype ontology is constructed in protégé and managed by Java web ontology language-application programming interface (OWL-API). As a result, our model can solve the problems of current e-tutor systems. Also, it can be used for different domain in e-tutor systems. It can reach the characteristics of standardization, reusability, flexibility, and open knowledge. By applying this model, we can avoid applying isolated databases. The constructed ontology can be used in the future to control adaptive intelligent e-tutor frameworks.
A small footprint printed cross-dipole antenna with wide impedance bandwidth and circular polarization Mustafa Hasan; Nasr Alkhafaji; Hussam AlAnsary; Azhar R. Mohsin
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp347-356

Abstract

Wideband circularly polarized (CP) cross-dipole antennas with flat, cavity and artificial magnetic conductor (AMC) reflectors are proposed. Each proposed antenna consists of a pair of driven dipoles, a pair of vacant-quarter printed rings, and a 50Ω coaxial probe. The boomerang shape has been adopted in the crossed-dipole. This shape makes the design more compact, so it can be a good candidate in the antenna array because of reducing the mutual coupling. All numerical simulation works have been done using the ANSYS electromagnetic (EM) software based on the finite element method (FEM) algorithm. The presented crossed-dipole with a cavity has the best performance compared to ones with conventional flat and AMC grounds. However, the crossed-dipole with the AMC ground is a low-profile structure. Thus, the paper investigates and discusses the results of the proposed strctures thoroughly. The obtained impedance bandwidth (IBW) is 42% (5.1-7.85 GHz) and the axial-ratio bandwidth (ARBW) is 7.72% (5.86-6.32 GHz) for the crossed-dipole with the conventional flat ground (i.e., reflector). Furthermore, the IBW and ARBW for the antenna with the cavity reflector are 50.37% (5.08-8.5 GHz) and 26.4% (5.72-7.46 GHz), respectively. The antenna with the AMC ground has the characterstics of the IBW and ARBW as 38.16% (5.36-7.89 GHz) and 15.16% (5.79-6.74 GHz), respectively. All structures are designed to operate for the C-band and wireless local area networks (WLAN) applications.
Fraudulent credit card transaction detection using soft computing techniques Aishwarya Priyadarshini; Sanhita Mishra; Debani Prasad Mishra; Surender Reddy Salkuti; Ramakanta Mohanty
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1634-1642

Abstract

Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.
A hybrid model based on convolutional neural networks and fuzzy kernel K-medoids for lung cancer detection Glori Stephani Saragih; Zuherman Rustam; Jane Eva Aurelia
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp126-133

Abstract

Lung cancer is the deadliest cancer worldwide. Correct diagnosis of lung cancer is one of the main tasks that is challenging tasks, so the patient can be treated as soon as possible. In this research, we proposed a hybrid model based on convolutional neural networks (CNN) and fuzzy kernel k-medoids (FKKM) for lung cancer detection, where the magnetic resonance imaging (MRI) images are transmitted to CNN, and then the output is used as new input for FKKM. The dataset used in this research consist of MRI images taken from someone who had lung cancer with the treatment of anti programmed cell death-1 (anti-PD1) immunotherapy in 2016 that obtained from the cancer imaging archive. The proposed method obtained accuracy, sensitivity, precision, specificity, and F1-score 100% by using radial basis function (RBF) kernel with sigma of {10­­-8, 10­­-4, 10­­-3, 5x10­­-2, 10­­-1, 1, 10­­4} in 20-fold cross-validation. The computational time is only taking less than 10 seconds to forward dataset to CNN and 3.85 ± 0.6 seconds in FKKM model. So, the proposed method is more efficient in time and has a high performance for detecting lung cancer from MRI images.
Combined fuzzy PID regulator for frequency regulation of smart grid and conventional power systems Smrutiranjan Nayak; Sanjeeb Kumar Kar; Subhransu Sekhar Dash
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp12-21

Abstract

In continually increasing area and structure of modern power system having burden demand uncertainties, the use of knowledgeable and vigorous frequency power strategy is essential for the satisfactory functioning of the Power system. A combined fuzzy proportional-integral-derivative (CFPID) controller is suggested for frequency supervision of the power system. To optimize the controller parameters, a review of sine and cosine work adjusted improved whale optimization algorithm (SCiWOA) has been utilized. The next practical application of power-system frequency control is performed by designing a CFPID controller using the proposed SCiWOA technique for a smart grid system having inexhaustible sources like sun oriented, wind, photovoltaic and capacity gadgets like a battery, flywheel just as module electric vehicles. The first advantages of the SCiWOA tuned CFPID controller over hybrid-particle-swarm-optimization and pattern-search (hPSO-PS) adjusted fuzzy proportional-integral (FPI) controller, hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) adjusted proportional-integral (PI) controller, genetic algorithm (GA) tuned proportional and integral (PI) controller, BFOA adjusted PI controller, jaya algoritm (JA) tuned PID with derivative filter (PIDN) controller and teaching learning based optimization (TLBO) tuned proportional-integral-derivative (PID) controller are demonstrated for the two-area non-reheat thermal power system. The second advantages of the SCiWOA tuned CFPID controller over artificial-bee-colony (ABC) tuned PID controller, SOSA tuned PID controller and Firefly algorithm (FA) tuned PID controller are demonstrated for two-area reheat thermal power system. It is seen that SCiWOA based CFPID controller is more effective in controlling the recurrence comparative with PID regulator.

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