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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 6,345 Documents
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Comparison of adaptive tuning fuzzy PID and Ziegler-Nichols PID for photovoltaic cooling system Badruzzaman, Yusnan; Vernandez, Aggie Brenda; Nursaputro, Septiantar Tebe; Larasati, Pangestuningtyas Diah
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1063-1074

Abstract

Renewable energy, particularly solar power, is widely recognized as a clean and sustainable resource, with rooftop photovoltaic (PV) systems playing a vital role in electricity generation. However, high temperatures can significantly reduce their efficiency, making effective cooling systems essential. This study proposes a proportional-integral-derivative (PID) based cooling control system for rooftop PV panels, integrating an adaptive Mamdani fuzzy logic controller to optimize PID parameters dynamically. The methodology includes system modeling, hardware and software implementation, and comparative testing between the Mamdani fuzzy-PID controller and the Ziegler-Nichols PID method. Experimental results show that both controllers effectively regulate PV panel temperature at 36 °C. The Ziegler-Nichols PID achieves faster settling time of 6.45 minutes with a steady-state error of 1.345%, whereas the Mamdani fuzzy-PID reduces the steady-state error to 0.93% but with a longer settling time of 9.15 minutes. These results indicate that the fuzzy-PID controller offers better accuracy and system stability, making it a promising solution for maintaining PV performance under varying environmental conditions. The key novelty of this study lies in its adaptive approach, where the Mamdany fuzzy-PID controller continuously adjust control parameters (Kp,Ki,Kd) in real time, resulting in more consistent and precise temperature regulation than conventional PID tuning methods.
Adaptive Lyapunov-based control for underactuated nonlinear system using deep neural network Haiyunnisa, Triya; Wibowo, Jony Winaryo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp717-728

Abstract

This paper proposes an adaptive Lyapunov-based control approach using deep neural networks (DNN) for underactuated nonlinear systems, with case studies on the Furuta pendulum and a wheeled path-following system. This approach combines simultaneous learning of the Lyapunov function V(x) to satisfy the positive-definite condition and the control law u(x) to satisfy negative definiteness of V ̇(x) thus ensuring the asymptotic stability of the system. The proposed model is validated using Python-based simulation. Results show that the proposed method significantly expands the region of attraction (RoA) compared to the linear quadratic regulator (LQR) method. In the Furuta pendulum, the RoA area in the [θ−θ˙] plane increased from 89.04% to 101.14% and in the [α−α˙] plane from 80.28% to 83.79%. Meanwhile, in the wheeled path-following system, the RoA within safety domain increased from 85.28% to 101.69%. Furthermore, robustness tests showed that the controller can maintain tracking performance on a sinusoidal path and reject short disturbances without excessive safety boundary violations. The resulting control signal remained smooth, non-oscillatory, and within the actuator saturation limits, ensuring safe and energy-efficient control. This approach offers a significant contribution by integrating Lyapunov stability theory, deep learning, and online adaptation, resulting a robust and practical for nonlinear underactuated systems.
FADTESE: A framework for automated deployment and effectiveness evaluation for big data tools Ho, Mony; Ang, Sokroeurn; Huy, Sopheaktra; Janarthanan, Midhunchakkaravarthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1051-1062

Abstract

Manual deployment of big data tools such as Hadoop, Sqoop, and Python is often slow, complex, and error prone because of extensive configuration steps, dependency conflicts, and inconsistent command-line execution. These challenges lead to unreliable installations and variations across systems. This study introduces framework for automated deployment and time, error, satisfaction evaluation (FADTESE), a unified framework that automates the installation of big data tools and evaluates its performance. The framework consists of two integrated components. The first is the automated deployment model, which validates environment readiness using the automation deployment readiness index (ADRI) and achieved a readiness value of 1.0 in this study. The second is the time, error, and satisfaction evaluation model, which quantifies improvements gained from automation and produced a score of 0.5941 through bootstrap resampling with ten thousand samples, indicating moderate effectiveness. The FADTESE script was technically validated across multiple Linux environments, including Ubuntu, Linux Mint, and AWS Ubuntu server systems. The performance evaluation involving eighty IT practitioners was conducted on Ubuntu systems to ensure consistent testing conditions and confirmed substantial gains in installation time, error reduction, and user satisfaction. Combining readiness and effectiveness yields a composite score of 0.5941 or 59.41%. FADTESE provides a reproducible and data driven method that standardizes big data deployment and improves reliability across local and cloud-based Linux environments.
Design and implementation of smart meter for optimizing and managing electrical energy in Morocco Bagayogo, Alhussein; Kabouri, Omar; El Makrini, Aboubakr; Azeroual, Mohamed; El Markhi, Hassane
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp663-674

Abstract

The growth of renewable energy sources necessitates the use of accurate and fast smart meter solutions. This article presents a low-cost internet of things (IoT) based smart meter adapted to the Moroccan electricity grid, supporting bidirectional energy measurement, DLMS/COSEM-based communication and control relays for automated energy flow management. The experimental validation shows a maximum measurement error of less than ±0.5%, satisfying the IEC-oriented accuracy requirements. The measured end-to-end latency is approximately 700 ms, including data acquisition (≈450 ms), signal processing (≈60 ms), data serialization (≈75 ms), network transmission (≈90 ms), and server-side processing (≈25 ms). These results demonstrate that the proposed system allows an almost real-time monitoring and control of imported and exported energy, which makes it suitable for the integration of residential renewable energies and the application of smart grids.
The developing a smart grid control system based on Konnex electrical equipment and internet of things technology Chuyen, Tran Duc; Tao, Mai Van; Co, Hoang Dinh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1020-1029

Abstract

In this research, the authors present a method for developing a smart grid control system based on Konnex (KNX) electrical equipment and internet of things (IoT) technology to control and monitoring electrical energy processes such as: voltage, current, frequency, and power for independent or grid-connected power systems in industry and civil use. The system includes: KNX electrical equipment (KNX-connectivity), IoT control board, Solar panels that produce electricity to supply the system, battery storage devices, converters and controllers, power consumption loads, and many measuring, switching and protection devices for the system. With computer control programming devices, software, and control algorithms, access is possible via website, computer, smartphone, iPhone, and iPad. The goal is to monitor electricity and automatically control the smart building system, which is being used for high-end apartment buildings (luxury housing estate); offices, hotels, and garden villas. The system was researched and tested at the practice workshop for industrial factories and enterprises, bringing high results. The system aims to save energy in the context of increasingly depleted fossil energy, both in Vietnam and around the world.
Energy management in smart grids using internet of things and price-based demand response with a hybrid EVO-PDACNN approach Raghvin, Manju Jayakumar; Bharamagoudra, Manjula R.; Dash, Ritesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp699-716

Abstract

Network control systems for energy distribution play an essential role when renewable energy sources (RES) expand and the smart grid (SG) infrastructure increases. A new approach to energy management (EM) in SGs combines energy valley optimizer (EVO) with pyramidal dilation attention convolutional neural network (PDACNN) to achieve its objectives. Through EVO-PDACNN, the system performs accurate energy consumption forecasting with PDACNN, while the EVO algorithm supports systematic scheduling capabilities. Due to its use, this method reduces the peak-to-average ratio (PAR) by 22% also the cost of electricity (COE) by 12%. This method performs better than the wind-driven bacterial forging algorithm (WBFA), genetic algorithm (GA), particle swarm optimization (PSO), modified elephant herd optimization algorithm (MEHOA), and ant colony optimization (ACO) because it has a new prediction ability and quick response. EVO-PDACNN establishes better performance through lower root mean square error (RMSE), together with mean squared error (MSE) and mean absolute error (MAE), which indicates enhanced cost efficiency and resource management capabilities for SGs. The method strengthens both energy forecasting and operational scheduling operations while effectively dealing with changes in supply and demand, which helps build resilient power systems.
Impact of electric vehicle demand forecasting on charging station infrastructure development Kronghinlad, Chartrin; Nilsiam, Yuenyong; Bhumpenpein, Nalinpat; Nuchitprasitchai, Siranee; Tangprasert, Sakchai
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp1010-1019

Abstract

This research addresses the challenge of forecasting electric vehicle (EV) demand in Thailand and its influence on the development of charging infrastructure. To improve predictive capability in environments with restricted historical data, we employed the grey model (GM) and genetic algorithms (GA) both independently and in combination. Using EV registration records from 2019 to 2023 obtained from the Automotive Information Center of Thailand, the optimized GM-GA hybrid model achieved markedly superior accuracy, with a mean absolute error (MAE) of 0.0016 and root mean squared error (RMSE) of 0.0031. These results demonstrate the model’s capacity to deliver precise forecasts despite data limitations, making it a valuable decision-making tool for charging station planning and deployment. The outcomes underscore the importance of forward-looking infrastructure strategies to support the growth of Thailand’s EV market and its transition toward sustainable mobility.
Cascaded speech enhancement system using deep learning method A, Kavitha; Chandra, Mahesh; Gupta, Vijay Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp806-817

Abstract

Here, a two-stage cascaded noise minimization from noisy speech is proposed for noise cancellation from highly corrupted speech signals. In the first stage, corrupted speech is passed through speech enhancement system based on wavelet domain adaptive filter using least mean square algorithm (WDAF-LMS) and performance is evaluated for noisy signal corrupted by babble noise, car noise and machine gun noises. Then this output is given to second stage for further improvement. This is fully connected deep neural network using stochastic gradient descent with momentum optimizer (FCDNN-SGDM) used to improve the quality of speech signal. The system is tested for highly corrupted noisy speech signals where noise signal power level is equal to or more than clean signal power. Input signal-to-noise ratio (SNR) level is taken as 0 dB and -5 to -13 dB. The proposed system improved the quality and intelligibility of speech at all SNR levels for all three noises.
Virtual decomposition with time delay control for underactuated robot manipulator Cheikh, Imane; Omali, Khaoula Oulidi; Faqihi, Hachmia; Benbrahim, Mohammed; Kabbaj, Mohammed Nabil
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp791-805

Abstract

The importance of controlling robot manipulators is undeniable. However, faults in these systems can significantly impact the workspace environment and personal safety. To address these challenges, a new adaptive approach has been proposed that easily adapts to a faulty actuator while precisely tracking its desired position. The virtual decomposition control (VDC) method decomposes the robot into subsystems, each with its sub-controller, while ensuring the overall system remains stable. Meanwhile, time delay estimation (TDE) is used to estimate unknown and uncertain parameters. A co-simulation was conducted to test the TD-VDC method on a 6 DoFs robot, which becomes underactuated during its running. The results of the root main square error of the proposed controller were lower of 6% than those of sliding mode control based on partial feedback linearization control (SMC-PFLC), which proves the proposal's effectiveness and efficiency.
Multi-objective optimization of distributed generation placement and sizing in active distribution networks considering harmonic distortion Ton, Trieu Ngoc; Le, Phong Minh; Le, Tan Minh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp598-607

Abstract

This paper presents a multi-objective optimization model for optimal placement and sizing of inverter-based distributed generation (DG) units in active distribution power systems (DPS), considering their impact on harmonic distortion. The model simultaneously minimizes total power losses and total harmonic distortion (THD), ensuring compliance with IEEE 519 standards. To solve this problem, the reptile search algorithm (RUN) is applied and compared with three metaheuristic algorithms: multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimizer (MOGWO), and multi-objective whale optimization algorithm (MOWOA). Simulation results on IEEE 33-bus and 69-bus systems show that reptile search algorithm (RUN) reduces power losses by up to 6.1% and THD by 21.7% compared to MOPSO. Moreover, the results confirm a strong correlation between DG output power and harmonic amplitudes, highlighting the importance of power quality aware DG planning.

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