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Design and performance evaluation of a soft-switched partial-power LLC converter for PV grid integration

10.11591/ijpeds.v17.i2.pp1130-1141
Sebin Davis Kurichiparambil , Varghese Jegathesan
This paper presents a soft-switched partial-power LLC converter integrated within a two-stage photovoltaic (PV) and grid-connected system. The proposed architecture combines the advantages of resonant operation and partial power processing to enhance conversion efficiency and reduce switching losses. Maximum power point tracking (MPPT) is achieved through frequency modulation of the LLC converter, while grid synchronization is maintained using a three-phase voltage-oriented control (VOC) inverter. Simulation results in MATLAB/Simulink demonstrate stable zero voltage switching (ZVS) and zero current switching (ZCS) across a wide irradiance range (400-1000 W/m²), enabling the system to achieve peak efficiencies above 98%, which is superior to typical transformerless and interleaved converter topologies reported in recent literature. The proposed soft-switched PPC-LLC architecture offers an efficient and scalable solution for next-generation PV grid interfaces by combining reduced processed power, robust resonant operation, and high-quality grid integration.
Volume: 17
Issue: 2
Page: 1130-1141
Publish at: 2026-06-01

High-efficiency two-stage LED driver with integrated PFC and LLC resonant converter for public lighting

10.11591/ijpeds.v17.i2.pp1084-1095
Marref Mohammed Amine , Seyf Eddine Bechekir , Mokhtaria Jbilou , Mostefa Brahami , Abdelber Bendaoud
This paper presents the design, implementation, and experimental validation of a 150 W two-stage light-emitting diode (LED) driver integrating a power factor correction (PFC) stage and a half-bridge LLC resonant converter for public lighting applications. The problem addressed is the insufficient power quality, limited efficiency, and poor harmonic performance of conventional LED drivers used in street lighting. The proposed method combines an advanced PFC front-end with an LLC resonant converter optimized using first harmonic approximation (FHA) to achieve high efficiency, stable output regulation, and soft-switching operation. Experimental results demonstrate a significant improvement in power quality, with the input current total harmonic distortion (THD) reduced from 134% to 17%, a near-unity power factor, a regulated LED output of 31.6 V/4.72 A, and a conversion efficiency exceeding 95%. The significance of this work lies in providing a high-performance, standards-compliant driver that supports reliable, energy-efficient, and grid-friendly public lighting with reduced operational costs.
Volume: 17
Issue: 2
Page: 1084-1095
Publish at: 2026-06-01

Harmonic analysis of grid-connected parallel H-bridge VSI and CSI with isolated DC sources

10.11591/ijpeds.v17.i2.pp1408-1417
Suroso Suroso , Winasis Winasis , Priswanto Priswanto
In a single-phase inverter system, parallel operation of inverters is a strategy to increase capacity, improve reliability, and increase the flexibility of the inverter system. This work discusses the basic operation of a novel parallel H-bridge current source inverter (H-BCSI) and H-bridge voltage source inverter (H-BVSI) operated in a grid-connected operation with isolated direct current (DC) sources equipped with power transformers. Each inverter circuit employed an independent current controller to regulate its alternating current (AC) output current. The proposed inverter system was tested for different operation conditions, and its characteristics were analyzed, especially for its harmonic profile. The test results showed that if the magnitude of the H-BCSI current was varied, while the H-BVSI current was kept constant, the total harmonic distortion (THD) value of load current was much lower than the THD values of H-BVSI current, H-BCSI current, and grid current, i.e., THD Iload ≤ 1%. This condition also occurred when the output current of the H-BVSI was increased gradually while the output current of H-BCSI was maintained constant. Moreover, a similar result was also obtained when both inverters’ output currents were varied simultaneously with the same value. The test results confirmed that the injected AC current of both inverters during parallel grid-connected operation worked well at unity power factor, and met the standards IEEE 1547 and IEC 61727, of which current THDs were ≤ 5%. The proposed grid-connected parallel inverter system worked, supplying a sinusoidal AC load current with high power quality.
Volume: 17
Issue: 2
Page: 1408-1417
Publish at: 2026-06-01

Multi-objective energy management optimization in electric vehicles using fuzzy logic and particle swarm optimization

10.11591/ijpeds.v17.i2.pp1025-1035
V. Lakshmi Devi , Damodhar Reddy , Srikanth Velpula , K. Kumar , Basi Reddy Avula
This paper proposes a hybrid energy management system (EMS) for electric vehicles by integrating fuzzy logic control (FLC) with particle swarm optimization (PSO) to improve power-split decision-making under dynamic driving conditions. The FLC is designed using state of charge (SoC) and vehicle speed as input variables and power split as the output. A set of fuzzy rules defines the EMS behavior, while PSO is employed to fine-tune decisions by maximizing an efficiency objective function defined as the closeness of the power split to an ideal reference. The simulation is implemented in Python using Colab-compatible packages such as scikit-fuzzy, DEAP, and matplotlib, ensuring accessibility and reproducibility. A test grid covering 10 SoC levels (10-100%) and 10 speed levels (10-120 km/h) is used to evaluate the system. Visualization tools, including heatmaps, 3D surface plots, and contour plots, are employed to represent the EMS behavior. The PSO-enhanced system achieved a maximum efficiency of 98.2% at an optimized SoC of 61.7% and a speed of 53.6 km/h, outperforming standalone fuzzy logic control. Tabulated results and statistical summaries validate the effectiveness of the proposed system.
Volume: 17
Issue: 2
Page: 1025-1035
Publish at: 2026-06-01

Enhancing grid performance through coordinated SVC-TCSC operation with PV support: A case study on IEEE 30-bus system under progressive loading

10.11591/ijpeds.v17.i2.pp1254-1264
Hafidha Reriballah , Latifa Smail , Ali Abderrazak Tadjeddine , Hocine Guentri , Rim Feyrouz Abdelgoui , Fatima Zohra Boudjella
Power systems face growing challenges of voltage instability, line congestion, and increased losses under rising demand. This study proposes a coordinated approach using two flexible AC transmission system (FACTS) devices: the static var compensator (SVC) and the thyristor controlled series capacitor (TCSC), together with photovoltaic (PV) generation, to enhance grid performance. The IEEE 30 bus test system is analyzed under normal and increased load conditions (5%, 10%, 15% load growth). Results show that coordinated SVC TCSC operation improves voltage profiles, reduces critical line loading by 14%, and lowers active and reactive losses by 10% and 23.8%, respectively, in the base case. Under a 15% load increase, integrating a 25 MW PV system with the coordinated FACTS restores the minimum voltage to 0.95 p.u., reduces line congestion by 27%, and decreases active and reactive losses by 35.5% and 53.5%. The combined FACTS PV strategy proves essential for maintaining stability and efficiency under high load growth. This integrated approach provides practical guidance for transmission operators toward resilient, loss aware, and renewable integrated smart grids.
Volume: 17
Issue: 2
Page: 1254-1264
Publish at: 2026-06-01

Improved control strategy for harmonic current mitigation in DFIG-based wind turbines supplying linear and nonlinear loads

10.11591/ijpeds.v17.i2.pp933-945
Hind Elaimani , Noureddine Elmouhi
Improving power quality is a major challenge in grid-connected wind energy systems, especially under mixed linear and nonlinear load conditions. This paper proposes an enhanced control strategy for harmonic current mitigation in a doubly fed induction generator (DFIG)-based wind turbine. The proposed approach integrates flux-oriented vector control with an active harmonic compensation algorithm implemented through the rotor-side converter (RSC). Unlike conventional methods that target only specific harmonic orders, the proposed strategy mitigates all current harmonics at the point of common coupling (PCC). Simulation studies conducted under various load conditions demonstrate that the method significantly reduces the total harmonic distortion (THD) and ensures near-sinusoidal stator currents. The results confirm the effectiveness and robustness of the proposed control approach in improving the power quality of DFIG-based wind energy conversion systems.
Volume: 17
Issue: 2
Page: 933-945
Publish at: 2026-06-01

Exploring player interaction and team cooperation in MMOG playability enhancement

10.11591/ijict.v15i2.pp644-654
Gong Xiaoxue , Lili Nurliyana Abdullah , Azrul Hazri Jantan , Noris Mohd Norowi , Fatimah Sidi , Gulmira Abildinova
The massively multiplayer online games (MMOGs) continue to grow in popularity, and it has become particularly important to understand the key factors that influence team playability. While existing research has focused primarily on system functionality and individual player experience, insufficient attention has been paid to the role of team dynamics in player satisfaction. This study focuses on the core variables that influence team playability, including teamwork, task dependency, team loyalty (TLO), and team relationships (TR), and explores how these variables work together to influence player experience. This study used a combination of exploratory research (multi-variates) and a questionnaire survey (N=1064) to initially construct a team playability model, which was validated by structural equation modeling (SEM). The results show that TR have a significant positive effect on teamwork efficiency, and captains with transformational leadership (TL) styles not only enhance TR but also further improve overall team effectiveness (TE) and player satisfaction. This study provides MMOG developers with theoretical support for designing game mechanics centered on team interaction to enhance overall playability and player stickiness.
Volume: 15
Issue: 2
Page: 644-654
Publish at: 2026-06-01

Deep learning-based optimization techniques for network lifetime enhancement in wireless sensor networks

10.11591/ijict.v15i2.pp623-633
Abhay Raghunath Gaidhani , Amol D. Potgantwar
Wireless sensor networks (WSNs) are integral to applications like environmental monitoring, healthcare, and surveillance, yet they face the critical challenge of limited energy resources, which shortens the network's operational lifespan. Addressing this issue, this paper explores deep learning-based optimization techniques as a solution to enhance network lifetime by efficiently managing energy consumption. We present a detailed review of the existing challenges in WSNs and examine various deep learning methods, including neural networks, deep reinforcement learning (DRL), and generative adversarial networks, specifically tailored for WSN optimization. In this study, we introduce a new reinforcement learning (RL) based optimization algorithm to prolong the network lifetime. The proposed technique is intended to smartly distribute the energy consumption among the network elements, leading to desirable performance with regard to the battery lifetime. The paper ends with a summary of design aspects and future research directions to improve the WSN performance further based on deep learning.
Volume: 15
Issue: 2
Page: 623-633
Publish at: 2026-06-01

Efficient email classification technique: a comparative study of header-only and full-content approaches

10.11591/ijict.v15i2.pp665-673
Worawit Kitikusoun , Nawaporn Wisitpongphan
The purpose of this research is to explore efficient techniques and sufficient features for organizational email classification, with a focus on identifying emails that are not beneficial for work to reduce the burden of email management. This study proposes a novel approach by comparing the performance of using email header features (Header-Only) versus full email data (Header + Body), aiming to evaluate the accuracy and processing time of widely used machine learning algorithms, including Random Forest, SVM, KNN, XGBoost, and ANN. The experiment was conducted using the Enron dataset, with key features extracted from email headers such as sender and recipient addresses and from the body content. The results show that using only header information provides classification performance comparable to using full email content. In particular, models such as Random Forest, XGBoost, and LightGBM achieved accuracy exceeding 95%, while reducing processing time by up to 21.66% in the Random Forest model. It is evident that classifying emails using header-only features is both highly accurate and resource-efficient. This research offers practical guidance for organizations in developing effective email filtering systems without compromising classification quality.
Volume: 15
Issue: 2
Page: 665-673
Publish at: 2026-06-01

Multiclass classification using variational quantum circuit on benchmark dataset

10.11591/ijict.v15i2.pp578-587
Muhammad Hamid , Bashir Alam , Om Pal
Classification is a major task in data science. Data classification is required in many industries such as healthcare, transport, and finance. Noisy intermediate-scale quantum (NISQ) era. Quantum computers are capable of solving complex data challenges and can be used for the classification of the data with minimum features. In this regard, quantum neural networks are being used extensively for data classification. In this paper, we employ variational quantum circuits for the task of multiclass classification. A hybrid approach is used for building the neural network. In which quantum circuits are used for the feedforward architecture, while in back-propagation, parameters are updated using a classical optimizer on classical computers. We have successfully demonstrated multiclass classification using the proposed approach on benchmark data sets. Our results show that variational quantum circuit (VQC) are a promising candidate for classification problems with fewer features. We have performed experiments on International Business Machines Corporation (IBM) quantum hardware and simulators.
Volume: 15
Issue: 2
Page: 578-587
Publish at: 2026-06-01

Performance improvement of DC microgrids via adaptive neuro-fuzzy inference system -optimized AI-tuned fractional order proportional-integral-derivative controllers

10.11591/ijict.v15i2.pp797-804
Debani Prasad Mishra , Sarita Samal , Manas Ranjan Sahu , Sonna Murari , Piyuskant Das , Surender Reddy Salkuti
This paper presents a novel approach to enhance the dynamic performance of direct current (DC) microgrids using an artificial intelligence (AI)-tuned fractional order proportional-integral-derivative (FO-PID) controller, further optimized through an adaptive neuro-fuzzy inference system (ANFIS). Conventional PID controllers tend to fail when it comes to dealing with microgrid environment-related non-linearities and uncertainties, particularly under changing load and generation situations. To remedy this, the suggested approach combines AI-tuned tuning algorithms for selecting initial parameters, and then ANFIS optimization to fine-tune the FOPID gains adaptively for better control precision. The performance of the hybrid control approach is tested through MATLAB simulations on a generic DC microgrid model that includes distributed energy resources, power electronic converters, and dynamic loads. Comparative evaluation against standard PID and independent FOPID controllers verifies remarkable advantages in terms of voltage regulation, stability, and transient response in various operating conditions. Amongst the achieved outcomes, it highlights the strength of the proposed ANFIS-optimized AI-tuned FOPID controller as a smart and robust strategy for real-time control of DC microgrids.
Volume: 15
Issue: 2
Page: 797-804
Publish at: 2026-06-01

Trust-based secure routing in IoT networks using machine learning for enhanced anomaly detection and risk mitigation

10.11591/ijict.v15i2.pp839-849
Sangeetha Krishnaswamy , Arulanandam Karalagan
The rapid growth of the internet of things (IoT) has led to the development of new challenges in ensuring secure and reliable data transmission. This paper proposes a trust-based secure routing protocol (TBSRP) designed to mitigate security threats such as routing attacks in IoT networks. The core innovation lies in the dual-layer trust evaluation mechanism, which combines reputation-based trust and behavioral analysis to dynamically adjust routing decisions based on real-time performance and historical behavior of network nodes. To enhance security, the protocol incorporates an adaptive threshold mechanism that adjusts trust criteria based on observed network conditions and an anomaly detection system utilizing machine learning (ML) algorithms for real-time monitoring of node behavior. Experimental evaluation demonstrates that TBSRP outperforms existing protocols (such as Ad hoc on-demand distance vector (AODV), trust-based AODV (TB-AODV), energy-efficient secure routing (ESR), and Secure AODV (SEC-AODV)) in key performance metrics, including packet delivery ratio (PDR), end-to-end delay, throughput, and routing overhead. The proposed protocol exhibits strong resilience to the increasing number of malicious nodes and varying network conditions, making it highly effective for securing IoT networks. This work contributes to the development of adaptive, scalable, and secure routing protocols for IoT environments, with the potential for further optimization through advanced ML techniques and real-world implementation.
Volume: 15
Issue: 2
Page: 839-849
Publish at: 2026-06-01

A review of sensemaking design elements: towards an affordances typology

10.11591/ijict.v15i2.pp488-496
Fadzlin Ahmadon , Murni Mahmud , Muna Azuddin
This study explores the intersection of interaction design and sensemaking within digital systems, aiming to identify and categorize key affordances that enhance user sensemaking. Starting with a focused literature review, key design elements such as tagging and annotation are identified, important for effective sensemaking in interaction design. Drawing on Maier's construct of affordances, the behaviours of these design elements are analyzed to derive specific affordances integral to enhancing user experience. The primary objective is to develop a generalized affordance typology that supports sensemaking across various digital systems. This typology organizes the derived affordances into broad themes such as effortless discovery, expressive freedom, collaborative engagement, cognitive support, insight enhancement, and user empowerment. This typology serves as a tool for interaction designers, facilitating the application of these themes in various design scenarios to create more intuitive and effective digital environment for sensemaking.
Volume: 15
Issue: 2
Page: 488-496
Publish at: 2026-06-01

Psychometric validation of the humor styles questionnaire among Indonesian pre-service teachers

10.11591/ijere.v15i3.38732
Ali Rachman , Noorhapizah Noorhapizah , Yogi Prihandoko , Nahdia Fitri Rahmaniah
This study aimed to develop and validate the Indonesian version of the humor styles questionnaire (HSQ-ID) for use in pre-service teacher education. A cross-sectional psychometric design was applied to a sample of 729 Indonesian pre-service teachers, using systematic translation, content validation, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) with the robust maximum likelihood (ML) estimator. HSQ-ID showed a stable four-factor structure, strong model fit (comparative fit index (CFI)=0.97, root mean square error of approximation (RMSEA)=0.045, standardized root mean square residual (SRMR)=0.040), and acceptable internal consistency across all subscales (Cronbach α=0.72–0.89). One-way analysis of variance (ANOVA) indicated significant differences in humor styles across 10 teacher specialization fields, suggesting that humor use is shaped by disciplinary and professional training contexts. These findings confirm that the HSQ-ID is a valid and reliable instrument for evaluating humor styles in Indonesian teacher education and can support future assessment-based pedagogical interventions.
Volume: 15
Issue: 3
Page: 2111-2120
Publish at: 2026-06-01

Bug safari: promoting ecological awareness in early childhood through nature-based learning

10.11591/ijere.v15i3.38526
Kazım Biber , Caner Börekci
This study examines the effectiveness of a nature-based educational program called bug safari, designed to enhance preschool children’s attitudes toward small creatures, particularly bugs, and to foster their ecological awareness. Developed within the framework of the Reggio Emilia approach, the program integrates multi-sensory and interdisciplinary learning methods, including observation, drama, storytelling, art activities, and parental involvement. The study was conducted in two preschool classrooms in Balıkesir, Türkiye. In the experimental group, bug safari activities were implemented once a week for six weeks, while the control group continued with the existing preschool curriculum. Data were collected using the 22-item bug awareness and ecological awareness questionnaire, developed by the researcher, and administered as both a pre-test and post-test. A mixed-design analysis of variance (ANOVA) revealed that the experimental group showed statistically significant improvements in bug awareness, understanding of the role of bugs in the ecosystem, and ecological consciousness, whereas no significant changes were observed in the control group. The findings indicated that nature-based programs involving direct experiences and active participation effectively promoted positive environmental attitudes and ecological awareness in early childhood. This study underscores the importance of integrating child centered, experiential, and nature-oriented approaches into preschool education to support cognitive, emotional, and behavioral development.
Volume: 15
Issue: 3
Page: 2217-2227
Publish at: 2026-06-01
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