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30,468 Article Results

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

An internet of things-telemedicine platform empowered by 5G mobile networks for Tunisian Rural places

10.11591/ijece.v16i3.pp1261-1271
Ibrahim Monia , Dadi Mohamed Bechir , Rhaimi Belgacem Chibani
With the advent of Internet of Things (IoT) technologies, offering new possibilities for remote healthcare delivery, the medicine sector has undergone significant advancements in recent years. New tools are used, and diagnostics have become more accurate. We suggest creating a platform that can be extended for several applications. This platform has been realized to attest and demonstrate how IoT technology offers devices that could be integrated to provide novel services like remote consultations. Our proposed platform contains novel functionalities such as real-time video calls, instantaneous messaging, live notifications, vital signs monitoring, and electronic health record access. This is accomplished with enhanced qualities of remote healthcare services. Added to this, healthcare access equity will be guaranteed. The paper emphasizes the potential of Laravel 11 as a framework offering powerful features for creating modern and high-performance applications. We have integrated Laravel Reverb, a powerful real-time communication package, to provide seamless real-time communication with users. With our application, notifications and interactions are dynamically created. This allows instant updates to delivery and engages the user experience. The database was designed based on the latest version of MySQL 8, coupled with the advanced capabilities of PHP 8.2. This combination provides unparalleled performance, scalability and reliability. Added to that, IoT’s technology usage helps to improve healthcare access and delivery, especially in underserved areas. Human and machine cooperation is a main factor of the 5th industry level. This is widely respected by our platform. This offers great help, especially for those isolated and underserved areas, as we hope.
Volume: 16
Issue: 3
Page: 1261-1271
Publish at: 2026-06-01

Advanced strategy for energy management and voltage stability in microgrid-a review

10.11591/ijape.v15.i2.pp880-893
Aswathi Ravindran , B. Rubini
Microgrids (MGs) have emerged as transformative solution for improving energy resilience, stability, and sustainability in modern power systems. By incorporating distributed energy resources (DERs), renewable energy sources (RES), and energy storage systems (ESS), microgrids can supply reliable and stable power to local loads while also supporting the main grid during disturbances. Despite their potential, the efficient operation of MGs depends heavily on well-designed energy management and control systems (EMCS). A key challenge lies in addressing inherent variability of RES such as solar and wind, which introduces uncertainty in generation, as well as the dynamic and unpredictable nature of consumer loads. These factors make strong, adaptive, and intelligent energy management strategies crucial for ensuring both voltage stability and reliable operation. This paper presents review of advanced strategies developed for energy management and voltage stability in microgrids. It explores state-of-the-art optimization techniques, intelligent control methods, and emerging management frameworks that aim to balance generation, storage, and load demand efficiently. The study critically analyzes current methodologies, highlights their limitations, and identifies crucial research gaps in literature. By synthesizing recent developments, the paper provides insights in to innovative approaches that can enhance system reliability, optimize resource utilization, and ensure stable microgrid operation under uncertain conditions.
Volume: 15
Issue: 2
Page: 880-893
Publish at: 2026-06-01

Engineering intelligence for a sustainable and resilient future: from foundations to real-world impact toward the SDGs

10.11591/ijece.v16i3.pp1075-1084
Tole Sutikno
The June 2026 issue of this journal presents a comprehensive body of research advancing efficient engineering intelligence from foundational theory to real-world deployment, with strong alignment to the Sustainable Development Goals (SDGs). A significant cluster addresses SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure) through innovations in microbial fuel cells, high-voltage insulation reliability, artificial intelligence (AI) based battery management systems, and energy-efficient LoRa/LoRaWAN frameworks. These works emphasize energy sustainability, system resilience, and infrastructure optimization. A second cluster focuses on advanced electronics, control, and communication systems, including memcapacitor design, hybrid model predictive control, reflectarray antennas, and embedded intelligence for autonomous systems, demonstrating efficiency-driven engineering across hardware and system levels. A dominant cluster highlights SDG 3 (Good Health and Well-being), with applications in medical imaging, sepsis detection, breast cancer classification, and mental health analysis, leveraging deep learning, transformers, and hybrid AI models. Finally, contributions aligned with SDG 4 (Quality Education) explore gamified learning systems, virtual reality adoption, and SDG-integrated educational information systems, while complementary studies in agriculture, finance, and Internet of Things (IoT) further demonstrate the societal impact of intelligent systems. Collectively, these works reinforce the role of efficient, scalable, and data-driven engineering in addressing global challenges.
Volume: 16
Issue: 3
Page: 1075-1084
Publish at: 2026-06-01

A survey of retrieval algorithms in ad and content recommendation systems

10.11591/ijece.v16i3.pp1518-1530
Yu Zhao , Fang Liu , Yuan Yuan , Yifan Dang
This paper presents a survey of retrieval algorithms used in advertising recommendation and organic content recommendation systems. Modern digital platforms rely on retrieval-based models to efficiently match users with relevant advertisements or personalized content. This survey reviews key techniques including inverted index methods, collaborative filtering, content-based filtering, hybrid recommendation models, and the two-tower neural network architecture widely used in large-scale recommendation systems. The paper compares the objectives, data utilization strategies, and evaluation metrics of ad targeting and organic retrieval systems. Practical challenges such as cold-start problems, data quality, scalability, and privacy considerations are also discussed. This survey further highlights the growing connection between industrial recommendation pipelines and emerging retrieval mechanisms used in large language model (LLM) systems. This survey provides insights into the design principles of modern retrieval systems and outlines future research directions at the intersection of recommendation systems and LLM.
Volume: 16
Issue: 3
Page: 1518-1530
Publish at: 2026-06-01

AI-enabled energy-aware routing approach for future-wireless sensor networks

10.11591/ijece.v16i3.pp1543-1561
Shamsher Singh , Mandeep Kumar
Next-generation wireless sensor networks (WSNs) demand intelligent, energy-aware communication mechanisms capable of sustaining long-term operation in environments with varying conditions and strict resource limitations. Traditional routing protocols often fail to optimize energy consumption under varying network densities, heterogeneous traffic patterns, and environmental uncertainties. This research proposes an AI-enabled energy-efficient routing protocol (AI-EERP) designed to enhance network lifetime, stability, and data delivery performance in next-generation WSNs. The protocol integrates machine learning–based node selection, adaptive clustering, and predictive residual-energy estimation to make optimized routing decisions in real time. Using AI-driven models, AI-EERP dynamically adjusts routing paths based on energy patterns, link quality, and network topology changes. The simulation outcomes clearly indicate that the proposed approach achieves notable gains in energy efficiency, packet delivery reliability, and network lifetime when compared with traditional routing protocols, including LEACH, PEGASIS, and HEED. The proposed approach establishes a robust and scalable framework for future intelligent WSN deployments across applications including smart cities, precision agriculture, environment-focused applications and automated industrial operations.
Volume: 16
Issue: 3
Page: 1543-1561
Publish at: 2026-06-01

Prostate magnetic resonance imaging/transrectal ultrasound registration using vision transformer and convolutional neural network

10.11591/ijece.v16i3.pp1188-1198
Hanae Mahmoudi , Hiba Ramadan , Jamal Riffi , Hamid Tairi
Multimodal registration of 3D medical images (3D-MReg) plays a key role in several medical applications and remains a very challenging task as it deals with multimodal images and volumetric objects at the same time. Recently, convolutional neural networks (CNNs) based approaches have been proposed to solve 3D-MReg. However, these techniques cannot preserve the global spatial context required for accurate affine registration since they rely on convolution and regional clustering operations. To solve these problems, we propose a supervised approach that combines both CNN and the vision transformer (ViT) to predict a dense displacement field (DDF). In a first step, our method investigates the power of ViT to capture global voxels dependencies for initial rigid alignment. Then we exploit the force of CNNs to focus on local details within pre-aligned concatenated input 3D moving and fixed images and estimate DDF, which is then applied to the moving labels. Our method has been validated in a prostate magnetic resonance imaging/transrectal ultrasound (MRI/TRUS) dataset and achieved promising results compared to previous work based on only CNNs.
Volume: 16
Issue: 3
Page: 1188-1198
Publish at: 2026-06-01

Enhancing support vector machine performance using particle swarm optimization for sentiment analysis

10.11591/ijict.v15i2.pp523-534
Christofer Satria , Anthony Anggrawan , Peter Wijaya Sugijanto , Husain Husain , I Nyoman Yoga Sumadewa , Victoria Cynthia Rebecca
Recently, social media has established itself as a leading platform in various sectors. Meanwhile, text extraction and sentiment analysis classification have attracted significant attention in research. Regrettably, traditional sentiment analysis often falls short of accurately capturing sentiment nuances. At the same time, machine learning has enabled more effective sentiment analysis, data mining, and classification, as well as the development of models that incorporate artificial intelligence. Therefore, the purpose of this study is to optimize sentiment analysis of public opinion in social media regarding Grand Prix motorcycle racing (MotoGP) and World Superbike (WSBK) events using machine learning and an optimized machine learning method. This study applies the support vector machine (SVM) machine learning method and enhances its performance through optimization by integrating it with the particle swarm optimization (PSO) algorithm. This study found that the SVM method achieved 80.15% accuracy, 75.63% recall, and 76.89% F1-score. In contrast, the SVM method combined with PSO achieves accuracies of 81.82%, 79.9%, and 79.62% for recall, precision, and F1-score, respectively, in classifying the sentiment of sporting events. The implications suggest that applying Hybrid SVM with PSO significantly enhances classification accuracy in sentiment analysis.
Volume: 15
Issue: 2
Page: 523-534
Publish at: 2026-06-01

Energy-aware inertial measurement units scheduling for wearable LoRa systems using quaternion features

10.11591/ijece.v16i3.pp1449-1465
Yudhi Adhitya , Indri Septiani
Wearable Internet of Things systems increasingly depend on inertial measurement units (IMUs) to capture human motion, yet continuous high-frequency sensing, on-device processing, and long-range (LoRa) communication impose significant energy and latency challenges for battery-powered devices. This study formulates a practical scheduling framework that optimizes IMU sampling, quaternion-based feature extraction, and transmission decisions within the wearable/LoRa architecture. The framework operates in discrete time windows of W=0.5−1 s, within which sensing, processing, and communication decisions are updated at the window level to balance energy consumption and responsiveness. The method models energy consumption, accuracy degradation at lower sampling rates, and communication constraints to define feasible operating modes and determine optimal configurations under varying activity levels. An empirical accuracy–frequency mapping and component-wise energy model support both offline optimization and lightweight online scheduling. The results show that the proposed framework can balance accuracy, responsiveness, and battery life by dynamically shifting between high-performance, balanced, and low-power surveillance states. This scheduling strategy extends operational lifetime while preserving motion-detection reliability and ensuring timely event transmission. The findings demonstrate the importance of energy-aware IMU management in long-range wearable systems and provide a foundation for adaptive sensing strategies in real-world deployments.
Volume: 16
Issue: 3
Page: 1449-1465
Publish at: 2026-06-01

Artificial intelligence-based battery management systems in electric vehicles: models, optimization, and future directions

10.11591/ijece.v16i3.pp1645-1654
Hassan Kassem , Tariq Bishtawi
The electric vehicle (EV) depends on the capabilities and durability of the main element of the car — the battery. Conventional battery management systems (BMS) can generally be challenged with regards to state estimation and lifespan forecasting in the face of complicated real-world scenarios. To address these limitations, this study examines how artificial intelligence (AI) has the potential to transform BMS operations. We introduce an in-depth discussion of AI-controlled BMS by examining the state-of-the-art models of precise state-of-charge and state-of-health estimation. The paper also goes into details of how machine learning and deep learning methods can optimize charging strategy, improve thermal management, and predictive diagnostics. The comparison between the data-driven solutions and the traditional methods is going to reveal that there is a high safety, efficiency, and battery life improvement. Lastly, we map the way ahead, taking into consideration issues such as edge computing, explainable AI, and the way of making the BMS a truly self-optimizing system, essential to the next generation of electric cars.
Volume: 16
Issue: 3
Page: 1645-1654
Publish at: 2026-06-01
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