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Comparative analysis for different passive filter topologies in grid-tied PV systems

10.11591/ijeecs.v42.i1.pp1-12
Shorouk Elsayed Ibrahim Mehrez , Asmaa Sobhy Sabik , Fady Wadie , Ibrahim A. Nassar
The enhancement of power quality in grid-connected photovoltaic (PV) systems requires the development of effective harmonic mitigation techniques. This paper addresses the design and evaluation of specific passive filters (RC, LC, and LCL filters) for a three-phase grid-tied PV system, aiming to mitigate harmonics in the power system. The paper also systematically calculates and optimally solves for the components required for the given system. The design of the parameters for all filter topologies within the 100-kW grid-connected PV array is thoroughly elaborated. Each topology is evaluated based on the total harmonic distortion (THD) content, which is obtained using fast fourier transform (FFT), as well as DC voltage and system efficiency. The results are presented to identify the best solutions for harmonic mitigation. The modified filter model demonstrated in this study effectively limits harmonic distortion at the output. It is shown that the proposed design addresses the issue of harmonic distortion in grid-connected inverters for PV systems. The goal of this paper is to identify the most reliable filter for extending the system’s lifespan. The results suggest that the LCL filter is superior, as the system’s DC voltage remained within the rated value and the system efficiency was higher compared to the RC filter. The performance and functionality of these filters were tested using MATLAB/Simulink.
Volume: 42
Issue: 1
Page: 1-12
Publish at: 2026-04-01

Hyperparameter tuning of MobileNetV2 on forest and land fire severity classification

10.11591/ijece.v16i2.pp964-972
Assad Hidayat , Imas Sukaesih Sitanggang , Lailan Syaufina
Forest and land fires pose significant environmental challenges, causing economic and ecological damage depending on their severity. This study proposes a deep learning-based classification model to assess fire severity using the MobileNetV2 architecture. A dataset of 560 post-fire images was categorized into five severity levels, with dataset preprocessing involving resizing, rescaling, and image augmentation. To enhance model performance, K-means clustering was applied for balanced data distribution across classes. The model was trained using grid search for hyperparameter tuning, with the optimal combination being a batch size of 8, learning rate of 0.0001, and dropout of 0.3. Training was conducted in 50 epochs, and evaluation using the confusion matrix demonstrated an accuracy of 85%, precision of 86%, and recall of 81%. The results indicate that MobileNetV2 effectively classifies post-fire severity levels, offering a reliable tool for post-disaster assessment. This study highlights the significance of dataset preprocessing and hyperparameter tuning in improving model accuracy. Future research should explore alternative architectures and expand the dataset to enhance model generalization. These findings can aid authorities in assessing fire impact, supporting mitigation strategies, and improving post-fire land management.
Volume: 16
Issue: 2
Page: 964-972
Publish at: 2026-04-01

Improving the performance of wireless sensor network using multi-hopping clustering partition

10.11591/ijeecs.v42.i1.pp81-92
Robby Rizky , Mustafid Mustafid , Teddy Mantoro , Wahyul Amien Syafei
Wireless sensor networks (WSNs) enable large-scale event monitoring; however, their performance is often constrained by low throughput. This study aims to develop a cluster-based routing protocol by implementing the multi-hopping clustering partition (MHCP) method. The MHCP process consists of three main stages: (i) cluster head (CH) selection, (ii) evaluation of node proximity to their respective CHs, and (iii) cluster partitioning to reduce intra-cluster variation. Four clusters were formed and interconnected through multi-hop communication, achieving throughput values of 142.0033, 244.1318, 119.0804, and 305.6159, respectively. In addition to the development of MHCP, the scientific contribution of this study is strengthened through the integration of the LEACH protocol and the K-means algorithm as a complementary methodological approach. LEACH improves energy efficiency through adaptive CH rotation, while K-means optimizes spatial node grouping. The combination of these methods ensures a balance between energy consumption and spatial proximity, resulting in improved throughput and extended network lifetime. Experimental results demonstrate that the proposed MHCP protocol achieves higher throughput than the conventional LEACH protocol across all clusters while maintaining acceptable delay and packet loss. These findings confirm that the integration of multi-hop communication and cluster partitioning effectively enhances data transmission efficiency and overall network performance in WSNs.
Volume: 42
Issue: 1
Page: 81-92
Publish at: 2026-04-01

Single-stage single-phase grid connected inverter proportional resonant and maximum power point tracking controllers for enhanced photovoltaic system performance

10.11591/ijece.v16i2.pp651-662
Abdelaziz Kabba , Abdellah Lassioui , Hassan El Fadil
The paper develops a current control methodology for a single-phase grid-tied DC/AC inverter applied to photovoltaic (PV) energy conversion systems. It incorporates an algorithm for finding the optimal voltage and current points to obtain maximum power point tracking (MPPT), the purpose of which is to ensure better energy extraction. This is followed by a proportional-integral (PI) controller to generate the reference current. In addition, a proportional-resonant (PR) controller is used to infinitely amplify the fundamental frequency signal, which makes it possible to eliminate the steady-state error. The analytical foundations of the PR controller are presented and substantiated through simulation studies implemented in MATLAB/Simulink. The phase-locked loop (PLL) is used for synchronization, enabling accurate phase detection of the grid voltage for effective power injection. An LCL filter is also implemented between the inverter and the grid. The results provided by the dedicated software confirm the effectiveness of the proposed control system.
Volume: 16
Issue: 2
Page: 651-662
Publish at: 2026-04-01

An innovative deep learning based approach for anomaly detection in intelligent video surveillance

10.11591/ijeecs.v41.i3.pp1105-1116
Megha G. Pallewar , Vijaya R. Pawar
Nowadays, anomaly detection has gained vital importance as security is a major concern everywhere. This work focuses on developing an intelligent video surveillance system capable of detecting anomalous activities in videos, utilizing the UCF Crime dataset as the primary source. The proposed model employed a multistage method uniting the convolutional neural networks (CNN) and long short-term memory (LSTM) networks. In the proposed approach, video frames serve as input to the CNN, which processes them to extract key features. These features are then passed to an LSTM network to capture temporal dependencies and identify anomalous events over time. This CNN-LSTM architecture successfully detects twelve distinct types of anomalous activities: abuse, arrest, arson, assault, burglary, explosion, fight, road accident, robbery, stealing, shoplifting, and vandalism. The dataset is divided into portions for training, testing, and validation, along with cross-validation to ensure model generalization. The system achieves an accuracy of 98.6%, reflecting a significant improvement of 4-5% over existing systems. This demonstrates the robustness of the proposed method in detecting anomalous behavior in video data.
Volume: 41
Issue: 3
Page: 1105-1116
Publish at: 2026-03-10

IoT-enabled digital twin with renewable energy for sustainable mudless eel aquaculture

10.11591/ijeecs.v41.i3.pp912-923
Muhammad Ferdiansyah , Lika Mariya , Taufik Rahman , Sugeng Dwiono
This research develops and tests a digital twin (DT)-based smart aquaculture system for mud-free eel farming through the integration of IoT sensing, artificial intelligence (AI)-based prediction, edge computing, and solar energy-based automation. The approach used is experimental systems engineering, which includes system design, hardware and software implementation, virtual replication, and physical-digital two-way synchronization. The system utilizes ESP32-based pH, temperature, dissolved oxygen (DO), ammonia (NH₃), and turbidity sensors, MQTT communication, and Raspberry Pi edge computing. Water quality prediction is performed using long short-term memory (LSTM) and random forest regression. The dataset consists of 30 days of real-time data covering water quality, actuator activity (aerator, pump, feeder), and energy production and consumption by IoT sensors and energy meters. Results show that LSTM excels by R² = 0.94; RMSE = 0.14; MAPE <5% and synchronization latency <1.5 seconds. Solar energy integration reduces energy consumption by 54 67%, whilst automation increases eel survival rate by 78% to 91%. The novelty of this research lies in the first integrated implementation of DT, AIoT, and solar energy-based automation in mud-free eel farming. The proposed framework provides a precise, scalable, and sustainable solution for the development of modern aquaculture.
Volume: 41
Issue: 3
Page: 912-923
Publish at: 2026-03-10

Overvoltage assessment of wind energy integration in low voltage distributed grids

10.11591/ijeecs.v41.i3.pp859-872
Farid Merahi , Badoud Abd Essalam
Large-scale integration of renewable energy (RE) resources into the electrical grid has increased significantly over the last decade, affecting the network at various nodes even at considerable distances from the common connection point. This paper presents an overvoltage assessment caused by the integration of two wind generators (WGs) into a low voltage distribution grid, which is structured into three zones. Two scenarios are studied, the first one considers the low voltage grid without WGs, representing its natural operating condition. In the second scenario, two WGs are connected in zone 3, inducing voltage rises at different nodes within the same zone, by reaching 7.9%, and affecting nodes located in other zones (Zone 1 and Zone 2). The simulation is performed using MATLAB/Simulink (R2025a), and the results obtained are compared to the standards test feeder IEEE 33-bus network, showing the overvoltage caused by WGs integration at nodes close to the connection point while improving voltage quality at distant nodes.
Volume: 41
Issue: 3
Page: 859-872
Publish at: 2026-03-10

Level of detail in UML models and its impact on model comprehension: a replication study

10.11591/ijeecs.v41.i3.pp1095-1104
Ariadi Nugroho , Michel R.V Chaudron
This replication study examines the impact of level of detail (LoD) in unified modeling language (UML) on model comprehension, replicating a controlled experiment, which involved 53 MSc students at Eindhoven University of Technology. Using the same UML model and experimental design, we conducted the study with 23 MSc Computer Science students at Bina Nusantara University, Indonesia. Consistent with the original findings, higher LoD was found to enhance comprehension correctness. However, the effect on comprehension efficiency was weaker and not statistically significant, likely due to the smaller sample size and contextual differences in subjects’ backgrounds. Furthermore, we found a potential disconnect between perception and actual comprehension performance in the subjects receiving UML model with low LoD. Specifically, while they viewed the model favourably, their actual understanding may have been impaired by the limited information and therefore the perceived clarity and ease of comprehension are not reflective of the true comprehension. Overall, this study reinforces the importance of LoD in UML modeling and highlights the need for further replication, particularly in contexts involving professional software engineers.
Volume: 41
Issue: 3
Page: 1095-1104
Publish at: 2026-03-10

Synthetic inertia controller of a wind power plant as a means of increasing the stability of electric power systems

10.11591/ijeecs.v41.i3.pp1117-1123
Makhmudov Tokhir Farkhadovich , Ramatov Adxam Nasiriddin o’gli
The article discusses the use of wind power plants as sources of synthetic inertia to enhance power system stability and reduce frequency fluctuations. This research explores the feasibility of implementing a synthetic inertia controller in wind power plants to decrease the magnitude of frequency oscillations during transient operating conditions. The growing integration of wind farms into modern power grids leads to a reduction in the overall kinetic energy, or inertia, available in the system. As a result, the grid may become more vulnerable to disturbances. When the system inertia is too low, frequency stability can be affected, especially when large generating units suddenly fail or disconnect from the grid. In general, a lower level of inertia in the system causes larger frequency deviations following an imbalance in active power. To overcome this issue, a synthetic inertia regulator for wind power plants has been developed, enabling wind turbines to support the grid and reduce the depth of frequency drops during transient events.
Volume: 41
Issue: 3
Page: 1117-1123
Publish at: 2026-03-10

Exploring word embeddings and clustering algorithms for user reviews

10.11591/ijeecs.v41.i3.pp1017-1024
Zuleaizal Sidek , Sharifah Sakinah Syed Ahmad
The rapid advancement of information technology has led to a significant surge in the volume of unstructured textual data. This has posed a major problem in terms of analyzing, organizing, and automatically clustering text for research purposes, which is crucial for extracting valuable insights. The process of manually clustering the unstructured data, such as customer reviews on the Internet, which capture the opinions of customers regarding products, services, and social events, requires significant financial resources, manpower, and time. Most of the studies are directed towards the analysis of sentiment in user reviews. In order to address the issues effectively, automated text clustering could assist in categorizing reviews into various themes, thereby simplifying the analysis process. Therefore, in this paper, we present and compare the result of experiment the combination of five text clustering techniques, namely K-means, fuzzy C-mean (FCM), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), and latent semantic analysis (LSA) with different embedding techniques, namely term frequency–inverse document frequency (TF-IDF), Word2Vec, and global vectors (GloVe). The experiments revealed that LDA is a reliable algorithm as it consistently produces good results across three-word embeddings. The highest Silhouette score recorded in the experiments was 0.66 using LDA and Word2Vec as word embedding. Simultaneously, the application of LSA in conjunction with Word2Vec yields superior outcomes, as evidenced by a Silhouette score of 0.65.
Volume: 41
Issue: 3
Page: 1017-1024
Publish at: 2026-03-10

Satellite-based assisted-offloading for energy-constrained edge networks

10.11591/ijeecs.v41.i3.pp935-945
Thembelihle Dlamini , Mengistu A. Mulatu , Sifiso Vilakati
As the need for global broadband internet connectivity increases, there is a need to consider the use of non-terrestrial networks (NTNs) to extend the network coverage to protected areas (e.g., national parks). Usually, protected areas are prohibited from having power lines thus lacking wireless connectivity. To over come this challenge, energy can be provided through the use of green energy from a solar photovoltaic (PV) system. Then, a green energy-based base station (BS) can be deployed within the area in order to provide mobile connectivity to visitors, as well as also using the NTNs to handle excess traffic or take over the traffic in the event the BS does not have sufficient green energy from stor age. In this paper, a hybrid wireless communication system is proposed to in clude BS sites located in a protected area and satellites in the low earth orbits (LEO), coupled with new offloading strategies, with the main goal of optimizing the trade-off between energy consumption and end-to-end delay for the green energy-based BS sites. For accuracy of our simulations, we consider real data from a solar photovoltaics system, traffic workloads, visitor’s location data, and satellite orbits from Starlink constellations. Our results demonstrate that the co existence of the BS and satellite achieve energy savings from 59% to 34%, with an average system delay of 0.83 seconds and a packet drop rate that ranges from 8.3% to 2.7%, when compared with our benchmark.
Volume: 41
Issue: 3
Page: 935-945
Publish at: 2026-03-10

A multimodal framework for explainable chest X-ray report generation

10.11591/ijeecs.v41.i3.pp1060-1069
Hamza Chehili , Nourhene Bougourzi , raida malak Makhlouf , hadjer Taib , Mustapha Bensaada
Chest X-ray (CXR) interpretation remains a challenging task due to overlapping anatomical structures, variability in disease presentation, and increasing clinical workload. Existing automated report-generation models provide promising results but often lack explicit interpretability, limited clinical alignment, and insufficient comparative evaluation with established baselines. This study proposes an explainable multimodal framework that combines a dual CNN encoder (ResNet-50 and EfficientNet-B0) with the Gemma-3 1B language model fine-tuned using low-rank adaptation (LoRA). Visual explanations are produced through Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance transparency in the decision process. Unlike prior image-to-text pipelines, our approach follows a findings-guided paradigm and integrates both visual and textual cues during generation. Experiments conducted on public datasets demonstrate consistent improvements over representative vision-language baselines reported in recent literature, with notable gains in BLEU, ROUGE, METEOR, and BERTScore. Generated reports show improved factual completeness and clinically relevant region-level attention. Limitations include the absence of evaluation against emerging foundation models and the need for anatomical- level explainability metrics. Future work will extend benchmarking to models such as M2-Transformer, MedCLIP-GPT, and R2Gen, and will explore clinical validation in real-world workflows.
Volume: 41
Issue: 3
Page: 1060-1069
Publish at: 2026-03-10

Intelligent artificial neural network-based control for solar electric vehicle charger

10.11591/ijeecs.v41.i3.pp885-893
Rajeshkumar Damodharan , Pradeep Kumar S
The performance of electric vehicle (EV) charging systems in response to sudden changes in solar irradiation and dynamic battery load variations. EV chargers must have effective power conversion and flexibility as the use of renewable energy sources increases. This paper suggests a charging system based on resonant converters that minimizes heat and losses in EV charging stations by enabling high-efficiency, soft-switching power transfer. For modern EV applications, the ability to manage large voltage fluctuations ensures reliable, quick, and portable charging. The artificial neural networks (ANN) controller overcomes the drawbacks of conventional Perturb and Observe (P&O) for solar DC-DC converters and PI control for resonant converter approaches. MATLAB simulation results demonstrate that the proposed system outperforms traditional techniques in terms of an ANN based controller, which enhances maximum power point tracking (MPPT) efficiency to 98.6%, reduces oscillations near the maximum power point by approximately 80%, and increases total EV charging efficiency by 3%. The ANN-based control to EV charging infrastructure greatly enhances overall system dependability and real-time responsiveness, making it a good fit for subsequent smart grid and renewable energy applications.
Volume: 41
Issue: 3
Page: 885-893
Publish at: 2026-03-10

A hybrid approach for measuring semantic similarity in lexically identical but ambiguous sentences

10.11591/ijeecs.v41.i3.pp954-965
Btissam El Janati , Adil Enaanai , Fadoua Ghanimi
This study addresses the critical challenge of semantic similarity and lexical disambiguation in natural language processing, focusing on sentences with structural and lexical ambiguities. We introduce an innovative hybrid approach that synergistically combines symbolic and neural methods to better align with human judgment. Our methodology dynamically integrates fuzzy Jaccard’s lexical precision with SBERT embeddings’ contextual sensitivity, enabling adaptive semantic ambiguity resolution. Experimental evaluation on 33 ambiguous sentences demonstrates that our approach significantly outperforms conventional artificial intelligence (AI) systems, achieving an 11.7% reduction in mean absolute error compared to reference models, with statistical analysis confirming robust results (d = -0.80, p < 0.001). This represents a 65% improvement in human evaluation alignment over existing methods. Our research contributes to advancing the field by showing that architectural intelligence can surpass mere parameter scaling, offering an effective solution for applications requiring both precision and interpretability, with promising directions for multilingual extension and explainable AI integration.
Volume: 41
Issue: 3
Page: 954-965
Publish at: 2026-03-10

Study of performance the 3-phase induction motor that drives by using scalar and vector control with variable speed loading

10.11591/ijeecs.v41.i3.pp894-911
Omran Alabedalkhamis , Baran Karahan , İbrahim İdiz , Hüseyin Alptekin , Enver Ediz Erol
Induction motor performance and efficiency greatly depend on the applied control technique, particularly in variable- and fixed-speed industrial applications. This paper aims to comparatively assess scalar control and vector control strategies for three-phase squirrel-cage induction motors. Using a simulation-based approach in MATLAB/Simulink, scalar control with sinusoidal pulse width modulation (SPWM) and vector control with space vector modulation (SVM) are built and analyzed under constant, variable, and bidirectional speed loading situations characteristic of a drive system. The results demonstrate that vector control provides greater speed regulation (about 93% compared to scalar control), reduced torque ripple (about 97% compared to scalar control), lower current stress (about 94% compared to scalar control), and improved dynamic responsiveness compared to scalar control, especially during transient operation. The paper is limited to numerical simulations. This paper’s biggest contribution is a clear, practical comparison which provides performance- and cost-oriented guidelines for selecting appropriate induction motor control strategies in severel applications.
Volume: 41
Issue: 3
Page: 894-911
Publish at: 2026-03-10
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