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30,938 Article Results

Design simulation and analysis of an MPPT technique using ANNs integral backstepping and SMC for PV systems

10.11591/ijpeds.v17.i2.pp1288-1303
Naoufal Zhani , Hassane Mahmoudi
This paper introduces the design of an innovative hybrid MPPT method called artificial neural networks-integral backstepping sliding mode control (ANN-IBSMC). This approach combines artificial neural networks (ANNs), which output the maximum power point voltage using inputs such as irradiance and temperature, with a robust control strategy. The designed controller aims to track the reference voltage with high accuracy and responsiveness by modifying the pulse width modulation of the DC-DC converter in the photovoltaic system. The IBSMC integrates the advantages of two control methods: the stability and accuracy of integral backstepping, and the robustness and fast response of sliding mode control (SMC). This combination enables improved precision, high convergence speed, enhanced robustness, and strong stability, the latter being ensured by the Lyapunov function. To evaluate the performance of the proposed controller, a comparative study is performed against other hybrid control techniques, such as the ANN-backstepping controller, the ANN-integral sliding mode controller, and the ANN-backstepping sliding mode controller, using MATLAB/ Simulink. A sensitivity and robustness analysis was carried out.
Volume: 17
Issue: 2
Page: 1288-1303
Publish at: 2026-06-01

A neural learning algorithm for online rotor resistance estimation in sensorless induction motor drive systems

10.11591/ijpeds.v17.i2.pp920-932
Tuan V. Pham , Nguyen H. Thai
This research proposes an advanced artificial neural network (ANN) framework optimized for the dynamic, real-time identification of rotor resistance (Rr) in sensorless induction motor (IM) drive systems. The proposed architecture introduces a self-tuning momentum factor within the neural learning update rule, which is adaptively modulated at each sampling interval. This modulation is governed by a Mamdani-based fuzzy inference system to ensure accelerated convergence and enhanced stability of the estimation process. Concurrently, the motor's angular velocity is estimated through a parallel ANN observer. Reliable identification of the time-varying rotor resistance is pivotal for compensating parametric sensitivity in flux observers, thereby optimizing the drive's control fidelity under varying thermal and load conditions. Comprehensive simulation and hardware-in-the-loop experimental results confirm that the proposed estimator tracks the actual Rr with high precision, maintaining steady-state errors within a 5% threshold.
Volume: 17
Issue: 2
Page: 920-932
Publish at: 2026-06-01

Torque ripple reduction in PMSM for FCEVs using ANFIS controller

10.11591/ijpeds.v17.i2.pp885-893
Shilpa Rao Hosabettu , Pushpa Rajesh Viswanathan
Globally, there is a growing emphasis on switching to green energy, particularly in the transportation sector, due to the effects of global warming, as seen by rising carbon footprints. Fuel cell electric vehicles (FCEVs) are one such technology that has attracted a lot of interest because of their availability, ease of use, high efficiency, and silent operation. Fuel cells are employed along with batteries to drive the vehicle much farther. Motors like permanent magnet synchronous motor (PMSM) provide the driving force for the vehicle, owing to their high torque at variable speeds and compactness. In such systems, it is necessary to have intelligent controllers that can align with the load requirement by means of a consistent and optimized power distribution. The torque ripple phenomenon, which has an impact on dynamic performance and operational stability, is one of the main limitations in the operation of PMSMs. In this work, smart control techniques, which are a combination of adaptive neuro fuzzy inference systems (ANFIS) and proportional-integral (PI) control, are employed to demonstrate the application of PMSM in conjunction with field-oriented control (FOC). Simulation results indicate that the proposed ANFIS-based FOC reduces torque ripple as compared to conventional PI control under varying load conditions.
Volume: 17
Issue: 2
Page: 885-893
Publish at: 2026-06-01

Performance assessment of PSO variants for optimal photovoltaic and DSTATCOM allocation in radial distribution networks

10.11591/ijpeds.v17.i2.pp946-957
Mohamed Kherchi , Hacene Mellah , Souhil Mouassa , Anwar Fellahi
This work presents a comparative evaluation of adaptive particle swarm optimization (PSO) variants for the optimal placement and sizing (OPS) of photovoltaic-based distributed generation (PV-DG) and DSTATCOM units in the standard IEEE 33-bus radial distribution network (RDN). Five adaptive PSO algorithms are investigated, namely adaptive acceleration coefficients PSO (AAC-PSO), autonomous particle groups PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sine-cosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). The optimization framework is structured as a single-objective problem focused on maximizing the active power loss index (APLI), which is used as a normalized indicator associated with active power loss reduction. To further assess the technical quality of the obtained solutions, two additional performance indicators are considered, namely the total voltage deviation (TVD) and the voltage stability index (VSI). The simulation outcomes indicate that the TVA-PSO algorithm exhibits superior overall performance compared to other evaluated variants in terms of convergence behavior and solution quality. In particular, it achieves the highest APLI value of 92.52%, corresponding to an active power loss reduction of 91.91%, with active power losses (APL) reduced from 210.99 kW to 17.07 kW. In addition, the obtained solution significantly improves the network voltage profile (VP) and enhances voltage stability. These findings provide evidence that the effectiveness of adaptive PSO strategies for optimizing PV-DG and DSTATCOM integration in RDN.
Volume: 17
Issue: 2
Page: 946-957
Publish at: 2026-06-01

Enhancing Bitcoin price forecasting: a comparative analysis of advanced time series models with hyperparameter optimization

10.11591/ijict.v15i2.pp535-544
Amine Batsi , Mohamed Biniz , Rachid El Ayachi
This paper evaluates state-of-the-art time series forecasting to predict next day Bitcoin prices via distinct architectures and methodologies in a real-time setting. We study six advanced models, KAN, TimesNet, NBEATS, NHITS, PatchTST and BiTCN, applied to a Jan 1, 2023, to Dec 1, 2024. We simulate real world applications via a rolling forecast strategy, in which we predict daily prices from the most recent data. The dataset consists of daily Bitcoin closing prices and data preprocessing and integrity checks for its constituent data. Additionally, rigorous accuracy and reliability were investigated using performance metrics such as the MAE, RMSE, MAPE, and R². NBEATS and NHITS were the top performers, achieving an R² score of 0.967, explaining complex patterns in volatile cryptocurrency data. The specific importance of model architecture and further hyperparameter optimization in achieving higher forecasting accuracy is highlighted in this study. The practical implications of these findings for the advancement of time series forecasting in financial markets are leveraged here, where timely and accurate forecasts are critical.
Volume: 15
Issue: 2
Page: 535-544
Publish at: 2026-06-01

Vietnamese EFL teachers’ cultural integration in business English classes: an ecological perspective

10.11591/ijere.v15i3.37919
Pham Thi Minh Thuy , Truong Minh Hoa
Cultural integration in English as a Foreign Language (EFL) instruction has become an important focus in Vietnamese universities, particularly in business and finance programs preparing students to navigate intercultural communication in global professional environments. While existing research has explored how language teachers include cultural elements in their instruction, limited attention has been given to understanding how these practices are shaped by the complex ecological systems where personal beliefs, institutional structures, resources, and sociocultural conditions interact dynamically. Addressing this gap, the present study investigates how EFL teachers at a Vietnamese public university integrate cultural content into their instruction. Guided by an ecological framework, the research employed a sequential mixed-methods design, collecting data through 67 questionnaires and 10 semi-structured interviews. Findings indicate that teachers prioritized international and target cultures, while local Vietnamese cultural content was largely underrepresented. Though teachers expressed strong commitment to fostering students’ intercultural competence for international business communication, their pedagogical practices were constrained by ecological factors like limited instructional time, rigid curricula, and a lack of localized, business-relevant resources. In response, several teachers leveraged personal agency and digital tools to adapt cultural content despite structural limitations. The study highlights the need for ecologically responsive cultural instruction in Business English classrooms. 
Volume: 15
Issue: 3
Page: 2618-2631
Publish at: 2026-06-01

Practices and strategies of informal assessments on grammar rules among second language learners

10.11591/ijere.v15i3.37717
Jason V. Chavez , Rolly G. Salveleon , Ma. Theresa L. Eustaquio , Haydee G. Adalia , Ma. Pilar T. Rosaldo , Joseph B. Quinto , Salita D. Dimzon , Sar-Ana M. Abdurasul , Rasmil T. Abdurasul , Ivy M. Nazareth
While informal assessment offers authentic insights into second language (L2) grammar acquisition, the specific strategies and implementation challenges remain underexplored. This study investigated the practices employed by L2 educators in conducting informal grammar assessments and the obstacles they encounter. Using a qualitative exploratory design, 20 purposively selected language educators from diverse linguistic regions in the Philippines participated in semi-structured interviews. Data were analyzed using reflexive thematic analysis. The findings revealed a pedagogical shift from static testing to stealth monitoring, characterized by contextualized micro-checks, gamified strategies to lower affective filters, and peer-scaffolded evaluation. However, significant challenges emerged, specifically the tension between assessment validity and reliability, as well as cognitive overload due to the dual burden of instruction and real-time data recording. The study concluded that while educators prioritize the authenticity of low-stakes assessment, effective implementation requires enhanced assessment literacy and structural support to mitigate subjectivity and operational fatigue.
Volume: 15
Issue: 3
Page: 2379-2389
Publish at: 2026-06-01

Real-time implementation and comparative analysis of fault-tolerant control strategies for induction motor drives

10.11591/ijpeds.v17.i2.pp894-907
Asmaa Hammou , Mokhtar Bendjebbar , Mohammed Benslimane
For nearly five decades, the induction motor has been the most widely used electrical machine in industry due to its robustness, simplicity, and low cost, supported by advances in power electronics enabling effective performance control. While DC motors were previously favored for their ease of speed and torque regulation, induction motors have gained prominence because they do not require brushes and involve fewer wear-prone components, resulting in reduced maintenance and improved reliability. Consequently, they are widely employed in industrial applications and emerging fields such as electric and hybrid vehicles. This study presents a comparative analysis of two fault-tolerant control (FTC) strategies: field-oriented control (FOC) and direct torque control (DTC). The evaluation focuses on sensitivity to parameter variations, dynamic performance, and steady-state behavior. Both strategies, classified under vector control techniques, are implemented in real time using a dSPACE platform to control an induction motor under an open-circuit fault in a two-level inverter. Results demonstrate that the DTC-based FTC approach offers superior robustness and stability compared to the IFOC-based method, particularly under fault conditions, load disturbances, and speed variations.
Volume: 17
Issue: 2
Page: 894-907
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

Adaptive notch filter: An alternative synchronizer for effective performance of active power filter under challenging grid conditions

10.11591/ijpeds.v17.i2.pp1221-1230
Yap Hoon , Kuew Wai Chew , Kenny Sau Kang Chu , Siti Zaliha Mohammad Noor
Harmonic distortion issues on modern power systems are becoming highly significant due to the increasing integration of renewable energy sources, electric vehicles, and smart technologies. These distortions, mainly caused by the operation of power electronics devices, potentially degrade overall system quality, increase losses, and shorten equipment lifespan if they are not properly mitigated. Shunt active power filters (SAPFs) are found to be most effective against current harmonics issues, but their performance strictly depends on accurate grid synchronization. In this paper, an alternative method developed based on the adaptive notch filter (ANF) concept is proposed for reliable grid synchronization under challenging conditions. The proposed ANF-based synchronizer is modelled in MATLAB/Simulink and benchmarked against the existing self-tuning filter (STF) method under four cases involving sinusoidal, distorted, noisy, and distortion-with-noise grid conditions. Simulation findings demonstrate that the proposed method enables the connected SAPF to effectively mitigate harmonics by providing low total harmonic distortions (2.71% to 2.82%) and minimal phase deviation (0.2° to 0.5°), while maintaining the accuracy of fundamental current between 94.48% to 97.21%. As a result, the overall power factor of the system is raised to near unity, confirming the ability of the proposed ANF-based method to serve as a better alternative for SAPF synchronization.
Volume: 17
Issue: 2
Page: 1221-1230
Publish at: 2026-06-01

Dual mode control of an integrated on-board charger powered BLDC drive

10.11591/ijpeds.v17.i2.pp1058-1068
Caroline Ann Sam , Varghese Jegathesan
The high adoption of electric vehicles in transportation has created a demand for compact, efficient, and cost-effective charging solutions for them. Conventional onboard chargers are often bulky, which adds to the overall cost of the drive system, whereas off-board charging infrastructure remains limited. In order to address these issues, this work illustrates the design and modelling of an active power factor corrected integrated onboard charger which gets reconfigured from the electric vehicle drive train components. The proposed circuit setup is designed to work in dual mode, i.e., in the role of a DC-DC converter while charging the vehicle battery and as a three-phase inverter while driving the vehicle. The front-end power factor correction circuit, in addition to the reconfigured DC-DC converter, charges the 24 V, 20 Ah lead acid battery under constant current constant voltage (CC-CV) mode, achieving a power factor close to unity. Modelling and control of the proposed 200 W reconfigurable converter-fed 24 V, 180 W brushless direct current (BLDC) drive is validated using MATLAB/ Simulink Software. Simulation results demonstrate a power factor of 0.996 in grid-connected operation with a total harmonic distortion (THD) of 4.96%. The proposed architecture achieves a compact structure with only 8 switches enabling charging, propulsion and regenerative braking operation. The proposed converter thus contributes to a cost-effective electric vehicle and provides the scope of future extension to vehicle to home (V2H), vehicle to load (V2L), and vehicle to vehicle (V2V) applications as well.
Volume: 17
Issue: 2
Page: 1058-1068
Publish at: 2026-06-01

Intelligent home automation framework using sensor fusion and machine learning for energy efficiency and thermal comfort

10.11591/ijict.v15i2.pp545-552
Franklin Ovuolelolo Okorodudu , Gracious Chukwuweike Omede , Etinosa Eugene Osawe
This paper presents an innovative, intelligent home automation framework integrating sensor fusion and machine learning to promote energy efficiency and thermal comfort in residential settings. Utilising low-cost hardware such as the Arduino Uno R3, passive Infrared (PIR) sensors, KY-018 photoresistors, and KY-028 temperature sensors, the system achieves a human presence detection accuracy of 95.3% via a random forest classifier. Over a three-month period, testing in several homes showed that the system is 99.7% reliable, responds in 1.2 seconds, and costs 85% less than commercial options. This research lays the groundwork for sustainable smart homes by providing a mathematical model for optimizing energy use and a unified modeling language (UML) model of the system architecture. These results show how important it is to have open-source technology that is cheap and could help smart building systems spread around the world. The study utilized a controlled experimental design featuring five families, with sensor data gathered at 10-second intervals over a three-month period. A random forest classifier trained on 10,000 labeled data points could correctly guess whether or not a person was present 94.8% of the time and 95.7% of the time. The framework is useful because it combines cheap sensors with a lightweight machine-learning pipeline that can work on small microcontrollers. This solves the long-standing problem of the cost performance gap seen in prior smart-home deployments.
Volume: 15
Issue: 2
Page: 545-552
Publish at: 2026-06-01

Neuro-fuzzy control on a permanent magnet synchronous generator integrated in a wind system

10.11591/ijpeds.v17.i2.pp1304-1312
Mohammed Aoumri , Ibrahim Yaichi , Harrouz Abdelkader , Patrice Wira
This paper introduces a control strategy for a synchronous generator in a wind energy system using an adaptive neuro-fuzzy approach. The suggested controller, based on neuro-fuzzy logic (NFLC), is meant to govern a permanent magnet synchronous generator (PMSG) often utilized in wind power applications. The generator's output voltage phase, phase current, reactive power, active power, angular velocity, and DC voltage are all under control. The adaptive neuro-fuzzy controller efficiently stabilizes all variables in a brief amount of time, according to simulation results. The effectiveness and robust performance of the suggested control system are verified by a number of simulated scenarios. The resilience of fuzzy logic control (FLC) and NFLC systems was compared. The study carefully tested the performance of both control techniques under varied operating settings and disturbance situations to determine their relative stability, flexibility, and efficacy in sustaining desired system behavior.
Volume: 17
Issue: 2
Page: 1304-1312
Publish at: 2026-06-01

Photovoltaic-inductive wireless charging for electric vehicles

10.11591/ijpeds.v17.i2.pp849-857
Azra Zaineb , P. Nagabushanam , Kalagotla Chenchireddy , Radhika Dora , Naresh Jella , Shabbier Ahmed Sydu
The growing demand for electric vehicles (EVs) necessitates efficient and eco-friendly charging methods. This study presents a photovoltaic-inductive wireless charging (PIWC) system, which integrates solar energy harvesting with inductive power transfer (IPT) to enable seamless operation without physical connectors. The system utilizes solar photovoltaic (PV) panels to generate renewable energy, which is then converted and transmitted wirelessly using resonant inductive coupling. This eliminates the need for physical connections, reducing wear and maintenance while supporting both stationary and dynamic charging applications. To enhance performance, maximum power point tracking (MPPT) controllers optimize solar energy utilization. Power electronics and control strategies regulate the energy transfer, ensuring efficient and stable operation. Additionally, IoT-based monitoring enables real-time system analysis and performance tracking. Through simulations and prototype evaluations, the system's feasibility, efficiency, and environmental impact are assessed. Results indicate that PIWC can minimize grid dependency, providing a sustainable, autonomous, and convenient charging solution for EVs. This innovation contributes to cleaner transportation and the advancement of renewable energy-driven mobility.
Volume: 17
Issue: 2
Page: 849-857
Publish at: 2026-06-01

Resilient EV charging station network design using AI algorithms

10.11591/ijpeds.v17.i2.pp1543-1552
Deepa Somasundaram , N. Krishnamoorthy , J. Vijay Anand , R. Priyanka , T. Santhana Krishnan , Kirubakaran Dhandapani
This paper proposes an AI-driven resilient network design framework for optimal electric vehicle (EV) charging station placement under stochastic demand and dynamic grid constraints. The proposed approach uniquely integrates long short-term memory (LSTM) based spatiotemporal demand forecasting with a hybrid genetic algorithm-particle swarm optimization (GA-PSO) model for multi-objective station placement. In addition, a deep reinforcement learning (DRL) agent is incorporated to enhance adaptive resilience under real-time grid disturbances. The framework minimizes installation cost, reduces user travel distance, and improves grid stability while ensuring equitable accessibility. The model is evaluated under multiple scenarios, including peak demand, station outages, renewable intermittency, and grid capacity reduction. Results demonstrate that the proposed hybrid AI framework achieves a resilience index of 0.92, reduces travel distance by 54%, and lowers installation cost by up to 16% compared to conventional approaches such as linear programming (LP) and K-means clustering. The integration of renewable energy further reduces peak grid dependency by 18%. The proposed methodology provides a scalable and practical solution for designing sustainable and resilient EV charging infrastructure in smart urban environments.
Volume: 17
Issue: 2
Page: 1543-1552
Publish at: 2026-06-01
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