International Journal of Power Electronics and Drive Systems (IJPEDS)
International Journal of Power Electronics and Drive Systems (IJPEDS, ISSN: 2088-8694, a SCOPUS indexed Journal) is the official publication of the Institute of Advanced Engineering and Science (IAES). The scope of the journal includes all issues in the field of Power Electronics and drive systems. Included are techniques for advanced power semiconductor devices, control in power electronics, low and high power converters (inverters, converters, controlled and uncontrolled rectifiers), Control algorithms and techniques applied to power electronics, electromagnetic and thermal performance of electronic power converters and inverters, power quality and utility applications, renewable energy, electric machines, modelling, simulation, analysis, design and implementations of the application of power circuit components (power semiconductors, inductors, high frequency transformers, capacitors), EMI/EMC considerations, power devices and components, sensors, integration and packaging, induction motor drives, synchronous motor drives, permanent magnet motor drives, switched reluctance motor and synchronous reluctance motor drives, ASDs (adjustable speed drives), multi-phase machines and converters, applications in motor drives, electric vehicles, wind energy systems, solar, battery chargers, UPS and hybrid systems and other applications.
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<![CDATA[THD and spectral performance analysis of two-triangle RPWM for inverter applications ]]>
<![CDATA[Jegadeeswari, G.]]>;
<![CDATA[Sundar, R.]]>;
<![CDATA[Manikandan, S. P.]]>;
<![CDATA[Poovannan, E.]]>;
<![CDATA[Rajarajachozhan, C.]]>;
<![CDATA[Batumalay, M.]]>;
<![CDATA[Kalpana, Sukumar]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp370-382
<![CDATA[Pulse width modulation (PWM) is essential for voltage source inverters (VSI) to generate high-quality voltage outputs. Conventional deterministic PWM generates predictable harmonics, causing clusters that increase acoustic noise. Random PWM (RPWM) disperses harmonic power over a wider frequency range, reducing noise and electromagnetic interference. Many RPWM techniques improve inverter quality, but only partially suppress dominant harmonics and lack effective harmonic spreading. Most studies focus on simulations with limited FPGA implementation or hardware validation. The use of digital tools like VHDL, ModelSim, and MATLAB co-simulation remains underutilized. This paper proposes two-triangle RPWM strategies to enhance harmonic dispersion and reduce total harmonic distortion (THD). Co-simulation results are shown for both SPWM and RPWM, along with comparisons of fundamental voltages, THD, and HSF across different modulation indexes. Additionally, synthesis data for the Xilinx XC3S500E FPGA processor is supplied. The last section offers a comparative analysis and experimental validation of SPWM and RPWM. These techniques enable enhanced inverter performance, lower acoustic noise, and process innovations in power electronic systems.]]>
<![CDATA[Improving voltage stability in isolated renewable energy microgrids using virtual synchronous generators ]]>
<![CDATA[Osman, Ahmad Supawi]]>;
<![CDATA[Abidin, Aidil Azwin Zainul]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp683-695
<![CDATA[The integration of renewable energy systems (RES) and distributed generation (DG) into microgrids introduces significant challenges in maintaining voltage stability due to intermittent generation and reduced rotational inertia. This systematic review critically examines advanced control strategies aimed at enhancing voltage resilience in isolated RES-driven microgrids. Particular focus is placed on virtual synchronous generators (VSGs), which emulate electromechanical dynamics of synchronous machines via state-space modeling, and model predictive control (MPC), which enables real-time control optimization under multi-constraint scenarios. The review synthesizes literature on coupling–decoupling behavior, impedance sensitivity, and dynamic voltage response under varying load conditions. Additionally, it evaluates the role of hardware-in-the-loop (HIL) platforms and Runge-Kutta-based simulations in validating control models for real-time deployment. A structured framework is proposed, aligning VSG-based inertia emulation with predictive control to address voltage dips, oscillations, and transient instabilities. The findings highlight both theoretical gaps and implementation opportunities for achieving robust voltage stabilization in next-generation microgrids.]]>
<![CDATA[Application of machine learning for production optimization and predictive maintenance in an iron processing plant ]]>
<![CDATA[Lahcen, Lakhdari]]>;
<![CDATA[Habbab, Mohamed]]>;
<![CDATA[Abdellah, Alhachemi Moulay]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp765-776
<![CDATA[The modern metallurgical industry requires advanced solutions for process optimization, cost reduction, and predictive maintenance. This paper proposes a unified simulation-based framework using machine learning (ML) to jointly address production optimization and maintenance prediction in a virtual iron processing environment. Several ML models, including random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine (SVM), and k-nearest neighbors (k-NN), were evaluated on synthetic datasets representing production, maintenance, and transport processes. A reproducible methodology was adopted, including preprocessing, time-aware data splitting, and cross-validation to prevent information leakage. Model performance was assessed using F1-score, area under the receiver operating characteristic curve (AUC), and regression metrics. Tree-based models achieved near-perfect classification performance (AUC ≈ 1, precision and recall > 0.99), while light gradient boosting machine (LightGBM) and CatBoost provided the best regression accuracy. Feature importance analysis using SHapley Additive exPlanations (SHAP) identified vibration and temperature as key maintenance indicators. Although based on simulation, the framework is designed for integration with supervisory control and data acquisition (SCADA) and the Industrial Internet of Things (IIoT), supporting real-time industrial deployment and alignment with operational key performance indicators.]]>
<![CDATA[Reliability-constrained optimal scheduling of PV-based microgrids using deterministic time-series forecasting and load prioritization strategies ]]>
<![CDATA[Wais, Dunya Sh.]]>;
<![CDATA[Abbood, Huda A.]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp250-266
<![CDATA[This paper presents an advanced MPC-based energy scheduling framework for islanded microgrids operating under uncertain and dynamic conditions where photovoltaic (PV) generation and energy storage systems (ESS) are integrated, and load management is hierarchically prioritized. The framework employs a hybrid ARIMA and random forest forecasting model to improve day-ahead and intra-day predictions of PV generation and load demand, enabling intelligent demand response, prioritized load shedding, and adaptive storage operation. Moreover, the proposed framework incorporates time-of-use (TOU) pricing and load importance weighting to minimize operational costs while ensuring a reliable power supply for critical loads. Simulation results across four operational scenarios demonstrate that the proposed method achieves approximately 32% improvement in critical load protection, 30% reduction in total operating cost, and 33.3% decrease in total load shedding compared to conventional MPC-based approaches. The proposed approach, therefore, provides a comprehensive, dynamic, and cost-efficient solution for microgrid scheduling and can be extended to multi-microgrid cluster applications in future research.]]>
<![CDATA[Grey wolf optimization approach to optimal backstepping control for buck converter output voltage regulation ]]>
<![CDATA[Mouslim, Sana]]>;
<![CDATA[Imodane, Belkasem]]>;
<![CDATA[Outana, Imane]]>;
<![CDATA[Oubella, M’hand]]>;
<![CDATA[Boulaoutaq, El Mahfoud]]>;
<![CDATA[Ajaamoum, Mohamed]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp640-652
<![CDATA[DC-DC converters are essential in regulating voltage levels within DC power systems, relying on high-efficiency electronic switching devices such as MOSFETs to ensure effective power conversion. Despite their widespread use, one of the major challenges encountered in practical implementations lies in accurately tuning controller parameters particularly for nonlinear approaches such as the backstepping controller. While recent studies have demonstrated the effectiveness of particle swarm optimization (PSO) in enhancing backstepping control performance, further improvements remain possible. In this work, we propose the grey wolf optimization (GWO) algorithm as an advanced and efficient technique for the optimal tuning of backstepping controller parameters. The goal is to minimize the voltage tracking error between the reference and the output of the DC-DC buck converter, ensuring enhanced dynamic response and stability. Additionally, the proposed control strategy has been experimentally implemented and validated in a photovoltaic context, demonstrating its practical relevance and strong potential for real-world energy conversion applications.]]>
<![CDATA[Development of a mathematical model for electric drive dynamics in belt conveyors: A Simulink-based analysis of transient behavior ]]>
<![CDATA[Alzyoud, Khalaf Y.]]>;
<![CDATA[Alkasassbeh, Jawdat S.]]>;
<![CDATA[Al-Rawashdeh, Ayman Y.]]>;
<![CDATA[Pavlov, Vlademer Е.]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp69-81
<![CDATA[This paper presents a detailed study of developing a mathematical model and experimental analysis of electric drive processes in belt conveyors. The proposed model simplifies the complex real mechanical system by substituting distributed parameters, such as the transported load's mass and the traction element's elasticity, with concentrated equivalents. A comprehensive investigation of key transient processes including stator currents speed, torque and resistance forces was performed using MATLAB's Simulink environment. The findings reveal significant differences in performance between the initial startup phase and operation under loaded conditions. To validate the model's accuracy, the authors employed numerical analyses utilizing regression metrics such as root mean square error (RMSE) and correlation coefficients. The results show that the proposed model significantly outperforms similar models in the literature with a notable RMSE of 12.5 A for stator current, reflecting an 18% improvement and 8.7 Nm for torque prediction, indicating a 15% enhancement. Furthermore, the model achieved a correlation coefficient of 0.98, confirming its high accuracy in experimental data fitting. By effectively capturing oscillatory phenomena during both unloaded and loaded startup conditions, this work establishes the model as a reliable representation of belt conveyor dynamics, setting a new benchmark in the field.]]>
<![CDATA[Fuzzy logic direct torque control of induction motors using three-level NPC inverter ]]>
<![CDATA[Chennane, Jamila]]>;
<![CDATA[Ouboubker, Lahcen]]>;
<![CDATA[Akhsassi, Mohamed]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp180-194
<![CDATA[Induction motor drives are extensively used for their robustness and efficiency, but precise control remains difficult under dynamic conditions. Conventional direct torque control offers a simple structure and fast response, but is limited by torque ripple, flux distortion, and poor low-speed performance. This paper proposes a fuzzy logic-based direct torque control (FDTC) combined with a three-level neutral point clamped (NPC) inverter. A fuzzy inference system (FIS) replaces the hysteresis comparators and switching table, while speed regulation is improved using a PI-fuzzy controller. MATLAB/Simulink simulations under speed variations and load disturbances demonstrate reduced torque and flux ripples, smoother flux trajectories, improved current waveforms, and faster transient response compared with classical DTC. These results confirm that the FDTC–NPC approach provides a robust and efficient solution for advanced applications such as industrial automation, renewable energy, and electric vehicles.]]>
<![CDATA[A novel technique for induction heating dryer with temperature and voltage control for power inverter ]]>
<![CDATA[Srivichai, Jeerapong]]>;
<![CDATA[Somsai, Kittaya]]>;
<![CDATA[Phuphanin, Akkachai]]>;
<![CDATA[Pornsuwancharoen, Nithiroth]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp438-452
<![CDATA[This study presents a novel prototype of an induction heating dryer integrating hysteresis control with phase-shifted pulse width modulation (PWM) for the first time. The system replaces conventional resistance heating, improving energy efficiency and thermal stability. The 2 kW prototype comprises a drying chamber and a hot air unit with controlled airflow of 1.5 m/s. Phase angle adjustment reduces voltage, current, and power consumption while maintaining the power factor within acceptable limits. The temperature control maintains stability within ±1 °C of the setpoint. The results demonstrate fast, energy-efficient, and precise drying, offering potential benefits for food processing and textile industries, and providing a foundation for future development of intelligent, energy-efficient induction dryers.]]>
<![CDATA[A framework for robust PID controller design: an optimization-based approach for inductive loads ]]>
<![CDATA[Tadjeddine, Ali Abderrazak]]>;
<![CDATA[Kamline, Miloud]]>;
<![CDATA[Smail, Latifa]]>;
<![CDATA[Djelaila, Soumia]]>;
<![CDATA[Reriballah, Hafidha]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp359-369
<![CDATA[This paper presents a comprehensive comparative study of proportional-integral-derivative (PID) controller tuning methodologies for inductive load applications across three representative scenarios. We systematically evaluate classical methods (Ziegler-Nichols, internal model control) against global optimization algorithms (genetic algorithm (GA), particle swarm optimization (PSO)) applied to resistor-resistor-inductor (RRL) circuit models. Results demonstrate that PSO achieves superior performance for moderate-to-slow systems, reducing settling time by 84% while completely eliminating overshoot compared to Ziegler-Nichols. The algorithm automatically discovers optimal PI controller structures, simplifying implementation. However, for ultra-fast systems (time constants < 1 ms), internal model control proves more reliable, achieving 0.84 ms settling with only 0.16% overshoot. Optimized controllers demonstrate exceptional robustness, maintaining stability under ±50% parameter variations and effectively rejecting disturbances. This research provides engineers with a scenario-based framework for method selection, moving beyond heuristic tuning to achieve previously unattainable performance levels. The findings establish optimization-based tuning as a systematic, reliable approach for high-performance control system design in industrial applications.]]>
<![CDATA[Design and improvement of dynamic performance of solar-powered BLDC motor for electric vehicles in agricultural applications ]]>
<![CDATA[Medegar, Savitri]]>;
<![CDATA[Sasikala, M.]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i1.pp168-179
<![CDATA[One of the most pressing environmental problems is the rapid increase in the production of greenhouse gases by transportation vehicles. This paper looks into SPEVs, or solar-powered electric vehicles. The answer to the problems of transportation-related pollution and fuel usage. In an electric vehicle, the power comes from a battery that may be charged by solar panels or any other external power source. By making use of the perturb and observe (P&O) maximum power point tracking (MPPT) controller, one can achieve maximum power. The DC voltage that the photovoltaic module produces is amplified when it is fed into a voltage source inverter (VSI) via this enhanced output. The tool for the job here is a buck-boost converter. To power their wheels, EVs rely on brushless direct current (BLDC) motors and variable speed inverters (VSIs), which transform DC power from solar panels into AC power. We compare the efficiency of electric vehicles (EVs) attained by raising converter voltages and battery state of charge (SoC) using a PI controller, and we look at the performance of photovoltaic (PV) and brushless linear direct current (BLDC) motors. We use MATLAB/Simulink to do the validation.]]>
