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.
Articles
2,721 Documents
<![CDATA[Improved efficiency of DC-DC converter through modified switched inductor-switched capacitor configuration using ANN optimization for photovoltaic sources ]]>
<![CDATA[Ramalingam Seyezhai]]>;
<![CDATA[A. S. Athish]]>;
<![CDATA[K. Ashwathy]]>;
<![CDATA[R. Akash Karthick]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp1142-1151
<![CDATA[In photovoltaic applications, the DC-DC converters are of utmost importance, allowing for the regulation of output voltages to satisfy system needs. A preliminary analysis of several converter topologies revealed that the switched inductor switched capacitor (SISC) configuration provides better performance with lower ripple, reduced stress, and better voltage boosting. The performance of the proposed topology is compared with different SISC topologies, and it is observed that the chosen configuration is suitable for photovoltaic sources. The efficiency of the suggested topology is further enhanced by using artificial neural networks (ANN) for regulating the switching frequency and duty cycle of the switch. In this work, two ANNs are used to train both switching frequency and duty cycle. For the training process, the Levenberg-Marquardt algorithm is used to achieve fast convergence with precise predictions. A prototype model is constructed and tested to validate the simulation results. The results prove that the projected converter achieves considerable efficiency and is suited for photovoltaic (PV) systems.]]>
<![CDATA[Performance optimization of hybrid renewable energy systems with real-time load forecasting using grey wolf-based predictive models ]]>
<![CDATA[Olumuyiwa Ajibola Awoniyi]]>;
<![CDATA[Evans Chinemezu Ashigwuike]]>;
<![CDATA[Chijioke Ejimofor]]>;
<![CDATA[Timothy Oluwaseun Araoye]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp1382-1395
<![CDATA[The performance optimization of hybrid renewable energy systems (HRES) is crucial for enhancing the efficiency, reliability, and sustainability of energy production. This study focuses on the integration of real-time load forecasting prediction using a grey wolf optimization (GWO)-based predictive model. The proposed methodology aims to address the challenges associated with the intermittent nature of renewable energy sources, such as solar and wind power, by providing accurate forecasts for load demands and solar irradiance. Real-time data from sensors and environmental parameters are incorporated to forecast the energy load and solar irradiance over short-term periods, which are then used to optimize the energy storage and generation components of the HRES. The GWO algorithm, known for its high accuracy and computational efficiency, is employed to optimize the dispatch of power from various sources while minimizing energy losses and ensuring system stability. The integration of GWO with real-time forecasting not only enhances the predictive capability of the system but also improves the overall economic viability of HRES by reducing operational costs and carbon emissions. This study demonstrates the potential of using intelligent optimization techniques and real-time forecasting for the sustainable operation of hybrid renewable energy systems, contributing to the development of smarter and more resilient energy grids.]]>
<![CDATA[Advanced soft-switching high-gain Re Boost Luo converter for enhanced efficiency in photovoltaic systems ]]>
<![CDATA[Vendoti Suresh]]>;
<![CDATA[Dondapati Ravi Kishore]]>;
<![CDATA[T. Vijay Muni]]>;
<![CDATA[P. Hari Krishna Prasad]]>;
<![CDATA[Pydi Bala Krishna]]>;
<![CDATA[A. V. G. A. Marthanda]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp1177-1187
<![CDATA[This work presents an innovative approach to improving efficiency and performance in photovoltaic (PV) systems through the development of a soft-switching high-gain Re Boost Luo converter. This converter integrates advanced soft-switching techniques to minimize switching losses, thereby enhancing overall system efficiency, which is crucial for applications requiring substantial voltage amplification from PV sources. The Re Boost Luo converter, with its inherent high-gain capability, facilitates superior voltage conversion ratios, enabling optimal energy extraction from PV panels across varying environmental conditions. The presented converter's design focuses on reducing electromagnetic interference (EMI) and alleviating stress on switching components, thereby extending their operational lifespan and reliability. Detailed modeling and performance analysis were carried out using the MATLAB/Simulink simulation environment, which allowed for comprehensive evaluation of the converter's functionality. Simulation results confirm that the converter achieves significant improvements in voltage gain, energy conversion efficiency, and system reliability, effectively addressing common challenges associated with high-voltage PV applications. This study underscores the converter's potential to advance renewable energy technologies by providing a robust solution for high-efficiency energy conversion in PV systems.]]>
<![CDATA[Smart adaptive CC-CV charger with PSO-accelerated load identification and fuzzy duty-cycle regulation ]]>
<![CDATA[Indhana Sudiharto]]>;
<![CDATA[Era Purwanto]]>;
<![CDATA[Muhamad Milchan]]>;
<![CDATA[Alifian Nur Rahmadika]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp1045-1057
<![CDATA[This paper presents an adaptive constant-current/constant-voltage (CC-CV) charger architecture, meticulously designed to address a key challenge in smart chargers. This challenge involves recognizing various battery types and applying the appropriate charging profile expeditiously, without requiring user intervention. The system integrates a particle swarm optimization (PSO) algorithm for ultra-fast load identification with a Mamdani-type fuzzy logic controller for precise duty cycle regulation. The PSO mechanism is capable of determining the optimal initial duty cycle in less than 500 milliseconds. Subsequent to this preliminary initiation, the fuzzy logic controller guarantees the effectiveness of current and voltage regulation during the charging phases. The simulation results obtained from this study validate the system's robustness, as evidenced by the consistent maintenance of voltage ripple below ±0.06 V and current ripple below ±0.04 A. These findings demonstrate the efficacy of the proposed approach in achieving fast, stable, and safe multi-load battery charging. The chemistry-agnostic design of the battery pack is extendable to any battery pack following the CC-CV paradigm, making it highly suitable for practical applications that demand flexibility and high reliability.]]>
<![CDATA[Fuzzy genetic control for linear speed in multi-machine systems ]]>
<![CDATA[Kaddouri Youssouf]]>;
<![CDATA[Bouchiba Bousmaha]]>;
<![CDATA[Baba Mohammed]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp908-919
<![CDATA[In today’s fast-moving industrial sectors which include paper, textile, and plastic manufacture the core of production quality is in the precise coordination of multi-drive systems. While PI controllers are the mainstay of the industry, they do have issues in that they struggle with the nonlinearity and dynamics of large-scale windings, which in turn causes instability and product integrity issues. To that end, this paper presents an optimized fuzzy-genetic controller (FLC-GA), which we put forward as a better linear speed synchronization solution. We used genetic algorithms in the tuning of fuzzy logic parameters, which also takes out the very time-consuming task of manual calibration, and at the same time sees a great increase in the system’s ability to deal with process variability. We put our FLC-GA through its paces in a head-to-head comparison with the classic PI and PI-PSO controllers. What we found was that our proposed controller did very well; we saw zero overshoot, a quick 0.5 s settling time, and the total elimination of tension ripples. Also, we saw from a 13.2% change in system inertia that the FLC-GA did a 65% better job in terms of speed accuracy and stability than what we see from standard PI control. We present the FLC-GA not only as a theoretical improvement but as a very robust, high-performance solution in the very tough field of continuous industrial synchronization.]]>
<![CDATA[Dual mode control of an integrated on-board charger powered BLDC drive ]]>
<![CDATA[Caroline Ann Sam]]>;
<![CDATA[Varghese Jegathesan]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp1058-1068
<![CDATA[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.]]>
<![CDATA[Performance assessment of PSO variants for optimal photovoltaic and DSTATCOM allocation in radial distribution networks ]]>
<![CDATA[Mohamed Kherchi]]>;
<![CDATA[Hacene Mellah]]>;
<![CDATA[Souhil Mouassa]]>;
<![CDATA[Anwar Fellahi]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp946-957
<![CDATA[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.]]>
<![CDATA[Physics-informed reinforcement learning for adaptive high-frequency injection in encoderless low-voltage PMSM drives ]]>
<![CDATA[Surendar Aravindhan]]>;
<![CDATA[Manoharan Kavitha]]>;
<![CDATA[J. Karthika]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp873-884
<![CDATA[It is difficult to control permanent magnet synchronous motor (PMSM) drives running at extra-low voltages with encoderless control because the back-EMF signal to estimate rotor position is weak, and this requires the injection of high-frequency (HF) signals. Traditional methods use constant or manually tuned injection levels, and these tend to cause large torque ripple, inaccurate estimation when under dynamic loading, and an inability to counteract parameter drift. The paper is related to the issue of online optimal HF injection amplitude choice in the encoderless 48 V PMSM drives and proposes a physics-inspired reinforcement learning (PIRL) system. This is aimed at obtaining the right low-speed positioning and reducing the torque ripple and power losses on different operating conditions. The suggested approach incorporates directly into the reinforcement learning reward terms the PMSM electromagnetic voltage equations, which restrict exploration to physically consistent space and enhance stability in the learning process. The PIRL agent is trained in a deep deterministic policy gradient architecture in a MATLAB/Simulink-Python co-simulation environment, based on which the PIRL agent adjusts the injection amplitude of HF in real time. Simulation outcomes show that the suggested methodology reaches approximately four times faster convergence with conventional reinforcement learning and reaches up to 65 percent of torque ripple reduction without a disturbed position estimation when operated in a speed range of 0-500 rpm. The findings show that physics-informed learning offers an efficient and energy-saving solution to adaptive encoderless control in extra-low-voltage PMSM drives, which has better resilience to changes in parameters with a low computational cost.]]>
<![CDATA[A neural learning algorithm for online rotor resistance estimation in sensorless induction motor drive systems ]]>
<![CDATA[Tuan V. Pham]]>;
<![CDATA[Nguyen H. Thai]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp920-932
<![CDATA[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.]]>
<![CDATA[Enhancing grid performance through coordinated SVC-TCSC operation with PV support: A case study on IEEE 30-bus system under progressive loading ]]>
<![CDATA[Hafidha Reriballah]]>;
<![CDATA[Latifa Smail]]>;
<![CDATA[Ali Abderrazak Tadjeddine]]>;
<![CDATA[Hocine Guentri]]>;
<![CDATA[Rim Feyrouz Abdelgoui]]>;
<![CDATA[Fatima Zohra Boudjella]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i2.pp1254-1264
<![CDATA[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.]]>
