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[Enhanced review on dynamic real-time digital simulation analysis of renewable energy integration using state space model ]]>
<![CDATA[Ahmad Supawi Osman]]>;
<![CDATA[Aidil Azwin Zainul Abidin]]>
<![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.pp1510-1521
<![CDATA[The modernization of electric power grids, driven by communication and electronic hardware advances alongside increasing renewable energy integration, introduces challenges like voltage fluctuations, weakened protection, and transient instability. High renewable penetration can trigger reverse power flow and voltage rise, complicating system control. Real-time digital simulations offer a non-destructive approach to analyze and optimize power system behavior under diverse conditions. Using platforms like Simulink Real-Time and RT-LAB with OPAL-RT, detailed studies of protection relays, circuit breakers, and control algorithms are efficiently conducted. This paper reviews real-time digital simulation techniques for renewable-integrated power systems, emphasizing state-space modeling for capturing system dynamics. Recent developments in predictive and event-based control strategies to enhance microgrid stability and operational efficiency are examined. Simulations of a three-bus system with transient analysis and event-based predictive control for energy management are discussed, demonstrating how real-time simulation platforms support renewable energy integration while maintaining grid stability.]]>
<![CDATA[Dual random optimized pulse width modulation controller for three-phase voltage source inverter driven brushless DC motor ]]>
<![CDATA[Halidu Abdul Mumin]]>;
<![CDATA[Solomon Nunoo]]>;
<![CDATA[Joseph Cudjoe Attachie]]>
<![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.pp966-980
<![CDATA[Brushless DC (BLDC) motors are widely employed in modern power electronic applications due to their high efficiency and dynamic performance. However, conventional pulse width modulation (PWM) techniques often generate concentrated harmonic components, leading to acoustic noise, torque ripple, and reduced inverter efficiency. This paper proposes an artificial neural network–assisted dual random pulse width modulation (ANN-DRPWM) strategy to enhance the output quality of a three-phase voltage source inverter driving a BLDC motor. In the proposed approach, supervised ANN training enables dual randomization of the carrier and modulation signals, effectively dispersing harmonic energy while maintaining improved DC-link voltage utilization. A passive LC filter is subsequently integrated to further suppress residual harmonics and ensure compliance with harmonic standards. The system is modeled and simulated in MATLAB/Simulink and evaluated against conventional sinusoidal PWM and flying capacitor multilevel inverter (FCMLI) techniques. Results demonstrate that the proposed ANN-DRPWM method achieves a post-filter total harmonic distortion (THD) of 2.17%, along with a 6-9% improvement in inverter efficiency and a noticeable reduction in torque ripple. Overall, the proposed strategy offers an efficient and intelligent modulation solution for high-performance BLDC motor drives, suitable for applications such as electric vehicles, renewable energy systems, and industrial drives.]]>
<![CDATA[Control strategy for the combined operation of grid-connected inverter and charger ]]>
<![CDATA[Quang-Tho Tran]]>;
<![CDATA[Quang-Sang Le]]>
<![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.pp1396-1407
<![CDATA[Solar power sources and electric vehicles (EVs) are increasingly used because of their environmental friendliness and sustainability. They are typically connected to the power grid through devices such as inverters and chargers to either generate or receive electrical energy. These devices contain a DC voltage bus. Therefore, the combined control of these two types of devices can improve their overall operational efficiency. This article proposes a grid-connected converter with an integrated battery-charging function. In addition, it presents a control strategy for the coordinated operation of this converter during both charging and power generation at the DC bus. In this algorithm, the battery is treated as a priority load, which allows the system to eliminate the AC-DC converter used in conventional chargers. A total peak power of 9 kWp is used to investigate the processes of power generation and battery charging. The total harmonic distortions of grid current are less than 2.86% in different operational cases and meet the grid codes. The obtained results are analyzed under varying irradiance conditions to verify the effectiveness of the proposed control method.]]>
<![CDATA[Resilient EV charging station network design using AI algorithms ]]>
<![CDATA[Deepa Somasundaram]]>;
<![CDATA[N. Krishnamoorthy]]>;
<![CDATA[J. Vijay Anand]]>;
<![CDATA[R. Priyanka]]>;
<![CDATA[T. Santhana Krishnan]]>;
<![CDATA[Kirubakaran Dhandapani]]>
<![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.pp1543-1552
<![CDATA[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.]]>
<![CDATA[An enhanced hybrid deep learning-quantum variational classifier framework for large-scale data analytics ]]>
<![CDATA[Yadlapti Suresh]]>;
<![CDATA[Venu Gopal Gaddam]]>;
<![CDATA[Challa Naga Venkata Jyothirmai]]>;
<![CDATA[Rokkam Veera Venkata Nagendra Bheema Rao]]>;
<![CDATA[Sreenivasulu Bolla]]>;
<![CDATA[Ankala Radhika]]>
<![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.pp1522-1532
<![CDATA[The rapid expansion of clinical data in modern healthcare requires analytical systems capable of uncovering intricate patterns and supporting accurate diagnostic decisions. Quantum machine learning (QML) offers significant potential for modeling higher-order feature interactions and accelerating computation beyond classical approaches. This paper introduces an improved hybrid architecture that fuses an inception-based attentional VGG (IAV) network with a quantum variational classifier (QVC) constructed using parameterized quantum circuits (PQCs). The framework begins with min-max normalization to stabilize heterogeneous clinical attributes and enhance training convergence. Deep discriminative features are then extracted through the IAV model, followed by quantum-driven classification using variational layers optimized by classical routines. The MIMIC-III clinical dataset is employed to validate the proposed system on large-scale healthcare records. Performance is measured using accuracy, precision, recall, and F1-score. The enhanced hybrid model achieves 97.28% accuracy, 97.16% precision, 96.65% recall, and a 97.38% F1-score, surpassing established methods including support vector machine (SVM) (89.23%), quantum support vector machine (QSVM) (90.13%), and QVKSVM (97.34%). The findings confirm that integrating deep learning with quantum variational optimization strengthens scalability, reduces computational overhead, and establishes a powerful foundation for next-generation healthcare analytics.]]>
<![CDATA[Photovoltaic-inductive wireless charging for electric vehicles ]]>
<![CDATA[Azra Zaineb]]>;
<![CDATA[P. Nagabushanam]]>;
<![CDATA[Kalagotla Chenchireddy]]>;
<![CDATA[Radhika Dora]]>;
<![CDATA[Naresh Jella]]>;
<![CDATA[Shabbier Ahmed Sydu]]>
<![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.pp849-857
<![CDATA[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.]]>
<![CDATA[Comparison of phase disposition, phase opposition, and phase disposition with variable frequency PWM techniques for harmonic reduction in cascaded multilevel inverters ]]>
<![CDATA[G. Nayana]]>;
<![CDATA[Savita D. Torvi]]>
<![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.pp858-872
<![CDATA[Renewable energy penetration in distributed generation systems significantly impacts the power quality of the output. The stochastic nature of the inverters provides variable voltage and variable frequency outputs, which is an advantage when used with photovoltaic (PV) and grid integration to the distribution grid, and also in induction motor drives. A primary source of power quality issues is the harmonics generated by the inverters. Multilevel inverters are commonly employed to mitigate these harmonics and improve power quality. Among the various multilevel inverter topologies, the cascaded multilevel inverter (CMLI) has gained prominence due to its simple structure, ease of control, and reduced component requirements. This paper presents a comprehensive review of multilevel inverter topologies that have influenced the evolution of the CMLI structure, along with an investigation into the application of advanced pulse width modulation (PWM) strategies for performance enhancement. In particular, phase disposition (PD), phase opposition disposition (POD), and phase disposition with variable frequency (PD-VF) PWM techniques are implemented on cascaded h-bridge (CHB) multilevel inverters configured for five-level, seven-level, and nine-level operations. A comparative evaluation of total harmonic distortion (THD) is conducted for each inverter configuration, both with and without the inclusion of an LC output filter, to assess waveform quality and harmonic mitigation capability. Furthermore, the harmonic suppression effectiveness of PD, POD, and PD-VF modulation methods is systematically analyzed across different voltage levels. The study also demonstrates that varying the carrier frequency in PD-VF modulation significantly influences THD performance, offering enhanced flexibility and expanded control possibilities in multilevel inverter applications.]]>
<![CDATA[Enhanced power quality in PV integrated EV fed microgrid using intelligent controller ]]>
<![CDATA[D. Balasubramanyam]]>;
<![CDATA[G. G. Raja Sekhar]]>;
<![CDATA[T. Vijay Muni]]>
<![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.pp1165-1176
<![CDATA[The need for electric cars (EVs) is steadily rising in the world due to the rise in emissions like CO2 and environmental effects brought on by conventional automobiles. EVs have also transformed the transportation industry. These days, EVs are popular because of their special qualities, which include lower noise pollution, carbon emissions, and operating expenses, as well as the capacity to operate in both grid-to-vehicle (V2G) and vehicle-to-grid (V2G) scenarios. Nevertheless, it affects the power distribution grid in a number of ways. There are various power concerns owing to the introduction of EVs in the distribution system, like instability of voltage, distortions in currents, harm onic distortions, power factor degradation, and fluctuations in voltage. The primary emphasis of this study is on mitigating PQ issues like harmonics produced in the distributed power network when electric vehicles are integrated at the distribution end. In order to reduce harmonics and enhance the distribution side's current profile, a dynamic active power filter (DAPF) with PSO-tuned ICC control technique is introduced. Performance of PSO-DAPF is validated with the help of MATLAB/Simulink, along with V2G and V2G operation.]]>
<![CDATA[Enhanced UPS inverter control using backstepping and fuzzy neural network for improved power quality ]]>
<![CDATA[G. Anjali Devi]]>;
<![CDATA[Swapna Ganapaneni]]>;
<![CDATA[L. Sirisaiah]]>;
<![CDATA[Lokesh Kotha]]>;
<![CDATA[Subhash Manchikanti]]>;
<![CDATA[Malligunta Kiran Kumar]]>;
<![CDATA[T. Rakesh]]>;
<![CDATA[K. V. Govardhan Rao]]>
<![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.pp1069-1083
<![CDATA[The rapid growth of sensitive digital infrastructures and automation systems has intensified the demand for uninterrupted and high-quality power delivery. To address this critical need, this paper proposes a novel hybrid intelligent control strategy for uninterruptible power supply (UPS) inverters that integrates backstepping control, fuzzy neural network (FNN) adaptation, and sliding mode gain compensation. The proposed approach ensures superior voltage regulation and robustness under nonlinear and dynamic load conditions while minimizing dependence on predefined system parameters. The backstepping controller establishes the Lyapunov-based stability framework, the FNN adaptively estimates system uncertainties in real time, and the sliding mode gain enhances resilience against external disturbances. This synergistic control integration enables fast dynamic response, reduced harmonic distortion, and improved system efficiency compared to conventional methods. Simulation and experimental validations demonstrate that the proposed controller achieves total harmonic distortion (THD) below 3%, voltage overshoot under 2%, and enhanced transient recovery, thereby ensuring reliable power quality for critical industrial and commercial applications. The study contributes a real-time feasible, adaptive, and robust UPS inverter control architecture, marking a significant advancement in intelligent power electronics for resilient energy systems.]]>
<![CDATA[Super-twisting MPPT enhanced via grey wolf optimization for dynamic PV operation ]]>
<![CDATA[Slimane Hadji]]>;
<![CDATA[Said Aissou]]>;
<![CDATA[Abdelhakim Belkaid]]>
<![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.pp1475-1485
<![CDATA[This paper introduces a hybrid maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems under rapidly varying irradiance conditions. The approach combines the super-twisting algorithm (STA), a second-order sliding mode control technique, with the grey wolf optimizer (GWO) in a coordinated framework where control action and parameter adaptation are jointly addressed. Unlike conventional MPPT methods that treat control and optimization separately, the proposed scheme improves transient response while limiting steady-state oscillations. The method is evaluated through MATLAB/Simulink simulations under multiple dynamic irradiance profiles, including fast-changing environmental conditions. Performance is assessed using complementary metrics, namely tracking efficiency, convergence dynamics, and root mean square error (RMSE), to provide an objective analysis. Results show that the STA-GWO strategy achieves faster convergence and improved stability compared to conventional SMC-GWO. It reaches an average tracking efficiency of 99.34%, compared to 99.19% for SMC-GWO, with reduced power fluctuations reflected by a lower RMSE. These improvements indicate a better trade-off between dynamic performance and steady-state accuracy. While this study is based on simulations, its findings require experimental validation. Future work will therefore include real-time implementation to confirm the practical applicability of the proposed approach.]]>
