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
2,781 Documents
<![CDATA[Enhanced voltage control and load compensation for DFIG-based wind energy systems using a stator-side series converter ]]>
<![CDATA[Preethicaa Ramesh]]>;
<![CDATA[Sowmmiya Uthayakumar]]>;
<![CDATA[Tole Sutikno]]>;
<![CDATA[Vigna Kumaran Ramachandaramurthy]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp2224-2246
<![CDATA[The increasing deployment of doubly-fed induction generator (DFIG)-based wind energy systems (WESs) requires effective voltage regulation and power-quality enhancement under varying operating conditions. This paper proposes a coordinated voltage regulation and load-compensation technique using a rotor-side converter (RSCR) and a stator-side series converter (SSSCR). The RSCR provides two-directional slip-power control through cascaded voltage and current control loops, while the SSSCR provides series voltage injection for voltage regulation and load compensation. The proposed system is developed in MATLAB/Simulink and validated using an OPAL-RT OP4512 real-time platform under variable wind speed, unbalanced and non-linear loading, and voltage sag/swell conditions. Quantitative results show reductions in overall current total harmonic distortion (THD) of 25.34% and 36.78% under non-linear and unbalanced load conditions, respectively. The voltage regulation error is reduced from 9% to 3%, while the settling time decreases from 0.24 s to 0.12 s under non-linear loading. The proposed configuration also reduces the voltage overshoot from 2.61% to 0.02% during speed transition. These results demonstrate the effectiveness of the coordinated RSCR-SSSCR configuration for simultaneous voltage regulation and load compensation.]]>
<![CDATA[Techno-economic optimization of hybrid renewable energy systems for low-carbon EV charging station ]]>
<![CDATA[Jeyagopi Raman]]>;
<![CDATA[Sudesh Nair Baskara]]>;
<![CDATA[Harpreet Kaur Channi]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp2039-2046
<![CDATA[The rapid adoption of electric vehicles (EVs) requires charging infrastructure that is sustainable, power-electronically efficient, and grid-compatible. This study presents a techno-economic and environmental optimization of a hybrid renewable energy-based EV charging station near Guru Nanak Dev Engineering College (GNDEC), Ludhiana, India, as a sustainable alternative to conventional petrol stations. Unlike studies based on assumed EV load profiles, the proposed framework integrates real-world e-rickshaw utilization data, inverter efficiency, and sensitivity analysis within a HOMER Pro optimization framework. The system combines solar photovoltaic (PV), battery storage, and grid supply through a bidirectional inverter to manage dynamic charging demand. Local solar irradiance (~5.6 kWh/m²/day) and actual e-rickshaw usage data were used as simulation inputs. Configurations were evaluated using net present cost (NPC), levelized cost of energy (LCOE), inverter efficiency, and CO₂ emissions. The optimal PV-battery-grid configuration achieved an NPC of $28,500, LCOE of $0.11/kWh, inverter efficiency of 95%, and approximately 95% lower CO₂ emissions than grid-connected configuration, with a 6.5-year payback period. The findings highlight the importance of power-electronic efficiency and energy management for economically viable, low-carbon EV charging infrastructure, supporting SDGs 7 and 9.]]>
<![CDATA[Multi-agent intelligence with privacy awareness for robust marine robotic electric drives ]]>
<![CDATA[Vishnu Kumar Mishra]]>;
<![CDATA[Megha Mishra]]>;
<![CDATA[T. Ram Kumar]]>;
<![CDATA[Yenna Geetha Reddy]]>;
<![CDATA[Sri Lavanya Sajja]]>;
<![CDATA[Gundla Rajesh]]>;
<![CDATA[Bandla Srinivasa Rao]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp1702-1713
<![CDATA[This paper introduces a privacy-preserving multi-agent intelligent model tailored towards improving the movement control and energy management of robotic marine electric motors. In particular, the research incorporates the use of a fractional-order neural network algorithm (FONNA) combined with federated learning, allowing for decentralization without compromising raw data security. The modeling process entails the application of a seven-phase voltage source inverter (VSI) to offer high-performance motor behavior and exceptional fault resilience. Some of the significant discoveries include the delivery of 96.96% peak efficiency at 55 kHz and decreasing dependency on high-bandwidth communication by 75% via gradient updates. Energy resilience remains an essential prerequisite for collaborative marine robotics in harsh offshore conditions where the electricity supply can be subjected to disruption. However, even with all these developments, robotic fleets for maritime purposes are faced with some major constraints when working together. For instance, the centralized approach is easily affected by system failures since all operations will be affected if one fails. The marine area poses harsh conditions in which communication bandwidth and electrical power may be difficult to provide.]]>
<![CDATA[Edge-AI robotic swarms for predictive maintenance in utility-scale solar farms: a systematic review and meta-analysis ]]>
<![CDATA[Luqman Assafat]]>;
<![CDATA[Mochamad Subchan Mauludin]]>;
<![CDATA[Singgih Dwi Prasetyo]]>;
<![CDATA[Yuki Trisnoaji]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp1831-1841
<![CDATA[The rapid expansion of utility-scale solar photovoltaic infrastructure poses critical operational challenges for maintaining reliability across large, dispersed networks. Conventional manual inspection and reactive maintenance approaches are increasingly ineffective, leading to reduced energy yield, increased downtime, and higher operational costs. This study presents a systematic review and meta-analysis evaluating the performance of autonomous robotic swarm technologies for predictive maintenance in solar farms, following PRISMA 2020 and Cochrane methodologies. A total of 20 eligible studies were analyzed using random-effects modeling, forest plot interpretation, subgroup evaluation, publication bias assessment, and sensitivity analysis. Results indicate a significant pooled effect size (Hedges g = 7.80) with substantial improvements in detection accuracy, efficiency, and cost reduction, supported by low publication bias and strong robustness. The discussion emphasizes the theoretical and practical implications of decentralized multi-agent coordination, while highlighting the limitations of heterogeneous methodologies and limited real-world validation. The study concludes that swarm robotics offers transformative potential for scalable, intelligent maintenance ecosystems and recommends future research that includes large-scale field deployments and standardized evaluation frameworks.]]>
<![CDATA[Adaptive predictive control for wind energy efficiency in real-time in smart grids ]]>
<![CDATA[Kadhim Hamzah Chalok]]>;
<![CDATA[Karrar Hameed Kadhim]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp2210-2223
<![CDATA[With the increasing global interest in renewable energy, advanced control strategies for wind energy conversion systems (WECSs) are critical for achieving not only maximum efficiency but also grid compatibility. This paper introduces a new adaptive, forecast-based predictive control algorithm to maximize the real-time energy efficiency of wind turbines integrated with smart grid architectures. The method couples a short-term wind-speed prediction module with a model-predictive control algorithm that uses the predictions to anticipate and adjust turbine operating conditions, such as pitch angle and rotor speed, to counteract the mechanical load from wind shear while maximizing power production. The analysis employs a dataset of 487 high-resolution cases of wind speed and wind turbine performance metrics to train and validate the adaptive model. The simulations were conducted in MATLAB/Simulink, using a model that captures the turbine’s nonlinear behavior and the electrical grid connection. The results indicate that the proposed controller increases the cycle-averaged power coefficient by approximately 8.2% and reduces tip-speed-ratio tracking error by roughly 70% relative to a conventional proportional-integral-derivative (PID) controller, while also reducing pitch and torque actuation, thereby lowering structural fatigue loads. This study is expected to provide a reliable solution to wind energy intermittency and facilitate the more stable and efficient integration of wind farms into modern smart grid systems.]]>
<![CDATA[Spatiotemporal digital twin for city-scale EV charging infrastructure using LSTM-GNN fusion and resilience-driven optimization ]]>
<![CDATA[Deepa Somasundaram]]>;
<![CDATA[B. Ravisankar]]>;
<![CDATA[Chavvakula Janaki Devi]]>;
<![CDATA[Ajay Babu Bathula]]>;
<![CDATA[Sabarimuthu Muthusamy]]>;
<![CDATA[K. Vinoth]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp2271-2280
<![CDATA[The rapid growth of electric vehicles (EVs) requires intelligent and resilient planning of charging infrastructure under dynamic urban conditions. This paper proposes a city-scale spatiotemporal digital twin (SDT) that integrates LSTM-GNN fusion with resilience-driven hybrid optimization (GA-PSO-deep reinforcement learning) for adaptive EV infrastructure management. The LSTM model captures temporal variations in charging demand, while the graph neural network (GNN) learns spatial dependencies across charging stations, mobility networks, and grid components. Unlike existing approaches, the proposed framework incorporates power electronics-aware modeling, including charger power conversion system (PCS) efficiency, switching losses, and harmonic distortion constraints, ensuring realistic grid interaction. The digital twin also considers energy system metrics such as transformer loading, voltage deviation, and renewable energy variability, along with EV drive-cycle characteristics like fast charging and battery limits. Simulation results show that the proposed model improves demand prediction accuracy by 14-22%, reduces grid overload probability by 35%, lowers operational cost by 18%, and achieves improved power quality performance compared to conventional methods. The system maintains a high resilience index (>0.92) under stress scenarios. Overall, this work presents a holistic AI-driven digital twin framework that enhances grid stability, supports sustainable EV integration, and enables scalable deployment of future smart charging infrastructure.]]>
<![CDATA[Comparison of acrylic and zinc blade materials on the performance of Darrieus vertical axis wind turbine ]]>
<![CDATA[Dina Maizana]]>;
<![CDATA[Andreas Tiopan Togatorop]]>;
<![CDATA[Indri Dayana]]>;
<![CDATA[Habib Satria]]>;
<![CDATA[Muhammad Irwanto Misrun]]>;
<![CDATA[Yanawati Yahya]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp2162-2171
<![CDATA[The performance of vertical axis wind turbine is strongly influenced by blade characteristics, yet the effect of blade material on turbine performance remains relatively underexplored, particularly for small scale Darrieus type turbines. This study experimentally investigated the influence of acrylic and zinc blade materials on the aerodynamics and energy conversion performance of a three bladed Darrieus vertical axis wind Turbine. A laboratory-scale experimental setup was employed to evaluated turbine rotational speed, torque, and electrical power under a controlled wind speed of 4.3 m/s. The results demonstrate the blade material significantly affects the turbine’s ability to extract and convert wind energy. The acrylic blade turbine achieved a power coefficient (Cp) of 0.293 and an overall conversion efficiency 62.81%, compared with 0.208 and 47.11%, respectively, for the zinc bladed turbine. The acrylic configuration therefore provided approximately 25% higher overall performance than zinc configuration. These findings experimentally validate the importance of blade material selection in the design of small scale Darrieus wind turbines and indicate that acrylic can provide a more effective alternative to zinc for improving wind energy conversion under low speed operating conditions. The results provide practical guidance for material selection in the development of lightweight, low speed vertical axis turbines for decentralized and small scale renewable energy applications.]]>
<![CDATA[Implementation of filter-clamped topology for transformerless three-phase PV inverters with reduced leakage current ]]>
<![CDATA[Mohammed Abdullah]]>;
<![CDATA[S. Venkata Padmavathi]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp1553-1563
<![CDATA[The growing prevalence of grid-connected photovoltaic (PV) systems has intensified the demand for efficient, reliable, and compact power conversion technologies. Transformerless photovoltaic (TLPV) systems provide improved efficiency and reduced size and costs compared to those using line-frequency or high-frequency isolation transformers. However, these inverters commonly exhibit leakage current because they lack galvanic isolation. Conventional topologies address this issue by adding extra semiconductor devices or employing complex control methods. This paper examines how a filter-clamped inverter (FCI) operates in grid-connected three-phase transformerless photovoltaic (TLPV) applications. However, the proposed inverter successfully reduces leakage current without additional components or control modifications. This makes it a compact and simplified alternative for transformerless PV applications. Additionally, the traditional full-bridge (FB) inverter with three phases, when compared to the FC inverter, has very high leakage current. Simulation results validate the FC inverter’s ability to minimize leakage current.]]>
<![CDATA[Digital twin driven federated multi-agent intelligence for autonomous renewable forecasting and smart grid optimization ]]>
<![CDATA[Pushpa Sreenivasan]]>;
<![CDATA[K. Gattaiah]]>;
<![CDATA[N. Hemalatha]]>;
<![CDATA[Radhey Shyam Meena]]>;
<![CDATA[Mallareddy Adudhodla]]>;
<![CDATA[V. S. Bhagavan]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp2029-2038
<![CDATA[The rapid growth of renewable energy integration has increased the complexity of smart grid operation due to the intermittent nature of distributed energy resources and continuously varying load demand. Existing approaches often rely on centralized control or combine only selected intelligent technologies, limiting scalability, data privacy, and autonomous decision-making. This paper proposes a digital twin-driven federated multi-agent intelligence (DT-FMAI) framework that integrates virtual system synchronization, privacy-preserving distributed learning, and cooperative multi-agent control within a unified architecture. Digital twins continuously mirror physical grid assets, federated learning enables collaborative forecasting without sharing raw data, and intelligent agents coordinate energy management in real time. The framework was implemented in MATLAB/Simulink with TensorFlow Federated and evaluated using renewable generation, weather, battery, and load datasets. Results demonstrate a 15-25% reduction in forecasting error, 10-18% improvement in voltage regulation, 92-96% load-matching efficiency, and 12-20% higher energy efficiency. These outcomes demonstrate the potential of the proposed framework for scalable, secure, and intelligent renewable-integrated smart grid operation.]]>
<![CDATA[THD reduction in a 13-level multilevel inverter using the angle control technique ]]>
<![CDATA[Dewan Ashikur Rahaman]]>;
<![CDATA[Aive Alamgir]]>;
<![CDATA[Md Ashraf Hossain]]>;
<![CDATA[Tapan Kumar Chakraborty]]>;
<![CDATA[Raisul Islam Rafi]]>;
<![CDATA[Raiyan Rahman Zihad]]>;
<![CDATA[Md. Abdullah Al Mahmud]]>;
<![CDATA[Samia Sarkar]]>
<![CDATA[ International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijpeds.v17.i3.pp1982-1993
<![CDATA[When compared to a traditional multilevel inverter, multilevel inverters can produce switched waveforms with lower amounts of harmonic distortion. Due to their capacity to provide excellent output waveforms at lower switching frequencies and without distortion, multilevel inverters have recently attracted more attention. The dynamic voltage restorer's multilevel topology reduces the output waveform's harmonic distortion without causing inverter power output losses. This study examines the most widely used topologies to determine how the sinusoidal switching angle affects total harmonic distortion (THD%). Among the crucial multilevel topologies, the cascaded H-bridge was selected. Because it needed fewer parts than the others. Asymmetric was selected for observation and simulations of output voltages up to Fifteen levels for equal and sinusoidal angles using PSIM software after a review of the literature. Appropriate switching angles were maintained in this research. With fewer H-bridges, higher-level multilevel inverters have been manufactured.]]>
