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[Comparation analysis of SSA and GWO algorithms for maximum power point tracking in standalone solar modules ]]>
<![CDATA[Indhana Sudiharto]]>;
<![CDATA[Mochammad Machmud Rifadil]]>;
<![CDATA[Muhammad Affid Febriansyah]]>
<![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.pp1962-1973
<![CDATA[Solar photovoltaic (PV) systems experience continuous output fluctuations due to changes in solar irradiance and operating temperature, reducing the effectiveness of power extraction. To improve energy harvesting capability, an adaptive maximum power point tracking (MPPT) method is required. This study evaluates the performance of the salp swarm algorithm (SSA) and grey wolf optimization (GWO) for MPPT control in a 100 Wp standalone PV system employing a single-ended primary inductor converter (SEPIC). The analysis focuses on tracking speed, efficiency, and stability under varying environmental conditions. Simulations were carried out in the ALTAIR PSIM Professional 2022.1.0.8 platform with irradiance levels ranging from 200-1000 W/m² and temperatures between 35-55 °C, including dynamic irradiance transitions. The obtained results show that SSA achieved a higher average tracking efficiency of 97.46% with a convergence time of 0.2276 s, while GWO produced 87.94% efficiency and a 0.2512 s convergence time. In addition, SSA demonstrated more stable tracking behavior and lower oscillation during low-irradiance operation. These results indicate that SSA provides better MPPT performance for compact standalone PV applications operating under fluctuating environmental conditions. Future work will involve hardware-based validation and real-time implementation.]]>
<![CDATA[Design of an integrated forecasting and scheduling model for power plants to balance solar and wind energy variability using real-time weather data ]]>
<![CDATA[Syafii Syafii]]>;
<![CDATA[Novizon Novizon]]>;
<![CDATA[Imra Nur Izrillah]]>
<![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.pp2112-2126
<![CDATA[The integration of variable renewable energy sources such as solar and wind creates challenges for power system stability and operational scheduling due to their intermittent characteristics. This study proposes an integrated forecasting and scheduling framework using real-time weather data for a hybrid renewable power system consisting of photovoltaic, wind, geothermal, and hydropower plants. Solar irradiance and wind speed data were collected using pyranometer and anemometer sensors and modeled using ARIMA for 24-hour-ahead forecasting. Based on AIC and BIC evaluation, ARIMA (2, 1, 2) and ARIMA (1, 1, 1) were selected for solar irradiance and wind speed forecasting, respectively. The forecasting results achieved MAPE values of 18.43% for solar irradiance and 14.12% for wind speed. The forecasted renewable outputs were integrated into a generation scheduling model, where geothermal power operated as a base-load unit and hydropower acted as a balancing source. The proposed scheduling strategy was evaluated through a 24-hour Newton-Raphson load flow simulation. Results showed that system power losses remained below 2% and bus voltage levels were maintained within acceptable limits, demonstrating reliable operation under fluctuating weather conditions.]]>
<![CDATA[Optimization of hybrid GA-PSO-based energy management with Six Sigma penalty in buildings ]]>
<![CDATA[K. N. Nurwijayanti]]>;
<![CDATA[Rustam Asnawi]]>;
<![CDATA[Handaru Jati]]>;
<![CDATA[Linda Faridah]]>;
<![CDATA[Effendi Dodi Arisandi]]>
<![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.pp2259-2270
<![CDATA[This study proposes the optimization of a hybrid genetic algorithm-particle swarm optimization (GA-PSO) building energy management combined with Six Sigma for quality control. The main problems include high energy consumption, large carbon emissions, and performance variability. Six Sigma is applied through control limits (UCL/LCL) and process capability index (Cpk) so that the solution is not only efficient but also stable. Using 30 days of operational data, the model evaluates daily energy consumption (kWh) and carbon emissions, then compares the baseline with pure GA, pure PSO, and GA-PSO+Six Sigma. The results show that GA-PSO reduces average energy consumption by 5.1% compared to GA and 3.5% compared to PSO. When combined with Six Sigma, the savings increased to 7.5% compared to GA and 6.6% compared to PSO, while reducing carbon emissions without compromising operational comfort. These findings present a measurable, sustainable, low-carbon building energy management model that is aligned with the decarbonization framework and ISO 50001 best practices.]]>
<![CDATA[FEA-based optimization of switches for reluctance motors ]]>
<![CDATA[Hiba Esam Aziz]]>;
<![CDATA[Abdullah K. Shanshal]]>;
<![CDATA[Imad Idan Abed Al-Khalaf]]>;
<![CDATA[Tamer Kamel]]>
<![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.pp1675-1687
<![CDATA[Switched reluctance motors (SRMs) are considered one of the important machines used in industry sectors due to their simple structure, robustness, and high-speed capability. However, the performance is often limited by high torque ripple and acoustic noise due to the uneven magnetic flux distribution. This paper presents an optimization study for improving flux distribution by shape design modifications of the stator and rotor geometry using finite element analysis (FEA) combined with the whale optimization algorithm (WOA). More specifically, FEA is utilized to calculate the magnetic field behavior, torque characteristics, and core losses for different structural geometries of the proposed design, while WOA systematically searches for the optimum values of shape parameters. Thus, the simulation results show that the proposed approach significantly improves the uniformity of flux distribution, hence reducing torque ripple and improving the efficiency. The integration of FEA with the WOA provides a practical and effective way to achieve better SRM performance without further complication of control algorithms.]]>
<![CDATA[Transformer fault diagnosis using dissolved gas analysis: a hybrid ensemble model with data preprocessing ]]>
<![CDATA[Fatima Zohra Boudjella]]>;
<![CDATA[Souhila Boudjella]]>;
<![CDATA[Nasiru Yahaya Ahmed]]>;
<![CDATA[Hazlee Azil Illias]]>;
<![CDATA[Smail Latifa]]>;
<![CDATA[Reriballah Hafidha]]>
<![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.pp1780-1793
<![CDATA[Failures and guaranteed dependability of the electrical grid, early fault diagnosis in power transformers is essential. By examining gas ratios suggestive of faults, dissolved gas analysis (DGA) continues to be a vital component for transformer health monitoring. Using four preprocessing techniques raw data, min-max normalization, logarithmic transformation, and square root transformation; this study suggests a machine learning method for fault detection using DGA gas ratios (such as CH₄/H₂, C₂H₂/C₂H₄). Random forest (RF), support vector machines (SVM), gradient boosted trees (GBT), and a hybrid RF-GBT model that uses prediction fusion were the four supervised classifiers assessed. Performance was assessed using accuracy, precision, recall, f1-score, and Cohen's kappa. Experimental results show that the hybrid RF-GBT model with logarithmic transformation achieves the highest performance, with 94.93% accuracy and 92.37% Cohen's kappa, significantly outperforming individual classifiers. Data preprocessing, particularly logarithmic and square root transformations, enhances diagnostic robustness by mitigating feature skewness. This study underscores the importance of tailored preprocessing and ensemble methods for reliable transformer fault diagnosis.]]>
<![CDATA[Deep learning-based intelligent islanding detection for grid-connected photovoltaic systems using convolutional neural networks ]]>
<![CDATA[Dondapati Ravi Kishore]]>;
<![CDATA[T. Vijay Muni]]>;
<![CDATA[K. Venkata Kishore]]>;
<![CDATA[V. Suresh]]>;
<![CDATA[S. Saahithi]]>;
<![CDATA[Thandava Krishna Sai Pandraju]]>;
<![CDATA[S. N. Chaitra]]>;
<![CDATA[B. Logeshwary]]>
<![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.pp1755-1767
<![CDATA[The increasing integration of photovoltaic (PV) systems into smart grids requires fast and reliable islanding detection to maintain grid stability and operational safety. Conventional detection methods often face challenges such as delayed response, reduced accuracy, and large non-detection zones under varying operating conditions. This paper proposes an intelligent islanding detection method for grid-connected PV systems using advanced artificial intelligence and deep learning techniques. Electrical parameters including voltage, current, frequency, and power signals are analyzed using signal processing methods and classified through a convolutional neural network (CNN) model developed in MATLAB/Simulink. Simulation results demonstrate that the proposed AI-based approach achieves rapid and accurate detection of islanding events with improved sensitivity and reduced false detections compared to conventional techniques. The proposed framework enhances the reliability, safety, and protection performance of modern photovoltaic power systems integrated with smart grids.]]>
<![CDATA[Toward energy-efficient AGVs: A review of mechanical design contributions and optimization framework ]]>
<![CDATA[Amizi Noor]]>;
<![CDATA[Khairur Rijal Jamaludin]]>;
<![CDATA[Wan Zuki Azman Wan Muhamad]]>;
<![CDATA[Faizir Ramlie]]>;
<![CDATA[Nolia Harudin]]>
<![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.pp2070-2085
<![CDATA[Energy optimization in automated guided vehicles (AGVs) is critical for improving efficiency and sustainability in intralogistics systems, particularly in path planning and scheduling applications. Various optimization approaches have been proposed from operational, computational, and energy supply perspectives. Although energy supply technologies offer advantages in energy storage and recovery, their integration into AGV systems remains limited due to technological maturity and implementation challenges. Consequently, control-based approaches, including artificial intelligence, have become dominant optimization strategies. While these methods improve operational performance, they also increase computational energy demand, highlighting the need for alternative approaches to reduce baseline power consumption. This paper reviews AGV energy optimization studies while emphasizing the potential of mechanical design as an alternative optimization scope. The review reveals that mobility inefficiencies such as slip, skid, and instability are commonly mitigated through control strategies rather than resolved at their mechanical source. To address this gap, a Taguchi-based mechanical optimization framework is proposed for evaluating multiple mechanical factors and parameter levels. The framework aims to reduce baseline power demand and minimize reliance on computationally intensive control strategies, contributing toward more energy-efficient AGV systems.]]>
<![CDATA[FPGA-based hardware-defined phase generation for capacitor-less permanent split capacitor motor operation ]]>
<![CDATA[Lertrat Phewngam]]>;
<![CDATA[Chaiwat Sirawattananon]]>;
<![CDATA[Anchasa Pramuanjaroenkij]]>
<![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.pp1728-1746
<![CDATA[Permanent split capacitor (PSC) motors are widely used due to their simple structure and reliability; however, conventional operation depends on a fixed passive capacitor to generate phase displacement between the main and auxiliary windings, limiting controllability and adaptability. This paper proposes an FPGA-based hardware-defined phase generation architecture for capacitor-less PSC motor operation, where the capacitor phase function is replaced by digitally synthesized phase-displaced excitation signals. The proposed system was implemented on a Tang Nano 4K FPGA using multi-channel PWM generation with hardware-based dead-time protection. Experimental validation was performed on a capacitor-less PSC motor using programmable phase relationships of 60°, 90°, and 120° over excitation frequencies of 20-50 Hz. The measured main and auxiliary winding currents were analyzed to evaluate the effectiveness of the electronically synthesized phase displacement and to reconstruct the resultant rotating magnetic field. Quantitative evaluation using circularity index (CI) and ellipticity ratio (ER) showed that the 90° excitation condition produced the most balanced magnetic field trajectory among the tested configurations. The results demonstrate that FPGA-based hardware-defined phase generation provides a flexible, deterministic, and experimentally validated alternative to conventional capacitor-based PSC motor operation, while future work will focus on closed-loop optimization and efficiency-torque evaluation.]]>
<![CDATA[Optocoupler-based closed-loop hysteresis control for buck converter: experimental results ]]>
<![CDATA[David Estrada Grisales]]>;
<![CDATA[José Ángel Buitrago Gutiérrez]]>;
<![CDATA[Fredy E. Hoyos]]>
<![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.pp1842-1851
<![CDATA[This work experimentally evaluates a simplified closed-loop on-off (hysteresis) controller for a single-phase buck converter using an LM324 comparator and an optocoupler-based gate drive to the IRF830 MOSFET. The controller compares the output voltage against a reference and drives the switch in a binary manner, yielding variable-frequency regulation with low component count. Performance is assessed for DC-DC regulation and dynamic reference tracking under sinusoidal, triangular, and rectangular waveforms at 1 Hz and 60 Hz, as well as under input-voltage droop, load perturbations, and real-time changes in reference waveform. Results show envelope-accurate tracking with fast transients and bounded ripple across all tested conditions, including representative grid-frequency operation. The controller maintains stability and preserves waveform fidelity despite supply and load disturbances; edge transients observed for rectangular references at 60 Hz are consistent with expected output-capacitor dynamics and remain bounded. The findings support the suitability of analogue on-off hysteresis with optocoupler isolation as a low-complexity approach for applications requiring efficient DC-DC conversion and dynamic signal tracking, especially where cost, simplicity, and rapid response are prioritized.]]>
<![CDATA[Optimal FOPID control for improved load frequency regulation of a renewable-integrated hybrid power system using the Puma optimizer ]]>
<![CDATA[Mitali Samal]]>;
<![CDATA[Chinmoy Kumar Panigrahi]]>;
<![CDATA[Deepak Kumar Gupta]]>
<![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.pp2013-2028
<![CDATA[This study addresses the challenge of frequency stabilization in a complex two-area hybrid power system subjected to load disturbances. Area 1 comprises thermal, hydro, and wind power generation, while Area 2 integrates thermal, hydro, and gas turbine units, presenting a diverse and realistic system for control strategies. The inherent nonlinearities and varying dynamics of these generation sources, particularly wind power, necessitate a robust and advanced control solution. The proposed Puma optimizer-driven fractional-order proportional-integral-derivative (FOPID) controller is developed to enhance dynamic performance under load disturbances and renewable energy fluctuations. The proposed Puma optimizer-based fractional-order proportional-integral-derivative (PO-FOPID) controller is rigorously validated against a Puma optimizer-based proportional-integral-derivative (PO-PID) controller, a PSO-tuned PID controller, and a PSO-tuned fractional-order PID (PSO-FOPID) controller. The settling time and peak overshoot/undershoot are considered for comparison, while the integral of time multiplied by the absolute error (ITAE) performance index is used as the objective function to be minimized. The findings highlight the potential of combining Puma optimizer with fractional-order control for reliable frequency regulation in modern hybrid power systems.]]>
