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International Journal of Power Electronics and Drive Systems (IJPEDS)
Published by Institute of Advanced Engineering and Science
ISSN : -     EISSN : 20888694     DOI : -
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
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.
Arjuna Subject : -
Articles 2,660 Documents
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Bidirectional AC/DC converter connecting AC and DC microgrids for smart grids Dung, Nguyen Van; Vinh, Nguyen The
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2549-2561

Abstract

This paper proposes a converter connecting two independent AC and DC microgrids in a flexible microgrid and smart grid system. With this converter, basic DC/DC converter types such as Flyback are used to develop the power circuit and controller for the converter that is capable of integrating the operating functions for the operation between microgrids. The converter uses bidirectional switching locking technology to simplify the control algorithm. The energy is converted in two directions, AC/DC and DC/AC, with different working principles of increasing and decreasing voltage according to the standards of the distribution grid and DC microgrid. The TDH value is significantly limited when using the recovery circuit solution. The converter is designed, simulated based on OrCAD software, and tested with a capacity in the range of 2-10 kW. The DC microgrid output voltage is 400 VDC, voltage is 220 VAC.
Enhanced integration of renewable energy and smart grid efficiency with data-driven solar forecasting employing PCA and machine learning Kathirvel, Jayashree; Sreenivasan, Pushpa; Vanitha, M.; Mohammed, Soni; Kumar, T. Sathish; Adaikalam, I. Arul Doss
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2645-2654

Abstract

A significant obstacle to preserving grid stability and incorporating renewable energy into smart grids is variations in solar irradiation. To improve solar power management's dependability, this research proposes a short-term solar forecasting framework powered by AI. Multiple machine learning models, such as long short-term memory (LSTM), random forest (RF), gradient boosting (GB), AdaBoost, neural networks (NN), K-Nearest neighbor (KNN), and linear regression (LR), are integrated into the suggested system, which also uses principal component analysis (PCA) for dimensionality reduction. The Abiod Sid Cheikh station in Algeria (2019-2021) provided real-world data for the model's validation. With a two-hour-ahead RMSE of 0.557 kW/m², AdaBoost had the most accuracy, whereas LR had the lowest, at 0.510 kW/m². In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. These findings highlight the efficiency of hybrid AI models based on PCA for accurate forecasting, which is crucial for smart grid stability.
Design of a static synchronous compensator for the north-south high-speed railway system Anh, An Thi Hoai Thu; Cuong, Tran Hung
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2369-2380

Abstract

The modern high-speed rail system plays a crucial role in driving the nation’s economic development. The problem of voltage imbalance caused by intermittent load movements is a significant challenge for energy management and distribution. When electric trains are connected to the three-phase grid, power quality degradation occurs, resulting in distortion and imbalance of the three-phase grid current and voltage, which in turn increases operating costs. This paper has proposed a linear control method using a PI controller for a static synchronous compensator (STATCOM) to directly control the amount of reactive power loss for electric trains. This solution will also bring good and stable voltage quality to electric trains so that electric trains can operate for a long time. The STATCOM device in this paper is a three-phase voltage source converter with a simple structure and can be easily controlled. This is considered a simple and effective solution to balance voltage, improve power factor, and enhance harmonic quality for railway trains, thereby achieving an optimal operating solution. This discussion can be simulated using MATLAB/Simulink software to determine the operation and control steps for STATCOM, thereby improving the quality of the power system. The simulation results of current, voltage, and reactive power response are presented. The simulation results have demonstrated that the proposed algorithm successfully achieves the set goals of ensuring voltage stability and providing the necessary amount of reactive power for the train, thereby improving the quality of the power grid for the North-South high-speed train in Vietnam.
Advanced thermal modeling of lithium-ion batteries: foundations for advanced capacity prediction Elkake, Abdelhadi; Laadissi, El Mehdi; Mossaddek, Meriem; Abdelhakim, Tabine; Hajajji, Abdelowahed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2699-2710

Abstract

Thermal modeling of lithium-ion batteries is crucial for optimizing their performance and reliability in applications such as electric vehicles and energy storage systems. This study introduces a novel thermal modeling framework to predict internal battery temperature as a function of current and ambient temperature. Three advanced methodologies, NN-LM, NN-BR, and GPM, were evaluated using drive cycle data across temperatures that vary from -20 °C to 25 °C. Among these, Gaussian process modeling (GPM) demonstrated the highest accuracy with an RMSE of 0.034%, while NN LM achieved an RMSE of 0.083%, offering a computationally efficient alternative suitable for real-time applications. The developed thermal model establishes a foundation for future research aimed at predicting battery capacity by incorporating the effects of internal temperature. Furthermore, accurate monitoring of internal temperature is critical for preventing thermal runaway by enabling early detection of unsafe thermal conditions. This work establishes a robust foundation for future research, aiming to develop real-time capacity prediction models, ultimately enhancing battery management systems under diverse operating conditions.
Energy resilience in disaster-prone regions: the role of portable and modular solar power systems Sutikno, Tole; Facta, Mochammad; Aji, Wahyu Sapto; Handayani, Lina; Arsadiando, Watra; Purnama, Hendril Satrian
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2879-2894

Abstract

Energy resilience is a critical requirement in disaster-prone regions, where electrical infrastructure is highly vulnerable to natural hazards and prolonged power outages. Portable and modular solar power systems have emerged as promising solutions for enhancing resilience by enabling decentralized, rapidly deployable, and grid-independent energy supply. This paper presents a comprehensive review of the role of portable and modular photovoltaic-based power systems in improving energy resilience from a power electronics perspective. The review synthesizes recent literature on resilience concepts, system architectures, and converter-based control strategies relevant to emergency energy applications. Particular emphasis is placed on DC-first and hybrid AC/DC architectures, modular converter topologies, battery management systems, and energy management strategies that support reliable and fault-tolerant operation under variable and uncertain conditions. Practical deployment and performance considerations, including scalability, robustness, monitoring, and usability in disaster environments, are also discussed. The findings indicate that well-designed portable and modular solar power systems can significantly reduce recovery time, improve operational continuity, and decrease reliance on centralized grids and fuel-based generators. This review identifies key technical challenges and research opportunities to guide future development of resilient power electronic-based energy systems for disaster response and recovery.
Performance enhancement of photovoltaic systems using hybrid LSTM-CNN solar forecasting integrated with P&O MPPT Fennane, Sara; Kacimi, Houda; Mabchour, Hamza; ALtalqi, Fatehi; Echchelh, Adil
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp696-708

Abstract

The increasing penetration of photovoltaic (PV) systems in smart grids highlights the need for reliable solutions to mitigate the inherent intermittency of solar energy. Short-term variability in solar irradiance remains a critical challenge for stable grid operation and efficient PV energy management. This paper proposes an integrated forecasting-control framework that combines short-term global horizontal irradiance (GHI) prediction with a conventional P&O MPPT strategy to enhance PV system performance. A hybrid LSTM-CNN architecture is developed to forecast one-step-ahead GHI under the semi-arid climatic conditions of Dakhla, Morocco, a region characterized by high solar potential and pronounced irradiance fluctuations. The forecasting model is validated using measured irradiance data from the National Renewable Energy Laboratory (NREL) via the National Solar Radiation Database (NSRDB). Predicted irradiance is then used to improve PV power estimation and support predictive maximum power point tracking (MPPT) operation. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed framework achieves accurate GHI forecasting, faster MPPT convergence, reduced steady-state oscillations, and improved PV power stability under rapidly changing irradiance. The proposed approach provides a practical and computationally efficient solution for enhancing the dynamic response and energy extraction efficiency of PV systems in smart grid applications.
Efficiency of squirrel-cage induction motors with copper and aluminum rotors Bunjaku, Ines Bula; Bula, Edin
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp223-237

Abstract

This study presents a method for estimating efficiency in three-phase squirrel-cage induction motors with copper and aluminum rotor cages. A detailed two-dimensional transient finite-element model of a 1.25 kW motor was created and analyzed under rated conditions (500 V, 50 Hz, 990 rpm, 75 °C) to determine torque, slip, losses, and efficiency. Finite-element results confirmed the copper rotor's advantage, with 11.0% higher efficiency (85.1% compared to 76.7%) and 37.5% lower rotor-cage losses (80 W compared to 128 W) compared to aluminum. For rapid efficiency prediction, both Mamdani-type fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using simulation data. The fuzzy system showed a maximum deviation of 0.8% for the copper rotor, while the neuro-fuzzy approach achieved effective nonlinear mapping for both rotor types with R² = 0.872 against finite-element benchmarks. Sensitivity tests with ±0.3% slip and ±15 W loss variations maintained estimation errors below 2.5%. This combined simulation and intelligent system methodology enables practical efficiency evaluation and rotor material comparison for motor condition assessment and industrial energy management.
A novel high-gain DC-DC converter with fuzzy logic control for hydrogen fuel cell vehicle applications Anusha, Gaddala; Sudhakar, A. V. V.; Rafikiran, Shaik; Deshmukh, Ram Ragotham; Basha, C. H. Hussaian
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp617-628

Abstract

Hydrogen fuel cell vehicles (HFCVs) are emerging as a sustainable alternative to conventional internal combustion engines due to their zero-emission characteristics and high energy efficiency. However, the low output voltage of fuel cells poses a significant challenge in meeting the high-voltage requirements of electric traction systems. To address this, this paper proposes a high-gain non-isolated switched-capacitor (SC) DC-DC converter integrated with a fuzzy logic controller (FLC) for efficient power management in hydrogen fuel cell vehicle applications. The proposed converter topology achieves a significant voltage step-up without the use of bulky magnetic components, making it lightweight and compact for automotive integration. A maximum power point tracking (MPPT) controller using fuzzy logic is used to recover optimum energy out of the fuel cell stack during different loads and conditions of the environment. MATLAB/Simulink simulation results validate the high voltage gain, stable operation, and improved dynamic response of the proposed converter under FLC control. The proposed intelligent control strategy enhances fuel cell utilization and ensures effective operation of HFCV powertrains.
Application of capacitor banks to enhance energy efficiency in aeration systems for fisheries cultivation Nugraha, I Made Aditya; Desnanjaya, I Gusti Made Ngurah
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp335-342

Abstract

Electrical energy consumption in aeration systems represents a major component of operational costs, primarily due to the low power factor of inductive equipment such as blowers. This study evaluates the effectiveness of capacitor banks in improving energy efficiency and their economic feasibility in small- to medium-scale aquaculture aeration systems. Over 90 days, measurements were conducted on energy consumption, current, voltage, and water quality parameters, including dissolved oxygen (DO) and pH in two systems: without and with capacitor banks. The results showed that the use of capacitor banks reduced daily energy consumption from 15.01 ± 0.45 kWh to 13.13 ± 0.45 kWh (savings of 12.51%), equivalent to approximately 56.4 kWh per month or 686.2 kWh per year. The average current decreased from 2.44 A to 1.88 A, while voltage, DO (6.50-6.64 mg/L), and pH (7.20-7.25) remained stable within the optimal range. Economic analysis revealed that an initial investment of IDR 1,500,000 has a payback period of 18 months, a net present value (NPV) of IDR 2.15-2.33 million (at 8% discount rate), and an internal rate of return (IRR) exceeding 50% per year. These findings demonstrate that the application of capacitor banks not only enhances energy efficiency and reduces power losses but is also highly feasible and profitable for practical adoption in aquaculture operations.
Comparative analysis of multi-output machine learning models for solar irradiance and wind speed forecasting: A case study in Tamil Nadu, India Selvi, S.; Shanti, N.; Dhandapani, Lakshmi; Bhoopathi, M.; Kumar, T. Sathish; Kavitha, P.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp786-796

Abstract

The growing share of wind and solar energy has created challenges in electrical networks, mainly due to intermittency, fluctuations, and uncertainty. These issues affect power system stability, grid operations, and the balance between supply and demand. To address this, accurate prediction of solar irradiance and wind speed is critical for integrating renewable energy into power systems. In this study, we propose a multi-output machine learning approach to predict both global horizontal irradiance (GHI) and wind speed simultaneously. The study uses historical meteorological data obtained from the National Solar Radiation Database (NSRDB) for Tamil Nadu, India. Six regression algorithms: linear regression, gradient boosting, random Forest, extreme gradient boosting (XGB), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) are tested under identical conditions. Model hyperparameters were tuned using GridSearchCV and Bayesian optimization to ensure robust performance. Before modeling, a comprehensive statistical analysis, including input feature distribution and correlation analysis, was conducted. Model accuracy was evaluated using RMSE, MAE, and R² metrics on both training and testing datasets. The results showed that ensemble tree-based methods outperformed the baseline linear model. Among them, CatBoost produced the best results for GHI prediction, while random forest delivered the most reliable wind speed forecasts, demonstrating strong predictive capability for renewable energy applications.

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