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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 65 Documents
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Search results for , issue "Vol 17, No 1: March 2026" : 65 Documents clear
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.
Impact and reliability analysis of voltage sags in a multi-pulse transformer-fed variable frequency drive system Govarthanan, R.; Palanisamy, K.; Paramasivam, S.
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.pp123-139

Abstract

In an industrial grid, variable frequency drives (VFDs) are the major appliances that contribute to harmonic pollution. To reduce the effects of this harmonic pollution and comply with the regulatory standards, multi-pulse transformers are used to cancel out the specific harmonics. The VFDs experience a different input current profile when fed through multi‑pulse transformers compared to direct grid connection. Despite the harmonic pollution reduction in the grid due to this implementation, the current stresses faced by the front-end devices will become higher. If the VFDs are designed only considering the impact of the direct grid consideration, the lifetime and reliability of the front-end devices will be a concern if operated with a multi-pulse feeder. This condition will be worse if there are presence of different types of sag events. This research details the effects of the reflected sags in the multi-pulse transformer’s secondary windings and the current stresses in the different front-end converter elements due to this. Also, a systematic methodology using the FIDES approach is used to estimate the reliability of the front-end converter. A 7.5 kW-rated VFD is fed with a 12-pulse transformer is used for this research.
Machine learning based models for solar energy Cherifi, Dalila; Dahbi, Abdeldjalil; Sebbane, Mohamed Lamine; Baali, Bassem; Kadri, Ahmed Yassine; Chaib, Messaouda
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.pp752-764

Abstract

Photovoltaic (PV) technology is one of the most promising forms of renewable energy. However, power generation from PV technologies is highly dependent on variable weather conditions, which are neither constant nor controllable, which can affect grid stability. Accurate forecasting of PV power production is essential to ensure reliable operation within the power system. The primary challenge of this study is to accurately predict photovoltaic energy production, considering that weather conditions, such as irradiance, temperature, and wind speed, are random variables. The key contribution of this article is developing a machine learning model to predict the energy production of a real PV power plant in Algeria. Using real measurements sourced from the Center of Renewable Energy Development (CDER) in Adrar, Algeria, in 2021. The data are from two PV power plants located in harsh desert climate conditions. The results presented in this study offer a comparison of several predictive methods applied to real-world data from a PV power plant situated in the Saharan Region. Our findings reveal that the artificial neural network (ANN) model yields the most accurate predictions of 94.96%, with the smallest prediction error: root mean square (RMSE) and mean absolute error (MAE) are 7.78% and 3.80%, respectively.
State of charge prediction for new and second-life lithium-ion batteries based on the random forest machine learning technique Sahhouk, Masoud A.; Aziz, Mohd Junaidi Abdul; Ardani, Mohd Ibthisham; Idris, Nik Rumzi Nik; Sutikno, Tole; Othman, Bashar Mohammad
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.pp487-501

Abstract

Accurate state of charge (SOC) estimation is a critical requirement for the safe and efficient operation of lithium-ion batteries (LIBs), particularly in second-life battery (SLB) applications where battery ageing, nonlinear degradation, and measurement noise introduce uncertainty. Although numerous SOC estimation techniques have been proposed, reliable prediction for new and second-life batteries under varied operating conditions remains challenging. In this study, a comparative investigation of the conventional coulomb counting (CC) method and a data-driven random forest (RF) model is conducted for SOC prediction in new and second-life LIBs. Experimental data are obtained from Murata US18650VTC5D cells under pulse discharge tests (PDT), constant discharge tests (CDT), and dynamic stress tests (DST) across a wide range of C-rates. PDT is conducted at 0.24 C, CDT at 0.2 C, 0.5 C, 1 C, and 2 C, while DST is performed at C-rates ranging from 0.5 C to 4 C at a controlled ambient temperature of 25 °C. The RF model is trained using voltage, current, and time features and evaluated against CC using MAE, MSE, RMSE, and R² metrics. Results show that RF consistently outperforms CC under all conditions, particularly for SLBs, achieving significantly lower errors and R² values approaching 0.998. These findings confirm the effectiveness of RF-based SOC estimation for intelligent battery management systems (BMS).
Design and implementation of a buck converter-based PV emulator using dynamic evolution control Samosir, Ahmad Saudi; Despa, Dikpride; Gusmedi, Herri; Ferbangkara, Sony
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.pp809-822

Abstract

This paper presents the design, simulation, and experimental implementation of a photovoltaic (PV) emulator based on a buck converter controlled using the dynamic evolution control (DEC) technique. The proposed system accurately reproduces the nonlinear current-voltage (I-V) and power-voltage (P-V) characteristics of a commercial GREEN CELL SM100-18P (100 Wp) PV module under standard test conditions (1000 W/m2, 25 °C). The electrical characteristics of the reference module are embedded in the controller through a lookup table (LUT), which is integrated with the DEC algorithm to enable adaptive real-time regulation of output voltage and current. System modeling and validation are first conducted in MATLAB/Simulink to analyze steady-state and transient performance. A hardware prototype based on an XL4016 buck converter and Arduino Nano microcontroller is then implemented, with real-time monitoring provided via an ILI9341 TFT display. Experimental results show that the emulator achieves a maximum power deviation of 0.8%, a normalized root mean square error (RMSE) of 0.015, a settling time of approximately 12 ms, overshoot below 1.5%, voltage ripple under 2%, and peak conversion efficiency of 94% near the MPP region. These results confirm that the proposed PV emulator provides accurate static and dynamic reproduction of PV characteristics, offering a low-cost, stable, and repeatable platform for laboratory-scale evaluation of PV-related power electronic converters.
An efficient grid-connected solar PV system with a fault-tolerant mechanism to mitigate the voltage disturbances Jayakumar, N.; Vighneshwari, B. Devi; Prema, V.
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.pp282-292

Abstract

One of the most effective renewable energy solutions for long-term power generation is a solar photovoltaic (PV) system that is connected to the grid. However, power quality and system reliability can be significantly impacted by grid-side voltage disturbances such as sag, swell, and faults. To reduce voltage fluctuations and improve grid stability, this study proposes an effective fault-tolerant (FT) solar PV system coupled with a dynamic voltage restorer (DVR). The adaptive DVR-based control method, which dynamically injects compensatory voltages based on disturbance amplitude to ensure uninterrupted and distortion-free power delivery, is the feature that makes this study unique. MATLAB/Simulink is used to model and simulate the system to assess its dynamic response under fault, sag, and swell situations. IEEE 519 standards are met by the suggested design, which produces average total harmonic distortion (THD) values of 0.59%, 1.16%, and 1.55% for 50%, 100% sag/swell, and three-phase fault circumstances, respectively. This indicates that even in challenging grid situations, the DVR can sustain high-quality voltage profiles. For implementation in renewable-rich or weak grid networks, the suggested FT-DVR configuration provides a workable and affordable solution that guarantees better voltage regulation, less harmonic distortion, and increased operational dependability for upcoming smart-grid integration.

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