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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 2,781 Documents
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Adaptive notch filter: An alternative synchronizer for effective performance of active power filter under challenging grid conditions Yap Hoon; Kuew Wai Chew; Kenny Sau Kang Chu; Siti Zaliha Mohammad Noor
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1221-1230

Abstract

Harmonic distortion issues on modern power systems are becoming highly significant due to the increasing integration of renewable energy sources, electric vehicles, and smart technologies. These distortions, mainly caused by the operation of power electronics devices, potentially degrade overall system quality, increase losses, and shorten equipment lifespan if they are not properly mitigated. Shunt active power filters (SAPFs) are found to be most effective against current harmonics issues, but their performance strictly depends on accurate grid synchronization. In this paper, an alternative method developed based on the adaptive notch filter (ANF) concept is proposed for reliable grid synchronization under challenging conditions. The proposed ANF-based synchronizer is modelled in MATLAB/Simulink and benchmarked against the existing self-tuning filter (STF) method under four cases involving sinusoidal, distorted, noisy, and distortion-with-noise grid conditions. Simulation findings demonstrate that the proposed method enables the connected SAPF to effectively mitigate harmonics by providing low total harmonic distortions (2.71% to 2.82%) and minimal phase deviation (0.2° to 0.5°), while maintaining the accuracy of fundamental current between 94.48% to 97.21%. As a result, the overall power factor of the system is raised to near unity, confirming the ability of the proposed ANF-based method to serve as a better alternative for SAPF synchronization.
Real-time selective harmonic elimination in multilevel inverters using embedded metaheuristic optimization on PYNQ-Z2 Taha Ahmad Hussein; Dahaman Ishak
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1914-1925

Abstract

This paper offers a structured methodology for regulating optimal control switching angles in multilevel inverters to achieve selective harmonic elimination (SHE) using embedded optimization algorithms on the PYNQ‑Z2 FPGA operating base. The proposed approach employs metaheuristic techniques including particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimization (GWO), slime mould algorithm (SMA), and whale optimization algorithm (WOA) to generate candidate switching angles across a wide range of modulation indices. For each modulation index, the most effective solution towards harmonic reduction and waveform quality is selected and implemented on the FPGA controller, enabling reliable real‑time operation with very low delay. Experimental validation of a 31‑level single‑phase inverter confirms the effectiveness of the method in eliminating selected harmonics and refining the fundamental output. Combining comparative algorithmic selection with FPGA‑based control demonstrates a systematic and efficient strategy for advanced inverter systems. A MATLAB/Simulink model is constructed to represent the 31-level inverter to enhance the practical aspect and study the frequency spectrum, where many harmonics that contribute to obtaining THD within the IEEE standards are eliminated. Also, the different types of losses are calculated based on the governing mathematical rates.
Design of a novel single-source 15-level inverter with self-balancing capacitors and sensorless PWM control Taoufiq El Ansari; Ayoub El Gadari; Youssef Ounejjar
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1950-1961

Abstract

Multilevel inverters (MLIs) are widely used in energy-conversion systems because they generate high-quality AC voltages with reduced harmonic distortion. However, existing MLIs often require numerous power devices, multiple DC sources, voltage sensors, or dedicated capacitor-balancing controllers, which increases both hardware cost and control complexity. This paper proposes a single-phase 15-level inverter derived from the Packed U-Cell structure. It employs a single DC source, three capacitors, and a reduced number of power switches. Based on the number of power switches, gate drivers, diodes, capacitors, DC sources, output levels, and total standing voltage per unit (TSVpu), the proposed topology achieves a lower cost function than the compared topologies reported in the literature. The proposed topology achieves a relatively low total standing voltage per unit (TSVpu) of 4.43. Sensorless open-loop SPWM offers inherent capacitor-voltage self-balancing, eliminating the need for voltage sensors or additional balancing loops. MATLAB/Simulink validation at 2 kHz and a modulation index of 1 covers steady-state operation, load transients, nonlinear loading, and DC-source voltage fluctuations. For a 50 Ω–20 mH load, the current THD is 2.20% without an output filter, confirming suitability for energy-conversion systems.
A novel machine learning based maximum power point tracking in interleaved buck-boost converter for portable solar-powered electric vehicle charging in rural areas C. Niranjana; P. K. Vineeth Kumar; J. J. Jijesh; G. B. Arjun Kumar; Dileep Reddy Bolla; K. N. Sunil Kumar
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2247-2258

Abstract

Need for off-grid electric vehicle (EV) charging solutions, using intelligent control systems, such as machine learning (ML)-based maximum power point tracking (MPPT), to harness solar energy, offers a means of optimizing efficiency even in the face of fluctuations. This innovative strategy combines clean energy, cutting-edge power electronics, and practical application, which makes it perfect for fostering innovation in areas with inadequate infrastructure. For rural areas without grid infrastructure, this paper presents a novel design and performance assessment of a portable solar-powered EV charging system. To maximize solar energy harvesting and charging efficiency, the system combines an interleaved buck-boost converter with an ML-driven MPPT algorithm. It is appropriate for small electric vehicles (EVs) like auto rickshaws because it uses a 48 V lithium iron phosphate (LiFePO₄) battery. A supervised regression model trained on real-time electrical (voltage, current, and power) and environmental (temperature, irradiance) parameters is used to implement the MPPT algorithm. The system was created using MATLAB/Simulink, and the key performance parameters were evaluated using real-time information. Analyses of the key performance metrics like charging efficiency, converter stability, and tracking accuracy show a superior energy harvesting efficiency of 97%.
Sliding-mode fuzzy logic controller based direct torque control and ripple minimization of induction motors S. Sujitha; Hima Bindu Eluri; P. B. Savitha; S. Venkateshwarlu
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1714-1727

Abstract

Conventional direct torque control is one of the most effective strategies for managing the torque of an induction machine (DTC), which is one of the most common types of machines. Instead, the DTC's inadequate control ability is made worse at low speeds by the audible flux and torque waves that are generated by its extremely low switching frequency. This is because the DTC's switching frequency is exceedingly low. In an effort to address these concerns, a variety of direct torque control strategies focused solely on flux and torque as their primary concerns. An improvement in DTC control and the elimination of ripples in induction motors are the goals of this research, which introduces a sliding mode fuzzy logic controller technique. The complexity of the algorithm, sensitivity of the parameters, the tracking speed, the switching loss, and the ripple reduction properties of the technique will all be thoroughly investigated. Simulation of the control mechanism is performed with MATLAB/Simulink in order to verify that it functions as intended.
Intelligent MPC for DFIG wind turbines Amira Lakhdara; Tahar Bahi; Amina Azizi; Amina Benabda
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2047-2057

Abstract

This paper presents and evaluates advanced control strategies to enhance power tracking and robustness in doubly fed induction generator systems operating under realistic and perturbed wind conditions. In addition to the conventional field-oriented control, we develop a model predictive control approach that determines the optimal rotor voltage vectors by minimizing a quadratic cost function, as well as a fuzzy-weighted model predictive control in which the cost weight is adjusted online based on the tracking error and its derivative. The dynamic models used accurately represent the key behaviors of the doubly fed induction generator and its rotor-side and grid-side converters. MATLAB/Simulink simulations are carried out using two wind scenarios: a smooth sinusoidal profile and a filtered stochastic profile, while a robustness test introduces variations in rotor parameters during operation. The results demonstrate that the fuzzy-weighted model predictive control achieves faster convergence, lower steady-state error, and improved robustness, all while maintaining reasonable converter effort and acceptable power quality.
Heuristic-based optimization for smart home energy management with renewable integration and energy storage systems M. J. Suganya; Y. Sukhi; J. C. Vinitha; J. Sumithra
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1747-1754

Abstract

The rapid growth of energy demand in urban areas emphasizes the necessity of efficient demand-side management (DSM) strategies in smart homes. This study presents a heuristic-based optimization framework for appliance scheduling in smart home environments that incorporates renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The objective is to minimize the electricity cost and the peak-to-average ratio (PAR) while satisfying the user comfort constraints. Three optimization techniques, genetic algorithm (GA), binary particle swarm optimization (BPSO), and wind-driven optimization (WDO), are implemented and evaluated in three scenarios: without renewable integration, with RSERs, and with both RSERs and ESSs. Simulation results show that BPSO has the lowest electricity cost and carbon emissions, while WDO has a faster convergence speed and competitive performance in PAR reduction. RSER and ESS integration has a major positive impact on energy efficiency, grid dependence, and sustainability. The results give useful insights to choose appropriate optimization methods and contribute to the development of effective, sustainable, and user-centric smart home energy management systems.
Design and implementation of a boost converter circuit based on an Arduino kit Ali S. Alhfidh; Farah Isam Hameed; Ali N. Hamoodi; Fawwaz Jassim Mohammed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp1630-1642

Abstract

This paper presents a DC motor speed control system based on an Arduino kit with a boost converter that enhances performance under variable load conditions. At load fluctuations, traditional control methods frequently show poor stability and efficiency. A boost converter is used to raise the input voltage to the DC motor. Arduino kit creates a real time pulse with modulation (PWM) signal to modify the duty cycle of the converter and provide dynamic voltage compensation; this system is able to maintain the speed constant in spite of changing with current and torque. The proposed practical connection board offers quicker response time and increases overall efficiency. An economical solution is provided by the combination of boost voltage regulation by Arduino-based PWM control. Results displayed the relationship between torque-speed and torque-current curves with and without the boost unit. Finally, it has been concluded that the boost output voltage is fixed at the desired value.
Hybrid AI-driven intelligent fault diagnosis and localization in modern power systems Deepa Somasundaram; M. Sowmya; R. Priya; Sandip D. Satav; P. Arthi Devarani; Jayashree Kathirvel
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2281-2290

Abstract

This paper presents a hybrid intelligent framework for fault diagnosis and localization in modern power distribution systems, addressing challenges such as noisy measurements, high-impedance faults (HIF), and uncertain operating conditions. The proposed approach integrates deep neural networks (DNN) for nonlinear feature extraction, support vector machines (SVM) for robust classification, and a fuzzy inference system for uncertainty-aware decision fusion, combining the strengths of deep learning, machine learning, and soft computing. A comprehensive dataset of over 12,000 fault instances is generated using IEEE 33-bus and 69-bus systems, covering multiple fault types (LG, LL, LLG, LLL), fault resistances (0.1-200 Ω), varying load conditions, and noise levels from 30 dB to -5 dB SNR. Wavelet-based denoising and hybrid feature extraction (time–frequency and statistical features) are employed to capture transient characteristics. The DNN generates discriminative feature embeddings, which are classified using an RBF-kernel SVM and further refined through fuzzy logic with Gaussian membership functions. Fault localization is performed using impedance-based estimation enhanced by learned correction. Results show that the proposed model achieves 98.5% classification accuracy, outperforming DNN (93.2%), SVM (90.4%), random forest (91.1%), and k-NN (88.6%). The model demonstrates strong noise robustness, with only -6% accuracy degradation at -5 dB SNR. It achieves fault localization error of 0.2-0.7 km and HIF detection with F1-score of 0.91. With inference latency of 45 ms (reduced to 28 ms after optimization), the system is suitable for real-time deployment, providing a scalable and reliable solution for smart grid fault monitoring.
Bayesian-optimized LSTM networks for accurate day-ahead photovoltaic power prediction Enas Ali Ahmed; Muna Hassan Hussein; Abdulkreem Mohammed Salih
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2172-2182

Abstract

Accurate day-ahead photovoltaic (PV) power forecasting is essential for effective energy management and grid balancing. This study proposes a Bayesian-optimized long short-term memory (LSTM) network for day-ahead PV power prediction. The model was evaluated using a PV-meteorological time-series dataset collected from a 500-kWp grid-connected PV installation in Mosul, Iraq, between January 1, 2021 and December 31, 2023 at an hourly sampling interval. After data-quality screening, 25,842 valid synchronized observations were retained from 26,280 timestamps. Each forecasting sample used the previous 24 hours of PV power, solar irradiance, ambient temperature, relative humidity, wind speed, and temporal indicators to predict the subsequent 24-hour PV power profile. The data were divided chronologically into training, validation, and independent test subsets. Preprocessing included duplicate removal, missing-value treatment, IQR-based outlier handling, temporal alignment, and min-max normalization fitted only on the training subset. Bayesian optimization tuned the LSTM architecture and training hyperparameters, while a hybrid MSE-MAE loss balanced large deviations and overall error. The proposed model achieved an MAE of 15.2 kW, an RMSE of 20.5 kW, a MAPE of 8.3%, and an R² of 0.93, outperforming linear regression and autoregressive integrated moving average (ARIMA) under the evaluated conditions.

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