Papers by International Journal of Power Electronics and Drive Systems
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
This paper proposes an AI-driven resilient network design framework for optimal electric vehicle ... more This paper proposes an AI-driven resilient network design framework for optimal electric vehicle (EV) charging station placement under stochastic demand and dynamic grid constraints. The proposed approach uniquely integrates long short-term memory (LSTM) based spatiotemporal demand forecasting with a hybrid genetic algorithm-particle swarm optimization (GA-PSO) model for multi-objective station placement. In addition, a deep reinforcement learning (DRL) agent is incorporated to enhance adaptive resilience under real-time grid disturbances. The framework minimizes installation cost, reduces user travel distance, and improves grid stability while ensuring equitable accessibility. The model is evaluated under multiple scenarios, including peak demand, station outages, renewable intermittency, and grid capacity reduction. Results demonstrate that the proposed hybrid AI framework achieves a resilience index of 0.92, reduces travel distance by 54%, and lowers installation cost by up to 16% compared to conventional approaches such as linear programming (LP) and K-means clustering. The integration of renewable energy further reduces peak grid dependency by 18%. The proposed methodology provides a scalable and practical solution for designing sustainable and resilient EV charging infrastructure in smart urban environments.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
This article explores the problem of adaptive control for nonlinear dynamic systems operating und... more This article explores the problem of adaptive control for nonlinear dynamic systems operating under uncertainty. It presents a model reference adaptive control (MRAC) method that integrates a neuro-fuzzy network with B-spline basis functions. The proposed approach allows effective approximation of nonlinear behaviors and ensures high control accuracy despite external disturbances and structural uncertainties within the system. The paper compares the performance of conventional linear MRAC with the neurofuzzy controller. Simulation results demonstrate that the neuro-fuzzy MRAC achieves superior stability and accuracy in closed-loop control. Additionally, the study examines the system's local stability under specific conditions of the learning rate. To address the challenge of computational complexity, a decomposition strategy dividing the controller into smaller sub-models is introduced, effectively mitigating the "curse of dimensionality." The findings support the applicability of neuro-fuzzy controllers for the intelligent control of a wide range of nonlinear systems.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
The rapid expansion of clinical data in modern healthcare requires analytical systems capable of ... more The rapid expansion of clinical data in modern healthcare requires analytical systems capable of uncovering intricate patterns and supporting accurate diagnostic decisions. Quantum machine learning (QML) offers significant potential for modeling higher-order feature interactions and accelerating computation beyond classical approaches. This paper introduces an improved hybrid architecture that fuses an inception-based attentional VGG (IAV) network with a quantum variational classifier (QVC) constructed using parameterized quantum circuits (PQCs). The framework begins with min-max normalization to stabilize heterogeneous clinical attributes and enhance training convergence. Deep discriminative features are then extracted through the IAV model, followed by quantum-driven classification using variational layers optimized by classical routines. The MIMIC-III clinical dataset is employed to validate the proposed system on large-scale healthcare records. Performance is measured using accuracy, precision, recall, and F1-score. The enhanced hybrid model achieves 97.28% accuracy, 97.16% precision, 96.65% recall, and a 97.38% F1-score, surpassing established methods including support vector machine (SVM) (89.23%), quantum support vector machine (QSVM) (90.13%), and QVKSVM (97.34%). The findings confirm that integrating deep learning with quantum variational optimization strengthens scalability, reduces computational overhead, and establishes a powerful foundation for next-generation healthcare analytics.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
The modernization of electric power grids, driven by communication and electronic hardware advanc... more The modernization of electric power grids, driven by communication and electronic hardware advances alongside increasing renewable energy integration, introduces challenges like voltage fluctuations, weakened protection, and transient instability. High renewable penetration can trigger reverse power flow and voltage rise, complicating system control. Real-time digital simulations offer a non-destructive approach to analyze and optimize power system behavior under diverse conditions. Using platforms like Simulink Real-Time and RT-LAB with OPAL-RT, detailed studies of protection relays, circuit breakers, and control algorithms are efficiently conducted. This paper reviews real-time digital simulation techniques for renewable-integrated power systems, emphasizing state-space modeling for capturing system dynamics. Recent developments in predictive and eventbased control strategies to enhance microgrid stability and operational efficiency are examined. Simulations of a three-bus system with transient analysis and event-based predictive control for energy management are discussed, demonstrating how real-time simulation platforms support renewable energy integration while maintaining grid stability.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
Maximizing power extraction from photovoltaic (PV) systems is crucial for their overall efficienc... more Maximizing power extraction from photovoltaic (PV) systems is crucial for their overall efficiency. However, under partial shading conditions (PSCs), the power-voltage curve shows several points of maximum power. This phenomenon often leads to traditional maximum power point tracking (MPPT) algorithms getting stuck at suboptimal local peaks, resulting in substantial energy losses. To solve this, we introduce a novel neuroevolutionary genetic algorithm (NEGA) for global MPPT. This hybrid algorithm integrates a neural network to intelligently guide the evolutionary search process, improving its GMPP tracking. The performance of the NEGA controller is rigorously compared against the widely used particle swarm optimization (PSO) algorithm via MATLAB/Simulink simulations across various irradiance scenarios. Results under severe PSCs demonstrate NEGA's superior tracking efficiency of 98.69%, far exceeding PSO's 76.02%. Moreover, NEGA achieves a faster convergence time of 0.1 s under dynamic irradiance, compared to 0.6s for PSO. The study concludes that NEGA is a robust and highly efficient solution for global MPPT, ensuring maximum power harvesting from PV systems under challenging operating conditions.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
This paper designs a power controller for power converters using fuzzy logic. The proposed contro... more This paper designs a power controller for power converters using fuzzy logic. The proposed controller will automatically adjust the frequency and voltage when the load changes to improve the power quality of the microgrid. Besides, the controller can realize accurate power sharing among the power converters in the microgrid, thereby suppressing the circulating current between the inverters. Furthermore, to ensure the control system operates stably and accurately during voltage and frequency adjustments, this paper employs a sliding-mode controller rather than a conventional proportional-integral controller. The proposed control method has a voltage deviation from the rated value when the load changes in the range of 1.5 Volts to 2.7 volts, and a frequency deviation from the rated value when the load changes in the range of 0.2 to 0.4 Hz. The accuracy of reactive power division is 100%. The proposed controller is simulated using MATLAB/ Simulink software, and the results obtained from the simulation have verified the effectiveness of the proposed method.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
This paper introduces a hybrid maximum power point tracking (MPPT) strategy for photovoltaic (PV)... more This paper introduces a hybrid maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems under rapidly varying irradiance conditions. The approach combines the super-twisting algorithm (STA), a second-order sliding mode control technique, with the grey wolf optimizer (GWO) in a coordinated framework where control action and parameter adaptation are jointly addressed. Unlike conventional MPPT methods that treat control and optimization separately, the proposed scheme improves transient response while limiting steady-state oscillations. The method is evaluated through MATLAB/Simulink simulations under multiple dynamic irradiance profiles, including fast-changing environmental conditions. Performance is assessed using complementary metrics, namely tracking efficiency, convergence dynamics, and root mean square error (RMSE), to provide an objective analysis. Results show that the STA-GWO strategy achieves faster convergence and improved stability compared to conventional SMC-GWO. It reaches an average tracking efficiency of 99.34%, compared to 99.19% for SMC-GWO, with reduced power fluctuations reflected by a lower RMSE. These improvements indicate a better trade-off between dynamic performance and steady-state accuracy. While this study is based on simulations, its findings require experimental validation. Future work will therefore include real-time implementation to confirm the practical applicability of the proposed approach.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
Renewable energy, particularly hydropower, is a key focus in reducing reliance on fossil fuels an... more Renewable energy, particularly hydropower, is a key focus in reducing reliance on fossil fuels and mitigating environmental impacts. Permanent magnet generator (PMG) has emerged as a highly efficient option for converting hydro-energy into electricity, offering advantages such as high efficiency, compact design, and minimal maintenance. This review explores the latest developments in PMG technology, particularly for small and medium-scale hydropower applications. A systematic review method was used to analyze 617 papers and narrow them down to 20 relevant studies. Key findings highlight advancements in PMG design, including modular stators, counter-rotating turbines, and cordless designs that enhance efficiency and adaptability in low-speed environments. However, significant challenges remain, including the high cost of magnetic materials like Neodymium Iron Boron (NdFeB), thermal stability issues, and more robust control systems to manage variable water flow conditions. The review concludes that while PMG holds great potential for hydropower applications, Further research is needed to optimize material usage, improve design, and reduce costs. Future work should focus on developing new magnetic materials and innovative rotor designs to ensure PMG can provide a scalable and sustainable solution for global energy needs.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
The rapid integration of photovoltaic (PV) systems into power networks poses significant challeng... more The rapid integration of photovoltaic (PV) systems into power networks poses significant challenges to grid stability, including reduced inertia, voltage fluctuations, and limited fault ride-through (FRT) capabilities. This study presents a comparative analysis of two inverter control strategies: the synchronous reference frame (SRF) controller and the virtual synchronous generator (VSG) controller. A high-fidelity MATLAB/Simulink model was developed, incorporating the effects of irradiance and temperature, maximum power point tracking (MPPT), and battery energy storage system (BESS) interaction. Standardized fault scenarios were applied at PV penetration levels ranging from 30% to 150% in accordance with IEEE-1547, IEEE-519, and IEC 61727 requirements. The results show that SRF control achieves superior harmonic suppression, with a total harmonic distortion (THD) consistently below 0.5%, confirming its suitability for strong grids prioritizing power quality. However, its stability deteriorated at higher penetration levels, with the voltage overshoot reaching approximately 16% and recovery times exceeding 3 s. In contrast, the VSG control demonstrates enhanced transient stability and effective FRT performance, with the overshoot limited to ≤5% and recovery achieved within 0.8 s across all operating conditions. The main contribution of this study lies in the direct benchmarking of the SRF and VSG control strategies under identical operating conditions using a unified evaluation framework, including an extended analysis beyond 100% PV penetration. The findings highlight a fundamental trade-off between harmonic performance and transient stability and provide practical guidance for selecting appropriate inverter control strategies for renewable-dominated power systems.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
The virtual synchronous generator (VSG) is commonly used to reproduce the inertial response of co... more The virtual synchronous generator (VSG) is commonly used to reproduce the inertial response of conventional synchronous machines. However, the VSG control architecture relies on controller chains, benchmark transformations, and parameter settings, including virtual inertia and damping, which limit its flexibility in highly dynamic environments. This paper proposes an innovative end-to-end control approach based on a neural network to fully replace the classical VSG control structure. The neural network developed is trained to directly generate inverter control signals from real-time electrical measurements, including voltages and currents, as well as active and reactive power. A dataset is generated from a detailed VSG model under different operating conditions, and then a multilayer neural network is trained using supervised learning with MATLAB. The resulting model is then integrated into a complete wind energy conversion chain simulated in Simulink. The simulation results demonstrate that control based on artificial neural networks ensures better frequency and voltage stability, more accurate tracking of the active power injected, and a significant improvement in power quality, with total harmonic distortion (THD) reduced to 0.04%, compared to 0.51% for conventional VSG control. These results confirm the potential of artificial intelligence-based approaches for the intelligent control of renewable energy systems.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
Grid-connected solar photovoltaic (GCPV) systems have become an essential part of modern electric... more Grid-connected solar photovoltaic (GCPV) systems have become an essential part of modern electricity generation due to their ability to harness clean, renewable energy, reduce greenhouse gas emissions, and lower dependence on fossil fuels. In Malaysia, initiatives promoting small-scale GCPV adoption among residential, commercial, and industrial users have been notably successful. However, concerns regarding power quality (PQ) within GCPV-integrated environments remain insufficiently explored. This study presents a comprehensive evaluation of the impact of GCPV generation on frequency fluctuations and voltage total harmonic distortion (THDV) within the Malaysian grid. The methodology involves empirical measurements of PQ at a selected GCPV installation, focusing on frequency fluctuation and THDV, and compares the results against Malaysian and international standards. These measurements form the basis for further statistical analysis, which includes descriptive analysis, process capability analysis, and Pearson correlation analysis. The study aims to provide insights into grid stability, the influence of GCPV output on PQ, and the relationship between environmental factors and PQ deviations. Findings reveal that GCPV generation has minimal impact on grid PQ, which remains within acceptable limits set by relevant standards. Furthermore, no significant correlation was observed between GCPV output and PQ deterioration. The results contribute to a deeper understanding of PQ challenges in GCPV systems and offer valuable guidance for regulators and utility providers to support the development of effective mitigation strategies to ensure the continued stability and efficiency of Malaysia's evolving power grid.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
Enhancing photovoltaic models' performance and dependability requires optimal parameter extractio... more Enhancing photovoltaic models' performance and dependability requires optimal parameter extraction. This paper presents a practical method for determining these values from experimental current-voltage data: the war strategy optimization algorithm. RTC France, PWP201, and STP6-120/36 are the three PV models to which the war strategy optimization algorithm was successfully applied. According to the findings, the RMSE values for RTC France were 0.0000077298; PWP201 was 0.0020528; and STP6-120/36 was 0.0014253. These results demonstrate the great potential of the warfare strategy optimization (WSO) to improve the accuracy of photovoltaic models and advance photovoltaic technology.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
In a single-phase inverter system, parallel operation of inverters is a strategy to increase capa... more In a single-phase inverter system, parallel operation of inverters is a strategy to increase capacity, improve reliability, and increase the flexibility of the inverter system. This work discusses the basic operation of a novel parallel H-bridge current source inverter (H-BCSI) and H-bridge voltage source inverter (H-BVSI) operated in a grid-connected operation with isolated direct current (DC) sources equipped with power transformers. Each inverter circuit employed an independent current controller to regulate its alternating current (AC) output current. The proposed inverter system was tested for different operation conditions, and its characteristics were analyzed, especially for its harmonic profile. The test results showed that if the magnitude of the H-BCSI current was varied, while the H-BVSI current was kept constant, the total harmonic distortion (THD) value of load current was much lower than the THD values of H-BVSI current, H-BCSI current, and grid current, i.e., THD Iload ≤ 1%. This condition also occurred when the output current of the H-BVSI was increased gradually while the output current of H-BCSI was maintained constant. Moreover, a similar result was also obtained when both inverters' output currents were varied simultaneously with the same value. The test results confirmed that the injected AC current of both inverters during parallel grid-connected operation worked well at unity power factor, and met the standards IEEE 1547 and IEC 61727, of which current THDs were ≤ 5%. The proposed gridconnected parallel inverter system worked, supplying a sinusoidal AC load current with high power quality.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
Solar power sources and electric vehicles (EVs) are increasingly used because of their environmen... more Solar power sources and electric vehicles (EVs) are increasingly used because of their environmental friendliness and sustainability. They are typically connected to the power grid through devices such as inverters and chargers to either generate or receive electrical energy. These devices contain a DC voltage bus. Therefore, the combined control of these two types of devices can improve their overall operational efficiency. This article proposes a grid-connected converter with an integrated battery-charging function. In addition, it presents a control strategy for the coordinated operation of this converter during both charging and power generation at the DC bus. In this algorithm, the battery is treated as a priority load, which allows the system to eliminate the AC-DC converter used in conventional chargers. A total peak power of 9 kWp is used to investigate the processes of power generation and battery charging. The total harmonic distortions of grid current are less than 2.86% in different operational cases and meet the grid codes. The obtained results are analyzed under varying irradiance conditions to verify the effectiveness of the proposed control method.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
The performance optimization of hybrid renewable energy systems (HRES) is crucial for enhancing t... more The performance optimization of hybrid renewable energy systems (HRES) is crucial for enhancing the efficiency, reliability, and sustainability of energy production. This study focuses on the integration of real-time load forecasting prediction using a grey wolf optimization (GWO)-based predictive model. The proposed methodology aims to address the challenges associated with the intermittent nature of renewable energy sources, such as solar and wind power, by providing accurate forecasts for load demands and solar irradiance. Real-time data from sensors and environmental parameters are incorporated to forecast the energy load and solar irradiance over shortterm periods, which are then used to optimize the energy storage and generation components of the HRES. The GWO algorithm, known for its high accuracy and computational efficiency, is employed to optimize the dispatch of power from various sources while minimizing energy losses and ensuring system stability. The integration of GWO with real-time forecasting not only enhances the predictive capability of the system but also improves the overall economic viability of HRES by reducing operational costs and carbon emissions. This study demonstrates the potential of using intelligent optimization techniques and real-time forecasting for the sustainable operation of hybrid renewable energy systems, contributing to the development of smarter and more resilient energy grids.
International Journal of Power Electronics and Drive System (IJPEDS) , 2026
This work focuses on studying and analyzing the photovoltaic power plant of Oued Nechou located i... more This work focuses on studying and analyzing the photovoltaic power plant of Oued Nechou located in the South of Algeria, in order to create its simulation model. This later can estimate its power production. To achieve this, all system parameters were introduced in the model according to the real data. Then, the characteristics of the photovoltaic panels were tested and plotted under different temperature and irradiation values to understand their influences on the electrical performances. In order to ensure the maximum energy production, photovoltaic panels were associated with converters controlled by a maximum power point tracking (MPPT) algorithm. Two different thin-film technologies of the PV panels (Amorphous silicon (a-Si) and cadmium telluride (CdTe) technologies) were simulated and tested under standard test conditions (STC) and compared with the real characteristics. The results show good accuracy. Subsequently, the real data of four seasons of the same year were introduced in the created model of Oued Nechou station. The obtained results of the simulation show that the performance of the produced energy is affected by the desert climatic conditions, especially the temperature and the solar radiation. However, the positive solar effect is higher than the negative thermal effect, which encourages investment by installing other photovoltaic stations in these areas known by the high and long duration of irradiance.
International Journal of Power Electronics and Drive System (IJPEDS), 2026
Solar-powered electric vehicle (EV) charging stations are essential in advancing low-carbon trans... more Solar-powered electric vehicle (EV) charging stations are essential in advancing low-carbon transportation. However, determining optimal locations remains challenging due to spatial, technical, and environmental constraints. This systematic review, conducted under the PRISMA 2020 framework, synthesizes optimization techniques for siting solar-powered EV charging stations from 15 peer-reviewed studies published between 2016 and 2024. The reviewed methods are classified into five major categories: geographic information systems (GIS)-based spatial models, multi-criteria decision-making (MCDM) frameworks, hybrid approaches integrating fuzzy logic and GIS, heuristic/metaheuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO), and artificial-intelligencebased models for predictive site selection. GIS-MCDM hybrid approaches were the most prevalent, offering improved robustness in spatial decisionmaking. Nevertheless, the literature reveals persistent gaps, including limited empirical validation, insufficient use of real-time data, and weak integration with smart-grid planning. This review provides a structured methodological classification, highlights sustainability considerations, and outlines a research roadmap toward intelligent, data-driven, and sustainable EV infrastructure planning aligned with global energy-transition goals.
International Journal of Power Electronics and Drive System (IJPEDS), 2026
Conventional automotive headlamp systems operate using fixed illumination levels and manual beam ... more Conventional automotive headlamp systems operate using fixed illumination levels and manual beam levelling, limiting adaptability to dynamic driving conditions such as vehicle load variation, speed changes, and ambient light fluctuations. These static systems may result in reduced visibility, increased glare, and inefficient energy usage. This paper presents a dual microcontroller-based smart headlight control system incorporating a dynamic load adjustment mechanism for real-time regulation of beam intensity and angle. Unlike conventional single-controller configurations, the proposed architecture distributes control tasks between two dedicated microcontrollers to enhance modularity and processing stability. The first controller performs adaptive intensity regulation through speed-dependent low-beam dimming and LDR-based high-beam glare control, while the second controller enables automatic beam levelling using rear suspension load sensing to compensate for vehicle pitch variations. The system was validated through Proteus simulation and hardware prototyping. Experimental results demonstrate low-beam modulation at 30%, 80%, and 100% brightness levels, high-beam voltage control from 0.04 V to 1.82 V, and adaptive beam angle adjustments under varying load conditions. Approximately 90% simulation-to-hardware agreement confirms system reliability. Compared to conventional systems, the proposed design offers improved adaptive illumination, glare mitigation, and energy-aware operation, supporting integration into modern LED-based automotive lighting platforms and electric vehicles.
International Journal of Power Electronics and Drive System (IJPEDS), 2026
Conventional power plants pose a threat to the environment because of their substantial carbon em... more Conventional power plants pose a threat to the environment because of their substantial carbon emissions. Photovoltaic (PV) systems are becoming more and more popular as a sustainable alternative for clean electricity generation. However, because weather and environmental factors vary, partial shadowing affects PV output. The stacked multi-cell converter (SMC) provides a practical way to improve power extraction under these circumstances. This paper suggests a hybrid control approach for a photovoltaic (PV)-based distributed system (DS) using an SMC that is based on the attentive evolutionary generative adversarial network (AEGAN) and prairie dog optimization (PDO) algorithm. The AEGAN forecasts load requirements, while the PDO maximizes converter control to improve reliability, efficiency, and power quality (PQ). Under various load and irradiation circumstances, the system is modelled and verified in MATLAB/Simulink. Results from simulations show that the AEGAN-PDO approach performs better in both dynamic and steady-state situations. Transient disturbances on the load side are rapidly reduced with minimal overshoot. In contrast to traditional particle swarm optimization (PSO), ant lion optimizer (ALO), and archerfish hunting optimizer (AHO) controllers, AEGAN-PDO maintains the lowest THD (1.1%), least power loss (0.24 MW), and best efficiency (98.59%). These results validate the AEGAN-PDO approach as a reliable and effective way to operate renewable-integrated power systems in realtime, promoting improved PQ and grid dependability.
International Journal of Power Electronics and Drive System (IJPEDS), 2026
This paper proposes a reliable power optimization strategy that maximizes the harvested power of ... more This paper proposes a reliable power optimization strategy that maximizes the harvested power of induction machines driven by wind, taking into account variable wind turbulence and uncertain machine parameters. This work explores the challenging task of designing type-2 fuzzy logic (T2FL) and conventional type-1 fuzzy logic (T1FL) controllers for wind energy conversion systems that exhibit multiple non-linearities. T2FL controllers are proficient in tackling uncertainties and offer quicker and more precise decision-making capabilities. The proposed approach is beneficial as it is independent of accurate wind turbine parameters, wind speed data, or additional sensors. Rather, it utilizes the mechanical rotor speed and the wind turbine power as input, which corresponds to maximum power point tracking (MPPT) through the management of the rotor speed via the machine-side converter. Real data validates the scheme against classical controllers, and via a set of simulations and statistical analyses, performance metrics like steady-state error, overshoot, tracking speed, and efficiency are widely assessed. The results show that the proposed scheme, which is independent of a dedicated wind speed sensor, demonstrates superior tracking performance, lower tracking errors, such as lower RMSE/MAE, and higher energy yield, although the wind speed and the system parameters change rapidly. Overall, this design provides more robust performance to random wind speed variations, increases operational efficiency and wind turbines' service life, and is low in adding mass and cost.
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Papers by International Journal of Power Electronics and Drive Systems