International Journal of Power Electronics and Drive Systems (IJPEDS)
The International Journal of Power Electronics and Drive Systems (IJPEDS) (p-ISSN: 2088-8694, e-ISSN: 2722-256X) is a peer-reviewed, open-access journal published by Institute of Advanced Engineering and Science (IAES). The journal is indexed in Scopus and included in the SCImago Journal Rank (SJR). IJPEDS provides an international platform for researchers, academics, and industry professionals to share scientific findings and advances in power electronics, electric drives, and energy systems. IJPEDS publishes original research articles, review papers, and technical contributions in the fields of power electronics, electric drives, and energy systems. The journal covers research areas including power converters and inverters, power semiconductor devices, control systems, electric machines, motor drives, renewable energy systems, electric vehicles, energy storage technologies, smart grids, power quality, modeling and simulation, EMI/EMC, and industrial applications. For detailed information about the journal's research areas and topics covered, please visit the Focus and Scope page. IJPEDS follows a double-blind peer-review process and adopts an open-access publication model. These publication policies support the dissemination of research in power electronics, electric drives, and related engineering disciplines. Current Issue Volume 17, Issue 2, June 2026 Explore the latest research contributions in power electronics, electric drives, renewable energy systems, and emerging energy technologies. View Issue →Archives → Guide for Authors Authors should prepare manuscripts according to the IJPEDS Author Guidelines and ensure that submissions comply with journal formatting requirements and publication ethics standards. New authors can follow the registration tutorial: Author Registration Guide Video. For further assistance regarding manuscript preparation or submission procedures, please contact the editorial office at ijpeds@iaesjournal.com and ijpeds.staff@gmail.com. <td width="33%" valign="top" style="border:1px solid #ddd;padding:15px;border-radius:6px;background:#fafafa;transition:all .3s ease;" onmouseover="this.style.background='#EAF4FC';this.style.borderColor='#0B5EA8';this.style.boxShadow='0 5px 15px rgba(11,94,168,0.18)';this.style.transform='translateY(-3px)';" onmouseout="this.style.background='#fafafa';this.style.borderColor='#ddd';this.style.boxShadow='none';this.style.transform='translateY(0)';"> Author Guidelines Instructions for manuscript preparation, submission requirements, and publication ethics. View Guidelines → <td width="33%" valign="top" style="border:1px solid #ddd;padding:15px;border-radius:6px;background:#fafafa;transition:all .3s ease;" onmouseover="this.style.background='#EAF4FC';this.style.borderColor='#0B5EA8';this.style.boxShadow='0 5px 15px rgba(11,94,168,0.18)';this.style.transform='translateY(-3px)';" onmouseout="this.style.background='#fafafa';this.style.borderColor='#ddd';this.style.boxShadow='none';this.style.transform='translateY(0)';"> Manuscript Template Download the official Microsoft Word and LaTeX manuscript templates. MS Word Template | LaTeX Template <td width="33%" valign="top" style="border:1px solid #ddd;padding:15px;border-radius:6px;background:#fafafa;transition:all .3s ease;" onmouseover="this.style.background='#EAF4FC';this.style.borderColor='#0B5EA8';this.style.boxShadow='0 5px 15px rgba(11,94,168,0.18)';this.style.transform='translateY(-3px)';" onmouseout="this.style.background='#fafafa';this.style.borderColor='#ddd';this.style.boxShadow='none';this.style.transform='translateY(0)';"> Online Submission Submit your manuscript through the official online submission system. Submit Manuscript →
Institute of Advanced Engineering and Science
Design and development of a portable electromagnetic coil accelerator for advanced defense applications
In this work, a prototype of a six-stage coil gun was designed and developed to accelerate and propel a ferromagnetic projectile as an alternative technology to traditional firearms, which rely on explosive power for defense applications. The electromagnetic coils were arranged along the length of the barrel and were energized one at a time, sequentially, from one end of the barrel to the other to accelerate and propel the projectile forward. This paper presents the design, simulation, and optimization of the barrel and projectile, including details of the electromagnetic coils, triggering and switching circuits, and pulsed current sources. Simulations were conducted using COMSOL Multiphysics software with both single-coil and multiple-coil configurations. The prototype model incorporates a non-ferromagnetic barrel to minimize the retarding magnetic field and hysteresis, which could otherwise reduce the projectile’s velocity. The barrel is equipped with IR sensors to detect the projectile’s movement and activate or deactivate the corresponding electromagnetic coils, ensuring efficient forward propulsion. Utilizing a capacitor bank and rapid charging circuits, the developed prototype unit is capable of propelling the projectile at a velocity of 419 m/s measured at a 10-meter distance and could fire every 2 milliseconds successfully. The prototype unit developed is a handheld rifle using a polyvinyl chloride (PVC) barrel and uses a projectile with dimensions meeting the defense application.
Copyright (c) 2026 M. Priyadharsini, Sunil Kumar Gupta, Manoj Gupta http://creativecommons.org/licenses/by-sa/4.0
Proximal policy optimization-based type II PPC for EV fast charging
In recent years, efficient and fast charging is critical for accelerating the adoption of electric vehicle (EV). However, traditional fully rated converters process the total power flow to the battery, but leading to excessive thermal stress, high energy losses, and quick battery degradation. Similarly, existing partial power converter (PPC) designs like type I and type II PPC, improve efficiency by processing only a fraction of the total power; however, they still face challenges such as additional isolation requirements, limited step-down performance, and lack of advanced control for fluctuating state of charge (SoC) conditions. To overcome these challenges, this research proposes a proximal policy optimization (PPO)-enhanced type II PPC for fast EV charging. Initially, the power is routed through a low-frequency (LF) isolation transformer and filtered to mitigate high-frequency noise. A portion of the power is partially processed through a SiC MOSFET-based phase-shifted full-bridge converter, while the remaining power bypasses directly to the battery. The PPO controller efficiently adjusts the phase shift angle in real time, optimizing switching cycles to reduce switching and thermal losses. The proposed PPO-type II PPC achieved better results in terms of peak efficiency (99.36%) and partial power handling (12.21%) when compared to existing type II PPC designs.
Copyright (c) 2026 Franco Aldrin Joseph Menezes, Gopala Reddy Krishnappa http://creativecommons.org/licenses/by-sa/4.0
Photovoltaic-inductive wireless charging for electric vehicles
The growing demand for electric vehicles (EVs) necessitates efficient and eco-friendly charging methods. This study presents a photovoltaic-inductive wireless charging (PIWC) system, which integrates solar energy harvesting with inductive power transfer (IPT) to enable seamless operation without physical connectors. The system utilizes solar photovoltaic (PV) panels to generate renewable energy, which is then converted and transmitted wirelessly using resonant inductive coupling. This eliminates the need for physical connections, reducing wear and maintenance while supporting both stationary and dynamic charging applications. To enhance performance, maximum power point tracking (MPPT) controllers optimize solar energy utilization. Power electronics and control strategies regulate the energy transfer, ensuring efficient and stable operation. Additionally, IoT-based monitoring enables real-time system analysis and performance tracking. Through simulations and prototype evaluations, the system's feasibility, efficiency, and environmental impact are assessed. Results indicate that PIWC can minimize grid dependency, providing a sustainable, autonomous, and convenient charging solution for EVs. This innovation contributes to cleaner transportation and the advancement of renewable energy-driven mobility.
Copyright (c) 2026 Azra Zaineb, P. Nagabushanam, Kalagotla Chenchireddy, Radhika Dora, Naresh Jella, Shabbier Ahmed Sydu http://creativecommons.org/licenses/by-sa/4.0
Comparison of phase disposition, phase opposition, and phase disposition with variable frequency PWM techniques for harmonic reduction in cascaded multilevel inverters
Renewable energy penetration in distributed generation systems significantly impacts the power quality of the output. The stochastic nature of the inverters provides variable voltage and variable frequency outputs, which is an advantage when used with photovoltaic (PV) and grid integration to the distribution grid, and also in induction motor drives. A primary source of power quality issues is the harmonics generated by the inverters. Multilevel inverters are commonly employed to mitigate these harmonics and improve power quality. Among the various multilevel inverter topologies, the cascaded multilevel inverter (CMLI) has gained prominence due to its simple structure, ease of control, and reduced component requirements. This paper presents a comprehensive review of multilevel inverter topologies that have influenced the evolution of the CMLI structure, along with an investigation into the application of advanced pulse width modulation (PWM) strategies for performance enhancement. In particular, phase disposition (PD), phase opposition disposition (POD), and phase disposition with variable frequency (PD-VF) PWM techniques are implemented on cascaded h-bridge (CHB) multilevel inverters configured for five-level, seven-level, and nine-level operations. A comparative evaluation of total harmonic distortion (THD) is conducted for each inverter configuration, both with and without the inclusion of an LC output filter, to assess waveform quality and harmonic mitigation capability. Furthermore, the harmonic suppression effectiveness of PD, POD, and PD-VF modulation methods is systematically analyzed across different voltage levels. The study also demonstrates that varying the carrier frequency in PD-VF modulation significantly influences THD performance, offering enhanced flexibility and expanded control possibilities in multilevel inverter applications.
Copyright (c) 2026 G. Nayana, Savita D. Torvi http://creativecommons.org/licenses/by-sa/4.0
Physics-informed reinforcement learning for adaptive high-frequency injection in encoderless low-voltage PMSM drives
It is difficult to control permanent magnet synchronous motor (PMSM) drives running at extra-low voltages with encoderless control because the back-EMF signal to estimate rotor position is weak, and this requires the injection of high-frequency (HF) signals. Traditional methods use constant or manually tuned injection levels, and these tend to cause large torque ripple, inaccurate estimation when under dynamic loading, and an inability to counteract parameter drift. The paper is related to the issue of online optimal HF injection amplitude choice in the encoderless 48 V PMSM drives and proposes a physics-inspired reinforcement learning (PIRL) system. This is aimed at obtaining the right low-speed positioning and reducing the torque ripple and power losses on different operating conditions. The suggested approach incorporates directly into the reinforcement learning reward terms the PMSM electromagnetic voltage equations, which restrict exploration to physically consistent space and enhance stability in the learning process. The PIRL agent is trained in a deep deterministic policy gradient architecture in a MATLAB/Simulink-Python co-simulation environment, based on which the PIRL agent adjusts the injection amplitude of HF in real time. Simulation outcomes show that the suggested methodology reaches approximately four times faster convergence with conventional reinforcement learning and reaches up to 65 percent of torque ripple reduction without a disturbed position estimation when operated in a speed range of 0-500 rpm. The findings show that physics-informed learning offers an efficient and energy-saving solution to adaptive encoderless control in extra-low-voltage PMSM drives, which has better resilience to changes in parameters with a low computational cost.
Copyright (c) 2026 Surendar Aravindhan, Manoharan Kavitha, J. Karthika http://creativecommons.org/licenses/by-sa/4.0
Torque ripple reduction in PMSM for FCEVs using ANFIS controller
Globally, there is a growing emphasis on switching to green energy, particularly in the transportation sector, due to the effects of global warming, as seen by rising carbon footprints. Fuel cell electric vehicles (FCEVs) are one such technology that has attracted a lot of interest because of their availability, ease of use, high efficiency, and silent operation. Fuel cells are employed along with batteries to drive the vehicle much farther. Motors like permanent magnet synchronous motor (PMSM) provide the driving force for the vehicle, owing to their high torque at variable speeds and compactness. In such systems, it is necessary to have intelligent controllers that can align with the load requirement by means of a consistent and optimized power distribution. The torque ripple phenomenon, which has an impact on dynamic performance and operational stability, is one of the main limitations in the operation of PMSMs. In this work, smart control techniques, which are a combination of adaptive neuro fuzzy inference systems (ANFIS) and proportional-integral (PI) control, are employed to demonstrate the application of PMSM in conjunction with field-oriented control (FOC). Simulation results indicate that the proposed ANFIS-based FOC reduces torque ripple as compared to conventional PI control under varying load conditions.
Copyright (c) 2026 Shilpa Rao Hosabettu, Pushpa Rajesh Viswanathan http://creativecommons.org/licenses/by-sa/4.0
Real-time implementation and comparative analysis of fault-tolerant control strategies for induction motor drives
For nearly five decades, the induction motor has been the most widely used electrical machine in industry due to its robustness, simplicity, and low cost, supported by advances in power electronics enabling effective performance control. While DC motors were previously favored for their ease of speed and torque regulation, induction motors have gained prominence because they do not require brushes and involve fewer wear-prone components, resulting in reduced maintenance and improved reliability. Consequently, they are widely employed in industrial applications and emerging fields such as electric and hybrid vehicles. This study presents a comparative analysis of two fault-tolerant control (FTC) strategies: field-oriented control (FOC) and direct torque control (DTC). The evaluation focuses on sensitivity to parameter variations, dynamic performance, and steady-state behavior. Both strategies, classified under vector control techniques, are implemented in real time using a dSPACE platform to control an induction motor under an open-circuit fault in a two-level inverter. Results demonstrate that the DTC-based FTC approach offers superior robustness and stability compared to the IFOC-based method, particularly under fault conditions, load disturbances, and speed variations.
Copyright (c) 2026 Asmaa Hammou, Mokhtar Bendjebbar, Mohammed Benslimane http://creativecommons.org/licenses/by-sa/4.0
Fuzzy genetic control for linear speed in multi-machine systems
In today’s fast-moving industrial sectors which include paper, textile, and plastic manufacture the core of production quality is in the precise coordination of multi-drive systems. While PI controllers are the mainstay of the industry, they do have issues in that they struggle with the nonlinearity and dynamics of large-scale windings, which in turn causes instability and product integrity issues. To that end, this paper presents an optimized fuzzy-genetic controller (FLC-GA), which we put forward as a better linear speed synchronization solution. We used genetic algorithms in the tuning of fuzzy logic parameters, which also takes out the very time-consuming task of manual calibration, and at the same time sees a great increase in the system’s ability to deal with process variability. We put our FLC-GA through its paces in a head-to-head comparison with the classic PI and PI-PSO controllers. What we found was that our proposed controller did very well; we saw zero overshoot, a quick 0.5 s settling time, and the total elimination of tension ripples. Also, we saw from a 13.2% change in system inertia that the FLC-GA did a 65% better job in terms of speed accuracy and stability than what we see from standard PI control. We present the FLC-GA not only as a theoretical improvement but as a very robust, high-performance solution in the very tough field of continuous industrial synchronization.
Copyright (c) 2026 Kaddouri Youssouf, Bouchiba Bousmaha, Baba Mohammed http://creativecommons.org/licenses/by-sa/4.0
A neural learning algorithm for online rotor resistance estimation in sensorless induction motor drive systems
This research proposes an advanced artificial neural network (ANN) framework optimized for the dynamic, real-time identification of rotor resistance (Rr) in sensorless induction motor (IM) drive systems. The proposed architecture introduces a self-tuning momentum factor within the neural learning update rule, which is adaptively modulated at each sampling interval. This modulation is governed by a Mamdani-based fuzzy inference system to ensure accelerated convergence and enhanced stability of the estimation process. Concurrently, the motor's angular velocity is estimated through a parallel ANN observer. Reliable identification of the time-varying rotor resistance is pivotal for compensating parametric sensitivity in flux observers, thereby optimizing the drive's control fidelity under varying thermal and load conditions. Comprehensive simulation and hardware-in-the-loop experimental results confirm that the proposed estimator tracks the actual Rr with high precision, maintaining steady-state errors within a 5% threshold.
Copyright (c) 2026 Tuan V. Pham, Nguyen H. Thai http://creativecommons.org/licenses/by-sa/4.0
Improved control strategy for harmonic current mitigation in DFIG-based wind turbines supplying linear and nonlinear loads
Improving power quality is a major challenge in grid-connected wind energy systems, especially under mixed linear and nonlinear load conditions. This paper proposes an enhanced control strategy for harmonic current mitigation in a doubly fed induction generator (DFIG)-based wind turbine. The proposed approach integrates flux-oriented vector control with an active harmonic compensation algorithm implemented through the rotor-side converter (RSC). Unlike conventional methods that target only specific harmonic orders, the proposed strategy mitigates all current harmonics at the point of common coupling (PCC). Simulation studies conducted under various load conditions demonstrate that the method significantly reduces the total harmonic distortion (THD) and ensures near-sinusoidal stator currents. The results confirm the effectiveness and robustness of the proposed control approach in improving the power quality of DFIG-based wind energy conversion systems.
Copyright (c) 2026 Hind Elaimani, Noureddine Elmouhi http://creativecommons.org/licenses/by-sa/4.0
Performance assessment of PSO variants for optimal photovoltaic and DSTATCOM allocation in radial distribution networks
This work presents a comparative evaluation of adaptive particle swarm optimization (PSO) variants for the optimal placement and sizing (OPS) of photovoltaic-based distributed generation (PV-DG) and DSTATCOM units in the standard IEEE 33-bus radial distribution network (RDN). Five adaptive PSO algorithms are investigated, namely adaptive acceleration coefficients PSO (AAC-PSO), autonomous particle groups PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sine-cosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). The optimization framework is structured as a single-objective problem focused on maximizing the active power loss index (APLI), which is used as a normalized indicator associated with active power loss reduction. To further assess the technical quality of the obtained solutions, two additional performance indicators are considered, namely the total voltage deviation (TVD) and the voltage stability index (VSI). The simulation outcomes indicate that the TVA-PSO algorithm exhibits superior overall performance compared to other evaluated variants in terms of convergence behavior and solution quality. In particular, it achieves the highest APLI value of 92.52%, corresponding to an active power loss reduction of 91.91%, with active power losses (APL) reduced from 210.99 kW to 17.07 kW. In addition, the obtained solution significantly improves the network voltage profile (VP) and enhances voltage stability. These findings provide evidence that the effectiveness of adaptive PSO strategies for optimizing PV-DG and DSTATCOM integration in RDN.
Copyright (c) 2026 Mohamed Kherchi, Hacene Mellah, Souhil Mouassa, Anwar Fellahi http://creativecommons.org/licenses/by-sa/4.0
High risk industries for advanced lightning protection system
Lightning strikes are a serious risk for high-risk facilities like oil and gas plants, mines, explosive storage, and data centers. These places hold the sensitive equipment and dangerous materials, and a lightning strike can cause major damage, leading to expensive downtime or even disastrous events such as fires or explosions. That’s why having a strong lightning protection system is not just a matter of following rules, but it is crucial for protecting both people and property. The complete lightning protection solutions designed to meet the specific needs of these critical industries. The services include lightning simulations and both isolated and attached lightning protection systems. This study investigates the real-time installation and testing of advanced lightning protection systems across high-risk industries like oil and gas plants, mines, explosive storage, and data centers. This ensures that the facility stays safe and continues to operate, even during severe weather. By investing in an effective lightning protection system, you can help secure your assets and keep everyone safe, focusing on what really matters in your industry.
Copyright (c) 2026 Kalagotla Chenchireddy, P. Nagabushanam, Radhika Dora, Vadthya Jagan, Shabbier Ahmed Sydu, Nunavath Praveen http://creativecommons.org/licenses/by-sa/4.0
Dual random optimized pulse width modulation controller for three-phase voltage source inverter driven brushless DC motor
Brushless DC (BLDC) motors are widely employed in modern power electronic applications due to their high efficiency and dynamic performance. However, conventional pulse width modulation (PWM) techniques often generate concentrated harmonic components, leading to acoustic noise, torque ripple, and reduced inverter efficiency. This paper proposes an artificial neural network–assisted dual random pulse width modulation (ANN-DRPWM) strategy to enhance the output quality of a three-phase voltage source inverter driving a BLDC motor. In the proposed approach, supervised ANN training enables dual randomization of the carrier and modulation signals, effectively dispersing harmonic energy while maintaining improved DC-link voltage utilization. A passive LC filter is subsequently integrated to further suppress residual harmonics and ensure compliance with harmonic standards. The system is modeled and simulated in MATLAB/Simulink and evaluated against conventional sinusoidal PWM and flying capacitor multilevel inverter (FCMLI) techniques. Results demonstrate that the proposed ANN-DRPWM method achieves a post-filter total harmonic distortion (THD) of 2.17%, along with a 6-9% improvement in inverter efficiency and a noticeable reduction in torque ripple. Overall, the proposed strategy offers an efficient and intelligent modulation solution for high-performance BLDC motor drives, suitable for applications such as electric vehicles, renewable energy systems, and industrial drives.
Copyright (c) 2026 Halidu Abdul Mumin, Solomon Nunoo, Joseph Cudjoe Attachie http://creativecommons.org/licenses/by-sa/4.0
FPGA-based sustainable EV charging controller with dynamic pricing for smart cities
The growing adoption of electric vehicles (EVs) poses significant challenges to the stability and efficiency of the power grid, particularly under dynamic electricity pricing and variable grid availability in smart cities. This paper presents a novel, low-latency, hardware-driven approach to smart EV charging control using a field-programmable gate array (FPGA) Arty A7-100T. A customized finite state machine (FSM) evaluates the state of charge (SoC) and real-time pricing across multiple time slots. Based on this evaluation, it generates charge eligibility for each EV and transmits these decisions-along with the SoC and pricing data-via UART to an external Python interface. The received data is processed in Python and used to dynamically plot the SoC, pricing, and charging permission for each EV across time slots-providing a clear, intuitive visualization of decision-making based on energy economics. The distributed hardware-enabled paradigm minimizes delay as well as external dependency, thus offering improved responsiveness for the grid. The implementation is shown to represent a scalable solution for solving vital issue of demand side management for the evolving EV ecosystem for smart cities.
Copyright (c) 2026 G. Sreeramulu Mahesh, K. Hemachandra Reddy, H. Pushparaj, G. Balagopal Reddy, N. Bharathesh Patel http://creativecommons.org/licenses/by-sa/4.0
Optimal selection of current control technique in multiphase DC-DC converters for dynamic load variations
Multiphase DC-DC converters are widely adopted in high-power applications such as electric vehicles (EVs) and renewable energy systems due to their ability to reduce current ripple, improve efficiency, and distribute thermal stress across multiple phases. However, under dynamic load variations, mismatches in passive components, device parameters, parasitic elements, and thermal effects can result in phase current imbalance. This imbalance degrades transient performance, increases circulating currents, and reduces overall system reliability. Therefore, selecting an appropriate current control strategy is essential to ensure accurate current sharing and stable output voltage regulation under varying operating conditions. This paper presents a comparative study and selection methodology for current control techniques for MCU-based interleaved DC-DC converters. Various current control strategies are evaluated in terms of dynamic response, steady-state current sharing accuracy, implementation complexity, and embedded feasibility. A 1 kW, 36 V-12 V three-phase interleaved buck converter using Gallium Nitride devices is modeled in MATLAB/Simulink and validated through hardware experimentation. The comparative results highlight the trade-offs among transient performance, current balancing accuracy, scalability, and embedded implementation complexity, providing a structured basis for selecting an appropriate current control technique as per application requirements.
Copyright (c) 2026 H. Swathi Hatwar, K. Suryanarayana, Anup Shetty http://creativecommons.org/licenses/by-sa/4.0
Hybrid control strategy for trajectory tracking and obstacle avoidance in differential wheeled robots: integrating PSO-NMPC, GA, and fuzzy logic
Mobile robots frequently encounter challenges in maintaining accurate trajectory tracking and effective obstacle avoidance in dynamic and uncertain environments. Traditional control methods, such as proportional integral derivative (PID) and standard MPC, often fail to provide the necessary adaptability and robustness for complex navigation tasks. To overcome these limitations, this study proposes a hybrid control framework for differential-drive wheeled robots that integrates particle swarm optimization–based nonlinear model predictive control (PSO-NMPC), adaptive neuro-fuzzy inference system (ANFIS) optimized by PSO, and genetic algorithm (GA) tuning. The PSO-NMPC computes optimal control inputs in real time while satisfying system constraints to ensure precise trajectory tracking, achieving an average RMSE of 0.0941 m (RMSEx = 0.0884 m, RMSEy = 0.0812 m). The ANFIS-PSO controller manages nonlinearities and environmental uncertainties for reliable obstacle avoidance, with an overall RMSE of 0.1084 m (RMSEx = 0.0761 m, RMSEy = 0.0772 m). The GA further optimizes key parameters and trajectories, ensuring global path refinement and robust obstacle clearance, achieving an overall RMSE of 0.1094 m (RMSEx = 0.1059 m, RMSEy = 0.0274 m). Simulation results in Matlab2024b confirm that the proposed hybrid framework provides precise trajectory tracking, smooth control, and robust obstacle avoidance, making it a promising solution for autonomous mobile robots operating in dynamic and uncertain environments.
Copyright (c) 2026 Abdennour Zeghida, Lotfi Farah, Halim Merabti, Abdelfateh Kerrouche http://creativecommons.org/licenses/by-sa/4.0
Multi-objective energy management optimization in electric vehicles using fuzzy logic and particle swarm optimization
This paper proposes a hybrid energy management system (EMS) for electric vehicles by integrating fuzzy logic control (FLC) with particle swarm optimization (PSO) to improve power-split decision-making under dynamic driving conditions. The FLC is designed using state of charge (SoC) and vehicle speed as input variables and power split as the output. A set of fuzzy rules defines the EMS behavior, while PSO is employed to fine-tune decisions by maximizing an efficiency objective function defined as the closeness of the power split to an ideal reference. The simulation is implemented in Python using Colab-compatible packages such as scikit-fuzzy, DEAP, and matplotlib, ensuring accessibility and reproducibility. A test grid covering 10 SoC levels (10-100%) and 10 speed levels (10-120 km/h) is used to evaluate the system. Visualization tools, including heatmaps, 3D surface plots, and contour plots, are employed to represent the EMS behavior. The PSO-enhanced system achieved a maximum efficiency of 98.2% at an optimized SoC of 61.7% and a speed of 53.6 km/h, outperforming standalone fuzzy logic control. Tabulated results and statistical summaries validate the effectiveness of the proposed system.
Copyright (c) 2026 V. Lakshmi Devi, Damodhar Reddy, Srikanth Velpula, K. Kumar, Basi Reddy Avula http://creativecommons.org/licenses/by-sa/4.0
Eco-friendly LED illumination using a modified non-inverting Cuk converter for sustainable lighting applications
Light-emitting diodes (LEDs) are essential to current lighting due to their perfect control, long lifespan, great energy efficiency, and environmental friendliness. However, issues like output ripple and uneven brightness could have an impact on both visual comfort and system performance. This research presents the design and implementation of a non-inverting Cuk (NI-Cuk) converter operating in discontinuous conduction mode (DCM), integrated with a valley-fill circuit (Vfc) that reduces voltage and current (V and I) ripple and improves lifetime. The study begins with an analysis of the classic Cuk (CCuk) converter, highlighting its shortcomings, including inverted output polarity and the high current stress across the switching device. A NI-Cuk is proposed to overcome the shortcomings of CCuk, delivering a positive output with higher efficiency. Vfc offers a faster steady-state response, reduces peak loads on components, and reduces losses. To confirm that the design and hardware prototype were developed, and the results are validated with the simulated outcomes. The approach's viability is confirmed by experimental results, and a comparison of CCuk, NI-Cuk, and NI-Cuk with and without Vfc is conducted using voltage regulation, efficiency, and ripple. The results show that the suggested converter assurances are a reliable, effective, and superior power source for LED lighting applications.
Copyright (c) 2026 B. Lakshmi Praba, Seyezhai Ramalingam http://creativecommons.org/licenses/by-sa/4.0
Smart adaptive CC-CV charger with PSO-accelerated load identification and fuzzy duty-cycle regulation
This paper presents an adaptive constant-current/constant-voltage (CC-CV) charger architecture, meticulously designed to address a key challenge in smart chargers. This challenge involves recognizing various battery types and applying the appropriate charging profile expeditiously, without requiring user intervention. The system integrates a particle swarm optimization (PSO) algorithm for ultra-fast load identification with a Mamdani-type fuzzy logic controller for precise duty cycle regulation. The PSO mechanism is capable of determining the optimal initial duty cycle in less than 500 milliseconds. Subsequent to this preliminary initiation, the fuzzy logic controller guarantees the effectiveness of current and voltage regulation during the charging phases. The simulation results obtained from this study validate the system's robustness, as evidenced by the consistent maintenance of voltage ripple below ±0.06 V and current ripple below ±0.04 A. These findings demonstrate the efficacy of the proposed approach in achieving fast, stable, and safe multi-load battery charging. The chemistry-agnostic design of the battery pack is extendable to any battery pack following the CC-CV paradigm, making it highly suitable for practical applications that demand flexibility and high reliability.
Copyright (c) 2026 Indhana Sudiharto, Era Purwanto, Muhamad Milchan, Alifian Nur Rahmadika http://creativecommons.org/licenses/by-sa/4.0
Dual mode control of an integrated on-board charger powered BLDC drive
The high adoption of electric vehicles in transportation has created a demand for compact, efficient, and cost-effective charging solutions for them. Conventional onboard chargers are often bulky, which adds to the overall cost of the drive system, whereas off-board charging infrastructure remains limited. In order to address these issues, this work illustrates the design and modelling of an active power factor corrected integrated onboard charger which gets reconfigured from the electric vehicle drive train components. The proposed circuit setup is designed to work in dual mode, i.e., in the role of a DC-DC converter while charging the vehicle battery and as a three-phase inverter while driving the vehicle. The front-end power factor correction circuit, in addition to the reconfigured DC-DC converter, charges the 24 V, 20 Ah lead acid battery under constant current constant voltage (CC-CV) mode, achieving a power factor close to unity. Modelling and control of the proposed 200 W reconfigurable converter-fed 24 V, 180 W brushless direct current (BLDC) drive is validated using MATLAB/ Simulink Software. Simulation results demonstrate a power factor of 0.996 in grid-connected operation with a total harmonic distortion (THD) of 4.96%. The proposed architecture achieves a compact structure with only 8 switches enabling charging, propulsion and regenerative braking operation. The proposed converter thus contributes to a cost-effective electric vehicle and provides the scope of future extension to vehicle to home (V2H), vehicle to load (V2L), and vehicle to vehicle (V2V) applications as well.
Copyright (c) 2026 Caroline Ann Sam, Varghese Jegathesan http://creativecommons.org/licenses/by-sa/4.0
Enhanced UPS inverter control using backstepping and fuzzy neural network for improved power quality
The rapid growth of sensitive digital infrastructures and automation systems has intensified the demand for uninterrupted and high-quality power delivery. To address this critical need, this paper proposes a novel hybrid intelligent control strategy for uninterruptible power supply (UPS) inverters that integrates backstepping control, fuzzy neural network (FNN) adaptation, and sliding mode gain compensation. The proposed approach ensures superior voltage regulation and robustness under nonlinear and dynamic load conditions while minimizing dependence on predefined system parameters. The backstepping controller establishes the Lyapunov-based stability framework, the FNN adaptively estimates system uncertainties in real time, and the sliding mode gain enhances resilience against external disturbances. This synergistic control integration enables fast dynamic response, reduced harmonic distortion, and improved system efficiency compared to conventional methods. Simulation and experimental validations demonstrate that the proposed controller achieves total harmonic distortion (THD) below 3%, voltage overshoot under 2%, and enhanced transient recovery, thereby ensuring reliable power quality for critical industrial and commercial applications. The study contributes a real-time feasible, adaptive, and robust UPS inverter control architecture, marking a significant advancement in intelligent power electronics for resilient energy systems.
Copyright (c) 2026 G. Anjali Devi, Swapna Ganapaneni, L. Sirisaiah, Lokesh Kotha, Subhash Manchikanti, Malligunta Kiran Kumar, T. Rakesh, K. V. Govardhan Rao http://creativecommons.org/licenses/by-sa/4.0
High-efficiency two-stage LED driver with integrated PFC and LLC resonant converter for public lighting
This paper presents the design, implementation, and experimental validation of a 150 W two-stage light-emitting diode (LED) driver integrating a power factor correction (PFC) stage and a half-bridge LLC resonant converter for public lighting applications. The problem addressed is the insufficient power quality, limited efficiency, and poor harmonic performance of conventional LED drivers used in street lighting. The proposed method combines an advanced PFC front-end with an LLC resonant converter optimized using first harmonic approximation (FHA) to achieve high efficiency, stable output regulation, and soft-switching operation. Experimental results demonstrate a significant improvement in power quality, with the input current total harmonic distortion (THD) reduced from 134% to 17%, a near-unity power factor, a regulated LED output of 31.6 V/4.72 A, and a conversion efficiency exceeding 95%. The significance of this work lies in providing a high-performance, standards-compliant driver that supports reliable, energy-efficient, and grid-friendly public lighting with reduced operational costs.
Copyright (c) 2026 Marref Mohammed Amine, Seyf Eddine Bechekir, Mokhtaria Jbilou, Mostefa Brahami, Abdelber Bendaoud http://creativecommons.org/licenses/by-sa/4.0
Integration of wind energy with a single-ended primary inductor converter and a brushless DC motor for water pumping system
This paper explores a simulation-based study on a renewable energy system that integrates wind energy with a single-ended primary inductor converter (SEPIC) to drive a brushless DC (BLDC) motor for water pumping applications. The proposed system addresses the challenge of regulating the variable output of wind turbines by employing a SEPIC converter to provide a stable direct current (DC) voltage supply to the BLDC motor. The novelty of this work lies in the combined modeling and performance analysis of the wind turbine, SEPIC converter, BLDC motor, and electronic commutation in MATLAB/Simulink, optimized for energy-efficient off-grid pumping. Simulation results demonstrate that the SEPIC converter effectively stabilizes the wind-generated voltage, ensuring reliable motor operation under varying wind conditions. The proposed system exhibits high efficiency, stable dynamic response, and low maintenance requirements, making it a practical solution for water pumping in wind-rich regions where solar irradiance is limited, particularly for off-grid water pumping applications.
Copyright (c) 2026 Hassan Abdi Abi, Abdullahi Mohamed Isak, Suleiman Abdullahi Ali, Yakub Hussein Mohamed, Sowdo Mursal Abdi, Abdirisakh Khalif Osman http://creativecommons.org/licenses/by-sa/4.0
High-gain DC-DC converter with advanced techniques: a review
This article provides an in-depth examination of recent advances in high-gain DC-DC converters, emphasizing soft-switching techniques and topological innovations that minimize voltage stress for renewable energy applications. High-gain DC-DC converters are crucial in photovoltaic and fuel-cell systems, where boosting low input voltages to higher levels must be achieved with high efficiency and compact design. Traditional boost converters fall short due to elevated switching stress, discontinuous input currents, and lower efficiency at high-gain levels. To address these limitations, this review categorizes and critically evaluates state-of-the-art converter topologies developed for high-gain operation. The main contributions of this review are as follows: i) A systematic classification of high-gain converter configurations with emphasis on their operational principles; ii) A detailed evaluation of soft-switching techniques, including zero voltage switching (ZVS) and zero current switching (ZCS), focusing on their roles in reducing switching losses and electromagnetic interference; iii) An analytical discussion on voltage stress mitigation methods and improved control strategies; and iv) An assessment of emerging trends in integrating advanced power electronics with renewable energy systems. These contributions collectively provide a comprehensive reference for researchers and engineers, supporting the development of next-generation high-performance DC-DC converters tailored for sustainable energy applications.
Copyright (c) 2026 Anitha Sagari Ravirala, T. Vijay Muni, T. Vinodita, K. Venkata Kishore, Ramoju Bheema Sankaram, Yuriy Yu Shvets http://creativecommons.org/licenses/by-sa/4.0
High step-up interleaved multilevel hybrid boost converter with switched-capacitor multiplier
The global integration of renewable energy sources like photovoltaics requires efficient high-step-up DC-DC converters. Conventional boost converters exhibit inherent limitations in achieving high voltage gain efficiently, particularly under high duty cycle operation, where switching losses, device stress, and output voltage ripple become significant. This paper proposes a novel hybrid DC-DC converter that integrates a four-phase interleaved input stage with a five-level switched-capacitor (SC) multiplier network. The proposed topology introduces a modular and structurally decoupled architecture, in which current conditioning and voltage boosting functions are independently realized. This enables scalable voltage gain through modular expansion without requiring extreme duty cycles or additional magnetic components. The interleaved stage reduces input current ripple and improves current sharing, while the multilevel SC network provides a high voltage conversion ratio and balanced voltage stress across components. Comprehensive simulations using PSIM software validate the converter's performance. With a 25 V input, the proposed converter achieves an output voltage of approximately 250 V (gain of 10), a high efficiency of 95.2%, output voltage ripple below 2%, and balanced capacitor voltages. The results confirm that the proposed converter offers an efficient, scalable, and high-performance solution for high step-up applications.
Copyright (c) 2026 Andi M. Nur Putra, Adrianti, Muhammad Imran Hamid http://creativecommons.org/licenses/by-sa/4.0
Design and performance evaluation of a soft-switched partial-power LLC converter for PV grid integration
This paper presents a soft-switched partial-power LLC converter integrated within a two-stage photovoltaic (PV) and grid-connected system. The proposed architecture combines the advantages of resonant operation and partial power processing to enhance conversion efficiency and reduce switching losses. Maximum power point tracking (MPPT) is achieved through frequency modulation of the LLC converter, while grid synchronization is maintained using a three-phase voltage-oriented control (VOC) inverter. Simulation results in MATLAB/Simulink demonstrate stable zero voltage switching (ZVS) and zero current switching (ZCS) across a wide irradiance range (400-1000 W/m²), enabling the system to achieve peak efficiencies above 98%, which is superior to typical transformerless and interleaved converter topologies reported in recent literature. The proposed soft-switched PPC-LLC architecture offers an efficient and scalable solution for next-generation PV grid interfaces by combining reduced processed power, robust resonant operation, and high-quality grid integration.
Copyright (c) 2026 Sebin Davis Kurichiparambil, Varghese Jegathesan http://creativecommons.org/licenses/by-sa/4.0
Improved efficiency of DC-DC converter through modified switched inductor-switched capacitor configuration using ANN optimization for photovoltaic sources
In photovoltaic applications, the DC-DC converters are of utmost importance, allowing for the regulation of output voltages to satisfy system needs. A preliminary analysis of several converter topologies revealed that the switched inductor switched capacitor (SISC) configuration provides better performance with lower ripple, reduced stress, and better voltage boosting. The performance of the proposed topology is compared with different SISC topologies, and it is observed that the chosen configuration is suitable for photovoltaic sources. The efficiency of the suggested topology is further enhanced by using artificial neural networks (ANN) for regulating the switching frequency and duty cycle of the switch. In this work, two ANNs are used to train both switching frequency and duty cycle. For the training process, the Levenberg-Marquardt algorithm is used to achieve fast convergence with precise predictions. A prototype model is constructed and tested to validate the simulation results. The results prove that the projected converter achieves considerable efficiency and is suited for photovoltaic (PV) systems.
Copyright (c) 2026 Ramalingam Seyezhai, A. S. Athish, K. Ashwathy, R. Akash Karthick http://creativecommons.org/licenses/by-sa/4.0
Design and analysis of a C4S DC-DC converter for sustainable solar energy systems
Efficient DC-DC power conversion is essential for sustainable solar photovoltaic systems. Conventional converters often suffer from leakage currents, higher circuit complexity, and limited flexibility in interfacing with grid-connected inverters. This study introduced a novel hybrid DC-to-DC converter based on the C4S (coupled capacitor combined Cuk-SEPIC) converter, proposed precisely for sustainable solar photovoltaic systems. The designed converter offers a dual output in the form of a bipolar direct current (DC) bus, allowing flexible combination with grid-connected inverters that receive either unipolar or bipolar DC inputs. This setup not only enables effective transfer of power to the grid but also efficiently removes the leakage currents without the necessity of lossy DC-link capacitors from the load-side current loop. Moreover, the magnetic cores are integrated by employing the input and output coupled capacitors, which considerably minimize ripple current and ensure the capability of power extraction from the PV unit. A fuzzy logic controller is employed to dynamically adjust the converter’s action under varying load conditions and solar irradiance. The proposed topology minimizes driver circuits, reduces system complexity, eliminates leakage current without requiring lossy DC-link capacitors, and improves reliability. Simulation results demonstrate stable voltage regulation, reduced ripple, improved efficiency, and superior dynamic response compared to conventional control methods. The proposed converter demonstrates its potential as a high-performance, intelligent, and energy-efficient process innovation for modern sustainable solar energy systems.
Copyright (c) 2026 G. Jegadeeswari, M. Vaigundamoorthi, R. Sundar, J. S. S. L. Bharani, C. Rajarajachozhan, M. Batumalay, S. P. Manikandan http://creativecommons.org/licenses/by-sa/4.0
Enhanced power quality in PV integrated EV fed microgrid using intelligent controller
The need for electric cars (EVs) is steadily rising in the world due to the rise in emissions like CO2 and environmental effects brought on by conventional automobiles. EVs have also transformed the transportation industry. These days, EVs are popular because of their special qualities, which include lower noise pollution, carbon emissions, and operating expenses, as well as the capacity to operate in both grid-to-vehicle (V2G) and vehicle-to-grid (V2G) scenarios. Nevertheless, it affects the power distribution grid in a number of ways. There are various power concerns owing to the introduction of EVs in the distribution system, like instability of voltage, distortions in currents, harm onic distortions, power factor degradation, and fluctuations in voltage. The primary emphasis of this study is on mitigating PQ issues like harmonics produced in the distributed power network when electric vehicles are integrated at the distribution end. In order to reduce harmonics and enhance the distribution side's current profile, a dynamic active power filter (DAPF) with PSO-tuned ICC control technique is introduced. Performance of PSO-DAPF is validated with the help of MATLAB/Simulink, along with V2G and V2G operation.
Copyright (c) 2026 D. Balasubramanyam, G. G. Raja Sekhar, T. Vijay Muni http://creativecommons.org/licenses/by-sa/4.0
Advanced soft-switching high-gain Re Boost Luo converter for enhanced efficiency in photovoltaic systems
This work presents an innovative approach to improving efficiency and performance in photovoltaic (PV) systems through the development of a soft-switching high-gain Re Boost Luo converter. This converter integrates advanced soft-switching techniques to minimize switching losses, thereby enhancing overall system efficiency, which is crucial for applications requiring substantial voltage amplification from PV sources. The Re Boost Luo converter, with its inherent high-gain capability, facilitates superior voltage conversion ratios, enabling optimal energy extraction from PV panels across varying environmental conditions. The presented converter's design focuses on reducing electromagnetic interference (EMI) and alleviating stress on switching components, thereby extending their operational lifespan and reliability. Detailed modeling and performance analysis were carried out using the MATLAB/Simulink simulation environment, which allowed for comprehensive evaluation of the converter's functionality. Simulation results confirm that the converter achieves significant improvements in voltage gain, energy conversion efficiency, and system reliability, effectively addressing common challenges associated with high-voltage PV applications. This study underscores the converter's potential to advance renewable energy technologies by providing a robust solution for high-efficiency energy conversion in PV systems.
Copyright (c) 2026 Vendoti Suresh, Dondapati Ravi Kishore, T. Vijay Muni, P. Hari Krishna Prasad, Pydi Bala Krishna, A. V. G. A. Marthanda http://creativecommons.org/licenses/by-sa/4.0
Implementation of sliding control with washout filter in boost and buck converters
This paper presents the analysis, design, and implementation of a two-stage power conversion system consisting of a boost converter and a buck converter. Both converters were controlled using a sliding-mode control based on a washout filter. The system was supplied with an alternating current (AC) voltage source that was rectified using a diode bridge. The main objectives are to improve the power factor (PF) in the boost stage and regulate the output voltage in the buck stage. In the first stage, sliding-mode control is applied to shape the input current according to the rectified voltage, increasing the PF and reducing harmonic distortion. In the second stage, the same control approach is used to maintain a constant output voltage under load variations and disturbances. This study includes the mathematical modeling of both converters, control design, and simulation results in PSIM. The results show that the proposed sliding-mode control strategy effectively enhances energy efficiency, stabilizes the output voltage, and significantly improves the PF, making it suitable for robust and efficient power conversion systems.
Copyright (c) 2026 Esteban Flórez Urrego, Fredy E. Hoyos, John E. Candelo-Becerra http://creativecommons.org/licenses/by-sa/4.0
Grid-connected battery energy storage system using interleaved bidirectional buck/boost converter and 3-level SVPWM inverter
This study presents a high-performance grid-connected battery energy storage system architecture designed to overcome the trade-off between DC-side current ripple and AC-side power quality. The proposed system synergistically integrates an interleaved bidirectional buck/boost converter, a three-level neutral-point clamped inverter, and an optimized LCL filter under a unified control strategy based on space vector pulse width modulation. Unlike conventional topologies that prioritize either subsystem individually, this integrated approach simultaneously minimizes battery current stress and ensures strict grid compliance. Extensive simulations in MATLAB/Simulink demonstrate that the proposed control strategy achieves a total harmonic distortion (THD) of just 2.93%, significantly outperforming the 40.25% THD of conventional two-level inverters and surpassing recent state-of-the-art benchmarks. The system exhibits robust dynamic response during rapid charging/discharging transitions and maintains stability under grid disturbances, such as 20% voltage dips. These results validate the architecture as a viable, high-efficiency solution for integrating modern renewable energy.
Copyright (c) 2026 Oumaymah Elamri, Jacob Wekalao http://creativecommons.org/licenses/by-sa/4.0
Dual-mode model predictive control for non-minimum phase boost converters
This paper aims to develop an efficient finite-set model predictive control (FS-MPC) strategy for DC-DC boost converters to improve voltage regulation while reducing computational complexity. The proposed approach introduces a split cost function that decouples voltage and current regulation, providing a simpler alternative to conventional long-horizon FS-MPC schemes used to address the converter’s non-minimum-phase (NMP) behavior. A current estimation technique is incorporated to eliminate the need for additional sensors, lowering hardware cost and improving robustness. Unlike existing FS-MPC methods that rely on horizon extension or extra measurements, the proposed strategy leverages the split cost structure to achieve comparable NMP compensation with significantly lower computational effort. The controller is implemented in real time using a hardware-in-the-loop (HIL) setup on a ZedBoard platform, with accurate data acquisition provided by an external ADC. Experimental results demonstrate that the proposed approach enhances voltage-tracking performance, eliminates overshoot and undershoot, reduces settling time by over 40%, and decreases computational effort by more than 80% compared to traditional FS-MPC methods.
Copyright (c) 2026 Jawhra El Hmidi, Anass Mansouri, Ali Ahaitouf http://creativecommons.org/licenses/by-sa/4.0
Adaptive notch filter: An alternative synchronizer for effective performance of active power filter under challenging grid conditions
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.
Copyright (c) 2026 Yap Hoon, Kuew Wai Chew, Kenny Sau Kang Chu, Siti Zaliha Mohammad Noor http://creativecommons.org/licenses/by-sa/4.0
Design of 7-level cascade asymmetric multilevel inverter for renewable energy applications using FPGA
The increasing focus on renewable energy has driven the need for efficient and reliable power converters. Multilevel inverters offer low harmonic distortion and high-quality output but often suffer from design complexity and excessive component count. This study presents the design and implementation of a 7-level cascaded asymmetric multilevel inverter optimized for renewable energy applications. The proposed topology utilizes a cascade structure with asymmetric DC voltage sources to generate seven voltage levels, providing a practical balance between performance and simplicity. The design was first validated through MATLAB/Simulink software to analyze circuit operation and evaluate the total harmonic distortion (THD) performance. Experimental evaluation was then conducted using a hardware prototype to verify simulation results. Without a filter, the THD from the simulation was 21.31%, while the experimental setup recorded a slightly higher value of 21.61%, indicating a marginal difference of 0.21%. With a filter, the simulation achieved a THD of 3.81%, whereas the experimental setup outperformed with a THD of 1.5%, showing a notable reduction of 2.31%. These findings confirm the proposed inverter’s capability to deliver superior power quality and operational efficiency. The combination of simulation and experimental validation demonstrates the practicality and reliability of the 7-level cascade asymmetric multilevel inverter for renewable energy applications.
Copyright (c) 2026 Afarulrazi Abu Bakar, Hazwaj Mhd Poad, Benjamin Ho Hao Xian, Tharnisha Sithananthan, Wahyu Mulyo Utomo, Triyanto Pangaribowo http://creativecommons.org/licenses/by-sa/4.0
Integrated reliability assessment of medium voltage networks incorporating voltage and reactive power performance indices
Energy system operation relies on synchronous protection and safety, with stable networks able to handle disruptions without abrupt changes. Due to the complexity of modern electrical systems, contingency analysis is essential for addressing issues in power system analysis. This study presents a comprehensive method for identifying the initial causes of cascading system failures in power systems. Case studies utilizing IEEE test systems with various cascade models demonstrate the effectiveness of this approach. The analysis aids in evaluating the possible impacts on the system and informs preventive strategies to avoid failures. This study integrates voltage stability and reactive power performance indices to create a framework for assessing the reliability of a medium-voltage power network operating at 33 kV. It evaluates N-1 and N-2 contingencies due to line and generator outages to identify and rank critical network components. In contrast to traditional reliability evaluations, the approach identifies weaknesses connected to voltage that impact system resilience. The proposed approach enables improved ranking of severe contingencies beyond conventional methods, supporting targeted reinforcement and enhancing voltage stability and overall system reliability.
Copyright (c) 2026 Dewan Ashikur Rahaman, Md. Minhaj Pathan, Aive Alamgir, Md. Masum Howlader, Kazi Mahtab Kadir, Md. Shazzad Dewan, Md. Yakub Hassan http://creativecommons.org/licenses/by-sa/4.0
Enhancing grid performance through coordinated SVC-TCSC operation with PV support: A case study on IEEE 30-bus system under progressive loading
Power systems face growing challenges of voltage instability, line congestion, and increased losses under rising demand. This study proposes a coordinated approach using two flexible AC transmission system (FACTS) devices: the static var compensator (SVC) and the thyristor controlled series capacitor (TCSC), together with photovoltaic (PV) generation, to enhance grid performance. The IEEE 30 bus test system is analyzed under normal and increased load conditions (5%, 10%, 15% load growth). Results show that coordinated SVC TCSC operation improves voltage profiles, reduces critical line loading by 14%, and lowers active and reactive losses by 10% and 23.8%, respectively, in the base case. Under a 15% load increase, integrating a 25 MW PV system with the coordinated FACTS restores the minimum voltage to 0.95 p.u., reduces line congestion by 27%, and decreases active and reactive losses by 35.5% and 53.5%. The combined FACTS PV strategy proves essential for maintaining stability and efficiency under high load growth. This integrated approach provides practical guidance for transmission operators toward resilient, loss aware, and renewable integrated smart grids.
Copyright (c) 2026 Hafidha Reriballah, Latifa Smail, Ali Abderrazak Tadjeddine, Hocine Guentri, Rim Feyrouz Abdelgoui, Fatima Zohra Boudjella http://creativecommons.org/licenses/by-sa/4.0
A hybrid simulation and hardware approach for a regenerative braking system in an electric motorcycle
Conventional electric motorcycles mostly depend on mechanical braking systems that dissipate kinetic energy as heat, resulting in significant energy losses, frequent battery recharging, and reduced operational efficiency. To address these limitations, a regenerative braking system (RBS) is designed and developed to recover and store kinetic energy during braking phases. The proposed RBS integrates a brushless DC (BLDC) motor that serves as a propulsion and energy regenerative unit, a lithium-ion battery for energy storage, and an Arduino microcontroller for real-time control and seamless system integration. A hybrid methodology combining MATLAB/Simulink simulations and hardware prototyping was adopted to evaluate system performance under various operating conditions. The simulation results demonstrated effective braking torque generation and back electromotive force (EMF) recovery to validate the system’s ability to convert kinetic energy into storable electrical energy. The proposed RBS achieved a theoretical energy recovery efficiency of approximately 70%, attributed to internal resistance and motor back EMF variations. These findings demonstrates the potential of regenerative braking in improving the energy efficiency of electric motorcycles, extending battery life, and reducing dependency on external charging. Furthermore, this study establishes a foundation for future RBS development incorporating lightweight materials, cost-effective components, and intelligent control strategies that can contribute to advancing sustainable and energy-efficient urban mobility solutions.
Copyright (c) 2026 Faris Anwar Amir Faisal, Siti Fauziah Toha, Nurul Muthmainnah Mohd Noor, Ahmad Syahrin Idris, Mohamad Osman Tokhi http://creativecommons.org/licenses/by-sa/4.0
Impact of integrating solar and wind DGs on voltage profiles and power losses in a practical distribution system
The rising inclusion of renewable energy sources into distribution networks has accelerated the adoption of distributed generation (DG) technologies such as solar photovoltaic (PV) and wind turbines. This paper explores the effect of solar and wind DG integration on voltage profiles, power losses, and economic performance in a practical 41-bus radial distribution system. Using the Power World Simulator (PWS) software, the load flow analysis is performed to evaluate different DG placement strategies and penetration levels using the loss sensitivity factor (LSF) method. The results indicate that optimal placement of solar and wind DGs notably improves voltage stability and effectively reduces both real and reactive power losses in the distribution system. Furthermore, the economic analysis demonstrates annual savings of ₹29.08 lakhs for solar DG and ₹33.40 lakhs for wind DG, with payback periods of approximately 11 years, indicating strong technical and financial feasibility. The findings highlight that strategic DG planning can simultaneously enhance system reliability, efficiency, and economic viability in modern distribution systems.
Copyright (c) 2026 Rudresha Sogilu Jayappa, Gopinath Harsha Rajappa, Kiran Kumar Gama Ramaiah, Shekhappa G. Ankaliki, S. Shruthi http://creativecommons.org/licenses/by-sa/4.0
Design simulation and analysis of an MPPT technique using ANNs integral backstepping and SMC for PV systems
This paper introduces the design of an innovative hybrid MPPT method called artificial neural networks-integral backstepping sliding mode control (ANN-IBSMC). This approach combines artificial neural networks (ANNs), which output the maximum power point voltage using inputs such as irradiance and temperature, with a robust control strategy. The designed controller aims to track the reference voltage with high accuracy and responsiveness by modifying the pulse width modulation of the DC-DC converter in the photovoltaic system. The IBSMC integrates the advantages of two control methods: the stability and accuracy of integral backstepping, and the robustness and fast response of sliding mode control (SMC). This combination enables improved precision, high convergence speed, enhanced robustness, and strong stability, the latter being ensured by the Lyapunov function. To evaluate the performance of the proposed controller, a comparative study is performed against other hybrid control techniques, such as the ANN-backstepping controller, the ANN-integral sliding mode controller, and the ANN-backstepping sliding mode controller, using MATLAB/ Simulink. A sensitivity and robustness analysis was carried out.
Copyright (c) 2026 Naoufal Zhani, Hassane Mahmoudi http://creativecommons.org/licenses/by-sa/4.0
Neuro-fuzzy control on a permanent magnet synchronous generator integrated in a wind system
This paper introduces a control strategy for a synchronous generator in a wind energy system using an adaptive neuro-fuzzy approach. The suggested controller, based on neuro-fuzzy logic (NFLC), is meant to govern a permanent magnet synchronous generator (PMSG) often utilized in wind power applications. The generator's output voltage phase, phase current, reactive power, active power, angular velocity, and DC voltage are all under control. The adaptive neuro-fuzzy controller efficiently stabilizes all variables in a brief amount of time, according to simulation results. The effectiveness and robust performance of the suggested control system are verified by a number of simulated scenarios. The resilience of fuzzy logic control (FLC) and NFLC systems was compared. The study carefully tested the performance of both control techniques under varied operating settings and disturbance situations to determine their relative stability, flexibility, and efficacy in sustaining desired system behavior.
Copyright (c) 2026 Mohammed Aoumri, Ibrahim Yaichi, Harrouz Abdelkader, Patrice Wira http://creativecommons.org/licenses/by-sa/4.0
Robust power optimization strategy for wind-driven induction machines using type-2 and type-1 fuzzy logic controllers
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.
Copyright (c) 2026 Driss Belkhiri, Boujemaa Nassiri, Mohamed Ajaamoum http://creativecommons.org/licenses/by-sa/4.0
A hybrid AEGAN-PDO strategy for power quality enhancement in PV-based distributed generation with stacked multi-cell converter
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 real-time, promoting improved PQ and grid dependability.
Copyright (c) 2026 B. N. Subhashini, P. Pramila http://creativecommons.org/licenses/by-sa/4.0
Design and implementation of a dual microcontroller-based smart headlight control system using a dynamic load adjustment mechanism
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.
Copyright (c) 2026 Liew Hui Fang, Rosemizi Abd Rahim, Muhammad Izuan Fahmi Romli, A. A. M. Ezanuddin, Shamshul Bahar Yaakob http://creativecommons.org/licenses/by-sa/4.0
Optimization techniques for siting solar-powered EV charging stations: A systematic review and methodological classification
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-intelligence-based models for predictive site selection. GIS-MCDM hybrid approaches were the most prevalent, offering improved robustness in spatial decision-making. 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.
Copyright (c) 2026 Linda Faridah, Rustam Asnawi1, Handaru Jati, Nurwijayanti Kusuma http://creativecommons.org/licenses/by-sa/4.0
Performance study of a real photovoltaic power station under desert conditions: case study
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.
Copyright (c) 2026 Fatma Bouchelga, Abderrahmane Khelfaoui, Abdeldjalil Dahbi http://creativecommons.org/licenses/by-sa/4.0
Performance optimization of hybrid renewable energy systems with real-time load forecasting using grey wolf-based predictive models
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 short-term 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.
Copyright (c) 2026 Olumuyiwa Ajibola Awoniyi, Evans Chinemezu Ashigwuike, Chijioke Ejimofor, Timothy Oluwaseun Araoye http://creativecommons.org/licenses/by-sa/4.0
Control strategy for the combined operation of grid-connected inverter and charger
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.
Copyright (c) 2026 Quang-Tho Tran, Quang-Sang Le http://creativecommons.org/licenses/by-sa/4.0
Harmonic analysis of grid-connected parallel H-bridge VSI and CSI with isolated DC sources
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 grid-connected parallel inverter system worked, supplying a sinusoidal AC load current with high power quality.
Copyright (c) 2026 Suroso, Winasis, Priswanto http://creativecommons.org/licenses/by-sa/4.0
Parameters optimization of solar PV cell using war strategy
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.
Copyright (c) 2026 Radouan Gouaamar, Seddik Bri http://creativecommons.org/licenses/by-sa/4.0
Comprehensive assessment and analysis of frequency fluctuation and voltage total harmonics distortion in Malaysia’s grid-connected solar PV systems: an empirical study
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.
Copyright (c) 2026 Hasif Mohamad, Khairul Anwar Ibrahim, Che Wan Mohd Faizal Che Wan Mohd Zalani, Zulkifli Ibrahim, Mohd Nor Hasli Mat Jusoh http://creativecommons.org/licenses/by-sa/4.0
Adaptive control of the virtual synchronous generator by deep neural networks for a wind high power conversion chain
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.
Copyright (c) 2026 Wijdane El Maataoui, Abdelouahed Abounada http://creativecommons.org/licenses/by-sa/4.0
Stability analysis of photovoltaic grid-connected power systems employing virtual synchronous generator control
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.
Copyright (c) 2026 Abdallah El Ghaly, Abdallah Hamdan, Mohamad Tarnini http://creativecommons.org/licenses/by-sa/4.0
Permanent magnet generator for small and medium-scale hydropower: a systematic review
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 analyse 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.
Copyright (c) 2026 Ngatono, Raja Nor Firdaus Kashfi Raja Othman, M. Nazri Othman, M. Zulkifli Ab Rahman http://creativecommons.org/licenses/by-sa/4.0
Super-twisting MPPT enhanced via grey wolf optimization for dynamic PV operation
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.
Copyright (c) 2026 Slimane Hadji, Said Aissou, Abdelhakim Belkaid http://creativecommons.org/licenses/by-sa/4.0
Innovative frequency and voltage controller for AC microgrid
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.
Copyright (c) 2026 Xuan Hoa Thi Pham, Hai Van Tran http://creativecommons.org/licenses/by-sa/4.0
Neuro-evolutionary genetic algorithm for global MPPT under partial shading conditions: a comparative analysis with PSO
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 neuro-evolutionary 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.
Copyright (c) 2026 Benlaria Ismail, Laidi Abdallah, Fenniche Ayoub, Belhadj Mohammed http://creativecommons.org/licenses/by-sa/4.0
Enhanced review on dynamic real-time digital simulation analysis of renewable energy integration using state space model
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 event-based 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.
Copyright (c) 2026 Ahmad Supawi Osman, Aidil Azwin Zainul Abidin http://creativecommons.org/licenses/by-sa/4.0
An enhanced hybrid deep learning-quantum variational classifier framework for large-scale data analytics
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.
Rokkam Veera Venkata Nagendra Bheema Rao
Copyright (c) 2026 Yadlapti Suresh, Venu Gopal Gaddam, Challa Naga Venkata Jyothirmai, Rokkam Veera Venkata Nagendra Bheema Rao, Sreenivasulu Bolla, Ankala Radhika http://creativecommons.org/licenses/by-sa/4.0
Adaptive control of nonlinear systems using neuro-fuzzy networks with B-spline functions
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 neuro-fuzzy 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.
Copyright (c) 2026 Oksana Porubay, Temurbek Abdullayev http://creativecommons.org/licenses/by-sa/4.0
Resilient EV charging station network design using AI algorithms
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.
Copyright (c) 2026 Deepa Somasundaram, N. Krishnamoorthy, J. Vijay Anand, R. Priyanka, T. Santhana Krishnan, Kirubakaran Dhandapani http://creativecommons.org/licenses/by-sa/4.0