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 the official publication of the Institute of Advanced Engineering and Science (IAES). The scope of the journal includes all issues in the field of power electronics, electric drives, and energy systems. Included are techniques for advanced power semiconductor devices; control in power electronics; low and high power converters (inverters, converters, controlled and uncontrolled rectifiers); control algorithms and techniques applied to power electronics; electromagnetic and thermal performance of electronic power converters and inverters; power quality and utility applications; renewable energy; electric machines; modelling, simulation, analysis, design and implementations of the application of power circuit components (power semiconductors, inductors, high frequency transformers, capacitors), EMI/EMC considerations; power devices and components; integrated and packaged; induction motor drives; synchronous motor drives; synchronous motor drives; permanent magnet motor drives; ASDs (adjustable speed drives); multi-phase machines and converters; applications in motor drives; electric vehicles; wind energy systems; solar; battery chargers; uninterruptible power supplies (UPS); and other applications. The IJPEDS uses a rolling submission process, allowing authors to submit at any time during the year without time restraints. This journal is a SCOPUS and ScimagoJR indexed journal, CiteScore: 3.2, SJR: 0.270, and SNIP: 0.639. POWER UP YOUR RESEARCH: Amplify Your Impact in Power Electronics and Drive Systems It conveys the journal's dedication to empowering researchers to enhance their research, increase their influence, and contribute to the advancement of power electronics and drive systems. IJPEDS Journal positions as a platform that supports and amplifies the impact of research in this field. Get to Know Us Better BECOMING AN AUTHOR SUBMISSION TEMPLATE SUBMISSION GUIDELINES IJPEDS POLICIES MEET OUR EDITOR CONTACT US
Institute of Advanced Engineering and Science
Modelling enhancement-mode GaN HEMTs with graded AlGaN barrier, graphene passivation, and dual field plates via TCAD
An enhancement-mode aluminum gallium nitride (AlGaN)/gallium nitride (GaN) HEMT is presented in which graphene-capped SiN passivation, a recessed composite p-GaN gate, dual discrete field plates, and a compositionally graded AlGaN barrier (35%→18% Al) are co-engineered in a single device. The novelty lies in integrating these four techniques within one enhancement-mode architecture to simultaneously suppress surface trapping and reduce gate leakage, strengthen electrostatic gate control, and redistribute the electric field for higher breakdown and more uniform field profiles. TCAD results indicate a threshold voltage (Vth) of +2.6 V, transconductance (gm) of 335 mS/mm, and breakdown voltage (Vbr) around 1 kV. The radio-frequency (RF) analysis shows a cut-off frequency (fT) of ~42.57 GHz, with favorable gate capacitance characteristics. These outcomes show that co-optimization delivers normally-off operation with improved breakdown and RF speed relative to using the techniques separately, supporting suitability for high-power, high-frequency applications.
Copyright (c) 2026 Mohiminur Rahman Ifty, Abdullah Al Fahad, Arnab Talukder, Satyazit Ghosh, Piash Sharma, Md. Sadik Al Rayhan http://creativecommons.org/licenses/by-sa/4.0
Design of the wireless EV charger to meet the performance requirement of SAE J2954 standard
To address the need for a reproducible design process for an efficient wireless electric vehicle (EV) charging system that guarantees compliance with the SAE J2954 standard, this paper proposes a systematic, flowchart-based optimization technique. Unlike methods that focus solely on coil performance, the proposed approach integrates standard-specific constraints, such as inductance and geometric limits, from the outset to ensure the final design meets stringent performance benchmarks for efficiency and misalignment tolerance. A circular flat spiral coil structure has been adopted for both the transmitter and receiver coils to enhance manufacturability and achieve uniform magnetic field distribution. A flowchart-based design technique has been developed to optimize key coil parameters, including the number of turns and coil diameters, subject to constraints of 200 µH inductance and a maximum outer diameter of 700 mm. Finite element analysis (FEA) simulations verify that the proposed design approach achieves maximum magnetic coupling under various air gap distances and misalignment conditions. An experimental validation of a 2-kW prototype demonstrates close agreement with simulations, achieving coil-to-coil efficiencies between 92.61% and 96.67%, and overall system efficiency exceeds 80% under all tested conditions. These results confirm that the proposed design method effectively meets performance requirements set by the SAE J2954 standard.
Copyright (c) 2026 Patcharapon Kaewnoen, Supapong Nutwong, Nattapong Hatchavanich, Ekkachai Mujjalinvimut http://creativecommons.org/licenses/by-sa/4.0
Comparative study of fuel economy and emissions for plug-in hybrid electric Payang Water Taxi on different driving cycles using ADVISOR
A new conceptual series-parallel plug-in hybrid vehicle for water transportation, known as the plug-in hybrid electric Payang Water Taxi (PHEPWT), is designed to improve vehicle fuel economy and significantly lower boat emissions. This article aims to analyze the fuel economy and emissions of PHEPWT, which are Hydrocarbons (HC), Carbon Monoxide (CO), and Nitrogen Oxides (NOx), with 6 driving cycles including Pulau Warisan river route, Kuala Terengganu river route, Kampung Laut river route, Seberang Takir river route, Pulau Kapas river route, and Tasik Kenyir river route. The analysis of the PHEPWT model will be compared with the existing powertrain architectures using water drive cycles by using the advanced vehicle simulator (ADVISOR). The results will be expected based on the fuel economy and emissions analysis that will show about 30-50% improvement in driving cycle for each driving cycle, and the fuel economy of the PHEPWT will indicate about 15-20% higher than that of the ADVISOR model. Also, for emission, the PHEPWT and ADVISOR models are based on the result of three-type emission such as HC, CO, and NOx, and show that the PHEPWT model has a lower emission compared to the ADVISOR model.
Copyright (c) 2026 Ahmad Luqmanul Hakim Ahmad Tarmizi, Siti Norbakyah Jabar, Salisa Abdul Rahman http://creativecommons.org/licenses/by-sa/4.0
Performance evaluation of cascaded H-bridge multilevel inverter with hybrid controller based PV system
Rising concerns about global warming demand renewable growth, which in turn needs efficient converter topologies to integrate renewable power. This article presents a single-phase, nine-level inverter to improve the performance of non-conventional power systems. Here, the foremost aim, based on the advanced techniques, to diminish the representation of switches with sources has been executed. This influences the appended preservation of generating energy against non-conventional power resources. This conquest during the statistic of switch refuses every switching loss, counting the cardinal-like driving circuit that details a minimization within convolution based on supervision track, consequently depreciating the disturbances with scope. The proposed inverter has a diminished production voltage total harmonic distortion (THD) with an ideal power factor. The cascaded H-bridge multilevel inverter (CHBMLI) topology is intended for the proposed method in support of the design, added ant-lion optimization (ALO) tuned fuzzy logic controller (FLC) methodical assessment for compensation. The presented arrangement is refined to diminish the energy losses, just as it is unified among reproducing systems that boost the smooth output voltage with reduced %THD. In addition, contraction in energy losses and amplification in efficiency are accomplished by producing transitional levels for the level elaboration system. Indeed, every completion related to the suggested arrangement is evaluated over the reproduction of MATLAB/Simulink and PROTEUS applications.
Copyright (c) 2026 C. Dinakaran, T. Padmavathi http://creativecommons.org/licenses/by-sa/4.0
A novel approach to flexible BTMS for 2-wheeler electric vehicle to avoid fire accidents-an Indian perspective
The Indian electric vehicles market share has significantly increased due to various government initiatives, increased fuel prices, and charging infrastructure. On the contrary, fire accidents of EV’s in India are no rarer due to inappropriate BTMS and its inability to work with different environmental conditions prevailing in India, so it has become one of the major concerns. Two-wheelers, being one of the most used modes of transport, are dominating the Indian roads; it well deserves an innovative BTMS that suits local environmental conditions for preventing thermal runaways and maintaining better performance of the battery. As we get to see diverse environmental conditions at different parts of India, it will be good if we can develop flexible BTMS. Major challenges being faced in the development of suitable BTMS are space and cost constraints. This paper focuses on the development of BTMS for electric two-wheelers, suitable for various environmental conditions, which fits in the available space with low additional cost. It also provides flexibility to drop or add some of the features based on one’s operational requirements or environmental conditions prevailing at the place of operation, which can be as easy as one can drop or choosing to have fog lamps, speakers, camera, and sunroof depending upon their requirement and budget.
Copyright (c) 2026 Mahmooda Mubeen, Gangishetti Srinivas http://creativecommons.org/licenses/by-sa/4.0
Optimal speed control of SRM with integration of switching variable proportional desaturation fuzzy logic regulator for EV application
SRMs are recognized for their high efficiency, strong torque output, and capability to operate at high speeds, making them well-suited for electric vehicle (EV) applications. Their high initial torque effectively overcomes the vehicle's inertia during the drive mode, enhancing overall performance. Switched reluctance machines (SRMs) come in various structural configurations such as 8/6, 6/4, and 4/2, offering flexible options for different vehicle requirements. Speed control in SRMs is typically managed through current regulation, which is handled by a current controller. Traditionally, a proportional integral (PI) controller is used to generate the required reference current. However, the PI regulator is prone to high damping and sensitivity to disturbances, leading to increased speed overshoot during startup and longer settling times. To address these limitations, a switched variable proportional desaturation PI (SVPDPI) controller is employed. Nevertheless, due to its relatively slow response, the PI component in the SVPDPI is replaced with a fuzzy logic module to enhance controller performance. This results in a switched variable proportional desaturation fuzzy logic (SVPDFL) regulator, which significantly reduces initial speed overshoot and improves settling time toward the desired reference speed. This paper presents a comparative analysis of these controllers, with simulations conducted using MATLAB/Simulink to evaluate performance improvements.
Copyright (c) 2026 Kadali Ravi Kumar, Narender Jatoth, Shaik Mohammed Mukassir http://creativecommons.org/licenses/by-sa/4.0
Development of a mathematical model for electric drive dynamics in belt conveyors: A Simulink-based analysis of transient behavior
This paper presents a detailed study of developing a mathematical model and experimental analysis of electric drive processes in belt conveyors. The proposed model simplifies the complex real mechanical system by substituting distributed parameters, such as the transported load's mass and the traction element's elasticity, with concentrated equivalents. A comprehensive investigation of key transient processes including stator currents speed, torque and resistance forces was performed using MATLAB's Simulink environment. The findings reveal significant differences in performance between the initial startup phase and operation under loaded conditions. To validate the model's accuracy, the authors employed numerical analyses utilizing regression metrics such as root mean square error (RMSE) and correlation coefficients. The results show that the proposed model significantly outperforms similar models in the literature with a notable RMSE of 12.5 A for stator current, reflecting an 18% improvement and 8.7 Nm for torque prediction, indicating a 15% enhancement. Furthermore, the model achieved a correlation coefficient of 0.98, confirming its high accuracy in experimental data fitting. By effectively capturing oscillatory phenomena during both unloaded and loaded startup conditions, this work establishes the model as a reliable representation of belt conveyor dynamics, setting a new benchmark in the field.
Copyright (c) 2026 Khalaf Y. Alzyoud, Jawdat S. Alkasassbeh, Ayman Y. Al-Rawashdeh, Vlademer Е. Pavlov http://creativecommons.org/licenses/by-sa/4.0
An innovative winding configuration to enhance 3-phase induction motor performance
A three-phase induction motor is extensively employed in the industrial sector due to its robustness and cost-effectiveness. An enhancement of this motor is underway to optimize its performance. Enhancing motor performance is intricately linked to escalating motor production expenses. Consequently, an innovative strategy is essential to enhance motor performance without incurring substantial extra expenses. This study aimed to introduce a novel approach for designing 3-phase induction motor coils to enhance motor performance without significant additional expenses. This study concentrated on the design of a 3-phase induction motor coil, rated at 1 HP, 380 V, 2 A, 4 poles, and 24 slots, and arranged in a Y-connection. We fabricated the coil using a dual-layer approach, creating magnetic pole pairs on each layer. The study's results demonstrated an improvement in output power, efficiency, load torque, and rotor speed of the new motor design, specifically by 19.32%, 16.26%, 18.48%, and 0.72%, respectively. Despite a 3.05% rise in motor coil current during peak load conditions, the motor's overall performance significantly improves, enhancing its capabilities without considerable additional expenses. This study claims that the suggested way can make other 3-phase induction motors work better without costing a lot more.
Copyright (c) 2026 Zuriman Anthony, Refdinal Nazir, Muhammad Imran Hamid http://creativecommons.org/licenses/by-sa/4.0
Linearity analysis of a brushed DC machine thermal system in response to speed input using transfer function
This study represents a preliminary step toward developing a real-time condition monitoring system for brushed DC machines by analyzing the linearity of their thermal behavior. The temperature response of an MY1016 DC motor was collected under no-load conditions at five different speed levels, ranging from 20% to 100% of the rated speed, until the motor reached steady-state conditions to emphasize the temperature increase due to speed variability. A transfer function model was identified using MATLAB’s System Identification Toolbox, and the system’s linearity was evaluated by analyzing the spread of pole values across different speeds. Results showed significant variability in the coefficient of variation (CV) for key components, with values ranging from 0.18 for the casing to 0.84 for the brush. These findings reveal significant deviations from linear thermal behavior, indicating that a single linear transfer function may be insufficient to model the system. This research highlights the need to validate linearity assumptions in thermal modeling and introduces a framework for assessing thermal variability under varying speed conditions.
Copyright (c) 2026 M. S. Mat Jahak, M. A. H. Rasid http://creativecommons.org/licenses/by-sa/4.0
Fuzzy adaptive sliding mode control with exponential reaching law for enhanced 4WD electric vehicle speed control
This paper discusses a novel fuzzy adaptive sliding mode control (FASMC) strategy for a four-wheel-drive (4WD) electric vehicle (EV), incorporating an exponential reaching law (ERL) and a fuzzy adaptive switching gain to enhance speed tracking. The classical SMC technique often suffers from the chattering problem, which can degrade the dynamic control performance of the electric vehicle. To address these challenges, the proposed hybrid controller employs an exponential reaching law to ensure fast convergence and reduced chattering, while a fuzzy logic-adaptation mechanism dynamically adjusts the switching gain to improve robustness against uncertainties and external disturbances. First, the mathematical model of the motor derived for achieving speed regulation using the classical SMC with an exponential reaching law based on indirect-field-oriented control (FOC). Then, the proposed control technique is designed to automatically adjust the ERL gain using a fuzzy logic controller to ensure precise vehicle speed control, optimizing the vehicle's dynamics under varying road conditions. This novel configuration enables the development of a 4WD EV control framework with an optimized controller, serving as the foundation for implementing our proposed study. The results validate the proposed method's superiority, delivering lower chattering, enhanced tracking precision, and greater robustness compared to traditional SMC while adhering to control standards. This control framework presents a viable advancement for 4WD EV motion management, supporting safer, more effective autonomous vehicle technologies.
Copyright (c) 2026 Abdelhamid Bouregba, Abdeldjabar Hazzab, Aissa Benhammou, Samir Hadjeri http://creativecommons.org/licenses/by-sa/4.0
Impact and reliability analysis of voltage sags in a multi-pulse transformer-fed variable frequency drive system
In an industrial grid, variable frequency drives (VFDs) are the major appliances that contribute to harmonic pollution. To reduce the effects of this harmonic pollution and comply with the regulatory standards, multi-pulse transformers are used to cancel out the specific harmonics. The VFDs experience a different input current profile when fed through multi‑pulse transformers compared to direct grid connection. Despite the harmonic pollution reduction in the grid due to this implementation, the current stresses faced by the front-end devices will become higher. If the VFDs are designed only considering the impact of the direct grid consideration, the lifetime and reliability of the front-end devices will be a concern if operated with a multi-pulse feeder. This condition will be worse if there are presence of different types of sag events. This research details the effects of the reflected sags in the multi-pulse transformer’s secondary windings and the current stresses in the different front-end converter elements due to this. Also, a systematic methodology using the FIDES approach is used to estimate the reliability of the front-end converter. A 7.5 kW-rated VFD is fed with a 12-pulse transformer is used for this research.
Copyright (c) 2026 R. Govarthanan, K. Palanisamy, S. Paramasivam http://creativecommons.org/licenses/by-sa/4.0
Modified firefly-optimized PI controller for BLDC motor performance under New European Driving Cycle conditions
This paper presents the application of a modified firefly algorithm (MFA) for tuning the proportional-integral (PI) speed controller of a brushless direct current (BLDC) motor drive, targeting improved overall dynamic performance of the motor drive system for electric vehicle (EV) applications. The controller’s effectiveness is evaluated under two variants of the New European Driving Cycle (NEDC) to replicate real-world driving scenarios. To validate the effectiveness of the proposed approach, a comparative study is carried out with two widely used optimization techniques, such as the standard firefly algorithm (FA) and particle swarm optimization (PSO). Comparative analysis reveals that the MFA-tuned controller delivers superior speed tracking accuracy, with significantly reduced speed error, speed ripple, and copper losses, when compared to controllers optimized using the standard firefly algorithm (FA) and particle swarm optimization (PSO). These improvements enhance both the energy efficiency and operational stability of the motor drive. Furthermore, the result of the experiment shows that the proposed controller demonstrates strong adaptability under varying load and speed conditions, positioning it as a robust solution for both electric vehicles and industrial motor control applications.
Copyright (c) 2026 Dibyadeep Bhattacharya, Raja Gandhi, Chandan Kumar, Karma Sonam Sherpa, Rakesh Roy http://creativecommons.org/licenses/by-sa/4.0
Implementation of finite control set-predictive torque control based on the dSpace DS1104 controller
Finite control set-predictive torque control (FCS-PTC) has emerged as a popular control method for induction motors (IMs) over the past decade. This paper provides a detailed hardware implementation of FCS-PTC with a constant weighting factor (WF) on 186 W of IM. The FCS-PTC is implemented using the DS1104 controller board programmed using C code. A comprehensive analysis of flux, torque, and the total harmonic distortion (THD) of the stator current is conducted and presented to establish a relationship between the WF and drive performance. The experimental results indicate that a higher WF results in improved performance in flux and THD current, but a poorer torque response. Conversely, a lower WF improves torque performance but sacrifices the flux and THD current. The analysis outcome will hopefully provide some guidelines for developing the WF selection techniques.
Copyright (c) 2026 Rozana Alik, Nik Rumzi Nik Idris, Norjulia Mohamad Nordin, Tole Sutikno http://creativecommons.org/licenses/by-sa/4.0
Design and improvement of dynamic performance of solar-powered BLDC motor for electric vehicles in agricultural applications
One of the most pressing environmental problems is the rapid increase in the production of greenhouse gases by transportation vehicles. This paper looks into SPEVs, or solar-powered electric vehicles. The answer to the problems of transportation-related pollution and fuel usage. In an electric vehicle, the power comes from a battery that may be charged by solar panels or any other external power source. By making use of the perturb and observe (P&O) maximum power point tracking (MPPT) controller, one can achieve maximum power. The DC voltage that the photovoltaic module produces is amplified when it is fed into a voltage source inverter (VSI) via this enhanced output. The tool for the job here is a buck-boost converter. To power their wheels, EVs rely on brushless direct current (BLDC) motors and variable speed inverters (VSIs), which transform DC power from solar panels into AC power. We compare the efficiency of electric vehicles (EVs) attained by raising converter voltages and battery state of charge (SoC) using a PI controller, and we look at the performance of photovoltaic (PV) and brushless linear direct current (BLDC) motors. We use MATLAB/Simulink to do the validation.
Copyright (c) 2026 Savitri Medegar, M. Sasikala http://creativecommons.org/licenses/by-sa/4.0
Fuzzy logic direct torque control of induction motors using three-level NPC inverter
Induction motor drives are extensively used for their robustness and efficiency, but precise control remains difficult under dynamic conditions. Conventional direct torque control offers a simple structure and fast response, but is limited by torque ripple, flux distortion, and poor low-speed performance. This paper proposes a fuzzy logic-based direct torque control (FDTC) combined with a three-level neutral point clamped (NPC) inverter. A fuzzy inference system (FIS) replaces the hysteresis comparators and switching table, while speed regulation is improved using a PI-fuzzy controller. MATLAB/Simulink simulations under speed variations and load disturbances demonstrate reduced torque and flux ripples, smoother flux trajectories, improved current waveforms, and faster transient response compared with classical DTC. These results confirm that the FDTC–NPC approach provides a robust and efficient solution for advanced applications such as industrial automation, renewable energy, and electric vehicles.
Copyright (c) 2026 Jamila Chennane, Lahcen Ouboubker, Mohamed Akhsassi http://creativecommons.org/licenses/by-sa/4.0
Improving the energy efficiency of two-speed motors through the use of new pole-switched windings
This article addresses the design and manufacturing of two-speed asynchronous motors with pole-changing windings. The need for developing two-speed motors with a single pole-changing winding is justified from the standpoint of energy and resource efficiency, as well as improved starting performance of high-power electric drives. An analysis of existing pole-changing winding designs is presented, highlighting their practical limitations in industrial applications. A new pole-changing winding with a 4/2 pole ratio and 48 stator slots was developed using the discrete spatial functions method based on star–delta–double star configurations. The electromagnetic characteristics of the proposed winding were analyzed. Based on this design, a new 4A200L8/4U3 two-speed motor was manufactured and tested under production conditions at the energy motors plant. Experimental results show that at p1 = 4 pole pairs the motor delivers P2 = 20 kW with efficiency η = 87%, cos φ = 0.82, I1 = 43 A at slip s = 2.35%, while at p2 = 2 pole pairs it develops P2 = 36 kW with efficiency η = 91.5%, cos φ = 0.906, I1 = 66 A at slip s = 1.5%. The results confirm more efficient utilization of the active magnetic core at lower polarity and demonstrate the feasibility of implementing such motors for energy-saving applications in heavy-duty drives requiring two equivalent operating speeds.
Copyright (c) 2026 Zhanat Issabekov, Dauletbek Rismukhamedov, Khusniddin Shamsutdionov, Shakhobiddin Husanov, Sabit Rismukhamedov, Bibigul Issabekova, Assemgul Zhantlessova http://creativecommons.org/licenses/by-sa/4.0
Enhancing the dynamic stability of electric power systems through the coordinated tuning of generator predictive controllers
The paper presents a method for the coordinated tuning of automatic voltage regulation (AVR) and automatic speed control (ASC) systems for a group of generators operating in parallel at a power plant. The method also involves solving the optimization problem using a genetic algorithm. The possibilities of using lead-lag elements in AVR and ASC, which impart predictive properties and improve damping characteristics of the controllers, are also considered. A model of a power plant operating in parallel with an electric power system is presented. This model demonstrates effective damping of oscillations under large disturbances when the proposed method is used to adjust the AVR and ASC control coefficients, along with a self-tuning lead-lag element. In this case, voltage oscillations and frequency overshoot disappear, and there is a significant reduction in the maximum deviations of these parameters. In the illustrative case study, the coordinated tuning of the controllers provides a 6% increase in the transmitted power limit and, as a consequence, the enhancement of the stability margin of the electric power system.
Copyright (c) 2026 Hristo Beloev, Yuri Bulatov, Andrey Kryukov, Konstantin Suslov, Yuliya Valeeva, Magdalena Dudek, Iliya Iliev http://creativecommons.org/licenses/by-sa/4.0
Efficiency of squirrel-cage induction motors with copper and aluminum rotors
This study presents a method for estimating efficiency in three-phase squirrel-cage induction motors with copper and aluminum rotor cages. A detailed two-dimensional transient finite-element model of a 1.25 kW motor was created and analyzed under rated conditions (500 V, 50 Hz, 990 rpm, 75 °C) to determine torque, slip, losses, and efficiency. Finite-element results confirmed the copper rotor's advantage, with 11.0% higher efficiency (85.1% compared to 76.7%) and 37.5% lower rotor-cage losses (80 W compared to 128 W) compared to aluminum. For rapid efficiency prediction, both Mamdani-type fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using simulation data. The fuzzy system showed a maximum deviation of 0.8% for the copper rotor, while the neuro-fuzzy approach achieved effective nonlinear mapping for both rotor types with R² = 0.872 against finite-element benchmarks. Sensitivity tests with ±0.3% slip and ±15 W loss variations maintained estimation errors below 2.5%. This combined simulation and intelligent system methodology enables practical efficiency evaluation and rotor material comparison for motor condition assessment and industrial energy management.
Copyright (c) 2026 Ines Bula Bunjaku, Edin Bula http://creativecommons.org/licenses/by-sa/4.0
Performance of high-speed train traction motor system in acceleration – cruise mode: theoretical analysis
This research is based on the development of a 480-kW traction motor for high-speed train (HST) applications. The performance of the traction motor system will be reviewed theoretically with the focus of the research on operational modes, namely acceleration mode and cruise mode. Motor performance will be reviewed more in-depth related to the development of mathematical models for torque function of time, power function of time and efficiency function of speed. The data used in this study are real data of 480 kW traction motor specifications and synthetic data of HST operations. The results of the traction motor performance analysis show that in the operational conditions of acceleration mode, the traction motor requires power and torque at the beginning reaching 4,838.13 Nm to overcome inertia, friction, aerodynamic force and increase speed. In cruise mode, the speed tends to be stable, so the power and torque decrease to 1,133.24 Nm. The results of the traction motor performance theoretically show that the traction motor can work well for variations in speed and track profile.
Copyright (c) 2026 Tri Widodo, Syamsul Kamar, Hilda Luthfiyah, Eko Syamsuddin Hasrito, Sofwan Hidayat, Meiyanne Lestari, Heru Basuki http://creativecommons.org/licenses/by-sa/4.0
Reliability-constrained optimal scheduling of PV-based microgrids using deterministic time-series forecasting and load prioritization strategies
This paper presents an advanced MPC-based energy scheduling framework for islanded microgrids operating under uncertain and dynamic conditions where photovoltaic (PV) generation and energy storage systems (ESS) are integrated, and load management is hierarchically prioritized. The framework employs a hybrid ARIMA and random forest forecasting model to improve day-ahead and intra-day predictions of PV generation and load demand, enabling intelligent demand response, prioritized load shedding, and adaptive storage operation. Moreover, the proposed framework incorporates time-of-use (TOU) pricing and load importance weighting to minimize operational costs while ensuring a reliable power supply for critical loads. Simulation results across four operational scenarios demonstrate that the proposed method achieves approximately 32% improvement in critical load protection, 30% reduction in total operating cost, and 33.3% decrease in total load shedding compared to conventional MPC-based approaches. The proposed approach, therefore, provides a comprehensive, dynamic, and cost-efficient solution for microgrid scheduling and can be extended to multi-microgrid cluster applications in future research.
Copyright (c) 2026 Dunya Sh. Wais, Huda A. Abbood http://creativecommons.org/licenses/by-sa/4.0
Modeling and analysis of batteryless off-grid photovoltaic with adaptive multi-motor
This paper presents a model of a batteryless off-grid photovoltaic (PV) system with an adaptive multi-motor load. This model is developed as an effort to enhance the power output of batteryless off-grid PV systems for motor loads. Instead of using a single large-capacity motor, as commonly done in previous studies, the model distributes the load into several smaller motors and controls them adaptively. This approach allows for better control of the total load impedance to support maximum power point (MPP) tracking. A case study involving three three-phase induction motors, each with an operating power of 200 W, is conducted, where the power production of the proposed model is analysed by comparing it with the theoretical MPP and a fixed-load motor system that represents a single large motor. Under 1000 W/m² irradiance and using an 852 Wp PV array, the proposed model achieves a power output of 842 W, which corresponds to 98.83% of the MPP. In contrast, the system without this model only generates 298 W, or just 35.02% of the MPP. The testing process spans a 5-second period during the motor starting state. The power production analysis of the proposed model is presented in graphical form using MATLAB/Simulink.
Copyright (c) 2026 I Wayan Sutaya, Ida Ayu Dwi Giriantari, Wayan Gede Ariastina, I Nyoman Satya Kumara http://creativecommons.org/licenses/by-sa/4.0
An efficient grid-connected solar PV system with a fault-tolerant mechanism to mitigate the voltage disturbances
One of the most effective renewable energy solutions for long-term power generation is a solar photovoltaic (PV) system that is connected to the grid. However, power quality and system reliability can be significantly impacted by grid-side voltage disturbances such as sag, swell, and faults. To reduce voltage fluctuations and improve grid stability, this study proposes an effective fault-tolerant (FT) solar PV system coupled with a dynamic voltage restorer (DVR). The adaptive DVR-based control method, which dynamically injects compensatory voltages based on disturbance amplitude to ensure uninterrupted and distortion-free power delivery, is the feature that makes this study unique. MATLAB/Simulink is used to model and simulate the system to assess its dynamic response under fault, sag, and swell situations. IEEE 519 standards are met by the suggested design, which produces average total harmonic distortion (THD) values of 0.59%, 1.16%, and 1.55% for 50%, 100% sag/swell, and three-phase fault circumstances, respectively. This indicates that even in challenging grid situations, the DVR can sustain high-quality voltage profiles. For implementation in renewable-rich or weak grid networks, the suggested FT-DVR configuration provides a workable and affordable solution that guarantees better voltage regulation, less harmonic distortion, and increased operational dependability for upcoming smart-grid integration.
Copyright (c) 2026 N. Jayakumar, B. Devi Vighneshwari, V. Prema http://creativecommons.org/licenses/by-sa/4.0
Solar power forecasting using a SARIMA approach for Indonesia's grid integration
Indonesia’s transition toward a renewable energy-dominated power grid is progressing to meet increasing energy demands while reducing dependence on fossil fuels. According to the National Energy General Plan, their goal is to have 23% of the energy mix come from renewables by 2025 and 31% by 2050. Accurate forecasting of photovoltaic (PV) power output is crucial to address the intermittent nature of solar energy and ensure grid stability. A seasonal autoregressive integrated moving average (SARIMA) model was developed to estimate day-ahead photovoltaic power output in Padang City, Indonesia. Using NASA solar irradiance data from March 1-31, 2024, the SARIMA(1,0,1)(4,0,3)24 model achieved high accuracy with an NRMSE of 4.19%. To evaluate its performance, a comparative evaluation was conducted between the SARIMA model and two machine learning methods, namely artificial neural network (ANN) and long short-term memory (LSTM), in which SARIMA achieved the lowest forecasting error. These findings indicate that SARIMA remains an effective and interpretable statistical method for short-term PV forecasting, supporting reliable energy planning and power grid operations towards Indonesia's renewable energy goals.
Copyright (c) 2026 Ricky Maulana, Syafii, Aulia http://creativecommons.org/licenses/by-sa/4.0
Mitigating harmonic distortion in grid-connected PV systems: a comparative evaluation of ANFIS and IC-based MPPT techniques
Integrating photovoltaic (PV) systems into power grids presents notable challenges related to power quality, especially concerning total harmonic distortion (THD) caused by power electronic converters, which do not comply with IEEE 519 and IEC 61000 standards. This study introduces an adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) controller designed to optimize energy extraction while concurrently mitigating harmonic distortion in three-phase grid-connected PV systems. Unlike conventional IC-MPPT methods, which compromise the quality of the power to monitor performance, the proposed ANFIS-MPPT strategy uses intelligent modulation index adjustment to achieve a dual goal. A comparison of the four irradiation levels (450-1000 W/m²) shows a higher performance: ANFIS-MPPT achieves 0.26% THD at 1000 W/m² compared to 0.44% at IC-MPPT (40.9% improvement), and 0.80% versus 1.12% at 450 W/m² (28.6% reduction). The five-layer ANFIS architecture, trained on temperature and radiation data, shows faster convergence and lower oscillation than the conventional approach. The results confirm that MPPT based on ANFIS is an effective solution to improve the energy quality of grid-integrated photovoltaic installations while maintaining optimal energy conversion efficiency.
Copyright (c) 2026 Bouledroua Adel, Mesbah Tarek, Kelaiaia Samia http://creativecommons.org/licenses/by-sa/4.0
Grid-tied photovoltaic system MPPT algorithms performance: comparative analysis
Between 2015 and 2024, global solar photovoltaic (PV) capacity rose significantly from 223.204 GW to 1624 GW, contributing to the reduction of greenhouse gas emissions associated with fossil-fuel-based power generation. Solar PV is recognized for its environmental benefits and is increasingly seen as a viable alternative for a long-term sustainable energy supply. However, the power output of PV systems is highly dependent on atmospheric conditions, particularly solar irradiation and temperature, which can cause fluctuations and reduce overall efficiency. To address this, maximum power point tracking (MPPT) techniques are employed to optimize energy extraction under varying environmental conditions. This study presents a comparative analysis of four MPPT algorithms, perturb-and-observe (P&O), incremental conductance (InC), fuzzy logic control (FLC), and artificial neural network (ANN) for grid-tied PV systems using MATLAB/Simulink. Each algorithm was evaluated under dynamic conditions to determine its tracking efficiency and responsiveness. The results show that while conventional methods like P&O and InC are simpler, they are less effective under rapidly changing conditions. FLC demonstrates faster convergence but requires greater computational resources. The intelligent controllers demonstrated superior performance: FLC achieved the highest power output of 1.019×10⁶ W with a corresponding voltage of 1.422×10⁴ V, while the ANN algorithm followed closely with 9.650×10⁵ W and 1.200×10⁴ V, respectively. The comparative insights gained from this analysis offer practical guidance for selecting MPPT controllers in real-world solar energy applications.
Copyright (c) 2026 Louis Nicase Nguefack, Kayode Timothy Akindeji, Abayomi A. Adebiyi http://creativecommons.org/licenses/by-sa/4.0
Application of capacitor banks to enhance energy efficiency in aeration systems for fisheries cultivation
Electrical energy consumption in aeration systems represents a major component of operational costs, primarily due to the low power factor of inductive equipment such as blowers. This study evaluates the effectiveness of capacitor banks in improving energy efficiency and their economic feasibility in small- to medium-scale aquaculture aeration systems. Over 90 days, measurements were conducted on energy consumption, current, voltage, and water quality parameters, including dissolved oxygen (DO) and pH in two systems: without and with capacitor banks. The results showed that the use of capacitor banks reduced daily energy consumption from 15.01 ± 0.45 kWh to 13.13 ± 0.45 kWh (savings of 12.51%), equivalent to approximately 56.4 kWh per month or 686.2 kWh per year. The average current decreased from 2.44 A to 1.88 A, while voltage, DO (6.50-6.64 mg/L), and pH (7.20-7.25) remained stable within the optimal range. Economic analysis revealed that an initial investment of IDR 1,500,000 has a payback period of 18 months, a net present value (NPV) of IDR 2.15-2.33 million (at 8% discount rate), and an internal rate of return (IRR) exceeding 50% per year. These findings demonstrate that the application of capacitor banks not only enhances energy efficiency and reduces power losses but is also highly feasible and profitable for practical adoption in aquaculture operations.
Copyright (c) 2026 I Made Aditya Nugraha, I Gusti Made Ngurah Desnanjaya http://creativecommons.org/licenses/by-sa/4.0
Development of numerical model-based photovoltaic emulator for half-cut cell PV panel with multiple peaks output characteristics curve emulation capability
This study introduces a photovoltaic (PV) emulator focusing on a developed numerical model specifically for half-cut cell PV panels under partial shading conditions (PSCs), addressing a gap in research focused on full-cell models. The emulator uses a DC-DC buck converter and PI control to accurately replicate half-cut cell PV panel characteristics. A cost-effective hardware prototype validated the model's effectiveness in emulating multi-peak PV behavior under dynamic PSCs with up to three peaks and user-defined shading. This flexible and affordable platform enables efficient testing of MPPT algorithms and grid integration for PV systems using increasingly prevalent half-cut cell technology. Simulation results show high accuracy, with MAPE in power as low as 0.175% under uniform irradiance conditions and less than 0.302% under multi-peaks PSCs. Hardware validation confirms reliability with low MAPE in the power of 0.499% under uniform conditions and below 0.614% multi-peak PSCs, demonstrating the developed half-cut cell PV panel numerical model's accuracy in reproducing dynamic shading effects for renewable energy research.
Copyright (c) 2026 Jordan S. Z. Lee, Jia Shun Koh, Rodney H. G. Tan, Nadia M. L. Tan, Thanikanti Sudhakar Babu http://creativecommons.org/licenses/by-sa/4.0
A framework for robust PID controller design: an optimization-based approach for inductive loads
This paper presents a comprehensive comparative study of proportional-integral-derivative (PID) controller tuning methodologies for inductive load applications across three representative scenarios. We systematically evaluate classical methods (Ziegler-Nichols, internal model control) against global optimization algorithms (genetic algorithm (GA), particle swarm optimization (PSO)) applied to resistor-resistor-inductor (RRL) circuit models. Results demonstrate that PSO achieves superior performance for moderate-to-slow systems, reducing settling time by 84% while completely eliminating overshoot compared to Ziegler-Nichols. The algorithm automatically discovers optimal PI controller structures, simplifying implementation. However, for ultra-fast systems (time constants < 1 ms), internal model control proves more reliable, achieving 0.84 ms settling with only 0.16% overshoot. Optimized controllers demonstrate exceptional robustness, maintaining stability under ±50% parameter variations and effectively rejecting disturbances. This research provides engineers with a scenario-based framework for method selection, moving beyond heuristic tuning to achieve previously unattainable performance levels. The findings establish optimization-based tuning as a systematic, reliable approach for high-performance control system design in industrial applications.
Copyright (c) 2026 Ali Abderrazak Tadjeddine, Miloud Kamline, Latifa Smail, Soumia Djelaila, Hafidha Reriballah http://creativecommons.org/licenses/by-sa/4.0
THD and spectral performance analysis of two-triangle RPWM for inverter applications
Pulse width modulation (PWM) is essential for voltage source inverters (VSI) to generate high-quality voltage outputs. Conventional deterministic PWM generates predictable harmonics, causing clusters that increase acoustic noise. Random PWM (RPWM) disperses harmonic power over a wider frequency range, reducing noise and electromagnetic interference. Many RPWM techniques improve inverter quality, but only partially suppress dominant harmonics and lack effective harmonic spreading. Most studies focus on simulations with limited FPGA implementation or hardware validation. The use of digital tools like VHDL, ModelSim, and MATLAB co-simulation remains underutilized. This paper proposes two-triangle RPWM strategies to enhance harmonic dispersion and reduce total harmonic distortion (THD). Co-simulation results are shown for both SPWM and RPWM, along with comparisons of fundamental voltages, THD, and HSF across different modulation indexes. Additionally, synthesis data for the Xilinx XC3S500E FPGA processor is supplied. The last section offers a comparative analysis and experimental validation of SPWM and RPWM. These techniques enable enhanced inverter performance, lower acoustic noise, and process innovations in power electronic systems.
Copyright (c) 2026 G. Jegadeeswari, R. Sundar, S. P. Manikandan, E. Poovannan, C. Rajarajachozhan, M. Batumalay, Sukumar Kalpana http://creativecommons.org/licenses/by-sa/4.0
Optimizing battery safety and performance: Hardware implementation and simulation analysis of protective measures, SoC Measurement, and cell balancing in BMS
This paper presents a dual-platform validation of a compact battery management system (BMS) combining an Arduino-based hardware prototype and a MATLAB/Simulink model for cross-validation. The hardware implements over-voltage, over-current, and over-temperature protections, state of charge (SOC) estimation using open-circuit voltage (OCV) and coulomb counting (CC), and both passive and active balancing. Experimental results show that SOC accuracy remains within ±2%, active balancing achieves 57% higher energy efficiency and 37% faster convergence than passive balancing, and thermal rise is limited to <5 °C. Limitations include fixed protection thresholds and the absence of physical validation of long-term aging effects. The dual-platform approach allows cross-validation of hardware and simulation, benchmarking SOC estimation methods, and quantifying energy and thermal trade-offs between balancing strategies. This approach offers a low-cost and reproducible validation pathway for EV-oriented BMS design.
Copyright (c) 2026 Atul Kumar Singh, C. P. Boopathy http://creativecommons.org/licenses/by-sa/4.0
Modeling and optimization of angular misalignment effects in resonant inductive wireless power transfer for electric vehicle charging
This paper presents an enhanced electromagnetic modeling and optimization study on the effects of angular misalignment in resonant inductive wireless power transfer (RIWPT) systems for electric vehicle (EV) charging. A detailed 3D model of a double-layer circular coil was developed in CST Studio Suite to investigate coupling degradation, energy loss, and efficiency behavior under angular deviations ranging from 0° to 25°, at a fixed air gap of 30 mm. Performance metrics including mutual inductance, magnetic field distribution, power transfer efficiency (PTE), and loss characteristics were analyzed to establish quantitative misalignment correlations. Results indicate a steady reduction in PTE from 99.979% at 0° to 88.441% at 25°, accompanied by corresponding increases in field asymmetry and energy dissipation. To mitigate these losses, an impedance-tuning strategy was applied by jointly optimizing transmitter-side series and parallel compensation capacitors, which improved PTE at 5° misalignment from 98.777% to 99.801%, restoring near-resonant operation. Additional analyses evaluated thermal impact, material robustness, and dynamic misalignment effects, providing a more holistic understanding of real-world charging scenarios. The study further discusses real-time tuning feasibility using embedded controllers and aligns performance with SAE J2954 and IEC standards for EV wireless charging. The findings establish validated design guidelines and adaptive tuning frameworks for achieving high-efficiency, misalignment-tolerant RIWPT systems, contributing toward robust and energy-efficient EV charging infrastructure.
Copyright (c) 2026 Samshul Munir Muhamad, Wan Muhamad Hakimi Wan Bunyamin, Rahimi Baharom http://creativecommons.org/licenses/by-sa/4.0
MPC and FOC for LVRT performance in hybrid renewable energy systems
This paper proposes a wind and solar energy-based hybrid generation system integrated with a photovoltaic (PV) array controlled using model predictive control (MPC) and a doubly fed induction generator (DFIG) wind turbine controlled using field-oriented control (FOC). The system employs cascaded-based and bridge-based structures for two renewable sources, and they are connected to an ordinary common load, and designed to meet the stringent conditions of low-voltage ride-through (LVRT) required during fault conditions and grid-side perturbations. In order to safeguard the power electronic converter from sharp voltage dips, a crowbar protection circuit is used on the rotor side of the DFIG. In order to verify the enhanced LVRT capability of the offered system, extensive modeling, control design, implementation steps, and numerous simulation results have been included. The use of sophisticated control methodologies and protective measures improves the reliability and stability of wind-solar power plants. Simulation results reveal that for a serious grid disturbance, the system manages to maintain the output voltage at 70% of its nominal value and keeps the waveform steady and sinusoidal. In addition, the control scheme ensures that the rotor current is not just sinusoidal but also well-balanced, yielding a steady-state electromagnetic torque. This combination of control and protective measures is paramount for achieving stability, power quality, and reliability in current hybrid renewable power systems.
Copyright (c) 2026 Oday Saad Fares, Riyadh G. Omar, Kassim A. Al-Anbarri http://creativecommons.org/licenses/by-sa/4.0
A novel hybrid PI and adaptive super-twisting sliding mode controller for high-performance integrated speed and flux regulation of IMDs
This paper presents a novel hybrid control strategy that integrates a proportional-integral (PI) regulator with an adaptive super-twisting sliding mode controller (ASTA) defined on a nonsingular terminal sliding mode control (NTSMC) surface for high-performance induction motor drives (IMDs). This enhanced hybrid PI-ASTA-NTSMC architecture jointly exploits the steady-state accuracy of PI control and the finite-time robustness of a higher-order sliding mode formulation. The adaptive mechanism of the super-twisting algorithm dynamically adjusts the switching gains according to the instantaneous sliding variable, ensuring consistent performance under time-varying loads and parameter variations. The NTSMC surface guarantees singularity-free finite-time convergence, while the adaptive ASTA law suppresses chattering and enhances disturbance rejection. Simulation results across multiple operating conditions show that the proposed controller significantly outperforms PI and PI-FOSMC schemes. It achieves the fastest transient, reducing settling time to 0.0407 s (39.4% and 31.5% faster than PI and PI-FOSMC), with overshoot lowered to 0.0091 rad/s and ISE/IAE minimized to 0.0035 and 0.0256, confirming its superior tracking precision. Additionally, reductions in the speed and torque RMSE indicate smoother control effort and improved closed-loop performance. The Lyapunov-based analysis confirms global finite-time stability of the overall system. With its enhanced robustness, low sensitivity to sampling noise, and continuous higher-order sliding structure that suppresses chattering, the proposed hybrid PI–ASTA–NTSMC offers a computationally efficient and practically attractive solution for integrated speed–flux control in industrial IM drives.
Copyright (c) 2026 Duc Thuan Le, Ngoc Thuy Pham http://creativecommons.org/licenses/by-sa/4.0
D-STATCOM control for distribution grids with distributed sources based on MMC structure using FCS-MPC algorithm
This paper proposes a D-STATCOM structure based on a modular multilevel converter (MMC) with the use of FCS-MPC control method for the purpose of compensating reactive power and stabilizing voltage in the distribution grid. The D-STATCOM is effectively used in cases involving non-sinusoidal and unstable voltages, which often occur in the distribution grid due to the effects of unbalanced nonlinear loads and power injection from renewable energy systems. The proposed structure also has the capability of reactive power compensating flexibility in fault conditions to stabilize the grid voltage. In this paper, a new control strategy, which is based on the combination of an outer PI controller and an inner FCS-MPC controller, was introduced. The outer PI controller is used to reduce static deviations in control values and to provide a reference value for the FCS-MPC controller. The inner FCS-MPC controller calculates the optimal switching state for the purpose of reducing the switching frequency of the MMC. The implementation process begins with the construction of a mathematical model and a control model. Simulations were carried out by MATLAB/Simulink to demonstrate the responsiveness of the control algorithm and the performance of D-STATCOM under the conditions of non-sinusoidal and unstable voltages.
Copyright (c) 2026 Pham Viet Phuong, Le Hoai Nam, Pham Chi Hieu, Tran Hung Cuong http://creativecommons.org/licenses/by-sa/4.0
A novel technique for induction heating dryer with temperature and voltage control for power inverter
This study presents a novel prototype of an induction heating dryer integrating hysteresis control with phase-shifted pulse width modulation (PWM) for the first time. The system replaces conventional resistance heating, improving energy efficiency and thermal stability. The 2 kW prototype comprises a drying chamber and a hot air unit with controlled airflow of 1.5 m/s. Phase angle adjustment reduces voltage, current, and power consumption while maintaining the power factor within acceptable limits. The temperature control maintains stability within ±1 °C of the setpoint. The results demonstrate fast, energy-efficient, and precise drying, offering potential benefits for food processing and textile industries, and providing a foundation for future development of intelligent, energy-efficient induction dryers.
Copyright (c) 2026 Jeerapong Srivichai, Kittaya Somsai, Akkachai Phuphanin, Nithiroth Pornsuwancharoen http://creativecommons.org/licenses/by-sa/4.0
High efficient DC-AC inverter for low wireless power transfer applications
The inverter's simplicity is an important aspect that must be considered especially for electronic devices, as adding the number of power switches increases the complexity and overall cost of the inverter. This work proposes an inverter design that converts DC into AC power. It receives 12 VDC as an input voltage, and it is composed of a boost converter that converts an input voltage of 5-20 VDC to an output voltage of 4-30 VDC and a pulse width modulation controller to produce a square wave with a frequency of 100 kHz to drive the switching MOSFET. The designed inverter can be operated on different loads ranging from 50 Ω to 1000 Ω, tested in both simulations and experimentally. The design was optimized by the LT Spice simulator. The proposed inverter has operating frequencies ranging from 40 kHz to 110 kHz, taking into account different loads. The obtained results showed that both simulation and experimental results converged, whereas the highest efficiency was 96.96% at 55 kHz at a fixed load of 100 Ω. On the other hand, the maximum achieved efficiency when the load was sweeping was 80% at a load of 50 Ω at a fixed frequency of 100 kHz.
Copyright (c) 2026 Kyrillos K. Selim, Hanem Saied Ebrahem Torad, Mostafa R. A. Eltokhy, Hesham F. A. Hamed, Mohamed Elzalik http://creativecommons.org/licenses/by-sa/4.0
Performance evaluation of dynamic voltage restorer using bidirectional impedance converter with UCAP
With the involvement of renewable energy sources, plug-in hybrid automobiles, and fault occurrence, power quality has degraded nowadays. The most effective device utilized in distribution systems to enhance power quality is the dynamic voltage restorer (DVR). For deep sags, DVR with storage topology is more beneficial, although it has challenges with converter and storage element rating. To address this, various converters and energy storage elements like ultracapacitors are reviewed. In this paper, a DVR with an ultra-capacitor (UCAP) using an impedance bidirectional converter is simulated, and power quality indices are compared with VSI-BDC. The simulation result reflects the enhanced capability of the suggested DVR in a wide range of operations, improved power quality indices, and its effectiveness in swell conditions. The control of DC link voltage with PI and model predictive control (MPC) were simulated and compared.
Copyright (c) 2026 A. Anitha, K. C. R. Nisha http://creativecommons.org/licenses/by-sa/4.0
A novel single-switch DC-DC converter using the coupled inductor with ultra-high voltage gain
This paper presents an extremely high step-up DC-DC converter using a quadratic topology and a coupled inductor (CI). The proposed converter (PC) utilizes a single switch, simplifying the control strategy and reducing switching losses. A passive clamp circuit recycles leakage energy, reducing voltage stress (VS) on the MOSFET and enabling the implementation of a low on-state resistance switch for higher efficiency. Additionally, the quadratic structure and passive clamp circuit contribute to higher voltage gain (VG) and better performance. The converter’s operating principles, steady-state analysis, and component selection criteria are discussed in detail. The influence of magnetizing inductance, duty cycle, and parasitic components on the VG is also investigated, along with the system’s dynamic response under input voltage and load variations to ensure stable operation. A comparative evaluation with existing converters highlights its advantages. The PC is verified through SIMPLIS simulations, where key performance metrics such as VG and switching stress are analyzed. Furthermore, a hardware prototype with a power rating of 300 W is built to confirm the theory and showcase the converter’s performance. Experimental results demonstrate high efficiency, stable operation, and substantial VG, validating the converter’s feasibility for renewable energy systems (RES).
Copyright (c) 2026 Kim-Anh Nguyen, Thai Anh Au Tran, Xuan Khanh Ho, Duong Thach Pham http://creativecommons.org/licenses/by-sa/4.0
State of charge prediction for new and second-life lithium-ion batteries based on the random forest machine learning technique
Accurate state of charge (SOC) estimation is a critical requirement for the safe and efficient operation of lithium-ion batteries (LIBs), particularly in second-life battery (SLB) applications where battery ageing, nonlinear degradation, and measurement noise introduce uncertainty. Although numerous SOC estimation techniques have been proposed, reliable prediction for new and second-life batteries under varied operating conditions remains challenging. In this study, a comparative investigation of the conventional coulomb counting (CC) method and a data-driven random forest (RF) model is conducted for SOC prediction in new and second-life LIBs. Experimental data are obtained from Murata US18650VTC5D cells under pulse discharge tests (PDT), constant discharge tests (CDT), and dynamic stress tests (DST) across a wide range of C-rates. PDT is conducted at 0.24 C, CDT at 0.2 C, 0.5 C, 1 C, and 2 C, while DST is performed at C-rates ranging from 0.5 C to 4 C at a controlled ambient temperature of 25 °C. The RF model is trained using voltage, current, and time features and evaluated against CC using MAE, MSE, RMSE, and R² metrics. Results show that RF consistently outperforms CC under all conditions, particularly for SLBs, achieving significantly lower errors and R² values approaching 0.998. These findings confirm the effectiveness of RF-based SOC estimation for intelligent battery management systems (BMS).
Copyright (c) 2026 Masoud A. Sahhouk, Mohd Junaidi Abdul Aziz, Mohd Ibthisham Ardani, Nik Rumzi Nik Idris, Tole Sutikno, Bashar Mohammad Othman http://creativecommons.org/licenses/by-sa/4.0
Investigation of efficiency and safety in wireless capacitive power transfer through a single-layer tissue phantom
Wireless power transfer (WPT) is a promising solution for implantable biomedical devices, offering an alternative to traditional implanted batteries and percutaneous connections, which are limited by short lifespans and high infection risks. Existing capacitive power transfer (CPT) systems for biomedical implants often utilize media such as animal meat or liquids to validate power transfer across the human body, but these materials exhibit inconsistent and inaccurate dielectric properties. To address this limitation, this study proposes a CPT system designed to operate with a single-layer tissue phantom that closely mimics the dielectric characteristics of human tissue. The system is integrated with a class-E LCCL resonant topology to enhance power transfer efficiency. In addition to evaluating performance, this work also investigates safety aspects in terms of electric field emission and specific absorption rate (SAR). Simulations using MATLAB Simulink and ANSYS HFSS reveal that at a 1 mm tissue gap, the electric field reaches 298.09 V/m and the SAR is 1.14 W/kg, which are both within established safety limits (614 V/m and 2 W/kg per 10 g of tissue). Furthermore, a 5 W, 1 MHz system operating across a 2 mm tissue gap demonstrates power transfer efficiencies of 40.61% for skin tissue and 20.53% for muscle tissue. These results validate the system’s safety and efficiency for powering deeply implanted biomedical devices.
Copyright (c) 2026 Yusmarnita Yusop, Amy Sarah Ngu, Cheok Yan Qi, N. B. Asan, Huzaimah Husin, Shakir Saat, Peter Adam Hoeher http://creativecommons.org/licenses/by-sa/4.0
Analysis of different converter topologies for EV applications
Electric vehicles (EVs) are gaining global prominence due to their high efficiency, low noise, and minimal carbon emissions. A critical aspect of EV performance lies in the interaction between energy storage systems (ESS) and power converters. Nonetheless, power delivery from storage units tends to be unreliable and needs strong converter units for effective and stable energy transmission. Several forms of direct current-to-direct current conversion systems used in electric vehicles are thoroughly examined in the paper, including both isolated and non-isolated designs such as those with the cuk, flyback, and push-pull architectures. The paper looks at converter categorization, control methods such as proportional-integral and artificial neural networks, as well as the method of modulation using unipolar and bipolar sinusoidal pulse-width modulation (PWM). Additionally, the role of optimization algorithms in improving converter performance is explored. Simulations were conducted using MATLAB/Simulink to evaluate each topology under varying load and input voltage conditions. The results demonstrate that the Push-Pull converter has the best efficiency for high-power applications, while the Cuk and Flyback converters are best for applications requiring continuous current and low-power, compact designs, respectively. This research offers insights for choosing optimal converter structures to improve energy efficiency and reliability of systems in electric vehicles.
Copyright (c) 2026 Bodapati Venkata Rajanna, Kondragunta Rama Krishnaiah, Sakimalla Prabhakar Girija, Shaik Hasane Ahammad, Mohammad Najumunnisa, Syed Inthiyaz, Gouthami Eragamreddy, Giriprasad Ambati, Nitalaksheswara Rao Kolukula http://creativecommons.org/licenses/by-sa/4.0
A three isolated port DC/DC converter for an energy storage system for renewable energy applications
The use of renewable energy sources like solar photovoltaic, wind, and fuel cells is gaining popularity due to growing environmental awareness, technological advancements, and declining production costs. Power electronic converters are usually used to convert the power from renewable sources to match the load demand and grid requirements. Among these, DC–DC converters are essential for improving system functionality and power density, especially in low-voltage renewable systems that require high voltage gain. This paper presents a systematic evaluation of five advanced DC-DC converter topologies: multi-port DC, boost multiport interleaved step-up, isolated bidirectional, voltage/current fed, and general resonant focusing on their structural complexity, component count, and potential application scenarios. In addition, a novel high-gain three-port resonant A DC-DC converter is proposed, incorporating galvanic isolation via a three-winding high-frequency transformer. The converter adopts a half-bridge resonant inverter and rectifier-based load port, resulting in a compact and cost-effective solution. A detailed analysis of the converter's operation, design considerations, and control strategy is conducted using PLECS simulation. Furthermore, an experimental setup is developed to validate the converter’s practical feasibility. The setup schematic and comprehensive comparative tables are included to support the evaluation and highlight the proposed design’s capabilities.
Copyright (c) 2026 Faruk Ahmeti, Dimitar Arnaudov, Sabrije Osmanaj http://creativecommons.org/licenses/by-sa/4.0
High voltage asymmetric converter for electrostatic particle accelerators
This work presents several topologies of asymmetric high voltage converters for electrostatic particle accelerators. The options are compared on the basis of their transfer functions and the magnetic components required, and the most suitable for the intended purpose is selected. Simulations and measurement results of the prototype, which has symmetrical voltage output and soft switching in the main transistor, are presented. The prototype built features output voltages of 10 kVand-10 kV, the converter uses a single common command ground for the transistors simplifying its drivers, and also by means of the presented snubber circuit it recovers energy during soft switching.
Copyright (c) 2026 Diego Alberto Fanego, Orlando Silvio Sandini, Hernan Emilio Tacca, Andres Juan Kreiner http://creativecommons.org/licenses/by-sa/4.0
Ferrite-based magnetic shielding for efficiency enhancement in resonant inductive wireless power transfer systems
This paper presents a detailed simulation-based investigation of ferrite-based magnetic shielding to enhance the efficiency and electromagnetic performance of resonant inductive wireless power transfer (RIPT) systems, with a particular emphasis on electric vehicle (EV) wireless charging applications. Two system configurations, a baseline coil-only system and a ferrite-shielded system, were modelled and simulated using CST Studio Suite 3D electromagnetic simulation software under identical geometric and electrical conditions to ensure a fair comparative evaluation. Key performance metrics, including power transfer efficiency (PTE), H-field distribution, and magnetic flux confinement, were analyzed to quantify the shielding impact. The ferrite-shielded configuration achieved a PTE improvement from 98.29% to 99.01%, demonstrating stronger flux concentration, reduced leakage, and lower electromagnetic interference (EMI) exposure. Additional analyses highlight the trade-offs in ferrite integration, including potential core loss, material cost, and thermal drift, while also discussing the system’s robustness against coil misalignment and its alignment with SAE J2954 and IEC 61980 standards for EV charging. The study is limited to a simulation-based approach without experimental validation; however, the findings establish a solid foundation for future hardware prototyping and hybrid shielding exploration, integrating ferrite and composite or metamaterial-based structures. Overall, this work contributes to the development of efficient, EMI-compliant, and thermally stable WPT systems suitable for next-generation EV charging infrastructures.
Copyright (c) 2026 Wan Muhamad Hakimi Wan Bunyamin, Rahimi Baharom http://creativecommons.org/licenses/by-sa/4.0
Multi-carrier PWM techniques to assess the performance of a 5-level diode clamped multilevel inverter fed PMSM drive
The advantages of multilevel inverters (MLIs) have led to their increased use in high- and medium-voltage power applications. These inverters reduce harmonic content, common-mode voltage, dv/dt stress on switches, and electromagnetic interference, among other things. In recent decades, drives for permanent magnet synchronous machines (PMSMs) that rely on inverters have become increasingly popular in both commercial and residential settings due to their great performance. Phase disposition (PD), phase opposition disposition (POD), and alternate phase opposition disposition (APOD) are three multi-carrier pulse width modulation (MCPWM) approaches that were simulated in this work to explore a 5-level DCMLI-fed PMSM. In order to create control pulses, each method compares reference signals with carrier signals that are either triangular or trapezoidal. Detailed comparisons with conventional three-level voltage source inverters (VSIs) are made based on the results. A 63.21 percent improvement in the total harmonic distortion (THD) of the output voltage and a 26.52% improvement in the THD of the stator current are both supported by experimental evidence.
Copyright (c) 2026 K. Lakshmi, T. Vijay Muni, P. Hari Krishna Prasad, Budi Srinivasa Rao, G. Nageswara Rao, K. B. Anilkumar http://creativecommons.org/licenses/by-sa/4.0
Enhancing SAPF performance with VOC and SVM for electrical networks depollution
This paper presents a significant enhancement in the filtering performance of shunt active power filters (SAPF) by leveraging the voltage oriented control(VOC) in combination with a three-level NPC inverter using space vector modulation (SVM). The VOC technique enables precise control of the SAPF by utilizing the orientation of the voltages, thereby optimizing harmonic compensation and reference tracking. Incorporating a three-level inverter allows for more refined voltage modulation, resulting in a substantial reduction in injected harmonic content. Simulation results from MATLAB/Simulink demonstrate the effectiveness of this approach. Before compensation, the measured total harmonic distortion (THD) reaches 27.98%, exceeding the IEEE 519-1992 standard threshold of 5%. However, after applying the SAPF, the THD drops to 0.85%, aligning with international standards for power quality. The figures included in the study illustrate the stability of the phase-locked loop(PLL)voltages and the noticeable improvement in the source current waveforms, which exhibit a near-sinusoidal profile after filtering. These findings validate the superiority of the VOC strategy coupled with an NPC inverter and SVM in effectively mitigating harmonic distortions and enhancing power quality in modern electrical networks.
Copyright (c) 2026 Kamal Bayoude, Mohamed Moutchou, Yassine Zahraoui http://creativecommons.org/licenses/by-sa/4.0
Reducing the cost, increasing efficiency, and improving the reliability of LED drivers are critical due to the widespread adoption of LED lighting. This paper presents a research study on a novel boost LED driver designed to minimize voltage pulsations across power switches, thereby reducing dynamic losses in all power components. A small number of Schottky diodes were used to reduce conduction losses. To reduce switching losses in semiconductors, a quasi-resonant switching (QRS) at zero current was implemented for driving transistors. The operating principle is analyzed using computer modeling and validated experimentally in critical conduction mode (CrCM). In the initial evaluation, one version of the proposed driver achieved a high efficiency of up to 98.7% at 120 W input power. Additionally, the size and value of the main inductor were significantly reduced. The proposed driver provides an efficient and scalable solution for high-power LED lighting. Lower dynamic losses and reduced impulse voltages create opportunities for integrating the control circuit and power switches into a single chip.
Copyright (c) 2026 Dzhunusbekov Erlan, Orazbayev Sagi http://creativecommons.org/licenses/by-sa/4.0
A novel high-gain DC-DC converter with fuzzy logic control for hydrogen fuel cell vehicle applications
Hydrogen fuel cell vehicles (HFCVs) are emerging as a sustainable alternative to conventional internal combustion engines due to their zero-emission characteristics and high energy efficiency. However, the low output voltage of fuel cells poses a significant challenge in meeting the high-voltage requirements of electric traction systems. To address this, this paper proposes a high-gain non-isolated switched-capacitor (SC) DC-DC converter integrated with a fuzzy logic controller (FLC) for efficient power management in hydrogen fuel cell vehicle applications. The proposed converter topology achieves a significant voltage step-up without the use of bulky magnetic components, making it lightweight and compact for automotive integration. A maximum power point tracking (MPPT) controller using fuzzy logic is used to recover optimum energy out of the fuel cell stack during different loads and conditions of the environment. MATLAB/Simulink simulation results validate the high voltage gain, stable operation, and improved dynamic response of the proposed converter under FLC control. The proposed intelligent control strategy enhances fuel cell utilization and ensures effective operation of HFCV powertrains.
Copyright (c) 2026 Gaddala Anusha, A. V. V. Sudhakar, Shaik Rafikiran, Ram Ragotham Deshmukh, C. H. Hussaian Basha http://creativecommons.org/licenses/by-sa/4.0
Analytical formulation of relationship between ionization current and extracted ion beam current in a Penning ion source
A study on the performance of the Penning-type internal ion source of the DECY-13 cyclotron has been conducted to evaluate the relationship between cathode current and extracted ion beam current, as well as the stability of the extracted beam. The DECY-13 cyclotron, developed at the Research Center of Accelerator Technology, BRIN, is designed to produce 13 MeV protons for radioisotope production. In the experiment, the cathode current was varied between 200-400 mA, while the magnetic field and extraction voltage at 1.25 T and 3 kV, respectively. The results indicate a clear power-law dependence between cathode current (Ic) and extracted beam current (Iext), expressed as Iext=343.8 Ic^1.42 . This relationship suggests that ionization efficiency increases sharply with cathode current. Stability tests at 400 mA cathode current showed that the extracted beam current remained stable at ~70 μA over 45 minutes, with only minor fluctuations. These findings demonstrate that cathode current is an effective parameter for controlling extracted beam current. The results contribute to a better understanding of ion source behavior in cyclotron systems and provide a foundation for further optimization of Penning ion sources for radioisotope production.
Copyright (c) 2026 Silakhuddin, Idrus Abdul Kudus, Bambang Murdaka Eka Jati, Dwi Satya Palupi, Taufik, Emy Mulyani, Heranudin http://creativecommons.org/licenses/by-sa/4.0
Grey wolf optimization approach to optimal backstepping control for buck converter output voltage regulation
DC-DC converters are essential in regulating voltage levels within DC power systems, relying on high-efficiency electronic switching devices such as MOSFETs to ensure effective power conversion. Despite their widespread use, one of the major challenges encountered in practical implementations lies in accurately tuning controller parameters particularly for nonlinear approaches such as the backstepping controller. While recent studies have demonstrated the effectiveness of particle swarm optimization (PSO) in enhancing backstepping control performance, further improvements remain possible. In this work, we propose the grey wolf optimization (GWO) algorithm as an advanced and efficient technique for the optimal tuning of backstepping controller parameters. The goal is to minimize the voltage tracking error between the reference and the output of the DC-DC buck converter, ensuring enhanced dynamic response and stability. Additionally, the proposed control strategy has been experimentally implemented and validated in a photovoltaic context, demonstrating its practical relevance and strong potential for real-world energy conversion applications.
Copyright (c) 2026 Sana Mouslim, Belkasem Imodane, Imane Outana, M’hand Oubella, El Mahfoud Boulaoutaq, Mohamed Ajaamoum http://creativecommons.org/licenses/by-sa/4.0
A novel adaptive constant power optimal efficiency control strategy for bidirectional DS-LCC wireless charger
This paper presents a novel adaptive constant power optimal efficiency control (ACPOEC) strategy that enables efficient constant power (CP) charging in a double-sided inductor-capacitor-capacitor (DS-LCC) wireless charger. The proposed control strategy is built upon triple-phase-shift (TPS) modulation and employs a pre-computed lookup table derived from offline optimization to achieve CP charging with corresponding optimal efficiency. The CP charger with the proposed strategy can eliminate switch-controlled capacitors (SCCs) in the topology. The proposed strategy is validated through simulation studies, achieving an efficiency range of 90.72% to 92.46%, which is also competitive with other advanced CP wireless charging systems. Compared with existing state-of-the-art CP wireless charging techniques, the wireless CP charger with the proposed ACPOEC strategy features a simplified topology, bidirectional power transfer capability, and competitive efficiency performance.
Copyright (c) 2026 Jiabo Yan, Mohd Junaidi Abdul Aziz, Nik Rumzi Nik Idris, Mohammad Al Takrouri, Tole Sutikno http://creativecommons.org/licenses/by-sa/4.0
Voltage compensation using fuel cell fed dynamic voltage restorer
One of the basic tasks of the dynamic voltage restorer (DVR) is to maintain voltage stability in distribution systems by correcting any deviations or disturbances in the three-phase supply. Whether they are increases or decreases. However, one of its disadvantages is its power source, as it cannot supply itself with power from the electrical grid like parallel compensators, which obtain power directly from the grid. This article presents an energy study of a dynamic voltage regulator (DVR) when operated using a power source represented by fuel cells, which are considered a clean and renewable source. On the other hand, excess energy from the regenerator or fuel cells can be output and injected into the distribution network for utilization via a parallel compensator (CP). The parallel compensator also compensates for reactive energy on the reactive load side to increase the power factor measured at the source side of the distribution system. This integrated system also uses neural networks to identify voltage disturbances and determine the voltages (modules/arguments) that must be added to the voltages in the power grid for correction. This analytical study was completed using a simulation system to confirm the effectiveness of this integrated system. The distinctive feature of this study is the integration of fuel cells and neural network-based control in the DVR system, providing a sustainable and intelligent alternative to conventional configurations, which makes it different from traditional DVRs that operate with batteries and supercapacitors. Its efficiency in compensating for voltage drops and surges is evident, and it also improves the power factor and ensures reliable operation of voltage-sensitive devices.
Copyright (c) 2026 Ryma Berbaoui, Rachid Dehini http://creativecommons.org/licenses/by-sa/4.0
Hybrid renewable energy for cold chain in Indonesia: technical and economic evaluation
Cold storage plays a crucial role in preserving temperature-sensitive products, particularly in the fisheries and food sectors. However, its operation is highly energy-intensive and often constrained by unstable electricity supply in many Indonesian regions. This study quantitatively evaluates a hybrid renewable energy system integrating photovoltaic (PV) panels, diesel generators, batteries, and the utility grid to ensure sustainable cold storage operations. Using measured load profiles, solar irradiation data, and annual operating costs, the system achieved a 60% reduction in diesel fuel consumption, 30-50% lower CO₂ emissions, and annual savings exceeding IDR 100 million compared to conventional generator-based systems. The system demonstrated 83.5% overall efficiency, with a payback period of 4.4 years and a positive net present value (NPV), confirming its economic viability. The novelty of this research lies in presenting the first comprehensive techno-economic analysis of a PV-diesel-battery-grid hybrid system specifically designed for fisheries-based cold storage facilities in Indonesia, considering local solar potential and grid reliability. Despite its feasibility, implementation challenges remain, including a lack of skilled technicians, limited financial incentives, and bureaucratic constraints. To overcome this, the study recommends PV subsidies, low-interest green loans, and public–private partnerships aligned with Indonesia's energy transition roadmap and cold chain development goals.
Copyright (c) 2026 I Made Aditya Nugraha, I Gusti Made Ngurah Desnanjaya, Anis Khairunnisa, Mahaldika Cesrany http://creativecommons.org/licenses/by-sa/4.0
Improving voltage stability in isolated renewable energy microgrids using virtual synchronous generators
The integration of renewable energy systems (RES) and distributed generation (DG) into microgrids introduces significant challenges in maintaining voltage stability due to intermittent generation and reduced rotational inertia. This systematic review critically examines advanced control strategies aimed at enhancing voltage resilience in isolated RES-driven microgrids. Particular focus is placed on virtual synchronous generators (VSGs), which emulate electromechanical dynamics of synchronous machines via state-space modeling, and model predictive control (MPC), which enables real-time control optimization under multi-constraint scenarios. The review synthesizes literature on coupling–decoupling behavior, impedance sensitivity, and dynamic voltage response under varying load conditions. Additionally, it evaluates the role of hardware-in-the-loop (HIL) platforms and Runge-Kutta-based simulations in validating control models for real-time deployment. A structured framework is proposed, aligning VSG-based inertia emulation with predictive control to address voltage dips, oscillations, and transient instabilities. The findings highlight both theoretical gaps and implementation opportunities for achieving robust voltage stabilization in next-generation microgrids.
Copyright (c) 2026 Ahmad Supawi Osman, Aidil Azwin Zainul Abidin http://creativecommons.org/licenses/by-sa/4.0
Performance enhancement of photovoltaic systems using hybrid LSTM-CNN solar forecasting integrated with P&O MPPT
The increasing penetration of photovoltaic (PV) systems in smart grids highlights the need for reliable solutions to mitigate the inherent intermittency of solar energy. Short-term variability in solar irradiance remains a critical challenge for stable grid operation and efficient PV energy management. This paper proposes an integrated forecasting-control framework that combines short-term global horizontal irradiance (GHI) prediction with a conventional P&O MPPT strategy to enhance PV system performance. A hybrid LSTM-CNN architecture is developed to forecast one-step-ahead GHI under the semi-arid climatic conditions of Dakhla, Morocco, a region characterized by high solar potential and pronounced irradiance fluctuations. The forecasting model is validated using measured irradiance data from the National Renewable Energy Laboratory (NREL) via the National Solar Radiation Database (NSRDB). Predicted irradiance is then used to improve PV power estimation and support predictive maximum power point tracking (MPPT) operation. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed framework achieves accurate GHI forecasting, faster MPPT convergence, reduced steady-state oscillations, and improved PV power stability under rapidly changing irradiance. The proposed approach provides a practical and computationally efficient solution for enhancing the dynamic response and energy extraction efficiency of PV systems in smart grid applications.
Copyright (c) 2026 Sara Fennane, Houda Kacimi, Hamza Mabchour, Fatehi ALtalqi, Adil Echchelh http://creativecommons.org/licenses/by-sa/4.0
Optimizing small-scale geothermal power: insights from long-term testing and system modifications of a 3 MW geothermal condensing power plant in Kamojang, Indonesia
This study presents the design, development, and performance evaluation of a 3 MW geothermal pilot power plant in Kamojang, Indonesia, developed by retrofitting a 2 MW backpressure turbine into a six-stage condensing turbine. With a 63.81% local content, the plant serves as one of Indonesia’s first demonstrations of small-scale condensing turbine technology. Multi-phase testing yielded a maximum net output of 2.2 MW, below the design target due to condenser vacuum inefficiencies, strainer pressure losses, and reduced turbine isentropic efficiency. Subsequent condenser and strainer modifications improved vacuum stability, reduced pressure drops, and enhanced specific steam consumption (SSC) and overall performance. Exergy analysis identified the condenser (16.1%) and turbine (9.5%) as the primary sources of exergy destruction, resulting in an overall exergy efficiency of 73.6%, higher than typical small-scale geothermal benchmarks. While operational performance improved significantly, sustaining long-term vacuum stability and optimizing turbine operation under variable steam conditions remain key challenges. Future work should focus on automated vacuum control, real-time monitoring, and advanced thermodynamic-electrical optimization to enhance system reliability. This study provides practical insights into turbine retrofitting, condenser stabilization, and integrated exergy evaluation, contributing to the advancement and localization of small-scale geothermal power technology in Indonesia.
Copyright (c) 2026 Lina Agustina, Suyanto, Budi Ismoyo http://creativecommons.org/licenses/by-sa/4.0
Intelligent single-axis solar tracking system for enhanced energy harvesting efficiency
The need for efficient clean energy solutions has increased due to population growth, climate change, and the development of industries. Among these, the most valuable clean and sustainable alternative is solar energy, i.e., photovoltaic (PV) technology. However, the key challenge is represented by maximizing the PV systems' efficiency. This paper proposes an IoT-enabled single-axis solar tracking system for improving PV by constantly aligning solar panels with the trajectory of the sun. To achieve that, an Arduino microcontroller is integrated with light-dependent resistors (LDRs). LRDs perform real-time detection of solar irradiance in order to adjust along with the azimuth axis. Based on the experimental results, the IoT-enabled single-axis solar tracking system improves energy harvesting by comparing with fixed-tilt PV. The proposed system outperforms the fixed-tilt PV by 22.5% in daily energy yield and average power output. Furthermore, tracking efficiency is better than fixed-tilt PV by 96.3% and tracking error of 3.7%.
Copyright (c) 2026 Adnan M. Al-Smadi, Deema Al-Shogran, Hazem Jihad Badarneh http://creativecommons.org/licenses/by-sa/4.0
Improving photovoltaic efficiency: a systematic study of P&O and INC MPPT techniques
Achieving high efficiency in photovoltaic (PV) systems under fluctuating irradiance and temperature conditions relies on effective maximum power point tracking (MPPT) techniques. Among the most commonly adopted approaches, perturb and observe (P&O) and incremental conductance (INC) are favored for their ease of implementation and operational flexibility. Nevertheless, a systematic comparison of their performance under dynamic conditions remains limited. This study conducts a comparative evaluation of P&O and INC algorithms using MATLAB/Simulink, with emphasis on tracking accuracy, convergence speed, and overall efficiency. A standard PV module is exposed to rapid variations in irradiance and temperature to examine algorithm robustness. The results indicate that although P&O achieves fast convergence in steady-state operation, it exhibits noticeable oscillations around the maximum power point, resulting in efficiency losses of up to 3%. Conversely, the INC method offers improved tracking precision and reduced oscillations, yielding efficiency gains of 2-4% over P&O in dynamic environments. These findings underline the trade-off between algorithmic simplicity and tracking accuracy, and provide practical guidance for selecting MPPT strategies in both grid-connected and standalone PV applications.
Copyright (c) 2026 Abdelkbir Jamaa, Ahmed Moutabir, Rachid Marrakh, Abderrahmane Ouchatti http://creativecommons.org/licenses/by-sa/4.0
The effects of surface albedo and photovoltaic system tilt angle on improving light energy utilization efficiency
The ground-surface reflection (albedo) significantly influences the amount of solar radiation absorbed by photovoltaic panels and, thus, the optimum tilt angle for maximizing annual energy generation. Nevertheless, the majority of design models presume a constant albedo value, therefore could not accurately represent actual field conditions. This study aims to identify the optimal tilt angle for each albedo value that maximizes the annual energy output of a stationary on-grid photovoltaic system of 20.48 kWp installed in Baghdad, Iraq. Seven albedo values, varying from 0.09 to 0.87, were simulated using PVsyst software, with the reference case established at an albedo of 0.2 and a tilt angle of 31°. The results indicate that the optimum tilt angle is directly proportional to the surface reflection. For albedo levels below the reference of 0.2 (0.18 and 0.09), increased energy generation occurred at reduced tilt angles of 30.5° and 29°, respectively. Conversely, for increased albedo values (i.e., exceeding the reference of 0.2, spanning from 0.25 to 0.87), greater tilt angles were necessitated, reaching 45° at an albedo of 0.87, where the annual energy rose from 35.212 to 36.999 MWh/yr, signifying a 5.07% increase relative to the reference condition. The results validate that the optimal tilt angle fluctuates with ground-surface albedo, as surface reflectivity affects solar irradiation and energy output. Integrating actual albedo values in photovoltaic models is crucial for precise tilt adjustment and enhanced system efficiency.
Copyright (c) 2026 Ahmed Daud Mosheer, Ahmed H. Duhis, Hussain A. Hammas http://creativecommons.org/licenses/by-sa/4.0
Machine learning based models for solar energy
Photovoltaic (PV) technology is one of the most promising forms of renewable energy. However, power generation from PV technologies is highly dependent on variable weather conditions, which are neither constant nor controllable, which can affect grid stability. Accurate forecasting of PV power production is essential to ensure reliable operation within the power system. The primary challenge of this study is to accurately predict photovoltaic energy production, considering that weather conditions, such as irradiance, temperature, and wind speed, are random variables. The key contribution of this article is developing a machine learning model to predict the energy production of a real PV power plant in Algeria. Using real measurements sourced from the Center of Renewable Energy Development (CDER) in Adrar, Algeria, in 2021. The data are from two PV power plants located in harsh desert climate conditions. The results presented in this study offer a comparison of several predictive methods applied to real-world data from a PV power plant situated in the Saharan Region. Our findings reveal that the artificial neural network (ANN) model yields the most accurate predictions of 94.96%, with the smallest prediction error: root mean square (RMSE) and mean absolute error (MAE) are 7.78% and 3.80%, respectively.
Copyright (c) 2026 Dalila Cherifi, Abdeldjalil Dahbi, Mohamed Lamine Sebbane, Bassem Baali, Ahmed Yassine Kadri, Messaouda Chaib http://creativecommons.org/licenses/by-sa/4.0
Application of machine learning for production optimization and predictive maintenance in an iron processing plant
The modern metallurgical industry requires advanced solutions for process optimization, cost reduction, and predictive maintenance. This paper proposes a unified simulation-based framework using machine learning (ML) to jointly address production optimization and maintenance prediction in a virtual iron processing environment. Several ML models, including random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine (SVM), and k-nearest neighbors (k-NN), were evaluated on synthetic datasets representing production, maintenance, and transport processes. A reproducible methodology was adopted, including preprocessing, time-aware data splitting, and cross-validation to prevent information leakage. Model performance was assessed using F1-score, area under the receiver operating characteristic curve (AUC), and regression metrics. Tree-based models achieved near-perfect classification performance (AUC ≈ 1, precision and recall > 0.99), while light gradient boosting machine (LightGBM) and CatBoost provided the best regression accuracy. Feature importance analysis using SHapley Additive exPlanations (SHAP) identified vibration and temperature as key maintenance indicators. Although based on simulation, the framework is designed for integration with supervisory control and data acquisition (SCADA) and the Industrial Internet of Things (IIoT), supporting real-time industrial deployment and alignment with operational key performance indicators.
Copyright (c) 2026 Lakhdari Lahcen, Mohamed Habbab, Alhachemi Moulay Abdellah http://creativecommons.org/licenses/by-sa/4.0
Enhanced adaptive reconfiguration for optimizing power generation and switching efficiency in PV arrays under PSC
Photovoltaic (PV) arrays suffer significant power losses under partial shading conditions (PSC), which can degrade system performance. This paper proposes a novel weighted objective function that balances power output maximization with switching action minimization during dynamic PV array reconfiguration. An enhanced firebug swarm optimization (FSO) algorithm is employed to optimize this function efficiently. Simulation results under five shading patterns demonstrate approximately 6% improvement in power output over conventional methods, while also reducing the number of switch operations. The proposed approach enhances energy yield and extends device lifespan, offering a robust solution for real-time PV optimization under PSC.
Copyright (c) 2026 D. Manimegalai, Kandadai Nagaratnam Srinivas, Gayathri Monicka Subarnan http://creativecommons.org/licenses/by-sa/4.0
Comparative analysis of multi-output machine learning models for solar irradiance and wind speed forecasting: A case study in Tamil Nadu, India
The growing share of wind and solar energy has created challenges in electrical networks, mainly due to intermittency, fluctuations, and uncertainty. These issues affect power system stability, grid operations, and the balance between supply and demand. To address this, accurate prediction of solar irradiance and wind speed is critical for integrating renewable energy into power systems. In this study, we propose a multi-output machine learning approach to predict both global horizontal irradiance (GHI) and wind speed simultaneously. The study uses historical meteorological data obtained from the National Solar Radiation Database (NSRDB) for Tamil Nadu, India. Six regression algorithms: linear regression, gradient boosting, random Forest, extreme gradient boosting (XGB), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) are tested under identical conditions. Model hyperparameters were tuned using GridSearchCV and Bayesian optimization to ensure robust performance. Before modeling, a comprehensive statistical analysis, including input feature distribution and correlation analysis, was conducted. Model accuracy was evaluated using RMSE, MAE, and R² metrics on both training and testing datasets. The results showed that ensemble tree-based methods outperformed the baseline linear model. Among them, CatBoost produced the best results for GHI prediction, while random forest delivered the most reliable wind speed forecasts, demonstrating strong predictive capability for renewable energy applications.
Copyright (c) 2026 S. Selvi, N. Shanti, Lakshmi Dhandapani, M. Bhoopathi, T. Sathish Kumar, P. Kavitha http://creativecommons.org/licenses/by-sa/4.0
Study of neural controller based MPPT in comparison with P&O for PV systems
This study investigated the performance of two prominent maximum power point tracking (MPPT) strategies: the established perturb and observe (P&O) technique and an artificial neural network (ANN)-based controller. Through simulations conducted in MATLAB/Simulink, a 50 W photovoltaic (PV) array was evaluated under dynamic irradiance and temperature variations. Notably, data generated by the P&O system served as the training dataset for the ANN model. The simulation results indicate that the ANN controller effectively and accurately identifies the PV system’s optimal operating point even amidst fluctuating environmental conditions. When compared to the conventional P&O method, the ANN approach demonstrated superior characteristics, including a significantly faster response, diminished oscillations around the maximum power point, and enhanced tracking accuracy during rapid environmental shifts. These findings underscore the substantial potential of ANN-based MPPT strategies for improving both the efficiency and operational stability of photovoltaic power systems.
Copyright (c) 2026 Mohamed Tayeb Boussabeur, Djaafar Toumi, Mourad Tiar, Abir Boucetta, Ikram Boucetta, Ahmed Ibrahim http://creativecommons.org/licenses/by-sa/4.0
Design and implementation of a buck converter-based PV emulator using dynamic evolution control
This paper presents the design, simulation, and experimental implementation of a photovoltaic (PV) emulator based on a buck converter controlled using the dynamic evolution control (DEC) technique. The proposed system accurately reproduces the nonlinear current-voltage (I-V) and power-voltage (P-V) characteristics of a commercial GREEN CELL SM100-18P (100 Wp) PV module under standard test conditions (1000 W/m2, 25 °C). The electrical characteristics of the reference module are embedded in the controller through a lookup table (LUT), which is integrated with the DEC algorithm to enable adaptive real-time regulation of output voltage and current. System modeling and validation are first conducted in MATLAB/Simulink to analyze steady-state and transient performance. A hardware prototype based on an XL4016 buck converter and Arduino Nano microcontroller is then implemented, with real-time monitoring provided via an ILI9341 TFT display. Experimental results show that the emulator achieves a maximum power deviation of 0.8%, a normalized root mean square error (RMSE) of 0.015, a settling time of approximately 12 ms, overshoot below 1.5%, voltage ripple under 2%, and peak conversion efficiency of 94% near the MPP region. These results confirm that the proposed PV emulator provides accurate static and dynamic reproduction of PV characteristics, offering a low-cost, stable, and repeatable platform for laboratory-scale evaluation of PV-related power electronic converters.
Copyright (c) 2026 Ahmad Saudi Samosir, Dikpride Despa, Herri Gusmedi, Sony Ferbangkara http://creativecommons.org/licenses/by-sa/4.0