Search Results (3,949)

This paper proposes a co-design methodology for the power and control stages of a bidirectional battery charger/discharger based on a boost converter topology. The approach ensures safe operation by limiting the battery current derivative, preventing abrupt transients that could degrade battery lifespan. The control strategy combines a cascade structure with an inner sliding mode current controller (for robustness and fast response) and an outer adaptive PI voltage loop (to regulate the DC-link voltage under varying load conditions). Additionally, the design constrains the switching frequency to reduce power losses. Experimental validation on a prototype converter demonstrates the effectiveness of the co-design framework, showing precise current/voltage regulation, adherence to switching frequency limits, and compliance with battery charging/discharging requirements. The results highlight the methodology’s potential to enhance efficiency and reliability in energy storage systems. The dynamic restrictions, overshoot lower than 5%, settling time shorter than 5 ms, and a battery current limitation less than 50 A/ms were always met with SMC and, in some cases, with the PI controller, but the results with SMC were always better: lower overshoot, shorter settling time, and greater restriction on the derivative of the battery current. In addition, the SMC system was 2.5–5.0% more efficient than the PI controller. Full article

Power converters are fundamental components in vehicle electrification systems. However, their inherently nonlinear and time-varying condition requires complex design procedures when conventional control strategies based on linear small-signal models are employed. This work proposes a simplified and hardware-oriented DC-DC converter control methodology that combines fuzzy logic and Neural Networks in a sequential manner. A fuzzy logic fuzzy controller is first used to generate a dataset of control actions under closed-loop operation. A lightweight neural network is then trained using the obtained data to approximate this mapping and subsequently replace the fuzzy controller in real-time operation. To validate the approach, a bidirectional buck–boost DC-DC converter is designed for applications in the powertrain of electric vehicles with 500 kHz switching frequency and 13 kW power rating. The control algorithm is embedded in an FPGA to demonstrate its suitability for hardware deployment. The experimental results show a reduction in RMSE of 33.7% and a decrease in the settling time of at least 51.7% when compared with a benchmark PID control. Full article

Given the expanding scale of offshore wind power development, strict spatial constraints on offshore platforms and multi-source power coupling present operational challenges during the collaborative transmission of near-shore and far-offshore wind power through a shared corridor. To address these issues, this paper proposes a collaborative transmission scheme based on the Self-Adaption Statcom and Line-Commutation Converter (SLCC). The technical and economic characteristics of three typical topologies—Modular Multilevel Converter (MMC) onshore grid connection, MMC direct transmission, and SLCC direct transmission—are compared and analyzed. The results demonstrate the advantages of the SLCC scheme in reducing the offshore platform footprint and lowering engineering costs. Furthermore, a hierarchical collaborative control strategy is designed to mitigate the power coupling between near-shore AC wind generation and far-offshore DC wind generation at the converter bus. The bottom layer utilizes a valve-side parallel Static Var Generator (SVG) to achieve reactive power self-balance and quasi-resonant suppression of specific harmonics. In the top layer, an LCC active power-following control strategy based on instantaneous power feedback is implemented. This achieves the logical decoupling of near-shore and far-offshore wind power transmission. The effectiveness of the proposed scheme in managing wind power fluctuations, riding through AC faults, and maintaining stable operation under weak grid conditions is verified using the PSCAD/EMTDC software. Full article

In recent years, the rapid expansion of electric vehicle (EV) charging infrastructure and the increasing penetration of renewable energy sources require highly efficient and dynamically robust power electronic interfaces. In photovoltaic (PV)-assisted EV charging stations and DC microgrids, bidirectional DC-DC converters (BDCs) are essential for managing power flow between PV arrays, battery energy storage systems, and the DC bus supplying EV chargers. This paper presents a novel voltage and current control design for a BDC operating in a PV-powered DC microgrid oriented to EV charging applications. Following a detailed mathematical model of the converter, a digital current controller and a predictive voltage regulator were developed using Model-Based Predictive Control (MBPC). The proposed cascade control structure enables accurate DC bus voltage regulation and seamless bidirectional power flow under dynamic load variations representative of EV charging and discharging scenarios. The control scheme was evaluated in MATLAB/SIMULINK^® and experimentally validated through Field-Programmable Gate Array (FPGA)-based test benches using an OPAL-RT real-time (RT) simulator, integrating the RT-LAB and RT-eFPGAsim environments. The predictive controller achieved precise regulation in both buck and boost modes, reaching efficiencies of 97.07% and 98.57%, respectively. The results demonstrate that integrating MBPC with RT validation provides high performance, fast dynamic response, and computational efficiency, making the proposed approach suitable for renewable-integrated EV charging stations and next-generation DC microgrid-based mobility systems. Full article

Power transformers are fundamental devices in electrical power transmission and distribution systems as they regulate voltage levels, which helps reduce system losses. However, their operation can be affected by temperature, with increases in temperature causing a decrease in their efficiency and lifetime. In addition, the presence of harmonics in the electrical current can cause overheating and distortions in transformer performance. A significant proportion of these harmonics are caused by the increasingly widespread use of DC renewable energies. To control such renewable sources, power inverters are used, which generate harmonics and cause overheating in transformers connected to the grid. One solution to this problem is to reduce the harmonic content generated by these converters to avoid transformer overheating and improve their lifetime. In this work, a modulation technique for H-bridge multilevel inverters is presented with the aim of reducing both harmonics and transformer heating. To test the technique’s effectiveness, the recommendations of a standard have been followed, which include the use of a dry transformer prototype for temperature measurements. The proposed technique has been compared with classical techniques for H-bridge multilevel inverters, and the experimental results indicate a reduction in the hottest-spot temperature. Full article

A strategy for controlling DC boost converters under disturbances is proposed through a disturbance-resistant reinforcement learning method. An iterative training process is designed using model-free deep reinforcement learning. A nonlinear disturbance observer is integrated to enhance the controller’s adaptability to disturbances. The proposed neural network controller achieves a maximum voltage deviation of 4.2% of the reference value, and a settling time of 6.5 ms in simulation studies. During experimental validation, the voltage deviation is limited to 6.0% with a settling time of 8 ms under the same operating condition. Simulation and experimental results demonstrate that the proposed control strategy is able to stabilize the output voltage under various disturbances, offering better performance under large-signal fluctuation. This work extends the practical deployment of reinforcement learning in the field of nonlinear control. Full article

This paper presents an integrated techno–economic framework for coordinated grid-to-vehicle and vehicle-to-grid (G2V–V2G) operation in unbalanced distribution networks. A hardware-compatible bidirectional charger with nested AC/DC and DC/DC control loops, together with a rule-based energy management system (EMS), enables seamless mode transitions while enforcing state-of-charge (SoC) and network constraints. A probabilistic Monte Carlo study on the IEEE 13-bus feeder shows that uncoordinated G2V charging induces adverse grid impacts such as voltage stress, line-ampacity violations, and transformer overloading, whereas EMS-driven V2G support improves voltage by 2–4%, reduces line loading by 15–25%, and lowers transformer stress by up to 10%. To align these technical benefits with economic incentives, a bi-level Stackelberg model is formulated where the utility updates locational energy prices based on combined voltage, line ampacity, transformer loading stress indices and EVs choose profit-maximizing nodes, modes and power levels. The interaction converges to a Stackelberg equilibrium with a clear win–win situation; the feeder’s average locational energy price falls entirely within the win–win region, yielding positive per-session profits for both the EV (≈$0.80) and the utility (≈$0.48) while reducing feeder stress. These results demonstrate that stress-aware locational pricing, combined with detailed converter-level control provides a technically robust and economically sustainable pathway for large-scale EV integration. Full article

This paper investigates the transient stability characteristics of a DC-coupled off-grid photovoltaic hydrogen production system. A nonlinear state-space model of the system is established by integrating the photovoltaic generation unit, the energy storage unit, and the electrolyzer unit. To enhance system dynamic performance, a virtual DC machine (VDCM) control strategy is introduced for the energy storage converter. Based on the nonlinear system model, a Takagi–Sugeno (TS) fuzzy model is constructed to approximate the system dynamics, and the largest estimated domain of attraction (LEDA) is derived using Lyapunov stability theory. Simulation studies are conducted to evaluate system stability under sudden photovoltaic power fluctuations caused by environmental disturbances, and the obtained LEDA is compared with the simulated attraction domain and the power boundary derived from the Lyapunov eigenvalue method. The results show that the LEDA obtained from the TS fuzzy model can effectively estimate the stability boundary of the system, although it remains slightly conservative. Furthermore, the impacts of VDCM control parameters and electrolyzer operating states on system stability are analyzed. Simulation results demonstrate that appropriate adjustment of system parameters can enlarge the LEDA and significantly improve the transient stability of the off-grid photovoltaic hydrogen production system. Full article

This paper presents the design, implementation, and experimental validation of a real-time embedded photovoltaic (PV) emulator based on the two-diode model, using a dSPACE DS1103 platform for hardware validation. The proposed system aims to accurately reproduce the electrical behavior of PV modules under varying environmental conditions, including irradiance and temperature variations. The emulator architecture combines a lookup-table-based modelling approach with a programmable DC power source, enabling deterministic real-time execution and efficient implementation. A multi-level control structure is employed, integrating inner-loop regulation, model-based reference generation, and feedback control to ensure accurate tracking of the PV current–voltage (I–V) characteristics. Experimental results demonstrate that the emulator achieves high accuracy, with an approximation error of approximately 1.2% under standard operating conditions. The system exhibits stable dynamic behavior characterized by a time constant of approximately 0.5 s, with performance maintained across different sampling intervals and load conditions. Additional simulations confirm that the two-diode model preserves high accuracy over a temperature range of 15–60 °C, with deviations below 2%. The results highlight that the two-diode model provides an optimal trade-off between modelling accuracy and computational complexity for real-time embedded applications. The proposed emulator offers a flexible and reliable platform for laboratory validation of photovoltaic behavior and provides the foundation for future testing of maximum power point tracking (MPPT) algorithms, power electronic converters, and embedded control strategies under controlled conditions. Full article

DC-bias may accumulate in bidirectional full-bridge LLC converters during reverse power transfer because the magnetizing branch lacks an inherent DC-blocking mechanism. This bias may cause asymmetric flux excitation in the transformer core, thereby increasing magnetic stress and even leading to core saturation. To address this issue, a confidence-scheduled hybrid DC-bias estimation and suppression method is proposed. An integration-based indicator is constructed for sensitive weak-bias detection, while a reduced-order extended Kalman filter (EKF) is introduced to improve noise immunity and dynamic tracking under strong-bias conditions. Moreover, a confidence-scheduling mechanism is developed to adaptively fuse the two estimates according to bias severity. Based on the fused estimate, a two-level suppression strategy is implemented for severe- and weak-bias conditions. Simulations and experiments on a 2 kW prototype verify that the proposed strategy achieves fast detection, highly accurate robust estimation with a steady-state error of less than 2%, and effective suppression over a wide operating range without additional bulky DC-blocking hardware. Full article

Bidirectional AC/DC converters in hybrid microgrids are prone to DC-bus voltage instability caused by source-side, grid-side, and load-side disturbances. Conventional linear active disturbance rejection control (LADRC) suffers from a trade-off between transient overshoot suppression and disturbance rejection capability, which limits its practical application. To address this issue, an improved LADRC strategy for bidirectional AC/DC converters is proposed in this paper. First, a linear tracking differentiator (LTD) is introduced to smooth the DC-bus voltage reference and suppress overshoot caused by abrupt command changes. Second, a proportional-derivative (PD) term is embedded into the linear extended state observer (LESO) to introduce phase lead compensation, thereby improving the observer phase characteristics without excessively increasing the observation bandwidth or amplifying high-frequency noise. Frequency domain analysis, MATLAB/Simulink simulations, and full-hardware prototype experiments are carried out to validate the proposed method. The simulation study covers grid voltage sag, photovoltaic-side source fluctuation, and DC-side load disturbance conditions. To further strengthen the experimental verification, hardware tests are conducted under grid voltage dip, PV-side voltage reduction, and DC-side load-switching conditions. The results consistently show that the proposed strategy can effectively reduce DC-bus voltage fluctuation and improve transient recovery performance compared with conventional LADRC. Therefore, the improved LADRC provides a practical and robust control solution for stabilizing bidirectional converters in AC/DC hybrid microgrids. Full article

The demand for reliable, compact, and highly dependable energy conversion systems has grown significantly due to their application in renewable energy systems and electric vehicles for transportation. One of the main converters used in this type of conversion system is the DC–AC converter, known as an inverter. The common study of inverter behavior has focused on addressing, in isolation, the topologies and modulation strategies that activate/deactivate the converter switches, whose main objectives are to improve power quality, increase power density under different operating conditions, and reduce losses. Some of the above objectives were addressed by oversized passive filters, which resulted in increased system volume, high cost, and reduced adaptability. This systematic review analyzes and organizes the state of the art regarding the relationship between the selection of inverter topology, modulation strategy (ranging from conventional modulation approaches to more advanced adaptive strategies), and optimization in conjunction with passive components to observe DC bus voltage management. The review was conducted following the PRISMA 2020 guidelines. A structured search was performed in IEEE Xplore, ScienceDirect, MDPI, and Scielo databases up to 2025, retrieving 9547 records. After duplicate removal and multi-stage screening of titles, abstracts, and full-text, 104 studies met the predefined technical inclusion criteria. Eligible studies were required to report quantitative performance metrics, validated modulation techniques, and explicit focus on inverter architectures or DC bus optimization. The selected studies were examined through comparative technical analysis of topology–modulation interaction, harmonic distortion performance, efficiency, and system-level integration. The study highlights the importance of taking a comprehensive approach at the complete system level by designing the elements addressed together, rather than being optimized in isolation for renewable energy and electric vehicle applications. Full article

While direct current (DC) ice-melting is currently adopted for some transmission lines, its application to 10 kV distribution transformers—often located in remote and rugged terrain—presents significant operational challenges. Disconnecting these transformers prior to ice-melting is a complex procedure that incurs substantial labor, material, and financial costs. Leaving transformers connected risks DC current flowing into idle windings, potentially causing damage. Furthermore, existing mobile DC ice-melting power supplies are bulky and impose stringent transportation requirements, rendering them unsuitable for use on mountain roads. To overcome these limitations, this paper proposes a compact, lightweight variable-frequency ice-melting device. The operating principle and output characteristics of the variable-frequency method are investigated in detail. Using Simulink, system modeling and simulation analyses are performed to obtain the voltage and current output characteristics, along with harmonic spectra. Simulation results demonstrate that the proposed device achieves significant miniaturization compared with conventional solutions: within the typical parameter range of conventional devices, the volume can be reduced by 44–58% and the weight by 43–52%. In addition, the selected LC filter parameters (L = 10.39 mH, C = 86.62 μF) represent an optimized compromise solution that effectively suppresses input harmonics while maintaining the output current total harmonic distortion (THD) within an acceptable limit of 3.6%. Experimental results further validate the feasibility of the variable-frequency ice-melting current. Based on a matrix converter topology, the proposed device enables flexible adjustment of the output melting voltage and frequency, exhibits excellent low-frequency performance and dynamic response, and maintains low output harmonic content—fully meeting the application requirements for variable-frequency ice-melting. The key novelty lies in a compact matrix-converter-based de-icing device with systematic low-frequency performance analysis, offering superior portability and adaptability over traditional DC solutions. Full article

Extreme fast charging (XFC) infrastructure is becoming increasingly necessary as the number of electric vehicles continues to grow. However, deploying such stations introduces several challenges related to power quality and compliance with regulatory standards. This work presents an alternative XFC station designed for charging multiple vehicles while ensuring low harmonic distortion in the grid currents, without the need for sinusoidal filters, by employing the Zero Harmonic Distortion (ZHD) converter. The proposed system offers galvanic isolation for each charging interface and supports additional functionalities, including the integration of Distributed Energy Resources (DERs) and the provision of ancillary services. These features are enabled through the combination of a bidirectional grid-connected active front-end operating at low switching frequency with high-frequency silicon carbide (SiC)-based dc/dc converters on the vehicle side. Hardware-in-the-loop (HIL) simulation results demonstrate a total demand distortion (TDD) of 1.12% for charging scenarios involving both 400 V and 800 V battery systems, remaining within the limits specified by IEEE 519-2022. Full article

The growing need for sustainable aviation propulsion has increased interest in hydrogen fuel cell systems as alternatives to combustion engines. This study presents the modeling, simulation, and optimization of a hybrid hydrogen–electric powertrain for the MIMIQ unmanned aerial vehicle (UAV). A 2 kW proton exchange membrane fuel cell is integrated with a 12S lithium-polymer battery via a DC–DC converter, enabling parallel power sharing and in-flight battery recharging. A MATLAB-based dynamic model was developed using mission power profiles derived from flight data and refined using momentum theory. The developed model was benchmarked through a comparative simulation of a combustion-based hybrid-electric powertrain variant of the same platform, demonstrating consistency in electrical and energetic behavior. Multi-objective optimization using NSGA-II was performed to maximize hover endurance and to minimize energy consumption while maximizing payload over a full mission. Results from this computational framework show that endurance is primarily constrained by hydrogen availability rather than battery capacity, with the fuel cell operating near its optimal efficiency region. The findings indicate that hydrogen–electric architectures offer improved endurance, reduced emissions and better scalability compared to combustion-based systems, supporting their suitability for long-endurance UAV applications. Full article