Search Results (3,959)

In recent years, the rapid evolution of large artificial intelligence (AI) models has placed unprecedented demands on the computing power of data center servers, driving an explosive growth in data center computing requirements. The power consumption of core computing components, represented by GPUs, has surged dramatically. When facing extremely high power densities, the traditional 12 V single-voltage power delivery architecture exposes severe limitations, including increased transmission link losses, thermal management difficulties, and low system efficiency. To address these challenges, this paper proposes and designs a hierarchical 54 V/12 V dual-plane multi-phase DC power delivery architecture for high-computing-power AI servers. By conducting refined hierarchical identification of system loads, this architecture introduces a 54 V high-voltage DC power plane for high-power loads while retaining the 12 V power plane for conventional loads. Within each power plane, multi-phase interleaved parallel Buck converters integrated with Turbo-COT control strategies and high-density DrMOS are deployed. Experimental results demonstrate that this power architecture exhibits excellent electrical characteristics: under steady-state conditions, the peak-to-peak (PK-PK) ripple voltage fluctuation amplitude of the 54 V power plane under different loads is compressed to between ±0.22% and ±0.26%, while the PK-PK ripple voltage fluctuation amplitude of the 12V power plane under different loads reaches ±0.66% to ±0.68%; in dynamic load step (0–50% and 50–100%) tests, the PK-PK voltage fluctuations of the 54 V plane are ±1.42% and ±1.33%, whereas the PK-PK voltage fluctuations of the 12 V power plane are ±2.36% and ±1.83%. Furthermore, the peak conversion efficiency of the 54 V power plane approaches 97%, and the maximum efficiency of the 12 V power plane reaches 94%, showing a measurable efficiency improvement under the tested conditions. The hierarchical multi-phase power delivery technology comprehensively reduces power supply link losses and enhances power stability, providing an important theoretical basis and engineering reference for the design of next-generation high-density AI servers and the optimization of green, energy-saving networks in data centers. Full article

Parallel DC/DC converter modules provide a feasible approach for achieving power scalability in various power conversion systems. This paper investigates an MOSFET-based lagging leg series diodes phase-shift full-bridge (LLSD-PSFB) converter and proposes a three-loop current-sharing control strategy for coordinated parallel operation. The strategy incorporates a voltage loop, a current loop, and a current-sharing loop to mitigate load current imbalance caused by MOSFET parameter mismatches and module inconsistencies. The operating principle and parameter design of the single-module LLSD-PSFB converter are analyzed, and an averaged model is established. Based on this model, a small-signal model of the parallel system is derived to evaluate system stability and current-sharing performance. Simulation results demonstrate that the proposed control scheme effectively improves current-sharing accuracy and dynamic response. An experimental prototype is developed to validate the theoretical and simulation results. The experimental results confirm that the proposed three-loop control strategy achieves high current-sharing precision and stable operation, demonstrating its effectiveness for parallel DC/DC converter systems and its potential for scalable high-power applications. Full article

The increasing integration of wind power reduces the equivalent inertia of power systems, leading to lower frequency nadirs and higher rate of change of frequency following disturbances. In Type-4 wind turbine systems, conventional maximum power point tracking (MPPT) may counteract the additional inertial power command during frequency support and cause secondary frequency dips during rotor-speed recovery. To address these issues, this paper proposes a virtual-inertia rate-of-change-of-frequency (VI-RoCoF) frequency-modulated Andronov-Hopf oscillator (AHO)-based adaptive inertia enhancement method together with an adaptive MPPT coordination strategy. The proposed method constructs a frequency-support demand from frequency deviation and VI-filtered RoCoF and embeds it into the instantaneous angular-frequency evolution of the AHO. Different from a conventional linear virtual-inertia controller that directly converts frequency-deviation and RoCoF signals into an algebraic power command, the proposed method realizes the additional support through a bounded limit-cycle frequency-forming process, thereby preserving phase continuity and nonlinear amplitude self-regulation during frequency modulation. Meanwhile, the adaptive MPPT strategy adjusts the power reference in stages to suppress the counteractive effect of conventional MPPT on inertial support and to ensure a smooth transition back to maximum power point tracking. Theoretical analysis shows that the proposed modulation maintains the limit-cycle stability of the AHO under bounded control constraints while improving the equivalent inertia and damping characteristics of the system. Simulation results, including both averaged-model and switching-level SPS simulations, demonstrate that, compared with conventional AHO-based, fixed-inertia AHO-based, and linear VI-RoCoF benchmark schemes without AHO dynamics, the proposed AHO-MPPT coordinated control strategy increases the frequency nadir, reduces the peak RoCoF, improves recovery-stage frequency dynamics, mitigates secondary frequency dips, maintains bounded AHO internal variables, and preserves DC-link voltage stability. Full article

DC series arc faults (SAFs) are a significant safety hazard on the DC side of photovoltaic (PV) systems, with current signals characterized by strong randomness, obvious non-stationarity, and concealed fault features, posing challenges for rapid and accurate detection. With the development of application models such as agricultural PV integration, photovoltaic greenhouses, solar-powered irrigation, and livestock energy supply, the demand for the safe operation of photovoltaic systems in agricultural production scenarios is becoming increasingly prominent. To address the difficulty in fully characterizing the multi-scale dynamic features and local amplitude disturbances of DC SAF signals, this paper proposes a SAF detection method based on multi-scale generalized amplitude-aware permutation entropy (MS-GAAPE). The method extracts MS-GAAPE from arc current signals at various scales using sliding window-based generalized coarse-graining, which preserves temporal sequence information while improving the characterization of local amplitude variations. Particle swarm optimization (PSO) is applied to optimize these multi-scale features, strengthening fault-related information and reducing interference. The optimized features are then processed by a support vector machine (SVM) for SAF detection. The dataset used contains 50,000 samples covering transient conditions such as voltage fluctuations and is divided into a training set and an independent test set in a 70% to 30% ratio. The training set is utilized for feature parameter determination, feature weight optimization, and classification model construction, while the independent test set is reserved solely for final performance evaluation. Experimental results demonstrate that the proposed method achieves excellent detection performance under various operating conditions and load levels, with an accuracy of 99.32% and a total detection time of 103.62 ms, meeting the requirements of the UL1699B standard, thus showcasing strong real-time detection capability and potential for embedded implementation. Full article

This paper presents an adaptive neural-network-based algorithm for chattering mitigation in sliding mode control (SMC) of parallel multicellular DC/AC power converters. Although conventional SMC provides strong robustness against parameter uncertainties, external disturbances, and load variations, its discontinuous control action often generates chattering, resulting in excessive switching activity and reduced converter performance. To address this limitation, a computationally efficient adaptive neural network is integrated into the SMC framework to approximate the discontinuous switching term and generate a smooth control signal. The proposed algorithm updates neural network parameters online through an adaptive learning mechanism, enabling real-time compensation of modeling uncertainties while preserving the inherent robustness of SMC. The resulting adaptive neural network sliding mode control (ANN-SMC) algorithm is formulated to ensure accurate output voltage tracking, balanced operation of converter cells, and reduced switching oscillations. Extensive simulation studies are conducted under different operating scenarios, including load variations and system disturbances. The performance of the proposed method is evaluated against classical SMC using quantitative indicators related to tracking accuracy, dynamic response, robustness, and chattering suppression. The results demonstrate that the ANN-SMC algorithm significantly reduces high-frequency oscillations while improving transient behavior and maintaining robust operation. These findings indicate that the proposed adaptive learning-based control algorithm constitutes an effective and scalable solution for advanced power conversion systems operating under uncertain conditions. Full article

This paper presents a comprehensive review of state-of-the-art developments in electric vehicle (EV) charging technologies, charging stations, and charging protocols, with particular emphasis on their integration with renewable energy sources (RESs). EV chargers are generally classified into on-board and off-board configurations. This study examines recent designs and advanced control strategies for both AC/DC and DC/DC power conversion stages, highlighting key technical aspects, recent innovations, and existing challenges. Furthermore, it provides an in-depth discussion of emerging multiport EV charger architectures that integrate photovoltaic (PV) systems, energy storage units, EVs, and the power grid within a unified framework. A comparative analysis is also presented to evaluate various converter topologies and energy management strategies used in the AC/DC and DC/DC stages of EV charging systems. Critical performance indicators such as power rating, output voltage level, efficiency, economic feasibility, and system complexity are also discussed. A comprehensive comparison is conducted among 13 review papers between 2015 and 2026, identifying key trends, methodological differences, and common findings. Full article

Conventional soft open points (SOPs) suffer from limited transfer capacity during distribution network faults. To address this issue, this paper proposes an integrated energy storage system and soft open point (ES-SOP) along with a power path dynamic reconfiguration method. The device consists of an M × N AC switch matrix, N AC/DC converters, and a common DC bus with energy storage. This structure provides three distinct power paths: a mechanical direct path, a third-party grid path, and an energy storage path. A seamless reconfiguration technology is developed to eliminate inrush currents during mechanical switching. It combines multi-unit virtual synchronous generator (VSG) pre-synchronization with a DC bus voltage droop coordination mechanism. The overall control follows a two-time-scale strategy. On a long time scale, a heuristic rule selects the most suitable healthy grid as the mechanical source. On a short time scale, the droop parameters of the converters are optimized to autonomously share the remaining power between the third-party grid path and the energy storage path. This allocation minimizes losses and requires no fast communication. Hardware-in-the-loop experiments verify the performance: the proposed method completely suppresses inrush current, keeps DC bus voltage fluctuation below 20 V during mode transitions, and achieves a transfer efficiency of approximately 98.5%. Full article

This study addresses the power dispatching of a DC-coupled three-phase grid-connected photovoltaic (PV) and energy storage-integrated system by proposing a peak shaving and valley-filling control architecture based on time-of-use (TOU) pricing. This research involves achieving maximum power-point tracking (MPPT) for PVMAs using a boost converter combined with the perturb and observe (P&O) method. A lithium-iron phosphate battery pack is integrated into the DC link via a bidirectional buck-boost converter, where charging and discharging control is executed according to peak and off-peak periods to regulate and stabilize the DC link voltage. Furthermore, bidirectional power flow control for peak and off-peak electricity consumption is realized using hysteresis current control and sinusoidal pulse-width modulation (SPWM) technologies within a smart inverter. By integrating the aforementioned power control architecture, the grid system can store energy from the utility during off-peak hours and release the stored energy during peak hours to reduce the load demand on the utility side. Initially, a simulation environment was established using Matlab/Simulink (2024b version) software, followed by control verification of the proposed system on a physical platform. The simulation and experimental results confirm that the integrated control architecture can precisely control the system’s DC link voltage at 800 V and stabilize the grid-connected AC voltage at an effective value (RMS) of 380 V. Moreover, under conditions of peak/off-peak switching and load variations, the system effectively demonstrates its stability and efficacy in performing valley filling and peak shaving. The proposed strategy achieves a power factor above 0.99 and a total harmonic distortion (THD) below 5%, regulates the DC-link voltage at 800 V with a steady-state error within 1.75%, and prevents up to 66.4 kWh of over-contract energy consumption per day under a 35 kW contract capacity, thereby contributing to sustainable energy management and economic savings. Full article

To satisfy the requirements of multi-level power output in wireless power transfer (WPT) systems, this paper proposes a multi-level power regulation strategy based on primary-side hardware reuse and inverter mode selection. The proposed approach reduces hardware complexity and control difficulty under diverse power demands, while eliminating the performance degradation of inverters induced by wide-range duty-cycle modulation. In this study, the configuration of the established system is first presented. Maintaining the inherent output connection of the full-bridge inverter, two operational modes realized via power device gating control are analyzed and deduced. On this basis, an analytical circuit model is constructed for the multi-level power output system incorporating primary-side hardware reuse, and the corresponding static characteristics of the system are investigated. Combined with the application of receiving coils with different specifications, a refined multi-level power output scheme relying on inverter mode selection is further formulated. Finally, experimental validation demonstrates that the prototype system achieves four discrete power levels simply through primary-side hardware reuse and mode switching, without modifying circuit connections or adjusting duty ratios. The maximum received power under each level reaches 405 W, 212 W, 101 W and 51 W, respectively; meanwhile, the corresponding DC–DC efficiency is maintained at 92.6%, 91.5%, 92.3% and 90.92%. Full article

To address the issue of transmission performance degradation caused by misalignment between the transmitting and receiving coils in wireless power transfer (WPT) systems, the optimal design for a bipolar reverse-wound magnetic coupler is proposed in this paper, considering both misalignment tolerance and wire consumption. Firstly, a bipolar reverse-wound magnetic coupler employing two concentric circular coils connected in reverse series is introduced. The calculation method for the mutual inductance between the transmitter and receiver coils of the proposed mechanism is investigated. Secondly, a circuit model for the WPT system based on this magnetic coupler is established. Subsequently, considering the mutual inductance variation characteristics and coil wire consumption, the design method based on particle swarm optimization for the bipolar reverse-wound magnetic coupler is proposed to enhance its misalignment tolerance. Simulation and experimental results demonstrate that, with the proposed optimization method, the receiving coil is permitted to misalign within a range covering 60% of the transmitter radius while maintaining mutual inductance fluctuations below ±5%. Compared to the conventional single circular transmitter, the proposed magnetic coupler achieves an approximately 20% improvement in misalignment tolerance under similar transmitter coil wire consumption conditions. The maximum receiving power of the experimental prototype is 203.67 W, while the DC-DC efficiency is 89.33%. Full article

DC microgrids are important platforms for renewable energy integration, energy storage interaction, and bidirectional power exchange. In these systems, multi-phase interleaved parallel DC-DC converters are widely used as key energy-router interfaces, but open-circuit faults in power devices may lead to current imbalance, waveform distortion, ripple redistribution, and system instability. To improve fault warning and diagnosis under variable operating conditions, this paper proposes a time–frequency dual-branch efficient fully convolutional network (EPFCN). The proposed model takes synchronized multi-channel voltage/current signals and their FFT-domain representations as complementary inputs. The time-domain branch extracts transient waveform features, while the FFT-domain branch captures spectral variation and harmonic-related information. An efficient channel attention (ECA) module is introduced to enhance fault-sensitive channel responses while maintaining a lightweight structure. An RT-LAB hardware-in-the-loop platform is established to construct a multi-condition diagnostic dataset covering one normal state and nine fault states. Experimental results show that the proposed EPFCN achieves high diagnostic accuracy, strong noise robustness, clear feature separability, and feasible edge-side inference performance. The proposed method provides an effective data-driven solution for online fault warning and diagnosis of multi-phase interleaved converters in DC microgrids. Full article

Sustainable EV charging infrastructure is fragmented by proprietary applications, vendor lock-in, and weakly time-differentiated pricing, blunting its contribution to urban-mobility decarbonisation. This paper asks whether an open-protocol, super-app-mediated photovoltaic–storage charging architecture can jointly resolve these three fragmentations under deployed field conditions and what its sustainability profile then looks like. We report a campus photovoltaic–storage microgrid integrating heterogeneous EV chargers under an open, vendor-neutral charging-control protocol with super-app authentication and payment replacing dedicated charging applications and a time-differentiated tariff aligned at the meter-interval level with the underlying utility wholesale rate; the deployment is exercised through a researcher-scheduled commissioning campaign of 13 sessions designed to establish functional correctness across the operating envelope rather than to measure user behaviour. Three results emerge across cross-vendor compatibility, onboarding friction, and grid alignment. First, basic message-level OCPP compatibility is sustained across two charger vendors under a single cloud management system—in sequential single-vendor sessions—including the full charging profile up to near-rated DC peak power. Second, the super-app-mediated workflow, which requires no charging-specific application installation and no new charger-operator account, structurally eliminates the dedicated application installation and the email/SMS/credit-card verification round-trips of conventional onboarding, compressing measured first-use end-to-end interaction to 31 s; relative to reconstructed commercial-operator baselines, this is, to the best of the authors’ knowledge, an order-of-magnitude reduction rather than a controlled benchmark. Third, mid-day energy delivery aligns incidentally with the utility off-peak window, not user-driven demand shifting, while PV-displacement and BESS-discharge contributions to charging are bracketed by scenario rather than being separately metered. The paper’s contribution is therefore a replicable, policy-embedded sustainable charging architecture validated at field scale within the New Taipei Net-Zero Carbon Demonstration Site Programme, with no claim of global novelty; the same architecture is structurally positioned to convert the observed incidental grid-friendliness into a deliberate, user-facing benefit via a hardware-free mid-day-discount redesign. Full article

Large-scale photovoltaic (PV)–hydrogen production systems are increasingly regarded as a promising solution for mitigating renewable energy curtailment and supporting the transition toward low-carbon energy systems. However, when connected to weak grids, such systems often suffer from insufficient voltage–frequency support capability and pronounced Direct current (DC) bus voltage fluctuations, which limit their operational stability and practical deployment. To address these challenges, this paper proposes a dual virtual motor coordinated control strategy for PV-based hydrogen production systems, integrating a grid-forming virtual synchronous generator (VSG) with a virtual DC motor (VDCM). By exploiting the complementary dynamic characteristics of grid-side converters and hydrogen production loads, the proposed approach enhances grid support capability while simultaneously providing inertia and damping to the hydrogen production DC bus without relying on additional physical energy storage. Dynamic response analysis is conducted to investigate the influence of virtual inertia and damping parameters on system stability. Simulation results under weak-grid conditions demonstrate that the proposed strategy effectively improves frequency and voltage support performance and significantly suppresses DC bus voltage fluctuations during load and power disturbances. The proposed control framework offers a practical and scalable solution for improving the operational robustness of PV–hydrogen production systems, contributing to the reliable integration of renewable energy and the development of green hydrogen infrastructure. Full article

Shipboard DC microgrids face critical stability challenges including low-inertia-induced voltage fluctuations and impedance mismatch caused by constant power loads (CPLs), which severely threaten system stability. Existing virtual DC machine (VDCM) control methods typically treat inertia support and impedance optimization as separate design objectives, lacking a unified frequency-domain design framework. To address this issue, this paper first establishes an accurate virtual impedance model for the standard VDCM controller, quantitatively revealing how its control parameters (J and D) shape the frequency-domain impedance characteristics and identifying potential stability conflicts. Building upon this model, a feedforward-compensated VDCM (FFC-VDCM) strategy is proposed, introducing a differential feedforward loop to actively reshape the converter output impedance in the critical mid-frequency range without interfering with the inertia support function. The impedance reshaping effect is quantified via impedance-based stability analysis; the proposed method improves the gain margin from $4.1$ dB (with conventional VDCM) to $8.6$ dB, along with a significant enhancement in the phase margin, confirming improved system robustness. Hardware-in-the-loop (HIL) experiments conducted under realistic shipboard conditions further confirm the theoretical analysis, demonstrating superior transient voltage regulation and validating the practical effectiveness of the proposed strategy. The FFC-VDCM provides a synergistic solution for concurrently improving inertia and stability in low-inertia DC microgrids. Full article

Laser brazing is commonly used for joining visible automotive body panels where both mechanical integrity and surface quality are required. The present work addresses optimization of a robotic laser brazing workstation intended for prototype vehicle production. During initial commissioning, irregular braze formation was associated with unstable filler wire feeding. Therefore, the wire feeding system was modified and subsequently evaluated together with the influence of laser power and wire feed speed on joint quality under a constant robot travel speed. Experimental joints were produced from electrolytically galvanized DC06 steel using CuSi3Mn1 filler wire. Joint performance was assessed by tensile testing and metallographic examination. Tensile strengths between 293 and 314 MPa were obtained, while fracture occurred exclusively in the base material outside the brazed region. Metallographic observations revealed regular braze geometry for parameter sets A, B and D, whereas excessive thermal input resulted in blowhole formation, zinc coating degradation and enlargement of the heat-affected zone. Quantitative evaluation showed a nearly linear increase in the HAZ area with increasing delivered energy (R² = 0.982). The results indicate that stable brazing conditions can be achieved through an appropriate balance between laser power and wire feed speed under constant robot travel speed conditions. The proposed parameter limits may serve as a practical guideline for robotic laser brazing of thin galvanized automotive sheets under prototype production conditions. Full article