Search Results (2,158)

The growing electrification of ports and maritime transport requires flexible power systems capable of supplying multiple voltage levels with high efficiency and power quality. Battery-based power barges offer a promising solution, but their power conversion systems must handle wide voltage and power ranges while remaining stable under dynamic operating conditions. This paper presents a scalable multi-stage power conversion architecture for battery-based power barges that can supply both low-voltage and high-voltage AC loads from a common DC source. The system combines isolated Dual Active Bridge (DAB) DC–DC converters with a three-level Neutral-Point-Clamped (NPC) inverter. An input-parallel output-series DAB configuration is used for high-voltage operation, enabling modularity and scalability within semiconductor limits. A coordinated control strategy ensures stable DC-link regulation, balanced module operation, and high-quality AC voltage generation. Simulation results confirm stable operation, fast dynamic response, a voltage THD below 4%, and overall efficiency above 95%, demonstrating the suitability of the proposed architecture for future power barge and port electrification applications. Full article

In the context of the coordinated development of high-proportion renewable energy integration and alternating current/direct current (AC/DC) hybrid transmission, the sending-end power grid faces challenges such as decreased system strength, contracted stability boundaries, and difficulties in covering high-risk operating conditions. This paper proposes a new renewable energy scale impact analysis method that integrates “typical scenario construction-scale ladder comparison–prediction-driven time series injection” in response to the operational constraints of AC/DC transmission. In terms of method implementation, firstly, a two-layer typical scenario system is constructed under unified transmission constraints and fixed grid boundaries: A regular benchmark scenario covers the main operating range, and a set of high-risk scenarios near the boundaries is obtained through multi-objective intelligent search, which is then refined through clustering to form a computable stress-test scenario library. Here, the boundary scenarios are generated by a multi-objective search that simultaneously drives multiple key section load rates towards their limits, subject to AC power-flow feasibility and operational constraints, and the resulting Pareto candidates are reduced into a compact stress-test library by clustering. Secondly, a ladder scenario with increasing renewable energy scale is constructed, and cross-scale comparisons are carried out within the same scenario system to extract the scale effect and critical laws of key safety indicators. Finally, data resampling and Gated Recurrent Unit multi-step prediction are introduced to generate wind power output time series, enabling the temporal mapping of prediction results to scenario injection quantities, and constructing a closed-loop input interface of “prediction–scenario–grid indicators”. The results demonstrate that the proposed hierarchical framework, under unified AC/DC export constraints, can effectively construct a compact stress-test scenario library with enhanced boundary-risk coverage and can reveal how transient voltage security evolves across renewable expansion scales. By coupling boundary-oriented scenario construction, cross-scale comparable assessment, and forecasting-driven time series injection, the framework improves engineering interpretability and practical applicability compared with conventional scenario sampling/reduction workflows. For the forecasting module, the Gated Recurrent Unit (GRU) model achieves MAPE = 8.58% and RMSE = 104.32 kW on the test set, outperforming Linear Regression (LR)/Random Forest (RF)/Support Vector Regression (SVR) in multi-step ahead prediction. Full article

Ripple currents on the direct current (DC) bus in variable frequency drive (VFD) systems originate from motor load current fluctuations and the high-frequency switching of power devices. The resulting Joule heating within the DC-link capacitors is a primary driver of lifespan degradation. To address the lack of systematic models for multi-phase H-bridge inverters and the over-design caused by empirical methods, this paper proposes a novel analytical method that incorporates the 2kπ/N phase difference of parallel units for precise ripple current quantification. First, a dynamic DC-link capacitor model is established based on a single-phase H-bridge inverter, and the expressions for the instantaneous, average, and root mean square (RMS) input currents are derived. Furthermore, by introducing the 2kπ/N phase difference (where k = 0, 1, …, N − 1) among N parallel H-bridge units, a universal analytical expression for the RMS input current and its harmonic spectrum in a multi-phase system is obtained. The analysis reveals that ripple current harmonics concentrate at 2m × f_sw (where m is a positive integer and f_sw is switching frequency) and their sidebands (2m × f_sw ± f_o, f_o is output fundamental frequency), and the coupling influence of modulation index and power factor angle on ripple amplitude is quantitatively characterized. A 12 × 160 kW twelve-phase H-bridge inverter is taken as a case study, and MATLAB (v2023b) simulations and hardware experiments demonstrate that the theoretical calculations are in close agreement with the simulated and measured results, with the errors of input current harmonic amplitudes all below 5%. Compared with traditional empirical design, the proposed method reduces the capacitor volume and cost by approximately 15–20% while ensuring system reliability. This method is directly extensible to other multi-phase inverter topologies, providing a theoretical foundation for the accurate selection of DC-link capacitors. Full article

This paper introduces a compact sub-6 GHz multiple-input multiple-output (MIMO) antenna array developed for 5G smartphone applications. The design employs eight planar inverted-F antenna (PIFA) elements arranged to realize dual-band and dual-polarized operation. The antenna achieves impedance bandwidths of 3.3–3.7 GHz (11.4%) and 5.3–5.8 GHz (10%), covering key sub-6 GHz fifth-generation (5G) bands. To enhance diversity performance, the elements are distributed along the edges of the smartphone mainboard, enabling excitation of orthogonal polarization modes while maintaining an overall board size of 75 mm × 150 mm on an FR4 substrate. Even without the use of dedicated decoupling structures, the closely spaced antenna elements exhibit satisfactory isolation levels, varying between −12 dB and −22 dB across the operating bands. The antenna array achieves wide impedance bandwidths of approximately 400 MHz at 3.5 GHz and more than 500 MHz at 5.5 GHz, supporting high data-rate communication. In addition, the proposed system demonstrates very low correlation and active reflection, with envelope correlation coefficient (ECC) values below 0.002 and total active reflection coefficient (TARC) levels better than −20 dB. User interaction effects are also investigated, and the results confirm acceptable SAR levels and stable radiation behavior in the presence of the human body. Owing to its planar, dual-band/dual-polarization capability and compliance with safety requirements, the proposed antenna represents a promising practical solution for contemporary 5G handheld devices and future multi-band mobile platforms. Full article

While critical to the global energy transition, China’s photovoltaic (PV) sector exemplifies the ‘green paradox’ of clean energy supply chains, where the rapid expansion of solar infrastructure generates significant upstream carbon emissions. This study provides a long-term (2000–2022) empirical examination of this tension, investigating the decoupling relationship between industrial growth and embodied carbon emissions. Employing a multi-regional input–output model, we quantify the evolving carbon footprint of China’s PV manufacturing. We then apply the Tapio decoupling framework—which measures whether emissions grow slower than, or decline relative to, economic output—and structural decomposition analysis to identify the key drivers of emission changes over two decades. Finally, we project future decarbonization pathways (2023–2030) under four policy scenarios using Monte Carlo simulations. Our findings reveal a fundamental transition: since 2015, technological progress has become the dominant force for emission reductions, contributing 78% to cumulative reductions and marking a shift from a ‘scale-driven’ to a ‘technology-driven’ growth model. However, rising global demand continues to push total emissions upward, resulting in ‘weak decoupling’ (emissions grow, but slower than output) rather than the ‘strong decoupling’ (absolute emissions decline) required for carbon neutrality. Scenario analysis indicates that strong decoupling is achievable by 2030 under ambitious policy and technology scenarios, with the Technological Breakthrough scenario projecting a 39% emission reduction alongside 103% output growth. Nevertheless, even under optimistic assumptions, approximately 29,000 tons of residual emissions remain due to the inherent energy intensity of upstream processes like polysilicon production. These findings support the development of differentiated policies that balance industrial competitiveness with carbon neutrality goals, highlighting that China’s PV sector—while enabling global decarbonization—must itself undergo a deep decarbonization transition. Full article

The paper presents four solutions to the voltage source inverter (VSI) control system with existing delays in the measurement channels and the middle switching frequency (25,600 Hz): Single-Input Single-Output Coefficient Diagram Method (SISO-CDM), Multi-Input Multi-Output Passivity-Based Control (MISO-PBC), Multi-Input Multi-Output One-Sample-Ahead Preview Controller (MISO-OSAP), and MISO-OSAP with Luenberger Observer (MISO-OSAP-LO). The theory, including adjustments to controller gains or to the coefficients of the characteristic equation of the closed-loop system, is presented. Simulations of the VSI operation with these control systems for the nonlinear load and the dynamic resistive load (per the requirements of the EN 62040-3 standard) are presented. The SISO-CDM and MISO-PBC are finally selected for experimental verification of the simulations. The results of the tests enable the selection of the control type for a particular VSI design based on its cost and an estimation of the advantages of the more expensive solution. The paper should help in engineering design according to the remarks in the paper. Full article

This paper presents a quadruple step-down (QSD) trans-inductor voltage regulator (TLVR) converter to accommodate the high-current and fast-transient requirements of processor power supplies. Evolved from dual-step-down (DSD) topology, the QSD configuration offers stronger load capacity; three additional flying capacitors are introduced between adjacent phases to break the 25% duty cycle constraint, thereby extending the output voltage range and accelerating the transient response. Moreover, the converter’s transient response is optimized to its full potential through both multi-phase simultaneous operation and the incorporation of the dedicated TLVR architecture. A modified adaptive on-time (AOT) controller supporting four-phase simultaneous operation is employed. Designed and verified via post-layout simulation in a 180 nm BCD process with all 6 V power transistors, the converter achieves a peak efficiency of 96.1% at 24 V input and 3 V output, as well as a maximum load capacity of 20 A. Under a 19 A load current step with a 19 ns rise time, it exhibits only a 37 mV output voltage droop and a 2 μs settling time, even with a 100 μF output capacitor. Full article

Hydropower plays a key regulating role in new-type power systems, and both forecasting accuracy and interpretability are critical for power dispatch. However, cascade hydropower forecasting is constrained by strong spatiotemporal coupling among multi-dimensional features, flow propagation delays, as well as the limited transparency of deep learning models. To tackle these issues, this paper develops a hybrid framework integrating Maximal Information Coefficient (MIC), the Long- and Short-term Time-series Network (LSTNet), and the SHapley Additive exPlanations (SHAP) interpretability method. First, an MIC-based nonlinear screening mechanism is employed to remove redundant noise and construct a high-quality input space. Second, an LSTNet model is developed to deeply extract spatiotemporal coupling features among cascade stations and flow evolution patterns, achieving high-accuracy forecasting of both system-level and station-level outputs. Finally, SHAP is used for global and local interpretability analysis to perform physics-consistency verification with respect to the model’s decision-making rationale. Experimental results indicate that the proposed approach achieves low errors in total output forecasting, reducing error levels by approximately 57–88% compared with Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Informer. Moreover, SHAP feature-dependence analysis reveals a nonlinear response change of station D around 7.8 MW, providing evidence for the physical consistency of the model outputs and improving model interpretability. Full article

Strong heterogeneity and ambiguous seismic responses hinder reliable sandstone thickness prediction when using a single seismic attribute in the lower sandstone interval of the Talang Akar Formation (hereafter abbreviated as the LTAF interval) in the B gas field, South Sumatra Basin. To address this challenge, we propose a seismic attribute fusion and reservoir sweet-spot prediction framework based on a multiscale convolutional neural network (CNN) integrated with a self-attention module. Multiple seismic attribute volumes are organized as multi-channel 2D attribute slices, and parallel convolutions with kernel sizes of 3 × 3, 5 × 5, and 7 × 7 are employed to capture spatial features ranging from thin-bed boundaries and channel morphology to sand-body assemblage distribution. The self-attention module explicitly models inter-attribute dependencies and performs adaptive weighted fusion to suppress noise and emphasize informative attributes. The network adopts a dual-output design, producing (i) a sandstone thickness prediction map at the same spatial resolution as the input and (ii) attribute importance scores for quantitative attribute selection and geological interpretation. Using 3D seismic data and well-constrained thickness labels, the proposed model achieves an R² of 0.8954, outperforming linear regression (R² = 0.8281) and random forest regression (R² ≈ 0.8453). The learned importance scores indicate that amplitude-related attributes (e.g., RMS amplitude and maximum amplitude) contribute most to thickness prediction, whereas frequency- and energy-related attributes show relatively lower contributions, which is consistent with bandwidth-limited resolution effects. Overall, the proposed framework unifies attribute fusion, thickness prediction, and interpretability within a single model, providing practical support for fine reservoir characterization and development optimization in heterogeneous sandstone reservoirs. Full article

A new architecture of FPGA-based Moore finite state machine (FSM) is proposed, as well as the corresponding method of synthesis. The proposed architecture of FSM circuit includes two cores of partial Boolean functions. The first core is based on functional decomposition, the second core is based on structural decomposition. Under certain conditions, the proposed method improves both spatial and temporal characteristics of FSM circuits. The FSM states have two codes. The first of them is a maximum binary code (MBC) having minimum possible number of bits. The second code is a partial state code representing a state as the element of some class of compatibility. The method can be applied if Moore FSM circuits are implemented using look-up table (LUT) elements of field-programmable gate arrays. To improve characteristics of resulting FSM circuits, the classes of pseudoequivalent states are used. This allows diminishing the numbers of literals in sum-of-products representing partial input memory functions. The first core is multi-level. For the second core, all partial functions are generated by single-LUT circuits. These cores form the first level of FSM circuit. The LUTs of the second level generate bits of MBCs. These codes are used by the third circuit level for generating both FSM outputs and partial state codes. An example of synthesis is shown. The experiments are conducted using a known library of benchmark Moore FSMs. The experiments show that the proposed approach can be used for complex FSMs where the total number of FSM inputs and state variables is at least twice the number of inputs of the base LUT. The results of experiments show that the proposed method allows improving both the spatial and temporal characteristics for complex FSMs compared with their counterparts based on other known design methods. Full article

In electric vehicle (EV) charging systems, DC series arc faults, due to their high concealment and severe hazard, have become one of the important causes of electric vehicle fire accidents. An improved hybrid arc fault model of a charging system was established in Simulink for preliminary study. The results show that the high-frequency noise generated by arc faults affects the output voltage quality of the charger, and this noise is conducted to the battery voltage. Arc faults in a real electric vehicle charging experimental platform were further investigated, where it was found that, during arc fault events, the charging system provides no alarm indication, and the current signals exhibit significant large-amplitude random disturbances and nonlinear fluctuations. Moreover, under normal conditions during vehicle charging startup and the pre-charge stage, the current waveforms also present high-pulse spike characteristics similar to arc faults. Finally, a carefully designed deep neural network-based arc fault detection algorithm, Arc_TCNsformer, is proposed. The current signal samples are directly input into the network model without manual feature selection or extraction, enabling end-to-end fault recognition. By integrating a temporal convolutional network for multi-scale local feature extraction with a sparse Transformer for contextual information aggregation, the proposed method achieves strong robustness under complex charging noise environments. Experimental results demonstrate that the algorithm not only provides high detection accuracy but also maintains reliable real-time performance when deployed on embedded edge computing platforms. Full article

In frequency division duplex (FDD) massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the sub-array multi-user (MU) hybrid beamforming architecture is highly attractive because of its low hardware cost and high energy efficiency. However, downlink channel state information (CSI) acquisition and hybrid beamformer optimization remain challenging due to the large feedback overhead and the non-convexity of the beamforming design. To address these issues, we propose an end-to-end deep learning (DL) framework that jointly optimizes pilot training, CSI feedback, and hybrid beamforming, overcoming the limitations of conventional independently designed modules. At the core of the network, we introduce the star efficient location attention (StarELA) module, which combines the implicit high-dimensional representation capability of star operations (element-wise multiplication) with the fine-grained feature localization of efficient location attention (ELA). In addition, for wideband digital beamformer generation, we exploit inter-subcarrier correlation and design a frequency–domain seed generation and interpolation upsampling strategy, which significantly reduces network parameters. Experimental results show that the proposed method approaches the upper-bound performance of conventional hybrid beamforming with ideal CSI, while consistently outperforming existing benchmark methods. Full article

This paper mainly considers the fractional parameter identification algorithms of the bilinear-in-parameter autoregressive (AR-BIP) system. The data filtering technique is introduced to improve the parameter estimation accuracy of the AR-BIP system, which involves using a filter to filter the data of the identification model. The filtering-based hierarchical fractional least mean square algorithm (F-HFLMS) and the filtering-based multi-innovation hierarchical fractional least mean square algorithm (F-MHFLMS) are proposed for effective and accurate parameter estimation of the AR-BIP system. Using the multi-innovation theory and expanding the scalar innovation into the innovation vector, the F-MHFLMS could take full advantage of the input and output data information of the system. The performance of the F-MHFLMS algorithm is compared with the F-HFLMS strategy for the AR-BIP system using the values of the mean square error (MSE) and the average predicted output error. The effectiveness and accuracy of F-HFLMS and F-MHFLMS algorithms are demonstrated under the numerical experimentation based on different noise variances, fractional orders and innovation lengths. Compared with the F-HFLMS algorithm, the F-MHFLMS algorithm can acquire more accurate and robust parameter estimation. Full article

Machine learning was employed to make fluid agnostic laminar heat transfer prediction in supercritical conditions, encompassing three fluids (sCO₂, sH₂O, sC₁₀H₂₂) representing a wide range of operating conditions. High-fidelity training data, consisting of both non-dimensional and dimensional (operating parameter) as inputs and Nu and T_wall as outputs, were generated from grid-converged, steady-state, computational fluid dynamic (CFD) simulations. The Random Forest (RF) algorithm outperformed the artificial neural networks (ANNs) across all scenarios on the small multi-fluid dataset (~1600 data points) employed during the training process. When using non-dimensional parameters as inputs, Nu prediction fidelities were better than T_wall predictions for both ML algorithms across both horizontal and vertical configurations. The RF model trained on data from a specific flow configuration (horizontal/vertical) could predict T_wall within an accuracy of +/−1% with dimensional, operational parameters as inputs while being agnostic to the working fluid. Furthermore, by including the gravity vector as an additional variable during the training process, the RF model could predict T_wall accurately in a mixed, multi-fluid dataset containing data from both horizontal and vertical configurations. Full article

In structured orchard environments, harvesting robots operate where rigid bodies (e.g., trunks, poles, and wires) coexist with flexible foliage. Strict avoidance of all obstacles significantly compromises operational efficiency. To address this, this study proposes an end-to-end autonomous harvesting framework characterized by an “avoid-rigid, push-through-soft” strategy. This framework explicitly propagates uncertainties from sensor data and reconstruction processes into the planning and policy phases. First, a multi-task perception network acquires 2D semantic masks of fruits and branches. Class probabilities and instance IDs are back-projected onto a 3D Gaussian Splatting (3DGS) representation to construct a decision-oriented, semantically enhanced 3D scene model. The policy network accepts multi-channel 3DGS rendered observations and proprioceptive states as inputs, outputting a continuous preference vector over eight predefined motion primitives. This approach unifies path planning and action decision-making within a single closed loop. Additionally, a dynamic action shielding module was designed to perform look-ahead collision risk assessments on candidate discrete actions. By employing an action mask to block actions potentially colliding with rigid obstacles, high-risk behaviors are effectively suppressed during both training and execution, thereby enhancing the robustness and reliability of robotic manipulation. The proposed method was validated in both simulation and real-world scenarios. In complex orchard scenarios, the proposed AE-TD3 algorithm achieves a harvesting success rate of 77.1%, outperforming existing RRT (53.3%), DQN (60.9%), and TD3 (63.8%) methods. Furthermore, the method demonstrates superior safety and real-time performance, with a collision rate reduced to 16.2% and an average operation time of only 12.4 s. Results indicate that the framework effectively supports efficient harvesting operations while ensuring safety. Full article