Search Results (576)

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

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article

Photovoltaic (PV) inverter fault prediction is critical for maintaining system reliability and minimizing energy loss. While recent studies have improved predictive accuracy using data-driven approaches, most evaluations remain focused on offline settings and do not address how probabilistic predictions are translated into operational decisions. This study investigates multi-horizon fault prediction for PV inverters under real-world constraints, with a particular focus on decision-level behavior. A modular prediction framework is implemented by combining transformer-based TimeXer embeddings with probabilistic classification using XGBoost. The model operates on sliding-window sensor data and produces fault probabilities across multiple future horizons. To support operational use, these probabilities are aggregated into a single risk score, and threshold-based alarm policies are evaluated through a systematic threshold sweep. The results show that predictive performance varies across horizons, with usable lead-time information concentrated in near-term predictions. Under severe class imbalance, imbalance-aware training significantly improves detection performance in precision–recall space, but performance remains sensitive to temporal variation. Most importantly, the threshold-sweep analysis reveals a structural trade-off between detection performance and alarm burden, where achieving moderate early-warning capability requires substantially increased alarm rates. These findings indicate that improving predictive accuracy alone is insufficient for practical deployment. Instead, decision-level behavior must be explicitly considered when designing predictive maintenance systems under operational constraints. Full article

Solar photovoltaic (PV)-powered electric vehicles (EVs) have gained greater significance in the present-day era of transportation across the globe. This proposed work presents an analysis of a five-level reduced switched-capacitor multilevel inverter (RSC-MLI)-powered permanent magnet synchronous motor (PMSM) drive for solar PV-powered battery vehicles enabled by a rat swarm optimization (RSO) maximum power point tracking (MPPT) control mechanism. The system proposed in this paper integrates solar PV arrays and battery storage systems for efficient power transfer to EVs for propulsion. In order to achieve fast, accurate tracking of the optimal maximum power point, the RSO technique is used. A five-level RSC-MLI is used in this study, which enables boosting the voltage and lowering switching losses in the system. The performance of the PMSM is further analyzed to obtain constant parameters, such as the velocity and torque of the electric vehicle. Full article

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

This research paper presents Volvo SmartCell, an AC battery technology that integrates modular multilevel converters and battery cells to form a unified system for electric vehicle propulsion and power supply. The research work addresses the broader challenge of reducing driveline cost and complexity by replacing traditional components such as inverters, onboard chargers, centralized DC/DC converters, vehicle control units and many more. SmartCell uses distributed Cluster Boards comprised of H-bridges which are controlled via wireless communication to generate AC voltage, deliver redundant low voltage power, and support cell level protection mechanisms. The prototype testing demonstrates that the system can supply traction power by engaging clusters according to the required voltage depending on motor speed, achieve AC grid charging by synthesizing sinusoidal voltages without a dedicated charger, and provide autonomous DC/DC operation through cluster level voltage regulation. Simulations further indicate that multilevel voltage generation can reduce switching losses and improve electric machine efficiency compared to conventional systems. Additional benefits include active cell balancing, support for mixed cell chemistries, and high redundancy through multiple independent power branches. Challenges remain in wireless bandwidth limitations and cost optimization of Cluster Boards. Ongoing development aims to enhance communication robustness and validate safety for non-isolated grid charging. Full article

The rapid electrification of road vehicles has fundamentally reshaped the priorities of noise, vibration, and harshness (NVH) engineering. In the absence of combustion-related broadband masking, tonal and order-related phenomena originating from the electric machine, inverter switching, and high-speed reduction gearing have become clearly perceptible and, in many cases, acoustically dominant. Consequently, drivetrain noise in electric vehicles can no longer be assessed at component level alone; it must be understood as a coupled system response shaped by excitation mechanisms, structural dynamics, transfer paths, radiation efficiency, and ultimately human perception. This review adopts a source-to-perception perspective and consolidates the principal physical mechanisms governing vibro-acoustic behavior in integrated electric drive units. Electromagnetic force harmonics and torque ripple are discussed alongside transmission-error-driven gear mesh excitation, while bearing and shaft nonlinearities are examined in the context of high-speed operation. In addition, ancillary thermoacoustic and aerodynamic contributions are considered, reflecting the increasingly integrated packaging of modern e-axle architectures. On this mechanism-oriented basis, dominant excitation types are linked to frequency-appropriate modeling strategies, spanning electromagnetic force extraction, multibody drivetrain simulation, structural finite element analysis, transfer path analysis, and acoustic radiation prediction. Particular attention is given to workflow integration across domains. Finally, the paper identifies research challenges that predominantly arise at system level, including multi-source interaction effects, installation-dependent transfer-path variability, emergent resonances in assembled structures, manufacturing-induced tonal artifacts, and the still limited correlation between predicted vibration fields and perceived sound quality. Full article

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

This paper presents a systematic experimental comparison of three sliding-mode-based current control strategies—traditional sliding mode control (SMC), fast terminal sliding mode control (FTSMC), and super-twisting sliding mode control (STSMC)—applied to a grid-connected five-level active neutral point clamped flying capacitor (5L-ANPC-FC) inverter. Unlike existing studies that typically investigate a single controller or topology, this work provides a fair, hardware-validated benchmark under identical operating conditions, enabling a clear assessment of convergence speed, harmonic performance, robustness, and implementation complexity. All controllers are designed within a unified framework and their stability is rigorously analyzed using Lyapunov theory. Experimental evaluations are conducted under steady-state operation, step changes in reference current, grid-voltage sag/swell, and DC-link voltage variations. The results demonstrate that while all three controllers ensure robust current tracking and inherent DC-side capacitor voltage balancing without additional control loops, FTSMC achieves the lowest grid-current total harmonic distortion (THD) and fastest convergence. STSMC effectively suppresses chattering, and traditional SMC offers a simple yet reliable baseline solution. The presented findings provide practical design guidelines for selecting appropriate sliding-mode controllers in high-performance multilevel inverter applications. Among the assessed control techniques, FTSMC has the most rapid dynamic response, characterized by a rise time of 0.1 ms and a minimal grid-current THD of 1.95%, indicating exceptional steady-state and transient performance. STSMC markedly diminishes chattering and ripple, attaining a THD of 2.04% with enhanced waveform smoothness relative to traditional SMC. Conversely, traditional SMC offers a more straightforward implementation but demonstrates elevated ripple and THD levels of around 2.29%, along with a peak current inaccuracy of 6–8%. The results underscore the trade-offs between implementation simplicity, dynamic responsiveness, and harmonic performance of the evaluated control techniques. 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

In recent years, industrial systems and power electronic equipment have imposed increasingly stringent requirements on power quality, and therefore, the realization of a high-quality power supply has garnered extensive research attention. Selective harmonic elimination pulse width modulation (SHEPWM) features superior harmonic suppression performance and can effectively attenuate specific sub-harmonics; however, solving the associated system of nonlinear transcendental equations remains a critical challenge, primarily due to its inherent computational complexity and the risk of convergence to local optima. To address these limitations, we propose a multi-strategy enhanced chaotic black-winged kite algorithm (CMBKA). The proposed CMBKA integrates three synergistic optimization strategies: logistic–tent chaotic mapping for uniform population initialization, golden sine strategy to balance global exploration and local exploitation, and Monte Carlo perturbation to avoid convergence to local optima. In contrast to BKA, the proposed CMBKA achieves markedly higher calculation accuracy for switching angles, which is systematically validated on a five-level modified packed U-cell (MPUC) inverter platform. Experimental results verify that the proposed CMBKA achieves a lower total harmonic distortion (THD) than does the BKA, while the targeted specific sub-order harmonics are effectively suppressed to below 0.05%, with a maximum voltage deviation of 2.3% between the simulation results and experimental hardware tests. This work provides a high-precision SHEPWM solution for multilevel inverters, offering significant potential for renewable energy systems requiring minimal harmonic pollution and high power density. Full article

This paper presents an efficient stand-alone photovoltaic water pumping system (PVWPS) intended for agricultural irrigation applications, operating without energy storage. The system employs a three-phase induction motor supplied by a three-level neutral point clamped (NPC) inverter. The proposed control strategy integrates the advantages of two distinct controllers to enhance both energy extraction and drive performance. On the photovoltaic side, a fuzzy logic-based maximum power point tracking (MPPT) algorithm is implemented to ensure continuous operation at the global maximum power point under rapidly varying irradiance conditions. On the motor drive side, a direct torque control (DTC) scheme is combined with the multilevel NPC inverter to regulate electromagnetic torque and stator flux. The use of a multilevel inverter significantly mitigates the inherent drawbacks of conventional DTC, notably torque and flux ripples, as well as stator current harmonic distortion. The overall control architecture maximizes power transfer from the photovoltaic generator to the pumping system, resulting in improved dynamic response and energy efficiency. The proposed system is validated through detailed MATLAB/Simulink simulations under abrupt irradiance variations and a realistic daily solar profile corresponding to August conditions in Kairouan, Tunisia. Simulation results demonstrate substantial performance improvements, including an 88% reduction in torque ripples, a 50% decrease in flux ripple, a 77.9% reduction in stator current THD, and a 33.3% enhancement in speed transient response compared to conventional DTC-based systems. Full article

Voltage status monitoring of transmission lines is critical for the safe operation of power systems. Traditional contact-based measurement methods pose safety risks and incur high costs. Therefore, this paper proposes a non-contact voltage Measurement method by inferring the actual voltage level of transmission lines. Firstly, the spatial electric field strength is constructed based on the simulated charge method. Then, the voltage of transmission lines is inverted according to the near-surface electric field strength. By incorporating Tikhonov regularization with multi-physical condition constraints and multi-start optimization strategies into traditional genetic algorithms, the instability issues in electric field inversion methods are resolved. The results indicate that under a 5% measurement noise, the voltage amplitude error is below 5%, and the phase error is less than 5°. The proposed improved genetic algorithm enhances the stability and reliability of the inversion process, providing an effective solution for voltage state monitoring of transmission lines. Full article

Accurate prediction of PM_2.5 concentration is crucial for air quality management and public health protection. However, existing methods often struggle to capture and interpret the nonlinear relationships among multiple atmospheric variables. This study proposes GT-iFormer, a novel interpretable deep learning model that integrates graph convolutional networks (GCNs), Temporal Convolutional Networks (TCNs), and inverted Transformer (iTransformer) for PM_2.5 concentration prediction. The model features a GTCN-Block that encapsulates GCN and TCN with residual-style fusion, preserving feature-level dependencies alongside temporal patterns to prevent information degradation. The Pearson correlation coefficients and KNN algorithm are innovatively integrated to build a data-driven graph structure, which allows GCNs to flexibly model the nonlinear relationships between pollutants and meteorological factors based on observed data. TCNs obtain multi-scale temporal patterns via causal dilated convolutions. Subsequently, the concatenated representations of GTCN-Block are input into iTransformer to model global inter-variable interactions using attention mechanisms along the axis of the variable. We incorporated SHAP (SHapley Additive exPlanations) analysis to expose feature importance and nonlinear relationships with PM_2.5 predictions. Results on the hour-level data of Beijing (2020–2021) and Shenzhen (2021) show that our proposed GT-iFormer surpasses all baseline models, with an RMSE of 8.781 μg/m³ and R² of 0.978 for Beijing, and an RMSE of 3.871 μg/m³ and R² of 0.957 for Shenzhen on single-step prediction, equating to RMSE reductions of 15.75% and 17.92%, respectively, over the best baseline model. The SHAP analysis shows clearly distinct regional patterns, with combustion sources dominant in Beijing (represented by CO at 28.231%), and traffic emissions dominant in Shenzhen (represented by NO₂ at 25.908%). Crucial threshold effects are established for all variables, with significant cross-city differences that can serve as general forecasts and guidance for city-specific air quality management policies. Full article

Unmanned aerial vehicle (UAV) forest fire detection is vital for forest safety. However, early-stage UAV fire scenarios often involve small targets, weak smoke signals, and strict onboard resource constraints, which pose significant challenges to existing detectors. To improve the speed and accuracy of UAV forest fire detection, this paper proposes a lightweight fire detection algorithm, AHE-YOLO, specifically designed for UAVs. The proposed method adopts a coordinated lightweight design to improve feature preservation and cross-scale representation under limited computational budgets. Specifically, the Adaptive Downsampling (ADown) module preserves shallow fire-related cues during spatial reduction, improving sensitivity to small flame and smoke targets. The high-level screening-feature fusion pyramid network (HS-FPN) introduces cross-scale attention to promote more discriminative multi-level feature interaction while reducing redundant computation. Furthermore, the Efficient Mobile Inverted Bottleneck Convolution (EMBC) module is employed to improve receptive-field efficiency and feature selectivity under lightweight constraints, further enhancing detection accuracy and inference speed. Finally, the performance of AHE-YOLO is comprehensively evaluated through ablation and comparative experiments on the same dataset. The final experimental results show that YOLO-AHE achieves a mean average precision (mAP) of 94.8% while reducing model parameters by 39.7%, decreasing FLOPs by 27.0%, and shrinking the model size by 36.4%. In addition, its inference speed improves by 16.5%. Beyond detection performance, the proposed framework supports sustainable forest monitoring by enabling early fire warning with reduced computational and energy demands, showing strong potential for real-time deployment on resource-constrained UAV and edge platforms. Full article