Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (106)

Search Parameters:
Keywords = multi-paralleled converters

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
33 pages, 13185 KB  
Article
Bi-Layer Model Predictive Control with Extended Horizons for Multi-Axis Underactuated Wave Energy Converters
by Xinrui Lu and Yuan Chen
J. Mar. Sci. Eng. 2025, 13(10), 1902; https://doi.org/10.3390/jmse13101902 - 3 Oct 2025
Abstract
In the field of wave energy, multi-axis wave energy converters (WECs) have emerged as a research priority owing to their enhanced energy absorption, leading to increased computational complexity. Conventional model predictive control (MPC) approaches have demonstrated limitations in the trade-off between real-time requirements [...] Read more.
In the field of wave energy, multi-axis wave energy converters (WECs) have emerged as a research priority owing to their enhanced energy absorption, leading to increased computational complexity. Conventional model predictive control (MPC) approaches have demonstrated limitations in the trade-off between real-time requirements and control performance. This paper proposes a bi-layer MPC strategy, including a long-term energy maximization layer and a short-term trajectory-tracking layer. First, a multi-axis underactuated WEC (MU-WEC) is proposed, which incorporates an inertial cable-driven parallel mechanism to absorb energy from multiple directions. In addition, a control-oriented dynamic model of a MU-WEC is established. Then, a bi-layer MPC strategy is proposed, which decouples computationally intensive optimization processes from time-sensitive real-time control, alleviating the computational burden significantly. Therefore, the upper layer achieves enhanced control performance by enabling extended prediction horizons, whereas the lower layer serves to ensure real-time requirements. Moreover, numerical simulations under irregular wave conditions demonstrate the performance of the proposed bi-layer MPC: under different waves, bi-layer MPC improves energy absorption by 127–311% over conventional MPC. This performance enhancement stems from the 5 times extension of the prediction horizon enabled by the reduced computational burden. Full article
(This article belongs to the Section Ocean Engineering)
20 pages, 1859 KB  
Article
Dynamic Weighted-Selection and Hybrid Modulation for Enhanced Performance of Multi-Source/Load Parallel AC-Link Universal Converters
by Abdulgafor Alfares
Energies 2025, 18(19), 5191; https://doi.org/10.3390/en18195191 - 30 Sep 2025
Abstract
This paper presents a novel open-loop modulation and control strategy for bidirectional, multi-source/load parallel AC-link power converters. While these converters offer advantages such as high-frequency operation and flexible power conversion capabilities, their application to complex systems such as nanogrids presents significant control challenges. [...] Read more.
This paper presents a novel open-loop modulation and control strategy for bidirectional, multi-source/load parallel AC-link power converters. While these converters offer advantages such as high-frequency operation and flexible power conversion capabilities, their application to complex systems such as nanogrids presents significant control challenges. Traditional control methods often struggle to efficiently manage power flow and charging/discharging processes, especially when dealing with multiple sources and loads of varying characteristics. To address these issues, this paper proposes a new control strategy that enables intelligent source and load selection while maintaining fast charging and discharging times. Simulation results demonstrate the effectiveness of the proposed approach. This research contributes to advancing the state-of-the-art in power electronics by providing a foundation for improved control of complex power conversion systems for renewable energy applications. Full article
Show Figures

Figure 1

28 pages, 8918 KB  
Article
A Multi-Channel Multi-Scale Spatiotemporal Convolutional Cross-Attention Fusion Network for Bearing Fault Diagnosis
by Ruixue Li, Guohai Zhang, Yi Niu, Kai Rong, Wei Liu and Haoxuan Hong
Sensors 2025, 25(18), 5923; https://doi.org/10.3390/s25185923 - 22 Sep 2025
Viewed by 208
Abstract
Bearings, as commonly used elements in mechanical apparatus, are essential in transmission systems. Fault diagnosis is of significant importance for the normal and safe functioning of mechanical systems. Conventional fault diagnosis methods depend on one or more vibration sensors, and their diagnostic results [...] Read more.
Bearings, as commonly used elements in mechanical apparatus, are essential in transmission systems. Fault diagnosis is of significant importance for the normal and safe functioning of mechanical systems. Conventional fault diagnosis methods depend on one or more vibration sensors, and their diagnostic results are often unsatisfactory under strong noise interference. To tackle this problem, this research develops a bearing fault diagnosis technique utilizing a multi-channel, multi-scale spatiotemporal convolutional cross-attention fusion network. At first, continuous wavelet transform (CWT) is applied to convert the raw 1D acoustic and vibration signals of the dataset into 2D time–frequency images. These acoustic and vibration time–frequency images are then simultaneously fed into two parallel structures. After rough feature extraction using ResNet, deep feature extraction is performed using the Multi-Scale Temporal Convolutional Module (MTCM) and the Multi-Feature Extraction Block (MFE). Next, these traits are input into a dual cross-attention mechanism module (DCA), where fusion is achieved using attention interaction. The experimental findings validate the efficacy of the proposed method using tests and comparisons on two bearing datasets. The testing findings validate that the suggested method outperforms the existing advanced multi-sensor fusion diagnostic methods. Compared with other existing multi-sensor fusion diagnostic methods, the proposed method was proven to outperform the five existing methods (1DCNN-VAF, MFAN-VAF, 2MNET, MRSDF, and FAC-CNN). Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
Show Figures

Figure 1

16 pages, 2827 KB  
Article
A Dual-Modality CNN Approach for RSS-Based Indoor Positioning Using Spatial and Frequency Fingerprints
by Xiangchen Lai, Yunzhi Luo and Yong Jia
Sensors 2025, 25(17), 5408; https://doi.org/10.3390/s25175408 - 2 Sep 2025
Viewed by 430
Abstract
Indoor positioning systems based on received signal strength (RSS) achieve indoor positioning by leveraging the position-related features inherent in spatial RSS fingerprint images. Their positioning accuracy and robustness are directly influenced by the quality of fingerprint features. However, the inherent spatial low-resolution characteristic [...] Read more.
Indoor positioning systems based on received signal strength (RSS) achieve indoor positioning by leveraging the position-related features inherent in spatial RSS fingerprint images. Their positioning accuracy and robustness are directly influenced by the quality of fingerprint features. However, the inherent spatial low-resolution characteristic of spatial RSS fingerprint images makes it challenging to effectively extract subtle fingerprint features. To address this issue, this paper proposes an RSS-based indoor positioning method that combines enhanced spatial frequency fingerprint representation with fusion learning. First, bicubic interpolation is applied to improve image resolution and reveal finer spatial details. Then, a 2D fast Fourier transform (2D FFT) converts the enhanced spatial images into frequency domain representations to supplement spectral features. These spatial and frequency fingerprints are used as dual-modality inputs for a parallel convolutional neural network (CNN) model with efficient multi-scale attention (EMA) modules. The model extracts modality-specific features and fuses them to generate enriched representations. Each modality—spatial, frequency, and fused—is passed through a dedicated fully connected network to predict 3D coordinates. A coordinate optimization strategy is introduced to select the two most reliable outputs for each axis (x, y, z), and their average is used as the final estimate. Experiments on seven public datasets show that the proposed method significantly improves positioning accuracy, reducing the mean positioning error by up to 47.1% and root mean square error (RMSE) by up to 54.4% compared with traditional and advanced time–frequency methods. Full article
(This article belongs to the Section Navigation and Positioning)
Show Figures

Figure 1

23 pages, 16714 KB  
Article
A Dual-Stream Dental Panoramic X-Ray Image Segmentation Method Based on Transformer Heterogeneous Feature Complementation
by Tian Ma, Jiahui Li, Zhenrui Dang, Yawen Li and Yuancheng Li
Technologies 2025, 13(7), 293; https://doi.org/10.3390/technologies13070293 - 8 Jul 2025
Cited by 2 | Viewed by 670
Abstract
To address the widespread challenges of significant multi-category dental morphological variations and interference from overlapping anatomical structures in panoramic dental X-ray images, this paper proposes a dual-stream dental segmentation model based on Transformer heterogeneous feature complementarity. Firstly, we construct a parallel architecture comprising [...] Read more.
To address the widespread challenges of significant multi-category dental morphological variations and interference from overlapping anatomical structures in panoramic dental X-ray images, this paper proposes a dual-stream dental segmentation model based on Transformer heterogeneous feature complementarity. Firstly, we construct a parallel architecture comprising a Transformer semantic parsing branch and a Convolutional Neural Network (CNN) detail capturing pathway, achieving collaborative optimization of global context modeling and local feature extraction. Furthermore, a Pooling-Cooperative Convolutional Module was designed, which enhances the model’s capability in detail extraction and boundary localization through weighted centroid features of dental structures and a latent edge extraction module. Finally, a Semantic Transformation Module and Interactive Fusion Module are constructed. The Semantic Transformation Module converts geometric detail features extracted from the CNN branch into high-order semantic representations compatible with Transformer sequential processing paradigms, while the Interactive Fusion Module applies attention mechanisms to progressively fuse dual-stream features, thereby enhancing the model’s capability in holistic dental feature extraction. Experimental results demonstrate that the proposed method achieves an IoU of 91.49% and a Dice coefficient of 94.54%, outperforming current segmentation methods across multiple evaluation metrics. Full article
Show Figures

Figure 1

19 pages, 3484 KB  
Article
Rolling Bearing Fault Diagnosis Model Based on Multi-Scale Depthwise Separable Convolutional Neural Network Integrated with Spatial Attention Mechanism
by Zhixin Jin, Xudong Hu, Hongli Wang, Shengyu Guan, Kaiman Liu, Zhiwen Fang, Hongwei Wang, Xuesong Wang, Lijie Wang and Qun Zhang
Sensors 2025, 25(13), 4064; https://doi.org/10.3390/s25134064 - 30 Jun 2025
Cited by 1 | Viewed by 551
Abstract
In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that [...] Read more.
In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that combines a spatial attention (SA) mechanism with a multi-scale depthwise separable convolution module. The proposed approach first employs the Gramian angular difference field (GADF) to convert raw signals. This conversion maps the temporal characteristics of the signal into an image format that intrinsically preserves both temporal dynamics and phase relationships. Subsequently, the model architecture incorporates a spatial attention mechanism and a multi-scale depthwise separable convolutional module. Guided by the attention mechanism to concentrate on discriminative feature regions and to suppress noise, the convolutional component efficiently extracts hierarchical features in parallel through the multi-scale receptive fields. Furthermore, the trained model serves as a pre-trained network and is transferred to novel variable-condition environments to enhance diagnostic accuracy in few-shot scenarios. The effectiveness of the proposed model was evaluated using bearing datasets and field-collected industrial data. Experimental results confirm that the proposed model offers outstanding fault recognition performance and generalization capability across diverse working conditions, small-sample scenarios, and real industrial environments. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
Show Figures

Figure 1

24 pages, 4986 KB  
Article
Research on Multi-Cycle Injection–Production Displacement Characteristics and Factors Influencing Storage Capacity in Oil Reservoir-Based Underground Gas Storage
by Yong Tang, Peng Zheng, Zhitao Tang, Minmao Cheng and Yong Wang
Energies 2025, 18(13), 3330; https://doi.org/10.3390/en18133330 - 25 Jun 2025
Viewed by 988
Abstract
In order to clarify the feasibility of constructing a gas storage reservoir through synergistic injection and production in the target reservoir, micro-displacement experiments and multi-cycle injection–production experiments were conducted. These experiments investigated the displacement characteristics and the factors affecting storage capacity during the [...] Read more.
In order to clarify the feasibility of constructing a gas storage reservoir through synergistic injection and production in the target reservoir, micro-displacement experiments and multi-cycle injection–production experiments were conducted. These experiments investigated the displacement characteristics and the factors affecting storage capacity during the multi-cycle injection–production process for converting the target reservoir into a gas storage facility. Microscopic displacement experiments have shown that the remaining oil is primarily distributed in the dead pores and tiny pores of the core in the form of micro-bead chains and films. The oil displacement efficiency of water flooding followed by gas flooding is 18.61% higher than that of gas flooding alone, indicating that the transition from water flooding to gas flooding can further reduce the liquid saturation and increase the storage capacity space by 2.17%. Single-tube long-core displacement experiments indicate that, during the collaborative construction of a gas storage facility, the overall oil displacement efficiency without a depletion process is approximately 24% higher than that with a depletion process. This suggests that depletion production is detrimental to enhancing oil recovery and expanding the capacity of the gas storage facility. During the cyclic injection–production stage, the crude oil recovery rate increases by 1% to 4%. As the number of cycles increases, the incremental oil displacement efficiency in each stage gradually decreases, and so does the increase in cumulative oil displacement efficiency. Better capacity expansion effects are achieved when gas is produced simultaneously from both ends. Parallel double-tube long-core displacement experiments demonstrate that, when the permeability is the same, the oil displacement efficiencies during the gas flooding stage and the cyclic injection–production stage are essentially identical. When there is a permeability contrast, the oil displacement efficiency of the high-permeability core is 9.56% higher than that of the low-permeability core. The ratio of the oil displacement efficiency between the high-permeability end and the low-permeability end is positively correlated with the permeability contrast; the greater the permeability contrast, the larger the ratio. The research findings can provide a reference for enhancing oil recovery and expanding the capacity of the target reservoir when it is converted into a gas storage facility. Full article
Show Figures

Figure 1

19 pages, 7758 KB  
Article
A Multi-Vector Modulated Model Predictive Control Based on Coordinated Control Strategy of a Photovoltaic-Storage Three-Port DC–DC Converter
by Qihui Feng, Meng Zhang, Yutao Xu, Chao Zhang, Dunhui Chen and Xufeng Yuan
Energies 2025, 18(12), 3208; https://doi.org/10.3390/en18123208 - 19 Jun 2025
Viewed by 559
Abstract
As a core component of the photovoltaic-storage microgrid systems, three-port DC–DC converters have attracted significant attention in recent years. This paper proposes a multi-vector modulated model predictive control (MVM-MPC) method based on vector analysis for a non-isolated three-port DC–DC converter formed by paralleling [...] Read more.
As a core component of the photovoltaic-storage microgrid systems, three-port DC–DC converters have attracted significant attention in recent years. This paper proposes a multi-vector modulated model predictive control (MVM-MPC) method based on vector analysis for a non-isolated three-port DC–DC converter formed by paralleling two bidirectional DC–DC converters. The proposed modulated MPC method utilizes three basic vectors to calculate the optimal switching sequence for minimizing the error vector. It can significantly minimize voltage ripple while maintaining the nonlinear and dynamic performance characteristics of a traditional MPC. MATLAB/Simulink R2024a simulations and hardware-in-loop (HIL) experimental results demonstrate that, compared with finite control set MPC and traditional two-vector modulated MPC methods, the proposed approach achieves remarkable reductions in current ripple and voltage ripple, along with excellent dynamic performance featuring smooth mode-switching. Full article
Show Figures

Figure 1

22 pages, 2386 KB  
Article
A Stochastic Framework for Saint-Venant Torsion in Spherical Shells: Monte Carlo Implementation of the Feynman–Kac Approach
by Behrouz Parsa Moghaddam, Mahmoud A. Zaky, Alireza Sedaghat and Alexandra Galhano
Symmetry 2025, 17(6), 878; https://doi.org/10.3390/sym17060878 - 4 Jun 2025
Viewed by 564
Abstract
This research introduces an innovative probabilistic method for examining torsional stress behavior in spherical shell structures through Monte Carlo simulation techniques. The spherical geometry of these components creates distinctive computational difficulties for conventional analytical and deterministic numerical approaches when solving torsion-related problems. The [...] Read more.
This research introduces an innovative probabilistic method for examining torsional stress behavior in spherical shell structures through Monte Carlo simulation techniques. The spherical geometry of these components creates distinctive computational difficulties for conventional analytical and deterministic numerical approaches when solving torsion-related problems. The authors develop a comprehensive mesh-free Monte Carlo framework built upon the Feynman–Kac formula, which maintains the geometric symmetry of the domain while offering a probabilistic solution representation via stochastic processes on spherical surfaces. The technique models Brownian motion paths on spherical surfaces using the Euler–Maruyama numerical scheme, converting the Saint-Venant torsion equation into a problem of stochastic integration. The computational implementation utilizes the Fibonacci sphere technique for achieving uniform point placement, employs adaptive time-stepping strategies to address pole singularities, and incorporates efficient algorithms for boundary identification. This symmetry-maintaining approach circumvents the mesh generation complications inherent in finite element and finite difference techniques, which typically compromise the problem’s natural symmetry, while delivering comparable precision. Performance evaluations reveal nearly linear parallel computational scaling across up to eight processing cores with efficiency rates above 70%, making the method well-suited for multi-core computational platforms. The approach demonstrates particular effectiveness in analyzing torsional stress patterns in thin-walled spherical components under both symmetric and asymmetric boundary scenarios, where traditional grid-based methods encounter discretization and convergence difficulties. The findings offer valuable practical recommendations for material specification and structural design enhancement, especially relevant for pressure vessel and dome structure applications experiencing torsional loads. However, the probabilistic characteristics of the method create statistical uncertainty that requires cautious result interpretation, and computational expenses may surpass those of deterministic approaches for less complex geometries. Engineering analysis of the outcomes provides actionable recommendations for optimizing material utilization and maintaining structural reliability under torsional loading conditions. Full article
(This article belongs to the Section Engineering and Materials)
Show Figures

Figure 1

19 pages, 1196 KB  
Article
Fixed-Time Event-Triggered Consensus Power-Sharing Control for Hybrid AC/DC Microgrid Parallel Bi-Directional Interconnect Converters
by Junjie Wu, Siyu Lyu, Benhua Qian, Chuanyu Jiang, Ziqaing Song and Jun Xiao
Mathematics 2025, 13(9), 1534; https://doi.org/10.3390/math13091534 - 7 May 2025
Viewed by 447
Abstract
Although power sharing in hybrid AC/DC microgrids (HMGs) has been widely researched, traditional power-sharing control is based on an infinite time consensus method, and the communication bandwidth is large. Therefore, this paper proposes a power-sharing strategy for HMG parallel bi-directional interconnected converters (BICs) [...] Read more.
Although power sharing in hybrid AC/DC microgrids (HMGs) has been widely researched, traditional power-sharing control is based on an infinite time consensus method, and the communication bandwidth is large. Therefore, this paper proposes a power-sharing strategy for HMG parallel bi-directional interconnected converters (BICs) considering fixed-time stabilization and event-triggered control. Firstly, every BIC has a well-designed local control method to generate the corresponding power reference for the BIC, which provides the basis for further research. Secondly, a fixed-time-based power-sharing controller is designed in order to improve the convergence speed of power-sharing control for HMG parallel BICs. Finally, an event-triggered method is applied to reduce the system communication bandwidth and the frequency of controller updates. In this paper, we first transform the parallel BIC control problem into a multi-agent system (MAS) consensus problem. Furthermore, a fixed time based on an event trigger consensus method is proposed at the secondary control level. The energy flow between the two subgrids can be shared according to the rated power of each BIC. Finally, the effectiveness of the proposed fixed-time event-triggered power-sharing control is verified through simulation and experiments. Full article
Show Figures

Figure 1

30 pages, 19525 KB  
Article
Disease Monitoring and Characterization of Feeder Road Network Based on Improved YOLOv11
by Ying Fan, Kun Zhi, Haichao An, Runyin Gu, Xiaobing Ding and Jianhua Tang
Electronics 2025, 14(9), 1818; https://doi.org/10.3390/electronics14091818 - 29 Apr 2025
Cited by 1 | Viewed by 849
Abstract
In response to the challenges of the low accuracy and high misdetection and omission rate of disease detection on feeder roads, an improved Rural-YOLO (SAConv-C2f+C2PSA_CAA+MCSAttention+WIOU) disease detection algorithm is proposed in this paper, which is an enhanced target detection framework based on the [...] Read more.
In response to the challenges of the low accuracy and high misdetection and omission rate of disease detection on feeder roads, an improved Rural-YOLO (SAConv-C2f+C2PSA_CAA+MCSAttention+WIOU) disease detection algorithm is proposed in this paper, which is an enhanced target detection framework based on the YOLOv11 architecture, for the identification of common diseases in the complex feeder road environment. The proposed methodology introduces four key innovations: (1) Switchable Atrous Convolution (SAConv) is introduced into the backbone network to enhance multiscale disease feature extraction under occlusion conditions; (2) Multi-Channel and Spatial Attention (MCSAttention) is constructed in the feature fusion process, and the weight distribution of multiscale diseases is adjusted through adaptive weight redistribution. By adjusting the weight distribution, the model’s sensitivity to subtle disease features is improved. To enhance its ability to discriminate between different disease types, Cross Stage Partial with Parallel Spatial Attention and Channel Adaptive Aggregation (C2PSA_CAA) is constructed at the end of the backbone network. (3) To mitigate category imbalance issues, Weighted Intersection over Union loss (WIoU_loss) is introduced, which helps optimize the bounding box regression process in disease detection and improve the detection of relevant diseases. Based on experimental validation, Rural-YOLO demonstrated superior performance with minimal computational overhead. Only 0.7 M additional parameters is required, and an 8.4% improvement in recall and a 7.8% increase in mAP50 were achieved compared to the initial models. The optimized architecture also reduced the model size by 21%. The test results showed that the proposed model achieved 3.28 M parameters with a computational complexity of 5.0 GFLOPs, meeting the requirements for lightweight deployment scenarios. Cross-validation on multi-scenario public datasets was carried out, and the model’s robustness across diverse road conditions. In the quantitative experiments, the center skeleton method and the maximum internal tangent circle method were used to calculate crack width, and the pixel occupancy ratio method was used to assess the area damage degree of potholes and other diseases. The measurements were converted to actual physical dimensions using a calibrated scale of 0.081:1. Full article
Show Figures

Figure 1

23 pages, 14157 KB  
Article
A Spatial–Frequency Combined Transformer for Cloud Removal of Optical Remote Sensing Images
by Fulian Zhao, Chenlong Ding, Xin Li, Runliang Xia, Caifeng Wu and Xin Lyu
Remote Sens. 2025, 17(9), 1499; https://doi.org/10.3390/rs17091499 - 23 Apr 2025
Cited by 1 | Viewed by 1050
Abstract
Cloud removal is a vital preprocessing step in optical remote sensing images (RSIs), directly enhancing image quality and providing a high-quality data foundation for downstream tasks, such as water body extraction and land cover classification. Existing methods attempt to combine spatial and frequency [...] Read more.
Cloud removal is a vital preprocessing step in optical remote sensing images (RSIs), directly enhancing image quality and providing a high-quality data foundation for downstream tasks, such as water body extraction and land cover classification. Existing methods attempt to combine spatial and frequency features for cloud removal, but they rely on shallow feature concatenation or simplistic addition operations, which fail to establish effective cross-domain synergistic mechanisms. These approaches lead to edge blurring and noticeable color distortions. To address this issue, we propose a spatial–frequency collaborative enhancement Transformer network named SFCRFormer, which significantly improves cloud removal performance. The core of SFCRFormer is the spatial–frequency combined Transformer (SFCT) block, which implements cross-domain feature reinforcement through a dual-branch spatial attention (DBSA) module and frequency self-attention (FreSA) module to effectively capture global context information. The DBSA module enhances the representation of spatial features by decoupling spatial-channel dependencies via parallelized feature refinement paths, surpassing the performance of traditional single-branch attention mechanisms in maintaining the overall structure of the image. FreSA leverages fast Fourier transform to convert features into the frequency domain, using frequency differences between object and cloud regions to achieve precise cloud detection and fine-grained removal. In order to further enhance the features extracted by DBSA and FreSA, we design the dual-domain feed-forward network (DDFFN), which effectively improves the detail fidelity of the restored image by multi-scale convolution for local refinement and frequency transformation for global structural optimization. A composite loss function, incorporating Charbonnier loss and Structural Similarity Index (SSIM) loss, is employed to optimize model training and balance pixel-level accuracy with structural fidelity. Experimental evaluations on the public datasets demonstrate that SFCRFormer outperforms state-of-the-art methods across various quantitative metrics, including PSNR and SSIM, while delivering superior visual results. Full article
Show Figures

Figure 1

25 pages, 1997 KB  
Review
Transient Synchronization Stability in Grid-Following Converters: Mechanistic Insights and Technological Prospects—A Review
by Yang Liu, Lin Zhu, Xinya Xu, Dongrui Li, Zhiwei Liang and Nan Ye
Energies 2025, 18(8), 1975; https://doi.org/10.3390/en18081975 - 11 Apr 2025
Cited by 2 | Viewed by 1371
Abstract
This paper investigates the transient synchronization stability mechanisms and technological advancements associated with grid-following (GFL) converters, providing a systematic review of the current research landscape and future directions in this field. The current literature lacks a comprehensive understanding of how outer-loop control dynamics [...] Read more.
This paper investigates the transient synchronization stability mechanisms and technological advancements associated with grid-following (GFL) converters, providing a systematic review of the current research landscape and future directions in this field. The current literature lacks a comprehensive understanding of how outer-loop control dynamics and grid-converter interactions critically influence transient stability mechanisms. This oversight often leads to incomplete or overly simplistic stability assessments, particularly under high penetration of renewable energy sources. Furthermore, existing stability criteria and analytical methodologies do not adequately address the compounded challenges arising from multi-control-loop coupling effects and systems with multiple parallel converters. These limitations underscore the inability of conventional methodologies to holistically model the transient synchronization behavior of GFL converters in modern power-electronics-dominated grids. To address these gaps, this work synthesizes a comprehensive review of modeling frameworks, analytical methodologies, transient stability mechanisms, and influence factors specific to GFL converters. First, based on the fundamental differences between synchronous generators and GFL, this paper summarizes the second-order equivalent model derived from phase-locked loop (PLL) dynamic. It conducts a comparative analysis of the applicability and limitations of conventional stability assessment methods, such as the equal-area criterion, phase portrait method, and Lyapunov functions, within power-electronics-dominated systems. It highlights potential mechanistic misinterpretations arising from neglecting outer-loop control and grid interactions. Second, the paper delineates the principal challenges inherent in the transient synchronization stability analysis of GFL converters. These challenges encompass the dynamic influences of multi-control-loop coupling effects and the imperative for advancing stability criterion research in systems with multiple parallel converters. Building on existing studies, the paper further explores innovative applications of artificial intelligence (AI) in transient stability assessment, including stability prediction based on deep learning, data-physics hybrid modeling, and human–machine collaborative optimization strategies. It emphasizes that enhancing model interpretability and dynamic generalization capabilities will be critical future directions. Finally, by addressing these gaps, this work provides theoretical foundations and technical references for transient synchronization stability analysis and control in high-penetration inverter-based resources (IBRs) grids. Full article
(This article belongs to the Special Issue Modeling, Simulation and Optimization of Power System)
Show Figures

Figure 1

17 pages, 6778 KB  
Article
A New Two-Stage Multiple-Parallel-Channel LED Driver Using a CLL-C Resonant Converter and Time Division Control Technique
by Duc Hung Tran, Zeeshan Waheed and Woojin Choi
Energies 2025, 18(5), 1215; https://doi.org/10.3390/en18051215 - 2 Mar 2025
Cited by 1 | Viewed by 1064
Abstract
This paper introduces a new two-stage multi-parallel-channel LED driver using a CLL-C resonant converter as the first stage and a Time Division Multiple Control circuit as the second stage. The first stage of the proposed converter topology has been developed from CLL-C topology [...] Read more.
This paper introduces a new two-stage multi-parallel-channel LED driver using a CLL-C resonant converter as the first stage and a Time Division Multiple Control circuit as the second stage. The first stage of the proposed converter topology has been developed from CLL-C topology with an additional inductor in the primary side and a capacitor in the secondary side. The converter provides a constant current at a resonant frequency with a Zero Phase Angle (ZPA), thus achieving Zero Voltage Switching (ZVS) turn-on, nearly Zero Current Switching (ZCS) turn-off for the switches, and ZCS for the diodes. The Time Division Multiple Control (TDMC) circuit was applied in the second stage to share the balanced current to each LED string. A 200 W prototype with five output channels was implemented to verify the superior advantages of the proposed topology with a maximum efficiency of 95.05%. Full article
Show Figures

Figure 1

20 pages, 5644 KB  
Article
Optimal Control of the Green Low-Carbon Base Station System Based on the Concept of Energy Router
by Guangyi Shao, Tong Liu, Yanjia Wang, Zongping Wang, Yuhui Wang and Qi Wang
Processes 2025, 13(1), 288; https://doi.org/10.3390/pr13010288 - 20 Jan 2025
Viewed by 1172
Abstract
This paper establishes an energy router system for green and low-carbon base stations, a −48 V DC bus multi-source parallel system including photovoltaic, wind turbine, grid power, and energy storage batteries, and studies the control strategy managing system energy distribution. Firstly, from the [...] Read more.
This paper establishes an energy router system for green and low-carbon base stations, a −48 V DC bus multi-source parallel system including photovoltaic, wind turbine, grid power, and energy storage batteries, and studies the control strategy managing system energy distribution. Firstly, from the perspective of system physical layer design, we combine multiple power circuits to complete the design of the system’s modular power conversion circuits and linearize the power electronic converters for modeling and analyze their stability. Different control strategies are proposed for different power converters to ensure the stable operation of the system. Secondly, from the perspective of overall energy optimal control, we construct system operating states and control algorithms based on the switching strategy of the energy router between different operating states of the system and use a heuristic algorithm based on rolling optimization to achieve the optimal control of the system at the physical level. Finally, we use Simulink to simulate and verify the state switching of the multi-source system, analyze control results according to the actual typical working conditions, and conduct experiments on the overall system. Simulations demonstrate that the system can achieve smooth transitions among various modes. The results of actual experiments show that the established multi-source system can save 60.28% of energy utilization costs annually, and the bus voltage control strategy can be effectively implemented while maintaining an appropriate voltage deviation. Full article
(This article belongs to the Topic Power System Dynamics and Stability, 2nd Edition)
Show Figures

Figure 1

Back to TopTop