Search Results (576)

Conventional approaches for distributed generation (DG) planning often fall short in addressing operational demands and regional control requirements within distribution networks. To overcome these limitations, this paper introduces a cluster-oriented DG planning method. In terms of cluster partitioning, this study breaks through the limitations of traditional methods that solely focus on electrical parameters or single functions. Innovatively, it partitions the distribution network by comprehensively considering multiple critical factors such as system grid structure, nodal load characteristics, electrical coupling strength, and power balance, thereby establishing a unique multi-level grid structure of **distribution network—cluster—node**. This partitioning approach not only effectively reduces inter-cluster reactive power transmission and enhances regional power self-balancing capabilities but also lays a solid foundation for the precise planning of subsequent distributed energy resources. It represents a functional expansion that existing cluster partitioning methods have not fully achieved. In the construction of the planning model, a two-layer coordinated siting and sizing planning model for distributed photovoltaics (DPV) and energy storage systems (ESS) is proposed based on cluster partitioning. In contrast to traditional models, this model for the first time considers the interaction between power source planning and system operation across different time scales. The upper layer aims to minimize the annual comprehensive cost by optimizing the capacity and power allocation of DPV and ESS in each cluster. The lower layer focuses on minimizing system network losses to precisely determine the PV connection capacity of each node within the cluster and the grid connection locations of ESS, achieving comprehensive optimization from macro to micro levels. For the solution algorithm, a two-layer iterative hybrid particle swarm algorithm (HPSO) embedded with power flow calculation is designed. Compared to traditional single particle swarm algorithms, HPSO integrates power flow calculations, allowing for a more accurate consideration of the actual operating conditions of the power grid and avoiding the issue in traditional methods where the current and voltage distribution are often neglected in the optimization process. Additionally, HPSO, through its two-layer iterative approach, is able to better balance global and local search, effectively improving the solution efficiency and accuracy. This algorithm integrates the advantages of the particle swarm optimization algorithm and the binary particle swarm optimization algorithm, achieving iterative solutions through efficient information exchange between the two layers of particle swarms. Compared with conventional particle swarm algorithms and other related algorithms, it represents a qualitative leap in computational efficiency and accuracy, enabling faster and more accurate handling of complex planning problems. Case studies on a real 10 kV distribution network validate the practicality of the proposed framework and the robustness of the solution technique. Full article

This paper investigates the problem of robust fusion state estimation for multi-sensor systems under the influence of time-correlated fading channels, incorporating a dynamic event-triggered mechanism (DETM). The randomly occurring parameter uncertainties are characterized by a stochastic variable following a Bernoulli distribution, while sensor measurements are transmitted to the corresponding estimators through time-correlated fading channels and dynamic event-triggered mechanisms. The DETM dynamically adjusts the triggering threshold via regulation and memory factors, enhancing adaptability in data transmission while effectively reducing redundant communication overhead. Furthermore, an augmented state model is constructed by integrating system states, channel coefficients, and the event-triggering mechanism, thereby comprehensively capturing the impact of dynamic environments on state estimation. Based on this model, a local state estimation algorithm is designed to ensure the convergence of the upper bound of the local estimation error covariance, which is further minimized at each time step through adaptive adjustment of local estimator gains. Subsequently, the local estimates obtained from multiple estimators are fused using the covariance intersection fusion strategy, improving the overall estimation accuracy. Simulation experiments demonstrate that the proposed recursive fusion state estimation framework significantly reduces communication overhead and enhances estimation performance in the presence of both time-correlated fading channels and randomly occurring parameter uncertainties, while maintaining an acceptable computational cost. Compared to the traditional Kalman filtering method, the proposed recursive fusion state estimation algorithm improves estimation accuracy by 58% while increasing computational time by only 32.4%. Additionally, the DETM effectively reduces communication frequency by 36.7% Full article

Offshore wind power has a broad development prospect, but with the development of offshore wind farms to the deep sea, the traditional high-voltage AC transmission has been difficult to adapt to the offshore wind power transmission distance and transmission capacity needs. A flexible DC transmission system applying modular multilevel converter is a common scheme for offshore wind power, which has been put into use in actual projects, but it is still facing the problems of high cost of offshore converter station platforms and high loss of collector systems. In order to improve the economy and reliability of the medium- and long-distance offshore wind power delivery systems, this paper proposes a diode rectifier-based medium-frequency AC pooling soft-direct low-cost delivery system for medium- and long-distance offshore wind power. Firstly, the mid-frequency equivalent model of the diode converter is established, and the influence of topology and frequency enhancement on the parameters of the main circuit equipment is analysed; then, the distribution parameters and transmission capacity of the mid-frequency cable are calculated based on the finite element modelling of the marine cable, and the transmission losses of the mid-frequency AC pooling system are then calculated, including the collector losses, converter valve losses, and transformer losses, etc. Finally, an economic analysis is carried out based on a specific example, comparing with the Jiangsu Rudong offshore wind power transmission project, in order to verify the economy of the medium-frequency AC flexible and direct transmission system of the medium- and long-distance offshore wind power using diode rectifier technology. Full article

This research investigates the role of fossil fuel energy, renewable energy, and education in terms of years of schooling and mean years of schooling on the economic growth of 19 selected Sub-Saharan African countries. The primary objective is to assess whether renewable energy and educational attainment serve as viable long-term drivers of economic development in a region still heavily reliant on fossil fuels. We employed the newly developed and robust econometric estimators, including “Residual Augmented Least Squares (RALS) co-integration”, to estimate long-term links among the facets of study. Moreover, “Pooled Mean Group–Autoregressive Distributed Lag model (PMG-ARDL) and Quantile Autoregressive Distributed Lag (QARDL)” econometric estimator was employed to estimate the long and short coefficients of the antecedents of study. The estimations obtained from the PMG-ARDL and QARDL estimators provide evidence that the coefficients of fossil fuel energy and renewable energy on economic growth are positive. But surprisingly, the magnitude of renewable energy is greater than fossil fuel energy in Sub-Saharan countries that still depend on fossil fuels. Moreover, human capital and capital stock boost economic growth in the countries studied. The outcomes reveal that not only quality but also quantity of education play a vital role in boosting economic development. To deepen the understanding of the observed effects, the study also explores the transmission channels through which renewable energy and education foster economic growth. Renewable energy contributes by lowering the marginal cost of electricity, encouraging green industrial transformation, and serving as a catalyst for technological innovation. Concurrently, improvements in education—measured by both expected and mean years of schooling—elevate labor productivity and facilitate the absorption and diffusion of new technologies across sectors, thereby stimulating sustained economic performance. The empirical results provide valuable insights for government officials and policymakers in specific Sub-Saharan African countries. Full article

With the continuous expansion of power grids, traditional manual inspection methods face numerous challenges, including low efficiency, high costs, and significant safety risks. As critical infrastructure in power transmission systems, power grid towers require intelligent recognition and monitoring to ensure the reliable and stable operation of power grids. However, existing methods struggle with accuracy and efficiency when processing large-scale point cloud data in complex environments. To address these challenges, this paper presents a comprehensive approach combining multi-sensor fusion and deep learning for power grid tower recognition. A data acquisition scheme that integrates LiDAR and a binocular depth camera, implementing the FAST-LIO algorithm, is proposed to achieve the spatiotemporal synchronization and fusion of sensor data. This integration enables the construction of a colored point cloud dataset with rich visual and geometric features. Based on the RandLA-Net framework, an efficient processing method for large-scale point cloud segmentation is developed and optimized explicitly for power grid tower scenarios. Experimental validation demonstrates that the proposed method achieves 90.8% precision in tower body recognition and maintains robust performance under various environmental conditions. The proposed approach successfully processes point cloud data containing over ten million points while effectively handling challenges such as uneven point distribution and environmental interference. These results validate the reliability of the proposed method in providing technical support for intelligent inspection and the management of power grid infrastructure. Full article

In this paper, we present a novel passive dual-tuned magnetic metasurface, which can enhance the field distribution produced by a closely placed radio-frequency coil for both ¹H and ²³Na 1.5 T MRI imaging. In particular, the proposed solution comprises a 5 × 5 capacitively loaded array, in which each unit-cell is composed of two concentric spiral coils. Specifically, the unit-cell internal spiral coil operates at the proton Larmor frequency (64 MHz), whereas the external is at the sodium one (17 MHz). Therefore, the paper aims to demonstrate the possibility of enhancing the magnetic field distribution in transmission and reception for 1.5 T MRI scanners by using the same metasurface configuration for imaging both nuclei, thus drastically simplifying the required instrumentation. We first describe the theoretical model used to design and synthetize the dual-tuned magnetic metasurface. Next, full-wave simulations are carried out to validate the approach. Finally, we report the experimental results acquired by testing the fabricated prototype at the workbench, observing a good agreement with the theoretical design and the numerical simulations. In particular, the metasurface increases the transmission efficiency T_x in presence of a biological phantom by a factor 3.5 at 17 MHz and by a factor 5 at 64 MHz, respectively. The proposed solution can pave the way for MRI multi-nuclei diagnostic technique with better images quality, simultaneously reducing the scanning time, the invasiveness on the patient and the overall costs. Full article

Triple glazing has garnered widespread attention as a key solution that balances sound insulation, building energy efficiency, and thickness-related cost-effectiveness. This study evaluated the acoustic performance of triple glazing, focusing on how cavity and glass thickness combinations performed under a fixed total thickness. Laboratory measurements were first conducted, which revealed that asymmetric triple glazing performed better for sound insulation than symmetric combinations. Based on this, 28 triple glazing combinations with a total thickness of 42 mm were selected to build finite element models, including window frames. These models were used to calculate the sound transmission loss curves, the weighted sound reduction index, and the distributions of structural stress and the displacement amplitude. The best sound insulation performance was achieved when the glass panes had unequal thicknesses, with the thickest pane positioned on the outermost layer (either on the source or the receiver side). The weighted sound reduction index $R_{\mathrm{w}}$ increased by 6 dB, and the combined value of $R_{\mathrm{w}}$ and the traffic noise spectrum correction ($R_{\mathrm{w}}$ + $C_{\mathrm{tr}}$) improved by up to 11 dB. The optimal combination was 8-12A-6-12A-4, and $R_{\mathrm{w}}$ reached 44 dB. Combinations with the thickest pane in the middle layer or with equal pane thicknesses exhibited a worse sound insulation performance. Variations in the cavity thickness had a smaller effect on sound insulation than changes in the glass’s thickness. A reasonable combination of glass thicknesses in triple glazing effectively reduced the displacement amplitude and improved the sound insulation performance. Full article

The dissemination of rumors can lead to significant economic damage and pose a grave threat to social harmony and the stability of people’s livelihoods. Consequently, curbing the dissemination of rumors is of paramount importance. The model in the text assumes that the population is homogeneous in terms of transmission behavior. This homogeneity is essentially a manifestation of translational symmetry. This paper undertakes a thorough examination of the impact of time delay on the dissemination of rumors within social networking services. We have developed a model for rumor dissemination, establishing the positivity and boundedness of its solutions, and identified the existence of an equilibrium point. The study further involved determining the critical threshold of the proposed model, accompanied by a comprehensive examination of its Hopf bifurcation characteristics. In the expression of the threshold $R_0$, the parameters appear in a symmetric form, reflecting the balance between dissemination and suppression mechanisms. Furthermore, detailed investigations were carried out to assess both the localized and global stability properties of the system’s equilibrium states. In stability analysis, the symmetry in the distribution of characteristic equation roots determines the system’s dynamic behavior. Through numerical simulations, we analyzed the potential impacts and theoretically examined the factors influencing rumor dissemination, thereby validating our theoretical analysis. An optimal control strategy was formulated, and three control variables were incorporated to describe the strategy. The optimization framework incorporates a specifically designed cost function that simultaneously accounts for infection reduction and resource allocation efficiency in control strategy implementation. The optimal control strategy proposed in the study involves a comparison between symmetric and asymmetric interventions. Symmetric control measures may prove inefficient, whereas asymmetric control demonstrates higher efficacy—highlighting a trade-off in symmetry considerations for optimization problems. Full article

A grid-connected converter is the interface between renewable energy power generation systems, such as solar power generation, wind power, hydropower, etc., and the power grid, responsible for the stable and efficient transmission of electric energy generated by renewable energy power generation systems to the grid. In order to realize local access for distributed photovoltaic power generation devices and energy storage devices, a composite three-port converter has the advantages of small size, low cost and high power density compared with a combined three-port converter. In view of the current problems of the existing compound three-port (AC/DC/DC) converters, such as DC and AC circulating current in current composite three-port converters and the harmonic control problem, the proposed compound three-port topology consists of a full-bridge inverter with six switching tubes, a zigzag transformer, two sets of filter inductors and two filter capacitors. Among them, the power frequency transformer adopts the zigzag connection method, which can effectively restrain the AC circulation and eliminate the DC magnetic flux of the iron core while introducing the third port. Firstly, the principle of AC/DC and DC/DC power conversion in the composite three-port topology is analyzed, which has higher efficiency than other topologies. Secondly, the topology control strategy is analyzed, and a two-loop hybrid current control method with improved current loop is proposed. When the DC-side voltage fluctuates, the DC offset of the battery can effectively improve the stability of the network side. Through the MATLAB/Simulink simulation experiment platform, the high efficiency of energy conversion and stable grid-connected operation characteristics are verified. Finally, the experiment of integrating into the power grid was carried out. Experiments were used to verify the effectiveness and feasibility of the proposed topology and strategy. The experimental results show that Total Harmonic Distortion (THD) can be controlled below 3%. Full article

This research presents a novel methodology based on data analysis for improving voltage stability in transmission systems. The proposal aims to determine a single distributed generator’s optimal location and sizing using the Fast Voltage Stability Index (FVSI) as the primary metric under $N-1$ contingency conditions. The developed strategy systematically identifies the most critical transmission lines close to instability through a frequency analysis of the FVSI in the base case and across multiple contingency scenarios. Subsequently, the weak buses associated with the most critical line are determined, on which critical load increases are simulated. The Distributed Generator (DG) sizing and location parameters are then optimized through a statistical analysis of the inflection point and the rate of change of the FVSI statistical parameters. The methodology is validated in three case studies: IEEE systems with 14, 30, and 118 buses, demonstrating its scalability and effectiveness. The results show significant reductions in FVSI values and notable improvements in voltage profiles under stress and contingency conditions. For example, in the 30-bus IEEE system, the average FVSI for all contingency scenarios was reduced by 26% after applying the optimal solution. At the same time, the voltage profiles even exceeded those of the base case. This strategy represents a significant contribution, as it is capable of improving the stability of the electrical power system in all $N-1$ contingency scenarios with overload at critical nodes. Using a single DG as a low-cost and highly effective corrective measure, the proposed approach outperforms conventional solutions through statistical analysis and a data-centric approach. Full article

In response to the Cybersecurity Law, organizations face numerous management and technical requirements. Detection techniques such as vulnerability scanning and penetration testing are employed to identify risks. Addressing these vulnerabilities demands substantial manpower, time, and financial resources. Security concerns also arise during digital file transmission and remediation efforts. This study proposes a security detection platform with step-by-step implementation guidelines, enabling resource-limited units to replicate the setup and address security gaps. It compares detection results between open-source and commercial tools, highlighting key differences and offering remediation strategies. Numerous digital files (e.g., test reports) are generated during testing. To ensure secure storage and sharing, the system integrates IOTA’s distributed ledger and IPFS, generating HASH values and uploading files on-chain to preserve integrity and authenticity. The objective is to deliver a scalable, cost-effective security detection framework that enhances system resilience while minimizing resource consumption. Full article

Selecting grid loss reduction strategies is crucial for energy-saving transformations, particularly in the context of electricity transmission and distribution pricing reforms. The optimization of strategic selection is not easy due to the vast number of grid devices, which leads to a multitude of possible strategy combinations. This paper presents an optimal model for selecting loss reduction strategies, aiming to minimize the sum of comprehensive investment costs and energy loss costs over the life cycle of the strategies. The energy loss costs include both direct expenses due to energy loss and indirect costs, namely, carbon emission penalties. The constraints include allowable voltage deviations, branch power transmission, the number of loss reduction measures, loss rates, and total investment limits. The model comprehensively considers both economic benefits and the social benefits of reduced carbon emissions. It can help companies better adapt to electricity transmission and distribution pricing reforms, reduce operational costs, and contribute to low-carbon development. Finally, the model is validated using the data provided by one provincial power grid company in China. The results show that the loss reduction reaches 13.9 MW and the reduced carbon emission per hour is 10.425 t. The proposed method is also compared with the enumeration method, which demonstrates its effectiveness and efficiency. Further research will be conducted on establishing functional relationships between electricity sales prices and line losses to incentivize companies to apply loss reduction measures under different pricing functions. Full article

With the increasing severity of data privacy and security issues, cross-organizational federated learning is facing challenges in communication efficiency and cost. Knowledge distillation, as an effective model compression technique, can reduce model size without significantly compromising accuracy, thereby lowering communication overhead. However, existing knowledge distillation methods either employ static distillation loss weights, ignoring bandwidth variations in communication networks, or fail to effectively account for bandwidth heterogeneity among different nodes, leading to communication bottlenecks. To enhance the overall system efficiency, there is an urgent need to find new methods that enable models to achieve optimal performance in resource-constrained environments. This paper proposes a communication optimization method based on mutual knowledge distillation (Fed-MKD) to address the bottleneck issues caused by high communication costs in cross-organizational federated learning. By leveraging a mutual distillation mechanism, Fed-MKD enables collaborative training of teacher and student models locally while reducing the frequency and size of global model transmissions to optimize communication. Our experimental results demonstrate that, compared to classical knowledge distillation methods, Fed-MKD significantly improves communication efficiency, with compression ratios ranging from 4.89× to 28.45×. Furthermore, Fed-MKD achieves up to 4.34× acceleration in convergence time across multiple datasets. These findings highlight the significant practical value of Fed-MKD in environments with heterogeneous data distributions and limited communication resources. Full article

To achieve commercial scalability, fiber-based quantum key distribution (QKD) systems must be integrated into existing optical communication infrastructures, rather than deployed exclusively on dedicated dark fibers. Integrating QKD into optical access networks (OANs) would be particularly advantageous, as these networks provide direct connectivity to end users for whom security is critical. Such integration can address the inherent security vulnerabilities in current OANs, which are primarily based on time-division multiplexing passive optical networks (TDM-PONs). However, integrating QKD into PONs poses significant challenges due to Raman noise and other detrimental effects induced by PON signals, which intensify as the launched power of PONs increases to support higher transmission speeds. In this study, we review recent advancements in both QKD and access network technologies, evaluate the technical feasibility of QKD-OAN integration, and propose cost-effective strategies to facilitate the widespread deployment of QKD in future access networks. Full article

With the frequent occurrence of extreme weather events worldwide, the compound frequency of drought and flood events has significantly increased, imposing multidimensional pressures on agricultural water resource management. Agricultural water consumption accounts for approximately 70%, with severe waste, as a large amount of water is lost during transmission and distribution. Faced with increasingly severe and frequent extreme weather, traditional drainage systems may become unsustainable. Identifying the factors influencing drainage time is crucial for efficient drainage. The MIKE URBAN model has significant potential in farmland waterlogging simulation and drainage network optimization. This study validated the model’s accuracy based on infiltration well overflow capacity experiments, with Average Relative Error (ARE) values of 2.29%, 6.52%, 4.41%, 3.17%, 4.37%, and 5.69%, demonstrating good simulation accuracy. The MIKE URBAN model was used to simulate drainage networks, explore factors affecting drainage time, establish an annual cost system for the drainage network, and optimize the network using a genetic algorithm with the objective of minimizing annual costs. Research findings: There is a clear negative correlation between the maximum inflow of infiltration wells and drainage time. As inflow increases, drainage becomes faster, but beyond 0.0075 m³/s (27 m³/h), the efficiency gains level off. This indicates that selecting infiltration wells with at least a 20% opening ratio is essential. Similarly, increasing the collector’s diameter enhances drainage efficiency significantly, though the effect follows a diminishing return pattern. While smaller lateral spacing improves local water collection, it may lead to flow congestion if the collector is undersized; conversely, larger spacing increases drainage paths and delays, even if the collector is large. An optimal spacing range of 100–150 m is suggested alongside the collector diameter. Lateral diameter also affects performance: increasing it reduces drainage time, but the benefit plateaus around 200 mm, making further enlargement cost-ineffective. The genetic algorithm helped to optimize the drainage network design. Utilizing the genetic algorithm, the drainage network was optimized in just 15 iterations. The fitness function value rapidly decreased from 351,000 CNY to 55,000 CNY and then stabilized, reducing the annual cost from 59,640.67 CNY to 45,337.86 CNY—a 24% savings—highlighting the approach’s effectiveness in designing efficient and economical farmland drainage systems. This study has shown that the simulation-based optimization of drainage networks provides a more rational and cost-effective approach to planning drainage infrastructure. Full article