Search Results (250)

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

Accurate localization of voltage sag sources is crucial for maintaining reliable and stable operation in microgrids with high penetration of distributed generation (DG). However, the complex topology, bidirectional and time-varying power flows, and measurement uncertainty make it difficult for these conventional model-based approaches to achieve high accuracy. To address these challenges, this paper proposes a two-stage voltage sag source localization method that integrates a data-driven spatio-temporal learning model with a model-based binary search refinement. In the first stage, an improved spatial-temporal graph convolutional network (STGCN) is developed to extract temporal and spatial correlations among voltage and current measurements, enabling section-level localization of sag sources. In the second stage, a binary search–based refinement strategy is applied within the candidate section to iteratively converge on the exact fault location with high precision and robustness. Simulations are conducted on a modified IEEE 33-node system with diverse PV output scenarios, covering combinations of fault types and locations. The results demonstrate that the proposed method maintains stable localization performance under high DG penetration and achieves high accuracy despite multiple fault types and noise interference. Full article

Background: Low-cost particulate matter sensors have enabled new opportunities for exposure monitoring but require evaluation before application in epidemiological studies. This study assessed the performance of the SPS30 sensor integrated into the ARMIE portable monitoring sensor-node under controlled laboratory conditions. Methods: Sensors were co-located with two comparison instruments—the optical DustTrak photometer and the combined Scanning Mobility Particle Sizer (SMPS) and Aerodynamic Particle Sizer (APS)—across multiple aerosol sources, including candle burning, cooking, cigarette smoke, and clean air, under both regular and high-humidity conditions. Calibration performance was evaluated using leave-one-sensor-out and leave-one-source-out approaches. Results: The ARMIE node demonstrated strong agreement with the DustTrak (r = 0.93–0.98) and maintained linear response characteristics across emission types. Calibration reduced mean errors and narrowed the limits of agreement. Agreement with the SMPS + APS was moderate (r = 0.74–0.94) and characterized by systematic underestimation at higher concentrations. Conclusions: Overall, the ARMIE node achieved high correlation with the DustTrak, demonstrating that low-cost optical sensors can reliably capture temporal variability in particle concentrations relative to mid-cost photometers. Full article

Rapid urbanization has brought severe threats to regional ecological security. Most research regards ecological security pattern (ESP) focuses on the current situation and ignores future land use and land cover (LULC) impacts. Therefore, this study proposed an ESP construction framework that integrates multi-scenario patch-generating land use simulation (PLUS) with ecosystem service value (ESV) evaluation based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Taking Hefei City as a case study, this study predicts land use types under the natural development scenario (NDS), ecological protection scenario (EPS), and economic development scenario (EDS) in 2030. Afterwards, ecological sources are identified by selecting four types of ecosystem services. Ecological corridors and nodes are identified by combining circuit theory and ecological resistance surfaces. The ESP is constructed based on a generic, landscape-scale connectivity-oriented perspective. The results showed that: (1) the LULC in Hefei City underwent significant changes between 2000 and 2020. The main manifestations are the reduction in cropland and increase in construction land. The expansion of construction land under EDS is the most significant. (2) The spatial distribution patterns of ESV for 2020 and three scenarios in 2030 exhibit marked heterogeneity. (3) According to the simulated scenarios in 2030, ecological corridors form a structure that is sparser in the central region and denser in the southern region; ecological pinch points are predominantly located within the Zipeng Mountain and the region situated to the south of Chaohu; ecological barrier points are mainly distributed at the edge of the built-up area and in the middle of long-distance ecological corridors. The ecological network structure under EPS has been expanded and reinforced. (4) Hefei City exhibits an ESP of “Four zones, Three screens, One network, Multiple nodes” as a whole, indicating an ecological security pattern with relatively higher potential ecological connectivity at the city scale. This study aims to provide scientific support for the development of Hefei City in society, economy and ecological security. Full article

The construction of urban ecological security patterns (ESPs) is an effective approach for managing ecological space and preventing the uncontrolled expansion of urban areas, thereby safeguarding the ecological security of urban agglomerations. This study focuses on the Changsha–Zhuzhou–Xiangtan Urban Agglomeration (CZTUA), utilizing an ESP framework based on ecosystem services, ecological sensitivity, landscape connectivity, and resistance surfaces (SSCR). The spatio-temporal evolution and driving forces of ESP were analyzed for 2010, 2015, and 2020. Based on this, the ecological control zones of the CZTUA were delineated according to ecosystem importance, and appropriate ecological improvement strategies were proposed. The findings revealed the following: (1) The number of ecological sources in the CZTUA decreased from 26 to 23, while their total area expanded from 1113.6 km² to 3013.96 km², indicating a “point-to-patch” development trend. Ecological corridors showed a “decrease–increase”trend in number, but their total length consistently contracted from 1025.69 km to 536.25 km, with greater emphasis on the efficiency and effectiveness of connecting habitats. Ecological nodes decreased from 14 to 5, while their aggregate area increased from 290.6 km² to 1796.48 km², mirroring changes in ecological sources. (2) Ecological sources, corridors, and nodes in the CZTUA are primarily located in the eastern mountainous and hilly regions, with a trend of expansion toward the western areas. The spatial distribution of corridors and nodes is shaped by these sources, with dense areas exhibiting short-distance networks and dispersal areas showing long-distance linear patterns. Node distribution shifts from entry/exit areas of ecological sources and corridors to the sources themselves. (3) The spatio-temporal evolution of the ESP in the CZTUA is driven by a dual-wheel mechanism of “natural foundation-policy regulation,” where precipitation and potential evapotranspiration serve as the primary natural drivers, manifested through water conservation. (4) The region is divided into three control levels: the core protected areas focus on ecological protection in the eastern mountainous and hilly regions; the ecological buffer areas emphasize ecological coordination in transitional landforms such as hills, medium-undulating mountains, and platforms; the intensive development areas, mostly located in platform, plain, and some hilly areas, prioritize ecological optimization. The three-tier control zones implement strategies of strict protection, buffering and coordination, and optimized development, respectively, providing a direct basis for the refined management of ecological spaces. Full article

With the continuous increase in the proportion of wind and solar power, the strong randomness and volatility of distributed new energy output have brought great challenges to the planning, regulation, and operation of the new distribution system. Distributed energy storage, with its characteristics such as scattered location distribution, flexible installation, small capacity, and diverse forms and application scenarios, is increasingly becoming an important resource and technical means to enhance the consumption capacity of new energy and ensure the safe and reliable operation of the power system. This paper proposes a collaborative planning method for distributed energy storage based on differentiated demands. First, the typical application scenarios of distributed energy storage are analyzed; secondly, the source–load matching degree and modularity are proposed as cluster division indicators. Voltage fluctuation, load fluctuation, and the net income of distributed energy storage are combined into multiple optimization objectives. Based on differentiated demands, a two-layer optimal configuration model of distributed energy storage is proposed and solved by using the improved particle swarm optimization algorithm. Finally, the feasibility and effectiveness of the proposed method were verified through a modified IEEE33 node simulation example. Full article

In graph theory and network design, the minimum cut is a fundamental measure of system connectivity and communication capacity. While prior research has largely focused on computing the minimum cut for a fixed source–sink pair, practical scenarios such as data center communication often demand a different objective: identifying the source node whose minimum cut to a designated sink is maximized. This task, which we term the Global Maximum Minimum Cut with Fixed Sink (GMMC-FS) problem, captures the goal of locating a high-capacity source relative to a shared sink node that aggregates multiple servers. The problem is of significant engineering importance, yet it is computationally challenging as it involves a nested max–min optimization. In this paper, we present a recursive reduction (RR) algorithm for solving the GMMC-FS problem. The key idea is to iteratively select pivot nodes, compute their minimum cuts with respect to the sink, and prune dominated candidates whose cut values cannot exceed that of the pivot. By recursively applying this elimination process, RR dramatically reduces the number of max-flow computations required while preserving exact correctness. Compared with classical contraction-based and Gomory–Hu tree approaches that rely on global cut enumeration, the proposed RR framework offers a more direct and scalable mechanism for identifying the source that maximizes the minimum cut to a fixed sink. Its novelty lies in exploiting the structural properties of the sink side of suboptimal cuts, which leads to both theoretical efficiency and empirical robustness across large-scale networks. We provide a rigorous theoretical analysis establishing both correctness and complexity bounds, and we validate the approach through extensive experiments. Results demonstrate that RR consistently achieves optimal solutions while significantly outperforming baseline methods in runtime, particularly on large and dense networks. Full article

Bacillus anthracis is the etiological agent of the zoonotic disease anthrax. The pathogen has colonized many regions of all inhabited continents. Increasing evidence points to a strong contribution of anthropogenic activities (trade) in this almost global spread. This article contributes further genomic data from 21 B. anthracis strains, including 19 isolated in Germany, aiming to support and detail the human role in anthrax dispersal. The newly sequenced genomes belong to the B. anthracis lineage predominant in China. This lineage is remarkable because of its phylogenetic structure. A polytomy with nine branches radiating from a central node was identified by whole-genome single-nucleotide polymorphism (wgSNP) analysis. Strains from Germany populate two among the nine branches. Detailed analysis of the polytomy indicates that it most likely emerged in China. We propose that the polytomy is the result of the import of contaminated animal products in a limited spatiotemporal frame, followed by the distribution of these products to different locations within China, where new B. anthracis lineages then became independently established. Currently available data point to Bengal as a likely geographic source of the original contamination, and the history of trade exchanges between Bengal and China agrees with the early fifteenth century as a likely time period. The subsequent exports to Germany would have occurred during the 19th century according to German trade history. Notably, Germany has been experiencing localized anthrax outbreaks from this trade heritage up into the 21st century. Full article

In traditional scheduling operations, dispatchers mainly rely on SCADA/EMS systems or personal experience. However, with access to a large number of new energy sources, the scale of the distribution network continues to expand, and its topology becomes increasingly complex, leading to potential security risks in scheduling operations. Therefore, it is very important to carry out risk assessments before scheduling operations. In this paper, risk theory is introduced into the field of distribution network scheduling operations, and a new risk assessment method is proposed considering various uncertain factors in the distribution network. In order to comprehensively analyze the influence of uncertainty factors in the operational process of a new distribution network, the output probability models of wind power, photovoltaic power, and load are first constructed in this study. Then, the improved Latin hypercube sampling method is used to extract the operating state of the distribution network system from the probability model, and the node voltage over-limit and line power flow overload are used as indicators to measure the severity of the consequences so as to establish a quantitative scheduling operation risk assessment system and analyze its framework in detail. Finally, simulation analysis is carried out in the improved IEEE-RTS79 test system: taking 15–25 lines from the operation state to the maintenance state as an example, this paper analyzes the influence of different locations and capacities of wind and solar access on the scheduling operation risk of distribution networks. The results can provide a reference for dispatchers to prevent risks before operation. Full article

This paper presents an optimization-based scheduling strategy for battery energy storage systems (BESS) in alternating current microgrids, considering both grid-connected and islanded operation. The study addresses two independent objectives: minimizing energy losses in the distribution network and reducing carbon dioxide emissions from dispatchable power sources. The problem is formulated using a full AC power flow model that simultaneously manages active and reactive power flows in BESS located in the microgrid, while enforcing detailed operational constraints for network components, generation units, and storage systems. To solve it, a parallel implementation of the Particle Swarm Optimization (PPSO) algorithm is applied. The PPSO is integrated into the objective functions and evaluated through a 24-h scheduling horizon, incorporating a strict penalty scheme to guarantee compliance with technical and operational limits. The proposed method generates coordinated charging and discharging plans for multiple BESS units, ensuring voltage stability, current limits, and optimal reactive power support in both operating modes. Tests are conducted on a 33-node benchmark microgrid that represents the power demand and generation from Medellín, Colombia. This is compared with two methodologies reported in the literature: Parallel Crow Search and Parallel JAYA optimizer. The results demonstrate that the strategy produces robust schedules across objectives, identifies the most critical network elements for monitoring, and maintains safe operation without compromising performance. This framework offers a practical and adaptable tool for microgrid energy management, capable of aligning technical reliability with environmental goals in diverse operational scenarios. Full article

Three-dimensional bulk fin-type field-effect transistors (FinFETs) have been the dominant devices since the sub-22 nm technology node. Electrical characteristics of scaled devices suffer from different process variation effects. Owing to the trapping and de-trapping of charge carriers, random interface traps (RITs) degrade device characteristics, and, to study this effect, this work investigates the impact of RITs on the DC/AC/RF characteristic fluctuations of FinFETs. Under high gate bias, the device screening effect suppresses large fluctuations induced by RITs. In relation to different densities of interface traps (D_it), fluctuations of short-channel effects, including potential barriers and current densities, are analyzed. Bulk FinFETs exhibit entirely different variability, despite having the same number of RITs. Potential barriers are significantly altered when devices with RITs are located near the source end. An analysis and a discussion of RIT-fluctuated gate capacitances, transconductances, cut-off, and 3-dB frequencies are provided. Under high D_it conditions, we observe ~146% variation in off-state current, ~26% in threshold voltage, and large fluctuations of ~107% and ~131% in gain and cut-off frequency, respectively. The effects of the random position of RITs on both AC and RF characteristic fluctuations are also discussed and designed in three different scenarios. Across all densities of interface traps, the device with RITs near the drain end exhibits relatively minimal fluctuations in gate capacitance, voltage gain, cut-off, and 3-dB frequencies. Full article

Maximizing the lifetimes of Wireless Sensor Networks (WSNs) is a prominent area of research. The energy hole problem is a major cause of network shutdown, where nodes within the Sink coverage deplete their energy faster due to the high energy cost of forwarding data from distant nodes to the Sink. Several research works have proposed solutions to address this issue, including the use of a mobile Sink to balance energy consumption throughout the network. However, most Sink mobility models overlook the energy consumption caused by overhearing, which is a critical factor in WSNs. In this paper, we introduce Linear Programming (LP) and Cuckoo Search (CS) metaheuristic optimization-based solutions to maximize the lifetime of WSNs by determining the optimal Sink sojourn points and associated durations. The proposed approaches consider the energy consumption levels of both reception and transmission, in addition to accounting for overhearing as an additional source of energy consumption. This allows for a comparison between the LP and CS solutions in terms of their effectiveness. To further enhance our solution, we apply the Travel Salesman Problem (TSP) to find the shortest path between the Sink sojourn points. By incorporating the TSP, we can optimize the routing path for the mobile Sink, thereby minimizing energy consumption and maximizing network lifetime. Test results demonstrate that the LP solution provides more accurate Sink sojourn times and locations, while the CS solution is faster, particularly for large WSNs. Moreover, our findings indicate that overlooking overhearing leads to a 48% decrease in WSN lifetime, making it essential to consider this factor if one is to achieve realistic results. Full article

To address the conflicts between high-penetration distributed photovoltaics (PV) integration causing voltage limit violations, reverse power flow issues, and the grid connection needs of industrial and commercial users, this paper proposes an optimal capacity planning method for distributed PV considering the user’s grid connection locations. This method effectively increases the acceptance capacity of the distribution transformer network for distributed PV while ensuring the safe and stable operation of the distribution network. First, the source–load uncertainty is considered, and the k-means clustering algorithm is used to select multiple typical daily probability scenarios. Then, the PV optimal connection node range is obtained through a PV site selection and sizing model. For the planning of nodes within the optimal range, an optimal capacity planning model focusing on the economic benefits of users is established. This model aims to optimize the improvement of wheeling cost and maximize the economic benefits of grid-connected users by determining the optimal PV access capacity for each node. Finally, for PV users outside this range, after determining the maximum allowable capacity for each node, the capacity margin and static voltage stability are comprehensively considered to evaluate the network access scheme. Simulation examples are used to verify the effectiveness of the proposed method, and the simulation results show that the proposed method can effectively increase the acceptance capacity of the distribution network for photovoltaic systems. By fully considering the wheeling cost collection strategy, the distributed PV acceptance capacity is increased by 20.14%, while both user benefits and the operational safety and economic performance of the distribution network are significantly improved, ultimately resulting in a 27.77% increase in total revenue. Full article

Air pollution threatens human and environmental health worldwide. A Harvard study in partnership with UK institutions found that fossil fuel pollution killed over 8 million people in 2018, accounting for 1 in 5 fatalities worldwide. Iraq, including the Kurdistan Region of Iraq, has a major environmental issue in that it ranks second worst in 2022 due to the high level of particulate matter, specifically PM_2.5. In this paper, a LoRa-based infrastructure for environmental monitoring in the Kurdistan Region has been designed and developed. The infrastructure encompasses end-node devices, an open-source network server, and an IoT platform. Two AirQNodes were prototyped and deployed to measure particulate matter values, temperature, humidity, and atmospheric pressure using manufacturer-calibrated PM sensors and combined temperature, humidity, and atmospheric sensors. An open-source network server is adopted to manage the AirQNodes and the entire network. In addition, an IoT platform has also been designed and implemented to visualize and analyze the collected data. The platform processes and stores the data, making it accessible for the public and decision-making parties. The infrastructure was tested and results validated by deploying two AirQNodes at separate locations adjacent to existing air quality monitoring stations as reference points. The findings demonstrated that the AirQNodes reliably mirrored the trends and patterns observed in the reference monitors. This research establishes a comprehensive infrastructure for monitoring air quality in the Kurdistan Region of Iraq. Furthermore, complete ownership of the system can be attained by possessing and overseeing the critical components of the infrastructure, which encompass the end devices, network server, and IoT platform. This integrated strategy is especially crucial for the Kurdistan Region of Iraq, where cost-efficiency and enduring sustainability are vital, yet such a structure is absent. Full article