Search Results (1,938)

To address the low accuracy of faulty feeder detection methods based on single-fault characteristics, we propose a faulty feeder detection method for resonant grounding systems that fuses multiple transient characteristics. First, we analyze the transient zero-sequence current fault characteristics of both faulty and healthy feeders during single-phase-to-ground (SPG) faults. Then, the transient zero-sequence current of each feeder is decomposed into intrinsic mode functions (IMFs) using variational mode decomposition (VMD), and a new signal was constructed by combining IMF1 and IMF2. Subsequently, transient energy and waveform similarity fault characteristics are extracted from the constructed signal, and a faulty feeder detection criterion based on multiple transient characteristics fusion is developed. Finally, extensive simulations and field data verify the proposed faulty feeder detection method. The results demonstrate that the method is robust against fault resistance, fault inception angle, fault location, and noise, achieving high accuracy in faulty feeder detection. This method can be widely applied to detect faulty feeders in resonant grounding systems. Full article

DC distribution systems are increasingly utilized in data centers, electric vehicle charging infrastructures, and microgrids due to their superior power conversion efficiency compared to AC systems. In DC networks, the protection coordination of overcurrent relays (OCRs) is essential for selectively isolating faults and maintaining operational stability. However, the integration of distributed energy resources (DERs), such as photovoltaics, introduces significant challenges by altering the magnitude and rate of change of fault currents. This study conducts a comprehensive analysis of various scenarios by varying both the fault location and the points of common coupling (PCC) for DER. The simulation results reveal that specific configurations lead to critical instances of protection mis-operation and failure to operate, which cause coordination failures and compromised coordination time intervals (CTIs). These findings demonstrate that conventional protection strategies may fail to ensure reliability in DER-integrated DC systems due to the dynamic nature of fault current characteristics. In this paper, these diverse scenarios and the resulting vulnerabilities in protection coordination were modeled and verified using PSCAD/EMTDC V5.0. Full article

To address the vulnerability of renewable-dominated power grids to cascading failures under extreme conditions and the limitations of existing methods in jointly handling vulnerability identification, energy storage allocation, and online control, this paper proposes an energy-storage-assisted coordinated defense strategy. First, a source-load uncertainty model is constructed and seven typical extreme operating scenarios are identified. Second, a cascading-failure evolution model that accounts for thermal accumulation is established to identify critical vulnerable branches. Third, for areas prone to local disconnection and weak terminal voltages, a coordinated ESS allocation model is developed by jointly considering active power, energy capacity, and reactive power support to determine candidate deployment locations and capacities. Finally, a graph neural network (GNN) is used to extract time-varying topological and electrical-state features, and proximal policy optimization (PPO) is employed to generate coordinated control commands for multiple ESSs, thereby linking overload suppression with voltage support. The results for the modified IEEE 39-bus system show that the proposed method identifies high-risk branches more accurately and forms an integrated defense chain covering identification, allocation, and control. The method reduces thermal stress in critical sections during the early stage of a fault, mitigates load shedding, and enhances system survivability. Full article

The coal resource-rich areas in Guizhou Province are located at the overlapping junction of the southern part of the third fold and subsidence zones of the Neocathaysian structural system and the Nanling latitudinal structural belt. These areas are characterized by well-developed folds and faults, complex coal seam structures, high in situ stress, and poor air permeability, which lead to low-efficiency conventional gas drainage and failure to achieve the expected results. In terms of enhancing coal seam permeability and improving gas drainage and utilization, research is urgently needed on the permeability enhancement mechanism of deep-hole blasting in outburst-prone coal seams under combined multi-stress action. By analyzing the influence law of coal mass fracture evolution before and after blasting, developing an experimental device for blasting permeability enhancement under combined multi-stress action, and conducting research on the pore variation law of coal mass before and after blasting, it is found that in situ stress is negatively correlated with coal mass pores, while blasting and gas stresses are positively correlated with pores. This study provides a theoretical basis and experimental evidence for permeability enhancement via deep-hole blasting in outburst-prone coal seams and further supports the selection of reasonable parameters for field tests to improve the gas drainage efficiency of outburst-prone coal seams. Full article

The Dongchuan District in Kunming City is located in the transition zone between the Yunnan–Guizhou Plateau and the Sichuan Basin. As a region with a copper mining history of over 2000 years, the district has experienced frequent landslides that pose serious threats to human lives, property, and ecological sustainability. Therefore, it is essential to compile a comprehensive landslide inventory and analyze the relationships between landslide spatial distribution and influencing factors for geological hazard prevention. High-resolution remote sensing imagery was interpreted to establish a landslide inventory, based on which the spatial distribution and geometric characteristics of landslides were systematically analyzed. The results show that a total of 1623 landslides were identified, with a total area of 10.36 km². Landslides predominantly occur at elevations of 1000–2000 m, on slopes of 20–45°, with aspects of 255–285°, and relief between 150 and 400 m, in areas with annual rainfall below 825 mm, within 1000 m of rivers and 3000 m of fault lines, and 1000–5000 m of mines. Four landslide clusters were delineated along the Xiao River Fault, highlighting the significant influence of the fault on the spatial distribution of landslides. Most landslides are longitudinal in planform, with travel distances (L) of 50–450 m and heights (H) from 25 to 350 m, both exhibiting allometric scaling with volume. The mean H/L ratio is 0.56 (corresponding to a mean reach angle of 29°), significantly higher than that in Baoshan City (21°). The results provide insights into landslide initiation mechanisms and spatial distribution patterns on the northern margin of the Yunnan–Guizhou Plateau, offering valuable data for landslide hazard assessment and sustainable regional development. Full article

Timely fault detection, classification, and localization are fundamental to enabling fast service restoration in modern distribution networks, and are especially vital for maintaining the reliability and resilience of smart city electricity infrastructures. A new AI-based method for classifying and localizing fault types is presented in this paper, which enhances situational awareness in smart distribution grids that supply dense urban loads and critical smart city services. The proposed approach targets various fault conditions, which include three-phase-to-ground, three-phase, two-phase-to-ground, two-phase, and single-phase-to-ground faults. The proposed method utilizes a wavelet-based signal processing technique to analyze the feeder’s current data captured by waveform measurement units (WMUs) and extracts features for fault analysis. As a result of these features, a multi-stage machine learning architecture incorporating deep learning components is developed to accurately determine the occurrence, type, and location of faults. To evaluate the performance of the proposed approach, simulations were conducted on a 16-bus distribution network. Results show a high level of accuracy in fault detection, classification, and localization. This indicates that the method can be a valuable tool for enhancing the resilience and intelligence of future power grids, as well as supporting self-healing and fast service restoration in smart city services. Full article

Automated Storage and Retrieval Systems (AS/RSs) are pivotal hubs in modern intelligent logistics, yet their operational efficiency is often constrained by the complex coupling of storage allocation, equipment scheduling, and path planning. This study proposes a systematic optimization framework to address these three critical control challenges. First, a multi-objective mathematical model for storage location allocation is established, considering efficiency, stability, and correlation. To solve this high-dimensional discrete problem, a Tabu Variable Neighborhood Search (TVNS) algorithm is proposed, integrating short-term memory mechanisms with multi-structure exploration to prevent premature convergence. Second, regarding stacker crane and forklift collaborative scheduling, a Pheromone-guided Artificial Hummingbird Algorithm (PT-AHA) is introduced. By incorporating pheromone feedback into foraging behavior, the algorithm significantly enhances global search capability to minimize total task completion time. Third, stacker crane path planning is modeled as a constrained Traveling Salesman Problem (TSP) and solved using a hybrid Simulated Annealing-Whale Optimization Algorithm (SA-WOA). Quantitative simulation results demonstrate that the TVNS algorithm improves storage allocation fitness by 1.1% over standard Genetic Algorithms, while the PT-AHA reduces task completion time (Makespan) by 21.9% for small-scale batches and consistently outperforms ACO by up to 3.6% in large-scale operations. Validation through an Intelligent Warehouse Management System (WMS) confirms that the integrated framework maintains high industrial resilience by triggering fault alarms and initiating recovery within 3.2 s during simulated equipment failures, providing a robust solution for enterprise-level deployments. Full article

When a line short-circuit fault occurs in a DC distribution network, the fault current rises quickly and affects a wide range, jeopardizing the safe operation of the system. In order to locate the fault quickly and accurately, this study proposes a fault localization method based on the Variational Mode Decomposition (VMD) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. First, the nonlinear relationship between the intrinsic principal frequency and fault distance is analyzed; then, the intrinsic principal frequency of the faulty traveling wave is extracted by using VMD, and the nonlinear relationship between the spectral energy of the principal frequency of the intrinsic frequency and the fault distance is fitted by training the Bi-LSTM network incorporating the attention mechanism. Finally, in response to the issue that a small amount of fault data in practical engineering is difficult to support the amount of data required for deep learning, a transfer learning method is used to locate the fault in the target domain. A small sample test of the target domain is carried out using the migration learning method. The experimental results show that the proposed method has high localization accuracy and good resistance to over-resistance and noise; compared with the traditional network training, the localization error based on migration learning is smaller, and the network convergence effect is better. Full article

In general, condition monitoring (CM) and noise, vibration and harshness (NVH) are often treated as separate disciplines, despite the fact that both rely on vibration measurements. CM relies on broadband statistical metrics such as RMS, kurtosis, and envelope analysis to detect faults. Meanwhile, NVH investigates tonal excitation mechanisms related to gear mesh frequency (GMF) and its modulation components. In this study, we investigate whether a numerical relationship can be established between classical CM indicators and physically based tonal excitation indicators derived from frequency–domain analysis. Using healthy and damaged benchmark gearbox recordings, Spearman correlation analysis was performed between broadband metrics and GMF-related tonal features, including GMF-band energy and absolute sideband energy. Results show moderate but statistically significant correlations between RMS, envelope peak amplitude, and tonal indicators, whereas kurtosis exhibits no meaningful association. Additionally, tonal response amplification in the damaged gearbox is shown to be non-uniformly distributed across sensor locations, indicating sensor-dependent structural sensitivity rather than uniform response growth. These findings demonstrate that broadband CM indicators partially encode changes in tonal excitation-related response, establishing a reproducible data-driven bridge between diagnostic condition monitoring and NVH excitation analysis. Full article

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, and expansion work. Managing a water supply network is a complex and complex process. A crucial challenge in water company management is detecting and locating hidden water leaks in the water supply network. Leak location in water distribution networks is a key challenge for utilities, as undetected leaks lead to water losses, increased energy consumption, and reduced service reliability. With the development of cyber-physical systems (CPSs), the integration of physical infrastructure with real-time digital monitoring has enabled more adaptive and responsive water operations. Data-driven decision-making in CPS in the water industry leverages classification and regression trees (C&RTs) to analyze real-time sensor data—such as pressure, flow, and consumption—to classify system states and predict potential faults. By transforming operational data into interpretable decision rules, C&RTs enable automated and timely maintenance actions that improve reliability, reduce water loss, and support intelligent infrastructure management. The aim of this study is to develop and evaluate AI-based optimization methods to enhance sustainability, efficiency, and resilience in the water industry by enabling autonomous, data-driven decision-making within CPSs, supporting smart industry practices, and addressing practical challenges associated with the actual implementation of smart water management solutions using simple solutions such as C&RTs. The accuracy of the best classifier was 86.15%. Further research will focus on using other types of decision trees that will improve classification accuracy. Full article

Located in the north of Yunnan Province, China, the Wuding geothermal area is a typical medium- and low-temperature geothermal system with strong hydrothermal activity and development potential as a clean and renewable energy resource. This study systematically investigates the main controlling factors of the Wuding geothermal field through field investigation, hydrochemical analysis, and stable isotope analysis, and puts forward a genetic model of the geothermal field. The results show that the Wuding geothermal field is a medium- to low-temperature, conduction-dominated geothermal system, and its geothermal water is predominantly of the Ca–HCO₃ (calcium bicarbonate) type. The recharge area lies at an altitude above 2250 m, which is speculated to be within the mountainous area in the southwest of the study area. The underground hot water in the area is immature water. The source water circulates to the deep heat storage zone along faults, rises to the surface through heat convection, and is exposed as hot springs. Upon discharge, the geothermal water mixes with shallow cold water, with cold-water dilution accounting for up to 85% of the total volume. Using the silica thermometer, cation thermometer, and silicon enthalpy model, the maximum temperature of heat storage is estimated to be 91 °C, with the depth of geothermal water circulation reaching 2200 m. The thermal reservoir is composed of dolomites of the Upper Cambrian Erdaoshui Formation (∈3e) and Sinian Dengying Formation (Zbd). Its heat source is heat flow from the upper mantle and the decay of radioactive elements. Continuous heat flow to the thermal reservoir is maintained through the fold fracture zone and faults in the core of the Hongshanwan anticline. The proposed genetic model of the Wuding geothermal field provides a scientific basis for the sustainable redevelopment and utilization of this geothermal resource and is of significance for regional low-carbon energy use and socio-economic sustainable development. Full article

On 4 December 2025, nearly two years after the 2024 Mw 7.0 Wushi earthquake, an Mw 5.8 event struck the nearby county of Aheqi, southwestern Tianshan. Owing to the subparallel strikes of both nodal planes and the interspersed hypocenter locations among regional structures in the reported focal mechanisms, the exact fault geometry of this event remains unresolved, impeding a better understanding of regional tectonic activity and the associated seismic hazards. To resolve this, we applied Interferometric Synthetic Aperture Radar (InSAR) technique to map the coseismic deformation and invert for the fault geometry and slip pattern. Significant tropospheric delays are mitigated using a moving-window linear model and a multi-interferogram weighted averaging strategy. The result shows significant uplift (~5.0 cm for ascending track and ~6.0 cm for descending track), indicating thrust-dominated mechanism. Bayesian inversion reveals two possible fault models: a 31.6° north-dipping blind thrust or a 54.4° south-dipping back-thrust. While both fault planes fit the InSAR observations, integrated evidence from the absence of back-thrust development conditions, the surface deformation pattern, and regional topography indicates that the north-dipping Aheqi fault is the causative structure. Together with the steeper Maidan fault to the north, it forms the Orogen Basin boundary along the southern Tianshan piedmont. Our findings highlight that resolving moderate blind-thrust seismogenic structures using InSAR requires integration with pre-existing structural and geomorphic evidence. Furthermore, Coulomb stress calculations indicate a rupture-promoting effect from the Wushi earthquake, which occurred on a reactivated fault, onto the Aheqi event, with stress loading exceeding 2 bar at the hypocenter. Thus, the potential for stress-driven sequential rupture between reactivated and present-day active structures necessitates an updated seismic hazard assessment in the southern Tianshan. Full article

Rainfall-induced landslides represent one of the most recurrent geohazards affecting the transportation network of southwestern Calabria (Italy). This study provides an integrated assessment of landslide occurrence and road damage along the Costa Viola by combining detailed geomorphological mapping, multi-temporal analyses, historical documentation (1950–2025), and GIS-based spatial data processing. A total of 261 landslides were mapped, affecting approximately 19% of the study area. Slides constitute the dominant movement type (66.7%), followed by complex landslides, flows, and falls. Landslide distribution is strongly controlled by geological and morphometric factors: more than 80% of mapped phenomena occur in highly fractured granitic and gneissic rocks, over 70% lie within 500 m of faults, and more than 90% are located within 300 m of streams. Slope gradient (25–55°) and local relief (350–550 m) further contribute to slope instability patterns. The historical dataset documents 237 landslide-induced road damage events over 75 years, with a marked increase in occurrence since the early 2000s. Most damage events affected the SS18 road and frequently corresponded to reactivations of pre-existing landslides, highlighting the long-term persistence of slope instability and the seasonal influence of intense autumn–winter precipitation. Overall, the results demonstrate that landslide hazard in the Costa Viola is governed by the interplay between structural, lithological, geomorphic, and climatic factors, compounded by anthropogenic modifications along road corridors. The combined landslide inventory and historical database provide a robust basis for risk mitigation, identification of critical road sectors, and future susceptibility and predictive modelling to support effective territorial planning. Full article

Unsupervised domain adaptation (UDA) has been extensively studied for bearing fault diagnosis under multiple operating conditions by mitigating distribution discrepancies across domains. However, in cross-domain imbalanced scenarios, bearing vibration signals are affected by both feature shift and class imbalance. Although a robust decision boundary learned from the source domain is critical for reliable transfer, classifier discriminability and robustness can be degraded by hard samples located near the boundary. As a result, the decision boundary may become ambiguous during adaptation, leading to degraded diagnostic performance in the target domain. To address these issues, a Maximum Margin Local Domain Adaptation (MMLDA) framework is proposed in which a multi-scale convolutional neural network is adopted as the backbone. Three core components are integrated into our framework: first, category-level reweighting to alleviate source-domain class imbalance; second, cross-domain local category alignment to reduce fine-grained feature discrepancies and feature shift; and finally, maximum-margin loss regularization to impose adaptive margin constraints on hard samples for improved decision boundary robustness. To evaluate the proposed method, cross-domain imbalanced transfer tasks under multiple operating conditions were constructed on two public bearing fault datasets, and comparative experiments were conducted. The results under different imbalance protocols demonstrate improved robustness and generalization of MMLDA. Full article

The fault tolerance of a swarm of small aerial vehicles (SAVs) is directly dependent on the reliability of data transmitted over communication channels. One of the key threats is the intentional distortion of signal sequences by an attacker, such as Barker codes or M-sequences, which are used for synchronization and control of the swarm. Such an attack can disable the entire swarm. The aim of this study is to develop a method for detecting such intrusions. The proposed algorithm analyzes mathematical expressions that describe the sidelobes’ levels of the autocorrelation function of the code. This approach not only detects unauthorized changes but also accurately identifies the location and magnitude of the distorted element. The conducted experiments confirm the high accuracy of the algorithm. The practical significance of the work lies in the possibility of integrating this method into the security subsystem of group interaction for small aerial vehicles. This creates a mechanism for active anomaly detection in communication channels: when a threat is detected, the swarm can respond promptly by switching to a backup channel, requesting data retransmission, or isolating the compromised channel, which in turn enhances the survivability and fault tolerance of the system’s functioning within the group. Full article