Significance: In line with the development and integration of new power systems, many large-scale renewable energy bases-particularly wind and photovoltaic-are being rapidly established in desert, Gobi, and other arid (DGA) regions across China and beyond. These regions are characterized by harsh climatic and geological conditions, making the reliable operation and rapid recovery of electrical infrastructure under extreme weather events increasingly critical. The increasing frequency and intensity of extreme weather events under global climate change further amplify this challenge. This study investigates the safe operation and resilience of large-scale renewable energy bases in DGA regions under extreme environmental conditions. This study aims to systematically review the impact of various extreme hazards on electrical equipment across the power system chain, assess the current state of disaster prevention and mitigation technologies, and identify critical technical needs for future development. This study provides a solid theoretical foundation and practical technological support for enhancing the resilience and intelligent transformation of modern power systems. Progress: A comprehensive review was conducted on recent domestic and international cases of power system failure and associated economic losses triggered by extreme weather events, including extreme low and high ambient temperatures, atmospheric icing, strong winds, sand and dust storms, earthquakes, lightning strikes, wildfires, floods, and secondary compound disasters. The analysis covers the full lifecycle of electrical infrastructure, including the power generation, transmission, and transformation stages. For each stage, critical threats to the operational security and structural integrity of key electrical equipment are identified. The results indicate that the unique environmental characteristics of DGA regions-high solar radiation, strong convective winds, large diurnal temperature variations, and frequent sandstorms-exacerbate the vulnerability of electrical equipment, particularly outdoor components such as transformers, insulators, switchgears, and towers. The primary types and impact mechanisms of extreme environmental factors on equipment in DGA regions are categorized. Their associated degradation modes, including material embrittlement due to low temperatures, overheating and insulation aging under extreme heat, salt fog and corrosion effects, mechanical fatigue from wind-induced vibration, and flashover risks due to pollution and icing, are discussed in detail. This study delineates the specific vulnerabilities of various types of electrical equipment and the main failure modes associated with each hazard. The current status of monitoring, early warning, emergency response, and disaster mitigation technologies is also critically analyzed. Although solutions such as online monitoring systems, structural reinforcement methods, seismic isolation devices, de-icing systems, and vibration-damping technologies have been proposed and partially implemented, many challenges remain. Despite promising results from pilot-scale deployments and demonstration projects, large-scale practical applications are hindered by technical bottlenecks. These include insufficient monitoring precision in complex environments, limited capacity for real-time online condition assessment, and reduced effectiveness in multihazard detection and degradation tracking. Furthermore, challenges in data integration, system interoperability, and long-term stability in harsh environments significantly undermine the reliability of disaster response systems in real-world engineering applications. Conclusions and Prospects: As the risks posed by extreme climate events continue to grow, transitioning from passive disaster response to active, intelligent risk management across the entire lifecycle of power systems is urgently needed. Future efforts should focus on creating a standardized, modular framework for disaster prevention and mitigation that can be rapidly adapted to a wide range of hazards. Intelligent decision-making platforms, supported by digital twin models, big data analytics, and AI-driven prediction algorithms, should be developed to provide real-time operational guidance under extreme conditions. Moreover, cross-disciplinary collaboration among meteorology, materials science, structural engineering, and electrical engineering is essential for designing equipment and systems inherently resistant to compound disasters.
Objective: With the marked increase in the frequency and intensity of extreme rainfall events, the growing likelihood of substation and road flooding poses a severe threat to the operation of power-traffic coupled networks. Existing studies have failed to fully quantify the impact of urban waterlogging disasters on power-traffic networks, and the interactive influence between the two networks under disaster scenarios remains poorly understood. To accurately identify critical nodes in the power-traffic coupled network and clarify key fault propagation links, a vulnerability assessment method that comprehensively integrates multidimensional influencing factors and the bidirectional influence mechanism between the power grid and the traffic network must be developed. Accordingly, this study proposes a substation-focused vulnerability assessment method for power nodes in a power-traffic coupled network, providing guidance for the planning and dispatch of power systems and traffic networks, as well as the allocation of disaster prevention materials. Methods: A research framework comprising "urban waterlogging modeling-coupling mechanism analysis-key node identification" is established. First, weather and geographic data were integrated into a two-dimensional hydrodynamic model, which incorporated the D8 single flow direction algorithm to establish an urban rainstorm waterlogging model. This model was used to map rainfall parameters to multi-period gridded urban waterlogging depths. Second, the power node failure mechanism was analyzed across different waterlogging depths, using Monte Carlo simulations to sample all grid nodes and obtain the operational state of the distribution network. Then, using traffic network parameters and electric vehicle charging models, the origin-destination analysis method and the Floyd algorithm were used to investigate traffic flow redistribution and charging loads in the urban traffic network, revealing the bidirectional influence mechanism under waterlogging conditions. An iterative "fault-diversion-redispatch-assessment" simulation was constructed to dynamically model the operating state of the coupled system under disaster scenarios. Finally, risk indicators, such as road and node saturation risks, were proposed from an operational perspective. By mapping the traffic network saturation risk to grid nodes and combining network topology with electrical indicators, a comprehensive evaluation method for identifying key nodes in the coupled system was developed based on the analytic hierarchy process, achieving accurate identification of weak links in the coupled system. Results: A case study involving a modified IEEE 33 bus system and a 32 nodes traffic network was conducted, which intuitively displayed the dynamic cascading failures within power-traffic coupled systems under urban waterlogging scenarios. The results showed that the proposed method accurately identified key nodes in the integrated network, fully verified the necessity of component-level identification from a coupling perspective. Conclusions: Based on an in-depth analysis of the fault propagation mechanisms of the power-traffic coupled network under urban waterlogging conditions, this study provides a new method for the vulnerability assessment of urban power-traffic coupled networks under extreme rainfall disasters. Future research will optimize the allocation of disaster prevention materials to more efficiently cope with possible waterlogging disasters.
Objective: As a crucial component of power systems, the valve hall plays a vital role in ensuring the safe and stable operation of converter stations, as well as the reliable transmission of electricity. However, earthquakes pose a significant threat to the integrity and functionality of these systems. Under seismic loading, due to the uniformity of the ground motion input and the mechanical and functional coupling between components, the dynamic responses of the equipment are correlated. This interdependence challenges the conventional assumption of independent component failures, highlighting the need for more advanced reliability modeling. Methods: To demonstrate the importance of considering response correlation between components, this study first analyzes the reliability of series and parallel systems under two extreme conditions: complete independence and complete correlation. The findings show that the correlation between equipment responses has a significant effect on system reliability, especially when the number of components increases or the failure probability varies widely. To quantify the correlations within the valve hall system, finite element simulations were conducted on both high-voltage and low-voltage valve halls of a ±800 kV converter station. The seismic responses of individual equipment were obtained under various ground motion inputs. Based on these data, a Gaussian copula-based method was employed to model the joint behavior of equipment responses. This method captures their statistical dependencies without assuming a predefined joint distribution. The analysis process mainly consists of marginal distribution modeling of the responses, transformation to uniform and normal distributions, determination of the correlation matrix, generation of independent normal samples, singular value decomposition of the correlation matrix, standard normal transformation, inverse mapping to the uniform domain, and generation of correlated samples. This approach enables the construction of a realistic joint distribution of equipment responses while preserving their marginal characteristics. Using the correlated samples generated through the Gaussian copula method, the system-level reliability of the valve hall was evaluated while accounting for dependent failure behavior. Results: The Gaussian copula method effectively modeled the correlation structure of variables using the correlation matrix, enabling accurate modeling of the correlation structure of the response states of the whole system. The assumption of complete independence of component states underestimated the actual system reliability. The degree of underestimation varied significantly with the peak ground acceleration (PGA), with the most pronounced effect observed at PGA=0.400g. Furthermore, neglecting the correlation between component failures resulted in an overestimation of the economic losses of the converter station, and the magnitude of this overestimation increased with the system's design life. Conclusions: The proposed reliability assessment framework incorporates equipment response correlations, yielding more accurate and realistic assessments of valve hall system performance under seismic conditions. This method also overcomes the limitations of traditional approaches based on independent assumptions, which are commonly adopted in large-scale system reliability analysis due to their computational simplicity. The proposed method is flexible and extensible, with broad application prospects in the reliability and risk assessment of complex infrastructure systems subjected to extreme events.
Objective: Transmission lines are the backbone of electric power transmission, and accurate control of their operating parameters is crucial for grid safety, stability, and disaster prevention. Under complex service conditions, conductors are subjected to coupled mechanical, meteorological, and geological loads. Conductor sag-a key parameter reflecting the mechanical state of transmission lines-is highly susceptible to abnormal variations beyond design safety margins due to typhoon-induced galloping, ice accumulation from heavy snow, and tower displacement caused by subsidence in goaf areas. Exceeding critical sag thresholds may lead to ground discharge (due to insufficient clearance), tower collapse (from excessive structural stress), or conductor breakage (especially over large rivers or valleys), jeopardizing grid transmission efficiency and infrastructure safety. Therefore, developing a new sag monitoring system based on advanced technology is essential for timely condition assessment and enhanced grid emergency response. This system detects gradual changes in the mechanical state of conductors during disaster evolution, providing accurate data support for pre-disaster early warning, in-disaster decision-making, and post-disaster reconstruction, ultimately improving the disaster resilience and operational reliability of transmission lines in complex environments. Methods: Based on laser point cloud data of transmission lines, this study designs methods for conductor tracing, missing data reconstruction, and sag calculation using a 3D point cloud k-dimensional tree (kd-tree) and simulated annealing (SA)-optimized penalized least squares B-spline smoothing. The workflow consists of three main steps: (1) Conductor tracing, in which a kd-tree index is built for the acquired point cloud to enable neighborhood searches and target conductor extraction; (2) Missing data reconstruction, in which the integrity of the extracted conductor point cloud is evaluated, and missing segments are reconstructed via SA-optimized penalized least squares B-spline fitting; (3) Sag calculation, in which the maximum sag is computed from the processed point cloud to obtain accurate sag values. Results: The effectiveness of the method was validated through six sets of transmission line point cloud experiments of varying scales, including data structure performance comparison, conductor tracing tests, data reconstruction experiments, sag calculation trials, and sag measurements under multiple operating conditions. The results demonstrate the following: (1) High efficiency for large-scale point clouds-tracing time for 10 million points was 45.30 s, and for 1 million points, it was 6.74 s; (2) High accuracy and robustness-successful conductor tracing and data reconstruction were achieved for a 712 m span with a 23.84% missing data rate (sag error less than 0.63%), and a root mean square (RMS) fitting error of 9.62 mm was obtained for a 320 m span with a 10.56% missing data rate; (3) Voxel downsampling of the point cloud reduced data density, slightly compromising measurement accuracy but significantly decreasing computational load and improving efficiency, thereby supporting deployment on portable platforms. Conclusions: This study proposes a sag measurement method for overhead transmission lines based on laser point cloud data. The method employs a kd-tree for spatial indexing and point cloud reconstruction, enables conductor tracing through neighborhood search, and uses SA-optimized penalized least squares B-spline fitting for shape reconstruction and recovery of missing conductor points. It addresses two major challenges: computational inefficiency due to large-scale point cloud data, and sag calculation errors caused by incomplete conductor point clouds. The study also establishes sag calculation formulas tailored to point cloud data, providing a valuable reference for future overhead transmission line inspections and a reliable monitoring tool for grid risk warning and analysis.
Objective: Transformers are critical assets in power transmission and distribution networks, ensuring reliable electricity delivery and overall system stability. As transformers age, their failure probability increases, leading to higher maintenance costs and outage risks. Effective maintenance planning is therefore essential for sustaining reliability, extending service life, and mitigating failures. However, limited maintenance resources make the efficient scheduling of transformer inspections and maintenance a major challenge in asset management. Traditional reliability-centered maintenance approaches, which rely on historical data and risk matrices, focus on system-level reliability while often overlooking the individual operational characteristics of transformers. Moreover, most existing strategies optimize a single objective, without achieving a systematic balance between maintenance cost and failure risk. Methods: To address these issues, this study proposes a cluster-based maintenance scheduling framework that explicitly considers asset heterogeneity and optimizes the trade-off between economic efficiency and reliability. The methodology integrates three components. First, a modified transformer failure rate model is developed by incorporating health index-based adjustments into a Weibull distribution, enabling individualized reliability assessments. The health index, derived from condition-monitoring data such as dissolved gas analysis indicators, provides a normalized, comprehensive condition score for each transformer. Second, the adjusted failure probabilities support asset-specific risk evaluation, allowing prioritized maintenance within each equipment cluster. The core decision variables define maintenance schedules—specifying when each asset is taken offline and serviced—while adhering to operational feasibility and utility constraints, including failure rate thresholds, health index limits, allowable maintenance windows, and resource restrictions. Third, a dual-objective optimization model, formulated as a mixed-integer linear programming problem, determines the optimal timing and sequencing of maintenance tasks. Adjustable weight parameters enable flexible trade-offs between minimizing maintenance cost and reducing failure risk. Results: The proposed approach was validated through this real-world case study, where simulation results showed a 12.6% reduction in total maintenance costs and an 8.2% decrease in average equipment failure risk compared with conventional methods. In addition, to analyze the impact of the maintenance coefficient on the optimization results, simulations were conducted using different coefficient values within a reasonable range while keeping other parameters constant. The results showed that larger maintenance coefficients led to poorer post-maintenance recovery, accelerated degradation, and an increase in average failure rate. Consequently, more maintenance actions were required to sustain system reliability, resulting in higher total costs. Moreover, by adjusting the reliability weight in the objective function while keeping other parameters unchanged, this study found that higher reliability weights corresponded to lower failure rates. When the reliability weight was set to 0.5, the model achieved the optimal balance between failure risk and maintenance cost, whereas overly low weights tended to maintain only the minimum acceptable maintenance intensity. Conclusions: This study presents a comprehensive, data-driven maintenance strategy that integrates Weibull-based degradation modeling, health-index-adjusted failure prediction, and optimization-based scheduling. The flexibility of the maintenance scheme is also influenced by the scale of substation assets. When the maintenance coefficient changes, the optimization strategy may remain unchanged for smaller substations due to limited equipment quantities. In addition, the reliability weight can partially affect the optimized maintenance schedule, and tuning this parameter within a reasonable range allows utilities to obtain cost-minimized solutions while maintaining the desired reliability level. The proposed framework effectively balances reliability and economic efficiency, visualizes system-wide failure trends, and supports informed decision-making for substation asset management.
Objective: The arc fault of oil-immersed transformers may result in explosions and corresponding fire accidents, which severely affect the safe operation of the power system. However, arc fault tests cannot be conducted on a large scale due to their high cost, implementation difficulty, and low repeatability. As a result, numerical simulations have become an important research method. Various types of simulation methods are available, and the validity and applicability of their numerical results and underlying physical processes need to be examined. Methods: Taking a 500 kV transformer as an example, this study used LS-DYNA to compare the effectiveness of the TNT equivalent method, instantaneous gas injection method, and uniform gas injection method in terms of spatial and temporal characteristics of arc fault explosion load of oil-immersed transformer. Thereafter, the response characteristics and failure modes of the structure, along with the influence of complex internal structures on the propagation of pressure, were explored. Results: The research results indicated the following: (1) The simulations revealed that the uniform gas injection method could reproduce the process of gas production during arc faults and efficiently reflect the effect of arc fault duration, with the generated peak pressure and pressure gradient closely matching the experimental data. The instantaneous gas injection method and TNT equivalent method yielded higher loads and might overestimate the structural responses. However, they could be employed for examining weak points of transformer structures during explosions because of their simple implementation. The uniform gas injection method was suited for precise response analysis and ultimate pressure limit analysis of transformer structures, which required high accuracy.(2) The arc fault load presented a rapid attenuation characteristic in space and time, which enhanced its local effect on the structure. However, due to the spatial limitations of the transformer structure and the space occupation of internal components, the reflection and superposition of pressure waves prevented the pressure from monotonically attenuating in time and space after reaching its peak. The multi-peak characteristics of the internal explosion pressure waves intensified the impulse within the complex structure.(3) Under the considered conditions, plastic deformation of the structure occurred in the turret structure immediately adjacent to the failure location. Bending deformation arose in the tank wall near the failure area, and substantial stress concentration was observed at the corner of the tank. Under high-energy conditions, the possible failure locations were found in the local area near the arc failure and the stress concentration points at the corners. The overall bending deformation could absorb energy and expand the fluid volume within the structure, which allowed the structure to withstand explosion loads.(4) An anti-explosion balance design concept for transformers was proposed, in which different load models were used for preliminary design, balance design, and pressure-relief design. The three stages corresponded to the static pressure bearing condition, the typical fault condition, and the rare occurrence condition. Conclusions: The uniform gas injection method effectively describes the gas generation process of the arc fault, and the load characteristics agree well with the test results. The arc fault load decays rapidly in time and space. Moreover, the complex pressure propagation path inside the transformer will considerably affect the local load. Based on the load and response characteristics, the stepwise balanced design concept adopting different levels of working conditions can improve the anti-explosion performance and realize economic benefits by simply strengthening the structure.
Objective: Frequent typhoons pose a significant threat to the safety of power systems. Accurate assessment of the vulnerability of transmission lines under typhoon conditions is therefore critical for enhancing grid resilience and ensuring a stable power supply. Methods: This study proposes a regional, full-process stochastic typhoon simulation method based on historical typhoon data and develops a vulnerability assessment framework for transmission lines that accounts for the joint distribution of extreme wind speed and wind direction. First, a comprehensive probabilistic framework was developed to simulate the full lifecycle of typhoons—from genesis and trajectory to eventual dissipation. This framework integrated nonparametric kernel density estimation with a Markov chain-based trajectory simulation algorithm to stochastically generate representative typhoon tracks, thereby enabling high-fidelity reconstruction of typhoon evolution. By incorporating near-surface wind-field characteristics, a joint probabilistic model of extreme wind speed and wind direction was further developed. This model captured the statistical dependence between wind speed and direction and effectively reflects the spatiotemporal variability of the wind field. Building upon this foundation, a multidimensional wind-induced fragility model was developed based on the structural failure mechanisms of transmission towers. The model comprehensively incorporated the coupling relationships among wind speed, wind direction angle, and spatial configuration parameters of transmission lines (e.g., alignment and span length). Through Monte Carlo simulations and high-dimensional probabilistic convolution techniques, the annual failure probabilities of different transmission towers within the target region were evaluated. Consequently, the model provided differentiated risk assessments for various line segments under typhoon-induced hazards. Notably, it enabled both the classification of risk levels at individual tower locations and the estimation of upper and lower bounds for failure probabilities for each tension section, providing quantitative support for identifying and reinforcing high-risk segments. Finally, a case study was conducted on a 220 kV double-circuit transmission line in Zhanjiang, Guangdong Province. Results: The results of this study validated the applicability and accuracy of the proposed assessment framework. The analysis demonstrated that the method effectively identified high-risk towers and vulnerable tension sections, with outputs that closely aligned with actual transmission line layout characteristics—highlighting the model's superiority in capturing the interactions between structural systems and wind fields. Distinct from traditional static or univariate assumption-based approaches, this study introduced a full-process stochastic simulation technique for typhoon track generation, coupled with a joint probabilistic modeling framework for extreme wind speed and direction, thereby enabling a comprehensive characterization of the impacts of extreme wind environments on transmission lines. Moreover, a multidimensional structural vulnerability model was developed that systematically integrated the spatial distribution, alignment, and span length of transmission towers to accurately assess the annual failure probability of different line segments under typhoon conditions. Validated through a representative case study, the proposed methodology demonstrated the capability to identify critical high-risk segments and quantify the overall vulnerability profile of transmission systems. Conclusions: The proposed approach, incorporating the joint effects of wind speed and direction, can not only identify high-risk spans of transmission lines but also effectively detect segments with significantly elevated failure probabilities. These findings provide a scientific basis for typhoon disaster mitigation and structural reinforcement of transmission lines.
Objective: Ultra-high voltage (UHV) transmission towers play an important role in long-distance power delivery, and their safe operation directly affects the stability and resilience of power systems. Under normal conditions, these towers are susceptible to deformation caused by stress induced by conductor suspension as well as geological hazards, extreme weather, etc. Current methods of routine inspection, including human inspection, unmanned aerial vehicle surveys, and in situ sensors, are costly and inefficient. Spaceborne synthetic aperture radar (SAR) provides large-area coverage and millimeter-level deformation sensitivity for landslide hazard assessment around power towers widely used by the State Grid. Methods: This study developed a high-resolution SAR-based approach to accurately extract the small structural deformation trend of UHV transmission towers. A novel SAR imaging intensity simulation and interferometric phase estimation method for transmission towers was developed. This method integrated three-dimensional light detection and ranging (LiDAR)-derived tower point cloud models with the radar range-Doppler equation to simulate the elevation phase of tower structures containing persistent scatterer (PS) points. To address the multiscattering effects and vertical occlusions inherent in lattice steel towers, a weighted sum model was developed for both intensity and phase simulations. Ascending and descending SAR data acquired by the China C-band Fucheng-1 satellite were processed over 8 months. In total, 39 SAR scenes covering two 500 kV transmission lines in Yubei District, Chongqing, were analyzed to conduct algorithm verification. To achieve subpixel accuracy in SAR geocoding, four corner reflectors (CRs) were deployed near a tower, with their positions precisely measured by a global navigation satellite system. After geometric calibration using CRs, the LiDAR point cloud data in the Fucheng-1 SAR imagery achieved a positioning accuracy within ±0.2 pixels, while the interferometric phase for strong scatterers, such as CRs, reached the sub-millimeter level. CR-based analysis further revealed a gradual settlement of approximately 8 mm over 8 months at one reflector site, highlighting the importance of stable benchmarks for long-term deformation monitoring. Results: Simulation experiments demonstrated that the proposed tower imaging model could reproduce key structural features, including the hollow lattice geometry and the scattering contributions of insulator strings. With a resolution of greater than 0.6 m, the simulated area with the power tower PS points showed favorable agreement with the hollow lattice texture of the power tower. Time-series analysis confirmed that PS points located on the tower structures maintained high coherence throughout the observation period, thereby enabling reliable extraction of deformation signals. Based on simulated tower interferometric phases, differential interferometry was performed without height-induced errors. Violin plots were used for statistically characterizing PS point deformation, and comparative analyses between strain-type and straight-type towers revealed structural differences. Comparative results indicated that strain-type towers exhibited a relatively stable condition, whereas time-series results revealed that straight-type towers exhibited more frequent and pronounced small deformation trend events. The correlation between environmental temperature variations and small deformation trends in transmission towers was weak. Interferograms with large temperature differences did not show an obvious trend in power tower structural deformation either. This trend might be influenced by multiple factors, including structural stiffness, insulator configuration, conductor tension, and external loading. Conclusions: This study verifies the feasibility of using high-resolution China C-band SAR satellites to monitor the small structural deformation trend of UHV transmission towers in time series datasets. Future work should incorporate structural temperature variations, power line stress conditions, and wind loads to develop physical mechanism-based models for explaining power tower deformation trends. Ultimately, the methodology presented in this study provides a foundation for analyzing such trends. It can be applied to assess structural stability under extreme events, such as geological hazards, earthquakes, and typhoons across different regions of China.
Objective: Electrical equipment is highly vulnerable to seismic hazards. Acquiring accurate seismic response data is crucial for post-earthquake damage assessment and emergency decision-making. Although conventional contact sensors are effective for capturing such data, they cannot be widely deployed on the equipment body due to monitoring constraints. Therefore, seismic response prediction methods based on time-series neural networks must be developed. Most existing studies have emphasized the optimization of neural network architectures, and dataset construction strategies have not been systematically and sufficiently investigated. Dataset construction directly influences the fitting accuracy and generalization ability of predictive models, ultimately determining the overall predictive performance. In this study, the effects of three key elements of dataset construction-ground-motion selection, amplitude scaling of records, and sample-size configuration-on the performance of time-series models were evaluated, and a scientifically grounded dataset construction workflow was proposed. Methods: A 500-kV transformer bushing was selected as the case study. A refined finite element model was developed and validated by shaking-table tests to determine its accuracy in terms of dynamic characteristics and response behavior, and the acceleration at the top oil reservoir was chosen as the prediction target. Two ground-motion selection strategies were adopted for dataset construction: spectrum-matched records and random selection constrained only by site type. Four amplitude scaling strategies were examined: conventional random, conventional fixed, extended-range random, and extended-range fixed scaling. Five sample-size levels of 80, 100, 120, 140, and 160 records were also configured to form multiple strategy combinations. A recursive long short-term memory neural network was used as the representative prediction model. Its performance was assessed based on mean squared error and peak response error, and repeated sampling and multiple independent training runs were performed to mitigate stochastic variability. Results: Spectrum-matched selection outperformed random selection based solely on the site type, yielding lower overall prediction errors in seismic response time series. Fixed scaling was superior to random scaling, and the introduction of extended-range scaling further enhanced the peak prediction accuracy. Although commonly used, random scaling considerably reduced the overall and peak prediction performance of the model and was not recommended for seismic response time-series prediction. Increasing the number of training samples improved the model accuracy, but marginal gains were observed at a sample size of 120-140 records. The combination of spectrum-matched selection and extended-range fixed scaling was the most effective strategy. Comparative tests with representative ground-motion records further confirmed that this strategy surpassed commonly used empirical approaches in terms of fitting accuracy, peak prediction capability, and training stability; it also enabled more accurate capture of abrupt response transitions and reduced phase errors. Conclusions: A recommended dataset construction workflow is proposed for the time-series prediction of electrical equipment modeled as linear elastic systems. The proposed process integrates finite element modeling and validation, spectrum-matched ground-motion selection, extended-range fixed scaling, and balanced sample-size configuration. The findings confirm that this workflow considerably improves both the prediction accuracy and overall stability of the model, offering systematic methodological support and practical engineering guidance for post-earthquake emergency assessment and response monitoring of electrical equipment. This approach can also be extended to other structural systems where dataset construction critically affects the model performance.
Objective: Unmanned aerial vehicle (UAV) inspection technology has become an essential tool for monitoring wildfires in transmission line corridors due to its flexibility, cost-effectiveness, and adaptability to remote and complex terrains. However, current research lacks initial wildfire image datasets from the perspective of UAV inspections, and existing algorithms struggle to accurately identify early-stage wildfires. This study constructs an initial wildfire image dataset from a UAV perspective and proposes the YOLOv11n-MCC detection model for transmission line corridors. Methods: Through the field inspections of national transmission line corridors for 174 h, covering 82 000 km with UAVs, 1 308 transmission line corridor inspection images were obtained, and a transmission line corridor inspection image dataset from the perspective of the UAV was constructed. This dataset was analyzed to identify the characteristics of the initial wildfire images. The early wildfire detection model, YOLOv11n-MCC, based on an enhanced version of YOLOv11n, was then proposed. First, part of the traditional convolutional network in YOLOv11n was replaced with multi-scale feature convolution(MFConv) to reduce computational load while improving feature extraction. Second, the C2PSA module in the backbone network was replaced with the spatialand channel synergy attention(SCSA) mechanism to improve target localization. Finally, C3k2_ABlock with convolutional additive token mixer(CATM) at its core was embedded to improve target representation and selection in complex backgrounds. Results: MFConv, SCSA attention, and C3k2_ABlock with CATM sequentially improved the YOLOv11n-MCC model's ability to detect targets in complex scenes. Comparative experiments revealed that the YOLOv11n-MCC model significantly outperforms the YOLOv11n baseline model in terms of accuracy, mAP50, parameter count, and giga floating-point operations per second(GFLOPS) for early mountain fire small-target detection, making it portable but still computationally efficient. Specifically, precision increased by 9.0 percentage point, recall by 3.8 percentage point, and mAP50 by 5.7 percentage point. In addition, the number of parameters and GFLOPS decreased by 0.149×106 and 0.2, respectively. The YOLOv11n-MCC architecture achieves enhanced multiscale feature representation while maintaining reduced computational complexity, thereby improving operational efficiency without compromising detection performance. Conclusions: The image dataset for transmission line corridor inspection constructed in this study can effectively support the training and testing of the improved fire detection algorithm. The proposed YOLOv11n-MCC model demonstrates stable performance in detecting small initial mountain fire targets and can be effectively applied to real-time wildfire detection in transmission line corridors using UAVs, thereby providing essential technical support for early wildfire detection. Future work will focus on examining the influence of varying smoke-to-fire ratios on model training to further enhance wildfire detection accuracy.
Objective: Overhead transmission lines in mountainous regions are highly susceptible to ice accretion and subsequent ice-shedding. The violent jumping vibration of conductors triggered by ice-shedding may result in severe hazards, including inter-phase flashover, hardware damage, or even conductor breakage. The extant literature principally utilizes numerical simulations to model the ice-shedding process and employs empirical formulas to predict the maximum jump height. However, these approaches are limited in their ability to reveal the underlying mechanisms and evolution laws of the physical quantities as theoretical models do. Furthermore, the maximum jump height alone is insufficient to meet the requirements of engineering risk assessment. To address these limitations, this study proposes a nonlinear theoretical model for a multi-span transmission line-insulator coupled system. This model can systematically predict both static and dynamic responses during the ice-shedding process. The model is expected to provide an efficient and reliable computational tool for forecasting and safety evaluation of transmission line ice-shedding hazards. Methods: A multi-span transmission line with equal span lengths and no elevation differences was selected as the object of study. Bundled conductors were simplified as single conductors, and the most unfavorable condition was considered by assuming that the entire middle span undergoes simultaneous ice-shedding. The process of ice-shedding by multi-span transmission lines comprises three fundamental static equilibrium stages: the initial equilibrium state, the static equilibrium state following icing, and the static equilibrium state after ice-shedding. The profile of the initial equilibrium state can be expeditiously obtained based on suspension theory. The integration of supplementary ice elements within the static equilibrium equations facilitates the derivation of tension and displacement distributions after icing. After the shedding of ice by the middle span, the system undergoes free vibration under the combined effects of the conductor and the ice self-weight, eventually reaching a new stable equilibrium. The incorporation of deformation compatibility conditions between conductors and suspension insulators facilitates the determination of tension and displacement distributions at the static equilibrium state following ice-shedding. The initial condition for the subsequent analysis was the static equilibrium state following the occurrence of icing. This was expanded to include the static equilibrium state after ice-shedding. The nonlinear coupled free vibration equations for all spans and insulator strings were established and solved simultaneously with the compatibility conditions. This approach enabled the determination of the complete time-history response of ice-shedding. The accuracy and efficiency of the proposed model were validated through comparisons with finite element simulations and existing static theoretical methods under various span numbers and ice thicknesses. Results: The comparative results demonstrated that, for ice thicknesses ranging from 5 to 25 mm and for 3-7 span configurations, the time-history responses predicted by the proposed model exhibited strong agreement with finite element results, with deviations in maximum jump height controlled within ±10%. Concurrently, the computational efficiency of the proposed method was notably superior to that of finite element methods. A thorough parametric analysis revealed that both the jump amplitude and the inter-span unbalanced tension exhibited an increase with greater ice thickness and span number. However, these variables tended to stabilize once the span number exceeded five. Furthermore, the displacement of suspension insulators facilitated the redistribution of local unbalanced tension across a greater number of spans. In addition, the magnitude of interspan unbalanced tension underwent rapid decay as the distance from the de-iced span increased. Conclusions: This introduces the nonlinear static and dynamic theories of flexible suspension cables into the study of transmission lines. It systematically develops a unified theoretical model for multi-span transmission line-insulator coupled systems. This model covers the static equilibrium state after icing, the static equilibrium state after ice-shedding, and the dynamic response process. The proposed method combines accuracy with efficiency, thereby enabling effective prediction of conductor jump responses induced by ice-shedding. In addition, it provides theoretical support for the refinement of anti-icing design formulas.
Objective: Transmission towers are highly sensitive to wind loading. Improving the wind resistance of transmission towers is important for ensuring grid reliability. Prestressed cables provide easy installation and minimal spatial intrusion. An internal cable reinforcement method was proposed to improve the tower. Four internal cables were installed at the main member of the tower to reduce horizontal wind loads. Methods: The applicability of the internal cable method was confirmed using theoretical analysis. A performance evaluation framework for transmission towers with internal cables was introduced. The framework determines the cable reinforcement scheme through theoretical analysis. Truss idealization, pinned-joint modeling, and small-deformation assumptions were adopted to investigate axial forces in main and diagonal members after cable reinforcement. The optimal reinforcement scheme was determined based on the comparative reduction in member axial forces arising from reinforcement. Nonlinear stability analysis was implemented on finite element models of transmission towers with and without internal cables. The member stability cycle criterion was performed to ascertain failure modes and critical wind speeds. Tower failure is regarded as either the buckling failure of a single member or the yielding of a main element below the cable installation location. Results: A 220 kV transmission tower was used as the case study. Using the framework, the reinforcement effect under extreme wind was explored. The results revealed that the axial-to-force ratio in the lower leg member improved considerably after internal cable installation, while the axial-to-force ratio in diagonal members reduced substantially under extreme wind loading. Local yielding in the lower leg member below the cable anchorage precipitated structural collapse. The failure mode changed from diagonal buckling to leg failure after reinforcing. The critical wind speed rose from 35.05 m/s to 40.36 m/s. The critical wind speed increased by 15.0%, which led to a 32.6% enhancement in horizontal load-bearing capacity. Then, the influence of cable prestress levels and cross-sectional areas were investigated. Under varying cable parameters, the critical wind speed of the tower increased by 13.0%-16.0%. Therefore, cable cross-sectional area and prestress exert only a minor effect on wind resistance performance. As cable prestress and cross-sectional area increased, the marginal gain in critical wind speed progressively diminished. Raising the cable cross-sectional area decreased its ultimate stress, with further area increases above 100.00 mm2 producing only marginal changes. Conclusions: The framework can be used to design cable-reinforcement schemes for transmission towers and evaluate the wind resistance performance of the reinforced tower-cable system. Cable reinforcement reduces axial forces in diagonal members along the tower. It reduces axial forces in lower leg members but raises them in upper leg members. For towers prone to leg-member buckling, internal cables with small inclination angles intensify compressive stresses in loaded legs. Therefore, external cables with larger angles are recommended. In towers where diagonal buckling governs failure, internal cable reinforcement substantially increases critical load capacity. The failure mode shifts to local yielding of lower leg members, and the capacity gain relies on the wind speed at which these members yield. Raising cable prestress or cross-sectional area decreases the critical wind speed for transmission towers with internal cables. The optimal wind resistance enhancement is realized with a cable prestress of 5.0 kN and a cross-sectional area of 100.00 mm2. The research results of this paper can provide a reference for the optimization of wind resistance design of transmission towers.
Objective: As a critical component of lifeline infrastructure, urban power systems (UPSs) are essential for maintaining city functionality and supporting daily societal needs. Accurately assessing the post-earthquake functionality (PEF) of power systems is crucial due to its great importance for emergency response, urban recovery, and social stability. Traditional connectivity-based assessment methodstend to overestimate the post-earthquake power supply capability because they ignore the physical constraints of power transmission and distribution. To address this limitation, this study proposes a comprehensive PEF assessment framework to accurately quantify UPS performance under seismic scenarios. The framework explicitly integrates topology, physical damage, power redistribution, and cascading failures, providing reliable support for emergency decision-making and recovery planning. Methods: The proposed framework consists of five interrelated steps. (1) A unified topological representation of the UPS is derived using single-line diagrams, in which buses serve as core units and sources, while transformers, lines, and switching stations are consistently abstracted across voltage levels. (2) A spatially distributed ground-motion field over the urban area is generated, and engineering demand parameters (e.g., peak ground acceleration) are assigned to different electrical facilities. (3) Seismic damage scenarios, i.e., damage to substations, lines, and switching stations, are generated using Monte Carlo simulations. Each damage state is mapped to a reduction in power supply capacity, allowing partial functionality loss rather than a binary failure mode. Damaged components are removed from the network, while components with no power supply are identified and excluded through connectivity analysis. (4) DC power flow is executed within each source-connected island, with a slack bus to enforce power balance. Cascading failures are simulated by iteratively removing the lines that exceed their rated capacity until the system reaches a stable state. Load curtailment follows explicit rules: priority users are supplied first, and remaining capacity is proportionally distributed among non-priority users based on load weight. (5) The load-supply performance index (ηLS) is employed to quantify the overall and regional PEF of the UPS under seismic scenarios, incorporating land-use classifications and spatial zoning for differentiated analysis. Results: A case study was conducted on a benchmark city model representing a medium-sized coastal city in southeastern China with over 500 land parcels and a total forecasted load of 1 873.46 MW. The PEF of the UPS was assessed with 10 000 Monte Carlo realizations considering a seismic scenario in which an Mw 6.5 earthquake occurred 10 km northeast of the city center. The results showed that the average ηLS was 46.87%, with the most severe functionality losses concentrated in the northeastern region near the epicenter. Functionality varied across land-use types: logistic and public service lands showed the highest median functionality levels (>70.00%), while industrial lands exhibited high variability (median approximately 62.99%). Residential, green space, and commercial lands showed mean functionality levels of approximately 45.00%. The earthquake epicenter location strongly influenced UPS performance, with the ηLS degrading to 35.69%-37.88% when the earthquake occurred to the west, north, and northwest of the city. Conclusions: This study validates the applicability of the proposed DC power flow-based functionality assessment method. Key findings include: (1) the framework captures the influence of network topology, seismic input randomness, power redistribution, and cascading failures on the PEF of UPS; (2) cascading failures significantly amplify functionality loss because partially damaged components may become non-functional due to overloading or islanding effects; (3) functionality differs across land-use types, with logistic and public service lands performing best, industrial lands showing high variability, and residential, green, and commercial lands maintaining stability close to the system average; (4) earthquake epicenter orientation strongly affects system functionality, highlighting the need to locate high-demand areas away from potential seismic sources and improve redundancy in critical transmission paths; and (5) current limitations, including the absence of post-earthquake redispatch modeling and the simplified power flow representation, will be addressed in future studies.
Objective: Extreme natural disasters and multi-point disturbances, such as earthquakes, typhoons, and equipment failures, significantly affect the stability and reliability of power systems. Traditional control measures often lack efficiency in addressing complex fault scenarios, leading to widespread load loss and prolonged recovery times. This study proposes a multilevel cooperative optimization method for load control in power systems, aiming to minimize load loss and enhance system stability. The method optimizes several control measures—stability-control load shedding, low-frequency and low-voltage load reduction, low-voltage tripping, and zonal operation—thereby improving system resilience to large-scale disturbances. It dynamically adjusts control strategies based on real-time fault conditions, improving recovery speed and anti-disturbance capability, and thus enhancing overall stability and recovery efficiency. Methods: The proposed method integrates multiple strategies to strengthen power system stability under complex fault scenarios. First, a regionally differentiated load-shedding strategy identifies high-risk areas and applies small-scale, multiple rounds of load shedding to avoid instability caused by large-scale load shedding. Second, dynamic threshold adjustment and cross-regional coordination mechanisms optimize load control delays in real time to respond quickly to frequency and voltage fluctuations. Wide-area measurements coordinate shedding and recovery across multiple regions, ensuring grid stability. Finally, a hybrid optimization mechanism combining genetic algorithms and model predictive control (MPC) is employed for real-time optimization. The genetic algorithm addresses nonlinear and multi-constrained problems, whereas MPC dynamically optimizes strategies based on real-time and predictive system states, enhancing response flexibility. Fault-scenario simulations using sequential Monte Carlo methods validate the adaptability of the proposed method under diverse conditions. Results: The simulation results showed that the proposed method significantly improved system performance under extreme fault conditions. The key results included: (1) Reduced overall load loss and accelerated recovery, enhancing system resilience during large-scale faults. (2) Improved real-time optimization through the combination of genetic algorithms and MPC, balancing load shedding across nodes and preventing localized losses. (3) Markedly better load-shedding effectiveness and recovery speed than traditional measures did, particularly in complex fault scenarios. (4) Consistent improvements in recovery speed and balanced load-shedding distribution in 100 simulated fault scenarios. Conclusions: The multilevel cooperative optimization method provides a systematic solution to enhance the resilience and stability of power systems under large-scale fault conditions. Combining multiple load control measures and optimizing them with genetic algorithms and MPC, it significantly strengthens the system's ability to withstand complex faults. The method not only reduces load loss but also accelerates recovery, thereby improving overall operational reliability. Future research may focus on advancing real-time adaptability and cross-regional coordination to further enhance the global optimization of power grids under increasingly frequent and severe fault scenarios.
Objective: Persistent strong aeolian vibrations in recent years have caused multiple conductor and strand breakages, as well as hardware wear and failure, across long-span and standard-span transmission lines throughout northwestern, northern, and northeastern China. To address the limitations of existing vibration dampers, specifically their suboptimal damping characteristics and restricted protection spans, this study develops an optimized design that effectively mitigates these fatigue risks. By enhancing the aeolian-vibration resistance of transmission lines, this approach protects conductors and fittings, thereby extending their service lives. Methods: A nonlinear energy sink method was used to optimize traditional vibration-damper designs, after which the vibration-mitigation performances of engineering prototypes were experimentally verified and theoretically evaluated. First, a theoretical nonlinear coupling model was established to analyze the vertical coupling vibration between the conductor and the nonlinear-stiffness damper, incorporating the damping, mass, and cubic stiffness of the oscillator. Next, nonlinear dynamic methods were used to decouple and solve the model. Thereafter, the numerical relationship between the displacement function and strain of the conductor and damper oscillator was clarified to maximize the proportion of oscillator vibration energy to the total energy of the coupled system. Based on this design, three critical coefficients were optimized and selected: the mass, nonlinear stiffness, and damping of the oscillator system. Results: The theoretical analysis indicated that the impact of the mass factor of the vibration-isolation device (oscillator) on the maximum dynamic bending strain of the conductor was negligible under low-intensity aeolian vibration. Conversely, the stiffness factor significantly impacted performance, necessitating rigorous analysis and optimization before structural design in transmission engineering. Furthermore, enhancing the damping characteristics was essential for effective vibration mitigation. Second, following authoritative industry testing standards, engineering prototypes of the nonlinear-stiffness damper were designed and customized using a 140 m experimental line as a typical example and experimental object. Comparative experiments were conducted to evaluate key performance indicators, including power characteristics and protection spans, thereby validating the optimization principles of the nonlinear stiffness and damping parameters. The related indicators, such as resonance frequency dispersion, maximum peak-to-valley ratio, and the effective damping frequency range, satisfied all standard engineering requirements, demonstrating that the device was suitable for deployment and applications on operational transmission lines. Finally, based on the power characteristics and anti vibration effect evaluation experiments in the existing standards, the data showed that the damping ratio of a single nonlinear-stiffness damper, which was greater than or equal to 1.5, represented a 140.0% increase over traditional FR-type anti-vibration hammers adapted to the same conductor model. Under identical strain-control thresholds, the optimized nonlinear-stiffness damper achieved a maximum protection span of 402 m, compared with 280 m by a traditional damper, representing a significant 43.6% increase. Conclusions: Overall, this study verified the effectiveness and technical advantages of the proposed design, demonstrating that the engineering application of the nonlinear energy sink theory significantly enhances aeolian-vibration resistance for high-voltage transmission lines.
Objective: With the growing complexity of modern power networks, coupled with the increasing frequency and intensity of natural hazards, there is an urgent need to develop advanced resilience assessment methods. Unlike conventional single-stage disaster events, multi-stage hazards such as earthquake-landslide-debris flow chains have successive and compounding impacts on electrical infrastructure. Such cascading events introduce severe uncertainty into the damage mechanisms and recovery processes of power systems, thereby threatening the continuity of electricity supply in disaster-affected regions. Traditional simulation-based approaches, especially those relying heavily on Monte Carlo techniques, often fail to capture the full dynamics of multi-stage shocks because of limited sampling or become computationally prohibitive when scaled to larger networks. To overcome these challenges, this study developed a probabilistic resilience assessment model that combines analytical probability calculations with optimization-based network analysis. The aim was to develop an accurate and efficient method for evaluating resilience in power systems under multi-stage disaster shocks. Methods: The proposed framework incorporates several method ological innovations. First, the entire power network is partitioned into smaller units through a community detection strategy, ensuring computational tractability while retaining the integrity of network interdependencies. Within these units, a mixed-integer programming model generates feasible power supply paths for each load node under normal operating constraints. This optimization-based representation identifies not only primary supply routes but also redundant paths that become critical in the event of failures. Second, the model incorporates probabilistic damage forecasting of transmission towers, which are among the most vulnerable components during seismic and secondary hazards. Instead of relying on scenario sampling, the model derives closed-form probability distributions of interruption durations during disasters and post-disaster recovery times. These distributions are obtained by analytically linking tower damage probabilities with repair processes, assuming realistic restoration practices. Finally, the resilience indicator is defined as the expected cumulative load-serving capability over the entire disaster cycle. By integrating the temporal evolution of service continuity, the framework captures both the degradation during hazard propagation and the recovery trajectory once repair efforts commence. This analytic approach eliminates the need for extensive sampling and significantly accelerates resilience estimation. Results: The proposed framework was validated using a modified IEEE 123-bus power network as the research object. This grid comprised one generation unit, 123 load centers, and 125 transmission lines. The application of the proposed method to this grid yielded several key research findings: First, the resilience of the power network exhibited an overall downward trend with increasing mainshock magnitude. It declined slowly under low-magnitude earthquakes with favorable disaster resistance. As the mainshock magnitude intensified and more chain-disasters occurred, the failure probability of transmission towers and lines increased, power supply redundancy was gradually weakened, and the network resilience dropped rapidly. Second, the established probabilistic resilience assessment model achieved markedly higher computational efficiency while guaranteeing satisfactory accuracy. By adopting analytical calculations, it realized accurate and efficient evaluation under the spatiotemporal evolution of earthquake disasters, effectively overcoming the excessive computational burden of Monte Carlo simulation. The resilience assessment results also revealed important system-level patterns: The resilience index of the studied grid was approximately 88.644 7% under a magnitude 7.0 earthquake, decreased to 76.854 9% under a magnitude 7.5, and further decreased to 58.413 4% under a magnitude 8.0. Although the resilience index declined significantly with increasing earthquake magnitude, the grid's supply path redundancy capability ensured that most electricity demand could still be met even under severe seismic events. These findings confirmed the crucial role of supply path redundancy and transmission tower reliability in maintaining stable electricity supply under cascading hazards. Conclusions: This study develops a novel probabilistic resilience assessment framework tailored for power networks exposed to multi-stage disaster shocks. By combining power unit partitioning, supply path optimization, and analytical probability modeling, the method addresses the limitations of simulation-heavy approaches and ensures the accuracy and efficiency of resilience estimation. The case study demonstrates the capability of the proposed method to quantify the interplay between disaster evolution, infrastructure vulnerabilities, and system recovery, offering highly relevant insights for planning resilient electricity infrastructure. This approach is particularly valuable for decision-makers and emergency planners, as it supports rapid assessment without sacrificing precision. In conclusion, the proposed method represents a significant advancement in resilience modeling for power systems. This study not only validates the feasibility of analytical probability-based approaches but also sets the stage for further research on integrating adaptive recovery strategies and resource-constrained repair models. In future work, the approach may be extended to consider real-time data integration, simultaneous restoration during hazard evolution, and multi-resource coordination, thereby enhancing the practical applicability of resilience assessment in real-world emergency contexts.
Objective: Change detection is highly sensitive to differential information in target features across multitemporal images and has been widely applied in various remote-sensing domains. It plays a crucial role in disaster emergency response and post-disaster damage assessment. However, most mainstream change detection methods are primarily designed for buildings, with limited applicability to roads. These methods struggle to capture the fine-grained and continuous change characteristics of roads. Furthermore, existing road detection approaches are predominantly single-temporal. Such approaches cannot perform end-to-end reconstruction of completely damaged or destroyed roads, which limits their performance in road damage detection. Moreover, publicly available datasets rarely include annotations for road damage information. The lack of specialized benchmark datasets for damaged roads therefore remains a challenge. To address these issues, this study developed a U-shaped bi-temporal transformer (U-BiFormer) for road damage change detection and constructed a bi-temporal damaged road (BTDR) dataset as a benchmark to validate the effectiveness of the proposed method. Methods: The proposed U-BiFormer consisted of three core modules: multiscale learning (MSL), residual transformer shortcut (RTS), and multilevel dense iteration decoding (MDID). MSL used the first three layers of ResNet-18 and a standard convolutional block to generate feature maps at four different scales. These multiscale feature maps enhanced and optimized the bi-temporal features, which were then fed into the RTS module. RTS consisted of two submodules: the Trans. block and the Res. block. The Trans. block modeled spatiotemporal global information within the bi-temporal features at different stages, containing high-level semantic information. The Res. block extracted difference maps that included low-level geographical localization information. By processing information at different hierarchical levels, RTS captured contextual dependencies at both long and short ranges, thereby improving performance. Instead of standard upsampling, MDID used dense blocks, which were particularly suitable for preserving road continuity. This module integrated feature maps from all preceding stages, enabling the fusion of coarse-grained and fine-grained features to support subsequent predictions. Finally, the proposed model employed separate prediction heads for the damage classification task and the change detection task. For damage classification, the class embedding vector was concatenated with semantic tokens to form new tokens, and the class embedding was then separated to predict the road damage level. For change detection, the reconstructed features were upsampled through several convolutional layers to generate the final visualization. Regarding the loss function, a combination of focal loss and Dice loss was used to achieve an optimal balance. Results: Experiments were conducted on the proposed BTDR dataset, including single-temporal road extraction experiments and bi-temporal change detection experiments. For the bi-temporal tasks, the results demonstrated that the proposed method performed well in damaged-category detection, with all evaluation metrics exceeding 82.00%. Furthermore, the method significantly outperformed mainstream models in the change detection task, achieving a mean intersection over union of 82.30%. In the single-temporal road detection task, the model also surpassed several mainstream road detection methods. Qualitative results visually confirmed the superiority of U-BiFormer. Conclusions: By constructing the specialized BTDR dataset and developing the U-BiFormer, this study achieves outstanding performance in road damage change detection and validates the effectiveness of each module. The proposed approach provides an efficient solution for rapid post-disaster response and reconstruction and offers significant engineering value.
Objective: Large-amplitude vibrations induced by conductor ice shedding can readily cause trip discharge in transmission lines, posing a serious threat to power grid security. Ice shedding often occurs under wind loads, leading to more complex conductor motion trajectories. Significant discrepancies exist between these dynamics and calculations based on current design codes that neglect wind effects, thereby increasing the probability of discharge failures. Therefore, accurate computation of conductor motion characteristics under combined ice shedding and wind load is essential to provide a scientific basis for setting electrical clearances in transmission lines. However, previous studies on coupled wind and ice-shedding effects generally assumed that the wind load acting on the remaining ice remains constant during vibration, neglecting the time-varying wind attack angle induced by conductor torsion after asymmetric ice shedding. This simplification ignores the dynamic variation of the wind attack angle during ice-shedding vibration, resulting in deviations between simulated and actual conditions. Consequently, such approaches may either overestimate or underestimate the influence of wind load on the ice-shedding trajectory. Methods: This study employs a user element subroutine to simulate the time-varying wind attack angle and corresponding wind load during ice shedding, using crescent-shaped ice accretion as an illustrative case. Finite element simulations are conducted to investigate the effects of wind speed, mean wind, fluctuating wind, and ice-shedding rate on the vertical jump height, horizontal displacement, and motion trajectory of conductors under coupled ice-wind conditions. Results: The results indicated that: (1) The torsional angle of the conductor during ice shedding varied significantly with wind speed. Higher wind speeds led to larger torsional angles, which markedly affected the vertical jump height, horizontal displacement, and motion trajectory. Neglecting this torsional effect introduced errors in calculating jump height and swing displacement that were inconsistent with actual behavior. (2) Conventional methods based on fixed wind loads either underestimated or overestimated the influence of wind on ice-shedding jump height. In a representative case, the calculated jump height based on a fixed wind attack angle deviated by up to 72.3% compared with that obtained under time-varying wind load. (3) The mean wind and fluctuating wind models exerted distinct effects on conductor dynamic responses. At lower wind speeds, differences in motion trajectories between the two models were negligible, whereas at higher wind speeds, fluctuating wind significantly increased jump height and horizontal swing distance. (4) The maximum vertical jump height occurred at an ice-shedding rate of 50%, while the horizontal swing amplitude was negatively correlated with the ice-shedding rate. The peak jump height and maximum horizontal swing did not occur simultaneously. After ice shedding, the conductor midpoint first reached the maximum jump height, after which the vertical displacement decreased while the horizontal displacement increased rapidly until the maximum swing position was reached. Conclusions: This study improves the computational accuracy of conductor ice-shedding dynamic responses under realistic coupled ice-wind conditions, provides guidance for the design of electrical clearances in ice-shedding-prone transmission lines, and enhances transmission line resilience against ice disasters. For refined and differentiated anti-icing design of transmission line sections susceptible to ice-shedding jumps, ice-shedding simulation models incorporating time-varying wind attack angles should be adopted. These models enable the calculation of conductor ice-shedding jump trajectories and envelopes, thereby providing a scientific basis for determining tower head dimensions and electrical clearances between conductors and ground wires, as well as between phases. This approach mitigates risks associated with insufficient electrical clearances while avoiding unnecessary material costs arising from overly conservative designs.
Objective: Transmission towers are essential components of power transmission systems, and their safety directly affects grid stability. With the increasing frequency of extreme weather events, damage from combined wind-rain loads has become more pronounced. Current design codes primarily address wind loads in isolation, neglecting the effects of wind-rain coupling. In addition, the effects of key factors, such as wind direction, on structural response remain unclear. This study investigates the displacement response of transmission tower-line systems under wind-rain coupling and clarifies how wind speed, rainfall intensity, wind direction angle, and measurement location influence this response. The goal is to provide reliable experimental data to optimize tower design against combined wind and rain loads. Methods: A 1:10-scaled aeroelastic model of a 110 kV cat-head transmission tower was designed and constructed based on similarity criteria. Comprehensive wind tunnel tests were conducted in a large-scale climate wind tunnel while considering three wind speeds (10.0, 14.0, and 18.0 m/s), three rainfall intensities (30, 60, and 90 mm/h), and three wind direction angles (45°, 60°, and 90°). A finite element model of the prototype tower was developed using advanced simulation software tools for modal analysis. A high-stiffness sensor support was designed to minimize vibration interference. Two laser displacement sensors were used to measure the root-mean-square (RMS) and peak displacements at the upper and lower sections (Measuring Points 1 and 2, respectively) of the tower top in a synchronous manner. Key similarity parameters, including length, wind speed, frequency, and stiffness, were strictly controlled to ensure the validity of the test results. Results: The experimental results yielded four key insights: (1) Wind speed had a non-linear strengthening effect on displacement response, with a significantly greater growth rate in the high wind speed range (14.0-18.0 m/s) than in the low wind-speed range (10.0-14.0 m/s). (2) Displacement at measuring point 1 consistently exceeded that at measuring point 2, indicating that the upper section of the tower top is the most vulnerable region under wind-rain coupling. (3) The 45° wind direction angle was identified as the most critical, producing a significantly larger displacement response than other angles. (4) A critical coupling effect between wind and rain was observed, with moderate increases in displacement response at lower rain intensities (≤60 mm/h) and wind speeds (≤14.0 m/s). Under extreme conditions (90 mm/h rain intensity and 18 m/s wind speed), the RMS displacement at measuring point 1 (45° wind angle) increased significantly compared with that under moderate conditions. Conclusions: This study systematically elucidated the displacement response characteristics of transmission towers under combined wind and rain loads, quantitatively assessing the influence of key environmental and structural factors. The results suggest that the upper section of the tower top should be prioritized for reinforcement in design. The combination of a 45° wind direction angle with extreme wind and rain conditions (18.0 m/s, 90 mm/h) constitutes a critical design scenario. In addition, wind direction angles of 60° and 90° can be classified as a "low-response group" allowing for potential design optimization. These findings provide a crucial experimental basis for advancing transmission tower design theory for combined wind and rain conditions, effectively balancing structural safety with economic efficiency.
Objective: Underground power utility tunnels are a crucial component of urban energy infrastructure. Their safe operation faces substantial challenges owing to the complex interactions among multidimensional risk factors, including human, equipment, environmental, and management factors. This study develops a quantitative risk propagation analysis framework that integrates static structural assessment with dynamic risk propagation modeling. The goal of this study is to systematically identify key risk factors for power utility tunnels and analyze the influence of inherent system risks and mitigation measures. In addition, this study aims to determine risk propagation thresholds, providing theoretical and practical guidance for risk prevention and control in power utility tunnels. Methods: This study adopted the work breakdown structure-risk breakdown structure approach to systematically identify 53 risk factors across 4 dimensions: human, equipment, environment, and management. Based on 101 typical accident cases from power utility tunnels, this study used the Apriori algorithm to extract 255 strong association rules. This study constructed a directed, weighted complex network to model risk propagation within power utility tunnels. For static analysis, this study calculated four network metrics: weighted in-degree, weighted out-degree, weighted betweenness centrality, and weighted out-clustering coefficient. This study used the expert grading method to determine the weights for each metric, yielding a comprehensive importance score for each node. For dynamic analysis, this study extended a susceptible-exposed-infected-recovered-susceptible (SEIRS) model by introducing a "controlled" (C) state, forming a dynamic risk propagation model (SEIRS-C). This study conducted Monte Carlo simulations across multiple scenarios to assess the impacts of various initial triggering factors, the overall risk propagation parameter (k, inherent system risk level), and the risk responsiveness (g, a comprehensive metric for control measure efficacy) on risk propagation. Results: The results from the static and dynamic analyses indicated the following: (1) The static analysis highlighted the importance of management and equipment risks. Key risk factors, including insulation degradation, failure to implement safety production responsibilities, malfunction or absence of alarm systems, and inadequate hazard identification and rectification, emerged as crucial to risk prevention. (2) Dynamic simulation showed that when management factors served as the initial activation node, the scope and duration of risk propagation were significantly greater than those when equipment factors served as the initial activation node. In addition, the management and environmental factors accounted for 70% of the top 10 nodes. (3) k was positively correlated with the speed and extent of risk propagation, whereas g determined the speed of risk mitigation. Moreover, a g value greater than 0.40 was necessary to prevent delays in risk control. Conclusions: This study establishes a combined static and dynamic framework for quantitative risk propagation analysis in power utility tunnels, effectively identifying key risk factors and proposing strategies for risk prevention and control optimization. Factors such as failure to implement safety production responsibilities, geological shifts, and insufficient safety inspections exhibit high static structural importance and dynamic risk propagation capacity and should be prioritized in prevention efforts. The findings suggest a dual approach to risk prevention: enhancing monitoring and source control at the static level and implementing process closure management to block high-risk propagation paths at the dynamic level. This study provides a scientific foundation for transitioning power utility tunnels risk management from experience-based methods to quantitative risk control.
Objective: As a novel, clean, and efficient energy source, hydrogen has emerged as a cornerstone of sustainable transportation, facilitating the rapid development and deployment of hydrogen-powered trains. While current research mainly explores relatively idealized open environments or simple confined volumes, the unique challenges posed by semi-enclosed, longitudinal tunnel geometries remain insufficiently investigated. Specifically, the dispersion and transport mechanisms of hydrogen following high-pressure leakage in the tunnel scenario, along with the resulting hydrogen concentration distribution, remain to be investigated. Furthermore, there is a lack of connection and analysis between the evolution of the velocity field and the variations in momentum flux following high-pressure hydrogen leakage. Methods: To investigate the evolution law of high-pressure hydrogen leakage and dispersion within tunnels, a numerical modeling approach was employed to establish physical geometry models of the tunnel and train. By integrating the turbulence model with the Molkov virtual nozzle model, the evolution of the velocity field, the variations in the hydrogen-leakage momentum flux, and the three-dimensional concentration distribution profiles were systematically analyzed. Results: After hydrogen leaked, its jet impinged on the tunnel ceiling, followed by rapid lateral dispersion and downward flow along the tunnel ceiling. During this process, the hydrogen momentum vector underwent multiple reorientations at the wall, which led to a rapid decrease in its momentum. Consequently, the momentum flux distribution exhibited a distinct gradient distribution along the tunnel ceiling. The velocity decayed significantly toward the tunnel exits, where density variations across the different zones remained negligible; the lateral momentum gradually homogenized. Hydrogen dispersion exhibited radial symmetry along the longitudinal axis of the tunnel ceiling. Furthermore, a high-concentration accumulation zone was identified within the flammable-hydrogen cloud surface layer, extending 0-60.0 m downstream from the leakage source. Laterally, the cloud expanded symmetrically under the influence of turbulent mixing, eventually spanning the entire width of the tunnel. Vertically, buoyancy-driven effects confined the hydrogen cloud accumulation to the upper 3.0-5.0 m of the tunnel, while concentrations in the lower strata remained consistently below the safety threshold. The initial momentum of the hydrogen jet significantly influenced its spatial distribution and dispersion. In the near-field region proximal to the leakage source, the high-velocity hydrogen jet impinged on the tunnel ceiling and was forced downward along the tunnel walls, thereby preventing hydrogen accumulation near the ceiling close to the wall. However, when the distance from the leakage source was relatively large, the velocity vector of hydrogen was lost, as hydrogen dispersion and transport at that time were mainly affected by buoyancy. Consequently, hydrogen rose from its previously downward-spread position and accumulated gradually on the tunnel ceiling. Therefore, a hydrogen-concentration peak appeared at a relatively distant position. Conclusions: The findings of this study provide basic data support for hydrogen-related parameters for analyzing hydrogen leakage in hydrogen-powered trains in tunnel scenarios, which is conducive to the application and promotion of hydrogen energy in non-traditional enclosed scenarios, such as tunnels.
