台风频发严重威胁电力系统安全, 开展输电线路台风易损性研究有利于提升电网韧性和保障供电稳定。该文基于台风历史数据, 提出了一种区域性台风全过程随机模拟方法, 并构建了考虑年风速极值-风向联合概率分布的输电线路易损性评估框架。首先, 通过建立台风起始、行进和终止全过程概率模型, 实现了台风路径随机模拟, 并结合近地表风场特性, 生成台风年风速极值-风向的联合概率分布; 其次, 利用基于塔体失效的线路风致易损性多维模型, 获得了目标区域输电线路各区段差异化台风灾害失效概率; 最后, 通过开展案例测试, 验证了该文所提易损性评估方法的可行性和有效性。研究结果表明: 该文所提评估方法可依据线路空间分布特征量化各基塔年失效概率, 识别高风险耐张区段和高失效概率线路区间, 进而评估整体失效水平。该文研究结果可为输电线路台风灾害防御和加固提供参考。

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.