覆冰是输电线路安全稳定运行重点关注的自然灾害之一, 随着中国超特高压输电线路的持续建设, 准确评估和预测导线覆冰成为线路防冰减灾的关键。为提高导线覆冰计算精度, 该文首先采用解析法和Euler法建立了导线水滴碰撞系数计算模型; 其次, 对比了这2种方法计算水滴碰撞系数的精度和效率; 最后, 分析了水滴粒径分布谱下水滴中值体积直径(median volume diameter, MVD)、风速和导线直径等对水滴碰撞系数计算误差的影响, 并提出了导线覆冰模拟中水滴粒径参数的优化选择方法。研究结果表明：采用解析法计算导线整体水滴碰撞系数的效率明显高于Euler法, 但误差较大; 使用水滴MVD计算水滴碰撞系数会导致一定误差, 且该误差随着水滴MVD、风速和导线直径的增加, 呈先减小后增大的趋势; 导线覆冰模拟时, 可根据水滴碰撞系数计算误差的变化, 动态选择水滴粒径参数, 以兼顾计算效率和精度。该文研究结果可为后续导线覆冰计算中合理选择水滴粒径参数提供参考。

Objective: Icing on overhead transmission lines presents a serious risk to the safety and stability of power grids, particularly amid the rapid expansion of ultra-high voltage networks in China. Accurate simulation of conductor icing is crucial for effective disaster prevention and mitigation. However, many existing models primarily depend on the median volume diameter (MVD) of droplets, often overlooking droplet size distribution (DSD) characteristics and leading to simulation errors. This study addresses this challenge by developing an optimized method for selecting droplet size parameters in icing simulations, thereby improving computational accuracy and efficiency. This work is essential for enhancing the reliability of icing predictions and reinforcing the resilience of power infrastructure under extreme weather conditions. Methods: This study employs two primary methodologies: the analytical and Eulerian methods. The analytical method, based on the Finstad's model, calculates the droplet collision coefficient (α₁) using MVD, whereas the Eulerian method leverages computational fluid dynamics to simulate air-droplet two-phase flow, incorporating DSD for higher precision. A comparative analysis of these methods is conducted to evaluate their efficiency and accuracy. In addition, this study investigates the impact of environmental parameters (wind speed, MVD, and conductor diameter) and droplet dispersion on α₁ errors. A dynamic selection strategy is proposed to determine when MVD could suffice or when DSD is necessary based on predefined error thresholds. Results: The key findings included the following: the analytical method outperformed the Eulerian method in computational speed but tended to overestimate α₁ due to unaccounted turbulence effects. Meanwhile, owing to the influence of DSD, directly using MVD to calculate α₁ in conductor icing simulations also introduced a certain error. The error diminished with higher wind speeds and larger MVD values. Using MVD alone introduced errors (Δα₁) in α₁ calculations, which exhibited a nonlinear trend: Δα₁ initially decreased to zero and then increased as MVD, wind speed, or conductor diameter increased. To avoid calculation errors in the conductor's α₁, one might consider using the DSD instead of MVD for computing α₁. However, this method involved significantly greater computational requirements and was therefore unsuitable for rapid assessment of conductor icing accumulation. This study identified critical thresholds where MVD can replace DSD without significant accuracy loss, optimizing computational resources. In detail, leveraging the high computational efficiency of the analytical method, Δα₁ was calculated. When this error was less than or equal to the maximum allowable error, MVD could be used in place of DSD, thereby achieving an optimal balance between computational efficiency and accuracy. The results were validated through case studies using experimental data from Pavlo et al. Conclusions: This study highlights the limitations of MVD-based icing simulations and underscores the importance of droplet dispersion characteristics. By integrating analytical and Eulerian approaches, this study provides a practical framework for dynamically selecting droplet size parameters, ensuring accurate and efficient icing predictions. The results show that, although MVD suffices under specific conditions, DSD is indispensable for scenarios involving highly dispersed droplets or smaller conductors. This study advances the field by offering a scalable solution for power grid resilience against icing hazards, with implications for academic research and industrial applications.