【摘要】 文章系统评估了复杂地形光伏场站极端风速的分布特征及其推算方法。通过融合中尺度再分析数据与CFD模拟技术,提出了一种适用于无测风塔区域的极端风速评估方法,以某山地风电场为例进行了验证。结果表明:首先,时序序列法在短距离（<10 km）丘陵地形中误差最小（10年重现期误差仅-0.3%）,而基准塔加速因子法可将复杂地形（距离>15 km或海拔差>200 m）的长重现期误差降低28%;其次,风加速因子α的可靠性是基准塔法精度的关键,当α大于1.5或小于0.7时,50年一遇风速误差呈指数增长(δV=8.3e^2.5a);最后,地形-距离-海拔的协同效应显著,当三者耦合时误差较单一因子预测值高40%。文章为光伏场站抗风设计提供了可靠的极端风速评估手段,对提升结构安全性和经济性具有重要意义。更多还原

【Abstract】 This study systematically evaluates the distribution characteristics and extrapolation methods of extreme wind speeds in photovoltaic power stations located in complex terrain.By integrating mesoscale reanalysis data with CFD simulation technologies,an extreme wind speed assessment method applicable to regions without anemometer towers is proposed and validated through a case study of a mountainous wind farm.The results indicate that the time-series method exhibits the smallest error in hilly terrain with short distances（<10 km）,with an error of merely-0.3% for the 10-year return period,whereas the reference tower acceleration factor method can reduce the long-return-period error in complex terrain（distance>15 km or altitude difference>200 m） by 28%.Furthermore,the reliability of the wind acceleration factor α is critical to the accuracy of the reference tower method;when or,the error of the 50-year return period wind speed increases exponentially(δV=8.3e^2.5a).Finally,the synergistic effect of terrain-distance-altitude is significant,with the error being 40% higher when these three factors are coupled compared to predictions based on a single factor.This study provides a reliable extreme wind speed assessment tool for wind-resistant design of photovoltaic power stations,and holds important implications for enhancing structural safety and economic efficiency.更多还原