由于微地形、微气象的影响,风电场风速的随机性特征复杂,为准确描述风速的随机性特征,提出基于分量相依性的风速随机性特征建模方法。首先对风速序列的随机性特征进行提取,采用变分模态分解（VMD）将风速分解为多个不同频率的模态分量,以序列自相关系数（AC）为指标,对风速成分进行划分,得到风速的波动性分量和随机性分量。然后,考虑风速随机性分量对波动性分量的相依性,以正态分布描述不同风速下的随机性特征,建立基于分量相依性的风速随机性模型。以华北张家口某风电场的运行数据为例,验证该方法的有效性。实验结果表明该文方法能更好地复现风速序列的随机性特征。
Abstract
Due to the influence of microtopography and micrometeorology, the randomness characteristics of wind speed in wind farms are complex. To accurately describe the randomness characteristics of wind speed, a feature modeling method based on component dependency is proposed. Firstly, the randomness of the wind speed series is extracted, and the wind speed is decomposed into several modal components with different frequencies by using the variational mode decomposition （VMD）. With the serial autocorrelation coefficient （AC） as the index, the wind speed component is divided, and the fluctuation component and randomness component of wind speed are obtained. Then, considering the dependence of the wind speed randomness component on the fluctuation component, the normal distribution is used to describe the randomness feature under different wind speeds, and a wind speed randomness model based on component dependency is established. The effectiveness of this method is verified by the operation data of a wind farm in Zhangjiakou, North China. The experimental results show that this method can better reproduce the random characteristics of wind speed series.
关键词
风速 /
随机性 /
特征提取 /
分量相依性 /
统计方法
Key words
wind speed /
randomness /
feature extraction /
component dependency /
statistical method
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基金
河北省自然科学基金创新群体项目（E2020202142）
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