计及风速不确定性及相关性的风电场分区虚拟惯量估计

随着以新能源为主体的新型电力系统建设持续推进，风电在全国各个区域电网的渗透率将高速提升。然而大规模风电场无法提供转动惯量支撑，引入虚拟惯量控制后亦缺少能够分区且精确的虚拟惯量评估方法。考虑了风场内风速的随机性及相关性，提出了基于Copula函数及聚类算法的风电场分区虚拟惯量估计方法。首先，考虑风速的尾流及时延效应，建立场内各风机风速的概率分布模型。其次，根据各风机的风速分布特性，采用双尺度谱聚类算法对场内风机进行聚类分区。然后，选取各区中心机组，构建最优Copula函数描述各分区间的风速相关性。最后，基于风电场机组分布和风速数据估计风电场内各分区的虚拟惯量储备。根据甘肃某风场的实际风速及出力数据构建仿真算例，仿真结果表明所提算法能有效实现风电场虚拟惯量的特征提取、聚类分区、惯量估计。

Virtual inertia estimation of wind farm zones with wind speed uncertainty and correlation

With the continuous advance of the construction of a new power system with renewable energy as the core, the penetration rate of wind power in the grid in various regions of China will increase rapidly. However, large-scale wind farms cannot provide the inertia support, and after the introduction of virtual inertia control, there is also a lack of partitioned and accurate virtual inertia evaluation methods. This paper considers the randomness and correlation of wind speed in a wind farm, and proposes a wind farm partition virtual inertia estimation method based on the Copula function and a clustering algorithm. First, the wake and delay effects of wind speed are considered. The probability distribution model of the wind speed of each turbine in the field is established. Secondly, from the wind speed distribution characteristics of each turbine, a spectral clustering algorithm is used to cluster the turbines. Then, the center turbine is selected in each area. The optimal Copula function is constructed to describe the wind speed correlation between each area. Finally, the partitioning method is used to estimate the virtual inertia reserve of each area in the wind farm. This paper constructs a simulation case based on the actual wind speed and output data of a wind farm in Gansu. The simulation results show that the algorithm proposed in this paper can effectively realize the feature extraction, clustering and division, and inertia estimation of the virtual inertia in the wind farm.This work is supported by the Science and Technology Project of State Grid Corporation of China (No. 522722191005).