利用贝叶斯线性回归结合混合Copula函数分析风电功率的相关性

分析风电场之间的出力相关性有助于合理规划风机的功率输送以及调度优化，从而提高传输线路的利用率。以冀北地区风场为例，首先分析了该区域风速的分布特性，然后利用贝叶斯线性回归算法建立混合Copula函数模型，拟合得到4个机群风速序列的联合分布，计算出风电场之间的出力相关性，并与其他相关性函数建模进行对比研究。研究结果表明，基于贝叶斯线性回归的混合Copula函数模型能够提高参数估计的精确性，从而使得计算出的相关性更为准确，并且由其拟合得到的出力概率分布与实际风场出力的概率分布较为一致。

Mix Copula Function Based Wind Power Correlation Analysis: A Bayesian Linear Regression Approach

Analyzing the output correlation among wind farms is beneficial to reasonably plan the power transmission and scheduling optimization, so as to improve the utilization rate of transmission lines. Taking wind field in northern Hebei region as an example, this paper analyzes the characteristics of wind power first, and then improves a mix Copula method to model the relationship among the wind power correlation structure. What’s more, we use the Bayesian linear regression method to establish a mixed Copula function model to calculate the correlation of wind speed sequences from different wind farm groups. In this way, we can fit the joint distribution function between them and analyze the impact of correlation on the joint output of wind farms. In addition, this method has been verified to be effective and accurate, it was also be compared with other correlation function modeling. The results show that the mixed Copula function model based on Bayesian linear regression can well calculate the correlation of wind power output, from which the output probability distribution obtained can get more accurate fitting results.