With the continuous expansion of the scale of wind power grid, there are a number of wind farms in the same region, and wind power has a strong regional correlation. Therefore, it is possible to construct a method that can accurately describe wind power output ,Which is of great significance to the operation of power system of large-scale wind farms. Based on the Pair Copula theory, a 7-D wind power correlation model is constructed and a number of wind farm output samples from Australia are analyzed. The results show that the Pair Copula model can describe the correlation dimension well, but with the increase of the dimension, the accuracy of the model decreases. To solve this problem, an optimization method based on Pair Copula model is proposed to optimize the correlation of wind farms by using the intelligent optimization algorithm (Particle swarm optimization algorithm and differential evolution algorithm). The simulation results show that the proposed method can improve the accuracy of the model greatly compared with the traditional method, which proves the validity and superiority of the proposed method and has important reference value for the operation of the multi-wind power system.%随着风电并网规模的不断扩大,出现了同一区域有多个风电场接入的情况.由于区域内不同风电场的出力具有较强的地域相关性,因此,构建一个能准确描述多风电场出力相关性的模型,对含大规模风电场电力系统的运行具有重大意义.文章基于Pair Copula理论构建了7维风电功率的相关性模型,并选取澳大利亚多个风电场出力样本进行实例分析.分析表明,Pair Copula模型能较好地描述高维相关性,但随着维度增加,模型精度有所下降.为此,文章利用智能优化算法(粒子进化算法),首次提出了一种基于Pair Copula模型参数优化的多风电场出力相关性优化建模方法.通过仿真分析表明,与传统方法相比,该方法大大提高了模型精度,验证了所提方法的有效性和优越性,对含多风电场电力系统的运行具有重要参考价值.
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