基于混合藤Copula和ILHS的概率电压稳定评估算法

为了评估具备复杂相关性的风电、光伏出力对电力系统电压稳定的影响，提出一种基于混合藤Copula和继承拉丁超立方采样(Inherit Latin Hypercube Sampling, ILHS)的概率电压稳定评估(Probabilistic Voltage Stability Evaluation, PVSE)算法。基于模糊C均值聚类对实际电网中风速和光照数据进行场景划分，利用AD距离确定不同场景中的最优藤结构，建立基于混合藤Copula的概率输入模型。基于ILHS在概率输入模型上采样，根据收敛条件逐渐增加样本点直至PVSE收敛，在PVSE过程中不断重复使用之前生成的样本点及计算结果，进而大幅提升概率分析效率。基于IEEE118节点系统对所提算法的有效性进行验证，结果表明，所提算法能准确刻画风光数据的相关性，并大幅提升PVSE的计算精度和速度。

Probabilistic Voltage Stability Evaluation Algorithm Based on Mixed Vine Copula and ILHS

In order to evaluate the impact of solar and photovoltaic output with complex correlation on power system voltage stability, a probabilistic voltage stability evaluation (PVSE) algorithm based on mixed vine Copula and inherit Latin hypercube sampling (ILHS) is proposed. Based on fuzzy c-means clustering, the scenarios of wind speed and solar radiation data in the practical power grid is divided. The AD distance is used to evaluate the optimal vine structure in different scenarios, and a probability input model based on mixed vine Copula is established. The sample points are sampled from the probability input model by using the ILHS, and the number of sample points is gradually increased according to the convergence criterion until PVSE converges. At the same time, the previously generated sample points and their corresponding results are continuously reused in the process of probability analysis, so as to greatly improve the efficiency of probability analysis. Based on the IEEE-118 bus system, the effectiveness of the proposed algorithm is verified. The results show that the proposed algorithm can accurately describe the correlation structure of multi-dimensional input data, and the calculation accuracy and speed of PVSE can be greatly improved by using the proposed algorithm.