目前基于相似度的聚类方法对风电出力场景进行聚类划分, 而相似度又大多采用欧式距离长短作为衡量依据, 其结果反映时间序列曲线的幅度大小差异, 未能反映出曲线的形态特征及变化趋势的不同. 本文提出一种基于高斯混合聚类的风电出力场景划分的方法, 即通过属于某一类的概率大小来判断最终的归属类别. 首先根据BIC准则, 肘部法则和轮廓系数分别确定GMM聚类和K-means聚类的最佳数量, 然后以某地区实际风电为研究对象, 提取该地区3年春季风电出力典型场景, 并对这两种聚类结果进行对比分析, 验证本文方法的有效性. 最后通过GMM聚类模型提取该地区各个季节风电出力典型场景.
At present, the clustering method based on similarity is used to classify the wind power output scene, and the similarity is mostly measured by the Euclidean distance. Hence, the results reflect the difference of the amplitude of the time series curve, not the difference of the morphological characteristics and changing trend of the curve. This study proposes a method of wind power output scene division based on Gaussian mixture clustering, that is, the final attribution category is judged by the probability of belonging to a certain category. Firstly, the optimal numbers of GMM clustering and K-means clustering are determined according to BIC criterion, elbow rule and contour coefficient, respectively. Then, taking the actual wind power in a certain area as the research object, the typical scenes of wind power output in spring in this area are extracted, and the two clustering results are compared and analyzed to verify the effectiveness of this method. Finally, the typical scenes of wind power output in each season in this region are extracted by GMM clustering model.