基于改进高斯混合模型的变电站负荷聚类算法

Substation Load Clustering Algorithm Based on Improved Gaussian Mixture Model

针对传统高斯混合模型(Gaussian mixture model,GMM)聚类算法中计算复杂、收敛速度慢和人为确定聚类数目时存在盲目性和主观性等不足,提出了一种基于改进GMM的变电站负荷聚类算法。以传统GMM聚类算法为基础,采用k均值(k-means)算法确定初始聚类中心。减少了GMM聚类算法迭代步骤,提高了输出结果的稳定性。输出不同聚类数下聚类结果的Davies-Bouldin(DB)指标、CalinskiHarabasz(CH)指标和轮廓系数(silhouette coefficient,SC),应用熵权法确定不同评价指标所占权重,构建聚类评价混合指数(cluster evaluation mixed index,CEM)。将聚类评价混合指数最大值对应的聚类个数作为最佳聚类数目,再次输入到改进GMM聚类算法中,得到变电站负荷聚类结果和聚类中心。结果表明,所提方法增强了传统GMM聚类算法的计算速度和稳定性,对变电站负荷具有良好的聚类综合能力,有助于实现聚类结果最优化。

A substation load clustering algorithm based on an improved Gaussian mixture model(GMM) is proposed to address the drawbacks of the conventional GMM clustering algorithm,such as complex calculation,slow rate of convergence,blindness and subjectivity in manually determining the number of clusters.Based on the conventional GMM clustering algorithm,the initial clustering center is chosen by the k-means algorithm.The reliability of the output results is increased while the number of iterations of the GMM clustering algorithm is decreased by using this algorithm.Under various clustering numbers,the algorithm generates the Davies-Boldin(DB) index,Calinski-Harabasz(CH) index,and silhouette coefficient(SC) of clustering results.The cluster evaluation mixed(CEM) index is created by using the entropy weight approach to calculate the weights of various evaluation indicators.The optimal number of clusters is calculated as the number of clusters that correspond to the maximum value of the CEM.This optimal number of clusters is then used as input into the improved GMM clustering algorithm to obtain the clustering results and clustering centers of the substation load.The illustration demonstrates that the suggested algorithm improves the computational efficiency and stability of the conventional GMM clustering algorithm and has good cluster synthesis capability for substation loads,which aids in clustering result optimization.