Research on Load Clustering Algorithms Based on Hierarchy and Fuzzy Theory

摘要

Aiming at the problem that the classification types are not easy to determine in the current fuzzy C-means classification, this paper proposes a power system load clustering method based on hierarchical and fuzzy theory, and introduces the concept of silhouette coefficient in the mathematical field into the power system load classification to measure the classification results. Aiming at the problem of the number of clusters in the original Fuzzy C-means clustering algorithm, the idea of decision tree classification in the hierarchical clustering algorithm is integrated into the original algorithm, and the improved algorithm is fused. The improved algorithm can avoid the influence of prior values on the classification results, and then determine the optimal number of classifications according to the silhouette coefficient index. Finally, the reliability and validity of the algorithm are verified by the load data of PJM market in the United States.Aiming at the problem of the number of clusters in the original fuzzy C-means clustering algorithm, the idea of decision tree classification in the hierarchical clustering algorithm is integrated into the original algorithm, and the improved algorithm is fused. The improved algorithm can avoid the influence of prior values on the classification results, and then determine the optimal number of classifications according to the silhouette coefficient.