Application of K-Means ++ Algorithm Based on t-SNE Dimension Reduction in Transformer District Clustering
Liping Liu, Qi Wang, Mei-na Dong, Ziyan Zhang, Yuan Li, Zezhong Wang, Shouqiang Wang · 2020 Asia Energy and Electrical Engineering Symposium (AEEES) · 2020
The classification of transformer districts is very important for line loss evaluation, load characteristic analysis and so on. In this paper, a Clustering technique of K-Means++ based on t-SNE dimension reduction is proposed and realized by programming. Firstly, the original matrix representing the electrical characteristic index of the transformer district are established. Secondly, the original normalized matrix is reduced by t-SNE dimension reduction method to obtain the main characteristic matrix. After that, K-Means++ is used to cluster the main characteristic matrix. Then, the accuracy of clustering results is evaluated by Silhouette Coefficient (SC) and Calinski-Harabaz (CH) index. Finally, selecting 1959 transformer districts as an example, simulation and calculation are performed to verify the effectiveness and practicable of the K-Means++ algorithm based on t-SNE dimension reduction in transformer district clustering.