基于改进自适应密度峰值算法的日负荷曲线聚类分析

电力市场的逐步开放以及大量可再生能源的接入使用户具有更多的用电自由,导致电力用户类型多样化、用户间负荷特性差异逐渐增大、负荷数据的类簇分布情况复杂化。为解决传统聚类算法面对不均衡负荷数据集时聚类效果不佳以及缺乏自适应能力等问题,提出一种改进自适应密度峰值聚类(Improved self-adaptive Density Peak Clustering,ISDPC)算法。首先,基于K-最近邻(K-Nearest Neighbor,KNN)和相对密度的思想定义了一种新的密度度量方式。然后在决策图中拟合分段函数得到最优类簇数目。最后,通过构造加权KNN图改进样本分配策略。试验结果表明,与传统聚类算法相比,所提方法聚类结果更加精确、具备自适应能力、鲁棒性更强。

Cluster analysis of daily load curves based on an improved self-adaptive density peak clustering algorithm

The opening electricity market and the incremental penetration of renewable energy provide more consumption choices for users. This results in diversification of power user patterns, increasing differences of load characteristics and giving a complex distribution of load clusters. An improved self-adaptive density peak clustering (ISDPC) algorithm is proposed to ameliorate the clustering results and adaptive abilities of traditional clustering methods for unbalanced load data. First, a new density metric is defined based on the K-nearest neighbor (KNN) and relative density. Secondly, the optimal number of clusters is obtained by a fitting partition function obtained from the decision graph. Finally, the allocation of strategy is improved by a weighted KNN graph. The experimental results show that clustering results obtained from the proposed method perform better in accuracy, robustness, and adaptability. This work is supported by the National Natural Science Foundation of China (No. 51877181).