针对现有的密度峰值聚类算法在数据局部波动较大或各类负荷数量分布不均匀导致的错分问题，提出了一种基于奇异谱分析和改进密度峰值聚类算法的电力负荷曲线聚类方法。首先，采用奇异谱分析将原始电力负荷数据分解为包含主要轮廓信息的低频分量和噪声成分的高频分量。然后，融合K近邻和自然最近邻思想重新定义了密度峰值聚类算法的局部密度，并对低频分量进行聚类。最后将其应用于真实的电力负荷数据集，与其他聚类算法进行对比，算例结果验证了所提方法在真实数据下的聚类有效性。

To address the misclassification issues in existing density peak clustering algorithms caused by significant local fluctuations in data or uneven distribution of load categories, a power load curve clustering method based on singular spectrum analysis and improved density peak clustering algorithm is proposed. Firstly, singular spectrum analysis is employed to decompose the original power load data into a low-frequency component containing the main contour information and a high-frequency component representing noise. Secondly, the local density of the density peak clustering algorithm is redefined by integrating the ideas of K-nearest neighbors and natural nearest neighbors, followed by clustering the low-frequency component. Finally, applied to real-world power load datasets, the proposed method is compared with other clustering algorithms, and case study results validate its effectiveness in real data.