针对现有聚类算法在提取典型负荷曲线时存在的非凸簇识别能力不足和参数敏感性等问题，提出基于改进密度峰值聚类（density peak clustering，DPC）算法的典型负荷曲线提取方法。首先，提出基于局部密度和相对距离的自适应聚类中心选取方法，解决传统DPC算法人为选择聚类中心的主观不确定性问题；其次，定义聚类交叉密度和聚类边界密度两个新参数，提出初始聚类校正策略，有效解决非聚类中心点的分配连带错误问题。通过6个二维数据集、4个多维数据集和1个实际REFIT电气负载测量数据集的对比实验表明，所提改进DPC算法在准确率（ACC）、调整兰德指数（ARI）和Fowlkes-Mallows指数（FMI）3个评价指标上均优于传统DPC、K-means和DBSCAN算法，其中ACC、ARI和FMI平均提升25.40 %、46.92 %和21.83 %。算例结果表明，所提改进DPC算法提取的典型负荷曲线更具代表性，可为电力系统灵活性资源优化调控提供更精准的数据支撑。

Aiming at the existing clustering algorithms′ lack of non-convex cluster identification ability and parameter sensitivity in extracting typical load curves, a typical load curve extraction method based on the improved density peak clustering (DPC) algorithm is proposed. Firstly, an adaptive cluster centre selection method based on local density and relative distance is proposed to solve the subjective uncertainty problem of artificially selecting cluster centres in the traditional DPC algorithm. Secondly, two new parameters of cluster cross-density and cluster boundary density are defined, and an initial cluster correction strategy is proposed to effectively solve the problem of assigning cascading errors to non-cluster centre points. Comparison experiments with six 2D datasets, four multidimensional datasets and one actual REFIT electrical load measurement dataset show that the proposed improved DPC algorithm outperforms the traditional DPC, K-means and DBSCAN algorithms in three evaluation indexes, namely, accuracy (ACC), adjustment of rand index (ARI) and Fowlkes-Mallows Index (FMI), where they are better than the traditional DPC, K-means and DBSCAN algorithms. Among them, ACC, ARI and FMI are improved by 25.40 %, 46.92% and 21.83 % on average. The results show that the typical load curve extracted by the proposed improved DPC algorithm is more representative, which can provide more accurate data support for the optimal regulation of power system flexibility resources.