针对配电台区复杂拓扑结构识别，以及统计数据缺失造成的识别准确率低的问题，融合最长公共子序列（longest common subsequence, LCSS）与动态时间规整（dynamic time warping, DTW）方法，提出了基于最长公共子序列动态时间规整距离（LCSS-DTW）的低压配电台区拓扑结构识别方法。依据在低压配电台区中，分布在同一电压相别的用户之间存在相似的电压波动，计算低压配电台区内用户之间的距离矩阵。利用LCSS的局部动态对齐的特性，结合DTW全局对齐的方式，能够更准确地捕捉到用户时序电压数据的相似性，以及确保时序数据的完整性，通过聚类算法，成功识别了配电台区用户的相序归属，并引入了随机数据缺失和异常数据干扰进行验证，结果表明，本方法具有较强的鲁棒性。在不同数据缺失和异常数据干扰条件下，方法的识别准确率保持在95%以上，较传统方法有明显提高，有效解决了数据缺失对拓扑结构识别准确率的影响。

To address the challenges of complex topology identification in low-voltage distribution networks and low recognition accuracy caused by missing statistical data, a topology identification method based on longest common subsequence-dynamic time warping(LCSS-DTW) was proposed. In low-voltage distribution networks, users on the same voltage phase exhibit similar voltage fluctuations. Based on this characteristic, a distance matrix between users was calculated. The method combines the local alignment capability of LCSS with the global alignment of DTW to better capture the similarity of users' time-series voltage data and ensure data completeness. A clustering algorithm was used to identify the phase association of users in the distribution network. Random missing data and abnormal data interference were introduced for validation. The results show that the proposed method exhibits strong robustness. Under different missing data and interference conditions, the recognition accuracy remains above 95%, which is significantly higher than that of traditional methods. The method effectively reduces the impact of data loss on topology identification accuracy.