The distribution network environment is complex, and distribution network synchronous phasor measurement units (D-PMU) is susceptible to interference, resulting in bad data that further impacts applications relying on measurement data. In order to improve the data quality of D-PMU, this paper proposes a density-based spatial clustering of applications with noise (DBSCAN) based bad data detection method for distribution network synchronous measurements that does not depend on system topology. First, the DBSCAN is used for anomaly data detection. The clustering results of DBSCAN are comprehensively evaluated using the silhouette coefficient and Dunn index. To address the need for preprocessing training and labeling data during detection, the sparrow search algorithm is used for adaptive parameter adjustment. On this basis, the K-Medoids algorithm for time series clustering is combined with the dynamic time warping algorithm to measure the similarity between different time series, thus solving the difficulty of distinguishing between disturbance data and bad data in D-PMU when the electrical connection is weak. This enhances both the accuracy of data processing and the robustness under noisy environments. Simulation and real data tests show that the proposed method can effectively distinguish real disturbance data and accurately identify bad D-PMU data.