Factors like low troubleshooting efficiency and untimely data updates make meter-to-transformer wiring relationships in low-voltage distribution networks deviate from the actual situation. To address this issue, a meter-to-transformer relationship identification method based on the combination of angle piecewise linear representation (APLR) and improved clustering by fast search find of density peaks (ICFSFDP) is proposed. Initially, inflection points in the voltage curve are extracted by analyzing the angle variations between neighboring segments, and the curve undergoes adaptive dimensionality reduction and reconstruction using APLR. Then, the ICFSFDP method is deployed to cluster the data sets after dimensionality reduction, and the optimal number of clusters is determined by identifying the minimum area enclosed by the fitted function and the coordinate axis within the decision graph. This allows the identification of central clustered and non-clustered consumers. Finally, the dynamic time warping (DTW) distance is utilized to measure the distance similarity between the central clustered and non-clustered consumers, obtaining meter-to-transformer relationships. The application of this method on both simulated and real data has validated its effectiveness. Results from the analytical cases indicate that this approach can analyze sequences with varied time intervals and dimensions without the need for manually setting clustering algorithm parameters, delivering a high accuracy in identifying meter-to-transformer relationships.