Weakly Supervised Optimization for Power Distribution Transformer Area Identification Based on Frequency-Domain Representation

Accurate identification of user–transformer relationships is fundamental to refined management, load forecasting, and fault diagnosis in low-voltage distribution networks. Traditional approaches often rely on costly manual inspection or complex physical modeling, which limits their scalability. This paper proposes a frequency-domain representation learning and weakly supervised optimization method for automatic transformer-area identification from large-scale user electricity data with incomplete labels. Specifically, the proposed method first applies the Fast Fourier Transform (FFT) to convert users’ voltage and current time series into robust frequency-domain feature vectors, effectively revealing intrinsic periodic structures while reducing noise interference. Then, under limited supervision, a deep metric learning framework is employed to optimize the embedding space such that users belonging to the same transformer area are clustered more compactly, while those from different areas are separated farther apart. Finally, a high-density clustering algorithm is applied in the optimized embedding space to complete the transformer-area partition for all users. Experimental results demonstrate that the proposed approach can effectively leverage limited label information and significantly improve transformer-area identification accuracy, providing an efficient and low-cost solution for digitalized operation and maintenance of low-voltage distribution networks.