Transformer–Customer Relationship Identification Using Hyperparameter-Optimized t-Distributed Stochastic Neighbor Embedding and Gaussian Mixture Clustering

The transformer–customer relationship is essential for the operation and management of low-voltage distribution systems. However, manual registration and verification of transformer–customer relationships are costly and prone to mismatches due to delayed archival updates. To address this issue, this paper proposes a data-driven method for transformer–customer relationship identification using smart meter voltage measurements. Specifically, raw voltage data obtained from Advanced Metering Infrastructure (AMI) are embedded into a low-dimensional feature space using t-distributed stochastic neighbor embedding (t-SNE). The key parameters of t-SNE are adaptively optimized using the proposed Dream Optimization Algorithm–Whale Optimization Algorithm (DOA-WOA), with the silhouette coefficient serving as the objective function. Subsequently, a Gaussian Mixture Model is applied to cluster the samples in the obtained low-dimensional feature space for transformer–customer relationship identification. The simulation results verify the effectiveness and robustness of the proposed method under different missing data rates and voltage measurement errors. Experiments conducted on real low-voltage distribution transformer datasets and incomplete data scenarios demonstrate that the proposed method achieves identification accuracies of 98.4% and 96.8%, respectively.