Two-Stage Transformer–Customer Relationship Identification Strategy for Low-Voltage Distribution Grid Using Physics-Guided Graph Attention Network

Accurate transformer–customer relationships are crucial for the efficient operation and high-quality service of the low-voltage distribution grid (LVDG). This paper proposes a novel two-stage transformer–customer relationship identification strategy for LVDG using physics-guided graph attention network (PGAT). First, considering both transient and steady-state voltage fluctuations, a modified piecewise aggregate approximation (MPAA) algorithm is developed to preprocess raw measurement data through compression and denoising while preserving key voltage correlation features. Second, electrical similarity among customers is explored using the Modified Piecewise Aggregate Approximation K-means (MPAA-K-means) algorithm, enabling preliminary identification of transformer–customer relationships. Then, a training paradigm based on PGAT is introduced to characterize node features constrained by grid topology and electrical properties, achieving refined identification of transformer–customer relationships. Finally, testing results on real LVDG demonstrate the effectiveness and accuracy of the proposed two-stage identification strategy, providing new insights for transformer–customer relationship identification.