Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Energies

DOI: 10.3390/en18164380

Authors: Yang Lei, Fan Yang, Yanjun Feng, Wei Hu, Yinzhang Cheng

Institutions: State Grid Hubei Electric Power Co., Ltd. (China), State Grid Corporation of China (China), State Grid Shanxi Electric Power Company Electric Power Research Institute, Southeast University

Language: English

Topic: Electricity Theft Detection Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,137