Low-Observability Distribution System State Estimation by Graph Computing with Enhanced Numerical Stability

In distribution systems, limited measurement configurations and communication constraints often result in a low success rate of data acquisition, posing challenges to both system observability and the real-time performance required by state estimation (SE). Consequently, the distribution system SE (DSSE) relies heavily on pseudo-measurements to supplement real-time data. However, existing pseudo-measurement models generally fail to adequately account for topology changes and may lead to numerical instability issues. To resolve these challenges, this paper presents a graph computing-based DSSE method with enhanced numerical stability for low-observability distribution systems. Specifically, a graph neural network (GNN) is employed to dynamically learn the coupling relationships between bus and branch electrical quantities to improve the credibility of pseudo-measurements. Additionally, a loop belief propagation (LBP) algorithm based on factor graphs is designed to capture the statistical discrepancies between real-time and pseudo-measurements, thus avoiding the impact of pseudo-measurement modeling on numerical stability. Numerical results on IEEE 33-bus and 95-bus test systems demonstrate that the proposed method effectively adapts to topology variations and significantly improves the accuracy of both pseudo-measurement modeling and DSSE.