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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Zijian Hu
1,
Hong Zhu
1,
Lan Lan
1,
Honghua Xu
1,
Zichen Liu
1,
Kexin Li
2,
Jie Li
2 and
Zhinong Wei
2,*
1
Nanjing Power Supply Company, State Grid Jiangsu Electric Power Company, Nanjing 210019, China
2
College of Electrical and Power Engineering, Hohai University, Nanjing 211000, China
*
Author to whom correspondence should be addressed.
Eng 2025, 6(7), 134; https://doi.org/10.3390/eng6070134 (registering DOI)
Submission received: 9 May 2025 / Revised: 15 June 2025 / Accepted: 18 June 2025 / Published: 21 June 2025
(This article belongs to the Section Electrical and Electronic Engineering)
Versions Notes

Abstract

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.
Keywords: distribution systems; state estimation; graph neural networks; pseudo-measurement modeling; factor graph distribution systems; state estimation; graph neural networks; pseudo-measurement modeling; factor graph

Share and Cite

MDPI and ACS Style

Hu, Z.; Zhu, H.; Lan, L.; Xu, H.; Liu, Z.; Li, K.; Li, J.; Wei, Z. Low-Observability Distribution System State Estimation by Graph Computing with Enhanced Numerical Stability. Eng 2025, 6, 134. https://doi.org/10.3390/eng6070134

AMA Style

Hu Z, Zhu H, Lan L, Xu H, Liu Z, Li K, Li J, Wei Z. Low-Observability Distribution System State Estimation by Graph Computing with Enhanced Numerical Stability. Eng. 2025; 6(7):134. https://doi.org/10.3390/eng6070134

Chicago/Turabian Style

Hu, Zijian, Hong Zhu, Lan Lan, Honghua Xu, Zichen Liu, Kexin Li, Jie Li, and Zhinong Wei. 2025. "Low-Observability Distribution System State Estimation by Graph Computing with Enhanced Numerical Stability" Eng 6, no. 7: 134. https://doi.org/10.3390/eng6070134

APA Style

Hu, Z., Zhu, H., Lan, L., Xu, H., Liu, Z., Li, K., Li, J., & Wei, Z. (2025). Low-Observability Distribution System State Estimation by Graph Computing with Enhanced Numerical Stability. Eng, 6(7), 134. https://doi.org/10.3390/eng6070134

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