Forecasting-aided State Estimation Framework for Distribution Networks with Multi-source Measurement Data Fusion

Yuanshi Zhang; Qirui Chen; Haizhou Liu; Qinran Hu; Bingxu Zhai

doi:10.35833/MPCE.2025.000259

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Forecasting-aided State Estimation Framework for Distribution Networks with Multi-source Measurement Data Fusion

Original Paper | 更新时间：2026-09-28

- Forecasting-aided State Estimation Framework for Distribution Networks with Multi-source Measurement Data Fusion
- Journal of Modern Power Systems and Clean Energy Vol. 14, Issue 5, Pages: 1806-1819(2026)
- 作者机构：
  
  1.School of Electrical Engineering, Southeast University, Nanjing 210096, China
  2.State Grid Jibei Electric Power Company, Beijing 100052, China
- 作者简介：
  
  Yuanshi Zhang received the B.Eng. and M.A.Sc. degrees from the Harbin Institute of Technology, Harbin, China, in 2013 and 2015, respectively, and the Ph.D. degree from the University of British Columbia, Vancouver, Canada, in 2021. Since 2021, he has been an Associate Professor with the School of Electrical Engineering, Southeast University, Nanjing, China. His research interests include power system optimization and control, high-voltage direct current (HVDC) system, and demand-side management. Email:yuanshizhang@seu.edu.cn
  Qirui Chen received the B.S. degree in electrical engineering from Nanjing Institute of Technology, Nanjing, China, in 2023. He is currently pursuing the M.S. degree in Southeast University. His research interests include optimization of smart terminal configuration of distribution network and state estimation. Email:220232967@seu.edu.cn
  Haizhou Liu received the B.Sc. degree in physics from Nanjing University,Nanjing, China, in 2019, and the Ph.D. degree in electrical engineering from Tsinghua University, Beijing, China, in 2024. He is currently a Lecturer at the School of Electrical Engineering, Southeast University,Nanjing, China. His research interest includes artificial intelligence (AI)-augmented decision-making framework across heterogeneous energy systems. Email:haizhou501@seu.edu.cn
  Qinran Hu
  Bingxu Zhai received the M.S. degree in electrical engineering from Tsinghua University, Beijing, China, in 2012. He is a Senior Expert of dispatch and operation at State Grid Jibei Electric Power Company, Beijing, China. His research interest includes operation and control of power system and renewable energy. Email:zhaibingxu@126.com
- 基金信息：
- DOI：10.35833/MPCE.2025.000259
  CLC：
- Received：07 April 2025，
  
  Revised：2025-07-25，
  
  Accepted：25 November 2025，
  
  Online First：11 February 2026，
  
  Published：15 September 2026
- 稿件说明：
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Zhang Yuanshi,Chen Qirui,Liu Haizhou,et al.Forecasting-aided State Estimation Framework for Distribution Networks with Multi-source Measurement Data Fusion[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1806-1819.
DOI：

Zhang Yuanshi,Chen Qirui,Liu Haizhou,et al.Forecasting-aided State Estimation Framework for Distribution Networks with Multi-source Measurement Data Fusion[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1806-1819. DOI： 10.35833/MPCE.2025.000259.

摘要

Abstract

Real-time state estimation underpins advanced applications in distribution networks by revealing system operating conditions with high fidelity. Intelligent measurement terminals increasingly combine heterogeneous measurements

including micro-phasor measurement units (μPMUs)

remote terminal units (RTUs)

and data transmission units (DTUs). Their complementary strengths can overcome traditional limits in time resolution and accuracy. However

heterogeneous formats

asynchronous updates

and complex temporal dynamics challenge conventional estimators. This paper proposes a unified real-time forecasting-aided state estimation (FASE) framework for distribution networks with multi-source measurement data fusion that fuses μPMU

RTU

and DTU data. A long short-term memory (LSTM)-based data imputation method is designed to effectively align delayed RTU and DTU updates with μPMU sampling

markedly reducing imputation errors versus linear and historical-average baselines. Unified measurement equations are formulated for all devices. An improved cubature Kalman filter (CKF) with adaptive robust weighting is adopted to enhance numerical stability and outlier resilience. To capture multimodal operating regimes driven by variable distributed energy resources

a Gaussian mixture model (GMM)-based approach is integrated into the distribution network state transition. Validated on the IEEE 33-bus and 118-bus systems

the proposed FASE framework achieves higher estimation accuracy and computational efficiency than extended Kalman filter (EKF)

unscented Kalman filter (UKF) and traditional CKF while meeting real-time constraints.

关键词

Keywords

references

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