A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
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Abstract (arXiv)
This paper presents a review of the literature on State Estimation (SE) in power systems. While covering some works related to SE in transmission systems, the main focus of this paper is Distribution System State Estimation (DSSE). The paper discusses a few critical topics of DSSE, including mathematical problem formulation, application of pseudo-measurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security. Both conventional and modern data-driven and probabilistic techniques have been reviewed. This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Aug 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1; 2025: 4; 2026: 1.
10 papers.
- 2023Deep Statistical Solver for Distribution System State Estimationcited by 9
- 2021False Data Injection Threats in Active Distribution Systems: A Comprehensive Surveycited by 5
- 2025Robust Power System State Estimation using Physics-Informed Neural Networkscited by 4
- 2022Learning Distribution Grid Topologies: A Tutorialcited by 3
- 2025Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Studycited by 2
- 2022Data-driven Models to Anticipate Critical Voltage Events in Power Systemscited by 0
- 2024Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operatorscited by 0
- 2025Learning a Generalized Model for Substation Level Voltage Estimation in Distribution Networkscited by 0
- 2025pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurementscited by 0
- 2026PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow with Continual Learningcited by 0
- 2026PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow with Continual Learning24 Feb 2026
- 2025pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements31 Oct 2025
- 2025Learning a Generalized Model for Substation Level Voltage Estimation in Distribution Networks17 Oct 2025
- 2025Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study9 Jul 2025
- 2025Robust Power System State Estimation using Physics-Informed Neural Networks8 Jul 2025
- 2024Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators29 Sep 2024
- 2023Deep Statistical Solver for Distribution System State Estimation4 Jan 2023
- 2022Data-driven Models to Anticipate Critical Voltage Events in Power Systems24 Jul 2022
- 2022Learning Distribution Grid Topologies: A Tutorial22 Jun 2022
- 2021False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey28 Nov 2021