Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation
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Abstract (arXiv)
Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2020.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.
- 2023Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Modelscited by 2
- 2024Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Methodcited by 2
- 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
- 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
- 2024Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method6 Sep 2024
- 2023Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models13 Nov 2023