Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work
By Junbo Zhao, A. Gómez-Expósito, Marcos Netto, Lamine Mili, Ali Abur, Vladimir Terzija, Innocent Kamwa, Bikash Chandra Pal, A. Singh, Junjian Qi and 2 more. IEEE Transactions on Power Systems, 2019
526 citations recorded by the source corpus.
This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.
Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/be5bb8bcdcf764da0c16b9f6e9abad8320bd8a56
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In Electrical and Electronic Engineering
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Citations in the corpus
- Physics-Informed Neural Networks for Power Systems (2019, unknown): “This shows the potential of physics informed neural networks to be used as a dynamic state estimator, when the model parameters are unknown [13].”
- A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids (2020, background): “nature [42].”