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Roles of dynamic state estimation in power system modeling, monitoring and operation

Roles of dynamic state estimation in power system modeling, monitoring and operation
Roles of dynamic state estimation in power system modeling, monitoring and operation
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 and novel system monitoring, control and protection tools to achieve better reliability and resiliency.
Dynamic state estimation, Kalman filtering, low inertia,monitoring,parameterestimation,powersystemstability, synchronous machines, synchrophasor measurements, converter interfaced generation, static state estimation
0885-8950
Zhao, Junbo
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Netto, Marcos
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Huang, Zhenyu
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Yu, Samson Shenglong
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Gomez-Exposito, Antonio
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Wang, Shaobu
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Kamwa, Innocent
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Akhlaghi, Shahrokh
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Mili, Lamine
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Terzija, Vladimir
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Meliopoulos, A.P. Sakis
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Pal, Bikash C.
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Singh, Abhinav Kumar
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Abur, Ali
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Bi, Tianshu
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Rouhani, Alireza
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Zhao, Junbo
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Netto, Marcos
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Huang, Zhenyu
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Yu, Samson Shenglong
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Gomez-Exposito, Antonio
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Wang, Shaobu
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Kamwa, Innocent
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Akhlaghi, Shahrokh
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Mili, Lamine
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Terzija, Vladimir
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Meliopoulos, A.P. Sakis
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Pal, Bikash C.
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Singh, Abhinav Kumar
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Abur, Ali
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Bi, Tianshu
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Rouhani, Alireza
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Zhao, Junbo, Netto, Marcos, Huang, Zhenyu, Yu, Samson Shenglong, Gomez-Exposito, Antonio, Wang, Shaobu, Kamwa, Innocent, Akhlaghi, Shahrokh, Mili, Lamine, Terzija, Vladimir, Meliopoulos, A.P. Sakis, Pal, Bikash C., Singh, Abhinav Kumar, Abur, Ali, Bi, Tianshu and Rouhani, Alireza (2020) Roles of dynamic state estimation in power system modeling, monitoring and operation. IEEE Transactions on Power Systems. (In Press)

Record type: Article

Abstract

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 and novel system monitoring, control and protection tools to achieve better reliability and resiliency.

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Roles of Dynamic State Estimation - Accepted Manuscript
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Accepted/In Press date: 27 September 2020
Keywords: Dynamic state estimation, Kalman filtering, low inertia,monitoring,parameterestimation,powersystemstability, synchronous machines, synchrophasor measurements, converter interfaced generation, static state estimation

Identifiers

Local EPrints ID: 444506
URI: http://eprints.soton.ac.uk/id/eprint/444506
ISSN: 0885-8950
PURE UUID: a170f53e-2019-45fb-a4a6-ca11b8f2b4d3
ORCID for Abhinav Kumar Singh: ORCID iD orcid.org/0000-0003-3376-6435

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Date deposited: 22 Oct 2020 16:30
Last modified: 22 Oct 2020 16:32

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Contributors

Author: Junbo Zhao
Author: Marcos Netto
Author: Zhenyu Huang
Author: Samson Shenglong Yu
Author: Antonio Gomez-Exposito
Author: Shaobu Wang
Author: Innocent Kamwa
Author: Shahrokh Akhlaghi
Author: Lamine Mili
Author: Vladimir Terzija
Author: A.P. Sakis Meliopoulos
Author: Bikash C. Pal
Author: Abhinav Kumar Singh ORCID iD
Author: Ali Abur
Author: Tianshu Bi
Author: Alireza Rouhani

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