The University of Southampton
University of Southampton Institutional Repository

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
28dc6b6e-c37b-4bd2-8336-b0010dcb8502
Netto, Marcos
ad2361f8-0c26-4efb-bd55-232fbd6326ad
Huang, Zhenyu
0aed8c31-b097-4fcc-811d-69fd0916de4f
Yu, Samson Shenglong
d8d8ba50-f030-4bc9-8e47-2e1aadbd4c6d
Gomez-Exposito, Antonio
09105f61-a289-42aa-a206-65f68c80fca9
Wang, Shaobu
cac44a8b-fdc5-44b5-bbae-39d8a2db43be
Kamwa, Innocent
f58f0495-2fd9-404f-ab43-322dffda07bf
Akhlaghi, Shahrokh
a3a5ab41-ca65-4213-b0a7-43ad11b2d141
Mili, Lamine
d9d3e578-859c-485c-93f6-5ebc165df1c2
Terzija, Vladimir
914536dc-940d-4642-945f-cbce4d2c8ec3
Meliopoulos, A.P. Sakis
920e5f14-0fdb-4ba5-85c5-66847d7a0abc
Pal, Bikash C.
c062978e-53eb-4d5d-ace8-746ccafa5fb0
Singh, Abhinav Kumar
6df7029f-21e3-4a06-b5f7-da46f35fc8d3
Abur, Ali
f62414be-a958-40ce-9ea7-778e42348e89
Bi, Tianshu
0ae46b40-ca70-44d3-acae-94e00f82f697
Rouhani, Alireza
adfbcf37-908a-435e-a4d5-4db0fbb0f42a
Zhao, Junbo
28dc6b6e-c37b-4bd2-8336-b0010dcb8502
Netto, Marcos
ad2361f8-0c26-4efb-bd55-232fbd6326ad
Huang, Zhenyu
0aed8c31-b097-4fcc-811d-69fd0916de4f
Yu, Samson Shenglong
d8d8ba50-f030-4bc9-8e47-2e1aadbd4c6d
Gomez-Exposito, Antonio
09105f61-a289-42aa-a206-65f68c80fca9
Wang, Shaobu
cac44a8b-fdc5-44b5-bbae-39d8a2db43be
Kamwa, Innocent
f58f0495-2fd9-404f-ab43-322dffda07bf
Akhlaghi, Shahrokh
a3a5ab41-ca65-4213-b0a7-43ad11b2d141
Mili, Lamine
d9d3e578-859c-485c-93f6-5ebc165df1c2
Terzija, Vladimir
914536dc-940d-4642-945f-cbce4d2c8ec3
Meliopoulos, A.P. Sakis
920e5f14-0fdb-4ba5-85c5-66847d7a0abc
Pal, Bikash C.
c062978e-53eb-4d5d-ace8-746ccafa5fb0
Singh, Abhinav Kumar
6df7029f-21e3-4a06-b5f7-da46f35fc8d3
Abur, Ali
f62414be-a958-40ce-9ea7-778e42348e89
Bi, Tianshu
0ae46b40-ca70-44d3-acae-94e00f82f697
Rouhani, Alireza
adfbcf37-908a-435e-a4d5-4db0fbb0f42a

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, 36 (3). (doi:10.1109/TPWRS.2020.3028047).

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.

Text
Roles of Dynamic State Estimation - Accepted Manuscript
Download (571kB)
Text
Roles_of_Dynamic_State_Estimation_in_Power_System_Modeling_Monitoring_and_Operation
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 27 September 2020
Published date: 30 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

Catalogue record

Date deposited: 22 Oct 2020 16:30
Last modified: 17 Mar 2024 03:56

Export record

Altmetrics

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

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×