A Comparative Study of Bayesian based filters for Dynamic State Estimation in Power Systems

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dc.contributor.authorBanu, S.
dc.contributor.authorJohnson, T.
dc.contributor.authorMoger, T.
dc.date.accessioned2026-02-06T06:36:07Z
dc.date.issued2021
dc.description.abstractDynamic state estimation of a power system is the first prerequisite for control and stability prediction under transient conditions. Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper, the popular variants of Kalman filter are used to estimate the main states of synchronous generators of an SMIB and IEEE 3-generators 9-bus test systems. A case study using a simple power system model is presented to illustrate the comparison between effectiveness of proposed approaches. © 2021 IEEE.
dc.identifier.citation2021 IEEE International Power and Renewable Energy Conference, IPRECON 2021, 2021, Vol., , p. -
dc.identifier.urihttps://doi.org/10.1109/IPRECON52453.2021.9640771
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/30267
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectCKF
dc.subjectDSE
dc.subjectEKF
dc.subjectPMU
dc.subjectUKF
dc.titleA Comparative Study of Bayesian based filters for Dynamic State Estimation in Power Systems

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