Derivative-free Kalman filtering based approaches to dynamic state estimation for power systems with unknown inputs
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Published version
Accepted version
Author(s)
Anagnostou, G
Pal, BC
Type
Journal Article
Abstract
This paper proposes a decentralized derivative-free
dynamic state estimation method in the context of a power system
with unknown inputs, to address cases when system linearisation
is cumbersome or impossible. The suggested algorithm tackles
situations when several inputs, such as the excitation voltage,
are characterized by uncertainty in terms of their status. The
technique engages one generation unit only and its associated
measurements, and it remains totally independent of other system
wide measurements and parameters, facilitating in this way the
applicability of this process on a decentralized basis. The robust-
ness of the method is validated against different contingencies.
The impact of parameter errors, process and measurement noise
on the unknown input estimation performance is discussed. This
understanding is further supported through detailed studies in a
realistic power system model.
dynamic state estimation method in the context of a power system
with unknown inputs, to address cases when system linearisation
is cumbersome or impossible. The suggested algorithm tackles
situations when several inputs, such as the excitation voltage,
are characterized by uncertainty in terms of their status. The
technique engages one generation unit only and its associated
measurements, and it remains totally independent of other system
wide measurements and parameters, facilitating in this way the
applicability of this process on a decentralized basis. The robust-
ness of the method is validated against different contingencies.
The impact of parameter errors, process and measurement noise
on the unknown input estimation performance is discussed. This
understanding is further supported through detailed studies in a
realistic power system model.
Date Issued
2017-04-13
Date Acceptance
2017-01-28
Citation
IEEE Transactions on Power Systems, 2017, 33 (1), pp.116-130
ISSN
1558-0679
Publisher
IEEE
Start Page
116
End Page
130
Journal / Book Title
IEEE Transactions on Power Systems
Volume
33
Issue
1
Copyright Statement
This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/L014343/1
Subjects
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
Dynamic state estimation
Kalman filters
phasor measurements
power system dynamics
state estimation
synchronous generator
unscented transformation
SYNCHROPHASOR ESTIMATION
FREQUENCY
PHASOR
DECOMPOSITION
ALGORITHM
0906 Electrical And Electronic Engineering
Energy
Publication Status
Published