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@article{Anagnostou:2017:10.1109/TPWRS.2017.2663107,
author = {Anagnostou, G and Pal, BC},
doi = {10.1109/TPWRS.2017.2663107},
journal = {IEEE Transactions on Power Systems},
pages = {116--130},
title = {Derivative-free Kalman filtering based approaches to dynamic state estimation for power systems with unknown inputs},
url = {http://dx.doi.org/10.1109/TPWRS.2017.2663107},
volume = {33},
year = {2017}
}
TY - JOUR
AB - This paper proposes a decentralized derivative-freedynamic state estimation method in the context of a power systemwith unknown inputs, to address cases when system linearisationis cumbersome or impossible. The suggested algorithm tacklessituations when several inputs, such as the excitation voltage,are characterized by uncertainty in terms of their status. Thetechnique engages one generation unit only and its associatedmeasurements, and it remains totally independent of other systemwide measurements and parameters, facilitating in this way theapplicability 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 noiseon the unknown input estimation performance is discussed. Thisunderstanding is further supported through detailed studies in arealistic power system model.
AU - Anagnostou,G
AU - Pal,BC
DO - 10.1109/TPWRS.2017.2663107
EP - 130
PY - 2017///
SN - 1558-0679
SP - 116
TI - Derivative-free Kalman filtering based approaches to dynamic state estimation for power systems with unknown inputs
T2 - IEEE Transactions on Power Systems
UR - http://dx.doi.org/10.1109/TPWRS.2017.2663107
UR - http://hdl.handle.net/10044/1/44258
VL - 33
ER -