@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}
}

Download

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  - 

Download