Imperial College London
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ProfessorBikashPal

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Power Systems
 
 
 
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Contact

 

+44 (0)20 7594 6172b.pal Website CV

 
 
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Assistant

 

Miss Guler Eroglu +44 (0)20 7594 6170

 
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Location

 

1104Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Anagnostou:2019:10.1109/TPWRS.2019.2909160,
author = {Anagnostou, G and Puthenpurayl, Linash K and Pal, B},
doi = {10.1109/TPWRS.2019.2909160},
journal = {IEEE Transactions on Power Systems},
pages = {3879--3890},
title = {Dynamic state estimation for wind turbine models with unknown wind velocity},
url = {http://dx.doi.org/10.1109/TPWRS.2019.2909160},
volume = {34},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper proposes a novel Kalman filtering based dynamic state estimation method, which addresses cases of models with a nonlinear unknown input, and it is suitable for wind turbine model state estimation. Given the complexity characterising modern power networks, dynamic state estimation techniques applied on renewable energy based generators, such as wind turbines, enhance operators’ awareness of the components comprising modern power networks. In this context, the method developed here is implemented on a doubly-fed induction generator based wind turbine, under unknown wind velocity conditions, as opposed to similar studies so far, where all model inputs are considered to be known, and this does not always reflect the reality. The proposed technique is derivative-free and it relies on the formulation of the nonlinear output measurement equations as power series. The effectiveness of the suggested algorithm is tested on a modified version of the IEEE benchmark 68-bus, 16-machine system.
AU  - Anagnostou,G
AU  - Puthenpurayl,Linash K
AU  - Pal,B
DO  - 10.1109/TPWRS.2019.2909160
EP  - 3890
PY  - 2019///
SN  - 0885-8950
SP  - 3879
TI  - Dynamic state estimation for wind turbine models with unknown wind velocity
T2  - IEEE Transactions on Power Systems
UR  - http://dx.doi.org/10.1109/TPWRS.2019.2909160
UR  - http://hdl.handle.net/10044/1/68202
VL  - 34
ER  -
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