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{Singh:2018:10.1109/TSP.2017.2788424,
author = {Singh, AK and Pal, BC},
doi = {10.1109/TSP.2017.2788424},
journal = {IEEE Transactions on Signal Processing},
pages = {1541--1550},
title = {Decentralized robust dynamic state estimation in power systems using instrument transformers},
url = {http://dx.doi.org/10.1109/TSP.2017.2788424},
volume = {66},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper proposes a decentralized method for estimation of dynamic states of a power system. The method remains robust to time-synchronization errors and high noise levels in measurements. Robustness of the method has been achieved by incorporating internal angle in the dynamic model used for estimation and by decoupling the estimation process into two stages with continuous updation of measurement-noise variances. Additionally, the proposed estimation method does not need measurements obtained from phasor measurement units; instead, it just requires analog measurements of voltages and currents directly acquired from instrument transformers. This is achieved through statistical signal processing of analog voltages and currents to obtain their magnitudes and frequencies, followed by application of unscented Kalman filtering for nonlinear estimation. The robustness and feasibility of the method have been demonstrated on a benchmark power system model.
AU  - Singh,AK
AU  - Pal,BC
DO  - 10.1109/TSP.2017.2788424
EP  - 1550
PY  - 2018///
SN  - 1053-587X
SP  - 1541
TI  - Decentralized robust dynamic state estimation in power systems using instrument transformers
T2  - IEEE Transactions on Signal Processing
UR  - http://dx.doi.org/10.1109/TSP.2017.2788424
UR  - http://hdl.handle.net/10044/1/55591
VL  - 66
ER  -
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