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{Cai:2017:10.1109/ACCESS.2017.2679006,
author = {Cai, L and Thornhill, NF and Kuenzel, S and Pal, BC},
doi = {10.1109/ACCESS.2017.2679006},
journal = {IEEE Access},
pages = {5631--5639},
title = {Real-time detection of power system disturbances based on k-nearest neighbor analysis},
url = {http://dx.doi.org/10.1109/ACCESS.2017.2679006},
volume = {5},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Efficient disturbance detection is important for power system security and stability. In this paper, a new detection method is proposed based on a time series analysis technique known as k nearest neighbor (kNN) analysis. Advantages of this method are that it can deal with the electrical measurements with oscillatory trends and can be implemented in real time. The method consists of two stages which are the off-line modelling and the on-line detection. The off-line stage calculates a sequence of anomaly index values using kNN on the historical ambient data and then determines the detection threshold. Afterwards, the on-line stage calculates the anomaly index value of presently measured data by readopting kNN and compares it with the established threshold for detecting disturbances. To meet the real-time requirement, strategies for recursively calculating the distance metrics of kNN and for rapidly picking out the kth smallest metric are built. Case studies conducted on simulation data from the reduced equivalent model of Great Britain power system and measurements from an actual power system in Europe demonstrate the effectiveness of the proposed method.
AU  - Cai,L
AU  - Thornhill,NF
AU  - Kuenzel,S
AU  - Pal,BC
DO  - 10.1109/ACCESS.2017.2679006
EP  - 5639
PY  - 2017///
SN  - 2169-3536
SP  - 5631
TI  - Real-time detection of power system disturbances based on k-nearest neighbor analysis
T2  - IEEE Access
UR  - http://dx.doi.org/10.1109/ACCESS.2017.2679006
UR  - https://doi.org/10.1109/ACCESS.2017.2679006
UR  - http://hdl.handle.net/10044/1/44867
VL  - 5
ER  -
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