Imperial College London
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DrIoannisKonstantelos

Faculty of Engineering, Department of Electrical and Electronic Engineering

Research Fellow
 
 
 
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i.konstantelos

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Konstantelos:2019:10.1109/TPWRS.2018.2859367,
author = {Konstantelos, I and Sun, M and Tindemans, S and Issad, S and Panciatici, P and Strbac, G},
doi = {10.1109/TPWRS.2018.2859367},
journal = {IEEE Transactions on Power Systems},
pages = {225--235},
title = {Using vine copulas to generate representative system states for machine learning},
url = {http://dx.doi.org/10.1109/TPWRS.2018.2859367},
volume = {34},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The increasing uncertainty that surrounds electricity system operation renders security assessment a highly challenging task; the range of possible operating states expands, rendering traditional approaches based on heuristic practices and ad hoc analysis obsolete. In turn, machine learning can be used to construct surrogate models approximating the system's security boundary in the region of operation. To this end, past system history can be useful for generating anticipated system states suitable for training. However, inferring the underlying data model, to allow high-density sampling, is problematic due to the large number of variables, their complex marginal probability distributions and the non-linear dependence structure they exhibit. In this paper we adopt the C-Vine pair-copula decomposition scheme; clustering and principal component transformation stages are introduced, followed by a truncation to the pairwise dependency modelling, enabling efficient fitting and sampling of large datasets. Using measurements from the French grid, we show that a machine learning training database sampled from the proposed method can produce binary security classifiers with superior predictive capability compared to other approaches.
AU  - Konstantelos,I
AU  - Sun,M
AU  - Tindemans,S
AU  - Issad,S
AU  - Panciatici,P
AU  - Strbac,G
DO  - 10.1109/TPWRS.2018.2859367
EP  - 235
PY  - 2019///
SN  - 0885-8950
SP  - 225
TI  - Using vine copulas to generate representative system states for machine learning
T2  - IEEE Transactions on Power Systems
UR  - http://dx.doi.org/10.1109/TPWRS.2018.2859367
UR  - https://ieeexplore.ieee.org/document/8418852
UR  - http://hdl.handle.net/10044/1/61964
VL  - 34
ER  -
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