Imperial College London
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DrIoannisKonstantelos

Faculty of Engineering, Department of Electrical and Electronic Engineering

Research Fellow
 
 
 
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Contact

 

i.konstantelos

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sun:2019:10.1109/TSG.2018.2873001,
author = {Sun, M and Konstantelos, I and Strbac, G},
doi = {10.1109/TSG.2018.2873001},
journal = {IEEE Transactions on Smart Grid},
pages = {5007--5020},
title = {A deep learning-based feature extraction framework for system security assessment},
url = {http://dx.doi.org/10.1109/TSG.2018.2873001},
volume = {10},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The ongoing decarbonisation of modern electricity systems has led to a substantial increase of operational uncertainty, particularly due to the large-scale integration of renewable energy generation. However, the expanding space of possible operating points renders necessary the development of novel security assessment approaches. In this paper we focus on the use of security rules, where classifiers are trained offline to characterize previously unseen points as safe or unsafe. This paper proposes a novel deep learning-based feature extraction framework for building security rules. We show how deep autoencoders can be used to transform the space of conventional state variables (e.g. power flows) to a small number of dimensions where we can optimally distinguish between safe and unsafe operation. The proposed framework is data-driven and can be useful in multiple applications within the context of security assessment. To achieve high accuracy, a novel objective-based loss function is proposed to address the issue of imbalanced safe/unsafe classes that characterizes electricity system operation. Furthermore, an R-vine copula-based model is proposed to sample historical data and generate large populations of anticipated system states for training. The superior performance of the proposed framework is demonstrated through a series of case studies and comparisons using the load and wind generation data from the French transmission system, which have been mapped to the IEEE 118-bus system.
AU  - Sun,M
AU  - Konstantelos,I
AU  - Strbac,G
DO  - 10.1109/TSG.2018.2873001
EP  - 5020
PY  - 2019///
SN  - 1949-3061
SP  - 5007
TI  - A deep learning-based feature extraction framework for system security assessment
T2  - IEEE Transactions on Smart Grid
UR  - http://dx.doi.org/10.1109/TSG.2018.2873001
UR  - http://hdl.handle.net/10044/1/65145
VL  - 10
ER  -
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