A deep learning-based feature extraction framework for system security assessment

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Title: A deep learning-based feature extraction framework for system security assessment
Authors: Sun, M
Konstantelos, I
Strbac, G
Item Type: Journal Article
Abstract: 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.
Issue Date: 1-Sep-2019
Date of Acceptance: 27-Sep-2018
URI: http://hdl.handle.net/10044/1/65145
DOI: https://doi.org/10.1109/TSG.2018.2873001
ISSN: 1949-3061
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 5007
End Page: 5020
Journal / Book Title: IEEE Transactions on Smart Grid
Volume: 10
Issue: 5
Copyright Statement: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (E
Funder's Grant Number: R96051 - EP/K036173/1
Keywords: 0906 Electrical and Electronic Engineering
0915 Interdisciplinary Engineering
Publication Status: Published
Online Publication Date: 2018-10-01
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Centre for Environmental Policy
Faculty of Natural Sciences



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