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A deep learning-based feature extraction framework for system security assessment
File | Description | Size | Format | |
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FINAL VERSION.pdf | Accepted version | 2.51 MB | Adobe PDF | View/Open |
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: | Electrical and Electronic Engineering Centre for Environmental Policy Faculty of Natural Sciences Faculty of Engineering |