Using vine copulas to generate representative system states for machine learning

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Title: Using vine copulas to generate representative system states for machine learning
Authors: Konstantelos, I
Sun, M
Tindemans, S
Issad, S
Panciatici, P
Strbac, G
Item Type: Journal Article
Abstract: 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.
Issue Date: Jan-2019
Date of Acceptance: 22-Jul-2018
ISSN: 0885-8950
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 225
End Page: 235
Journal / Book Title: IEEE Transactions on Power Systems
Volume: 34
Issue: 1
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: PO: 5510854 - WVR3114N
Keywords: Science & Technology
Engineering, Electrical & Electronic
data clustering
machine learning
Monte Carlo simulation
parametric statistics
principal component analysis
risk assessment
stochastic dependence
uncertainty analysis
0906 Electrical and Electronic Engineering
Publication Status: Published
Online Publication Date: 2018-07-24
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Centre for Environmental Policy
Faculty of Natural Sciences

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