Using Vine Copulas to Generate Representative System States for Machine Learning

Using Vine Copulas to Generate Representative System States for Machine Learning

Konstantelos, Ioannis (Imperial College London)
Sun, Mingyang (Imperial College London)
Tindemans, Simon H. (TU Delft Intelligent Electrical Power Grids)
Issad, Samir (Reseau de Transport d'Electricite)
Panciatici, Patrick (Reseau de Transport d'Electricite)
Strbac, Goran (Imperial College London)

2019

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 nonlinear 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 modeling, 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.

Copulas
data clustering
machine learning
Monte Carlo simulation
parametric statistics
principal component analysis
risk assessment
stochastic dependence
uncertainty analysis

http://resolver.tudelft.nl/uuid:958ca590-cc08-498b-9c73-a5e20e589355

https://doi.org/10.1109/TPWRS.2018.2859367

2019-07-01

0885-8950

IEEE Transactions on Power Systems, 34 (1), 225-235

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2019 Ioannis Konstantelos, Mingyang Sun, Simon H. Tindemans, Samir Issad, Patrick Panciatici, Goran Strbac