Conference paper
Deep Learning for Power System Security Assessment
Technical University of Denmark1
Skolkovo Institute of Science and Technology2
Center for Electric Power and Energy, Centers, Technical University of Denmark3
Electric Power Systems, Center for Electric Power and Energy, Centers, Technical University of Denmark4
Department of Electrical Engineering, Technical University of Denmark5
Energy Analytics and Markets, Center for Electric Power and Energy, Centers, Technical University of Denmark6
Security assessment is among the most fundamental functions of power system operator. The sheer complexity of power systems exceeding a few buses, however, makes it an extremely computationally demanding task. The emergence of deep learning methods that are able to handle immense amounts of data, and infer valuable information appears as a promising alternative.
This paper has two main contributions. First, inspired by the remarkable performance of convolutional neural networks for image processing, we represent for the first time power system snapshots as 2-dimensional images, thus taking advantage of the wide range of deep learning methods available for image processing.
Second, we train deep neural networks on a large database for the NESTA 162-bus system to assess both N-1 security and small-signal stability. We find that our approach is over 255 times faster than a standard small-signal stability assessment, and it can correctly determine unsafe points with over 99% accuracy.
Language: | English |
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Publisher: | IEEE |
Year: | 2019 |
Pages: | 1-6 |
Proceedings: | 13<sup>th</sup> IEEE PowerTech Milano 2019 |
ISBN: | 1538647222 , 1538647230 , 9781538647226 and 9781538647233 |
Types: | Conference paper |
DOI: | 10.1109/PTC.2019.8810906 |
ORCIDs: | Thams, Florian and Chatzivasileiadis, Spyros |
NESTA 162-bus system Power system stability Security Stability criteria Training Two dimensional displays convolutional neural networks deep learning deep learning methods deep neural networks image processing learning (artificial intelligence) neural nets power engineering computing power system images power system operator power system security power system security assessment sheer complexity small-signal stability assessment