A Generic Data Generation Framework for Short Circuit Detection Training of Neural Networks

Wang M, Kordowich G, Jäger J (2022)


Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Publisher: VDE

Conference Proceedings Title: Conference Proceedings, 2 – 4 November 2022 in Kassel, Germany

Event location: Kassel

ISBN: 978-3-8007-6013-8

URI: https://ieeexplore.ieee.org/document/10104226

Abstract

Artificial Neural Networks excel in pattern recognition tasks and can therefore be used to identify short circuits in power systems. As short circuits do not frequently occur in real world grids, simulation data must be used to train neural networks. This paper describes a framework that can be used to automatically create large datasets for the training process of Neural Networks. Tests show that the framework is fast, adaptable, and works out of the box for most grid models. Additionally, a short circuit detection training is presented, that proves the applicability for Neural Network training purposes.

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How to cite

APA:

Wang, M., Kordowich, G., & Jäger, J. (2022). A Generic Data Generation Framework for Short Circuit Detection Training of Neural Networks. In Conference Proceedings, 2 – 4 November 2022 in Kassel, Germany. Kassel: VDE.

MLA:

Wang, Minxiao, Georg Kordowich, and Johann Jäger. "A Generic Data Generation Framework for Short Circuit Detection Training of Neural Networks." Proceedings of the PESS + PELSS 2022, Kassel VDE, 2022.

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