Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
D. Hauer, M. Bittner, S. Cejka, R. Mosshammer, F. Kintzler, T. Leopold, S. Wilker:
"Re-enacting rare multi-modal real-world grid events to generate ML training data sets";
Vortrag: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE),
Kyoto, Japan;
20.06.2021
- 23.06.2021; in: "2021 IEEE International Symposium on Industrial Electronics (ISIE)",
IEEE,
(2021),
ISSN: 2163-5145;
S. 1
- 6.
Kurzfassung englisch:
Today's energy grids are facing huge challenges caused by the growing diversity of energy consumers and producers as well as an ongoing increase of renewable energy sources and e-mobility. Hence, it is essential that the grids continuously evolve by introducing new monitoring, protection and optimization concepts including machine learning (ML) approaches. To overcome the lack of existing monitoring data for rare real-world grid events, this paper presents a concept for generating training data sets for ML approaches based on a multi-modal grid simulation tool. The simulation tool as well as the proposed semi-automated data generation approach are introduced and the concept is verified based on a real-world battery storage maintenance event.
Schlagworte:
Smart grids, Simulation, Deep learning
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/ISIE45552.2021.9576350
Elektronische Version der Publikation:
https://publik.tuwien.ac.at/files/publik_301313.pdf
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.