Abstract
Modeling and simulation of electrical power systems are becoming increasingly important approaches for the development and operation of novel smart grid functionalities - especially with regard to data-driven applications as data of certain operational states or misconfigurations can be next to impossible to obtain. The DeMaDs framework allows for the simulation and modeling of electric power grids and malfunctions therein. Furthermore, it serves as a testbed to assess the applicability of various data -driven malfunction detection methods. These include data mining techniques, traditional machine learning approaches as well as deep learning methods. The framework's capabilities and functionality are laid out here, as well as explained by the means of an illustrative example.
| Originalsprache | Englisch |
|---|---|
| Titel | Proceedings |
| Erscheinungsort | Aachen, Germany |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 979-8-3503-1122-8 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 5 Apr. 2023 |
| Veranstaltung | 2023 Open Source Modelling and Simulation of Energy Systems (OSMSES) - RWTH Aachen, Aachen, Deutschland Dauer: 27 März 2023 → 29 März 2023 https://go.fzj.de/osmses2023 |
Konferenz
| Konferenz | 2023 Open Source Modelling and Simulation of Energy Systems (OSMSES) |
|---|---|
| Kurztitel | OSMSES |
| Land/Gebiet | Deutschland |
| Stadt | Aachen |
| Zeitraum | 27/03/23 → 29/03/23 |
| Internetadresse |
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
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SDG 7 – Erschwingliche und saubere Energie
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SDG 9 – Industrie, Innovation und Infrastruktur
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SDG 13 – Klimaschutzmaßnahmen
Research Field
- Power System Digitalisation
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