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.
Original language | English |
---|---|
Title of host publication | Proceedings |
Place of Publication | Aachen, Germany |
Number of pages | 6 |
ISBN (Electronic) | 979-8-3503-1122-8 |
DOIs | |
Publication status | Published - 5 Apr 2023 |
Event | 2023 Open Source Modelling and Simulation of Energy Systems (OSMSES) - RWTH Aachen, Aachen, Germany Duration: 27 Mar 2023 → 29 Mar 2023 https://go.fzj.de/osmses2023 |
Conference
Conference | 2023 Open Source Modelling and Simulation of Energy Systems (OSMSES) |
---|---|
Abbreviated title | OSMSES |
Country/Territory | Germany |
City | Aachen |
Period | 27/03/23 → 29/03/23 |
Internet address |
Research Field
- Power System Digitalisation
Keywords
- Data-driven approach
- malfunction detection
- modeling and simulation
- electric power systems
- smart grids