  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.
