Hotz, Christian DanielChristian DanielHotzBecker, ChristianChristianBecker2020-02-272020-02-272019Conference on Sustainable Energy Supply and Energy Storage Systems (NEIS 2019)http://hdl.handle.net/11420/5044Shifting paradigms in electrical power generation, transmission and consumption will affect system dynamics and may negatively influence its small signal stability in the long run. A smaller stability margin calls for smart methods to monitor the current state of the power system to be able to detect critical situations immediately. This paper proposes a method based on artificial neural networks that is capable of providing an online supervision service, which works in real-time due to its low demand for computational resources. Additionally, the requirements regarding system state information of such a monitoring system are investigated to assess the measurement and communication setup necessary for its proper functionality and thus its applicability to real power systems.enMLE@TUHHConference Paperhttps://ieeexplore.ieee.org/document/9000480Conference Paper