TY - CHAP U1 - Konferenzveröffentlichung A1 - Linke, Manuela A1 - Messmer, Tobias A1 - Micard, Gabriel A1 - Wenzel, Adrian A1 - Schubert, Gunnar A1 - Kindl, Matthias A1 - Minde, Adrian T1 - Artificial neural network based decision support system for the present power grid accounting for the successful integration of renewable energy sources such as PV systems T2 - 36th European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC 2019), 9-13 September, Marseille, France N2 - We present an alternative approach to grid management in low voltage grids by the use of artificial intelligence. The developed decision support system is based on an artificial neural network (ANN). Due to the fast reaction time of our system, real time grid management will be possible. Remote controllable switches and tap changers in transformer stations are used to actively manage the grid infrastructure. The algorithm can support the distribution system operators to keep the grid in a safe state at any time. Its functionality is demonstrated by a case study using a virtual test grid. The ANN achieves a prediction rate of around 90% for the different grid management strategies. By considering the four most likely solutions proposed by the ANN, the prediction rate increases to 98.8%, with a 0.1 second increase in the running time of the model. KW - Grid Management KW - Distributed System Operators (DSO) KW - Grid Integration KW - Smart Grids KW - Artificial Neural Networks Y1 - 2019 SN - 3-936338-60-4 SB - 3-936338-60-4 U6 - https://doi.org/10.4229/EUPVSEC20192019-6CV.1.21 DO - https://doi.org/10.4229/EUPVSEC20192019-6CV.1.21 SP - 1895 EP - 1898 ER -