Reducing Measurements of Voltage Sensitivity via Uncertainty-Aware Predictions
| Vortragende(r) | Anton Winter | |
|---|---|---|
| Vortragstyp | Bachelorarbeit | |
| Betreuer(in) | Bela Böhnke | |
| Termin | Fr 29. April 2022 | |
| Vortragsmodus | in Präsenz | |
| Kurzfassung | Due to the energy transition towards weather-dependent electricity sources like wind and solar energy, as well as new notable loads like electric vehicle charging, the voltage quality of the electrical grid suffers. So-called Smart Transformers (ST) can use Voltage Sensitivity (VS) information to control voltage, frequency, and phase in order to enhance the voltage quality. Acquiring this VS information is currently costly, since you have to synthetically create an output variability in the grid, disturbing the grid even further. In this thesis, I propose a method based on Kalman Filters and Neural Networks to predict the VS, while giving a confidence interval of my prediction at any given time. The data for my prediction derives from a grid simulation provided by Dr. De Carne from the research center Energy Lab 2.0. | |