Training of Artificial Neural Networks Based on Feed-in Time Series of Photovoltaics and Wind Power for Active and Reactive Power Monitoring in Medium-Voltage Grids
| dc.contributor.author | Dipp, Marcel | |
| dc.contributor.author | Menke, Jan-Hendrik | |
| dc.contributor.author | Wende - von Berg, Sebastian | |
| dc.contributor.author | Braun, Martin | |
| dc.contributor.editor | David, Klaus | |
| dc.contributor.editor | Geihs, Kurt | |
| dc.contributor.editor | Lange, Martin | |
| dc.contributor.editor | Stumme, Gerd | |
| dc.date.accessioned | 2019-08-27T12:55:31Z | |
| dc.date.available | 2019-08-27T12:55:31Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Today, there is already a significant injection of renewable energies at the medium-voltage level, which requires the use of reliable monitoring methods. In addition to tracking electrical parameters such as line current or bus voltage magnitudes, precise knowledge of the active and reactive power feed-in is becoming increasingly relevant in order to provide the necessary information for optimization strategies at higher voltage levels. For this reason, we have developed a method to monitor the active and reactive power for the medium-voltage level with very low measurement density, which is based on artificial neural networks (ANN). The actual training of ANN is accomplished with photovoltaics (PV) and wind feed-in time series based on real weather data to ensure realistic monitoring of the injection. The presented method is applied to a German medium-voltage grid to evaluate the estimation accuracy. | en |
| dc.identifier.doi | 10.18420/inf2019_71 | |
| dc.identifier.isbn | 978-3-88579-688-6 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/25023 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-294 | |
| dc.subject | artificial neural networks | |
| dc.subject | active and reactive power monitoring | |
| dc.subject | time series | |
| dc.title | Training of Artificial Neural Networks Based on Feed-in Time Series of Photovoltaics and Wind Power for Active and Reactive Power Monitoring in Medium-Voltage Grids | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 557 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 545 | |
| gi.conference.date | 23.-26. September 2019 | |
| gi.conference.location | Kassel | |
| gi.conference.sessiontitle | Digitalisierung des Energiesystems |
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