Institute of Electrical Power and Energy Technology
Institute of Electrical Power and Energy Technology
EN
EN
Contact
Simon Stock, M. Sc.
E-6 Elektrische Energietechnik
- Elektrische Energietechnik
Office Hours
Jederzeit
Harburger Schloßstraße 36,
21079 Hamburg
Building HS36, Room C3 0.006
Phone: +49 40 42878 2378
E-mail:
simon.stock@tuhh.de
Research Projects
Applications of AI in distribution system operation
VeN²uS
Networked grid protection systems - Adaptive and interconnected
Publications
TUHH Open Research (TORE)
2023
-
Reinforcement learning based coordination of virtual inertia provision from inverter-dominated distribution grids
32nd IEEE International Symposium on Industrial Electronics (ISIE 2023)
Publisher DOI -
Bayesian physics-informed neural networks for robust system identification of power systems
IEEE Belgrade PowerTech (2023)
Publisher DOI
2022
-
Modelling the influence of virtual inertia in distribution systems on frequency stability
IEEE Power and Energy Student Summit 2022 and Power Electronics Student Summit: (2022)
-
Application of physics-based graph convolutional network in real-time state estimation of under-determined distribution grids
IEEE PES Innovataive Smart Grid Technologies Europe, ISGT Europe (2022)
Publisher DOI -
Interconnected network protection systems - the basis for the reliable and safe operation of distribution grids with a high penetration of renewable energies and electric vehicles
CIRED Porto Workshop 2022: 548-552 (2022)
Publisher DOI -
Real-time inertia estimation in an inverter-dominated distribution grid using a physics-informed recurrent neural network
CIRED Porto Workshop 2022: 1403 (2022)
Publisher DOI
2021
-
Applications of artificial intelligence in distribution power system operation
IEEE Access 9 : 150098-150119 (2021-11)
Open Access | Publisher DOI -
A Modified Branch-Current Based Algorithm for Fast Low Voltage Distribution Grid State Estimation using Smart Meter Data
Von Komponenten bis zum Gesamtsystem fur die Energiewende (ETG-Kongress 2021)
Courses
Stud.IP
| |||||||||||||||||||||||
|
Supervised Theses
ongoing
completed
2021
- Hund, P. (2021). Modellierung eines elektrischen Netzes zur Demonstration des Einflusses von virtueller Trägheit durch umrichterbasierte Energieanlagen.
- Hund, P. (2021). Koordinierte Bereitstellung von virtueller Trägheit durch erneuerbare umrichterbasierte Energieanlagen in Verteilnetzen mithilfe von künstlicher Intelligenz.
- Möller, P. (2021). Erfassung der Knotenspannung in Niederspannungsnetzen auf Basis von dezentralen Messeinrichtungen mithilfe von Machine learning.
- Plant, R. (2021). Estimation of Power System Inertia in an Inverter-Dominated Distribution Grid Using Machine Learning.
2020
- Dressel, M. (2020). Modellierung der Zustandsschätzung eines elektrischen Netzes mit Hilfe von Graph neuronalen Netzen.
- Schmidt, M. (2020). Vorhersage von zuverlässig bereitstellbarer Regelleistung aus Erneuerbaren Energien mithilfe von neuronalen Netzen.
