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IEET > Staff > Research Assistants > Anna-Lena Steen, M.Sc.

Anna-Lena Steen

M.Sc.
Research Assistant

Contact

Anna-Lena Steen, M. Sc.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Office Hours
nach Vereinbarung
Harburger Schloßstraße 22a,
21079 Hamburg
Building Harburger Schloßstraße 22a, Room 2.017
Phone: +49 40 42878 4091
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Research Project

KoLa
Optimized Load Management and Flexibility Coordination for Electrified Urban Public Transport

KoLa

Optimized Load Management and Flexibility Coordination for Electrified Urban Public Transport

Federal Ministry for Economic Affairs and Climate Action (BMWK); Duration: 2022 to 2026

to the project page

Publications

TUHH Open Research (TORE)

2024

2023

2022

Courses

Stud.IP
zur Veranstaltung in Stud.IP Studip_icon
Machine Learning in Electromagnetic Compatibility (EMC) Engineering (VL)
Untertitel:
This course is part of the module: Machine Learning in Electrical Engineering and Information Technology
Semester:
SoSe 24
Veranstaltungstyp:
Vorlesung (Lehre)
Veranstaltungsnummer:
lv3006_s24
DozentIn:
Prof. Dr. sc. techn. Christian Schuster, Dr. Cheng Yang
Beschreibung:

Electromagnetic Compatibility (EMC) Engineering dealswith design, simulation, measurement, and certification of electronic andelectric components and systems in such a way that their operation is safe,reliable, and efficient in any possible application. Safety is herebyunderstood as safe with respect to parasitic effects of electromagnetic fieldson humans as well as on the operation of other components and systems nearby.Examples for components and systems range from the wiring in aircraft and shipsto high-speed interconnects in server systems and wirless interfaces for brainimplants. In this part of the course we will give an introduction to thephysical basics of EMC engineering and then show how methods of MachineLearning (ML) can be applied to expand todays physcis-based approaches in EMCEngineering.
Leistungsnachweis:
m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul>
ECTS-Kreditpunkte:
1
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Heimatinstitut: Institut für Theoretische Elektrotechnik (E-18)
In Stud.IP angemeldete Teilnehmer: 2

Supervised Theses

ongoing

2024

  • Ahmed, Taha (2024). Development of an iterative multi-agent coordination framework for congestion prevention in low voltage grids.

  • Busch, Marcel (2024). Entwicklung eines Netzmodells zur szenarienbasierten Untersuchung von Engpässen in heutigen und zukünftigen städtischen Verteilnetzen.

  • Krammer, Friederike (2024). Entwicklung eines Algorithmus zur Koordinierung flexibler Prosumer zur Netzengpassvermeidung in Niederspannungssträngen.

  • Wilke, Jan Jakob (2024). Definition leistungsbasierter Netzregeln zur Engpassvermeidung in elektrischen Verteilnetzen.

completed
TU Hamburg
Institute of Electrical Power and Energy Technology (ieet) E-6
Prof. Dr.-Ing. Christian Becker
Harburger Schloßstraße 36
21079 Hamburg
E-mail : ieet(at)tuhh.de
Phone : +49 40 42878 3213
Fax : +49 40 42878 2382
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