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Institute of Electrical Power and Energy Technology
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IEET > Staff > Research Assistants > Robert Annuth, M.Sc.

Robert Annuth

M.Sc.
Research Assistant

Contact

Robert Annuth M. Sc.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Office Hours
nach Vereinbarung
Harburger Schloßstraße 22a,
21079 Hamburg
Building HS22a, Room 2.002
Phone: +49 40 42878 2837
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Research Project

SuSy
Sustainable DC-Systems – DC-Power Systems on Ships

SuSy

Sustainable DC-Systems – DC-Power Systems on Ships

Federal Ministry for Economic Affairs and Climate Action (BMWK); Duration: 2021 to 2024

to the project page

Publications

TUHH Open Research (TORE)

2024

2023

Courses

Stud.IP
link to course in Stud.IP Studip_icon
Machine Learning in Electrical Engineering and Information Technology
Semester:
SoSe 24
Course type:
Lecture
Course number:
lv3004_s24
Lecturer:
Prof. Dr. sc. techn. Christian Schuster, Prof. Dr.-Ing. Christian Becker, Prof. Dr. Alexander Kölpin, Gerhard Bauch, Dr. Maximilian Stark, Dr. Davood Babazadeh, Dr. Cheng Yang, PD Dr.-Ing. habil. Rainer Grünheid, Simon Stock, M.Sc.
Description:
This master course, a collaborative effort between the Institute of Communications, the Institute for High-Frequency Engineering, the Institute for Power Systems, and the Institute for Theoretical Electrical Engineering, is designed to unveil the synergies between machine learning and our respective fields of expertise. In an age defined by rapid technological advancement, machine learning stands as a catalyst for innovation, offering transformative possibilities across diverse sectors. From optimizing communication systems to enhancing power grid efficiency, and from refining signal processing techniques to enabling autonomous systems, the integration of machine learning techniques holds immense promise for addressing contemporary challenges. Throughout this course, we will delve into the theoretical underpinnings, practical methodologies, and tangible applications of neural networks and machine learning algorithms. By delving into algorithmic design, data analysis, and optimization techniques, we aim to equip you with the skills and insights needed to navigate the complexities of modern engineering landscapes.
Performance accreditation:
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 credit points:
6
Stud.IP informationen about this course:
Home institute: E-8 Nachrichtentechnik
Registered participants in Stud.IP: 101
Postings: 4
Documents: 25

Supervised Theses

ongoing

2024

  • Görner, T. (2024). Vergleich der Effizienz von Windparks mit AC- und DC-Sammelnetzen.

  • Wichmann, A. (2024). Untersuchung von Dynamik und Stabilität in DC-Sammelnetzen von Offshore-Windparks.

completed

2024

  • Schultheiß, J. (2024). Impedanzbasierte Stabilitätsanalyse zur Bewertung der Stabilitätsgrenzen von DC- und AC-Netzen.

2023

  • Görner, T. (2023). Ermittlung der Stabilitätsgrenzen von DC-Microgrids und Validierung durch transiente Simulationen.

  • Langendörfer, T. (2023). Entwicklung eines Konzepts zur Auswahl von Netzstrukturen und Regelkonzepten für DC-Microgrids.

  • Rudolf, D. (2023). Optimierung der Energieeffizienz in DC-Microgrids: Eine Untersuchung der Rolle von DC-DC Wandlern.

  • Wichmann, A. (2023). Entwicklung eines Konzeptes zur datenbasierten Ermittlung des Alterungszustandes von Fahrzeugbatterien.

  • Winkel, J. (2023). Configurable gate driver circuits for IGBT based inverters with adjustable piecewise voltage and current gradient for industrial application.

2022

  • Nußbaum, F. (2022). Untersuchung der Systemstabilität von maritimen DC-Micro Grids durch Betrachtung von Lastflüssen und möglichen Fehlerfällen.

  • Nußbaum, F. (2022). Optimierung von Betriebszuständen und Spannungsregelung eines DC-Microgrids durch Reinforcement Learning.

  • Winter, K. (2022). Entwicklung eines verteilten Messsystems zur frequenzabhängigen Netzimpedanzbestimmung über einen breiten Frequenzbereich bis 500 kHz.

TU Hamburg
Institute of Electrical Power and Energy Technology (ieet) E-6
Prof. Dr.-Ing. Christian Becker
Harburger Schloßstraße 22a
21079 Hamburg
E-mail : ieet(at)tuhh.de
Phone : +49 40 42878 3213
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