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IEET > Staff > Research Assistants > Béla Wiegel, M.Sc.

Béla Wiegel

M.Sc.
Research Assistant

Contact

Béla Wiegel, M. Sc.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Harburger Schloßstraße 36,
21079 Hamburg
Building HS36, Room C2 1.001
Phone: +49 40 42878 2240
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Research Projects

EffiziEntEE
Efficient integration of high shares of renewable energies in technically and economically integrated energy systems

EffiziEntEE

Efficient integration of high shares of renewable energies in technically and economically integrated energy systems

Federal Ministry for Economic Affairs and Climate Action (BMWK); Duration: 2022 to 2025

to the project page
CyEntEE
I³-Lab Cyber Physical Energy Systems – Sustainability, Resilience and Economics

I³-Lab

CyEntEE

Cyber Physical Energy Systems – Sustainability, Resilience and Economics

Hamburg University of Technology (TUHH); Duration: 2020 to 2023

to the project page

Publications

TUHH Open Research (TORE)

2024

2023

2022

2021

Courses

Stud.IP
link to course in Stud.IP Studip_icon
Machine Learning Applications in Electric Power Systems (VL)
Subtitle:
This course is part of the module: Machine Learning in Electrical Engineering and Information Technology
Semester:
SoSe 24
Course type:
Lecture
Course number:
lv3008_s24
Lecturer:
Prof. Dr.-Ing. Christian Becker, Dr. Davood Babazadeh, Simon Stock, M.Sc.
Description:

This part of the course focuses on how to utilize ML methods to model and operate electric power systems. Electric power systems consist of generation units such as PV, loads or consumers and the grid that connects those actors and supports to transport energy. This part of the course helps to understand the data-driven modelling of generation units (e.g. PV & fuel cells), modelling of load behavior, and to formulate and solve a state estimation problem for distribution grids using neural networks.

This part of the course includes lectures to introduce the basics that are followed by practical examples and coding.

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:
1
Stud.IP informationen about this course:
Home institute: Elektrische Energietechnik (E-6)
Registered participants in Stud.IP: 3

Supervised Theses

ongoing
completed

2022

  • Kaya, E. (2022). Simulation des Lebenszyklus‘ einer Lithium Ion Zelle in den stationären EP and instationären EV Anwendungsfällen.

  • Pauelsen, F.-T. (2022). Implementierung eines Maximum-Power-Point-Tracker für Photovoltaikanlagen in Modelica.

  • Rücker, J. (2022). Dynamische Untersuchung des Verhaltens elektrischer Komponenten auf Quartiersebene hinsichtlich der Spannungshaltung.

  • Rüffert, J. (2022). Charakterisierung von Zellen in Verteilnetzen anhand von Bewertungskriterien und die Auswirkungen von punktuell und zeitlich begrenzt auftretenden Lasten.

2021

  • Helmrich von Elgott, L. (2021). Optimierter Einsatz dezentraler Flexibilität zur Betriebsführung intelligenter sektorgekoppelter Verteilnetze.

  • Zwinzscher, S. (2021). Entwicklung einer Methodik zur dynamischen Berechnung der Flexibilität eines auf Power-to-Heat basierenden Nahwärmenetzes.

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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