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Institute of Electrical Power and Energy Technology
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IEET > Staff > Research Assistants > Mirco Fabian Woidelko, M.Sc., M. A.

Mirco Fabian Woidelko

M.Sc., M.A.
Research Assistant

Contact

Mirco Woidelko, M.Sc., M.A.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Office Hours
nach Vereinbarung
Harburger Schloßstraße 22a,
21079 Hamburg
Building HS22a, Room 2.004
Phone: +49 40 42878 4093
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Research Project

DisrupSys
Disruptive functions and technology for angle-based integrated grid operation in converter-dominated power systems with predominantly renewable energy supply

DisrupSys

Disruptive functions and technology for angle-based integrated grid operation in converter-dominated power systems with predominantly renewable energy supply

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

to the project page

Publications

TUHH Open Research (TORE)

Courses

Stud.IP
link to course in Stud.IP Studip_icon
Seminare.EIM: Seminar on Electromagnetic Compatibility and Electric Power Systems (Bachelor/Master-ET)
Semester:
WiSe 22/23
Course type:
Seminar
Lecturer:
Prof. Dr.-Ing. Christian Becker, Dr. Anna Katharina Kirf, Kathleen Potzahr, Prof. Dr. sc. techn. Christian Schuster, Marwan Mostafa, M.Sc., Mirco Woidelko, M.Sc., M.A., Dr. Cheng Yang, Christoph Klie, M.Sc., Johannes Heise, M.Sc., Dr.-Ing. Jan-Peter Heckel, Simon Stock, M.Sc., Hanko Ipach, M.Sc., Robert Annuth, M.Sc., Béla Wiegel, M.Sc., Tom Steffen, M.Sc.
Description:
Due to the energy transition, an increasing number of renewable energy plants are installed and connected to the grid. Their integration into the existing grid structure leads to challenging problems, which need to be solved to keep the grid stable. In addition to the conventional methods, the increasing availability of computational power allows to explore more computationally expensive algorithms and techniques. Besides optimization algorithms, machine learning approaches have gained popularity in the field of power engineering. Machine learning is a very diverse approach also used in multiple other disciplines and can be tailored to solve various problems. It offers a broad variety of techniques, networks, and algorithms with multiple advantages. In this joint seminar, the different applications for optimization techniques and machine learning in electrical energy systems are presented and discussed. The Seminar is open for Bachelor and Master Students in the electrical engineering program of TUHH. PhD students from both Institutes present their current research, while Bachelor/Master students give presentations on topics related to either power technology or electromagnetic compatibility.
Area classification:
Studiendekanat Elektrotechnik, Informatik und Mathematik
Stud.IP informationen about this course:
Home institute: Elektrotechnik, Informatik und Mathematik
Registered participants in Stud.IP: 33
Postings: 2
Documents: 4

Supervised Theses

ongoing
completed

2023

  • Babendererde, A. (2023). Regelung eines Umrichters zum Anschluss eines Wasserstoffspeicherkraftwerks an die Höchstspannungsebene.

2022

  • Lim, I. (2022). Modelling and Integration of a Hydrogen Storage Power Plant in the 10-Machine New-England Power System.

  • Lindner, J. (2022). Primärregelungskonzepte für einen Batteriepufferspeicher eines Wasserstoffspeicherkraftwerkes.

  • Rieckborn, N. (2022). Modellierung des Umwandlungsprozesses eines Wasserstoffspeicherkraftwerks.

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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