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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
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: E-6 Elektrische Energietechnik
Registered participants in Stud.IP: 3

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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