Simon Stock

M.Sc.
Research Assistant

Contact

Simon Stock, M. Sc.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Office Hours
Jederzeit
Harburger Schloßstraße 36,
21079 Hamburg
Building HS36, Room C3 0.006
Phone: +49 40 42878 2378
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Research Projects

Applications of AI in distribution system operation

Applications of AI in distribution system operation

Hamburg University of Technology (TUHH); Duration: 2020 to 2024

VeN²uS
Networked grid protection systems - Adaptive and interconnected

VeN²uS

Networked grid protection systems - Adaptive and interconnected

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

Research Focus

Optimal operation and energy managment in electrical distribution grids (Smart Grids) using artifical intelligence

Publications

TUHH Open Research (TORE)

2023

2022

2021

Courses

Stud.IP
zur Veranstaltung in Stud.IP Studip_icon
Quantitative Research Methods
Untertitel:
Module M1263: Quantitative Research Methods (L1714)
Semester:
WiSe 23/24
Veranstaltungsart:
Project Seminar Module
Veranstaltungstyp:
Projektgruppe (Organisation)
Veranstaltungsnummer:
M1263 / L1714
DozentIn:
Prof. Dr. Christian Ringle
Beschreibung:
Participants will understand the use, requirements, advantages, and disadvantages of quantitative methods. Examples illustrate the application of quantitative methods and their use to address business problems. The course involves three parts: - The first part of the course focuses on an introduction to quantitative research methods. - The second part of the course involves working on a seminar thesis. Participants are in teams invited to describe selected quantitative research methods and to address simple research questions with the described method. Students are expected to write a short (empirical) paper that applies methods learned in this course to a research question of their choice. - The third part is the final presentation of the results from the group work. Participants will present their own small research projects and discuss the results in the plenum. Participants are invited to join the discussions as a part of the final grade.
TeilnehmerInnen:
Students of the study degree program Mechanical Engineering and Management (MEM)
Voraussetzungen:
Basic knowledge in business administration and quantitative methods.
Lernorganisation:
Sessions by appointment.
Leistungsnachweis:
Written thesis and presentation.
ECTS-Kreditpunkte:
6
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Heimatinstitut: Management and Decision Sciences [MDS] (W-9)
In Stud.IP angemeldete Teilnehmer: 13
Anzahl der Dokumente im Stud.IP-Downloadbereich: 3

Supervised Theses

ongoing
completed

2021

  • Hund, P. (2021). Modellierung eines elektrischen Netzes zur Demonstration des Einflusses von virtueller Trägheit durch umrichterbasierte Energieanlagen.

  • Hund, P. (2021). Koordinierte Bereitstellung von virtueller Trägheit durch erneuerbare umrichterbasierte Energieanlagen in Verteilnetzen mithilfe von künstlicher Intelligenz.

  • Möller, P. (2021). Erfassung der Knotenspannung in Niederspannungsnetzen auf Basis von dezentralen Messeinrichtungen mithilfe von Machine learning.

  • Plant, R. (2021). Estimation of Power System Inertia in an Inverter-Dominated Distribution Grid Using Machine Learning.

2020

  • Dressel, M. (2020). Modellierung der Zustandsschätzung eines elektrischen Netzes mit Hilfe von Graph neuronalen Netzen.

  • Schmidt, M. (2020). Vorhersage von zuverlässig bereitstellbarer Regelleistung aus Erneuerbaren Energien mithilfe von neuronalen Netzen.