DI Dr. techn. David Fellner, BSc.
Program Director Renewable Energy Engineering
Senior Lecturer/Researcher
Education
- Vienna University of Technology | Vienna, Austria
2020 – 2024 Doctoral program in Engineering Sciences Area of concentration: Computer Sciences; Grade: with distinction Thesis: ‘Data Driven Detection of Misconfigurations in Power Distribution Systems’ - 2016 – 2019 MSc Energy and Automation Engineering
Thesis: Investigation of various solutions for improved voltage quality in weak distribution networks - 2012 – 2016 BSc Electrical Engineering and Information Technology
Thesis: Weight estimation with mobile load cells - NTNU | Trondheim, Norway
Spring 2018 ERASMUS Semester Abroad Program in Master of Science in Electric Power Engineering - Politecnico di Milano | Milan, Italy
Spring 2015 ERASMUS Semester Abroad Program in Ingegneria Elettrica (Electric Engineering)
Professional Career
- Program Director at the University of Applied Sciences Technikum Vienna
Since December 2023 × Head of the ‘Renewable Energies’ degree program (Bachelors & Masters) - Involved in COIN research project ‘GridEdge’ treating the expansion of lab infrastructure incorporating PV and EVSE as well as a DC microgrid used for contract research
- Lectures on the electricity grid and project works/ thesis’ on (DC) power grids as well as grid simulation and AI applications on the power grid
- Research Engineer at the Austrian Institute of Technology AIT
Oct. – Nov. 2023 × Research projects on data-driven smart grid solutions - DeMaDsPilot: data-driven malfunction detection
- Parmenides: AI-based state estimation
- Trainer at Technikum Wien Academy
2022 -2023 × Training for the OVE on ‘Power Quality’ - External Lecturer at the University of Applied Sciences Technikum Vienna
- 2019 – 2023: Lectures and laboratory classes on
– Energy distribution systems in urban environments
– Components of energy distribution systems
– Electricity Grids
– Supervision of Bachelor thesis - PhD Candidate at the Austrian Institute of Technology AIT
April 2019 – September 2023 - Dissertation topic: ‘Data Driven Detection of Misconfigurations in Power Distribution Systems’
– Machine Learning application development and testing
– Data collection in laboratories and through automized grid simulations
– 8 peer-reviewed publications, one book chapter (to be published) - FFG funded research project ‘DeMaDs: Data driven detection of malfunctioning devices in power distribution systems’ conducted
– Proposal, project management, and execution - Support in research projects and proposal work (PoSyCo, TheBuilding, EASE)
Competences
- Programming languages: Python, C, Java, MATLAB, SQL
- Simulation software: POWERFACTORY, LABVIEW, MAPLE, SIMULINK