David Fellner

David Fellner is a Senior Lecturer and Program Director for Renewable Energy Engineering at the University of Applied Sciences Technikum Vienna. He leads the Renewable Energies degree program (Bachelors & Masters) and is involved in the COIN research project 'GridEdge', which focuses on expanding lab infrastructure incorporating PV, EVSE, and DC microgrids for contract research.

His educational background includes:

• Doctoral program in Engineering Sciences (Computer Sciences concentration) at Vienna University of Technology (2020-2024), graduating with distinction. Dissertation: 'Data Driven Detection of Misconfigurations in Power Distribution Systems'
• MSc in Energy and Automation Engineering (2016-2019)
• BSc in Electrical Engineering and Information Technology (2012-2016)
• ERASMUS Semester at NTNU, Trondheim, Norway (Spring 2018)
• ERASMUS Semester at Politecnico di Milano, Italy (Spring 2015)

Dr. Fellner's research focuses on the intersection of power systems engineering and data science, particularly applying machine learning techniques to power distribution systems. His work addresses critical challenges in modern grid management including misconfiguration detection, anomaly identification, and reliability improvement. He has developed frameworks for data-driven malfunction detection in power systems, with applications in both public and industrial grids. His expertise spans DC microgrids, renewable energy integration, and AI applications for grid monitoring and optimization.

His publication record demonstrates a clear trajectory of increasingly sophisticated applications of machine learning to power system challenges. Starting with foundational work on data-driven malfunction detection, his research has evolved to include transformer profile disaggregation, deep learning applications, and the development of open-source frameworks like DeMaDs. The publications reveal a strong focus on practical implementations that address real-world grid issues, particularly as renewable energy sources become more integrated into power distribution systems.

Dr. Fellner has been actively involved in research projects including DeMaDsPilot (data-driven malfunction detection), Parmenides (AI-based state estimation), and support for projects like PoSyCo, TheBuilding, and EASE. He has also provided training for OVE on 'Power Quality' and has supervised numerous Bachelor theses.

His laboratory work centers around the COIN research project 'GridEdge', which expands lab infrastructure incorporating photovoltaics, electric vehicle supply equipment, and DC microgrids for contract research purposes. This facility enables practical testing and validation of his theoretical work on grid monitoring and optimization.