Julian Oelhaf
Julian Oelhaf, M. Sc.
About: I am a PhD researcher at FAU Erlangen-Nürnberg working on applying machine learning to power systems. My focus is on making electricity grids more reliable and resilient by improving how faults are detected, classified, and managed. I enjoy bridging research and practice, and I work closely with industry to bring new AI solutions into real-world grid operations.
Focus Areas
- Fault Management: smarter detection, classification, and localization of grid faults
- Protection & Restoration: AI-driven strategies to speed up response and recovery after disturbances
- Digital Twins & Data: large-scale simulations and data pipelines for testing new protection approaches
- Anomaly Detection: modern AI methods to identify unusual events and improve system monitoring
Approach & Tools
- Machine Learning & AI (deep learning, reinforcement learning, transformers)
- Handling complex time-series data at scale with reproducible pipelines
- Simulation and real data: PowerFactory EMT, PMU/SCADA, and digital twins
Collaboration Opportunities
- Joint pilot projects with utilities, TSOs/DSOs, and technology providers
- Benchmarking and validation of new AI-based protection methods
- Advisory and knowledge transfer on ML adoption for grid applications
Academic CV
- Since 2024:
PhD Student at the Pattern Recognition Lab, FAU Erlangen-Nürnberg - 2021 – 2024
Master’s degree in Computer Science, FAU Erlangen-Nürnberg - 2017 – 2020
Bachelor’s degree in Aerospace Computer Science, JMU Würzburg
Projects
2024
-
Coordinated grid protection based on machine learning methods
(Third Party Funds Single)
Term: July 1, 2024 - June 30, 2027
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Publications
2026
Unpublished Publications
A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management
(2026)
DOI: 10.48550/arXiv.2509.09053
BibTeX: Download
, , , , , , :
2025
Conference Contributions
A Graph Neural Network-Based Approach for Power System Protection
IEEE Kiel PowerTech (Kiel, June 29, 2025 - July 3, 2025)
In: PowerTech 2025, Kiel: 2025
DOI: 10.1109/PowerTech59965.2025.11180650
URL: https://ieeexplore.ieee.org/document/11180650
BibTeX: Download
, , , , :
Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
Fault and Disturbance Analysis Conference (Atlanta, GA, May 5, 2025 - May 6, 2025)
In: Fault and Disturbance Analysis Conference 2025 2025
DOI: 10.48550/arXiv.2505.17763
BibTeX: Download
, , , , :
Verification of neural network based power system protection schemes
19th IET Conference on Developments in Power System Protection (DPSP Europe 2025) (Bilbao, April 1, 2025 - April 3, 2025)
DOI: 10.1049/icp.2025.1062
BibTeX: Download
, , , , , :
A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (Hyderabad, April 6, 2025 - April 11, 2025)
In: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New York City: 2025
DOI: 10.1109/ICASSP49660.2025.10890544
BibTeX: Download
, , , , , , :
Bachelor/Master Thesis
Type | Title | Status |
---|---|---|
MA thesis | Reinforcement Learning for Adaptive Protection in Power Grids | running |
Project | Reinforcement Learning for Centralized Fault Coordination in Power Systems | running |
Project | Latent Space Modeling for Event Detection in Power Grid Data | running |
MA thesis | Advanced Machine Learning-Based High Demand Forecasting of Household Energy Consumption for Enhancing Grid Operations | running |
MA thesis | Deep Learning-Based Fault Detection and Classification in Power System Protection: A Comparative Study | finished |
MA thesis | Wind Power Forecasting through Probabilistic Machine Learning Models | finished |