Julian Oelhaf
Lehrstuhl für Informatik 5 (Mustererkennung)
Research associates
Address
Martensstraße 3
91058 Erlangen
I am a PhD researcher at FAU Erlangen-Nürnberg and the Pattern Recognition Lab / LME, working at the interface of machine learning, power systems, and reliable infrastructure. My research focuses on AI-based methods for power system protection, especially fault detection, fault classification, fault line identification, and fault localization in future electricity grids.
Modern power systems are changing rapidly due to renewable energy sources, power-electronic converters, decentralized generation, and increasingly complex grid operation. These developments challenge classical protection schemes, which were designed for more predictable grid dynamics. My work investigates how machine learning can support fast, robust, and transparent protection decisions under these changing conditions.
Beyond academic research, I am interested in bringing machine learning methods closer to real-world grid operation. This includes reproducible benchmarks, scalable data pipelines, simulation-based validation, and collaboration with utilities, grid operators, technology providers, and industrial partners working on digital and resilient energy infrastructure.
- 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
2024
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Coordinated grid protection based on machine learning methods
(Third Party Funds Single)
Project leader: , ,
Term: July 1, 2024 - June 30, 2027
Acronym: Netzschutz-KI
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
2026
Conference Contributions
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Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
In: 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain: 2026
URL: https://arxiv.org/abs/2509.22458
BibTeX: Download
Unpublished Publications
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Feature Selection for Fault Prediction in Distribution Systems
(2026)
DOI: 10.48550/arXiv.2603.25274
BibTeX: Download - , , , , , , :
Robustness Evaluation of Machine Learning Models for Fault Classification and Localization in Power System Protection (Conference contribution, accepted)
20th International Conference on Developments in Power System Protection (DPSP 2026) (London, UK, March 2, 2026 - March 6, 2026)
DOI: 10.48550/arXiv.2512.15385
BibTeX: Download
2025
Journal Articles
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A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management
In: International Journal of Electrical Power & Energy Systems Volume 172 (2025), Article No.: 111257
ISSN: 0142-0615
DOI: 10.1016/j.ijepes.2025.111257
URL: https://www.sciencedirect.com/science/article/pii/S0142061525008051
BibTeX: Download
Conference Contributions
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Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection
33rd European Signal Processing Conference (EUSIPCO 2025) (Palermo, September 8, 2025 - September 12, 2025)
In: 2025 33rd European Signal Processing Conference (EUSIPCO) 2025
URL: https://ieeexplore.ieee.org/document/11226584
BibTeX: Download - , , , , :
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
Current Theses & Projects
| Title | Type | Student | Period | Status |
|---|---|---|---|---|
| Known Operator Learning for Fault Localization in Electric Power Grids | MA thesis | Sagar Sikdar | Feb 2026 – Aug 2026 | running |
| Federated Learning for Local Fault Analysis in Power Systems | MA thesis | Lukas Bayer | running | |
| Deep Learning for Fault Localization in High-Voltage Power Grids | MA thesis | Muhammad Zain | running | |
| Parameter Efficient Finetuning of Universal Time Series Transformers for Energy Forecasting | MA thesis | Aliullah Aliullah | Mar 2026 – Aug 2026 | running |
| Conventional vs. Reinforcement Learning–Based Relays for Power System Protection | Project | Pushpak Mitra | Jan 2026 – Jun 2026 | running |
| Reproducible Reinforcement Learning on a Real-World Power Grid Control Problem | Project | Alexander Luce | Mar 2026 – Jun 2026 | running |