Julian Oelhaf

Julian Oelhaf, M. Sc.

Researcher

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room: Room 09.157
Martensstr. 3
91058 Erlangen

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

  • 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

  • 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)

2026

Unpublished Publications

2025

Journal Articles

Conference Contributions

Type Title Status
Project Evaluating Time-Frequency Representations for Intelligent Fault Analysis in Power System Protection running
Project Multi-Task Learning for Integrated Fault Analysis in Power System Protection running
MA thesis Reinforcement Learning for Adaptive Protection in Power Grids running
Project Reproducible Reinforcement Learning on a Real-World Power Grid Control Problem open
Project Reinforcement Learning for Centralized Fault Coordination in Power Systems finished
Project Transformer-Based Forecasting Model for Fault Detection in Power System Protection finished
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