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)

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 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