Universal and Relay-Generalizable Machine Learning for Protection in Power Grids

Type: MA thesis

Status: open

Supervisors: Julian Oelhaf, Siming Bayer, Andreas Maier

Universal and Relay-Generalizable Machine Learning for Protection in Power Grids

Motivation

Modern power system protection relies on local relay measurements obtained via current and voltage transformers at circuit breaker locations. Machine learning approaches have demonstrated strong performance for fault detection, classification, and localization tasks.

However, existing work typically trains and evaluates models on the same relay locations, implicitly assuming fixed spatial measurement points. In real-world deployment, protection algorithms must generalize across:

  • Different relay locations
  • Changing grid configurations
  • Varying short-circuit levels
  • Diverse operating conditions

A key open research question is therefore:

“Can machine learning models learn spatially invariant fault representations that generalize across relay locations while still allowing local adaptation?”

This thesis addresses this question through a structured two-layer learning approach.

Research Objectives

The thesis investigates the design of a universal, relay-generalizable protection model that:

  1. Learns generic fault representations independent of spatial location
  2. Quantifies cross-relay generalization behavior
  3. Enables efficient fine-tuning for specific installation points
  4. Research Questions

The work will address the following scientific questions:

  1. How large is the cross-relay generalization gap in fault detection and localization tasks?
  2. Which relay pairs exhibit “easy” vs. “hard” transfer and why?
  3. Can domain generalization strategies improve spatial invariance?
  4. Does universal pretraining reduce the amount of local data required for adaptation?

Methodology

Step 0 – Baseline: Cross-Relay Generalization Analysis

  • Train a model on measurements from a single relay.
  • Evaluate on all other relays using a leave-one-relay-out protocol.
  • Construct a full transfer matrix across all relay pairs.
  • Analyze:
    • Same-line vs. cross-line transfer
    • Influence of topology and infeed
    • Spatial symmetry effects

This establishes the relay generalization gap.

Step 1 – Universal Pretraining

  • Train a neural network using domain-randomized grid simulations.
  • Learn grid-invariant fault representations.
  • Incorporate robustness against:

Step 2 – Location-Specific Fine-Tuning

  • Adapt the pretrained model to a specific relay.
  • Extend from fault detection to fault localization.

Expected Contributions

The thesis is expected to provide:

  • A formal evaluation framework for relay generalization
  • Quantitative analysis of spatial transfer behavior
  • Identification of invariance-inducing training strategies
  • Practical recommendations for ML-based protection deployment

Required and Recommended Background

Required Knowledge

  • Solid Python programming skills
  • Basic supervised learning and evaluation metrics

Strongly Recommended Lectures

  • Pattern Recognition
  • Pattern Analysis
  • Introduction to Machine Learning
  • Deep Learning
  • Machine Learning for Time Series
  • Advanced Deep Learning
  • Machine Learning for Signal Processing

Students with background in either machine learning or power systems are encouraged to apply.

Project Type

Master Thesis (research-oriented)
Start date: flexible (by arrangement)

Possibility to start as a project and later extend to a full Master thesis.

Application

Send one PDF to: julian.oelhaf@fau.de

Subject:
Application | Master Thesis | Relay-Generalizable Protection |

Email body (max. 200 words):
Short motivation and your earliest start date.

Attach as one PDF:

  • CV
  • Transcript of records (dated)
  • Optional: links to code projects (GitHub, portfolio, etc.)

📌 Incomplete applications will not be considered.