Changhun Kim

Education

• M.S. in Artificial Intelligence, Friedrich-Alexander University Erlangen-Nürnberg, Germany (Sep 2022 – Mar 2025)
  • Thesis: RetNetHTR – Leveraging Retentive Networks for Efficient and Accurate Handwritten Text Recognition
  • Teaching Assistant: Advanced Deep Learning, Introduction to Machine Learning
  • Research Assistant, Medical Imaging AI Lab
• B.S. in Computer Science, University of Seoul, South Korea (Mar 2014 – Feb 2021)
  • Undergraduate Research Assistant, Environmental System Toxicology Lab
  • Two-year absence due to mandatory military service

Experience

• Research Assistant, AI Medical Imaging Lab, FAU Erlangen-Nürnberg
  • Project: Self-supervised denoising of fluorescence microscopy images
• Researcher, Urban Big Data and AI Institute, University of Seoul (Jan 2021 – Sep 2022)
  • Project: Graph Neural Networks for molecular toxicity prediction
• AI Educational Content Technical Quality Assurance , MODULABS, South Korea (May 2021 – Oct 2021)
• Research Intern, Environmental System Toxicology Lab, University of Seoul (Sep 2018 – Dec 2019)
  • Project: Machine learning for imbalanced toxicity data prediction

2024

• GridAssist - Assistenzsysteme für eine optimierte automatisierte Systemführung in Verteilnetzen

  (Third Party Funds Single)

  Term: December 1, 2024 - November 30, 2027
  Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
  Abstract

  TP3: Automated Optimal System Management in MS/HS Networks & Network Restoration

  AP 3.1: Automated Bottleneck and Fault Management Based on Machine Learning Methods

  AP 3.2: Stable Grid and Supply Restoration

  
  

  TP5: Competence Team for Interfaces and Databases

  AP 5.1: Creation of a Knowledge Database for Heterogeneous Power Grid Data Sources

  Automated Grid Management and Restoration through AI and Data Integration

  The increasing complexity and volatility of modern power grids necessitate intelligent, automated systems for optimal operation and rapid restoration. In TP3, we develop AI-driven solutions for automated bottleneck and fault management (AP 3.1) as well as stable and systematic grid restoration (AP 3.2) in medium- and high-voltage (MS/HS) networks. These systems leverage machine learning techniques—ranging from supervised and unsupervised learning to reinforcement learning—with a hybrid training approach incorporating domain knowledge via Physics-Informed Neural Networks (PINNs) and Known-Operator Learning. This ensures reliable, explainable decision-making in real-time operations and minimizes data requirements in safety-critical environments.

  In bottleneck management, predictive and event-triggered AI models autonomously suggest topology adjustments and flexibility deployment, factoring in a wide range of grid constraints and operational targets. Fault management employs multi-source data analysis for real-time fault detection, location, isolation, and resupply, utilizing both existing sensors and proposed novel measurements. For restoration, a dedicated assistance system is developed using a two-stage training process: supervised learning of standardized grid restoration procedures followed by reinforcement learning in a high-fidelity simulation environment. A single-agent model competes across simulation instances to evolve optimized strategies for power system recovery.

  To support these applications, TP5 (AP 5.1) establishes a unified graph-based knowledge database for integrating heterogeneous data sources—from GIS, SCADA, and metering systems—into a cohesive representation. This structure enables complex system queries and machine-readable context for AI models, forming the backbone of advanced operational analytics.

  The entire system architecture and algorithms are tested in AP 6.3 through a hybrid field test utilizing real-time simulation with OPAL-RT and live grid snapshots from Lechwerke’s control center. Assistance systems interface with the simulation via standard protocols, ensuring vendor-independent implementation and practical transferability. Experienced grid operators validate the system in realistic operational conditions, ensuring alignment with the requirements of future grid operations and energy transition goals.

  →More information

Research Project: GridAssist – AI-based techniques for automated congestion control and fault mangement in power grids, while also buidling a robust knowledge database that integrates and harmonizes heterogeneous power grid data from multiple sources.

Research Areas: Congestion and Fault mangement in Electrical Grid, Graph Neural Networks,  Time Series Data Analysis, and Reinforcement Learning for Decision-Making in Power Systems.

No publications found.