Kateryna Morozovska

Kateryna Morozovska is a Researcher at the KTH Royal Institute of Technology, affiliated with the Division of Decision and Control Systems and the School of Electrical Engineering and Computer Science. Her work bridges computational methods and energy systems development, focusing on physics-informed machine learning for renewable energy integration and power transformer optimization.

Education

• B.S. and M.S. in Electrical Mechanics from Zaporizhzhya National Technical University (2013)
• European Energy Masters program with mobility at DTU (Denmark), TU Delft (Netherlands), and NTNU (Norway)
• PhD in Electrical Engineering from KTH (2020) on 'Dynamic rating for applications in renewable energy'
• Licentiate from KTH (2019)

Research Focus
Kateryna's research emphasizes Physics-Informed Neural Networks (PINNs) for power system optimization, including transformer thermal modeling, cellulose degradation analysis, and renewable energy integration. Her projects explore dynamic rating techniques to enhance grid efficiency and sustainability, funded by Vinnova and applied in PV-power plants and wind farms.

Scientific Contributions
She has developed frameworks for transformer cost analysis, wind farm sizing, and sensor placement optimization using PINNs and MILP. Her work addresses environmental trade-offs in wind energy, such as raw material mining impacts, and investigates thermodynamic challenges in nanocellulose and power systems.

Affiliations
Kateryna collaborates with industry partners through the PINN Summer School and contributes to SweGRIDS and Mendeley communities. Her teaching includes hands-on PINN training and multi-GPU machine learning.