Graduation Term
Fall 2024
Degree Name
Master of Science (MS)
Department
Department of Mathematics
Committee Chair
Mehdi Karimi
Committee Member
Gaywalee Yamskulna
Committee Member
Papa Sissokho
Abstract
Rapid advancements in data-driven deep learning models have led to their increased usage in recent years. Highly-effective models for conducting complex decision-making tasks have been developed. Given their success, data-driven deep learning models are being developed and improved in a variety of areas, one of which is renewable energy. In this thesis, we discuss the development of data-driven, deep learning models for optimizing wind farm layouts for maximum power output. We include a background for wind energy, the physics of fluid flow and power production, and two types of neural networks: graph neural networks and physics-informed neural networks. Additionally, we discuss the construction and training of neural networks. Ultimately, we provide a physics-informed graph neural network capable of performing wind farm layout optimization for maximum power production of a wind farm under varying conditions.
Access Type
Thesis-Open Access
Recommended Citation
Martin, Emma R., "Applications of Physics-Informed Graph Neural Networks in Wind Farm Layout Optimization" (2024). Theses and Dissertations. 2025.
https://ir.library.illinoisstate.edu/etd/2025