Reduced-Order Modeling and Data-driven Techniques for Control of Grid-Connected Wind Farms

Abstract

This thesis focuses on improving the commercial viability of wind energy systems through modeling, control, and analysis. In the area of modeling, we propose computationally scalable mathematical models that are suitable for real-time control applications. These models are then utilized to systematically analyze the effects of high wind penetration on the grid. Additionally, recognizing the importance of wind energy in providing ancillary services, we propose a control platform that integrates forecasting tools with economic and aerodynamic models to maximize energy value streams. The research presented in this thesis has the potential to enhance the performance and profitability of wind energy systems, contributing to the growth and sustainability of renewable energy sources.