Applying cooperative control techniques to improve performance of wind farms

With the rise of renewable energy sources as a viable means towards a zero-emissions planet, the exploration of maximizing the efficiency of these systems has become an active and necessary topic of research. One energy source that is rapidly increasing in power production across the globe is using wind power. Individual horizontal axis wind turbines (HAWTs) operate by using the aerodynamic force of wind to spin the rotor blades and generate power. Currently wind farm control methods for single turbines in practice use look-up tables for operational parameters that are based on offline optimization solutions. However, once multiple turbines are grouped together to form a wind farm, inter-turbine wake dynamics arise, effectively coupling turbine performances. Wind profiles at downstream turbines suffer from turbulence caused from upstream blades spinning. As such, for large wind farms (e.g. collections of several large turbines) both acknowledging that coupling occurs based on turbine operation and designing control frameworks to counteract detrimental turbulence effects is crucial in maximizing the power extraction of a farm. With that in mind, one novel insight guides the research presented herein: treating wind farms as a collective entity and noting the wind acts as a shared resource. Through that lens and acknowledging upstream effects as the dominant factor leading to diminished power extraction guides the construction of distributed control schemes. Cooperative methods, where upstream turbines intentionally lessen their own power extraction in order to minimize downstream turbulence effects, are one such method to handle this problem. This thesis covers control techniques over a variety of regimes, ranging from centralized control, to distributed H_\infty robust controllers, state-feedback controllers, and multi-objective optimization methodologies. Using a variety of wind turbine models and wake paradigms we have designed methods to improve overall performance of wind farms.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Industrial Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Buccafusca, Lucas

Contributors dc:contributor

• Beck, Carolyn L
• Srikant, Rayadurgam
• Stipanovic, Dusan
• Etesami, Rasoul

• Wind Energy
• Controls
• Optimization
• Renewable Energy

Statement dc:rights

• Copyright 2022 by Lucas Buccafusca

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115482

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115482