Keywords

Cooperative control; optimal planning; wind turbines; wind farms; wake interaction; optimization; stability; simulation; scalability

Abstract

Wind energy is on an upswing due to climate concerns and increasing energy demands on conventional sources. Wind energy is attractive and has the potential to dramatically reduce the dependency on non-renewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. Wake interferences among wind turbines can lower the overall efficiency considerably, while offshore conditions pose increased loading on wind turbines. In wind farms, wind turbines* wake affects each other depending on their positions and operation modes. Therefore it becomes essential to optimize the wind farm power production as a whole than to just focus on individual wind turbines. The work presented here develops a hierarchical power optimization algorithm for wind farms. The algorithm includes a cooperative level (or higher level) and an individual level (or lower level) for power coordination and planning in a wind farm. The higher level scheme formulates and solves a quadratic constrained programming problem to allocate power to wind turbines in the farm while considering the aerodynamic effect of the wake interaction among the turbines and the power generation capabilities of the wind turbines. In the lower level, optimization algorithm is based on a leader-follower structure driven by the local pursuit strategy. The local pursuit strategy connects the cooperative level power allocation and the individual level power generation in a leader-follower arrangement. The leader, could be a virtual entity and dictates the overall objective, while the followers are real wind turbines considering realistic constraints, such as tower deflection limits. A nonlinear wind turbine dynamics model is adopted for the low level study with loading and other constraints considered in the optimization. The stability of the algorithm in the low level is analyzed for the wind turbine angular velocity. Simulations are used to show the advantages of the method such as the ability to handle non-square input matrix, non-homogenous dynamics, and scalability in computational cost with rise in the number of wind turbines in the wind farm.

Notes

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Graduation Date

2015

Semester

Summer

Advisor

Xu, Yunjun

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Degree Program

Mechanical Engineering

Format

application/pdf

Identifier

CFE0005899

URL

http://purl.fcla.edu/fcla/etd/CFE0005899

Language

English

Release Date

August 2020

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Campus-only Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic

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