Data-driven cooperative control for wind farm power maximization
Abstract/Contents
- Abstract
- Among the various renewable energy sources, wind power has proven effective for large-scale energy production. To increase wind power production, it is essential not only to increase the number of wind farms but also to operate them efficiently. Conventionally, for a given wind condition, each individual wind turbine maximizes its own power production without taking into consideration the conditions of other wind turbines. Under this greedy control strategy, the wake formed by the upstream wind turbine, resulting in reduced wind speed and increased turbulence intensity inside the wake, would affect and lower the power production of downstream wind turbines. This thesis investigates a cooperative wind farm control approach to optimally coordinate the control actions (i.e., the operational conditions) of the wind turbines. The optimally coordinated control actions minimize the wake interference among the wind turbines and would therefore increase the total wind farm power production. To determine the optimum coordinated control actions, two methods are discussed in this thesis. First, the optimum coordinated control actions for wind turbines are determined using an analytical approach by employing mathematical optimization. In this approach, the total wind farm power is expressed as a function of the control actions of all the wind turbines. The wind farm power function is then maximized using sequential quadratic programming to determine the optimum coordinated control actions for the wind turbines. The effectiveness of the cooperative control strategy is studied using an example wind farm site and available wind data. For the second approach, the optimum coordinated control actions of the wind turbines are derived using the input (control actions of wind turbines) and output (wind farm power) data of a target wind farm. For real-time, data-driven wind farm control, an optimization algorithm should be able to improve target wind farm power production by executing as few trial actions as possible using the wind farm power monitoring data. To achieve this goal, a Bayesian Ascent (BA) algorithm is developed by incorporating into the Bayesian Optimization framework a trust region strategy that regulates the search domain. Numerical simulations using the wind farm power function show that the BA algorithm can be as effective as the analytical approach. Wind tunnel experiments with scaled wind turbines are conducted to further demonstrate the effectiveness of the data-driven BA algorithm for real-time control. Experimental results show that the BA algorithm can achieve a monotonic increase in the total wind farm power production using a small number of trial actions and demonstrate the potential of the BA algorithm for the real-time wind farm control problem.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Park, Jinkyoo |
|---|---|
| Associated with | Stanford University, Department of Civil and Environmental Engineering. |
| Primary advisor | Law, K. H. (Kincho H.) |
| Thesis advisor | Law, K. H. (Kincho H.) |
| Thesis advisor | Baker, Jack W |
| Thesis advisor | Murray, Walter |
| Advisor | Baker, Jack W |
| Advisor | Murray, Walter |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jinkyoo Park. |
|---|---|
| Note | Submitted to the Department of Civil and Environmental Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Jinkyoo Park
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...