Next Article in Journal
Review of Reactive Power Dispatch Strategies for Loss Minimization in a DFIG-based Wind Farm
Previous Article in Journal
Experimental Investigation on CO2 Methanation Process for Solar Energy Storage Compared to CO2-Based Methanol Synthesis
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Energies 2017, 10(7), 852; doi:10.3390/en10070852

A Data-Driven, Cooperative Approach for Wind Farm Control: A Wind Tunnel Experimentation

1
Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
2
Department of Civil Engineering, Chonbuk National University, Jeonju 5896, Korea
3
Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
*
Author to whom correspondence should be addressed.
Received: 23 March 2017 / Revised: 12 June 2017 / Accepted: 16 June 2017 / Published: 27 June 2017
(This article belongs to the Section Electrical Power and Energy System)
View Full-Text   |   Download PDF [16605 KB, uploaded 27 June 2017]   |  

Abstract

This paper discusses a data-driven, cooperative control strategy to maximize wind farm power production. Conventionally, every wind turbine in a wind farm is operated to maximize its own power production without taking into account the interactions between the wind turbines in a wind farm. Because of wake interference, such greedy control strategy can significantly lower the power production of the downstream wind turbines and, thus, reduce the overall wind farm power production. As an alternative to the greedy control strategy, we study a cooperative wind farm control strategy that determines and executes the optimum coordinated control actions for maximizing the total wind farm power production. To determine the optimum coordinated control actions of the wind turbines, we employ a data-driven optimization method that seeks to find the optimum control actions using only the power measurement data collected from the wind turbines in a wind farm. In particular, we employ the Bayesian Ascent (BA) algorithm, a probabilistic optimization method constructed based on Gaussian Process regression and the trust region concept. Wind tunnel experiments using 6 scaled wind turbine models are conducted to assess (1) the effectiveness of the cooperative control strategy in improving the power production; and (2) the efficiency of the BA algorithm in determining the optimum control actions of the wind turbines using only the input control actions and the output power measurement data. View Full-Text
Keywords: wind farm control; Bayesian Ascent algorithm; wind tunnel experiment; data-driven optimization wind farm control; Bayesian Ascent algorithm; wind tunnel experiment; data-driven optimization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Park, J.; Kwon, S.-D.; Law, K. A Data-Driven, Cooperative Approach for Wind Farm Control: A Wind Tunnel Experimentation. Energies 2017, 10, 852.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top