Lidar assisted wake redirection in wind farms: A data driven approach

Harsh S. Dhiman; Dipankar Deb; Aoife M. Foley

doi:10.1016/j.renene.2020.01.027

Lidar assisted wake redirection in wind farms: A data driven approach

Harsh S. Dhiman, Dipankar Deb^*, Aoife M. Foley

^*Corresponding author for this work

School of Mechanical and Aerospace Engineering

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

26 Downloads (Pure)

Abstract

Lidar based wind measurement is an integral part of wind farm control. The major issues and challenges in power maximization include the potential losses due to wake effect observed among wind turbines. This manuscript presents a wake management technique that utilizes lidar simulations for wake redirection.
The proposed methodology is validated for 2-turbine and 15-turbine wind farm layouts involving a PI control based yaw angle correction. Yaw angle misalignment using wake center tracking of the upstream turbines is used to increase the power generation levels. Results of wake center estimation are compared with a Kalman filter based method. Further, the velocity deficit and overall farm power improvement by yaw angle correction is calculated. Results reveal a 1.7% and 0.675% increase in total wind farm power for two different wind speed cases.

Original language	English
Pages (from-to)	484-493
Journal	Renewable Energy
Volume	152
Early online date	13 Jan 2020
DOIs	https://doi.org/10.1016/j.renene.2020.01.027
Publication status	Published - 01 Jun 2020

Keywords

Center of Wake
Lidar
Transfer function
Velocity deficit
Wake effect
Yaw angle

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment

Access to Document

10.1016/j.renene.2020.01.027Licence: Unspecified

Lidar assisted wake redirection in wind farms: A data driven approach
Copyright 2020 Elsevier. This manuscript is distributed under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Accepted author manuscript, 2.33 MBLicence: CC BY-NC-ND

Fingerprint Dive into the research topics of 'Lidar assisted wake redirection in wind farms: A data driven approach'. Together they form a unique fingerprint.

Cite this

@article{ca3cb6bd17cb4490ab19f65f41ac069e,

title = "Lidar assisted wake redirection in wind farms: A data driven approach",

abstract = "Lidar based wind measurement is an integral part of wind farm control. The major issues and challenges in power maximization include the potential losses due to wake effect observed among wind turbines. This manuscript presents a wake management technique that utilizes lidar simulations for wake redirection.The proposed methodology is validated for 2-turbine and 15-turbine wind farm layouts involving a PI control based yaw angle correction. Yaw angle misalignment using wake center tracking of the upstream turbines is used to increase the power generation levels. Results of wake center estimation are compared with a Kalman filter based method. Further, the velocity deficit and overall farm power improvement by yaw angle correction is calculated. Results reveal a 1.7% and 0.675% increase in total wind farm power for two different wind speed cases.",

keywords = "Center of Wake, Lidar, Transfer function, Velocity deficit, Wake effect, Yaw angle",

author = "Dhiman, {Harsh S.} and Dipankar Deb and Foley, {Aoife M.}",

year = "2020",

month = jun,

day = "1",

doi = "10.1016/j.renene.2020.01.027",

language = "English",

volume = "152",

pages = "484--493",

journal = "Renewable Energy",

issn = "0960-1481",

publisher = "Elsevier BV",

}

TY - JOUR

T1 - Lidar assisted wake redirection in wind farms: A data driven approach

AU - Dhiman, Harsh S.

AU - Deb, Dipankar

AU - Foley, Aoife M.

PY - 2020/6/1

Y1 - 2020/6/1

N2 - Lidar based wind measurement is an integral part of wind farm control. The major issues and challenges in power maximization include the potential losses due to wake effect observed among wind turbines. This manuscript presents a wake management technique that utilizes lidar simulations for wake redirection.The proposed methodology is validated for 2-turbine and 15-turbine wind farm layouts involving a PI control based yaw angle correction. Yaw angle misalignment using wake center tracking of the upstream turbines is used to increase the power generation levels. Results of wake center estimation are compared with a Kalman filter based method. Further, the velocity deficit and overall farm power improvement by yaw angle correction is calculated. Results reveal a 1.7% and 0.675% increase in total wind farm power for two different wind speed cases.

AB - Lidar based wind measurement is an integral part of wind farm control. The major issues and challenges in power maximization include the potential losses due to wake effect observed among wind turbines. This manuscript presents a wake management technique that utilizes lidar simulations for wake redirection.The proposed methodology is validated for 2-turbine and 15-turbine wind farm layouts involving a PI control based yaw angle correction. Yaw angle misalignment using wake center tracking of the upstream turbines is used to increase the power generation levels. Results of wake center estimation are compared with a Kalman filter based method. Further, the velocity deficit and overall farm power improvement by yaw angle correction is calculated. Results reveal a 1.7% and 0.675% increase in total wind farm power for two different wind speed cases.

KW - Center of Wake

KW - Lidar

KW - Transfer function

KW - Velocity deficit

KW - Wake effect

KW - Yaw angle

U2 - 10.1016/j.renene.2020.01.027

DO - 10.1016/j.renene.2020.01.027

M3 - Article

AN - SCOPUS:85078262522

VL - 152

SP - 484

EP - 493

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -