Title: An error reduction algorithm to improve lidar turbulence estimates for wind energy

Abstract

Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors frommore » instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.« less

Authors:

Newman, Jennifer F.; Clifton, Andrew

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• Search DOE PAGES for ORCID "0000-0001-9698-5083"

Publication Date:

Fri Feb 10 00:00:00 EST 2017

Research Org.:

National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Org.:

USDOE Office of Energy Efficiency and Renewable Energy (EERE)

OSTI Identifier:

1343361

Alternate Identifier(s):

OSTI ID: 1324227

Report Number(s):

NREL/JA-5000-66374
Journal ID: ISSN 2366-7451

Grant/Contract Number:  

LDRD Contract No. DE-AC36-08GO28308; AC36-08GO28308

Resource Type:

Published Article

Journal Name:

Wind Energy Science (Online)

Additional Journal Information:

Journal Name: Wind Energy Science (Online) Journal Volume: 2 Journal Issue: 1; Journal ID: ISSN 2366-7451

Publisher:

European Wind Energy Association - Copernicus

Country of Publication:

Germany

Language:

English

Subject:

17 WIND ENERGY; lidar; turbulence; power prediction; machine learning