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Title: An Error-Reduction Algorithm to Improve Lidar Turbulence Estimates for Wind Energy

Currently, cup anemometers on meteorological (met) towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability. However, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install met towers at potential sites. As a result, remote sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. While lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount of uncertainty surrounding the measurement of turbulence with lidars. This uncertainty in lidar turbulence measurements is one of the key roadblocks that must be overcome in order to replace met towers with lidars for wind energy applications. In this talk, a model for reducing errors in lidar turbulence estimates is presented. Techniques for reducing errors from instrument noise, volume averaging, and variance contamination are combined in the model to produce a corrected value of the turbulence intensity (TI), a commonly used parameter in wind energy. In the next step of the model, machine learning techniques are used to furthermore » decrease the error in lidar TI estimates.« less
Authors:
;
Publication Date:
OSTI Identifier:
1330445
Report Number(s):
NRL/PR-5000-67000
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 18th Meeting of the Power Curve Working Group, 10 August 2016, Boulder, Colorado
Research Org:
NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))
Sponsoring Org:
NREL's Laboratory Directed Research and Development Program
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY lidar; turbulence; wind energy; light detection and ranging; NREL