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

Newman, Jennifer; Clifton, Andrew

doi:10.5194/wes-2-77-2017

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

Conference · Mon Oct 24 00:00:00 EDT 2016

DOI:https://doi.org/10.5194/wes-2-77-2017· OSTI ID:1330445

Newman, Jennifer;

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 further decrease the error in lidar TI estimates.

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

Sponsoring Organization:: NREL's Laboratory Directed Research and Development Program

DOE Contract Number:: DE-AC36-08GO28308

OSTI ID:: 1330445

Report Number(s):: NREL/PR-5000-67000; MainId:24726; UUID:fd069dae-3c66-e611-a722-d89d67132a6d; MainAdminID:13200

Resource Relation:: Journal Volume: 2; Journal Issue: 1; Conference: Presented at the 18th Meeting of the Power Curve Working Group, 10 August 2016, Boulder, Colorado

Country of Publication:: United States

Language:: English

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Comparison of methods to derive radial wind speed from a continuous-wave coherent lidar Doppler spectrum Held, Dominique P.; Mann, Jakob Atmospheric Measurement Techniques, Vol. 11, Issue 11 https://doi.org/10.5194/amt-11-6339-2018	journal	January 2018
Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity: Floating lidar as an advanced offshore wind speed measurement technique Gottschall, Julia; Gribben, Brian; Stein, Detlef Wiley Interdisciplinary Reviews: Energy and Environment, Vol. 6, Issue 5 https://doi.org/10.1002/wene.250	journal	May 2017
Using a Virtual Lidar Approach to Assess the Accuracy of the Volumetric Reconstruction of a Wind Turbine Wake Fuertes, Fernando; Porté-Agel, Fernando Remote Sensing, Vol. 10, Issue 5 https://doi.org/10.3390/rs10050721	journal	May 2018
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Title: An Error-Reduction Algorithm to Improve Lidar Turbulence Estimates for Wind Energy

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