Date of Award

Spring 1-1-2012

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Aerospace Engineering Sciences

First Advisor

Julie K. Lundquist

Second Advisor

Jeffrey P. Thayer

Third Advisor

Kenneth E. Jansen

Abstract

This study examines the influences of modern multi-megawatt wind turbine generator wakes on wind profiles. Principles of wind LIDAR technology and operation are discussed in preparation for analysis of the LIDAR dataset. Surface and wind LIDAR observations were collected from June 30, 2011 to August 16, 2011 in central Iowa. Two identical Windcube LIDAR systems were compared for two days at the beginning of the observation period and found to agree with good correlation in both wind speed and wind direction measurements at 20m vertical intervals from 40m to 220m above the surface. For the remainder of the field campaign, one LIDAR was located 2 rotor diameters (D) directly south of a wind turbine; the other LIDAR was moved 3 D North of the same wind turbine. Data from the two LIDAR dataset was filtered for the prevailing southerly flow in order to simultaneously capture inflow and waked conditions with the respective LIDAR. Data were compared between the upwind and downwind LIDARs for horizontal and vertical wind speed, wind shear, wind direction, wind directional shear, horizontal and vertical turbulence intensity, turbulent kinetic energy, and the power law coefficient (alpha). Results indicate measurable reductions in waked wind speeds at heights spanning the wind turbine rotor (40m to 120m). Turbulent and wind shear quantities increase in the wake of the turbine rotor. Results also indicate that the power law coefficient below turbine hub height may be a parameter that quickly identifies whether the downwind LIDAR was sampling turbine wake or free flow conditions. Changes in quantities downwind of the wind turbine are also shown to vary with inflow wind speed and time of day. Results are consistent with the few observations available from other studies; this dataset contributes higher temporal and spatial resolution data to provide a dataset which will be useful for turbine wake model validation.

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