How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

Patrick Murphy,Paul Fleming,Julie K Lundquist

doi:10.5194/wes-5-1169-2020

Abstract

Abstract. Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, also known as shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), βbulk (a measure of bulk rotor-disk-layer veer), βtotal (a measure of total rotor-disk-layer veer), and rotor-equivalent wind speed (REWS; a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWSθ, although statistically significant differences in power production do not occur between REWS and REWSθ at our site. When REWS differs from the hub-height wind speed (as measured by either the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW or up to 5 % of the rated power for a single 1.5 MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

Highlights

Wind energy is already the second-largest source of renewable energy in the United States and is the fastest-growing source of renewable energy, providing 6.3 % of the total energy in the United States (EIA, 2017)
Wind farm operators and control engineers rely on wind turbine power curves to predict the power production of a given model of turbine for various inflow wind speeds (Brower, 2012)
REWSN-nacelle transfer function (NTF) (Fig. 8a) exhibits a wide distribution, REWSL (Fig. 8b) is centered more tightly around zero, likely because the rotor-equivalent wind speed (REWS) is calculated from lidar values and some variation in the wind occurs between the lidar and the nacelle

Summary

Introduction

Wind energy is already the second-largest source of renewable energy in the United States and is the fastest-growing source of renewable energy, providing 6.3 % of the total energy in the United States (EIA, 2017). As wind energy continues to grow, so will the challenge of predicting power output and integrating that power with the rest of the electric grid (Marquis et al, 2011; Woodford, 2011; Xie et al, 2011; Vittal and Ayyanar, 2013; Heier, 2014; Heydarian-Forushani et al, 2014; Sarrias-Mena et al, 2014). Wind farm operators and control engineers rely on wind turbine power curves to predict the power production of a given model of turbine for various inflow wind speeds (Brower, 2012). The inflow wind speeds are typically measured by instrumentation on top of the nacelle at or near hub height, where the blades of a turbine connect to its hub. Wind turbines are designed to optimize these inflow wind speeds by orienting themselves into the inflow.

Methods

Results

Conclusion