Newman, Jennifer | Search NASA

DOE OSTI2018

L-TERRA [SWR-15-34]

Lidar (light detection and ranging) instruments are often used in wind energy studies in place of meteorological towers, as they can collect measurements at higher heights than met towers and are more easily moved to different parts of a site. While lidars can accurately measure mean wind speeds, they frequently measure different values of turbulence intensity (TI) than would be measured by a cup anemometer on a tower. Discrepancies in TI values are caused by several factors, including instrument noise and volume averaging. The inability of lidars to accurately measure turbulence is a significant barrier to replacing met towers with lidars in the future. There is currently no accepted standard process for quantifying and removing these errors in lidar TI measurements. The Software is an error reduction algorithm for lidar-measured turbulence. The algorithm is trained using co-located lidar and meteorological tower data. Lidar-measured parameters, such as wind speed, shear, and TI, will be used to predict the difference between the lidar- and tower-measured TI. The algorithm will be disseminated as a set of computer code that can be used by lidar users and manufacturers to correct lidar TI data in real time or in post-processing.

Newman, Jennifer↗

DOE OSTI2017

Development of a Dynamic Lidar Uncertainty Framework

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote-sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote-sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote-sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards for quantifying remote sensing device uncertainty for power performance testing consider uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device and are generally fixed, leading to climatic uncertainty values that apply to the entire measurement campaign. However, real-world experience and a consideration of the fundamentals of the measurement process have shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we describe the development of a new dynamic lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from a field measurement site to assess the ability of the framework to predict errors in lidar-measured wind speed. The results show how uncertainty varies over time and can be used to help select data with different levels of uncertainty for different applications, for example, low uncertainty data for power performance testing versus all data for plant performance monitoring.

17 WIND ENERGY↗