Field Measurement of Wind Speed Using LIDAR in a Coastal Area

• Main Authors: Chien-ChengHuang, 黃健成
• Other Authors: Yu-Ting Wu
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/d2m2a5

碩士 === 國立成功大學 === 工程科學系 === 105 === Because of increasing demand from wind energy applications, analysis of wind data obtained from field measurements has become very important. The study focuses on the analysis of wind speed data obtained from a remote sensing device, a Leosphere Windcube LIDAR (light detection and ranging) device, and compares the data measured by the propeller and sonic anemometers on a meteorological mast. We carried out field measurements where a LIDAR device installed next to the mast was deployed in the Changhua Coastal Industrial Park onshore wind farm in January of 2015 and in July of 2016, respectively. The setup of the LIDAR included a total of 12 measurement heights ranging from 40m to 140m above the ground to collect wind speed data at those levels. A LIDAR device is capable of sensing the moving velocity of aerosols by faithfully following the dynamics of flows in the atmosphere. This allows the measurement of speed and flow direction through the tracking of particle motion. The LIDAR device measures only the velocity magnitude along laser beams (also called light-of-sight velocity; LOS velocity) with the application of the Doppler effect of light in space. It cannot directly measure the velocity components of local wind speeds. The velocity components must be converted from the LOS velocity components with a conversion technique. In this study, we propose two methods for the flow conversion and compare the converted wind speed data with the data measured from anemometers installed on the meteorological mast. The proposed conversion methods are used to calculate the 10-min mean wind speed, 10-min mean wind direction and 10-min turbulence intensity at heights of 40m, 50m, and 70m. The results provide a comparison of the 10-min mean wind speed, 10-min wind direction, and 10-min turbulence intensity obtained from LIDAR at all heights with the mean characteristics obtained from anemometers installed on the meteorological mast. Then, based on the normal turbulence model, it is possible to obtain the suitable turbine class for a local wind condition. An energy spectrum analysis also enabled observations as to whether the inertial subrange exists and also provoked a discussion of the comparison between the two methods in spectrum plots at a height of 70 m.