Complex terrain wind shear analysis

Programme overview

In the preliminary assessment of wind resources, surveys, and refined site selection, the placement of lidar and power supply are issues that need to be carefully considered. The location should avoid deep valleys, cliffs and other highly turbulent terrain. Since there is usually no mains power supply during the measurement, solar cells, small wind turbines or fuel power sources are required for power supply.
According to weather conditions such as heavy fog, the integrated wind speed deep learning prediction software package of the lidar can be expanded. The lidar can continuously optimize the wind speed prediction through the method of machine learning according to the actual measured wind speed, the terrain conditions, seasons and other conditions. When the lidar's detection range is limited due to such weather as heavy fog, the forecast data output can be adopted to ensure the data acquisition rate within the entire measurement range.


Features of the scheme

In the preliminary assessment of wind resources, surveys, and refined site selection, the placement of lidar and power supply are issues that need to be carefully considered. The location should avoid deep valleys, cliffs and other highly turbulent terrain. Since there is usually no mains power supply during the measurement, solar cells, small wind turbines or fuel power sources are required for power supply.
According to weather conditions such as heavy fog, the integrated wind speed deep learning prediction software package of the lidar can be expanded. The lidar can continuously optimize the wind speed prediction through the method of machine learning according to the actual measured wind speed, the terrain conditions, seasons and other conditions. When the lidar’s detection range is limited due to such weather as heavy fog, the forecast data output can be adopted to ensure the data acquisition rate within the entire measurement range.

Application cases