The space-time structure of turbulence for lidar-assisted wind turbine control

• Published in: Renewable energy 2022-08, Vol.195, p.293-310
• Main Authors: Guo, Feng, Mann, Jakob, Peña, Alfredo, Schlipf, David, Cheng, Po Wen
• Format: Article
• Language: English
• Subjects: Atmospheric stability; evolution; lidar; Lidar-assisted control; space and time; Turbulence evolution; Turbulence spectral model; turbulent flow; wind; Wind turbine; wind turbines
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2022.05.133

Lidar-assisted wind turbine control has been proven to be beneficial for turbine load reduction. It relies on the preview of incoming turbulence provided by a nacelle lidar, which allows the turbine controller to react to the flow disturbances before their impact on the turbine. When assessing its benefits, previous studies mainly use the standard turbulence parameters suggested by the IEC 61400-1 standard and assume Taylor's frozen hypothesis. Based on atmospheric turbulence observations, the parameters of the Mann spectral turbulence model differ significantly from the values recommended in the standard, and they vary, e.g., with atmospheric stability. Also, turbulence evolution from upstream to the rotor position conflicts with the frozen turbulence hypothesis, and its effect on the preview quality by a nacelle lidar still needs quantification. This work presents a simple method to extend the Mann model to account for the temporal evolution of turbulence using a space-time spectral velocity tensor. Under various atmospheric stability conditions, we derive the space-time tensor parameters and evaluate the space-time tensor using data from a five-beam lidar and a meteorological mast, we found good agreement between the space-time tensor and the measurement data. In addition, a numerical method for simulating a four-dimensional (4D) space-time velocity field based on the space-time tensor is presented. In the end, we analyze the importance of including the temporal evolution of turbulence for assessing lidar wind preview quality. Lidar-assisted wind turbine control has been proven to be beneficial for turbine load reduction. It relies on the preview of incoming turbulence provided by a nacelle lidar, which allows the turbine controller to react to the disturbances prior their impact on the turbine. This work presents a simple method to extend the Mann model to account for the temporal evolution of turbulence using a space-time spectral velocity tensor. Under various atmospheric stability conditions, we derive the space-time tensor parameters and evaluate the space-time tensor using data from a five-beam lidar and a meteorological mast, we found good agreement between the space-time tensor and the measurement data. We analyze the importance of including the temporal evolution of turbulence for assessing lidar wind preview quality. Figure 1: Typical nacelle lidar measurements over a wind field. The evolved turbulence (more transparent) illustrates the actual