LiSBOA: Enhancing LiDAR-Based Wind Turbine Wake and Turbulence Characterization in Complex Terrain

The Light Detection and Ranging (LiDAR) data analysis method has emerged as a powerful and versatile tool for characterizing atmospheric conditions and modeling light propagation through various media. In the context of renewable energy, particularly wind energy, LiDAR is increasingly utilized to analyze wind flow, turbine wake effects, and turbulence in complex terrains. This study focuses on advancing LiDAR data interpretation through the development and application of the LiDAR Statistical Barnes Objective Analysis (LiSBOA) method. LiSBOA enhances the capacity of scanning LiDAR systems by enabling more precise optimization of scan configurations and improving the retrieval of wind statistics across Cartesian grids. Unlike conventional approaches, LiSBOA offers fine-grained control over azimuthal resolution and spatial filtering, which allows for the detailed reconstruction of wind fields and turbulence structures. These capabilities are crucial for accurately simulating wind turbine wakes and power capture, particularly in environments with variable atmospheric stability and complex topography. Field deployments and comparative assessments against traditional meteorological mast data demonstrate the effectiveness of LiSBOA. The method reduces wind velocity estimation errors to within 3% and increases the accuracy of turbulence intensity measurements by over 4%. Such improvements are significant for enhancing wind resource assessment, optimizing turbine placement, and refining control strategies for operational turbines. LiSBOA represents a robust advancement in LiDAR data processing for wind energy applications. By addressing limitations in spatial resolution and measurement uncertainty, it supports more reliable modeling of wake interactions and flow variability. This work contributes to improving the efficiency and reliability of wind energy systems through advanced remote sensing and statistical analysis techniques.