A Novel Wind Turbine Clutter Detection Algorithm for Weather Radar Data

Wind turbine radar echoes exhibit significant scattering power and Doppler spectrum broadening effects, which can interfere with the detection of meteorological targets and subsequently impact weather prediction and disaster warning decisions. In operational weather radar applications, the influence of wind farm on radar observations must be fully considered by meteorological departments and related institutions. In this paper, a Wind Turbine Clutter Classification Algorithm based on Random Forest (WTCDA-RF) classification is proposed. The level-II radar data is processed in blocks, and the spatial position invariance of wind farm clutter is leveraged for feature extraction. Samples are labeled based on position information, and valid samples are screened and saved to construct a vector sample set of wind farm clutter. Through training and optimization, the proposed WTCDA-RF model achieves an ACC of 90.92%, a PRE of 89.37%, a POD of 92.89%, and an F1-score of 91.10%, with a CSI of 83.65% and a FAR of only 10.63%. This not only enhances the accuracy of weather forecasts and ensures the reliability of radar data but also provides operational conditions for subsequent clutter removal, improves disaster warning capabilities, and ensures timely and accurate warning information under extreme weather conditions.