With the rapid growth of the wind power industry, many commercial utility-scale wind turbines have been built across the country. These extremely large man-made structures are reported to have negative impact on nearby radars due to their complex scattering mechanisms. Various forms of clutter effect caused by wind turbines in the radar vicinity are generally referred to as the Wind Turbine Clutter (WTC).
Due to the lack of awareness on this newly recognized clutter, many wind farms have been built in the Line of Sight (LOS) coverage of operational radars, potentially affecting their performance.
Weather radar is the one affected most by WTC because the target of interest is precipitation particles, which is spatially inseparable from the wind turbine within the clutter contaminated resolution volume. Our study thus focuses on analyzing the cause of different types of clutter effects by wind turbines, characterizing the radar signatures of such clutter and mitigating the clutter effect for weather radar.
The Micro-Doppler signature of the WTC reveals interesting time-variant spectrum features which are closely related to the instantaneous motions of the wind turbine. The complex motions of a wind turbine can be mostly characterized by three rotations: roll, pitch and yaw. Electromagnetic (EM) characterization of such a dynamic electrical large target is challenging. Various scattering mechanisms are analyzed and the back scattered field and RCS of the wind turbine are computed using commercial EM solver and a hybrid high-frequency approximation approach developed from our study.
Field measurements were carried out by deploying the mobile radar to wind farms. The measurements give us the first non-aliased Doppler spectrum of wind turbines. In order to synchronize the wind turbine motion with radar data acquisition, the Radar Wind Turbine Testbed (RWT2) was developed for indoor scaled measurements, which includes the scaled wind turbine model and the scatterometer. Both frequency and time domain measurements were made to characterize the statistics of return signal from the wind turbine model.
Several mitigation schemes developed from our study will be discussed, including the telemetry based method, the Adaptive Spectrum Processing (ASP) and the mitigation scheme for moment data based on the Maximum A Posterior (MAP) criteria. A thorough analysis of utilizing LOS avoidance to prevent WTC at the first place will be presented at the end
