Wind Turbine Clutter Mitigation via Nonconvex Regularizers and Multidimensional Processing

2019 ◽  
Vol 36 (6) ◽  
pp. 1093-1104
Author(s):  
Yinan Hu ◽  
Faruk Uysal ◽  
Ivan Selesnick
Keyword(s):  
Wind Turbine ◽  
Weather Radar ◽  
Spectral Width ◽  
Previous Method ◽  
Weather Data ◽  
Doppler Velocity ◽  
Radar Systems ◽  
Previous Formulation ◽  
Clutter Mitigation

AbstractThis paper generalizes a previous formulation of signal separation problem for dynamic wind turbine clutter mitigation at weather radar systems. In this modified formulation, we use nonconvex regularizers together with multichannel overlapping group shrinkage (MOGS) to penalize weather signals and adopt multidimensional processing. We show the restored weather signals in plan position indicator (PPI) format and, to demonstrate the improvement, compare them with the ones produced by the previous method in reflectivity, spectral width, and Doppler velocity estimates of weather data. The improvement results from a better characterization of the sparsities of the weather radar returns. During the course of experiments, we observe that the proposed method successfully mitigates the wind turbine clutter and dramatically increases the signal-to-clutter ratio, even for different weather and wind turbine signatures. In addition, when the wind turbine clutter is weak in the mixture, our algorithm manages to attenuate the ground clutters and produces clutter-free weather signals favorable for further processing.

scholarly journals Automatic Detection of Wind Turbine Clutter for Weather Radars

2010 ◽  
Vol 27 (11) ◽  
pp. 1868-1880 ◽  
Author(s):  
Kenta Hood ◽  
Sebastián Torres ◽  
Robert Palmer
Keyword(s):  
Wind Turbine ◽  
Weather Radar ◽  
Real Data ◽  
Radar Data ◽  
Detection Algorithm ◽  
Weather Data ◽  
Atmospheric Conditions ◽  
Fuzzy Logic Algorithm ◽  
Weather Radars ◽  
Anomalous Propagation

Abstract Wind turbines cause contamination of weather radar signals that is often detrimental and difficult to distinguish from cloud returns. Because the turbines are always at the same location, it would seem simple to identify where wind turbine clutter (WTC) contaminates the weather radar data. However, under certain atmospheric conditions, anomalous propagation of the radar beam can occur such that WTC corrupts weather data on constantly evolving locations, or WTC can be relatively weak such that contamination on predetermined locations does not occur. Because of the deficiency of using turbine locations as a proxy for WTC, an effective detection algorithm is proposed to perform automatic flagging of contaminated weather radar data, which can then be censored or filtered. Thus, harmful effects can be reduced that may propagate to automatic algorithms or may hamper the forecaster’s ability to issue timely warnings. In this work, temporal and spectral features related to WTC signatures are combined in a fuzzy logic algorithm to classify the radar return as being contaminated by WTC or not. The performance of the algorithm is quantified using simulations and the algorithm is applied to a real data case from the radar facility in Dodge City, Kansas (KDDC). The results illustrate that WTC contamination can be detected automatically, thereby improving the quality control of weather radar data.

scholarly journals Spectral width of SuperDARN echoes: measurement, use and physical interpretation

2006 ◽  
Vol 24 (1) ◽  
pp. 115-128 ◽  
Author(s):  
P. V. Ponomarenko ◽  
C. L. Waters
Keyword(s):  
Physical Interpretation ◽  
Spectral Width ◽  
Radar Data ◽  
Doppler Velocity ◽  
Coherent Scatter ◽  
Plasma Irregularities ◽  
Radar Systems ◽  
Radar Network ◽  
High Latitude Region ◽  
Latitude Region

Abstract. The Doppler velocity and spectral width are two important parameters derived from coherent scatter radar systems. The Super Dual Auroral Radar Network (SuperDARN) is capable of monitoring most of the high latitude region where different boundaries of the magnetosphere map to the ionosphere. In the past, the spectral width, calculated from SuperDARN data, has been used to identify the ionosphere footprints of various magnetosphere boundaries. In this paper we examine the way the spectral width is presently estimated from the radar data and describe several recommendations for improving the algorithm. Using the improved algorithm, we show that typical spectral width values reported in the literature are most probably overestimated. The physical interpretation of the cause of various magnitudes of the spectral width is explored in terms of the diffusion and dynamics of ionospheric plasma irregularities.

scholarly journals Wind turbine impact on operational weather radar I/Q data: characterisation and filtering

2017 ◽  
Vol 10 (5) ◽  
pp. 1739-1753 ◽  
Author(s):  
Lars Norin
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Negative Impact ◽  
Weather Radar ◽  
Rapid Expansion ◽  
Weather Radars ◽  
Radar Systems ◽  
Increasing Demand ◽  
Yaw Angle ◽  
The Impact

Abstract. For the past 2 decades wind turbines have been growing in number all over the world as a response to the increasing demand for renewable energy. However, the rapid expansion of wind turbines presents a problem for many radar systems, including weather radars. Wind turbines in the line of sight of a weather radar can have a negative impact on the radar's measurements. As weather radars are important instruments for meteorological offices, finding a way for wind turbines and weather radars to co-exist would be of great societal value.Doppler weather radars base their measurements on in-phase and quadrature phase (I/Q) data. In this work a month's worth of recordings of high-resolution I/Q data from an operational Swedish C-band weather radar are presented. The impact of point targets, such as masts and wind turbines, on the I/Q data is analysed and characterised. It is shown that the impact of point targets on single radar pulses, when normalised by amplitude, is manifested as a distinct and highly repeatable signature. The shape of this signature is found to be independent of the size, shape and yaw angle of the wind turbine. It is further demonstrated how the robustness of the point target signature can be used to identify and filter out the impact of wind turbines in the radar's signal processor.

scholarly journals Wind turbine impact on operational weather radar I/Q data: characterisation and filtering

2017 ◽  
Author(s):  
Lars Norin
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Negative Impact ◽  
Weather Radar ◽  
Rapid Expansion ◽  
Weather Radars ◽  
Radar Systems ◽  
Increasing Demand ◽  
Yaw Angle ◽  
The Impact

Abstract. For the past two decades wind turbines have been growing in number all over the world as a response to the increasing demand for renewable energy. However, the rapid expansion of wind turbines presents a problem for many radar systems, including weather radars. Wind turbines in line-of-sight of a weather radar can have a negative impact on the radar's measurements. As weather radars are important instruments for meteorological offices, finding a way for wind turbines and weather radars to co-exist would be of great societal value. Doppler weather radars base their measurements on in-phase and quadrature phase (I/Q) data. In this work a month worth of recordings of high resolution I/Q data from an operational Swedish C-band weather radar are presented. The impact of point targets, such as masts and wind turbines, on the I/Q data is analysed and characterised. It is shown that the impact of point targets on single radar pulses is manifested as a distinct and highly repeatable signature. The shape of this signature is found to be independent of the size, shape, and yaw angle of the wind turbine. It is further demonstrated how the robustness of the point target signature can be used to identify and filter out the impact of wind turbines in the radar's signal processor.

Characterization of turbulent wake of wind turbine by coherent Doppler lidar

2014 ◽  
Author(s):  
Songhua Wu ◽  
Jiaping Yin ◽  
Bingyi Liu ◽  
Jintao Liu ◽  
Rongzhong Li ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Turbulent Wake ◽  
Doppler Lidar ◽  
Coherent Doppler Lidar
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