Mitigation of Wind Turbine Clutter for Weather Radar by Signal Separation

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.

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Estimating reflectivity values from wind turbines for analyzing the potential impact on weather radar services

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-1477-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 1477-1509

Author(s):

I. Angulo ◽

O. Grande ◽

D. Jenn ◽

D. Guerra ◽

D. de la Vega

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Weather Radar ◽

Radar Signal ◽

Weather Radars ◽

Potential Impact ◽

Processing Techniques ◽

Signal Processing Techniques ◽

Do So

Abstract. The World Meteorological Organization (WMO) has repeatedly expressed concern over the increasing number of impact cases of wind turbine farms on weather radars. Since nowadays signal processing techniques to mitigate Wind Turbine Clutter (WTC) are scarce, the most practical approach to this issue is the assessment of the potential interference from a wind farm before it is installed. To do so, and in order to obtain a WTC reflectivity model, it is crucial to estimate the Radar Cross Section (RCS) of the wind turbines to be built, which represents the power percentage of the radar signal that is backscattered to the radar receiver. This paper first characterizes the RCS of wind turbines in the weather radar frequency bands by means of computer simulations based on the Physical Optics theory, and then proposes a simplified model to estimate wind turbine RCS values. This model is of great help in the evaluation of the potential impact of a certain wind farm on the weather radar operation.

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Insights into wind turbine reflectivity and RCS and their variability using X-band weather radar observations

10.5194/amt-2020-384 ◽

2020 ◽

Author(s):

Martin Lainer ◽

Jordi Figueras i Ventura ◽

Zaira Schauwecker ◽

Marco Gabella ◽

Montserrat F.-Bolaños ◽

...

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Weather Radar ◽

Least Square ◽

Clear Correlation ◽

Radar Observations ◽

Ordinary Least Square ◽

The North ◽

X Band ◽

Polarimetric Weather Radar

Abstract. The increasing need of renewable energy fosters the expansion of wind turbine sites for power production throughout Europe with manifold effects, both on the positive and negative side. The latter concerns, among others, radar observations in the proximity of wind turbine (WT) sites. With the aim of better understanding the effects of large, moving scatterers like wind turbines on radar returns, MeteoSwiss performed two dedicated measurement campaigns with a mobile X-band Doppler polarimetric weather radar (METEOR 50DX) in the north-eastern part of Switzerland in March 2019 and March 2020. Based on the usage of a X-band radar system, the performed campaigns are up to now unique. The main goal was to quantify the effects of wind turbines on the observed radar moments, to retrieve the radar cross section (RCS) of the turbine themselves, and to investigate the conditions leading to the occurrence of the largest RCS. Dedicated scan strategies, consisting of PPI (Plan Position Indicator), RHI (Range-height Indicator) and fixed-pointing modes, were defined and used for observing a wind park consisting of three large wind turbines. During both campaigns, measurements were taken in 24/7 operation. The highest measured maxima of horizontal reflectivity (ZH) and RCS reached 78.5 dBZ respectively 44.1 dBsm. A wind turbine orientation (yawing) stratified statistical analysis shows no clear correlation with the received maximum returns. However, the median values and 99th percentiles of ZH and RCS show different enhancements for specific relative orientations. Further, we show, based on investigating correlations and an OLS (ordinary least square) model analyses, that the fast changing rotor blade angle (pitch) is a key parameter, which strongly contributes to the variability of the observed returns.

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Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations

Atmospheric Measurement Techniques ◽

10.5194/amt-14-3541-2021 ◽

2021 ◽

Vol 14 (5) ◽

pp. 3541-3560

Author(s):

Martin Lainer ◽

Jordi Figueras i Ventura ◽

Zaira Schauwecker ◽

Marco Gabella ◽

Montserrat F.-Bolaños ◽

...

Keyword(s):

Wind Turbine ◽

Cross Section ◽

Wind Turbines ◽

Weather Radar ◽

Radar Cross Section ◽

Least Square ◽

Clear Correlation ◽

Radar Observations ◽

X Band ◽

Polarimetric Weather Radar

Abstract. The increasing need of renewable energy fosters the expansion of wind turbine sites for power production throughout Europe with manifold effects, both on the positive and negative side. The latter concerns, among others, radar observations in the proximity of wind turbine (WT) sites. With the aim of better understanding the effects of large, moving scatterers like wind turbines on radar returns, MeteoSwiss performed two dedicated measurement campaigns with a mobile X-band Doppler polarimetric weather radar (METEOR 50DX) in the northeastern part of Switzerland in March 2019 and March 2020. Based on the usage of an X-band radar system, the performed campaigns are up to now unique. The main goal was to quantify the effects of wind turbines on the observed radar moments, to retrieve the radar cross-section (RCS) of the turbines themselves and to investigate the conditions leading to the occurrence of the largest RCS. Dedicated scan strategies, consisting of PPI (plan position indicator), RHI (range–height indicator) and fixed-pointing modes, were defined and used for observing a wind park consisting of three large wind turbines. During both campaigns, measurements were taken in 24/7 operation. The highest measured maxima of horizontal reflectivity (ZH) and RCS reached 78.5 dBZ and 44.1 dBsm, respectively. A wind turbine orientation (yawing) stratified statistical analysis shows no clear correlation with the received maximum returns. However, the median values and 99th percentiles of ZH show different enhancements for specific relative orientations. Some of them remain still for Doppler-filtered data, supporting the importance of the moving parts of the wind turbine for the radar returns. Further, we show, based on investigating correlations and an OLS (ordinary least square) model analysis, that the fast-changing rotor blade angle (pitch) is a key parameter, which strongly contributes to the variability in the observed returns.

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