Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations
<p>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 t...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-05-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/14/3541/2021/amt-14-3541-2021.pdf |
| _version_ | 1819080359644495872 |
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| author | M. Lainer J. Figueras i Ventura J. Figueras i Ventura Z. Schauwecker M. Gabella M. F.-Bolaños R. Pauli J. Grazioli |
| author_facet | M. Lainer J. Figueras i Ventura J. Figueras i Ventura Z. Schauwecker M. Gabella M. F.-Bolaños R. Pauli J. Grazioli |
| author_sort | M. Lainer |
| collection | DOAJ |
| description | <p>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 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">24</mn><mo>/</mo><mn mathvariant="normal">7</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="27pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="be594f87eccfced10a3804f797404ac0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-14-3541-2021-ie00001.svg" width="27pt" height="14pt" src="amt-14-3541-2021-ie00001.png"/></svg:svg></span></span> operation. The highest measured maxima of horizontal reflectivity (<span class="inline-formula"><i>Z</i><sub>H</sub></span>) and RCS reached <span class="inline-formula">78.5</span> <span class="inline-formula">dBZ</span> and <span class="inline-formula">44.1</span> <span class="inline-formula">dBsm</span>, 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 <span class="inline-formula"><i>Z</i><sub>H</sub></span> 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.</p> |
| first_indexed | 2024-12-21T19:43:38Z |
| format | Article |
| id | doaj.art-0a53c743093f45a8989f7a262aa0f5a6 |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-21T19:43:38Z |
| publishDate | 2021-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-0a53c743093f45a8989f7a262aa0f5a62022-12-21T18:52:23ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482021-05-01143541356010.5194/amt-14-3541-2021Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observationsM. Lainer0J. Figueras i Ventura1J. Figueras i Ventura2Z. Schauwecker3M. Gabella4M. F.-Bolaños5R. Pauli6J. Grazioli7Federal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, SwitzerlandFederal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, Switzerlandnow at: Météo-France, Toulouse, FranceFederal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, SwitzerlandFederal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, SwitzerlandFederal Office for Defence Procurement, armasuisse, Science and Technology, Sensorik, SwitzerlandMilitary Aviation Authority (MAA), SwitzerlandFederal Office of Meteorology and Climatology, MeteoSwiss, Locarno-Monti, Switzerland<p>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 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">24</mn><mo>/</mo><mn mathvariant="normal">7</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="27pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="be594f87eccfced10a3804f797404ac0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-14-3541-2021-ie00001.svg" width="27pt" height="14pt" src="amt-14-3541-2021-ie00001.png"/></svg:svg></span></span> operation. The highest measured maxima of horizontal reflectivity (<span class="inline-formula"><i>Z</i><sub>H</sub></span>) and RCS reached <span class="inline-formula">78.5</span> <span class="inline-formula">dBZ</span> and <span class="inline-formula">44.1</span> <span class="inline-formula">dBsm</span>, 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 <span class="inline-formula"><i>Z</i><sub>H</sub></span> 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.</p>https://amt.copernicus.org/articles/14/3541/2021/amt-14-3541-2021.pdf |
| spellingShingle | M. Lainer J. Figueras i Ventura J. Figueras i Ventura Z. Schauwecker M. Gabella M. F.-Bolaños R. Pauli J. Grazioli Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations Atmospheric Measurement Techniques |
| title | Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations |
| title_full | Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations |
| title_fullStr | Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations |
| title_full_unstemmed | Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations |
| title_short | Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations |
| title_sort | insights into wind turbine reflectivity and radar cross section rcs and their variability using x band weather radar observations |
| url | https://amt.copernicus.org/articles/14/3541/2021/amt-14-3541-2021.pdf |
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