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Estíbaliz Montes, Alberto Arnedo, Ruth Cordón, Rafael Zubiaur Barlovento Recursos Naturales S.L.

Influence of wind shear and seasonality on the power curve and annual energy production of wind turbines. Estíbaliz Montes, Alberto Arnedo, Ruth Cordón, Rafael Zubiaur Barlovento Recursos Naturales S.L. Hall 3, Stand #3748 Logroño, Spain brn@barlovento-recursos.com

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Estíbaliz Montes, Alberto Arnedo, Ruth Cordón, Rafael Zubiaur Barlovento Recursos Naturales S.L.

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  1. Influence of wind shear and seasonality on the power curve and annual energy production of wind turbines Estíbaliz Montes, Alberto Arnedo, Ruth Cordón, Rafael Zubiaur Barlovento Recursos Naturales S.L. Hall 3, Stand #3748 Logroño, Spainbrn@barlovento-recursos.com European Wind Energy Conference and Exhibition 2009 CS3 Resource assessment and siting

  2. Introduction Influence of wind shear on AEP Use of lidar for wind profile assessment Results Conclusion OUTLINE

  3. Power curve function not only of horizontal wind speed P(ws). Other parameters influence the performance: Wind shear, turbulence, … Some of these parameters depend on atmospheric stability. The influence of wind shear and seasonality on power curve has been investigated. Experimental works: Power curve tests at different sites have been carried out, including wind shear assessment. The influence of wind shear and seasonality on AEP results is assessed. Lidar has been evaluated as complementary equipment for power curve tests. INTRODUCTION

  4. Seasonality of wind shear is well known

  5. EXPERIMENT Power curve tests at different sites: • Site 1. Flat terrain, near sea level, South of Spain. • Site 2. Complex terrain, about 1000 m a.s.l., Centre of Spain. • Site 3. Near flat terrain, about 300 m a.s.l. North of Spain. Turbine power: < 1MW, 1-2 MW, > 2MW Tests according IEC 61400-12-1. Additional equipments for the assessment of wind shear. Site 3 includes also lidar measurements.

  6. Category 1. α < 0.12 Category 2. 0.12 ≤ α < 0.17 Category 3. 0.17 ≤ α EXPERIMENT Datasets have been split into subsets: Power curves and AEP have been calculated for each Site, dataset and subset.

  7. Similar AEP differences for three sites. Influence of wind shear on power curve is similar.

  8. EXPERIMENT • Results: • Better power performance for lowerα:Category 1. • Similar differences in AEP for three sites. • Similar results for different turbine sizes. • Wind Shear not the only cause of seasonal AEP differences.

  9. LIDAR MEASUREMENTS The measurement plan at Site 3 includes: • Validation of measurements (wind speeds, wind profile). • Filtering criteria development. • Assessment of results.

  10. LIDAR MEASUREMENTS Validation of measurements: • Many uncorrelated data => lidar filtering necessary.

  11. LIDAR MEASUREMENTS Filtering criteria: • Cloudiness. • Rain. • Data availability at all levels.

  12. LIDAR MEASUREMENTS Assessment of results: • High correlation of wind speeds (lidar-mast). • Slope aprox. 1 • Same wind shear results, But • Low data availability. VMAST,TH = 1.002 VLIDAR,TH R2=0.94

  13. CONCLUSIONS • Wind shear influences the power curve, • For the same hub height average wind speed, • AEP is lower for big α values. • Wind shear variations can be found at test sites: • by sector, by season, then: • Measurements of wind shear shall be included in the tests. • Lidar measurements can be helpful, but • lidar valid data availability need to be improved. • Site specific power curves are needed for • wind resource assessment.

  14. CONSIDERATION Power curve is used as a wind turbine performance characteristic. The improvements in the Power curve concept need to be consistent with the wind resource assessment practices: measurements, wind field models, wake models.

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