Validation and Comparison betweenWAsP and Meteodyn Predictions for    a Project in Complex Terrain Meteodyn WT users meeti...
Hatch•   Employee-owned, Projects in more than 150 countries    – 8000 employees worldwide               l          ld id ...
Overview•   Review of models•   Presentation of a test case•   Results and comparisons•   Conclusions and investigations  ...
Review of models                   4
Why is CFD a goodalternative to linear models ?• CFD is now well recognized by the wind  community• Overpass linear model ...
Why is CFD a goodalternative to linear models ?• Some questions remains:   – Can we quantify the uncertainty and errors   ...
Why is CFD a good alternative t linear models ?  lt    ti to li        d lCFD Models (Meteodyn) Linear Models (WAsP) •   P...
Presentation of a test case                              8
A test case• Comparison between WAsP and  Meteodyn on a potential project• Project covers an area of 11km x 8km       j• E...
A test case• Forest diversity, varies among :         diversity   – Completely logged area (no trees)   – 15m high trees  ...
A test case               Altitude       Masts              RIX (%)                 (m)        M1       540       10.1    ...
A test case              12
A test case              13
Meteodyn settings• Topographical information :   – Roughness : 0.6 for trees   – Elevation Contour : 5m within project are...
Meteodyn settings• Model:  – Robust forest model (convergence issues with    the dissipative model)  – Near neutral stabil...
Results and comparisons                          16
Results – Wind Speeds• Cross-Prediction Matrix   – Predictors : Synthesis performed with the     « Predictor » mast   – Pr...
Results - Errors• Cross-Prediction Matrix   –   12 x 12 matrix = 132 cross predictions   –   For both WAsP and Meteodyn   ...
Results - Errors• Absolute errors                    WAsP       Meteodyn  Min Error         0.0%         0.0%  Max Error  ...
Results - Errors• Generally, errors have the same sign  (positive/negative)                    40.0%                    30...
Masts   Altitude (m)       RIX (%)                                                                         M1          540...
Results - Errors• RIX dependency:  – WAsP : Error increase sharply when RIX >    15%  – Meteodyn : Error is more constant ...
Results - Errors              • RIX dependency:                    – Ri Ø suggests correcting WAsP with ∆RIX              ...
Results - Errors              • RIX dependency:                    – E                      Error increases when ∆RIX incr...
Results - Uncertainty• 11 estimates of wind speed for each mast• Uncertainty is estimated with the standard  deviation of ...
Results - Uncertainty• Caracterize the repeatability of an  estimate• Uncertainty can be reduced on average            y  ...
Conclusions and investigations                  27
Conclusions• For this project Meteodyn shows better           project,  results for error and uncertainty  compared to WAs...
Conclusions• However :  – WAsP results are without any correction which    is often performed (like RIX correction for    ...
Conclusions• Further investigations and questions :   – How do they compare when correcting WAsP                 y        ...
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Hatch Ltd. Validation and comparison WAsP and meteodyn 2011

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Validation and Comparison between
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Hatch Ltd. Validation and comparison WAsP and meteodyn 2011

  1. 1. Validation and Comparison betweenWAsP and Meteodyn Predictions for a Project in Complex Terrain Meteodyn WT users meeting– Paris (France), March 21 and 22, 2011 Gilles Boesch, Wind Project Analyst j y Salim Chemanedji, Senior Project Manager Martin Hamel, Project Manager Hatch (Montreal), Canada
  2. 2. Hatch• Employee-owned, Projects in more than 150 countries – 8000 employees worldwide l ld id – EPCM, integrated teams, project and construction management – Consulting – process technologies and business process, – Serving mining & metals, infrastructure and energy• For Wind Power projects: – Wind resource assessment – Geotech engineering, foundation design – Turbine evaluation and selection – Total project and construction management – Interconnection assessment, Electrical engineering – Environmental assessment 2
  3. 3. Overview• Review of models• Presentation of a test case• Results and comparisons• Conclusions and investigations 3
  4. 4. Review of models 4
  5. 5. Why is CFD a goodalternative to linear models ?• CFD is now well recognized by the wind community• Overpass linear model limitations for p complex terrain• Reduce the modelling uncertainty• R d Reduce financial risks fi i l i k But CFD must be used with care since it is more complex 5
  6. 6. Why is CFD a goodalternative to linear models ?• Some questions remains: – Can we quantify the uncertainty and errors associated to these models ? – What are the criteria for chosing linear or CFD models ? – Do CFD models always perform better than linear models ? Usually difficult to assess because only few meteorological masts are available within a project to perform cross-predictions 6
  7. 7. Why is CFD a good alternative t linear models ? lt ti to li d lCFD Models (Meteodyn) Linear Models (WAsP) • Pros • Pros – Suitable for complex terrain – Easy and fast computation – Calibration f the it C lib ti of th site – Good performance in possible (forest, stability, relatively flat terrain mesh etc.) – Is already a standard – Built-in features (energy, • Cons extreme winds turbulence winds, etc.) – High errors for complex terrain • Cons – Calibration is difficult to – Solid expertise needed perform (when possible) – Calculation time C l l ti ti 7
  8. 8. Presentation of a test case 8
  9. 9. A test case• Comparison between WAsP and Meteodyn on a potential project• Project covers an area of 11km x 8km j• Equipped with 12 meteorological masts (recording from 6 months to 6 years of data)• Relatively complex (deep valleys, ridges, rolling mountains) g )• Mix of coastal and inland areas 9
  10. 10. A test case• Forest diversity, varies among : diversity – Completely logged area (no trees) – 15m high trees – Regrowth• RIX variations (Ruggedness Index) – % of slopes >30% in a 3500m radius – 2 to 25 over the entire project – 2.7 to 22.4 at the meteorological masts Variety of conditions to evaluate the behavior of the models 10
  11. 11. A test case Altitude Masts RIX (%) (m) M1 540 10.1 M2 560 11.0 M3 421 22.4 22 4 M4 420 17.9 M5 448 15.1 M6 521 16.6 16 6 M7 560 8.0 M8 433 22.1 M9 440 11.8 11 8 M10 665 14.3 M11 567 2.7 M12 540 12.1 12 1 11
  12. 12. A test case 12
  13. 13. A test case 13
  14. 14. Meteodyn settings• Topographical information : – Roughness : 0.6 for trees – Elevation Contour : 5m within project area• Mesh : – Mapping area covering all met masts – Mesh independency tests (variation of the Radius) – Minimum horizontal resolution : 30m – Minim m vertical resol tion : 5m Minimum ertical resolution – 3 460 000 cells in the prevailing direction 14
  15. 15. Meteodyn settings• Model: – Robust forest model (convergence issues with the dissipative model) – Near neutral stability class – 30 degrees directional steps• Data: – Measured data – Quality controlled – At 50m or 60m high – Extrapolated to long term with standard MCP method 15
  16. 16. Results and comparisons 16
  17. 17. Results – Wind Speeds• Cross-Prediction Matrix – Predictors : Synthesis performed with the « Predictor » mast – Predicted : Wind Speed at the « Predicted Sp Mast » Predicted M1 M2 M3 … M12 M1 M1 measured M1 predicts M2 M2 M2 predicts ictor M2 measured M1 M3 M3 measured Predi … … M12 M12 measured 17
  18. 18. Results - Errors• Cross-Prediction Matrix – 12 x 12 matrix = 132 cross predictions – For both WAsP and Meteodyn – No correction is applied to both models output – Correction often applied with WAsP because of wind speed inconsistencies in complex terrain• Converted into a Relative Error Matrix : V predicted − Vmeasured %E = Vmeasured• Resulting in 132 relative error values for each cross-prediction h di ti 18
  19. 19. Results - Errors• Absolute errors WAsP Meteodyn Min Error 0.0% 0.0% Max Error 34.0% 14.1% Average 7.1% 4.7%• On average, Meteodyn reduces the error by 35%.• S Some exceptions : 33 cases out of 132 ti t f show better results with WAsP 19
  20. 20. Results - Errors• Generally, errors have the same sign (positive/negative) 40.0% 30.0% 20.0%Relativ Error (%) WAsP 10.0% Meteodyn y ve 0.0% -10.0% -20.0%• The difference is in the magnitude g 20
  21. 21. Masts Altitude (m) RIX (%) M1 540 10.1 M2 560 11.0 M3 421 22.4 M4 420 17.9Results - Errors M5 M6 M7 M8 448 521 560 433 15.1 16.6 16 6 8.0 22.1 M9 440 11.8 M10 665 14.3 M11 567 2.7 M12 540 12.1• Comparison at each mast Error comparison 25.0% 20.0%Average Error) 15.0% E WAsP 10.0% Meteodyn 5.0% 0.0% M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 Met Masts 21
  22. 22. Results - Errors• RIX dependency: – WAsP : Error increase sharply when RIX > 15% – Meteodyn : Error is more constant RIX influence on cross-prediction errors 25.00% 20.00% 15.00% Averag Error (%) Wasp Meteodyn 10.00% ge 5.00% 0.00% 0.0 5.0 10.0 15.0 20.0 25.0 RIX (%) 22
  23. 23. Results - Errors • RIX dependency: – Ri Ø suggests correcting WAsP with ∆RIX Ris t ti WA P ith (between 2 masts) – Correction based on a correlation between Error and ∆RIX for each cross-prediction E d f h di ti – Open question : Can we correct Meteodyn based on the RIX ? Error vs dRIX - Meteodyn Error vs dRIX - Wasp 40.0% 40.0% 30.0% y = 0.5552x 30.0% y = 1.0632x R² = 0.6345 R² = 0.7025 20.0% 20.0% %) Error (% %) 10.0% 10 0% 10.0% 10 0%Error (% 0.0% 0.0% ‐30.0% ‐20.0% ‐10.0% -10.0% 0.0% 10.0% 20.0% 30.0% ‐30.0% ‐20.0% ‐10.0% -10.0% 0.0% 10.0% 20.0% 30.0% -20.0% -20.0% -30.0% -30.0% ∆RIX (%) ∆RIX (%) 23
  24. 24. Results - Errors • RIX dependency: – E Error increases when ∆RIX increases i h i – Error and ∆RIX seem to be correlating – The slope is lower for Meteodyn Meteodyn is less sensitive to site topography differences Error vs dRIX - Meteodyn Error vs dRIX - Wasp 40.0% 40.0% 30.0% y = 0.5552x 30.0% y = 1.0632x R² = 0.6345 R² = 0.7025 20.0% 20.0% %) Error (% %) 10.0% 10 0% 10.0% 10 0%Error (% 0.0% 0.0% ‐30.0% ‐20.0% ‐10.0% -10.0% 0.0% 10.0% 20.0% 30.0% ‐30.0% ‐20.0% ‐10.0% -10.0% 0.0% 10.0% 20.0% 30.0% -20.0% -20.0% -30.0% -30.0% ∆RIX (%) ∆RIX (%) 24
  25. 25. Results - Uncertainty• 11 estimates of wind speed for each mast• Uncertainty is estimated with the standard deviation of the errors Uncertainty Uncertainty Uncertainty Masts RIX (%) WAsP Meteodyn Reduction M1 4.6% 4 6% 2.4% 2 4% 1.9 19 10.1 10 1 M2 4.4% 3.1% 1.4 11.0 M3 7.8% 3.1% 2.5 22.4 M4 4.2% 2.5% 1.7 17.9 M5 2.9% 2.7% 1.1 15.1 6 M6 2.8% 2.7% 1.0 16.6 66 M7 4.7% 3.5% 1.4 8.0 M8 5.7% 3.4% 1.7 22.1 M9 3.0% 2.3% 1.3 11.8 M10 4.2% 2.8% 1.5 14.3 M11 4.8% 3.1% 1.5 2.7 M12 3.4% 2.5% 1.4 12.1 25
  26. 26. Results - Uncertainty• Caracterize the repeatability of an estimate• Uncertainty can be reduced on average y g by 1.5 when using Meteodyn.• No trend with regards to the RIX• The separation distance is more important regarding the uncertainty Numbers are site-specific and must be considered with care ! 26
  27. 27. Conclusions and investigations 27
  28. 28. Conclusions• For this project Meteodyn shows better project, results for error and uncertainty compared to WAsP• Significant advantages : – Cost reduction : Need for less meteorological masts per project – Financial risks reduction : Lower uncertainty increases P75 or P99 value 28
  29. 29. Conclusions• However : – WAsP results are without any correction which is often performed (like RIX correction for example) – Results are specific to this site – Some cases are better predicted with WAsP – Other projects with lower RIX show equivalent results between both models 29
  30. 30. Conclusions• Further investigations and questions : – How do they compare when correcting WAsP y g with the RIX ? – Can we correct Meteodyn’s results in a certain way ? (RIX or other) – Why does WAsP better predict the wind speed at some points ? • Mesh refinement ? • Forest model ? • Roughness ? – What are the criteria for defining a terrain in terms of complexity (use of WAsP vs Meteodyn) ? – How many met tower should we use in complex terrain ? 30

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