Tutorials

Here you can find html-rendered PyWAsP notebooks/tutorials to help you get started with PyWAsP. The notebooks themselves (and associated data) can be found here: https://gitlab.windenergy.dtu.dk/WAsP/pywasp-examples.

Classical WAsP step-by-step

• Classical resource assessment step-by-step
  • Introduction
  • The situation
  • Working with WAsP to provide a prediction
  • Import packages
  • Observed wind climate
  • Topography data
    • Creating topography map
    • Displaying terrain and roughness data
    • Output Locations
    • Site effects
  • The generalized wind climate calculation
  • Estimating wind power
  • Estimating wind farm production

Calculate a resource grid

• PyWAsP: From observed wind climate to resource map
  • Introduction
  • Import packages
  • Observed wind climate
  • Topography data
    • Terrain data (SRTM)
    • Roughness data (CORINE LandCover)
  • Site Effects
    • Create TopographyMap object
    • Roughness Rose
    • Output Locations
    • Site Effects Calculation
      • Plotting z0meso
      • Plotting Roughness Change Speedups
  • Generalization and downscaling with PyWAsP
  • Predicted Wind Climates

Experiment with the response of WAsP’s Roughness Change model

• Experiment with response of WAsP’s Roughness Change modelling
  • Create single-contour map of roughness change
  • Setup the test cases
  • Create a column of “speedups”
  • Examine the speedup results

Evaluating roughness maps at Østerild

• Evaluating roughness maps at Østerild
  • Reading Østerild wind data
• Preparing map files
• Using landcover (LC) maps
• Roughness and landcover around masts
• Exploring roughness around a point: the roughness rose
• Comparison of observed and modelled wind, per roughness maps
• Horizontal transects of power density
• Vertical profile of power density
• Changing properties of the landcover data

Importing a WAsP workspace

• PyWAsP: Working with a WAsP workspace (WWH file)
  • Finding objects in the workspace
  • Importing the inputs
    • The observed wind climate
    • The vector map
    • The wind turbine generator and the turbine sites
    • The mesoscale climate and the air density
    • The WAsP parameters
  • Importing the WAsP results
    • The generalised wind climate
    • Predicted wind climates and site effects
    • Annual energy production
    • Anything else: the low-level member data
  • Repeating the WAsP calculation with pywasp
  • Calculating a resource grid over the wind farm

Losses and uncertainties

• PyWAsP: modeling losses and uncertainty of AEP
  • Introduction
  • The situation
  • Import packages
  • Wind farm production: from predicted wind climate to potential AEP
  • From potential AEP to net AEP (P50) at PCC (Point of Connection)
  • Summary overview of a loss table
  • The total loss and total loss factor
  • Obtaining the P50
  • From net AEP (P50) to AEP Gaussian distribution
  • Summary overview of an uncertainty table
  • The sensitivity factor
  • Total uncertainty factor
  • Obtaining a Px AEP value

Working with polygon and line maps

• Working with polygon and line maps
  • Acquire a CORINE polygon map
  • Visualizing the outputs: Raster
  • Visualizing the outputs: Vector
  • Reprojection and Benchmarking
  • Handling Partial Metadata: Roughness-only and ID-only
  • Topological Edge Case: Polygons with Holes
  • Topological Edge Case: Overlapping Polygons
  • Topological Edge Case: Missing Vertices (Snapping)
  • Disconnected Polygons and Site Effects
  • Using an external roughness length
  • When to use check_errors

Generalized wind climate interpolation

• Generalized wind climate interpolation
  • Prepare Generalized Wind climate cuboid
  • GWC interpolation for the windkit spatial structures
  • Performance
  • Case study using WRF data

Meridian convergence and wind direction rotation

• Grid Convergence and Wind-Direction Consistency
  • Learning goals
  • Part 1 - Why Grid Convergence Matters in PyWAsP
  • Part 2 - Historical Context: WRF, ALPHA, and NEWA
    • 2.1 WRF grid rotation and the ALPHA variable
    • 2.2 What is NEWA?
    • 2.3 The manual 2019-2023 extension
  • Part 3 - Diagnosing the error
    • 3.1 Time series of direction difference
    • 3.3 Using different projections
  • Part 4 - PyWAsP 2.1 Behavior
    • 4.1 Affected APIs
  • Part 5 - Recommended Workflows
  • Part 6 - Extreme Projection Demonstration
    • 6.1 Geostrophic turning and residual probability mass
    • 6.2 Single-step versus true-north two-step rotation
    • 6.3 Polar comparison
  • Part 7 - Practical Checklist
  • Part 8 - Migration Guide: PyWAsP < 2.1 and WAsP GUI Wind Climates
  • Glossary
    • Appendix A - Internal Direction-Rotation Notes
  • Appendix B - Direction-sign conventions in the Part 6 example
    • B.1 Natural versus effective grid convergence
    • B.2 Why the corrected peak moves from north
    • B.3 Function sign summary

Understanding the BZ orographic model

• Understanding the BZ Orographic Model
  • The BZ model in practical terms
  • A Gaussian hill and four sites
    • How the model sees the hill
    • Directional speed-up and turning
  • Preparing elevation rasters with missing cells
    • Boundary-connected missing cells
    • Reading the rightmost panel
    • An enclosed missing cell
  • A minimal real-terrain example
  • Practical guidance
  • References