WindFarmer software: advanced training

This course goes beyond our introductory level courses to look at some advanced features of WindFarmer software. We will discuss how to improve the accuracy of your results, and how to calculate more than just your annual energy production.

Contact us:

Robert Rawlinson-Smith Robert Rawlinson-Smith
Business Lead, Bladed

For more information about our training courses

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Training Information

  • Duration:

    1 day

Additional information:
WindFarmer is widely acknowledged as a powerful piece of software, and an increased understanding of its functionality will help you to leverage your investment.

Course outline:
In the morning we will look at how to make the most of the features which give WindFarmer its accurate modelling:

  • Its range of wake models leads the industry, and we will discuss how to select the one most appropriate for your wind farm.
  • The ‘Association Method’ is a technique which eliminates the errors typically introduced when a wind speed frequency distribution is approximated to a Weibull distribution.
  • Noise propagation modelling is increasingly important, and we will look at how to design a turbine layout in a noise constrained area using WindFarmer’s layout optimiser.

In the afternoon, we will discuss the range of outputs which WindFarmer can generate and how these can be used for energy analysis, operational diagnostics and short term forecasting. This will centre around the ‘Flow and Performance Matrix’, part of the Turbulence Intensity module. This matrix, when combined with a little post-processing in Excel, can be used for many tasks such as:

  • A 12 x 24 table of wind farm output through the day and across the year
  • Prediction of wind farm output for a given wind speed and direction
  • Modelling the effect of wind farm curtailment

Who should attend?
This course will be of interest both to experienced users of WindFarmer, and to users of other software thinking of making a switch. It incorporates “hands-on” exercises with the software.