WindQuest – Smarter Tools

WindQuest – Smarter Tools

►  What is Wind Quest

WindQuest is our engine to power lean wind farm development. We are not simply a consultancy running industry standard tools. We stripped back the requirements and built an internal set of tools from the ground up to be a number of things:

  • Lightweight and flexible
  • Modular and interconnected
  • Swift and powerful
  • Comprehensive and integrated

When we need to be fast and dynamic, WindQuest allows us to be. When we need to dive into great detail, WindQuest has the reach.

►  Overview of WindQuest

►  WindQuest Powering Sophistication

We’ve looked at every aspect of the prospecting and site development process and studied to see where automation can improve the process. Here’s a three of our most pioneering examples:

► Example 1: Wind Map Ensembling

Improving wind map accuracy through finding a consensus

Estimating the variations in wind resource over country-size areas requires reliance on mesoscale wind maps. Comparing multiple independent mesoscale maps we see significant variations between different maps – a sure sign of significant inaccuracies. Improved results can be obtained by ensembling multiple maps, thereby gaining a consensus opinion and reducing the effect of random errors within individual wind maps. WindQuest is setup to calibrate and ensemble as many source maps as are available. In every country to date with ground-based measurements suitable for validation, the WindQuest map has been more accurate than any of the source maps.

► Example 2: Terrain Characterisation of Sites

Understanding the importance of local terrain

There are physical characteristics of a site that are known to be desirable in promoting good wind resources. These include limited areas of steep slopes that create turbulent and detached wind flow, good exposure in prevailing wind directions, height gains that promote speed ups and ridges that are perpendicular to prevailing wind directions. WindQuest defines special parameters to identify areas with these favourable characteristics, to provide an invaluable independent opinion  where wind flow models are known to be poor-quality.

WindQuest’s terrain characterisation has been validated against operational wind farm locations – in a study of ### wind turbines in Karnataka, India, 86% of turbines appeared in the best 5% of terrain scores. No wind maps – purely based on local terrain.

► Example 3: Long-term Analyses

Investigating hundreds of long-term approaches

To put on-site wind measurements in context of the windiness of the measurement period, reanalysis climate data is used as a reference data source. Industry standard approaches look at a handful of different approaches before picking a single preferred approach.

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Any one approach can have substantial bias, so instead at every site WindQuest studies multiple nodes from multiple reanalysis data sets, averaged to multiple averaging periods and clipped to multiple long-term periods. The result is a thousand or more possible approaches which combine to provide a distribution of final wind speeds, from which the mean and spread can be interrogated to obtain a consensus opinion for the long-term wind speed at the mast.

This approach reduces the dependence on any one data sets and improves accuracy of the final long-term wind speed.