Posters

Abstract

Static yaw misalignments are where the direction the turbine tries to point, is not the direction that maximises energy capture. As a result, energy capture, and so revenues, are reduced. There are a number of hardware solutions that try to address this problem, for example nacelle mounted lidar, additional anemometry on the nosecone of the turbine, ground-based lidar, use of upwind met masts, and even drone based solutions, but, these come with a significant cost and are not easily scalable. A reliable data driven approach using widely available SCADA data would be preferable. Here we present the interim results of a WeDoWind Open Data Exploration Challenge, that is focused on identifying static yaw misalignments at two operational wind farms using openly available SCADA data. Different methodologies are compared, including an open-source analysis provided in OpenOA, highlighting the benefits and drawbacks of each approach. Some key results are: * A zero-degree yaw misalignment is not necessarily optimal for energy capture. * Each turbine can have a different static yaw misalignment. * An individual static yaw misalignment can vary with time, wind speed and wind direction. * The wind speed measurement can be influenced by the yaw angle, complicating the analysis. * Wakes also complicate the analysis further. Whilst these issues are addressed individually in the analysis methodology, the main issue with the current results is around validation, as there is no way to know the "correct" static yaw misalignment. Application of known static yaw misalignments, or independent hardware measurements, would therefore be of benefit, as too would application of the methodologies on a wider variety of turbine types and data sets. People are therefore encouraged to access the OpenOA open-source static yaw misalignment analysis and apply it to their data sets. This can help confirm if the approach works as expected, contributing to a tool that could be of significant benefit to the wind industry.