Wind Turbines: The Yaw Misalignment Challenge
Together with Nadara, develop and compare methods for identifying yaw misalignment–driven underperformance using high-frequency SCADA data sampled at 15-second resolution from operating wind turbines.
Working with nature, backed by shareholders, and with the support of local communities, Nadara is a NextGen Independent Power Producer (IPP+) that develops, owns and operates renewable energy sites across Europe and the US. Our technologies include over 4GW of installed onshore wind, solar photovoltaic, biomass and energy storage, and our IPP+ services encompass energy optimisation, trading, and flexibility solutions that maximise value across the energy chain, supporting energy security and creating long-term value that powers homes, businesses, and change.
Challenge Owner
Postdate 11.07.2026
Energy
Data
Description
The goal of this challenge is to develop and compare methods for identifying yaw misalignment–driven underperformance using high-frequency SCADA data sampled at 15-second resolution from operating wind turbines.
Participants are asked to analyse the provided SCADA data and submit a list of turbines and time periods for which yaw misalignment is identified, together with an estimate of the yaw misalignment angle. The analysis should focus on detecting power underperformance attributable to yaw misalignment, rather than other operational or environmental effects. For each turbine and analysis window, participants should indicate whether yaw misalignment is detected and, where applicable, provide an estimate of the yaw misalignment angle (in degrees).
Ground truth data is provided using the WindFit measurement system, which gives Nadara with yaw misalignment data for a subset of instrumented turbines. Participants submit predicted yaw misalignment angles for held-out SCADA data from one turbine.
The leaderboard will rank submissions by the accuracy of their yaw offset estimates, regardless of method type.
Take part in this challenge HERE!
Impact
Yaw misalignment occurs when a wind turbine rotor is not correctly aligned with the wind direction. Even small misalignment angles can lead to significant power losses and increased mechanical loads, making yaw misalignment one of the most impactful sources of underperformance in wind farms.
Traditionally, wind turbine performance monitoring relies on standard SCADA data aggregated over 10-minute intervals. While suitable for high-level performance tracking, 10-minute SCADA often lacks the temporal resolution needed to reliably identify yaw misalignment, as control actions, wind direction variability, and transient effects are averaged out.
In contrast, high-frequency SCADA data sampled at 15-second intervals preserves dynamic turbine behaviour, yaw controller response, and short-term power fluctuations. This higher temporal resolution enables more accurate identification of yaw-related underperformance patterns and allows for direct estimation of yaw misalignment angles. Nadara aims to leverage 15-second high-frequency SCADA data to develop robust, data-driven methods for detecting yaw misalignment that go beyond the limitations of traditional 10-minute analysis.
Data Set
High-resolution SCADA between 2023-01-01 and 2024-12-31 is provided for 16 turbines across two anonymised sites. The test SCADA is published; only its labels are withheld. You can use it freely for unsupervised or transductive work. The evaluation of test data results will be done at the end of the challenge.
turbine_locations_PPP.csv and turbine_locations_SSS.csv give the layout one file per site. x_m is easting, y_m northing.
Signals: Power, WindSpeed, WindDir, NacDir, PitchAngle, RotSpeed, GenSpeed. Vane angle can be derived as WindDir - NacDir, wrapped to ±180°.
train.parquet carries every day, labelled or not, so you can use the full two years. yaw_misalignment_deg is null on unlabelled days.
Needed Skills
- Time-series data analysis experience
- Optional: knowledge of wind energy systems
- To take part in this challenge, registration is required on the WeDoWind platform.