Presentations

Abstract

This presentation shares a new open-source wake steering case study from the Hill of Towie wind farm where an innovative wind farm control system has recently been deployed. The data includes multiple months of 1Hz turbine and LiDAR measurements (two LiDARs: a ground-mounted vertical profiling LiDAR and a nacelle-mounted LiDAR). The control system combines multiple optimisation approaches including collective yaw control and wake steering. It is understood that this will become only the 2nd open data set of a multi-month wake steering trial on a commercial wind farm (SMARTEOLE [1] being the first), thus supporting academia and industry on demonstrating the benefits and viability of such wind farm control strategies. The presentation will elaborate on multiple open analyses of the data including: * Detailed measurement of inflow conditions available thanks to a ground-mounted and nacelle-mounted LiDAR. * Detailed measurements and comparisons to models of wake alteration during wake steering thanks to a nacelle-mounted LiDAR on the downwind turbine.  * AEP uplift and uncertainty analysis using the publicly available wind-up tool [2] using both LiDARs and neighbour turbines as reference. Wind inflow conditions are one of the largest determinants of turbine performance and loading, and LiDARs are unique in their abilities to characterise inflow. In addition to hub height wind speeds, measured quantities of relevance include rotor equivalent wind speeds, vertical shear and veer profiles, temporal and spatial turbulence characteristics, yaw alignment, and inflow angles. The LiDARs are equipped with met stations, so also report air temperatures, humidities and pressures. The nacelle-mounted LiDAR additionally measures time varying inclination and roll, which are useful to understand turbine loadings. The wake strength and horizontal deflection (ie the effectiveness of steering in changing the wake’s trajectory) are measured at various ranges by the nacelle-mounted LiDAR. Combined with lidar-derived TI measures, these are compared to predictions from open wake models such as FLORIS. The presentation aims to progress familiarity of wake steering within the industry with a practical example, showing that (with the right controller) wake steering is safe, predictable, and valuable. The presentation also aims to progress the industry’s ability to accurately measure AEP uplift with uncertainty quantification for complex upgrades such as wind farm control. This is achieved by sharing all data and source code for an open uplift analysis of this real-world case study. The open-source dataset will be a valuable resource to technical stakeholders in the wind industry. Containing all the original SCADA and LiDAR channels logged at 1Hz or faster, the dataset is applicable to a wide range of research use cases. The process of creating an open-source wind farm dataset and ensuring it has good findability, accessibility, interoperability, and reusability (FAIR) will also be presented. References [1] Thomas Duc, & Eric Simley. (2022). SMARTEOLE Wind Farm Control open dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7342466 [2] wind-up Python package v0.4.5, https://pypi.org/project/res-wind-up/0.4.5/, November 20, 2025