LiDAR-based detection of wind gusts: An experimental study of gust propagation speed and impact on wind power ramps
Mathieu Pichault, Claire Vincent, Grant Skidmore, Jason Monty
Abstract
Understanding the complex behaviours inherent to the wind is a key challenge in improving the accuracy of short-term prediction models. Turbulent wind gusts are one of several factors expected to contribute to uncertainties in forecasts and can also cause turbine damage in extreme cases. This study aims to (1) evaluate how spatial gust properties map to temporal variations in power generation at the wind turbine and farm scale and (2) assess the validity of Taylor’s frozen turbulence hypothesis for gust impact prediction. First, a gust identification scheme building upon existing research is presented. Gusts are extracted from LiDAR-based near-horizontal wind fields collected throughout a year-long measurement campaign at an onshore wind farm. The tendency of gusts to induce wind power ramps is investigated, and their propagation speed is compared against the background flow. Results show that gusts are associated with increased power variability at the turbine scale and the wind farm scale, with the most noticeable effect being at the turbine level. Gusts with length scales less than one kilometre generally propagate at the rate of the background flow, whereas larger gusts travel marginally faster. The study emphasises the importance of gusts for short-term power forecasting applications and affirms the use of Taylor’s frozen turbulence hypothesis for gust impact prediction.