Short-term wind forecasting via surface pressure measurements: stochastic modeling and optimal sensor placement

Adjustments to the turbine blade pitch, generator torque, and nacelle direction (yaw) are conventional strategies for increasing energy production and lowering operation and maintenance costs by countering the effects of wind field variability on wind plants. However, in the absence of effective short-term forecasting tools, almost all modern-day plants rely on data collected at or just behind the wind turbine to adjust their settings, and consequently always lag optimal operation conditions. We propose a short-term forecasting framework that can enable model-based control systems to preemptively adapt ahead of atmospheric variations in improving turbine efficiency and reducing structural loads and failures. Our approach relies on a combination of linear stochastic estimation and Kalman filtering algorithms to assimilate and process real-time nacelle-mounted anemometer and surface air pressure readings with the predictions of a stochastic reduced-order model of the hub-height velocity field. We also utilize a convex optimization framework to identify optimal layouts for the placement of pressure sensors across a wind farm that ensure a desirable level of accuracy in predicting the speed and direction of the incoming wind. Our results serve as a proof of concept for a wind forecasting strategy based on ground-level pressure sensor measurements as an economical alternative to those that rely on doppler LiDAR capabilities.