Wind plant optimization opportunities based on scanning-based measurements and advanced measurement analysis techniques

Scanning remote sensing instruments can provide high resolution spatial measurements of atmospheric winds across a region (e.g. a wind plant). Scanning radar such as Texas Tech University’s Ka-band (TTUKa) Doppler radars have the ability to document wind plant complex flow structure and variability at scales of motion relevant to wind energy using both single- and dual-Doppler data collection approaches. Therefore, scanning instruments should facilitate the development and validation of improved model parameterization schemes designed to reduce the wind plant underperformance gap (i.e. the difference between preconstruction energy estimates and the actual energy production). However, even if the underperformance gap is eliminated, today’s wind plants are far from optimized. Therefore, the comprehensive research question of this dissertation was: To what extent can analysis of scanning-based wind measurements be used to facilitate the development of ‘smart’ (i.e. more efficient) wind plants? To explore this research question, the TTUKa radars collected ABL wind and wind plant complex flow measurements using both single- and dual-Doppler data collection approaches to (1) establish and validate innovative measurement analysis techniques to remove several principal limitations of scanning-based instruments and (2) examine the effectiveness of two commonly used wind plant wake mitigation control strategies (i.e. strategically offsetting wind turbine yaw and blade pitch) in varying atmospheric conditions.

Innovative measurement analysis techniques were developed to determine boundary layer advective properties (speed and direction), which through a robust space-to-time conversion process allow sub-volume time scale (i.e. less than the measurement revisit period) temporal wind field variability to be resolved at individual locations within the measurement domain. Also, methods were established to extract atmospheric turbulence intensity information by analyzing spatial wind field variability across defined analysis areas of a single scanned volume. These advanced analysis techniques should significantly improve the utility of scanning-based measurements in both the wind energy and atmospheric science communities.

This research also highlighted some of the difficulties associated with the effective implementation of control-based wake mitigation including (1) the ability to accurately implement the desired control changes, (2) identifying reliable data sources and methods to allow these control changes to be accurately quantified, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions (e.g. boundary layer streak orientation, atmospheric stability). For example, wake length and meandering are sensitive to boundary layer stability in such a way as to suggest wind plant control effectiveness may be increased in the stable boundary layer relative to the unstable boundary layer.