Understanding wind turbine wake mixing and recovery is critical for improving the power generation and structural stability of downwind turbines in a wind farm. In the field, where incoming flow and turbine operation are constantly changing, the rate of wake recovery can be significantly influenced by dynamic wake modulation, which has not yet been explored. Here we present the first investigation of dynamic wake modulation in the near wake of an operational utility-scale wind turbine, and quantify its relationship with changing conditions. This experimental investigation is enabled using novel super-large-scale flow visualization with natural snowfall, providing unprecedented spatiotemporal resolution to resolve instantaneous changes of the wake envelope in the field. These measurements reveal the significant influence of dynamic wake modulation, which causes an increase in flux across the wake boundary of 11% on average, on wake recovery, providing insights into necessary modifications to traditional wake and farm models. Further, our study uncovers the direct connection between dynamic wake modulation and operational parameters readily available to the turbine controller such as yaw error, blade pitch, and tip speed ratio. These connections pave the way for more precise wake prediction and control algorithms under field conditions for wind farm optimization.
Bibliographical noteFunding Information:
This work was supported by the National Science Foundation CAREER award ( NSF-CBET-1454259 ), Xcel Energy through the Renewable Development Fund (grant RD4-13 ) as well as IonE of University of Minnesota. We also thank the students and the engineers from St Anthony Falls Laboratory, including S. Riley, T. Dasari, B. Li, Y. Wu, J. Tucker, C. Ellis, J. Marr, C. Milliren and D. Christopher for their assistance in the experiments. Appendix A
- Flow visualization
- Wind energy
- Wind turbine