Abstract:We study dynamic induction control for mitigating the wake losses of a pair of inline wind turbines. In order to explore control strategies that account for unsteady interactions with the flow, we employ optimal control and adjoint-based optimization in combination with large-eddy simulations. The turbines are represented with an actuator line model. We consider a simple uniform inflow case with two NREL 5 MW turbines spaced 5 diameters apart and find that optimal control leads to 25% gains compared to standar… Show more
“…The wall shear stress is computed according to the standard log law 14,20 . In wake flow simulations, a fringe zone is used to adjust the flow from the downstream wake state to an undisturbed inflow condition [21][22][23] . The vertical profiles of mean wind speed and turbulence intensity in the streamwise direction obtained from the precursor simulation are shown in Fig.…”
To fulfill the increasing need for large power generation by wind turbines, the concept of multi-rotor wind turbines has recently received attention as a promising alternative to conventional massive single-rotor wind turbines. To shed light on the viability of this concept, large-eddy simulation is employed in this study to compare wake flow properties of a multi-rotor wind turbine with those of a single-rotor turbine. The wake of a multi-rotor turbine is found to recover faster at short downwind distances, where the whole wake is characterized as an array of localized high velocity-deficit regions associated with each rotor. However, as the wake moves downstream, rotor wakes start interacting with each other until they eventually form a single wake. This transition from a wake array to a single wake adversely affects the initial fast recovery of multirotor turbine wakes. A budget analysis of mean kinetic energy is performed to analyze the energy transport into the wake before and after this transition. In addition, the effect of different geometrical configurations on wake characteristics of a multi-rotor turbine was examined. We found that the recovery rate of multi-rotor turbine wakes is enhanced by the increase of rotor spacing, whereas the number and rotation direction of rotors do not play a significant role in the wake recovery. A simple analytical relationship is also developed to predict the streamwise distance at which the transition from a wake array to a single wake occurs for multi-rotor wind turbines.
“…The wall shear stress is computed according to the standard log law 14,20 . In wake flow simulations, a fringe zone is used to adjust the flow from the downstream wake state to an undisturbed inflow condition [21][22][23] . The vertical profiles of mean wind speed and turbulence intensity in the streamwise direction obtained from the precursor simulation are shown in Fig.…”
To fulfill the increasing need for large power generation by wind turbines, the concept of multi-rotor wind turbines has recently received attention as a promising alternative to conventional massive single-rotor wind turbines. To shed light on the viability of this concept, large-eddy simulation is employed in this study to compare wake flow properties of a multi-rotor wind turbine with those of a single-rotor turbine. The wake of a multi-rotor turbine is found to recover faster at short downwind distances, where the whole wake is characterized as an array of localized high velocity-deficit regions associated with each rotor. However, as the wake moves downstream, rotor wakes start interacting with each other until they eventually form a single wake. This transition from a wake array to a single wake adversely affects the initial fast recovery of multirotor turbine wakes. A budget analysis of mean kinetic energy is performed to analyze the energy transport into the wake before and after this transition. In addition, the effect of different geometrical configurations on wake characteristics of a multi-rotor turbine was examined. We found that the recovery rate of multi-rotor turbine wakes is enhanced by the increase of rotor spacing, whereas the number and rotation direction of rotors do not play a significant role in the wake recovery. A simple analytical relationship is also developed to predict the streamwise distance at which the transition from a wake array to a single wake occurs for multi-rotor wind turbines.
“…These findings have important implications for the design of advanced wind farm control algorithms that use blade pitch to regulate turbine axial induction (e.g. Yılmaz & Meyers 2018), which cannot expect the wake to react immediately to changes in blade pitch. Figure 5.( a ) Sample change in blade pitch angle at a constant rate (dashed black line) and the resulting wake expansion response (solid red line).…”
Section: Resultsmentioning
confidence: 98%
“…In addition to gaining insight into wake behaviours occurring during normal utility-scale wind turbine operation, improved understanding of the dynamic wake is crucial for the design of recently proposed advanced wind farm control algorithms, including thrust optimization (Goit & Meyers 2015; Munters & Meyers 2017; Shapiro et al. 2017; Munters & Meyers 2018 b ; Yılmaz & Meyers 2018), yaw angle modification (Gebraad, Fleming & van Wingerden 2015; Raach et al. 2017, 2018; Fleming et al.…”
Section: Introductionmentioning
confidence: 99%
“…(2017, 2018) used simulations to investigate the effectiveness of a lidar-based closed-loop control framework, and observed a period of inverse wake deflection in response to a 5 step change in yaw angle. Yılmaz & Meyers (2018) and Munters & Meyers (2018 b ) took advantage of changes in wake width induced by periodic blade pitch changes to induce successive vortex rings in the wake of a simulated turbine, thereby re-energizing the wake through their interactions. The wind farm optimization conducted by Munters & Meyers (2018 a ) yielded similar behaviours for thrust and analogous yaw oscillations that induced wake meandering.…”
The current study uses large eddy simulations to investigate the transient response of a utility-scale wind turbine wake to dynamic changes in atmospheric and operational conditions, as observed in previous field-scale measurements. Most wind turbine wake investigations assume quasi-steady conditions, but real wind turbines operate in a highly stochastic atmosphere, and their operation (e.g. blade pitch, yaw angle) changes constantly in response. Furthermore, dynamic control strategies have been recently proposed to optimize wind farm power generation and longevity. Therefore, improved understanding of dynamic wake behaviours is essential. First, changes in blade pitch are investigated and the wake expansion response is found to display hysteresis as a result of flow inertia. The time scales of the wake response to different pitch rates are quantified. Next, changes in wind direction with different time scales are explored. Under short time scales, the wake deflection is in the opposite direction of that observed under quasi-steady conditions. Finally, yaw changes are implemented at different rates, and the maximum inverse wake deflection and time scale are quantified, showing a clear dependence on yaw rate. To gain further physical understanding of the mechanism behind the inverse wake deflection, the streamwise vorticity in different parts of the wake is quantified. The results of this study provide guidance for the design of advanced wake flow control algorithms. The lag in wake response observed for both blade pitch and yaw changes shows that proposed dynamic control strategies must implement turbine operational changes with a time scale of the order of the rotor time scale or slower.
“…Such prediction from individual turbines can be further integrated into the turbine controllers to improve the control strategy of a wind farm for enhancing overall power production and/or mitigating structural damages, constituting the concept of "smart" wind farms in the future. Such concept has been demonstrated in some recent studies, including dynamic pitch variation used to move wake breakdown closer to the turbine [73,74], and yaw steering that deflects wakes away from downstream turbines to improve overall power production of a wind farm [65,75]. It is worth noting that although our study has revealed some statistical correlations between near-wake behavior and individual turbine parameters, the wake behavior in the field is influenced by multiple intertwining physical processes and cannot be readily predicted with a single turbine parameter.…”
This paper provides a review of the general experimental methodology of snow-powered flow visualization and super-large-scale particle imaging velocimetry (SLPIV), the corresponding field deployments and major scientific findings from our work on a 2.5 MW utility-scale wind turbine at the Eolos field station. The field measurements were conducted to investigate the incoming flow in the induction zone and the near-wake flows from different perspectives. It has been shown that these snowpowered measurements can provide sufficient spatiotemporal resolution and fields of view to characterize both qualitatively and quantitatively the incoming flow, all the major coherent structures generated by the turbine (e.g., blade, nacelle and tower vortices, etc.) as well as the development and interaction of these structures in the near wake. Our work has further revealed several interesting behaviors of near-wake flows (e.g., wake contraction, dynamic wake modulation, and meandering and deflection of nacelle wake, etc.), and their connections with constantly-changing inflows and turbine operation, which are uniquely associated with utility-scale turbines. These findings have demonstrated that the near wake flows, though highly complex, can be predicted with substantial statistical confidence using SCADA and structural response information readily available from the current utility-scale turbines. Such knowledge can be potentially incorporated into wake development models and turbine controllers for wind farm optimization in the future.
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