Wind turbines are usually clustered in wind farms which causes the downstream turbines to operate in the turbulent wakes of upstream turbines. As turbulence is directly related to increased fatigue loads, knowledge of the turbulence in the wake and its evolution are important. Therefore, the main objective of this study is a comprehensive exploration of the turbulence evolution in the wind turbine’s wake to identify characteristic turbulence regions. For this, we present an experimental study of three model wind turbine wake scenarios that were scanned with hot-wire anemometry with a very high downstream resolution. The model wind turbine was exposed to three inflows: laminar inflow as a reference case, a central wind turbine wake, and half of the wake of an upstream turbine. A detailed turbulence analysis reveals four downstream turbulence regions by means of the mean velocity, variance, turbulence intensity, energy spectra, integral and Taylor length scales, and the Castaing parameter that indicates the intermittency, or gustiness, of turbulence. In addition, a wake core with features of homogeneous isotropic turbulence and a ring of high intermittency surrounding the wake can be identified. The results are important for turbulence modeling in wakes and optimization of wind farm wake control
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tract Micro wind turbines can be structurally integrated on top of the solid base of noise barriers near highways. A number of performance factors were assessed with holistic experiments in wind tunnel and in the field. The wind turbines underperformed when exposed in yawed flow conditions. The theoretical cosθ theories for yaw misalignment did not always predict power correctly. Inverter losses turned out to be crucial especially in standby mode. Combination of standby losses with yawed flow losses and low wind speed regime may even result in a net power consuming turbine. The micro wind turbine control system for maintaining optimal power production underperformed in the field when comparing tip speed ratios and performance coefficients with the values recorded in the wind tunnel. The turbine was idling between 20%–30% of time as it was assessed for sites with annual average wind speeds of three to five meters per second without any power production. Finally, the field test analysis showed that inadequate yaw response could potentially lead to 18% of the losses, the inverter related losses to 8%, and control related losses to 33%. The totalized loss led to a 48% efficiency drop when compared with the ideal power production measured before the inverter. Micro wind turbine’s performance has room for optimization for application in turbulent wind conditions on top of noise barriers. https://doi.org/10.3390/en14051288
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In this report, the details of an investigation into the e ect of the low induction wind turbines on the Levelised Cost of Electricity (LCoE) in a 1GW o shore wind farm is outlined. The 10 MW INNWIND.EU conventional wind turbine and its low induction variant, the 10 MW AVATAR wind turbine, are considered in a variety of 10x10 layout configurations. The Annual Energy Production (AEP) and cost of electrical infrastructure were determined using two in-house ECN software tools, namely FarmFlow and EEFarm II. Combining this information with a generalised cost model, the LCoE from these layouts were determined. The optimum LCoE for the AVATAR wind farm was determined to be 92.15 e/MWh while for the INNWIND.EU wind farm it was 93.85 e/MWh. Although the low induction wind farm o ered a marginally lower LCoE, it should not be considered as definitive due to simple nature of the cost model used. The results do indicate that the AVATAR wind farms require less space to achieve this similar cost performace, with a higher optimal wind farm power density (WFPD) of 3.7 MW/km2 compared to 3 MW/km2 for the INNWIND.EU based wind farm.
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This paper assesses wind resource characteristics and energy yield for micro wind turbines integrated on noise barriers. An experimental set-up with sonic anemometers placed on top of the barrier in reference positions is realized. The effect on wind speed magnitude, inflow angle and turbulence intensity is analysed. The annual energy yield of a micro wind turbine is estimated and compared using data from a micro-wind turbine wind tunnel experiment and field data. Electrical energy costs are discussed as well as structural integration cost reduction and the potential energy yield could decrease costs. It was found that instantaneous wind direction towards the barrier and the height of observation play an influential role for the results. Wind speed increases in perpendicular flows while decreases in parallel flow, by +35% down to −20% from the reference. The azimuth of the noise barrier expressed in wind field rotation angles was found to be influential resulted in 50%–130% changes with respect to annual energy yield. A micro wind turbine (0.375 kW) would produce between 100 and 600 kWh annually. Finally, cost analysis with cost reductions due to integration and the energy yield changes due to the barrier, show a LCOE reduction at 60%–90% of the reference value. https://doi.org/10.1016/j.jweia.2020.104206
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The dynamic inflow effect denotes the unsteady aerodynamic response to fast changes in rotor loading due to a gradual adaption of the wake. This does lead to load overshoots. The objective of the paper was to increase the understanding of that effect based on pitch step experiments on a 1.8 m diameter model wind turbine, which are performed in the large open jet wind tunnel of ForWind – University of Oldenburg. The flow in the rotor plane is measured with a 2D laser Doppler anemometer, and the dynamic wake induction factor transients in axial and tangential direction are extracted. Further, integral load measurements with strain gauges and hot-wire measurements in the near and close far wake are performed. The results show a clear gradual decay of the axial induction factors after a pitch step, giving the first direct experimental evidence of dynamic inflow due to pitch steps. Two engineering models are fitted to the induction factor transients to further investigate the relevant time constants of the dynamic inflow process. The radial dependency of the axial induction time constants as well as the dependency on the pitch direction is discussed. It is confirmed that the nature of the dynamic inflow decay is better described by two rather than only one time constant. The dynamic changes in wake radius are connected to the radial dependency of the axial induction transients. In conclusion, the comparative discussion of inductions, wake deployment and loads facilitate an improved physical understanding of the dynamic inflow process for wind turbines. Furthermore, these measurements provide a new detailed validation case for dynamic inflow models and other types of simulations.
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Dynamic inflow effects occur due to the rapid change of the rotor loading underconditions such as fast pitch steps. The paper presents a setup suitable for the investigation ofthose effects for non-axisymmetric rotor conditions, namely individual pitch steps. Furthermore, insights into the relevant phenomena are gathered. An individual pitch control capable model wind turbine is set up in a wind tunnel in order to conduct measurement under controllable conditions. During the execution of the collective and individual pitch steps, the loads and the operational parameters are recorded by the onboard sensors. Meanwhile, simulations engineering aeroelastic codes are run in order to evaluate their accuracy for predicting the relevant phenomena. Results show distinct behaviour of the rotor loads during an individual pitch step, which differs from the loads under collective steps. The free vortex wake simulations are able to predict the turbines’ response satisfactory while the blade element momentum tools show deviations from the measurements. The findings serve as a basis for discussion and future work.
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The dynamic inflow effect describes the unsteady aerodynamic response to fast changes in rotor loading due to the inertia of the wake. Fast changes in turbine loading due to pitch actuation or rotor speed transients lead to load overshoots. The phenomenon is suspected to be also relevant for gust situations; however, this was never shown, and thus the actual load response is also unknown. The paper’s objectives are to prove and explain the dynamic inflow effect due to gusts, and compare and subsequently improve a typical dynamic inflow engineering model to the measurements. An active grid is used to impress a 1.8m diameter model turbine with rotor uniform gusts of the wind tunnel flow. The influence attributed to the dynamic inflow effect is isolated from the comparison of two experimental cases. Firstly, dynamic measurements of loads and radially resolved axial velocities in the rotor plane during a gust situation are performed. Secondly, corresponding quantities are linearly interpolated for the gust wind speed from lookup tables with steady operational points. Furthermore,simulations with a typical blade element momentum code and a higher-fidelity free-vortex wake model are performed. Both the experiment and higher-fidelity model show a dynamic inflow effect due to gusts in the loads and axial velocities. An amplification of induced velocities causes reduced load amplitudes. Consequently, fatigue loading would be lower. This amplification originates from wake inertia. It is influenced by the coherent gust pushed through the rotor like a turbulent box. The wake is superimposed on that coherent gust box, and thus the inertia of the wake and consequently also the flow in the rotor plane is affected. Contemporary dynamic inflow models inherently assume a constant wind velocity. They filter the induced velocity and thus cannot predict the observed amplification of the induced velocity. The commonly used Øye engineering model predicts increased gust load amplitudes and thus higher fatigue loads. With an extra filter term on the quasi-steady wind velocity, the qualitative behaviour observed experimentally and numerically can be caught. In conclusion, these new experimental findings on dynamic inflow due to gusts and improvements to the Øye model enable improvements in wind turbine design by less conservative fatigue loads.
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Key to reinforcement learning in multi-agent systems is the ability to exploit the fact that agents only directly influence only a small subset of the other agents. Such loose couplings are often modelled using a graphical model: a coordination graph. Finding an (approximately) optimal joint action for a given coordination graph is therefore a central subroutine in cooperative multi-agent reinforcement learning (MARL). Much research in MARL focuses on how to gradually update the parameters of the coordination graph, whilst leaving the solving of the coordination graph up to a known typically exact and generic subroutine. However, exact methods { e.g., Variable Elimination { do not scale well, and generic methods do not exploit the MARL setting of gradually updating a coordination graph and recomputing the joint action to select. In this paper, we examine what happens if we use a heuristic method, i.e., local search, to select joint actions in MARL, and whether we can use outcome of this local search from a previous time-step to speed up and improve local search. We show empirically that by using local search, we can scale up to many agents and complex coordination graphs, and that by reusing joint actions from the previous time-step to initialise local search, we can both improve the quality of the joint actions found and the speed with which these joint actions are found.
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