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This study employs a UAV anemometry system to assess the wind field around a yawed wind turbine, particularly focusing on its wake during operational conditions. The research findings reveal that the evolution of wind turbine wakes follows distinct patterns at various downstream distances. Turbulence intensity notably amplifies within regions characterized by significant fluctuations in mean wind speed. Specifically, in yawed conditions, the areas with the highest turbulence generated by the rotor coincide with zones exhibiting pronounced variations in mean wind speed. The heightened turbulence within the wake region to some extent constrains the safety and economic viability of wind farms. Turbulence intensity increases significantly in the region where the average wind speed changes greatly, that is, under the yaw state of the wind turbine, this region is characterized with the strongest turbulence generated by the rotor. The UAV anemometry system's wind speed assessment closely matches predictions from the Y-3DJGF model, accounting for wake experience coefficient adjustments. Furthermore, in yaw conditions, the wind turbine's wake trajectory exhibits some deviation from the incident flow's direction, with an initial increase in slope followed by gradual stabilization. As the downstream distance increases, the trajectory will eventually establish a consistent trend with the incident flow. The cost-effective and flexible UAV anemometry system enhances wind field measurements, offering an innovative approach for wind energy sector research and engineering. • Development of a UAV-based anemometry system for wake measurement of yawed wind turbines. • Investigation of wake characteristics under yawed conditions, revealing significant reductions in wake wind speed and increased turbulence intensity, especially at 2D downstream. • Experimental validation of wake trajectory deviations under yaw conditions, highlighting changes in slope and eventual alignment with incident flow downstream. • Validation of wind speed measurements using the UAV anemometry system against predicted results from the Y-3DJGF model. • Application of the UAV anemometry system in wind resource assessment, layout optimization, and power generation efficiency forecasting for wind farms. [ABSTRACT FROM AUTHOR]

The wake of wind turbine in a complex terrain site is investigated by a modified delayed detached eddy simulation (MDDES) and a hybrid framework to predict the wind turbine wake effects in a real wind farm are presented. The wind field and turbine wake flows over the real terrain are simulated for different directions and validated by comparing with those measured by a LiDAR in the wind farm. Subsequently, the wake characteristics over the escarpment are systematically investigated by numerical simulations and the terrain effects on the turbine wake flow are clarified. A hybrid framework is then utilized for a wind farm over complex terrain. In this framework, the effects of local terrain and surface roughness on the wind speed, wind direction and turbulence intensity are taken into account by the numerical simulation, while the wind turbine wakes are represented by a new Gaussian-based analytical wake model. Finally, the applicability of the proposed framework is verified by comparison with the numerical simulation results of wake over the escarpment. • Numerical simulations on the wind turbine wake over complex terrain are conducted and validated by the field measurement. • The wind turbine wake characteristics over the escarpment with different elevation are systematically investigated. • A hybrid framework is utilized for wind turbine wake prediction over complex terrain and its applicability is verified. [ABSTRACT FROM AUTHOR]

A new wake model for wind turbines considering ambient turbulence intensity and thrust coefficient effects is proposed by numerical and analytical studies. Firstly, two kinds of operating condition with different thrust coefficients under two types of inflow with different ambient turbulence intensity are simulated for a model and a utility-scale wind turbine by using large eddy simulation (LES). The predicted mean velocity and turbulence intensity in the wakes of two wind turbines are compared with those obtained from the wind tunnel tests to validate numerical models. Subsequently, a new wake model is proposed to predict the mean velocity and turbulence intensity distribution in the wake regions of wind turbines. The model is derived based on the axial symmetry and self-similarity assumption for wake deficit and added turbulence intensity. All parameters of the proposed model are determined as the function of ambient turbulence intensity and thrust coefficient identified based on the various large eddy simulations. The velocity deficit and added turbulence intensity in the wake predicted by the new wake model show good agreement with the LES simulations and experimental results in the near and far wake regions. [ABSTRACT FROM AUTHOR]

A numerical wake model that is capable of simulating wind turbine wake effects over complex terrains is proposed in this work. Currently, full computational fluid dynamics (CFD) simulations are required to simulate wake effects over complex terrains. Due to their high computational cost, it is difficult to apply expensive CFD simulations in the wind farm design process as optimization algorithms often require large number of solution evaluations. The proposed wake model solves a simplified variation of the Navier-Stokes equations, in which simplifications and assumptions have been implemented in order to reduce computational cost while maintaining accuracy. This model was validated by comparing with full CFD simulations with reasonable accuracy. In general, the model produces accurate results while keeping the computational cost two orders of magnitude lower than that of full CFD simulations. The low computation time highlights the model's potential to be used for optimizing wind farm layouts on complex terrains. [ABSTRACT FROM AUTHOR]

Previous studies on wake of horizontal-axis wind turbines (HAWTs) mainly focus on the spatial distribution but ignore the time-varying characteristics of the wake profile which highly affect the operation strategy of the downstream HAWTs to ensure the high-efficiency and low-fatigue load. This paper proposed a mathematical method for studying the spatial-temporal inhomogeneity of wake profiles. Considering the anisotropy of wake expansion, the turbulence intensity was modified to avoid repeated experimental calculation of empirical parameters such as wake expansion coefficient. In addition, the super-Gaussian function was used to modify the near wake model, and the modified near wake model was combined with four typical far wake models to obtain four temporal wake models that can express the time-varying characteristics of the wake. The accuracy of the improved four temporal wake models were verified by wind tunnel test, and the relative errors were analyzed. Results show that the prediction errors of the modified two-dimensional temporal wake model are basically within 4%, which is smaller than the other three wake models. The method presented in this paper can effectively describe the spatial distribution and time-varying characteristics of wake expansion, and can provide a reference for optimizing the control strategy of HAWTs. • The spatial-temporal inhomogeneity characteristic of wake is discussed. • Four temporal wake models are proposed to express spatial-temporal profile of the wake. • These four temporal wake models are based on four typical far wake models. • The temporal wake models are verified using measured data of wind tunnel test. • The temporal wake models have good accuracy. [ABSTRACT FROM AUTHOR]