a b s t r a c tThe wake evolution measured downstream of multi-megawatt wind turbines located in flat and complex terrains are described here. These high-resolution measurements at full-scale Reynolds number conditions are made with an instrumented drone that is equipped with a suite of sensors and detail the characteristics of the mean flow and turbulent kinetic energy in the evolving wake. Reynolds decomposition yields the nature of turbulent fluctuations in surface layer, and this decomposition is used to detail the turbulence statistics, degree of anisotropy and friction velocity. These measurements are shown to be suited for the further development of three-dimensional wake models that are currently under intensive development.
The aerodynamic characteristics of wakes in complex terrain have a profound impact on the energy yield of wind farms and on the fatigue loads on wind turbines in the wind farm. In order to detail the spatial variations of the wind speed, wind direction, and turbulent kinetic energy (TKE) in the near-wake, comprehensive drone-based measurements at a multi-megawatt (MW) wind turbine that is located in complex terrain have been conducted. A short-time Fourier transform (STFT)-based analysis method is used to derive time-localized TKE along the drone's trajectory. In upstream and in the near-wake, the vertical profiles of wind speed, wind direction, and TKE are detailed. There is an increase in the TKE from upstream to downstream of the wind turbine, and whereas, the characteristic microscale length scales increase with increasing height above the ground upstream of the turbine, in the near-wake the microscale lengths are of constant, smaller magnitude. The first-ever measurements of the pressure field across a multi-MW wind turbines rotor plane and of the tip vortices in the near-wake are also reported. It is shown that the pitch between subsequent tip vortices, which are shed from the wind turbines blades, increases in the near-wake as the wake evolves. These details of the near-wake can have an important effect on the subsequent evolution of the wake and must be incorporated into the three-dimensional (3D) field wake models that are currently under intensive development.
The expanded integration of wind energy imposes technical challenges to maintain system reliability. In order to tackle these challenges, comprehensive reliability models for wind turbines and related factors are essential. Proposed algorithm classifies Wind Turbine Generator (WTG) components based on their impact on WTG output. There upon, the WTG has a composite three-state reliability model which aggregates WTG foremost components. The chronological operation conditions of each component is obtained using state duration sampling method. Precise Wind Farms (WFs) reliability assessment requires accurate Wind Speed (WS) forecasting methods which acknowledge WSs propagation through WFs terrains. Thus, WS variations are developed based on Weibull distribution. Offered algorithms are integrated to estimate the capacity factor of some WFs using Monte Carlo simulation method. The implied WS data are recorded in certain locations in Egypt which are candidates to host WFs. The utilized simulation environments are MATLAB and Simulink.
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