Determining an Appropriate Parameter of Analytical Wake Models for Energy Capture and Layout Optimization on Wind Farms

Abstract

The wake of a wind turbine is a crucial factor that decreases the output of downstream wind turbines and causes unsteady loading. Various wake models have been developed to understand it, ranging from simple ones to elaborate models that require long calculation times. However, selecting an appropriate wake model is difficult because each model has its advantages and disadvantages as well as distinct characteristics. Furthermore, determining the parameters of a given wake model is crucial because this affects the calculation results. In this study, a method was introduced of using the turbulence intensity, which can be measured onsite, to objectively define parameters that were previously set according to the subjective judgement of a wind farm designer or general recommended values. To reflect the environmental effects around a site, the turbulence intensity in each direction of the wind farm was considered for four types of analytical wake models: the Jensen, Frandsen, Larsen, and Jensen–Gaussian models. The prediction performances of the wake models for the power deficit and energy production of the wind turbines were compared to data collected from a wind farm. The results showed that the Jensen and Jensen–Gaussian models agreed more with the power deficit distribution of the downstream wind turbines than when the same general recommended parameters were applied in all directions. When applied to energy production, the maximum difference among the wake models was approximately 3%. Every wake model clearly showed the relative wake loss tendency of each wind turbine.

Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore

“…It is better exploration abilities, diminished susceptibility to being trapped in local minima, and because it does not suffer from premature convergence. PSO is an algorithm built on the basis of swarm intelligence to solve optimization problems, e.g., search spaces [24], and it has been successfully applied in many areas where optimization problems need to be resolved [25]. Figure 7 shows the convergent process of Pareto optimization by the interaction of turbine layout design variables with EA and PSO, two objective functions AEPs, and the posterior loss.…”