Wake Effect Evaluation And Wind Turbine Yawing Optimization In Offshore Wind Farms Based On An Improved Gaussian Model

The wake effect leads to wind velocity deficit at downstream positions,resulting in decline of the overall output power in wind farms.At present,optimizing the layout of wind turbines is mostly used to avoid the output reduction caused by the wake effect.But it cannot be used in wind farms that have been built,and the effect could also be poor where wind direction changes frequently.Based on this background,this dissertation studies the decline in the overall output power in wind farms caused by the wake effect and the optimal yawing strategy to improve the overall output power.Based on the Gaussian distribution model,this dissertation proposes a Gaussian yawing wake model considering non-symmetrical shape with the study of the yawed wake shape.Then the Monte Carlo sampling is introduced to evaluate the decline of the overall output power caused by the wake effect in wind farms of different scales.And an optimal yawing strategy based on genetic algorithm is proposed to make the downstream wind turbines avoid the peak area of the wake region to obtain an improvement of the overall output power.First of all,in order to accurately calculate the wake effect,this dissertation introduces the mass conservation and momentum conservation of the yawed wake region,and proposes a Gaussian yawing wake model considering non-symmetrical shape.Then with comparing the calculation results of the model proposed in this article with experimental data,the accuracy of the proposed model is verified.Subsequently,this dissertation studies the influence of different factors on the wake effect in wind farms,and proposes an evaluation method of wake effect based on Monte Carlo sampling.Firstly,on the basis of the single wake model,the wake superposition effect is taken into consideration,then a 3-D Gaussian multi-wake model is built in MATLAB,and the wake effect under different conditions are calculated.Finally the Monte Carlo sampling is introduced to evaluate the influence of wake effect on the output power of wind farms.The simulation results show that the overall output power of wind farms is notably reduced due to the wake effect.And such decline is much more pronounced in large wind farms because of the heavier superposition.To reduce the impact of wake effect,yawing strategies are then introduced.And an optimal yawing strategy based on genetic algorithm is proposed.The variation of the optimal yaw strategy and its effect under different incoming wind conditions are studied.The simulation results demonstrate that downstream wind turbines could avoid the peak region of wake effect using this strategy,thereby achieving a significant improvement of the overall output power in wind farms.This dissertation conducts the experimental analyses on the evaluation of the wake effect and the yawing strategy,providing a scientific theoretical basis for the engineering application,and making up for the deficiencies of existing research in this field.