Gaussian与GA风电场尾流软测量建模与优化

Modeling and optimization of wind farm wake soft sensing based on Gaussian and GA

由于风电场内机组间存在尾流效应,影响风电场整体发电量,导致风电场收益降低。而尾流效应又很难观测,为削弱风电场尾流效应对发电量的影响,对尾流软测量、尾流优化方法展开研究。根据过程机理建模方法,基于Jensen尾流模型和高斯(Gaussian)风速模型,建立风电场机组尾流模型,基于模型进行尾流仿真计算,分析尾流效应对机组发电功率的影响,并采用遗传算法(GA)对风电机组偏航角进行优化,合理优化机组间的尾流影响,最后基于实际风场案例进行仿真实验研究。通过研究发现,应用所述理论与方案,实验风场整体发电量可提升1.5%,在提升发电收益的同时也减少碳排放。

Due to the wake effect between units in the wind farm,the overall power generation of the wind farm is affected,resulting in the reduction of the income of the wind farm.In order to weaken the influence of wind farm wake effect on power generation,the wake soft sensing and wake optimization methods are studied.According to the process mechanism modeling method,the wake model of wind farm unit is established based on Jensen wake model and Gaussian wind speed model.The wake simulation calculation is carried out based on the model to analyze the influence of wake effect on unit power.Genetic algorithm(GA)is used to optimize the yaw angle of wind turbine unit and reasonably optimize the wake effect between units,Finally,the simulation experiment is carried out based on the actual wind field case.It is found that the application of the theory and scheme can increase the overall power generation of the experimental wind farm by 1.5%,improve the power generation income and reduce the carbon emission at the same time.