基于小波逆变换方法的风力机风场数值模拟研究

Simulation of wind turbine stochastic wind field based on inverse wavelet method

针对风力机随机风场的特点,采用小波逆变换方法,仿真出相互独立的风力机随机风场,并对其进行空间相关性修正。以1.5 MW风力机为算例,分析了3种风功率频谱的差异特点,基于空间变化的改进Von karman功率频谱模型,采用Littlewood-Paley小波基作为小波逆变换的正交基函数,利用小波系数和功率频谱密度函数的关系进行了空间相关性修正,模拟出风场36个节点风速时程,并通过时域分析、频域分析以及时频联合分析,得到模拟频谱与目标频谱吻合较好的结论。该结论表明了风力机风场的非平稳性、局部相似性和间隙性,以及高非线性风速功率频谱的逼近性,证明了小波逆变换方法在模拟风力机随机风场的有效性和准确性。

The wavelet inverse transformation method is used to investigate stochastic wind field fea- ture. In a wind field where wind turbines are supposed to be erected, independent wind speeds are sim- ulated, and these speeds are then corrected by a correlation approach. Taking a 1.5 MW wind turbine as a numerical example, different features of three wind power spectrums are analyzed. The stochastic wind time history at 36 locations in the wind field is simulated based on a space variation improved Von karman power spectrum model,which uses Littlewood-Paley wavelet function as orthogonalbasis, and corrects space correlation by taking advantage of the relation between waveletcoefficient and power spectral densities. It is found that the simulated power spectral densities are consistent with the target power spectral density in time domain, frequency domain and time-frequency analyses. The results show that the stochastic winds have the properties of non-stationary, local-similarity and intermitten- cy, and the wind field approximates wind speed of high nonlinear power spectrum,which proves that the method presented in this paper is effective and accuracy for wind turbine field simulation.