相位随机化傅里叶变换模型用于合成风速的产生
Phase-randomised Fourier transform model for the generation of synthetic wind speeds
摘要：
风力涡轮机设计和控制的日益复杂性产生了对高质量数据的需求。因此，可以用计算机生成的替代数据来扩展相对有限的一组测得的风数据，例如，以进行能量产生和机械负载的可靠统计研究。本文提出了一种数据驱动的，统计模型的生成现实的替代时间序列，是基于相位随机傅里叶变换。该模型模拟了一个遍历的伪随机过程，利用迭代秩重排序过程产生的合成时间序列，具有目标数据的功率谱密度，同时收敛到目标数据的概率分布与任意的，用户定义的精度。两个建立的数据驱动的建模技术生成替代风速的比较。在所选模型的测试用例中给出的相同输入条件下对所提出的模型进行测试，并根据与目标统计描述符的一致性对其性能进行研究。仿真结果表明，该模型能够以高保真度再现输入数据集的统计描述符，并能够捕捉风速的非平稳日变化和季节变化。
英文摘要：
The increasing sophistication of wind turbine design and control generates a need for high-quality data. Therefore, the relatively limited set of measured wind data may be extended with computer-generated surrogate data, e.g. to make reliable statistical studies of energy production and mechanical loads. This paper presents a data-driven, statistical model for the generation of realistic surrogate time series that is based on the phase-randomised Fourier transform. The proposed model simulates an ergodic, pseudo-random process that makes use of an iterative rank-reordering procedure to yield synthetic time series that possess the power spectral density of the target data and concurrently converges to the probability distribution of the target data with an arbitrary, user-defined precision. A comparison with two established data-driven modelling techniques for generating surrogate wind speeds is presented. The proposed model is tested under the same input conditions given in the test cases of the selected models, and its performance is investigated in terms of the agreement with the target statistical descriptors. Simulation results show that the proposed model can reproduce with high fidelity the statistical descriptors of the input datasets and is able to capture the nonstationary diurnal and seasonal variations of the wind speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
