Spectrally consistent upscaling of wind speed time series data
Über dieses Buch
As the share of wind energy grows, reliable engineering computations for wind speed modeling become critical for forecasting, stability assessment, and risk-informed design. A central challenge is that available wind records are often limited to coarse time averages, such as 10-minute or hourly means, which suffice for energy yield estimates but miss the high-frequency variability needed for reliability analysis, fatigue assessment, and extreme-event studies. Building on the Temporal Enhanced Fourier Transform (TEFT), we develop an integrated storm-hazard reliability framework that transforms coarse storm wind histories into high-resolution structural loading scenarios. The approach estimates the spectrum implied by averaged data, corrects for attenuation due to temporal averaging, and reconstructs a finer time grid by adjusting Fourier coefficients under consistency constraints. These constraints enforce agreement with observed moments and marginal distributions, while stochastic sampling generates ensembles that reflect uncertainty. The procedure avoids direct reliance on measured high-rate data, yet maintains realistic maxima, means, variance, distributional fit, and spectral slope. Results show that the generated high-resolution series align with observed characteristics and support reliability computations. The method enables more faithful storm-to-structure simulations for grid stability, structural reliability of onshore and offshore assets, and extreme-event risk analysis, thereby strengthening the reliability and reproducibility of engineering decisions for critical infrastructure systems.
