Characterizing nonstationary wind speed using empirical mode decomposition

This paper explores how to characterize nonstationary wind speed that can be modeled as a deterministic time-varying mean wind speed component plus a stationary random process for the fluctuating wind speed component. The time-varying mean wind speed is naturally extracted from the nonstationary wind data using the empirical mode decomposition (EMD). The proposed approach is then applied to the wind data recorded by the anemometers installed in the Tsing Ma suspension Bridge during Typhoon Victor to find its time-varying mean wind speed, probability distribution of fluctuating wind speed, wind spectrum, turbulence intensity, and gust factor. The resulting wind characteristics are compared with those obtained by the traditional approach based on a stationary wind model. It is found that most of nonstationary wind data can be decomposed into a time-varying mean wind speed plus a well-behaved fluctuating wind speed admitted as a stationary random process with a Gaussian distribution. The time-varying mean wind speed identified by EMD at a designated intermittency frequency level is more natural than the traditional time-averaged mean wind speed over the certain time interval. The proposed approach can also be applied to stationary wind speed with the same output as obtained by the traditional approach. It is concluded that the proposed approach is more appropriate than the traditional approach for characterizing wind speed.