Extraction of optimal time-varying mean of non-stationary wind speeds based on empirical mode decomposition
Kang Cai,Xiao Li,Lun-hai Zhi,Xu-liang Han
Abstract
The time-varying mean (TVM) component of non-stationary wind speeds is commonly extracted utilizing empirical mode decomposition (EMD) in practice, whereas the accuracy of the extracted TVM is difficult to be quantified. To deal with this problem, this paper proposes an approach to identify and extract the optimal TVM from several TVM results obtained by the EMD. It is suggested that the optimal TVM of a 10-min time history of wind speeds should meet both the following conditions: (1) the probability density function (PDF) of fluctuating wind component agrees well with the modified Gaussian function (MGF). At this stage, a coefficient p is newly defined as an evaluation index to quantify the correlation between PDF and MGF. The smaller the p is, the better the derived TVM is; (2) the number of local maxima of obtained optimal TVM within a 10-min time interval is less than 6. The proposed approach is validated by a numerical example, and it is also adopted to extract the optimal TVM from the field measurement records of wind speeds collected during a sandstorm event.
Key Words
Gaussian function; non-stationary wind speed model; empirical mode decomposition; probability density function; optimal time-varying mean
Address
Kang Cai — School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei, Anhui 230009, China; Architecture and Civil Engineering Research Center, City University of Hong Kong Shenzhen Research Institute, Shenzhen, China
Xiao Li — Architecture and Civil Engineering Research Center, City University of Hong Kong Shenzhen Research Institute, Shenzhen, China; Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong
Lun-hai Zhi — School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei, Anhui 230009, China
Xu-liang Han — Architecture and Civil Engineering Research Center, City University of Hong Kong Shenzhen Research Institute, Shenzhen, China; Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong
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