Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series

Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.

Self-Similar Characteristic for the Ramp Structures of Wind Speed

Time series of wind speed are composed of large and small ramp structures. Data analysis reveals a power law relation between the linear slope of ramp structures and the time scale. This suggests that these ramp structures of wind speed have a self-similar characteristic. The lower limit of the self-similar scale range was 2 s. The upper limit is unexpectedly large at 27 rain. Data are collected from grassland, city, and lake areas. Although these data have different underlying surfaces, all of them clearly show a power law relation, with slight differences in their power exponents.

Paradigm of Numerical Simulation of SpatialWind Field for Disaster Prevention of Transmission Tower Lines

Numerical simulation of the spatial wind field plays a very important role in the study of wind-induced response law of transmission tower structures.A reasonable construction of a numerical simulation method of the wind field is conducive to the study of wind-induced response law under the action of an actual wind field.Currently,many research studies rely on simulating spatial wind fields as Gaussian wind,often overlooking the basic non-Gaussian characteristics.This paper aims to provide a comprehensive overview of the historical development and current state of spatial wind field simulations,along with a detailed introduction to standard simulation methods.Furthermore,it delves into the composition and unique characteristics of spatial winds.The process of fluctuating wind simulation based on the linear filter AR method is improved by introducing spatial correlation and non-Gaussian distribution characteristics.The numerical simulation method of the wind field is verified by taking the actual transmission tower as a calculation case.The results show that the method summarized in this paper has a broader application range and can effectively simulate the actual spatial wind field under various conditions,which provides a valuable data basis for the subsequent research on the wind-induced response of transmission tower lines.

Simulating Temporally and Spatially Correlated Wind Speed Time Series by Spectral Representation Method

In this paper,it aims to model wind speed time series at multiple sites.The five-parameter Johnson mdistribution is deployed to relate the wind speed at each site to a Gaussian time series,and the resultant-Z(t)dimensional Gaussian stochastic vector process is employed to model the temporal-spatial correlation of mwind speeds at different sites.In general,it is computationally tedious to obtain the autocorrelation functions Z(t)(ACFs)and cross-correlation functions(CCFs)of Z(t),which are different to those of wind speed times series.In order to circumvent this correlation distortion problem,the rank ACF and rank CCF are introduced to Z(t)characterize the temporal-spatial correlation of wind speeds,whereby the ACFs and CCFs of can be analytically obtained.Then,Fourier transformation is implemented to establish the cross-spectral density matrix Z(t)mof,and an analytical approach is proposed to generate samples of wind speeds at different sites.Finally,simulation experiments are performed to check the proposed methods,and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds,and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds.

COMBINING SINGULAR SPECTRUM ANALYSIS AND PAR(p) STRUCTURES TO MODEL WIND SPEED TIME SERIES

Singular spectrum analysis （SSA） is a technique that decomposes a time series into a set of components, such as, trend, harmonics, and residuals. Leaving out the residual components and adding up the others, the time series can be smoothed. This procedure has been used to model Brazilian electricity consumption and flow series. The PAR（p）, periodic autoregressive models, has been broadly used in modelling energy series in Brazil. This paper presents an approach of this decomposition method, by fitting the PAR（p）, considering its multivariate version known as multivariate SSA （MSSA）. The method was applied to a vector of two wind speed series recorded at two locations in the Brazilian Northeast region. The obtained results, when compared to the univariate decomposition of each series, were far superior, showing that the spatial correlation between the two series were considered by MSSA decomposition stage.