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.

Wind and Photovoltaic Power Time Series Data Aggregation Method Based on an Ensemble Clustering and Markov Chain

Reducing the input wind and photovoltaic power time series data can improve the efficiency of time sequential simulations.In this paper,a wind and photovoltaic power time series data aggregation method based on an ensemble clustering and Markov chain(ECMC)is proposed.The ECMC method can effectively reduce redundant information in the data.First,the wind and photovoltaic power time series data were divided into scenarios,and ensemble clustering was used to cluster the divided scenarios.At the same time,the Davies-Bouldin Index(DBI)is adopted to select the optimal number of clusters.Then,according to the temporal correlation between wind and photovoltaic scenarios,the wind and photovoltaic clustering results are merged and reduced to form a set of combined typical day scenarios that can reflect the characteristics of historical data within the calculation period.Finally,based on the Markov Chain,the state transition probability matrix of various combined typical day scenarios is constructed,and the aggregation state sequence of random length is generated,and then,the combined typical day scenarios of wind and photovoltaic were sampled in a sequential one-way sequence according to the state sequence and then are built into a representative wind and photovoltaic power time series aggregation sequence.A provincial power grid was chosen as an example to compare the multiple evaluation indexes of different aggregation methods.The results show that the ECMC aggregation method improves the accuracy and efficiency of time sequential simulations.