CN104573249A - Time-variant ARMA model based non-stable wind speed simulation method - Google Patents

Abstract

The invention discloses a time-variant ARMA model based non-stable wind speed simulation method. The time-variant ARMA model based non-stable wind speed simulation method comprises the following steps of 1 determining parameters of a target non-stable wind speed simulated wind speed model and a time-variant ARMA model; 2 dispersing approximate non-stable fluctuating wind speed into a plurality of stable fluctuating wind speeds within a short time interval, and meanwhile adopting a non-uniform modulation function to modulate a power spectrum of each section of stable fluctuating wind speeds into an evolution spectrum; 3 establishing the time-variant ARMA model of non-stable wind speed and performing non-stable wind speed simulation; 4 outputting and displaying a simulation result and comparing the power spectrum of simulation point wind speeds and respective and a self-correlation function and a cross-correlation function with a target power spectrum, a target self-correlation function and a target cross-correlation function. The time-variant ARMA model based non-stable wind speed simulation method is used for fluctuating wind speed non-stability simulation and ensuring that the frequency component of the fluctuating wind speed changes with time, namely a time-varying characteristic of the power spectrum.

Description

Based on time become the non-stationary wind speed simulation method of arma modeling

Technical field

The present invention relates to and a kind ofly adopt time series analysis to simulate the method for non-stationary wind speed, specifically a kind of based on time become the non-stationary wind speed simulation method of arma modeling.

Background technology

For large span spatial structure, Longspan Bridge, (surpassing) high building structure, the buildings or structures such as tall and slender structure (as stay-supported mast, television tower, chimney etc.), wind load is one of control load of structural wind resistance design.And first the Wind resistant analysis of carrying out structure will obtain the sample data of wind load, determine that the Main Means of wind load has wind tunnel test, field measurement and numerical simulation etc. at present.Along with the develop rapidly of computer technology and people are to the further investigation of stochastic process numerical simulation technology, adopt method for numerical simulation the to obtain arbitrariness of the condition such as feature that Wind Velocity History curve can consider place, wind spectrum signature, buildings, the load that simulation is obtained is as far as possible close to the actual wind-force of structure, the arbitrariness of some statistical property can be met simultaneously, and more representative than physical record, be thus widely used in Practical Project.

Non-stationary property is as the ubiquitous a kind of phenomenon of the various random load of occurring in nature (as atmospheric turbulence of boundary layer, thunderstorm high wind and earthquake etc.), its amplitude and frequency are all time dependent, when therefore carrying out numerical simulation to fluctuating wind under some specific environment, the non-stationary of wind is the factor that must consider.Particularly in downburst, (namely strong in Thunderstorm Weather down draft clashes ground, and propagated along earth's surface to surrounding by rum point have sudden and destructive a kind of high wind), it is extremely strong non-stationaryly produces larger dynamic response to structure possibly.The numerical simulation of current stochastic process is mainly divided into two classes: one is the spectral representation method based on trigonometric series superposition, and one is the homing method based on linear filter technology.Natural wind is considered as ergodic stationary Gaussian process by these traditional wind speed simulation methods usually approx, and simulates according to existing pulsating wind power spectrum.For spacious, the good optimum weather wind of smooth place upward stability, above-mentioned stationarity supposition can meet substantially.But a large amount of actual test data analysis shows, under the complicated landform of harsher wind conditions, many wind speed records do not meet this stationarity requirement.Non-stationary fluctuating wind particularly under complicated landform harsher wind conditions, when adopting steady wind speed to suppose, Non-stationary Data needs to give up, and this can cause larger analytical error, as turbulence intensity value can be over-evaluated, and then affects the accuracy of subsequent analysis.

Time series analysis (Time series analysis) is a kind of statistical method of Dynamic Data Processing.The present invention is based on theory of random processes and mathematical statistics method, the statistical law that research random data sequence is deferred to, for solving practical problems.Autoregressive moving-average model (Autoregressive moving average model, be called for short: arma modeling) be the important method of search time sequence, be made up of with " mixing " based on moving average model (being called for short MA model) autoregressive model (being called for short AR model).The present invention is based on nonstationary wind speed model, adopt time series autoregressive moving-average model and consider time variation---i.e. TARMA (Time-Varying ARMA) method of arma modeling coefficient, numerical simulation is carried out to non-stationary fluctuating wind stochastic process.

Summary of the invention

The object of the present invention is to provide a kind of based on time become the non-stationary wind speed simulation method of arma modeling, it is simulated for the non-stationary of fluctuating wind speed, guarantees that its frequency content is time-varying, i.e. the time variation of power spectrum; Simultaneously by the power spectrum of simulation points wind speed and from, cross correlation function with target power is composed and target oneself, cross correlation function carry out the validity that contrasts to guarantee to simulate.

For achieving the above object, design of the present invention is: non-stationary fluctuating wind speed is separated into the short time sequence that some sections can be approximately steady fluctuating wind speed in enough short time interval Δ t, by non-uniformly modulated function, modulation is carried out to power spectrum according to " Evolutionary Spectral " theory and obtain time-varying power spectrum and Evolutionary Spectral, finally by the TARMA time-varying model of the non-stationary fluctuating wind speed set up and the Evolutionary Spectral based on modulation generates non-stationary fluctuating wind speed, generate the spatial coherence that wind speed considers fluctuating wind simultaneously.

According to foregoing invention design, the present invention adopts following technical proposals: a kind of based on time become the non-stationary wind speed simulation method of arma modeling, it is characterized in that, it comprises the following steps:

The first step, determines that the Wind speed model of target non-stationary wind speed simulation becomes each parameter of arma modeling in time;

Second step, by approximate for non-stationary fluctuating wind speed discrete be steady fluctuating wind speed in some short time intervals, the power spectrum of every section of steady fluctuating wind speed is modulated to Evolutionary Spectral by employing non-uniformly modulated function simultaneously;

3rd step, set up non-stationary fluctuating wind speed time become arma modeling, and carry out non-stationary wind speed simulation;

4th step, exports and display simulation result, by the power spectrum of simulation points wind speed and from, cross correlation function with target power is composed and target oneself, cross correlation function contrast.

Preferably, steady fluctuating wind two parts of average wind and zero-mean are become when down wind non-stationary anemometer is shown as deterministic by the described first step.

Preferably, the non-uniformly modulated function representation in described second step be as shown in the formula:

$A(\mathrm{\ω},t)=\sqrt{\frac{{\tilde{U}}_j\left(t\right)}{U_j}{\left[\frac{1+50\frac{\mathrm{\ω}{z}_j}{2\mathrm{\π}{U}_j}}{1+50\frac{\mathrm{\ω}{z}_j}{2\mathrm{\π}{\tilde{U}}_j\left(t\right)}}\right]}^{5/3}}$

In formula: ω is circular frequency; z _jfor space point vertical ground height; for becoming mean wind speed during point place, space; for some place, space non-stationary wind speed statistical average wind speed.

Preferably, the Wind speed model of described non-stationary wind speed simulation is expressed as following formula:

$U(x,y,z,t)={\stackrel{\‾}{U}}^{*}(x,y,z,t)+u^{*}(x,y,z,t)$

In formula, be space (x, y, z) place time become average wind, u ^*t () is the steady fluctuating wind process of zero-mean.

Preferably, become arma modeling time described and adopt following formula:

$U\left(t\right)=\underset{i=1}{\overset{p}{\mathrm{\Σ}}}{A}_i\left(t\right)U(t-\mathrm{i\Δt})+\underset{j=0}{\overset{q}{\mathrm{\Σ}}}{B}_j\left(t\right)X(t-\mathrm{j\Δt})$

Wherein, U (t) is zero-mean nonstationary random process vector, A _i(t) for time become autoregressive coefficient matrix, B _j(t) for time become slip regression coefficient matrix, p is Autoregressive, and q is slip regression order, and to be variance be X (t) 1, the white noise sequence of normal distribution.

Of the present invention based on time become the non-stationary wind speed simulation method tool of arma modeling and have the following advantages: the non-stationary pulsating wind power spectrum of each point of the present invention's simulation and target average power spectra compare, and simulation each point wind speed from, cross correlation function and target oneself, cross correlation function contrast, results contrast coincide.Fluctuating wind speed amplitude with time to become mean wind speed size relevant, time to become mean wind speed larger then fluctuating wind speed amplitude larger, the wind field characteristic of this and reality matches.

Accompanying drawing explanation

Fig. 1 is the non-stationary wind speed simulation point schematic diagram along ground vertical direction;

Fig. 2 (a) and Fig. 2 (b) is downburst moves different phase downburst model schematic towards buildings direction;

Fig. 3 be based on time become the non-stationary wind speed simulation method design frame diagram of arma modeling;

Fig. 4 be based on time become the non-stationary wind speed simulation method program process flow diagram of arma modeling;

Fig. 5 (a) to Fig. 5 (c) is each virtual space point along the non-stationary wind speed of ground vertical height 10m, 25m and 80m place 800s and fluctuating wind speed schematic diagram; Fig. 5 (d) to Fig. 5 (f) is the non-stationary fluctuating wind speed schematic diagram of a virtual space point along ground vertical height 10m, 25m and 80m place 800s;

Fig. 6 (a) to Fig. 6 (c) is the schematic diagram of the non-uniformly modulated function of each virtual space point; Fig. 6 (d) to Fig. 6 (f) is the schematic diagram of the time-varying power spectrum of each virtual space point; The contrast schematic diagram that the simulated power spectrum of each virtual space point of Fig. 6 (g) to Fig. 6 (i) and target are composed;

Fig. 7 (a) to Fig. 7 (f) be each virtual space point along ground vertical height 10m, 25m, 80m, 10m and 25m, 25m and 80m, 10m and 80m place from, cross correlation function target and target oneself, the contrast schematic diagram of cross correlation function.

Embodiment

The present invention is adopted to further describe the simulator program of non-stationary fluctuating wind speed (for downburst) below in conjunction with accompanying drawing.

The non-stationary wind speed simulation method becoming arma modeling when the present invention is based on comprises the steps:

The first step, determines the Wind speed model (comprising Wind speed model, virtual space point, terrain parameter and simulation duration etc.) of target non-stationary wind speed simulation, the change each parameter of arma modeling (Autoregressive and slip regression order) in time of target power spectrum.Steady fluctuating wind two parts of average wind and zero-mean are become when being shown as deterministic by down wind non-stationary anemometer.

In the above-mentioned first step, the Wind speed model of non-stationary wind speed simulation is expressed as following formula (1):

$U(x,y,z,t)={\stackrel{\‾}{U}}^{*}(x,y,z,t)+u^{*}(x,y,z,t)---\left(1\right)$

In formula, be space (x, y, z) place time become average wind, u ^*t () is the steady fluctuating wind process of zero-mean.From above-mentioned steady Wind speed model and nonstationary wind speed model, if total down wind record U (t) itself is strict stationary stochastic process, then become the constant value average wind that average wind U (t) will be degenerated in the steady wind model of tradition time.In fact, the definition in nonstationary wind speed model is exactly the simplest model describing non-stationary process: stationary process+trend term.

As adopted the downburst model shown in Fig. 2 (a), Fig. 2 (b) to carry out non-stationary wind speed simulation to the point of three shown in Fig. 1, time become the Autoregressive p=16 of arma modeling, slip regression order q=1.Simulation points is positioned at along downburst moving direction and distance downburst thunderstorm center 3500m.Downburst Wind speed model adopts Oseguera and Bowles mean wind speed model, the vertical distributed model of Vicroy, maximum wind velocity V in vertical distribution wind speed _max=80m/s, residing height and position Z _max=67m; The radial maximum wind velocity V of certain At The Height in wind speed field _{r, max}=47m/s, with downburst central horizontal distance r _max=1000m, radical length scale-up factor R _r=700m; Thunderstorm intensity time variations following formula (2) represents:

$\mathrm{\Π}=\left\{\begin{array}{cc}t/5,& (0\≤t\≤5\min )\\ e^{-(t-5)/20},& (t>5\min )\end{array}\right.---\left(2\right)$

Downburst point-to-point speed V _o=8m/s.Upper cut-off frequency is 4 π rad, N=2^11, consider that downburst self moves, simulated time interval of delta t=0.1s, simulation duration is 800s simultaneously.

Second step, by approximate for non-stationary fluctuating wind speed discrete be steady fluctuating wind speed in some short time interval Δ t, the power spectrum of every section of steady fluctuating wind speed is modulated to Evolutionary Spectral by employing non-uniformly modulated function simultaneously; Select existing pulsating wind power spectrum (as Kaimal spectrum), below for Kaimal spectrum, adopt Kaimal non-uniformly modulated function that power spectrum is modulated to Kaimal Evolutionary Spectral.

The power spectrum of Kaimal is adopted to be expressed as formula (3):

$S\left(\mathrm{\ω}\right)=\frac{1}{2}\frac{200}{2\mathrm{\π}}{u}_{*}^2\frac{z_j}{U_{z_j}}\frac{1}{{[1+50\frac{\mathrm{\ω}{z}_j}{2\mathrm{\π}{U}_{z_j}}]}^{5/3}}---\left(3\right)$

Kaimal non-uniformly modulated function representation is as shown in the formula (4):

$A(\mathrm{\ω},t)=\sqrt{\frac{{\tilde{U}}_j\left(t\right)}{U_j}{\left[\frac{1+50\frac{\mathrm{\ω}{z}_j}{2\mathrm{\π}{U}_j}}{1+50\frac{\mathrm{\ω}{z}_j}{2\mathrm{\π}{\tilde{U}}_j\left(t\right)}}\right]}^{5/3}}---\left(4\right)$

In formula: ω is circular frequency; z _jfor space point vertical ground height; for becoming mean wind speed during point place, space; for some place, space non-stationary wind speed statistical average wind speed.

3rd step, set up non-stationary fluctuating wind speed time become arma modeling, and frame diagram according to Fig. 3 and the process flow diagram shown in Fig. 4 carry out non-stationary wind speed simulation, namely consider the time variation of ARMA matrix of coefficients and the spatial coherence of fluctuating wind, the 3rd step can adopt Matlab language.

3rd step comprises following concrete steps:

Assuming that adopt as shown in the formula (5) for M some non-stationary fluctuating wind speed of simulation:

U(t)＝[u ₁(t) u ₂(t) … u _M(t)] ^T(5)

Become the formula of arma modeling at that time as shown in the formula shown in (6):

$U\left(t\right)=\underset{i=1}{\overset{p}{\mathrm{\Σ}}}{A}_i\left(t\right)U(t-\mathrm{i\Δt})+\underset{j=0}{\overset{q}{\mathrm{\Σ}}}{B}_j\left(t\right)X(t-\mathrm{j\Δt})---\left(6\right)$

Wherein, U (t) is zero-mean nonstationary random process vector, A _i(t) for time become autoregressive coefficient matrix (MxM rank), B _j(t) for time become slip regression coefficient matrix (MxM rank), p is Autoregressive, and q is slip regression order, and to be variance be X (t) 1, the white noise sequence of normal distribution, and meet as shown in the formula (7):

$R_{\mathrm{xx}}(t,\mathrm{j\Δt})=E\left[X\left(t\right)X(t-\mathrm{j\Δt})\right]=\left\{\begin{array}{c}{I}_m,j=0\\ 0,j\≠0\end{array}\right.---\left(7\right)$

From formula (7), calculating fluctuating wind speed U (t) key is to determine A _i(t), B _j(t) matrix of coefficients.

Work as t=t ₀time, formula (7) is as shown in the formula (8):

$U\left(t_0\right)=\underset{i=1}{\overset{p}{\mathrm{\Σ}}}{A}_i\left(t_0\right)U(t_0-\mathrm{i\Δt})+\underset{j=0}{\overset{q}{\mathrm{\Σ}}}{B}_j\left(t_0\right)X(t_0-\mathrm{j\Δt})---\left(8\right)$

U is taken advantage of in the right side in both sides simultaneously ^t(t ₀-k Δ t) (k=1,2 ... p), and get mathematical expectation, must as shown in the formula (9):

$E\left[U\left(t_0\right)U^T(t_0-\mathrm{k\Δt})\right]=\underset{i=1}{\overset{p}{\mathrm{\Σ}}}E\left[A_i\left(t_0\right)U(t_0-\mathrm{i\Δt})U^T(t_0-\mathrm{k\Δt})\right]+\underset{j=0}{\overset{q}{\mathrm{\Σ}}}E\left[B_j\left(t_0\right)X(t_0-\mathrm{j\Δt})U^T(t_0-\mathrm{k\Δt})\right]---\left(9\right)$

According to the definition of related function, namely as shown in the formula (10):

$R_{\mathrm{uu}}\left(\mathrm{k\Δt}\right)=\underset{i=1}{\overset{q}{\mathrm{\Σ}}}{A}_i\left(t_0\right)R_{\mathrm{uu}}\left[(k-i)\mathrm{\Δt}\right]+\underset{j=1}{\overset{q}{\mathrm{\Σ}}}{B}_j\left(t_0\right)R_{\mathrm{xu}}\left[(k-j)\mathrm{\Δt}\right]---\left(10\right)$

X is taken advantage of the right side in both sides again to formula (8) simultaneously ^t(t ₀-l Δ t) (l=1,2 ... q), and get mathematical expectation, must as shown in the formula (11):

$E\left[U\left(t_0\right)X^T(t_0-\mathrm{l\Δt})\right]=\underset{i=1}{\overset{p}{\mathrm{\Σ}}}E\left[A_i\left(t_0\right)U(t_0-\mathrm{i\Δt})X^T(t_0-\mathrm{l\Δt})\right]+\underset{j=0}{\overset{q}{\mathrm{\Σ}}}E\left[B_j\left(t_0\right)X(t_0-\mathrm{j\Δt})X^T(t_0-\mathrm{l\Δt})\right]---\left(11\right)$

Namely as shown in the formula (12):

$R_{\mathrm{ux}}\left(\mathrm{l\Δt}\right)=\underset{i=1}{\overset{q}{\mathrm{\Σ}}}{A}_i\left(t_0\right)R_{\mathrm{ux}}\left[(l-i)\mathrm{\Δt}\right]+\underset{j=1}{\overset{q}{\mathrm{\Σ}}}{B}_j\left(t_0\right)R_{\mathrm{xx}}\left[(l-j)\mathrm{\Δt}\right]---\left(12\right)$

In addition, current output U (t can be found out from formula (9) ₀) only depend on current input X (t ₀) and input X (t in the past ₀-τ), and with input X (t in the future ₀+ τ) irrelevant (τ > 0), i.e. U (t ₀) and X (t ₀+ τ) separate, then have: R _ux(-τ)=0, τ > 0 and R _ux(-τ)=R _xu(τ).

As j=0, k-j > 0, then R _xu[(k-j) Δ t]=0.

Formula (11) and formula (12) being merged launches as shown in the formula (13):

Wherein,

According to Wei Na-Xin Qin formula, must as shown in the formula (14):

$\left\{\begin{array}{c}{R}_{\mathrm{uu}}^{\mathrm{ii}}\left(\mathrm{j\Δt}\right)={\∫}_{-\∞}^{\∞}{S}_{\mathrm{ii}}(\mathrm{\ω},\mathrm{j\Δt})\·e^{\mathrm{i\ω}\·\mathrm{j\Δt}}\mathrm{d\ω}\\ R_{\mathrm{uu}}^{\mathrm{ik}}\left(\mathrm{j\Δt}\right)={\∫}_{-\∞}^{\∞}{S}_{\mathrm{ik}}(\mathrm{\ω},\mathrm{j\Δt})\·e^{\mathrm{i\ω}\·\mathrm{j\Δt}}\mathrm{d\ω}\end{array}\right.---\left(14\right)$

[R can be obtained _uu(j Δ t)], now, also need known [R _ux(l Δ t)] can A be tried to achieve _i(t), B _j(t) matrix of coefficients.

Make q=0 in formula (10), must as shown in the formula (15):

$U\left(t_0\right)=\underset{i=1}{\overset{q}{\mathrm{\Σ}}}{\stackrel{\‾}{A}}_i\left(t_0\right)U(t_0-\mathrm{i\Δt})+B_0\left(t_0\right)X\left(t_0\right)---\left(15\right)$

X is taken advantage of in the right side in both sides simultaneously ^t(t ₀-l Δ t) (l=0,1,2 ... q) and get mathematical expectation, must as shown in the formula (16):

$\left\{\begin{array}{c}{R}_{\mathrm{ux}}\left(\mathrm{l\Δt}\right)=\underset{i=1}{\overset{q}{\mathrm{\Σ}}}{\stackrel{\‾}{A}}_i\left(t_0\right)R_{\mathrm{ux}}\left[(l-i)\mathrm{\Δt}\right],l=\mathrm{1,2},...q\\ R_{\mathrm{ux}}\left(0\right)=B_0\left(t\right),l=0\end{array}\right.---\left(16\right)$

U is taken advantage of the right side in both sides again to formula (15) simultaneously ^t(t ₀-k Δ t) (k=0,1,2 ... p), get mathematical expectation, must as shown in the formula (17):

$\left\{\begin{array}{c}{R}_{\mathrm{uu}}\left(\mathrm{k\Δt}\right)=\underset{i=1}{\overset{p}{\mathrm{\Σ}}}{\stackrel{\‾}{A}}_i\left(t_0\right)R_{\mathrm{uu}}\left[(k-i)\mathrm{\Δt}\right],k=\mathrm{1,2},...p\\ R_{\mathrm{uu}}\left(0\right)=\underset{i=1}{\overset{p}{\mathrm{\Σ}}}{\stackrel{\‾}{A}}_i\left(t_0\right)R_{\mathrm{uu}}(-\mathrm{i\Δt})+B_0\left(t_0\right)R_{\mathrm{xu}}\left(0\right),k=0\end{array}\right.---\left(17\right)$

Expansion $R_{\mathrm{uu}}\left(\mathrm{k\Δt}\right)=\underset{i=1}{\overset{q}{\mathrm{\Σ}}}{\stackrel{\‾}{A}}_i\left(t_0\right)R_{\mathrm{uu}}\left[(k-i)\mathrm{\Δt}\right],$ Must as shown in the formula (18):

By [the R drawn above _uu(j Δ t)] and equation can try to achieve

The second formula from formula (16), (17) again, and can obtain $R_{\mathrm{uu}}\left(0\right)=\underset{i=1}{\overset{p}{\mathrm{\Σ}}}{\stackrel{\‾}{A}}_i\left(t_0\right)R_{\mathrm{uu}}(-\mathrm{i\Δt})+B_0\left(t_0\right)B_0^T\left(t_0\right),$ Namely as shown in the formula (19):

$B_0\left(t_0\right)B_0^T\left(t_0\right)=R_{\mathrm{uu}}\left(0\right)-\underset{i=1}{\overset{p}{\mathrm{\Σ}}}{\stackrel{\‾}{A}}_i\left(t_0\right)R_{\mathrm{uu}}(-\mathrm{i\Δt})---\left(19\right)$

So far, above formula right-hand member matrix is all obtained, and carries out Cholesky decomposition to the right equation, can try to achieve B ₀(t ₀), then substitute into formula (16) recursion and try to achieve matrix [R _ux(l Δ t)].By the matrix [R drawn _uu(j Δ t)] and [R _ux(l Δ t)] substitute into formula (13), just can determine TARMA time-varying model coefficient matrices A _i(t), B _jt (), finally obtains t ₀moment wind speed.

4th step, exports and display simulation result, as shown in Figure 5, by the power spectrum of simulation points wind speed and from, cross correlation function with target power compose and target certainly, cross correlation function contrasts, and guarantees the validity of simulation wind speed.

Step be above based on the establishment of Matlab platform based on time become arma modeling the calculation procedure of non-stationary wind speed simulation method carry out analysis & verification.

As can be seen from Fig. 5 (a) in Fig. 5 (f), fluctuating wind speed amplitude with time to become mean wind speed size relevant, time to become mean wind speed larger then fluctuating wind speed amplitude larger, the wind field characteristic of this and reality matches.For further illustrating the validity of simulation, to non-stationary fluctuating wind speed simulated power spectrum and the target average power spectra of each point (shown in Fig. 6 (a) to Fig. 6 (i)) compares, and the downburst Kaimal non-uniformly modulated function at three difference places is shown in Fig. 6.And from, cross correlation function and target oneself, cross correlation function compare (shown in Fig. 7 (a) to Fig. 7 (f)), as can be seen from the figure results contrast coincide.Can find out that from Fig. 7 cross correlation function figure the fluctuating wind speed cross correlation of each point weakens with the increase of distance.

Those skilled in the art can carry out various remodeling and change to the present invention.Therefore, present invention covers the various remodeling in the scope falling into appending claims and equivalent thereof and change.

Claims (5)

1. based on time become the non-stationary wind speed simulation method of arma modeling, it is characterized in that, it comprises the following steps:

The first step, determines that the Wind speed model of target non-stationary wind speed simulation, target power spectrum becomes in time the parameter of arma modeling, determine simultaneously each virtual space point time become mean wind speed;

Second step, adopts non-uniformly modulated function that target power spectrum is modulated to Evolutionary Spectral, simultaneously by fluctuating wind speed steady in some short time intervals discrete for non-stationary fluctuating wind speed;

3rd step, set up non-stationary fluctuating wind speed time become arma modeling, according to determine above time become arma modeling parameter and and Evolutionary Spectral carry out the simulation of non-stationary fluctuating wind speed;

4th step, according to time become mean wind speed and the non-stationary fluctuating wind speed that generates determines final non-stationary wind speed and output display analog result, simultaneously by the power spectrum of simulation points wind speed and from, cross correlation function with target power is composed and target oneself, cross correlation function contrast.

2. according to claim 1 based on time become the non-stationary wind speed simulation method of arma modeling, it is characterized in that, when down wind non-stationary anemometer is shown as deterministic by the described first step, become steady fluctuating wind two parts of average wind and zero-mean.

3. according to claim 1 based on time become the non-stationary wind speed simulation method of arma modeling, it is characterized in that, the non-uniformly modulated function representation in described second step be as shown in the formula:

In formula: ω is circular frequency; for space point vertical ground height; for becoming mean wind speed during point place, space; for some place, space non-stationary wind speed statistical average wind speed.

4. according to claim 1 based on time become the non-stationary wind speed simulation method of arma modeling, it is characterized in that, the Wind speed model of described non-stationary wind speed simulation is expressed as following formula:

In formula, it is space place time become average wind, it is the steady fluctuating wind process of zero-mean.

5. according to claim 1 based on time become the non-stationary wind speed simulation method of arma modeling, it is characterized in that, become arma modeling time described and adopt following formula:

Wherein, for zero-mean nonstationary random process vector, for time become autoregressive coefficient matrix, for time become slip regression coefficient matrix, p is Autoregressive, and q is slip regression order, to be variance be 1, the white noise sequence of normal distribution.