CN103678940A - Method for estimating uncertainty of wind speed fluctuation based on effective turbulence intensity instantaneous model - Google Patents

Abstract

The invention relates to a method for estimating wind speed fluctuation uncertainty based on an effective turbulence intensity instantaneous model, and solves the problems that error bands in the same width are given in an area with low wind speed and an area with high wind speed and an overlarge prediction error exists, does not accord with actual conditions and is not favorable for making a reasonable scheduling plane for a power grid in a method for estimating the uncertainty of wind speed fluctuation based on the current theoretical model. Instantaneous standard deviation of turbulence residual difference is obtained through a Mallat wavelet decomposition and reconstruction algorithm, and the uncertainty of the wind speed fluctuation is estimated by fitting through the effective turbulence intensity instantaneous model according to average wind speed. The method is used for predicting the power of a wind power plant, so that the reasonable scheduling plant is made for the power grid, spinning reserve is determined, and the operation of the power grid is safely and economically guaranteed.

Description

Fluctuations in wind speed uncertainty estimation method based on effective turbulence intensity instantaneous model

Technical field

The present invention relates to the fluctuations in wind speed uncertainty estimation method based on effective turbulence intensity instantaneous model, relate to forecasting wind speed and uncertainty estimation field.

Background technology

Current, a lot of key areas all relate to research and the application of forecasting wind speed and uncertainty estimation, as electric system and railway construction field etc.Take field of power as example, and wind-power electricity generation is one of study hotspot of new forms of energy power industry in current electric system.Because wind-powered electricity generation fluctuation has stronger uncertainty, after large-scale wind power field access electrical network, the safety and economic operation control to electrical network etc. is brought to significant impact; And wind farm power prediction is the important foundation addressing this problem accurately, can help electrical network to formulate rational operation plan, determine spinning reserve, safety economy ground guarantees the operation of electrical network.Yet wind is the power source of wind-powered electricity generation unit output, therefore, wind speed is the topmost influence factor of wind power, and wind speed is carried out to Accurate Prediction, is the important prerequisite of wind power prediction.At present, the prediction for wind speed and wind power can be divided into deterministic forecast and uncertain prediction.The forecast result of deterministic forecast generally only needs to provide the occurrence that wind speed is at a time put, and temporal resolution is 15 minutes; Uncertain prediction is further the error band of deterministic forecast result in the regular period to be analyzed, to provide the predicated error band under a certain degree of confidence, the i.e. fluctuation range of wind speed in one period; Standby because of the operation that wind-powered electricity generation error causes for determining electrical network, the safe and highly efficient operation of electrical network is had great importance.The extensive error that current Statistical Learning Theory is model provides theoretical foundation, but Statistical Learning Theory basis is the probability distribution of broad sense, the fiducial interval providing is often very loose, the region little at wind speed all provides identical wide error band with the large region of wind speed, there is excessive prediction error, do not there is practical value.Excessive prediction error is estimated will to cause more conservative safety to arrange, and occurs more redundancy rotation stand-by heat.

Summary of the invention

The present invention take for solving the uncertainty estimation method that existing theoretical model is basic fluctuations in wind speed, the region little at wind speed all provides identical wide error band with the large region of wind speed, have excessive prediction error, neither tallying with the actual situation can not be for helping electrical network to formulate the problem of rational operation plan; Solution causes the safety of too guarding to arrange because prediction error estimation is excessive, and the problem that occurs too much redundancy rotation stand-by heat.And the method for the fluctuations in wind speed uncertainty estimation based on effective turbulence intensity instantaneous model is provided, its concrete steps are as follows:

Step 1, according to the spectrum gap of lower atmosphere layer motion power spectrum, determine decomposition scale;

Step 2, take Mallat wavelet decomposition and restructing algorithm as instrument carries out the decomposition of wind speed seasonal effect in time series and reconstruct to actual measurement air speed data, obtain mean wind speed and corresponding turbulence residual error;

Step 3, utilize Mallat wavelet decomposition algorithm to square after turbulence residual error be reconstructed again the variance obtaining after filtering after decomposing, the square root that counts of the variance after filtering is the instantaneous standard deviation of turbulence residual error;

Step 4, ask the effective turbulence intensity I corresponding with mean wind speed, formula is as follows:

$I=\frac{\mathrm{\σ}}{\stackrel{\‾}{V}}$

Wherein, σ is the instantaneous standard deviation of turbulence residual error,

for a hour level mean wind speed;

Step 5, according to quadratic polynomial approximating method, utilize 3 σ principles to reject wild point, effective turbulence intensity model is carried out to matching;

Step 6, the effective turbulence intensity models fitting result of basis and forecasting wind speed result are carried out uncertainty estimation.

Advantage of the present invention:

The present invention is incorporated into effective turbulence intensity model among fluctuations in wind speed uncertainty estimation, by having set up effective turbulence intensity instantaneous model, utilize hourly average wind speed and the same period wind speed standard deviation internal relation, realization is to following a period of time wind series uncertainty estimation, thereby can provide the uncertain fluctuation range of forecast wind speed.And the present invention not only on the basis of several effective turbulence intensity mathematical model in IEC standard, has provided again a kind of new matching relational expression; And, according to effective turbulence intensity instantaneous model, carry out uncertainty estimation, at wind speed, the large and little given uncertain fluctuation range of wind speed in region of wind speed is different, and uncertainty and wind speed self size is relevant, makes the present invention have more practicality in the estimation of forecasting wind speed ambiguity is applied; In addition, the present invention can be for othernesses such as region, landforms, season and blower fan height and wake effects, carry out the instantaneous statistical modeling of effective turbulence intensity, the present invention estimates that in forecasting wind speed ambiguity the accuracy in application has improved approximately 10%, therefore can help electrical network to formulate rational operation plan; Avoid causing the safety of too guarding to arrange because prediction error estimation is excessive, and avoid occurring too much redundancy rotation stand-by heat.

Accompanying drawing explanation

Fig. 1 is the schematic flow sheet of method of the present invention;

Fig. 2 is the power spectrum (continuous spectrum of air motion) of lower atmosphere layer motion; Wherein, amplitude is amplitude;

Fig. 3 is certain wind energy turbine set actual measurement wind series of month;

Fig. 4 is hour level mean wind speed component of actual measurement air speed data;

The remaining turbulence residual error after hour level mean wind speed of removing that Fig. 5 is actual measurement air speed data;

Fig. 6 is 8 layers of decomposition algorithm schematic diagram of Mallat small echo;

Fig. 7 is 8 layers of restructing algorithm schematic diagram of Mallat small echo;

Fig. 8 is the standard deviation of actual measurement air speed data turbulence residual error;

The effective turbulence intensity of Fig. 9 for utilizing actual measurement number to obtain;

Figure 10 is that matching obtains effective turbulence intensity instantaneous model;

Figure 11 is the versatility checking of the power law model of wind field 1;

Figure 12 is the versatility checking of the power law model of wind field 2;

Figure 13 is the versatility checking of the power law model of wind field 3;

Figure 14 is that the momentary fluctuation of hourly average wind speed is uncertain;

Figure 15 is the wind speed forecast result with prediction error band that utilizes the present invention to provide of blower fan in wind field 1.

Embodiment

Embodiment one: the uncertainty estimation method of the fluctuations in wind speed based on effective turbulence intensity instantaneous model in present embodiment, its concrete steps are as follows:

Step 1, according to the spectrum gap of lower atmosphere layer motion power spectrum, determine decomposition scale;

Step 2, take Mallat wavelet decomposition and restructing algorithm as instrument carries out the decomposition of wind speed seasonal effect in time series and reconstruct to actual measurement air speed data, obtain hour level mean wind speed and corresponding turbulence residual error;

Step 3, utilize Mallat wavelet decomposition algorithm to square after turbulence residual error be reconstructed again the variance obtaining after filtering after decomposing, the square root that counts of the variance after filtering is the instantaneous standard deviation of turbulence residual error;

Step 4, ask the effective turbulence intensity I corresponding with hour level mean wind speed, formula is as follows:

$I=\frac{\mathrm{\σ}}{V}$

Wherein, σ is the instantaneous standard deviation of turbulence residual error, and V is a hour level mean wind speed;

Step 5, according to quadratic polynomial approximating method, utilize 3 σ principles to reject wild point, effective turbulence intensity model is carried out to matching;

Step 6, the effective turbulence intensity models fitting result of basis and forecasting wind speed result are carried out uncertainty estimation.

Embodiment two: in present embodiment, a kind of uncertainty estimation method of fluctuations in wind speed and the difference of embodiment one are: in step 2, the wavelet basis of wavelet decomposition is db10.All the other are identical with embodiment one.

Embodiment three: in present embodiment, a kind of uncertainty estimation method of fluctuations in wind speed is with the difference of embodiment two: the Shannon's sampling theorem of being combined during wavelet reconstruction in step 2 obtains a hour level mean wind speed.All the other are identical with embodiment two.

Embodiment four: in present embodiment, a kind of uncertainty estimation method of fluctuations in wind speed and the difference of embodiment three are: in step 2, the specific algorithm of wavelet decomposition and reconstruct is as follows:

Signal f (t) is at metric space V _jwith wavelet space W _jbe projected as

(formula 8)

Might as well establish

d _j,k=< f (t), ψ _j,k(t) >, by $V_j=V_{j+1}\&CirclePlus;W_{j+1},$ Can obtain

(formula 9)

Two scaling Equations by scaling function can obtain

(formula 10)

By scaling function orthogonality, can be obtained

(formula 11)

By the two scaling Equations of wavelet function, can be obtained

(formula 12)

Above three equations of simultaneous can obtain:

$c_{j+1,n}=\underset{k=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{c}_{j,k}{h}^{*}(k-2n)$ (formula 13)

$d_{j+1,n}=\underset{k=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}{c}_{j,k}{g}^{*}(k-2n)$ (formula 14)

$c_{j,k}=\underset{n=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}h(k-2n)c_{j+1,n}+\underset{n=-\&infin;}{\overset{\&infin;}{\mathrm{\&Sigma;}}}g(k-2n)d_{j+1,n}$ (formula 15).

All the other are identical with embodiment three.

Embodiment five: in present embodiment, a kind of uncertainty estimation method of fluctuations in wind speed and the difference of embodiment four are: the effective turbulence intensity model for matching in step 5 is

$\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{V}}=\mathrm{\&alpha;}\&times;{\stackrel{\&OverBar;}{V}}^{-\mathrm{\&beta;}}+B$ (formula 20)

Wherein, α and β are fitting constant, adopt quadratic polynomial approximating method to obtain; B is constant, is the minimum turbulence intensity in high wind speed region.All the other are identical with embodiment four.

Embodiment six: in present embodiment, a kind of uncertainty estimation method of fluctuations in wind speed and the difference of embodiment five are: in step 6, uncertainty estimation utilizes the variation of formula 20 to obtain

$\mathrm{\&sigma;}\left(\stackrel{\&OverBar;}{V}\right)=\mathrm{\&alpha;}{\&times;\stackrel{\&OverBar;}{V}}^{1-\mathrm{\&beta;}}$ (formula 21)

According to wind speed, uncertain definition obtains in conjunction with (formula 21)

$V\left(t\right)=\stackrel{\&OverBar;}{V}\left(t\right)\&PlusMinus;\mathrm{\&sigma;}\left(\stackrel{\&OverBar;}{V}\right)$ (formula 22)

Wherein,

for hour wind speed mean value, complete uncertainty estimation.All the other are identical with embodiment six.

Embodiment

Step 1, if Fig. 2 is lower atmosphere layer motion power spectrum (continuous spectrum of air motion), the time of middle faint variation or space scale district (spectrum gap) are probably about 1 hour.Therefore, take and air motion can be decomposed into 2 scale component as separation in 1 hour, one is regular foreseeable average flow (synoptic process) yardstick, and one is turbulent flow (turbulent flow process) yardstick that cannot predict.

Step 2, to take Mallat wavelet decomposition and restructing algorithm be instrument, and select db10 as wavelet basis, carries out the decomposition of wind speed seasonal effect in time series and reconstruct.First, actual measurement air speed data (as shown in Figure 3, sampling interval is 5s) is carried out to 8 layers of decomposition; In conjunction with Shannon's sampling theorem, the 8th layer and the 7th layer is reconstructed, the cycle that obtains is for other hourly average wind speed of hour level, as shown in Figure 4; Below 1～6 layer is reconstructed, obtains original wind series and remove the turbulence residual error after hourly average wind speed, as shown in Figure 5.If air speed data sampling interval is 1S, need to carry out be reconstructed after 10 layers of decomposition, just can obtain as hour mean wind speed in level cycle and corresponding turbulence residual error.

Step 3, first, utilizes Mallat wavelet decomposition algorithm in step 1 to carry out 8 layers of decomposition to the turbulence residual error after carrying out square; Then, utilize Mallat wavelet reconstruction algorithm that the 7th layer and the 8th layer is reconstructed, obtain the variance after filtering; Finally, ask for the square root that counts of variance after filtering.Thereby, be also just equivalent to ask for that be applied to effective turbulence intensity modeling and the instantaneous standard deviation corresponding turbulence residual error of the mean wind speed same period, as shown in Figure 8.

Step 4, ask the effective turbulence intensity I corresponding with hour level mean wind speed, formula is as follows:

$I=\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{V}}$

Wherein, σ is the instantaneous standard deviation of turbulence residual error,

for a hour level mean wind speed;

Step 5, according to quadratic polynomial method, utilize 3 σ principles to reject wild point, and according to effective turbulence intensity model, as shown in the formula carrying out matching shown in 20;

$\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{V}}=\mathrm{\&alpha;}\&times;{\stackrel{\&OverBar;}{V}}^{-\mathrm{\&beta;}}+B$ (formula 20)

Wherein, α and β are fitting constant, adopt quadratic polynomial approximating method to obtain;

B is constant, generally gets the minimum turbulence intensity in high wind speed region.

In order to prove its validity, with reference to the good turbulence intensity model of two kinds of fitting effect providing in IEC standard, the normal turbulence model in IEC61400-1 second edition and the third edition as shown in the formula 18 and formula 19 shown in:

$\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{V}}=I_{15}\&times;(15/\stackrel{\&OverBar;}{V}+a)/(a+1)$ (formula 18)

$\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{V}}=\frac{I_{\mathrm{ref}}(0.75\&times;\stackrel{\&OverBar;}{V}+b)}{\stackrel{\&OverBar;}{V}}$ (formula 19)

Wherein, I ₁₅for the turbulence intensity eigenwert under 15m/s wind speed;

be 10 minutes mean wind speeds;

I _reffor the reference value of turbulence intensity, tri-grades of minute A, B, C;

A, b are constant, by real data matching, are obtained.

According to the value of Fig. 9 B, be 0.1016.Utilize formula 20, the matching actual measurement air speed data corresponding with blower fan obtains effective turbulence intensity instantaneous model, as Figure 10, wherein, α ≈ 0.1760, β ≈ 0.7962.And the wind field data fitting compliance test result of other two zoness of different of process, as shown in Figure 12～13, proves that formula 20 has good versatility.Wherein, three parameters of effective turbulence intensity instantaneous model in Figure 12 are respectively α=0.6742, β=1.0995, B=0.0894; In Figure 13, three parameters are respectively α=0.2596, β=0.9160, B=0.0729.

In step 6, step 6, uncertainty estimation utilizes the variation of formula 20 to obtain

$\mathrm{\&sigma;}\left(\stackrel{\&OverBar;}{V}\right)=\mathrm{\&alpha;}\&times;{\stackrel{\&OverBar;}{V}}^{1-\mathrm{\&beta;}}$ (formula 21)

According to definition $V\left(t\right)=\stackrel{\&OverBar;}{V}\left(t\right)\&PlusMinus;\mathrm{\&sigma;}\left(\stackrel{\&OverBar;}{V}\right)$ (formula 22)

Wherein,

for a hour level mean wind speed, complete wind speed uncertainty estimation.

According to above-mentioned step, can complete the fluctuations in wind speed uncertainty estimation based on the instantaneous modeling of effective turbulence intensity, obtain the momentary fluctuation scope of wind speed, as shown in Figure 15 and Figure 16.As can be seen from the figure: the uncertainty estimation result that the present invention provides is when different wind speed size, and uncertainty is different, more meets with actual conditions; And the present invention both can solve the wake effect impact between blower fan, can solve again this stochastic volatility with the problem of the factors vary such as season, weather, geographical conditions; Therefore, the invention solves the very loose problem of the extensive estimation of error fiducial interval of general statistical model.Thereby, can relatively accurately and practicably to real-time prediction result, carry out probability interval estimation, thereby for optimizing decision and the power grid stability analysis of scheduling provides abundant information, help electrical network to formulate rational operation plan, avoid causing the safety of too guarding to arrange because prediction error estimation is excessive, and avoid occurring too much redundancy rotation stand-by heat.

Claims (5)

1. the fluctuations in wind speed uncertainty estimation method based on effective turbulence intensity instantaneous model, is characterized in that carrying out in accordance with the following steps:

Step 1, according to the spectrum gap of lower atmosphere layer motion power spectrum, determine decomposition scale;

Step 2, take Mallat wavelet decomposition and restructing algorithm as instrument carries out the decomposition of wind speed seasonal effect in time series and reconstruct to actual measurement air speed data, obtain hour level mean wind speed and corresponding turbulence residual error;

Step 3, utilize Mallat wavelet decomposition algorithm to square after turbulence residual error be reconstructed again the variance obtaining after filtering after decomposing, the square root that counts of the variance after filtering is the instantaneous standard deviation of turbulence residual error;

Step 4, ask the effective turbulence intensity I corresponding with hour level mean wind speed, formula is as follows:

$I=\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{V}}$

Wherein, σ is the instantaneous standard deviation of turbulence residual error, for a hour level mean wind speed;

Step 5, according to quadratic polynomial approximating method, utilize 3 σ principles to reject wild point, effective turbulence intensity model is carried out to matching;

Step 6, the effective turbulence intensity models fitting result of basis and forecasting wind speed result are carried out uncertainty estimation.

2. the fluctuations in wind speed uncertainty estimation method based on effective turbulence intensity instantaneous model according to claim 1, the wavelet basis that it is characterized in that wavelet decomposition in step 2 is db10.

3. the fluctuations in wind speed uncertainty estimation method based on effective turbulence intensity instantaneous model according to claim 2, adopts Shannon's sampling theorem to obtain a hour level mean wind speed while it is characterized in that in step 2 wavelet reconstruction.

4. the fluctuations in wind speed uncertainty estimation method based on effective turbulence intensity instantaneous model according to claim 3, is characterized in that in step 5, the effective turbulence intensity model for matching is

$\frac{\mathrm{\&sigma;}}{\stackrel{\&OverBar;}{V}}=\mathrm{\&alpha;}\&times;{\stackrel{\&OverBar;}{V}}^{-\mathrm{\&beta;}}+B$ Formula (20)

Wherein, α and β are fitting constant, adopt quadratic polynomial approximating method to obtain; σ is the instantaneous standard deviation of turbulence residual error; B is constant, is the minimum turbulence intensity in high wind speed region.

5. according to the fluctuations in wind speed uncertainty estimation method based on effective turbulence intensity instantaneous model described in claim 1,2,3 or 4, it is characterized in that in step 6, uncertainty estimation utilizes formula (21) to carry out,

$V\left(t\right)=\stackrel{\&OverBar;}{V}\left(t\right)\&PlusMinus;\mathrm{\&sigma;}\left(\stackrel{\&OverBar;}{V}\right)$ Formula (21)

Wherein, $\mathrm{\&sigma;}\left(\stackrel{\&OverBar;}{V}\right)=\mathrm{\&alpha;}\&times;{\stackrel{\&OverBar;}{V}}^{1-\mathrm{\&beta;}}$ Formula (22)

for a hour level mean wind speed, complete uncertainty estimation.

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