CN104133989A - Icing loss considered wind power plant time sequence output power calculation method - Google Patents

Abstract

The invention discloses a wind power plant time sequence output power calculation method in mountainous complex terrains under a condition that icing loss is considered. Influence on output power by an internal cable and/ or overhead line of a wind turbine generator and a window power plant is considered while calculation is carried out; an obtained result is used for evaluating the reliability of an electric system power generation system accessed into the wind power plant; time sequence output power is subjected to clustering analysis to establish a multimode probability model of the wind power plant; and the multimode probability model of the wind power plant can be further used for evaluating the reliability of a power generation and transmission system. An influence factor, especially the influence on the wind power plant output power by the icing congelation loss of the wind power plant, of the wind power plant output power is perfected, an icing congelation loss simulation result is consistent with the reality, and the evaluation precision of the reliability of the wind power plant can be improved.

Description

Take into account the wind energy turbine set sequential output power calculating method of icing loss

Technical field

The invention belongs to field of power, relate in particular to a kind of wind energy turbine set sequential output power calculating method of taking into account icing loss.

Background technology

Along with the access of wind-powered electricity generation large-scale cluster formula, electric system is also brought to certain impact.Therefore need the electric system containing large-scale wind power field to carry out reliability assessment.Along with the actual of wind-powered electricity generation is incorporated into the power networks, many problems also start to highlight.Recur three large-scale wind-powered electricity generation unit off-grids because cable fault causes Jiuquan, Guazhou County and body in Zhangjiakou Area, Hebei Province in February, 2011 to April, weather extremes also can affect greatly the output power of wind energy turbine set, Dec as annual in Southwestern China portion area is between the March of Second Year, under the effect of the outside physical environment such as temperature, humidity, circuit in wind energy turbine set and wind-powered electricity generation unit can produce icing, cause that wind-powered electricity generation unit failure rate is high, output power weakens.

In reliability assessment for electricity generation system, the model of wind energy turbine set adopts multimode probability model and sequential output power model at present.But in existing numerous model, all do not take into account the impact of above-mentioned factor.

The present invention is by considering the factors such as icing loss, cable fault, analogue simulation obtains the output power model of wind energy turbine set, temporal model can be used for to the assessment of the reliability of electricity generation system, also output power model can be carried out obtaining after cluster the multimode probability model of wind energy turbine set, for generating and transmitting system reliability assessment.

For the use of theoretical icing model, main problem is input, and owing to being subject to the impact of icing, the air speed data recording is not necessarily accurate.In addition,, in order to detect beginning and the end of nature icing, also need air themperature and height of cloud base value accurately.And obtaining of these parameters is very difficult, can says icing theoretical modeling and predict the development of also depending on this system.And icing model often presents certain periodicity.A part is the temperature cycles rise and fall due to round the clock, in addition due to the caused disengaging of dynamics.Up to now, the detailed modeling of this process is also very limited, therefore the present invention is directed to this icing loss, obtaining after service data and icing lost data, builds the relation of output power and equivalent wind speed from external characteristics.Improve the influence factor of Power Output for Wind Power Field.

Obtain output power curve, these timing curve data can be used for the reliability assessment of temporal model, on the one hand the data of timing curve are done to cluster, can obtain more realistic and accurate multimode probability model, are further used for the assessment of generating and transmitting system.

Summary of the invention

Lose the impact on wind energy turbine set sequential output power in order to analyze icing, the present invention proposes a kind of wind energy turbine set sequential output power calculating method of taking into account icing loss, comprise the steps:

The data of step 1, each wind-powered electricity generation unit of collection wind energy turbine set, comprising: wind-powered electricity generation unit quantity N _wT, each sea level elevation wind energy turbine set time period t=1,2 ... wind speed { v in T ₁, v ₂... v _t..., v _tand wind direction { d (v ₁), d (v ₂) ... d (v _t) ..., d (v _t);

Gather wind-powered electricity generation unit self parameter, comprising: wind wheel radius r ₀, sweeping area A _rotor, incision wind speed v _in, wind rating v _rated, cut-out wind speed v _out, rated power P _rated, hub height H;

Gather wind energy turbine set inner member failure rate and repair rate, comprising: wind-powered electricity generation unit failure rate λ _wT, wind-powered electricity generation unit repair rate μ _wT, cable fault rate λ _cA, cable repair rate μ _cA;

Statistics during the loss of collection icing in each time period, comprising: time period T during icing _iceinterior t _icethe actual wind speed of icing hour i Fans blower fan output power under corresponding wind speed t _icethe loss electric weight that congeals of i Fans in the individual time period

Step 2, by wind energy turbine set time period t _ice=1 ... T _iceinterior wind speed statistics, calculate icing loss during equivalent wind speed be calculated as follows:

$\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}P\left(v_{i.t_{\mathrm{ice}}}\right)-\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{P}_{\mathrm{loss}.i.t_{\mathrm{ice}}}=\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}P\left(V_{\mathrm{eq}.i.t_{\mathrm{ice}}}\right)---\left(3\right)$

be t _icethe actual wind speed of i Fans in the individual icing time period, for the blower fan output power under corresponding wind speed, be t _icethe loss electric weight that congeals of i Fans in the individual time period, be t _icethe equivalent wind speed of i Fans in the individual time period, for the blower fan output power under corresponding equivalent wind speed; Statistical equivalent wind speed probability distribution, simulates with piecewise function;

Step 3, carry out simulation calculation; Use Monte Carlo sampling to obtain the sequential wind speed model of wind energy turbine set, during considering icing loss, corresponding wind speed is modified, obtain sequential equivalent wind speed model; Use the sampling of illiteracy Taka sieve to obtain the time sequence status of each wind-powered electricity generation unit and each cable and/or pole line; Set up mountain area complex-terrain model, try to achieve the wind speed at variant height wind-powered electricity generation unit place; Set up the wake effect model of multi-overlapped, revise the wind speed at each wind-powered electricity generation unit place; In conjunction with the time sequence status of wind-powered electricity generation unit and the power characteristic of wind-powered electricity generation unit, try to achieve the output power of each wind-powered electricity generation unit; In conjunction with time sequence status and the wiring of wind energy turbine set internal electric of cable and/or pole line, try to achieve the sequential output power of wind energy turbine set.

In described step 3, sequential wind speed model adopts Weibull fitting of distribution, and Weibull probability distribution is as follows:

$f\left(v\right)=\left(\frac{k}{c}\right){\left(\frac{v}{c}\right)}^{k-1}\exp [-{\left(\frac{v}{c}\right)}^k]---\left(1\right)$

K is form parameter, and c is scale parameter, and v is wind speed; Form parameter and scale parameter draw by measured data statistics; Adopt maximum likelihood estimate, false wind field gas velocity statistical sample is v={v ₁, v ₂, v ₃..., v _n, n represents statistical sample number, utilizes maximum likelihood estimate to solve the concrete calculation expression of form parameter k and scale parameter c as follows:

$\left\{\begin{array}{c}k={(\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{v}_i^k\ln v_i}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{v}_i^k}+\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\ln v_i)}^{-1}\\ c={\left(\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\left(v_i\right)}^k\right)}^{1/k}\end{array}\right.---\left(2\right)$

In formula, form parameter k adopts process of iteration to solve, thereby tries to achieve dimensional parameters c.

Described mountain area complex-terrain model is based on Lissaman wake effect model, the place difference of different wind-powered electricity generation units installations, has different sea level elevations, and wind speed with altitude changes and changes, the wind speed profile inequality that causes wind energy turbine set, its situation of change represents with following formula:

$\frac{v\left(h\right)}{v_0}={\left(\frac{h}{h_0}\right)}^{\mathrm{\α}}---\left(4\right)$

In formula: v ₀for being highly h ₀the wind speed recording, m/s; V (h) is at the wind speed highly recording for h, m/s; α is wind speed with altitude variation factor, generally gets α=1/7; Obtaining thus wind speed increases along with highly increasing; Obtained the wind speed decreased coefficient d of subdued topography by lossless Bei Nuli equation _fwind speed decreased coefficient d with complex-terrain _crelation is as follows:

$\frac{d_C}{d_F}={\left(\frac{v_0}{v}\right)}^2---\left(5\right)$

So wind speed is everywhere:

$\begin{array}{c}v(x,h)=v\left(h\right)(1-d_C)\\ =v_0[1-(1-\sqrt{1-C_T}){\left(\frac{h_0}{h}\right)}^{2\mathrm{\α}}{{\left(\frac{r_0}{r}\right)}^2}^{}]{\left(\frac{h}{h_0}\right)}^{\mathrm{\α}}\end{array}---\left(6\right)$

Wherein, C _tfor thrust coefficient, r is the radius that is subject to wake effect impact.

The wake effect model of described multi-overlapped is based on Lissaman wake effect model, supposes at a distance of being x _ijwT _iand WT _jsea level elevation be respectively h _i, h _j, downstream wind-powered electricity generation unit WT _jbe subject to upstream wind-powered electricity generation unit WT _iwind speed computing formula after impact is as follows:

$\begin{array}{c}{v}_j(x_{\mathrm{ij}},h_j)=v_i[1-(1-\sqrt{1-C_T})\·{\left(\frac{h_i}{h_j}\right)}^{2\mathrm{\α}}\\ \·{\left(\frac{r_j}{r_i\left(x_{\mathrm{ij}}\right)}\right)}^2\·\left(\frac{A_{\mathrm{shad}.\mathrm{ij}}}{A_{\mathrm{rotor}}}\right)]{\left(\frac{h_j}{h_i}\right)}^{\mathrm{\α}}\end{array}---\left(7\right)$

In formula: A _rotorfor wind wheel sweeping area, a _shad.ijfor the area of lap, r _iand r _jbe respectively upstream wind-powered electricity generation unit WT _iwith downstream wind-powered electricity generation unit WT _jwind wheel radius; Computing formula is tried to achieve by following formula:

$\begin{array}{c}{A}_{\mathrm{shad}.\mathrm{ij}}=r_i^2\left(x_{\mathrm{ij}}\right){\cos }^{-1}\left(\frac{r_i^2\left(x_{\mathrm{ij}}\right)+d_{\mathrm{ij}}^2+\mathrm{\Δ}{h}^2-r_j^2}{2r_i\left(x_{\mathrm{ij}}\right)\·\sqrt{\mathrm{\Δ}{h}^2+d_{\mathrm{ij}}^2}}\right)\\ +r_j^2{\cos }^{-1}\left(\frac{r_j^2+d_{\mathrm{ij}}^2+\mathrm{\Δ}{h}^2-r_i^2\left(x_{\mathrm{ij}}\right)}{2r_j\·\sqrt{\mathrm{\Δ}{h}^2+d_{\mathrm{ij}}^2}}\right)\\ -r_i\left(x_{\mathrm{ij}}\right)\·\sqrt{\mathrm{\Δ}{h}^2+d_{\mathrm{ij}}^2}\\ \·\sin \left[{\cos }^{-1}\left(\frac{r_i^2\left(x_{\mathrm{ij}}\right)+d_{\mathrm{ij}}^2+\mathrm{\Δ}{h}^2-r_j^2}{2r_i\left(x_{\mathrm{ij}}\right)\·\sqrt{\mathrm{\Δ}{h}^2+d_{\mathrm{ij}}^2}}\right)\right]\end{array}---\left(8\right)$

Δ h=|h in formula _j-h _i|, be that the sea level elevation of two wind-powered electricity generation units is poor; d _ijfor upstream wind-powered electricity generation unit WT _iwith downstream wind-powered electricity generation unit WT _jbetween level interval; Wake effect is not paid attention in the situation of sowing: the wind-powered electricity generation unit fault of upstream, there is no impact to downstream wind-powered electricity generation unit; Wind-powered electricity generation unit power characteristic is as follows:

$P\left(v\right)=\left\{\begin{array}{cc}0,& v>v_{\mathrm{out}}\mathrm{or\; v}<v_{\mathrm{in}}\\ P_{\mathrm{rated}}\frac{v^3-v_{\mathrm{in}}^3}{v_{\mathrm{rated}}^3-v_{\mathrm{in}}^3},& v_{\mathrm{in}}\&le;v\&le;v_{\mathrm{rated}}\\ P_{\mathrm{rated},}& v_{\mathrm{rated}}\&le;v\&le;v_{\mathrm{out}}\end{array}\right.---\left(9\right)$

V, v _in, v _rated, v _outbe respectively current actual wind speed, incision wind speed, wind rating and cut-out wind speed, P _ratedfor the rated power of blower fan.

In described step 3, obtain the time sequence status { S of wind-powered electricity generation unit _wT.1, S _wT.2... S _wT.t.S _wT.T, wherein, the wind-powered electricity generation set state in certain moment s _wTi.t(i=1 ... N _wT, t=1,2,3 ... T) expression wind-powered electricity generation unit i is at the state in t moment, and its value can only be 0 and 1, represents that respectively blower fan is in stopping transport and running status, N _wTfor blower fan sum, T is simulation T.T.; Obtain the time sequence status { S of cable and/or pole line _cL.1, S _cL.2... S _cL.t... S _cL.T, wherein, certain moment cable and/or pole line state s _cLm.t(m=1,2,3 ... N _cL, t=1 ... T) expression cable and/or pole line m are at the state in t moment, and its value can only be 0 and 1, represent that respectively cable and/or pole line are in stopping transport and running status, N _cLfor being cable and/or pole line number, T is simulated time;

Equivalent wind speed in step 2 is brought into formula (9) and is tried to achieve the P that exerts oneself of t moment wind-powered electricity generation unit i _wTi.t, then try to achieve in conjunction with the t moment wind-powered electricity generation unit i of wind-powered electricity generation set state and exert oneself as P _wTi.twith S _wTi.tproduct: P _wTi.ts _wTi.t;

Try to achieve in conjunction with the t moment wind-powered electricity generation unit i of wind-powered electricity generation set state and cable and/or pole line state exert oneself for this branch road output power sum is so wind energy turbine set comprises many chain type branch roads, tries to achieve after every branch road sum, is added the output that obtains the wind energy turbine set t moment.

The idiographic flow of the simulation calculation in described step 3, comprising:

1) read in the residing position coordinates of each wind-powered electricity generation unit of wind energy turbine set;

2) simulation time initialization, t is hourage, from t=1;

3) during judging whether to enter icing; According to the feature of ice-covering area, during being icing February Dec to next year, calculate simulated time since 1 o'clock on the 1st January, during 1-the 1416th hour and 8017-the 8760th hour are icing, and as criterion;

4), if during entering icing, adopt equivalent wind speed modeling wind speed; Otherwise, adopt Weibull distribution simulation wind speed;

5) read in the wind speed and direction of t hour;

6) Monte Carlo sampling obtains the wind-powered electricity generation unit of normal operation and fault stoppage in transit;

7) calculate the wind speed at each wind-powered electricity generation unit place according to wake effect correlation formula (4)-(8);

8) calculate each wind-powered electricity generation unit real power output according to wind-powered electricity generation unit power characteristic formula (9);

9) set simulation time Y=8760 hour herein, 1 year, hourage t=t+1, judged whether simulation time finishes, and t>Y finishes to calculate, and t<Y proceeds to calculation process 3).

Beneficial effect of the present invention has been to provide a kind of method of setting up wind energy turbine set wind-powered electricity generation unit icing model, the perfect influence factor of Power Output for Wind Power Field, particularly wind energy turbine set icing congeals and loses the impact on Power Output for Wind Power Field, the simulation ice-coating loss result that congeals conforms to actual, can improve the Evaluation accuracy of wind energy turbine set reliability.

Brief description of the drawings

Fig. 1 is the flow process of simulation calculation;

Fig. 2 is wind farm wind velocity probability distribution statistical and fitted figure, and column curve is the distribution of actual count wind speed, and smooth curve is Weibull Probability Distribution Fitting;

The wind direction statistics of Fig. 3 wind energy turbine set, rose circle diagram, N, NNE in figure, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, NNW, represents north, northeast by north, northeast, northeast by east, east, southeast by east, the southeast, southeast by south, south, swbs, southwest, southwest by west, west, NW b W, northwest, NW b N respectively;

Fig. 4 is the probability distribution of equivalent wind speed during the icing by calculating gained loses, and can find out well matching of Weibull distribution, so adopt piecewise function simulation;

Fig. 5 is the powertrace of wind-powered electricity generation unit;

Fig. 6 is partial occlusion wake effect model under complex-terrain, v ₀for original wind speed, WT _iupwindturbine, WT _jthe downstream wind-powered electricity generation unit (x that downwindturbine is respectively upstream wind-powered electricity generation unit and affected by wake effect _i, y _i, z _i) (x _j, y _j, z _j) be respectively its coordinate, d _ij, x _ijfor not being horizontal axis distance, the front and back horizontal range of two wind-powered electricity generation units, v _tfor process WT _iafterwards with the wind speed at this wind-powered electricity generation unit place, v _j(x _ij) be downstream wind-powered electricity generation unit WT _jbe subject to upstream wind-powered electricity generation unit WT _iwind speed after impact, A ₀for sweeping area, A _{x, j}for wind-powered electricity generation unit WT _iat wind-powered electricity generation unit WT _jgo out wake effect influence area, r _i(y _ij) be the radius of this area, A _shadowfor front two-part lap area, angle θ is central angle corresponding to overlapping area, r ₀for wind wheel radius;

Fig. 7 is corresponding multiple wake effect model, v ₀for original wind speed, 1#, 2# philosophy are wind turbine group #;

Fig. 8 is wind energy turbine set wind-powered electricity generation unit layout, and transverse and longitudinal axle is denotation coordination respectively, and black side's point represents wind-powered electricity generation unit installation place, and the height of wind-powered electricity generation unit is labeled in wind-powered electricity generation unit installation place;

Fig. 9 is cabling diagram;

Figure 10 is the sequential output power curve of wind energy turbine set;

Figure 11 is the cabling diagram of the inner branch road of wind energy turbine set, and bus represents bus, and arrow represents power flow direction, and WT represents wind-powered electricity generation unit, WT1 ... WT9, WT10 represents wind turbine group #, and CL represents cable or pole line CL1, and CL10 represents cable number.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that following explanation is only exemplary, instead of in order to limit the scope of the invention and to apply.

The present invention proposes a kind of wind energy turbine set sequential output power calculating method of taking into account icing loss, comprise the steps:

The data of step 1, each wind-powered electricity generation unit of collection wind energy turbine set, comprising: wind-powered electricity generation unit quantity N _wT, each sea level elevation wind energy turbine set time period t=1,2 ... wind speed { v in T ₁, v ₂... v _t..., v _tand wind direction { d (v ₁), d (v ₂) ... d (v _t) ..., d (v _t); The wind direction statistics that is illustrated in figure 3 wind energy turbine set, wind direction equal angles is divided into 16 directions, presents rose circle diagram;

Gather wind-powered electricity generation unit self parameter, obtain under the parameter of wind-powered electricity generation unit:

Wind wheel radius r ₀=45m

Sweeping area A _rotor=6232m ²

Incision wind speed v _in=3m/s

Wind rating v _rated=12m/s

Cut-out wind speed v _out=25m/s

Rated power P _rated=2MW

Hub height H=60m

Wind energy turbine set inner member failure rate and repair rate: wind-powered electricity generation unit failure rate λ _wT=0.012 times/year, wind-powered electricity generation unit repair rate μ _wT=30 days; Cable fault rate λ _cA=0.008 times/year, cable repair rate μ _cA=12 days.

Statistics during the loss of collection icing in each time period, comprising: time period T during icing _iceinterior t _icethe actual wind speed of icing hour i Fans blower fan output power under corresponding wind speed t _icethe loss electric weight that congeals of i Fans in the individual time period

Step 2, by wind energy turbine set time period t _ice=1 ... T _iceinterior wind speed statistics, calculate icing loss during equivalent wind speed be calculated as follows:

$\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}P\left(v_{i.t_{\mathrm{ice}}}\right)-\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{loss}.i.t_{\mathrm{ice}}}=\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}P\left(V_{\mathrm{eq}.i.t_{\mathrm{ice}}}\right)---\left(3\right)$

be t _icethe actual wind speed of i Fans in the individual icing time period, for the blower fan output power under corresponding wind speed, be t _icethe loss electric weight that congeals of i Fans in the individual time period, be t _icethe equivalent wind speed of i Fans in the individual time period, for the blower fan output power under corresponding equivalent wind speed; Statistical equivalent wind speed probability distribution, simulates with piecewise function;

Step 3, the sampling of utilization Monte Carlo obtain the sequential wind speed model of wind energy turbine set, during considering icing loss, corresponding wind speed are modified, and obtain sequential equivalent wind speed model; Use the sampling of illiteracy Taka sieve to obtain the time sequence status of each wind-powered electricity generation unit and each cable and/or pole line; Set up mountain area complex-terrain model, try to achieve the wind speed at variant height wind-powered electricity generation unit place; Set up the wake effect model of multi-overlapped, revise the wind speed at each wind-powered electricity generation unit place; In conjunction with the time sequence status of wind-powered electricity generation unit and the power characteristic of wind-powered electricity generation unit, try to achieve the output power of each wind-powered electricity generation unit; In conjunction with time sequence status and the wiring of wind energy turbine set internal electric of cable and/or pole line, try to achieve the sequential output power of wind energy turbine set.

Wherein, in step 3, sequential wind speed model adopts Weibull fitting of distribution, is wind farm wind velocity probability distribution statistical and matching as shown in Figure 2, and column curve is the distribution of actual count wind speed, and smooth curve is Weibull Probability Distribution Fitting.

Weibull probability distribution is as follows:

$f\left(v\right)=\left(\frac{k}{c}\right){\left(\frac{v}{c}\right)}^{k-1}\exp [-{\left(\frac{v}{c}\right)}^k]---\left(1\right)$

K is form parameter, and c is scale parameter, and v is wind speed; Form parameter and scale parameter draw by measured data statistics.Adopt maximum likelihood estimate, false wind field gas velocity statistical sample is v={v ₁, v ₂, v ₃..., v _n, n represents statistical sample number, utilizes maximum likelihood estimate to solve the concrete calculation expression of form parameter k and scale parameter c as follows:

$\left\{\begin{array}{c}k={(\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{v}_i^k\ln v_i}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{v}_i^k}+\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\ln v_i)}^{-1}\\ c={\left(\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(v_i\right)}^k\right)}^{1/k}\end{array}\right.---\left(2\right)$

In formula, form parameter k can adopt process of iteration to solve, thereby tries to achieve dimensional parameters c.

During icing, equivalent wind speed is revised with piecewise function, is calculated as follows:

$\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}P\left(v_{i.t_{\mathrm{ice}}}\right)-\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{loss}.i.t_{\mathrm{ice}}}=\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}P\left(V_{\mathrm{eq}.i.t_{\mathrm{ice}}}\right)---\left(3\right)$

be t _icethe actual wind speed of i Fans in the individual icing time period, for the blower fan output power under corresponding wind speed, be t _icethe loss electric weight that congeals of i Fans in the individual time period, be t _icethe equivalent wind speed of i Fans in the individual time period, for the blower fan output power under corresponding equivalent wind speed.

Mountain area complex-terrain model is based on Lissaman wake effect model, and the place difference of different wind-powered electricity generation units installations, has different sea level elevations, and wind speed with altitude changes and changes, and causes the wind speed profile inequality of wind energy turbine set.Fig. 6 is shown in by concrete model, v ₀for original wind speed, WT _iupwindturbine, WT _jthe downstream wind-powered electricity generation unit (x that downwind turbine is respectively upstream wind-powered electricity generation unit and affected by wake effect _i, y _i, z _i) (x _j, y _j, z _j) be respectively its coordinate, d _ij, x _ijfor not being horizontal axis distance, the front and back horizontal range of two wind-powered electricity generation units, v _tfor process WT _iafterwards with the wind speed at this wind-powered electricity generation unit place, v _j(x _ij) be downstream wind-powered electricity generation unit WT _jbe subject to upstream wind-powered electricity generation unit WT _iwind speed after impact, A ₀for sweeping area, A _{x, j}for wind-powered electricity generation unit WT _iat wind-powered electricity generation unit WT _jgo out wake effect influence area, r _i(y _ij) be the radius of this area, A _shadowfor front two-part lap area, angle θ is central angle corresponding to overlapping area, r ₀for wind wheel radius.Its situation of change can have following formula to represent:

$\frac{v\left(h\right)}{v_0}={\left(\frac{h}{h_0}\right)}^{\mathrm{\&alpha;}}---\left(4\right)$

In formula: v ₀for being highly h ₀the wind speed recording, m/s; V (h) is at the wind speed highly recording for h, m/s; α is wind speed with altitude variation factor, generally gets α=1/7.Obtaining thus wind speed increases along with highly increasing.Can be obtained the wind speed decreased coefficient d of subdued topography by lossless Bei Nuli equation _fwind speed decreased coefficient d with complex-terrain _crelation is as follows:

$\frac{d_C}{d_F}={\left(\frac{v_0}{v}\right)}^2---\left(5\right)$

So wind speed is everywhere:

$\begin{array}{c}v(x,h)=v\left(h\right)(1-d_C)\\ =v_0[1-(1-\sqrt{1-C_T}){\left(\frac{h_0}{h}\right)}^{2\mathrm{\&alpha;}}{{\left(\frac{r_0}{r}\right)}^2}^{}]{\left(\frac{h}{h_0}\right)}^{\mathrm{\&alpha;}}\end{array}---\left(6\right)$

Wherein, C _tfor thrust coefficient, r is the radius that is subject to wake effect impact.

The wake effect model of multi-overlapped is based on Lissaman wake effect model, is corresponding multiple wake effect model as shown in Figure 7, v ₀for original wind speed, 1#, 2# philosophy are wind turbine group #.Suppose at a distance of being x _ijwT _iand WT _jsea level elevation be respectively h _i, h _j, downstream wind-powered electricity generation unit WT _jbe subject to upstream wind-powered electricity generation unit WT _iwind speed computing formula after impact is as follows:

$\begin{array}{c}{v}_j(x_{\mathrm{ij}},h_j)=v_i[1-(1-\sqrt{1-C_T})\&CenterDot;{\left(\frac{h_i}{h_j}\right)}^{2\mathrm{\&alpha;}}\\ \&CenterDot;{\left(\frac{r_j}{r_i\left(x_{\mathrm{ij}}\right)}\right)}^2\&CenterDot;\left(\frac{A_{\mathrm{shad}.\mathrm{ij}}}{A_{\mathrm{rotor}}}\right)]{\left(\frac{h_j}{h_i}\right)}^{\mathrm{\&alpha;}}\end{array}---\left(7\right)$

In formula: A _rotorfor wind wheel sweeping area, a _shad.ijfor the area of lap, r _iand r _jbe respectively upstream wind-powered electricity generation unit WT _iwith downstream wind-powered electricity generation unit WT _jwind wheel radius; Computing formula can be tried to achieve by following formula:

$\begin{array}{c}{A}_{\mathrm{shad}.\mathrm{ij}}=r_i^2\left(x_{\mathrm{ij}}\right){\cos }^{-1}\left(\frac{r_i^2\left(x_{\mathrm{ij}}\right)+d_{\mathrm{ij}}^2+\mathrm{\&Delta;}{h}^2-r_j^2}{2r_i\left(x_{\mathrm{ij}}\right)\&CenterDot;\sqrt{\mathrm{\&Delta;}{h}^2+d_{\mathrm{ij}}^2}}\right)\\ +r_j^2{\cos }^{-1}\left(\frac{r_j^2+d_{\mathrm{ij}}^2+\mathrm{\&Delta;}{h}^2-r_i^2\left(x_{\mathrm{ij}}\right)}{2r_j\&CenterDot;\sqrt{\mathrm{\&Delta;}{h}^2+d_{\mathrm{ij}}^2}}\right)\\ -r_i\left(x_{\mathrm{ij}}\right)\&CenterDot;\sqrt{\mathrm{\&Delta;}{h}^2+d_{\mathrm{ij}}^2}\\ \&CenterDot;\sin \left[{\cos }^{-1}\left(\frac{r_i^2\left(x_{\mathrm{ij}}\right)+d_{\mathrm{ij}}^2+\mathrm{\&Delta;}{h}^2-r_j^2}{2r_i\left(x_{\mathrm{ij}}\right)\&CenterDot;\sqrt{\mathrm{\&Delta;}{h}^2+d_{\mathrm{ij}}^2}}\right)\right]\end{array}---\left(8\right)$

Δ h=|h in formula _j-h _i|, be that the sea level elevation of two wind-powered electricity generation units is poor, d _ijfor upstream wind-powered electricity generation unit WT _iwith downstream wind-powered electricity generation unit WT _jbetween level interval.Wake effect is not paid attention in the situation of sowing: the wind-powered electricity generation unit fault of upstream, there is no impact to downstream wind-powered electricity generation unit.Wind-powered electricity generation unit power characteristic is as follows:

$P\left(v\right)=\left\{\begin{array}{cc}0,& v>v_{\mathrm{out}}\mathrm{or\; v}<v_{\mathrm{in}}\\ P_{\mathrm{rated}}\frac{v^3-v_{\mathrm{in}}^3}{v_{\mathrm{rated}}^3-v_{\mathrm{in}}^3},& v_{\mathrm{in}}\&le;v\&le;v_{\mathrm{rated}}\\ P_{\mathrm{rated},}& v_{\mathrm{rated}}\&le;v\&le;v_{\mathrm{out}}\end{array}\right.---\left(9\right)$

V, v _in, v _rated, v _outbe respectively current actual wind speed, incision wind speed, wind rating and cut-out wind speed, P _ratedfor the rated power of blower fan.The powertrace of wind-powered electricity generation unit as shown in Figure 5, incision wind speed v _in=3m/s; Wind rating v _rated=12m/s, cut-out wind speed v _out=25m/s, rated power P _rated=2MW.

Fig. 9 is cabling diagram, comprises that power supply is that wind-powered electricity generation unit, bus, transformer and cable are connected, and connected mode is that chain type connects, and connects 10 typhoon group of motors, totally 8 cable 80 typhoon group of motors, total installation of generating capacity 160MW on every cable.Wind-powered electricity generation unit is connected to bus through cable after step-up transformer, after further boosting, accesses electrical network.In conjunction with cable and or time sequence status and the wiring of wind energy turbine set internal electric of pole line, try to achieve the sequential output power of wind energy turbine set, be the sequential output power curve of wind energy turbine set as shown in figure 10.

Time sequence status { the S of the wind turbine group that obtains _wT.1, S _wT.2... S _wT.t.S _wT.T, wherein, the wind-powered electricity generation set state in certain moment s _wTi.t(i=1 ... N _wT, t=1,2,3 ... T) expression wind-powered electricity generation unit i is at the state in t moment, and its value can only be 0 and 1, represents that respectively blower fan is in stopping transport and running status, N _wTfor blower fan sum, T is simulation T.T.; Obtain the time sequence status { S of cable and/or pole line _cL.1, S _cL.2... S _cL.t... S _cL.T, wherein, certain moment cable and/or pole line state s _cLm.t(m=1,2,3 ... N _cL, t=1 ... T) expression cable and/or pole line m are at the state in t moment, and its value can only be 0 and 1, represent that respectively cable and/or pole line are in stopping transport and running status, N _cLfor cable and/or pole line number, T is simulated time.

In conjunction with the time sequence status of wind-powered electricity generation unit and the power characteristic of wind-powered electricity generation unit, try to achieve the output power of each wind-powered electricity generation unit.Equivalent wind speed in step 2 is brought into formula (9) and is tried to achieve the P that exerts oneself of t moment wind-powered electricity generation unit i _wTi.t, in conjunction with the state of wind-powered electricity generation unit, final t moment wind-powered electricity generation unit i exerts oneself as P _wTi.twith S _wTi.tproduct: P _wTi.ts _wTi.t.

In conjunction with time sequence status and the wiring of wind energy turbine set internal electric of cable and/or pole line, try to achieve the sequential output power of wind energy turbine set.Internal electric connection layout is shown in Fig. 9 and Figure 11

Taking Figure 11 as example, when the t moment, wind-powered electricity generation unit i exert oneself for this branch road output power sum is so wind energy turbine set comprises many chain type branch roads, tries to achieve after every branch road sum, is added the output that obtains the wind energy turbine set t moment.

The idiographic flow of the simulation calculation in step 3 as shown in Figure 1, comprising:

1) read in the residing position coordinates of each wind-powered electricity generation unit of wind energy turbine set, as shown in Figure 8, transverse and longitudinal axle is denotation coordination respectively, black side point expression wind-powered electricity generation unit installation place, and the height of wind-powered electricity generation unit is labeled in wind-powered electricity generation unit installation place;

2) simulation time initialization, t is hourage, from t=1;

3) during judging whether to enter icing.According to the feature of ice-covering area, during being icing February Dec to next year.Calculate simulated time since 1 o'clock on the 1st January, during 1-the 1416th hour and 8017-the 8760th hour are icing, and as criterion;

4), if during entering icing, adopt equivalent wind speed modeling wind speed; Otherwise, adopt Weibull distribution simulation wind speed.Formula (3) is shown in asking for of equivalent wind speed, and the probability distribution graph of equivalent wind speed is shown in Fig. 4, can find out well matching of Weibull distribution, so adopt piecewise function simulation.

5) read in the wind speed and direction of t hour;

6) Monte Carlo sampling obtains the wind-powered electricity generation unit of normal operation and fault stoppage in transit;

7) calculate the wind speed at each wind-powered electricity generation unit place according to wake effect correlation formula (4)-(8); Computation model-Lissaman model of single wind energy conversion system wake flow

8) calculate each wind-powered electricity generation unit real power output according to wind-powered electricity generation unit power characteristic formula (9);

9) set simulation time Y=8760 hour herein, 1 year.Hourage t=t+1, judges whether simulation time finishes, and t>Y finishes to calculate, and t<Y proceeds to calculation process 3).

The above; only for preferably embodiment of the present invention, but protection scope of the present invention is not limited to this, is anyly familiar with in technical scope that those skilled in the art disclose in the present invention; the variation that can expect easily or replacement, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.

Claims (6)

1. take into account the wind energy turbine set sequential output power calculating method of icing loss for one kind, it is characterized in that, comprise the steps:

The data of step 1, each wind-powered electricity generation unit of collection wind energy turbine set, comprising: wind-powered electricity generation unit quantity N _wT, each sea level elevation wind energy turbine set time period t=1,2 ... wind speed { v in T ₁, v ₂... v _t..., v _tand wind direction { d (v ₁), d (v ₂) ... d (v _t) ..., d (v _t);

Gather wind-powered electricity generation unit self parameter, comprising: wind wheel radius r ₀, sweeping area A _rotor, incision wind speed v _in, wind rating v _rated, cut-out wind speed v _out, rated power P _rated, hub height H;

Gather wind energy turbine set inner member failure rate and repair rate, comprising: wind-powered electricity generation unit failure rate λ _wT, wind-powered electricity generation unit repair rate μ _wT, cable fault rate λ _cA, cable repair rate μ _cA;

Statistics during the loss of collection icing in each time period, comprising: time period T during icing _iceinterior t _icethe actual wind speed of icing hour i Fans blower fan output power under corresponding wind speed t _icethe loss electric weight that congeals of i Fans in the individual time period

Step 2, by wind energy turbine set time period t _ice=1 ... T _iceinterior wind speed statistics, calculate icing loss during equivalent wind speed be calculated as follows:

$\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}P\left(v_{i.t_{\mathrm{ice}}}\right)-\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{loss}.i.t_{\mathrm{ice}}}=\underset{t_{\mathrm{ice}}=1}{\overset{T_{\mathrm{ice}}}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}P\left(V_{\mathrm{eq}.i.t_{\mathrm{ice}}}\right)---\left(3\right)$

be the actual wind speed of i Fans in tice icing time period, for the blower fan output power under corresponding wind speed, be t _icethe loss electric weight that congeals of i Fans in the individual time period, be t _icethe equivalent wind speed of i Fans in the individual time period, for the blower fan output power under corresponding equivalent wind speed; Statistical equivalent wind speed probability distribution, simulates with piecewise function;

Step 3, carry out simulation calculation; Use Monte Carlo sampling to obtain the sequential wind speed model of wind energy turbine set, during considering icing loss, corresponding wind speed is modified, obtain sequential equivalent wind speed model; Use the sampling of illiteracy Taka sieve to obtain the time sequence status of each wind-powered electricity generation unit and each cable and/or pole line; Set up mountain area complex-terrain model, try to achieve the wind speed at variant height wind-powered electricity generation unit place; Set up the wake effect model of multi-overlapped, revise the wind speed at each wind-powered electricity generation unit place; In conjunction with the time sequence status of wind-powered electricity generation unit and the power characteristic of wind-powered electricity generation unit, try to achieve the output power of each wind-powered electricity generation unit; In conjunction with time sequence status and the wiring of wind energy turbine set internal electric of cable and/or pole line, try to achieve the sequential output power of wind energy turbine set.

2. method according to claim 1, is characterized in that, in described step 3, sequential wind speed model adopts Weibull fitting of distribution, and Weibull probability distribution is as follows:

$f\left(v\right)=\left(\frac{k}{c}\right){\left(\frac{v}{c}\right)}^{k-1}\exp [-{\left(\frac{v}{c}\right)}^k]---\left(1\right)$

K is form parameter, and c is scale parameter, and v is wind speed; Form parameter and scale parameter draw by measured data statistics; Adopt maximum likelihood estimate, false wind field gas velocity statistical sample is v={v ₁, v ₂, v ₃..., v _n, n represents statistical sample number, utilizes maximum likelihood estimate to solve the concrete calculation expression of form parameter k and scale parameter c as follows:

$\left\{\begin{array}{c}k={(\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{v}_i^k\ln v_i}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{v}_i^k}+\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\ln v_i)}^{-1}\\ c={\left(\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left(v_i\right)}^k\right)}^{1/k}\end{array}\right.---\left(2\right)$

In formula, form parameter k adopts process of iteration to solve, thereby tries to achieve dimensional parameters c.

3. method according to claim 1, it is characterized in that, described mountain area complex-terrain model is based on Lissaman wake effect model, the place difference that different wind-powered electricity generation units are installed, there is different sea level elevations, and wind speed with altitude change and change, cause the wind speed profile inequality of wind energy turbine set, its situation of change represents with following formula:

$\frac{v\left(h\right)}{v_0}={\left(\frac{h}{h_0}\right)}^{\mathrm{\&alpha;}}---\left(4\right)$

In formula: v ₀for being highly h ₀the wind speed recording, m/s; V (h) is at the wind speed highly recording for h, m/s; α is wind speed with altitude variation factor, generally gets α=1/7; Obtaining thus wind speed increases along with highly increasing; Obtained the wind speed decreased coefficient d of subdued topography by lossless Bei Nuli equation _fwind speed decreased coefficient d with complex-terrain _crelation is as follows:

$\frac{d_C}{d_F}={\left(\frac{v_0}{v}\right)}^2---\left(5\right)$

So wind speed is everywhere:

$\begin{array}{c}v(x,h)=v\left(h\right)(1-d_C)\\ =v_0[1-(1-\sqrt{1-C_T}){\left(\frac{h_0}{h}\right)}^{2\mathrm{\&alpha;}}{{\left(\frac{r_0}{r}\right)}^2}^{}]{\left(\frac{h}{h_0}\right)}^{\mathrm{\&alpha;}}\end{array}---\left(6\right)$

Wherein, C _tfor thrust coefficient, r is the radius that is subject to wake effect impact.

4. method according to claim 1, is characterized in that, the wake effect model of described multi-overlapped is based on Lissaman wake effect model, supposes at a distance of being x _ijwT _iand WT _jsea level elevation be respectively h _i, h _j, downstream wind-powered electricity generation unit WT _jbe subject to upstream wind-powered electricity generation unit WT _iwind speed computing formula after impact is as follows:

$\begin{array}{c}{v}_j(x_{\mathrm{ij}},h_j)=v_i[1-(1-\sqrt{1-C_T})\&CenterDot;{\left(\frac{h_i}{h_j}\right)}^{2\mathrm{\&alpha;}}\\ \&CenterDot;{\left(\frac{r_j}{r_i\left(x_{\mathrm{ij}}\right)}\right)}^2\&CenterDot;\left(\frac{A_{\mathrm{shad}.\mathrm{ij}}}{A_{\mathrm{rotor}}}\right)]{\left(\frac{h_j}{h_i}\right)}^{\mathrm{\&alpha;}}\end{array}---\left(7\right)$

In formula: A _rotorfor wind wheel sweeping area, a _shad.ijfor the area of lap, r _iand r _jbe respectively upstream wind-powered electricity generation unit WT _iwith downstream wind-powered electricity generation unit WT _jwind wheel radius; Computing formula is tried to achieve by following formula:

$\begin{array}{c}{A}_{\mathrm{shad}.\mathrm{ij}}=r_i^2\left(x_{\mathrm{ij}}\right){\cos }^{-1}\left(\frac{r_i^2\left(x_{\mathrm{ij}}\right)+d_{\mathrm{ij}}^2+\mathrm{\&Delta;}{h}^2-r_j^2}{2r_i\left(x_{\mathrm{ij}}\right)\&CenterDot;\sqrt{\mathrm{\&Delta;}{h}^2+d_{\mathrm{ij}}^2}}\right)\\ +r_j^2{\cos }^{-1}\left(\frac{r_j^2+d_{\mathrm{ij}}^2+\mathrm{\&Delta;}{h}^2-r_i^2\left(x_{\mathrm{ij}}\right)}{2r_j\&CenterDot;\sqrt{\mathrm{\&Delta;}{h}^2+d_{\mathrm{ij}}^2}}\right)\\ -r_i\left(x_{\mathrm{ij}}\right)\&CenterDot;\sqrt{\mathrm{\&Delta;}{h}^2+d_{\mathrm{ij}}^2}\\ \&CenterDot;\sin \left[{\cos }^{-1}\left(\frac{r_i^2\left(x_{\mathrm{ij}}\right)+d_{\mathrm{ij}}^2+\mathrm{\&Delta;}{h}^2-r_j^2}{2r_i\left(x_{\mathrm{ij}}\right)\&CenterDot;\sqrt{\mathrm{\&Delta;}{h}^2+d_{\mathrm{ij}}^2}}\right)\right]\end{array}---\left(8\right)$

Δ h=|h in formula _j-h _i|, be that the sea level elevation of two wind-powered electricity generation units is poor; d _ijfor upstream wind-powered electricity generation unit WT _iwith downstream wind-powered electricity generation unit WT _jbetween level interval; Wake effect is not paid attention in the situation of sowing: the wind-powered electricity generation unit fault of upstream, there is no impact to downstream wind-powered electricity generation unit; Wind-powered electricity generation unit power characteristic is as follows:

$P\left(v\right)=\left\{\begin{array}{cc}0,& v>v_{\mathrm{out}}\mathrm{or\; v}<v_{\mathrm{in}}\\ P_{\mathrm{rated}}\frac{v^3-v_{\mathrm{in}}^3}{v_{\mathrm{rated}}^3-v_{\mathrm{in}}^3},& v_{\mathrm{in}}\&le;v\&le;v_{\mathrm{rated}}\\ P_{\mathrm{rated},}& v_{\mathrm{rated}}\&le;v\&le;v_{\mathrm{out}}\end{array}\right.---\left(9\right)$ V, v _in, v _rated, v _outbe respectively current actual wind speed, incision wind speed, wind rating and cut-out wind speed, P _ratedfor the rated power of blower fan.

5. method according to claim 1, is characterized in that, obtains the time sequence status { S of wind-powered electricity generation unit in described step 3 _wT.1, S _wT.2... S _wT.t.S _wT.T, wherein, the wind-powered electricity generation set state in certain moment s _wTi.t(i=1 ... N _wT, t=1,2,3 ... T) expression wind-powered electricity generation unit i is at the state in t moment, and its value can only be 0 and 1, represents that respectively blower fan is in stopping transport and running status, N _wTfor blower fan sum, T is simulation T.T.; Obtain the time sequence status { S of cable and/or pole line _cL.1, S _cL.2... S _cL.t... S _cL.T, wherein, certain moment cable and/or pole line state s _cLm.t(m=1,2,3 ... N _cL, t=1 ... T) expression cable and/or pole line m are at the state in t moment, and its value can only be 0 and 1, represent that respectively cable and/or pole line are in stopping transport and running status, N _cLfor being cable and/or pole line number, T is simulated time;

Equivalent wind speed in step 2 is brought into formula (9) and is tried to achieve the P that exerts oneself of t moment wind-powered electricity generation unit i _wTi.t, then try to achieve exerting oneself as P in conjunction with the t moment wind-powered electricity generation unit i of wind-powered electricity generation set state _wTi.twith S _wTi.tproduct: P _wTi.ts _wTi.t;

Try to achieve in conjunction with the t moment wind-powered electricity generation unit i of wind-powered electricity generation set state and cable and/or pole line state exert oneself for this branch road output power sum is so wind energy turbine set comprises many chain type branch roads, tries to achieve after every branch road sum, is added the output that obtains the wind energy turbine set t moment.

6. method according to claim 1, is characterized in that, the idiographic flow of the simulation calculation in described step 3, comprising:

1) read in the residing position coordinates of each wind-powered electricity generation unit of wind energy turbine set;

2) simulation time initialization, t is hourage, from t=1;

3) during judging whether to enter icing; According to the feature of ice-covering area, during being icing February Dec to next year, calculate simulated time since 1 o'clock on the 1st January, during 1-the 1416th hour and 8017-the 8760th hour are icing, and as criterion;

4), if during entering icing, adopt equivalent wind speed modeling wind speed; Otherwise, adopt Weibull distribution simulation wind speed;

5) read in the wind speed and direction of t hour;

6) Monte Carlo sampling obtains the wind-powered electricity generation unit of normal operation and fault stoppage in transit;

7) calculate the wind speed at each wind-powered electricity generation unit place according to wake effect correlation formula (4)-(8);

8) calculate each wind-powered electricity generation unit real power output according to wind-powered electricity generation unit power characteristic formula (9);

9) set simulation time Y=8760 hour herein, 1 year, hourage t=t+1, judged whether simulation time finishes, and t>Y finishes to calculate, and t<Y proceeds to calculation process 3).