CN106684905A - Wind power plant dynamic equivalence method with wind power forecast uncertainty considered - Google Patents

Abstract

The invention discloses a wind power plant dynamic equivalence method with the wind power forecast uncertainty considered. The method includes the steps that a probability density function and parameters of wind speed and power errors in a wind power forecast are determined; sample matrix inversion is used for conducting multi-scenario sampling to obtain a sample in accordance with forecast error probability distribution of a wind turbine unit; a two-dimensional statistic grid with the wind speed errors and the power errors as coordinates is established to count the sample frequency of each wind turbine in the statistic grid; the k-means clustering algorithm is used for conducting wind turbine clustering based on the improved KL distance; stand-alone equivalence is conducted on the wind turbine unit and a current collection system in each cluster. According to the method, the wind speed and power forecast error uncertainty is considered through multi-scenario sampling based on probability distribution, and therefore the method has high accuracy under the condition of wind power forecast errors, can better serve in forewarning calculation of power system dispatching, and has certain project application value.

Description

A kind of wind power plant Dynamic Equivalence for considering wind-powered electricity generation uncertainty in traffic

Technical field

The present invention relates to wind power plant equivalence method research field, in particular it relates to a kind of consider wind-powered electricity generation uncertainty in traffic Wind power plant Dynamic Equivalence.

Background technology

Large-scale wind power is accessed and brought challenges to all many-sides of operation of power networks, for the dynamic characteristic of wind-electricity integration system Carrying out research can help power grid operation personnel's development risk to estimate and science decision, and then improve the permeability of wind-powered electricity generation.Big In the wind farm grid-connected emulation of type, if be modeled to every typhoon group of motors, not only workload is great, and can influence to calculate Speed, precision and convergence, therefore, it is necessary to study its dynamic equivalent model on the premise of wind power plant output accuracy is ensured. In existing research, main wind power plant Dynamic Equivalence has unit equivalent method, half equivalent method and multimachine equivalent method etc..It is single Machine equivalent method refers to that all Wind turbines in wind power plant are equivalent for 1 machine；Half equivalent method refers to the wind-force for retaining wind turbine Machine part, its generator model is equivalent for 1 machine；Multimachine equivalent method refers to according to operating point equivalence Cheng Duotai by Wind turbines Machine.Wherein, multimachine equivalent method is widely adopted because of its high precision, easy-operating advantage.

Multimachine is equivalent to be divided into multiple groups by Wind turbines in wind power plant according to operation characteristic first, then to the wind in each group It is equivalent that group of motors carries out unit.Need to select that the index of its running status can be reflected during wind turbine component group, wind speed, fan rotor turns Speed, Wind turbines state variable, running of wind generating set control area, Wind turbines wind speed, rotating speed, stator voltage, q axles stator electricity Stream and active power, wind speed, rotating speed and propeller pitch angle overall target etc. have been proposed as wind turbine component group index.However, Current wind power plant dynamic equivalent research is based on deterministic data, it is believed that the data that wind turbine component group is used are defined exact figures According to that is, in the absence of error.But in the early warning of electric power system dispatching is calculated, point group's data of wind power plant multimachine Equivalent Model will From wind power prediction data, its error is inevitable and with randomness.In the case where wind-powered electricity generation predicts accurate scene, use Traditional multimachine Equivalent Model based on deterministic data has the degree of accuracy higher；However, most of scenes of wind-powered electricity generation prediction are Inaccurate, if now wind power plant can give simulation calculation band still using the multimachine equivalence method based on deterministic data point group Carry out larger error.

In sum, present inventor has found above-mentioned technology extremely during the present application technical scheme is realized There is following technical problem less：

Conventionally, as most of scenes of wind-powered electricity generation prediction are inaccurate, if now wind power plant still uses base In the multimachine equivalence method of deterministic data point group, then larger error can be brought to simulation calculation, therefore existing wind power plant etc. There is the technical problem that accuracy is poor, causes simulation calculation error larger in value method.

The content of the invention

The invention provides a kind of wind power plant Dynamic Equivalence for considering wind-powered electricity generation uncertainty in traffic, solve existing Be present the technical problem that accuracy is poor, causes simulation calculation error larger in wind power plant equivalence method, realize in wind power Predict under the scene for error occur that there is preferable accuracy, in the case where wind-powered electricity generation uncertainty in traffic is considered, can be more preferable Serve the technique effect that the early warning of electric power system dispatching is calculated.

In order to solve the above technical problems, this application provides a kind of wind power plant dynamic for considering wind-powered electricity generation uncertainty in traffic etc. Value method, it is theed improvement is that, methods described is comprised the steps based on wind-powered electricity generation uncertainty in traffic point group：

A, the probability distribution and its parameter that determine wind speed and power error in wind-powered electricity generation prediction, many scenes are carried out using the method for inverting Sampling, obtains obeying the sample of Wind turbines predicated error probability distribution；

The Two-dimensional Statistical grid of B, foundation with air speed error and power error as coordinate, statistics is per Fans in statistics grid Interior sample frequency；

C, based on improved KL distance, i.e., improved KL divergences (Kullback-Leibler Divergence), KL divergences It is called relative entropy (Relative Entropy), blower fan point group is carried out using k-means (k- averages) clustering algorithm； Kullback-Leibler is English name-to, and the country is referred to as KL divergences,

D, that unit is carried out to Wind turbines parameter in each group and network parameter is equivalent.

Further, the step A is comprised the following steps：

A1, according to priori or historical forecast error information, it is determined that the wind speed error delta in prediction per Fans wind-powered electricity generations v_wWith power error Δ P_wCumulative distribution function F (Δ v_w)、F(ΔP_w)；

A2, for every Fans generate N_sRandom number c between individual (0,1), i.e. probability, wherein, the wind of same Fans Speed is identical with the random number c of power sample；

A3, solution equation F (Δ v_w)=c, F (Δ P_w)=c, N can be obtained per Fans_sGroup includes air speed error Δ v_w With power error Δ P_wTwo-dimensional array, namely in each group of two-dimensional array, comprising the wind being calculated by same random number c Fast error delta v_wWith power error Δ P_w。

Further, the step B comprises the steps：

B1, for air speed error Δ v_wWith power error Δ P_w, its excursion is averagely reasonably divided into M_v、M_PIt is individual Interval, respectively with Δ v_wWith Δ P_wAs abscissa and ordinate, a latticed interval range for two dimension can be obtained, it is interval In the range of include M_v×M_PIndividual grid, counts the N per Fans respectively_sSample frequency of the group two-dimensional array in each grid, i-th Sample frequency of the two-dimensional array of Fans in l-th grid is E_i(l), l ∈ [1, M_v×M_P]；

Whether B2, the sample frequency judged in each grid are 0, and a minimum ε is superimposed if for 0, are carried out down if being not 0 One step.

Further, the step C comprises the steps：

C1, selection k Fans are used as initial cluster center；

C2, to any one Fans, calculate its improved KL distance for arriving k cluster centre, by the blower fan be grouped into distance most Group where small cluster centre, the improved KL distance computing formula between the i-th Fans and jth Fans is：

d_KL(E_i,E_j)=[D_KL(E_i||E_j)+D_KL(E_j||E_i)]/2 (1)

Wherein, d_KL(E_i,E_j) it is improved KL distance between the i-th Fans and jth Fans, D_KL(E_i||E_j) it is i-th Blower fan is to the KL distances between jth Fans, D_KL(E_j||E_i) for jth Fans to the KL distances between the i-th Fans, both Computing formula be：

Wherein, E_iL () is sample frequency of the two-dimensional array of the i-th Fans in l-th grid, E_jL () is jth typhoon Sample frequency of the two-dimensional array of machine in l-th grid, l ∈ [1, M_v×M_P]。

Average sample frequency of each blower fan in grid is counted in C3, calculating group, it is assumed that have catwalk blower fan in k-th group, its Average sample frequency in l-th grid is：

Wherein, E_rL () is sample frequency of the two-dimensional array of r Fans in l-th grid, l ∈ [1, M_v×M_P]。 By that analogy, catwalk blower fan can be obtained in M_v×M_PAverage sample frequency E in individual grid_{av_k}(l), as new cluster Central value, calculates k group its average sample frequency, obtains k new cluster centre value；

C4, judgement：If all cluster centre values keep constant, or update times reach the upper limit, then turn C5, otherwise return C2；

C5, output cluster result.

Further, the step D comprises the steps：

D1, equivalence is carried out to the Wind turbines parameter in wind turbine group：

It is constant to Wind turbines parameter aggregation based on Wind turbines output characteristics before and after equivalence, to wind-powered electricity generation in wind turbine group The wind speed v of unit, wind sweeping area A, capacity S, active-power P, reactive power Q, shafting inertia time constant H, axis rigidity system Number K and shafting damped coefficient D parameters carry out equivalence according to equation below respectively：

In formula：n_wIt is Wind turbines number in wind turbine group；v_eq、v_iWind turbines is total respectively in wind turbine group The wind speed of wind speed and the i-th typhoon group of motors；A_eq、A_iTotal wind sweeping area of Wind turbines and i-th respectively in wind turbine group The wind sweeping area of Wind turbines；S_eq、S_iThe appearance of the total capacity of Wind turbines and the i-th typhoon group of motors respectively in wind turbine group Amount；P_eq、P_iThe active power of total active power of Wind turbines and the i-th typhoon group of motors respectively in wind turbine group；

D2, equivalence is carried out to network parameter：

Equivalence to line impedance is carried out based on the constant principle of equivalent front and rear voltage loss, is calculated as follows：

In formula：n_wIt is Wind turbines number, n in group_fIt is Wind turbines number, Z in trunk line type blower fan branch road in wind power plant_g It is g sections of branch impedance in trunk line type branch road；

Equivalent admittance Y over the ground_eqIt is calculated as follows：

In formula：Y is admittance over the ground.

The excellent effect that has of technical scheme that the present invention is provided is：

Technical scheme in above-mentioned the embodiment of the present application, at least has the following technical effect that or advantage：

The wind power plant Dynamic Equivalence of the consideration wind-powered electricity generation uncertainty in traffic that the present invention is provided, determines that wind-powered electricity generation predicts apoplexy The probability distribution and its parameter of speed and power error, many scene sampling are carried out using the method for inverting, and obtain obeying Wind turbines prediction The sample of probability of error distribution；Set up with air speed error and Two-dimensional Statistical grid of the power error as coordinate, statistics is per Fans Sample frequency in statistics grid；Based on improved KL distance, blower fan point group is carried out using k-means clustering algorithms；According to point Group's result, it is constant to Wind turbines parameter aggregation based on Wind turbines output characteristics before and after equivalence, damaged based on voltage before and after equivalence The constant principle of consumption carries out equivalence to network parameter, and simulation result shows, the wind power plant Dynamic Equivalence that the present invention is provided by In consideration wind-powered electricity generation uncertainty in traffic (i.e. the randomness of wind-powered electricity generation predicated error), thus there is the field of error in wind power prediction There is preferable accuracy, early warning that can be with better services in electric power system dispatching is calculated, to improving wind power plant dynamic etc. under scape The accuracy of value model and the security and stability of wind-electricity integration system operation have certain meaning.

Brief description of the drawings

Accompanying drawing described herein is used for providing further understanding the embodiment of the present invention, constitutes of the application Point, do not constitute the restriction to the embodiment of the present invention；

Fig. 1 is the schematic flow sheet of the wind power plant Dynamic Equivalence of consideration wind-powered electricity generation uncertainty in traffic in the application.

Specific embodiment

The invention provides a kind of wind power plant Dynamic Equivalence for considering wind-powered electricity generation uncertainty in traffic, solve existing Be present the technical problem that accuracy is poor, causes simulation calculation error larger in wind power plant equivalence method, realize in wind power Predict under the scene for error occur that there is preferable accuracy, in the case where wind-powered electricity generation uncertainty in traffic is considered, can be more preferable Serve the technique effect that the early warning of electric power system dispatching is calculated.

The present invention provides a kind of wind power plant Dynamic Equivalence for considering wind-powered electricity generation uncertainty in traffic, its flow chart such as Fig. 1 It is shown, comprise the steps：

A, the probability distribution and its parameter that determine wind speed and power error in wind-powered electricity generation prediction, many scenes are carried out using the method for inverting Sampling, obtains obeying the sample of Wind turbines predicated error probability distribution

A1, according to priori or historical forecast error information, it is determined that the wind speed error delta in prediction per Fans wind-powered electricity generations v_wWith power error Δ P_wCumulative distribution function F (Δ v_w)、F(ΔP_w)；

A2, for every Fans generate N_sRandom number c between individual (0,1), i.e. probability, wherein, the wind of same Fans Speed is identical with the random number c of power sample；

A3, solution equation F (Δ v_w)=c, F (Δ P_w)=c, N can be obtained per Fans_sGroup includes air speed error Δ v_w With power error Δ P_wTwo-dimensional array, namely in each group of two-dimensional array, comprising the wind being calculated by same random number c Fast error delta v_wWith power error Δ P_w。

The Two-dimensional Statistical grid of B, foundation with air speed error and power error as coordinate, statistics is per Fans in statistics grid Interior sample frequency

B1, for air speed error Δ v_wWith power error Δ P_w, its excursion is averagely reasonably divided into M_v、M_PIt is individual Interval, respectively with Δ v_wWith Δ P_wAs abscissa and ordinate, a latticed interval range for two dimension can be obtained, it is interval In the range of include M_v×M_PIndividual grid, counts the N per Fans respectively_sSample frequency of the group two-dimensional array in each grid, i-th Sample frequency of the two-dimensional array of Fans in l-th grid is E_i(l), l ∈ [1, M_v×M_P]；

Whether B2, the sample frequency judged in each grid are 0, and a minimum ε is superimposed if for 0, are carried out down if being not 0 One step.

C, based on improved KL distance, carry out blower fan point group using k-means clustering algorithms

C1, selection k Fans are used as initial cluster center；

C2, to any one Fans, calculate its improved KL distance for arriving k cluster centre, by the blower fan be grouped into distance most Group where small cluster centre, the improved KL distance computing formula between the i-th Fans and jth Fans is：

d_KL(E_i,E_j)=[D_KL(E_i||E_j)+D_KL(E_j||E_i)]/2 (15)

Wherein, d_KL(E_i,E_j) it is improved KL distance between the i-th Fans and jth Fans, D_KL(E_i||E_j) it is i-th Blower fan is to the KL distances between jth Fans, D_KL(E_j||E_i) for jth Fans to the KL distances between the i-th Fans, both Computing formula be：

Wherein, E_iL () is sample frequency of the two-dimensional array of the i-th Fans in l-th grid, E_jL () is jth typhoon Sample frequency of the two-dimensional array of machine in l-th grid, l ∈ [1, M_v×M_P].Each blower fan is in statistics grid in C3, calculating group In average sample frequency, it is assumed that have catwalk blower fan in k-th group, its average sample frequency in l-th grid is：

Wherein, E_rL () is sample frequency of the two-dimensional array of r Fans in l-th grid, l ∈ [1, M_v×M_P]。

By that analogy, catwalk blower fan can be obtained in M_v×M_PAverage sample frequency E in individual grid_{av_k}(l), as New cluster centre value, calculates k group its average sample frequency, obtains k new cluster centre value；

C4, judgement：If all cluster centre values keep constant, or update times reach the upper limit, then turn C5, otherwise return C2；

C5, output cluster result.

D, that unit is carried out to Wind turbines parameter in wind turbine group and network parameter is equivalent

D1, equivalence is carried out to the Wind turbines parameter in wind turbine group：

It is constant to Wind turbines parameter aggregation based on Wind turbines output characteristics before and after equivalence, to wind-powered electricity generation in wind turbine group The wind speed v of unit, wind sweeping area A, capacity S, active-power P, reactive power Q, shafting inertia time constant H, axis rigidity system Number K and shafting damped coefficient D parameters carry out equivalence according to equation below respectively：

In formula：n_wIt is Wind turbines number in wind turbine group；v_eq、v_iWind turbines is total respectively in wind turbine group The wind speed of wind speed and the i-th typhoon group of motors；A_eq、A_iTotal wind sweeping area of Wind turbines and i-th respectively in wind turbine group The wind sweeping area of Wind turbines；S_eq、S_iThe appearance of the total capacity of Wind turbines and the i-th typhoon group of motors respectively in wind turbine group Amount；P_eq、P_iThe active power of total active power of Wind turbines and the i-th typhoon group of motors respectively in wind turbine group；

D2, equivalence is carried out to network parameter：

Equivalence to line impedance is carried out based on the constant principle of equivalent front and rear voltage loss, is calculated as follows：

In formula：n_wIt is Wind turbines number, n in group_fIt is Wind turbines number, Z in trunk line type blower fan branch road in wind power plant_g It is g sections of branch impedance in trunk line type branch road；

Equivalent admittance Y over the ground_eqIt is calculated as follows：

In formula：Y is admittance over the ground.

Technical scheme in above-mentioned the embodiment of the present application, at least has the following technical effect that or advantage：

The wind power plant Dynamic Equivalence of the consideration wind-powered electricity generation uncertainty in traffic that the present invention is provided, determines that wind-powered electricity generation predicts apoplexy The probability distribution and its parameter of speed and power error, many scene sampling are carried out using the method for inverting, and obtain obeying Wind turbines prediction The sample of probability of error distribution；Set up with air speed error and Two-dimensional Statistical grid of the power error as coordinate, statistics is per Fans Sample frequency in statistics grid；Based on improved KL distance, blower fan point group is carried out using k-means clustering algorithms；According to point Group's result, it is constant to Wind turbines parameter aggregation based on Wind turbines output characteristics before and after equivalence, damaged based on voltage before and after equivalence The constant principle of consumption carries out equivalence to network parameter, and simulation result shows, the wind power plant Dynamic Equivalence that the present invention is provided by In consideration wind-powered electricity generation uncertainty in traffic (i.e. the randomness of wind-powered electricity generation predicated error), thus there is the field of error in wind power prediction There is preferable accuracy, early warning that can be with better services in electric power system dispatching is calculated, to improving wind power plant dynamic etc. under scape The accuracy of value model and the security and stability of wind-electricity integration system operation have certain meaning.

, but those skilled in the art once know basic creation although preferred embodiments of the present invention have been described Property concept, then can make other change and modification to these embodiments.So, appended claims are intended to be construed to include excellent Select embodiment and fall into having altered and changing for the scope of the invention.

Obviously, those skilled in the art can carry out various changes and modification without deviating from essence of the invention to the present invention God and scope.So, if these modifications of the invention and modification belong to the scope of the claims in the present invention and its equivalent technologies Within, then the present invention is also intended to comprising these changes and modification.

Claims (5)

1. it is a kind of consider wind-powered electricity generation uncertainty in traffic wind power plant Dynamic Equivalence, it is characterised in that the wind in methods described Group of motors is to carry out a point group based on wind-powered electricity generation uncertainty in traffic, is comprised the steps：

A, the probability distribution and its parameter that determine wind speed and power error in wind-powered electricity generation prediction, carry out many scenes and take out using the method for inverting Sample, obtains obeying the sample of Wind turbines predicated error probability distribution；

The Two-dimensional Statistical grid of B, foundation with air speed error and power error as coordinate, statistics is per Fans in statistics grid Sample frequency；

C, based on improved KL distance, carry out blower fan point group using k-means clustering algorithms；

D, that unit is carried out to Wind turbines parameter in each group and network parameter is equivalent.

2. wind power plant Dynamic Equivalence as claimed in claim 1, it is characterised in that the step A is comprised the following steps：

A1, according to pre-conditioned or historical forecast error information, it is determined that the wind speed error delta v in prediction per Fans wind-powered electricity generations_wAnd work( Rate error delta P_wCumulative distribution function F (Δ v_w)、F(ΔP_w)；

A2, for every Fans generate N_sRandom number c between individual 0 to 1, i.e. probability, wherein, the wind speed and work(of same Fans The random number c of rate sample is identical；

A3, solution equation F (Δ v_w)=c, F (Δ P_w)=c, N can be obtained per Fans_sGroup includes air speed error Δ v_wAnd power Error delta P_wTwo-dimensional array, namely in each group of two-dimensional array, comprising the air speed error being calculated by same random number c Δv_wWith power error Δ P_w。

3. wind power plant Dynamic Equivalence as claimed in claim 2, it is characterised in that the step B comprises the steps：

B1, for air speed error Δ v_wWith power error Δ P_w, its excursion is averagely divided into M_v、M_PIndividual interval, respectively with Δv_wWith Δ P_wAs abscissa and ordinate, obtain including M in a latticed interval range for two dimension, interval range_v×M_P Individual grid, counts the N per Fans respectively_sSample frequency of the group two-dimensional array in each grid, the two-dimensional array of the i-th Fans Sample frequency in l-th grid is E_i(l), l ∈ [1, M_v×M_P]；

Whether B2, the sample frequency judged in each grid are 0, if 0 one minimum ε of superposition, if not 0 carries out step C.

4. wind power plant Dynamic Equivalence as claimed in claim 3, it is characterised in that the step C comprises the steps：

C1, selection k Fans are used as initial cluster center；

C2, to any one Fans, calculate its improved KL distance for arriving k cluster centre, it is minimum that the blower fan is grouped into distance Group where cluster centre, the improved KL distance computing formula between the i-th Fans and jth Fans is：

d_KL(E_i,E_j)=[D_KL(E_i||E_j)+D_KL(E_j||E_i)]/2 (1)

Wherein, d_KL(E_i,E_j) it is improved KL distance between the i-th Fans and jth Fans, D_KL(E_i||E_j) it is the i-th Fans To the KL distances between jth Fans, D_KL(E_j||E_i) it is jth Fans to the KL distances between the i-th Fans, both meters Calculating formula is：

$D_{KL}\left(E_i\right|\left|E_j\right)=\underset{l\∈\[1,M_v\×M_P\]}{\Σ}{E}_i\left(l\right)ln\frac{E_i\left(l\right)}{E_j\left(l\right)}---\left(2\right)$

$D_{KL}\left(E_j\right|\left|E_i\right)=\underset{l\∈\[1,M_v\×M_P\]}{\Σ}{E}_j\left(l\right)ln\frac{E_j\left(l\right)}{E_i\left(l\right)}---\left(3\right)$

Wherein, E_iL () is sample frequency of the two-dimensional array of the i-th Fans in l-th grid, E_jL () is jth Fans Sample frequency of the two-dimensional array in l-th grid, l ∈ [1, M_v×M_P]；

C3, calculate average sample frequency of each blower fan in grid is counted in group, it is assumed that have catwalk blower fan in k-th group, it is the Average sample frequency E in l grid_{av_k}For：

$E_{av\_k}\left(l\right)=\frac{1}{T}\underset{r=1}{\overset{T}{\Σ}}{E}_r\left(l\right)---\left(4\right)$

Wherein, E_rL () is sample frequency of the two-dimensional array of r Fans in l-th grid, l ∈ [1, M_v×M_P]；

By that analogy, catwalk blower fan is obtained in M_v×M_PAverage sample frequency E in individual grid_av__k(l), as new cluster Central value, calculates k group its average sample frequency, obtains k new cluster centre value；

C4, judgement：If all cluster centre values keep constant, or update times reach the upper limit, then turn C5, otherwise return to C2；

C5, output cluster result.

5. wind power plant Dynamic Equivalence as claimed in claim 1, it is characterised in that the step D comprises the steps：

D1, equivalence is carried out to the Wind turbines parameter in each group：

It is constant to Wind turbines parameter aggregation based on Wind turbines output characteristics before and after equivalence, to Wind turbines in wind turbine group Wind speed v, wind sweeping area A, capacity S, active-power P, reactive power Q, shafting inertia time constant H, axis rigidity COEFFICIENT K and Shafting damped coefficient D parameters carry out equivalence according to equation below respectively：

$v_{eq}=\sqrt[3]{\frac{1}{n_w}\underset{i=1}{\overset{n_w}{\Σ}}{v}_i^3}---\left(5\right)$

$A_{eq}=\underset{i=1}{\overset{n_w}{\mathrm{\Σ}}}{A}_i---\left(6\right)$

$S_{eq}=\underset{i=1}{\overset{n_w}{\Σ}}{S}_i---\left(7\right)$

$P_{eq}=\underset{i=1}{\overset{n_w}{\Σ}}{P}_i---\left(8\right)$

$Q_{eq}=\underset{i=1}{\overset{n_w}{\Σ}}{Q}_i---\left(9\right)$

$H_{eq}=\frac{\underset{i=1}{\overset{n_w}{\Σ}}{S}_i{H}_i}{\underset{i=1}{\overset{n_w}{\Σ}}{S}_i}---\left(10\right)$

$K_{eq}=\frac{\underset{i=1}{\overset{n_w}{\Σ}}{S}_i{K}_i}{\underset{i=1}{\overset{n_w}{\Σ}}{S}_i}---\left(11\right)$

$D_{eq}=\frac{\underset{i=1}{\overset{n_w}{\Σ}}{S}_i{D}_i}{\underset{i=1}{\overset{n_w}{\Σ}}{S}_i}---\left(12\right)$

In formula：n_wIt is Wind turbines number in wind turbine group；v_eq、v_iTotal wind speed of Wind turbines respectively in wind turbine group With the wind speed of the i-th typhoon group of motors；A_eq、A_iTotal wind sweeping area of Wind turbines and the i-th typhoon electricity respectively in wind turbine group The wind sweeping area of unit；S_eq、S_iThe capacity of the total capacity of Wind turbines and the i-th typhoon group of motors respectively in wind turbine group； P_eq、P_iThe active power of total active power of Wind turbines and the i-th typhoon group of motors respectively in wind turbine group；

D2, equivalence is carried out to network parameter：

Equivalence to line impedance is carried out based on the constant principle of equivalent front and rear voltage loss, is calculated as follows：

$Z_{eq}=\frac{\underset{i=1}{\overset{n_w}{\Σ}}\left(\underset{g=1}{\overset{i}{\Σ}}\right(Z_g\underset{s=g}{\overset{n_f}{\Σ}}{P}_s\left)P_i\right)}{{\left(\underset{i=1}{\overset{n_w}{\Σ}}{P}_i\right)}^2}---\left(13\right)$

In formula：n_wIt is Wind turbines number, n in group_fIt is Wind turbines number, Z in trunk line type blower fan branch road in wind power plant_gIt is dry G sections of branch impedance in wire type branch road；

Equivalent admittance Y over the ground_eqIt is calculated as follows：

$Y_{eq}=\underset{i=1}{\overset{n_w}{\Σ}}{Y}_i---\left(14\right)$

In formula：Y is admittance over the ground.