CN109802440B - Offshore wind farm equivalence method, system and device based on wake effect factor - Google Patents

Abstract

The invention discloses an equivalent method, system and device for an offshore wind farm based on a wake effect factor, wherein the method includes the following steps: acquiring offshore wind farm parameters, wherein the offshore wind farm parameters include the origin of the offshore wind farm. Wind speed, position information of each wind turbine in the offshore wind farm, wind wheel radius, attenuation constant and thrust coefficient; determine the wake effect factor of each wind turbine according to the parameters of the offshore wind farm; According to each wind turbine group and its equivalent parameters, the equivalent model of the offshore wind farm is established. The method is simple and easy to implement, occupies less memory space and has high accuracy, and can be applied to the research of large-scale offshore wind power.

Description

Equivalent method, system and device for offshore wind farm based on wake effect factor

technical field

The invention relates to the technical field of wind power generation, in particular to an equivalent method, system and device for an offshore wind farm based on a wake effect factor.

Background technique

Compared with onshore wind power generation, offshore wind power generation has many obvious advantages, mainly in: (1) Offshore wind turbines can reduce the occupation of land resources, and the sea has a large continuous space suitable for large-scale wind power projects, which is very suitable for large-scale wind power projects. (2) Compared with onshore wind power, offshore wind power is close to the traditional power load center, which is conducive to the consumption of the power grid and reduces the investment cost and power loss caused by long-distance power transmission; (3) The offshore wind speed is higher than that of onshore wind power. The wind speed should be 20% to 100% higher, and the power generation efficiency will also be improved accordingly. In most cases, the flat sea surface is stable and the wind speed is stable, which is conducive to the wind turbine to make more effective and full use of wind energy and reduce the fatigue load on the wind turbine, and ultimately improve the wind turbine. life cycle and generate more power.

There are a large number of scattered wind turbines in large offshore wind farms. Under the action of a certain wind direction, the wind speed of the wind turbines located in the downwind direction is often lower than the wind speed of the wind turbines located in the upwind direction. This phenomenon is called wake effect. , which is the main cause of energy loss in offshore wind farms. At the same time, the simulation of large offshore wind farms often has hundreds of wind turbines. If the model of each wind turbine is built in detail, it will greatly increase the complexity of the simulation, resulting in long calculation time, low resource utilization, and even lead to the "Curse of Dimensionality".

For this reason, the wind farm is equivalently modeled in the related technology, and the wake effect is considered in the wind farm equivalent. Each wind turbine calculates the real-time wake effect, which requires a large amount of calculation and a high degree of complexity of the model, and does not consider the influence range of the wake effect. In addition, this technology adopts the k-means algorithm for grouping, although the calculation is simple and easy, but the accuracy is not high, and it is easy to fall into the local optimum.

SUMMARY OF THE INVENTION

The present invention aims to solve one of the technical problems in the related art at least to a certain extent. To this end, an object of the present invention is to propose an equivalent method for offshore wind farms based on wake effect factors, which is simple and easy to implement, occupies less memory space and has high accuracy, and can be applied to large-scale offshore wind farms. Research.

In order to achieve the above object, an embodiment of the first aspect of the present invention proposes an equivalent method for an offshore wind farm based on a wake effect factor, which includes the following steps: acquiring offshore wind farm parameters, wherein the offshore wind farm parameters include offshore wind farm parameters The incoming wind speed of the field, the position information of each wind turbine in the offshore wind farm, the radius of the wind wheel, the attenuation constant and the thrust coefficient; the wake effect factor of each wind turbine is determined according to the parameters of the offshore wind farm; The wind turbines in the offshore wind power are grouped according to the wake effect factor; the equivalent parameters of each wind turbine group are calculated respectively; the offshore wind farm equivalent model is established according to each wind turbine group and its equivalent parameters.

The wake effect factor-based equivalent method for offshore wind farms in the embodiment of the present invention groups wind turbines based on the wake effect factor as the principle of unit grouping, and then calculates the equivalent parameters of each unit group. This method is simple and easy to implement, occupies less memory space and has high accuracy, and is suitable for large-scale offshore wind power research.

In addition, the wake effect factor-based equivalent method for offshore wind farms in the above embodiments of the present invention may also have the following additional technical features:

Optionally, the determining the wake effect factor of each wind turbine according to the offshore wind farm parameter includes: calculating the wake effect influence degree of each wind turbine according to the offshore wind farm parameter; The degree of effect influence determines the wake effect factor of each wind turbine.

Optionally, the influence degree of wake effect of each wind turbine is calculated by the following formula:

Among them, σ _i is the influence degree of the wake effect of the ith wind turbine, v _in is the wind speed of the incoming flow, v _i is the wind speed at the ith wind turbine, i=1,2,...,n, n is the number of wind turbines in the offshore wind farm.

Optionally, the wake effect factor is negatively correlated with the influence degree of the wake effect.

Optionally, when the wake effect influence degree is greater than the first preset value, the wake effect factor is determined to be 0; when the wake effect influence degree is greater than the second preset value and less than or equal to the first preset value , the wake effect factor is determined to be 1; when the influence degree of the wake effect is greater than the third preset value and less than or equal to the second preset value, the wake effect factor is determined to be 2; when the influence degree of the wake effect is less than or When it is equal to the third preset value, the wake effect factor is determined to be 3.

Optionally, the first preset value is 0.95, the second preset value is 0.7, and the third preset value is 0.4.

Optionally, the grouping of the wind turbines in the offshore wind power according to the wake effect factor includes: randomly selecting one of the n wake effect factors as the first cluster center Y1, and calculating the remaining The distance between the n-1 wake effect factors and Y1; select the wake effect factor with the largest distance from Y1 as the second cluster center Y2, and calculate the n-2 wakes other than Y1 and Y2 respectively. Distances D1(x _j , Y1) and D2(x _j , Y2) between effectors and Y1 and Y2, where j=1,2,...,n-2; select min[ The wake effect factor in D1(x _j , Y1), D2(x _j , Y2)] is used as the next cluster center until the number of cluster centers reaches m; The k-means algorithm groups n wind turbines.

Optionally, the equivalent parameters include generator equivalent parameters, transformer equivalent parameters, wind speed equivalent parameters and control equivalent parameters, wherein the generator equivalent parameters are:

Among them, S and S _eq respectively represent the generator capacity before and after the equivalence, x _m and x _{m_eq} represent the generator excitation reactance before and after the equivalence, respectively, x ₁ and x _{1_eq} represent the generator stator reactance before and after the equivalence, x ₂ and x _{2_eq} respectively represent the generator rotor reactance before and after the equivalence, r ₁ and r _{1_eq} respectively represent the generator stator resistance before and after the equivalence, and r ₂ and r _{2_eq} respectively represent the generator rotor resistance before and after the equivalence;

The equivalent parameters of the transformer are:

Among them, S _T and S _{T_eq} respectively represent the transformer capacity before and after the equivalent value, and Z _T and Z _{T_eq} respectively represent the transformer impedance before and after the equivalent value;

When calculating the wind speed equivalent parameters, first determine the power of each wind turbine according to the wind speed and the wind speed-power curve at each wind turbine, calculate the average value of the power, and then according to the average value, the wind speed - the power curve determines the equivalent parameters of the wind speed;

In the control equivalence parameters, S _eq =mS, and other control parameters are the same as the control parameters before the equivalence.

In order to achieve the above purpose, the second aspect of the present invention provides an equivalent system for an offshore wind farm based on a wake effect factor, including: an acquisition module for acquiring offshore wind farm parameters, wherein the offshore wind farm parameters Including the incoming wind speed of the offshore wind farm, the position information of each wind turbine in the offshore wind farm, the radius of the wind wheel, the attenuation constant and the thrust coefficient; the determination module is used for determining each wind turbine according to the parameters of the offshore wind farm The wake effect factor of , which is used to establish an equivalent model of an offshore wind farm according to each wind turbine group and its equivalent parameters.

The offshore wind farm equivalent system based on the wake effect factor in the embodiment of the present invention groups the wind turbines with the wake equivalent factor as the unit grouping principle, and then calculates the equivalent parameters of each unit group, and according to the grouping results and Equivalent parameters are used to establish an equivalent model of offshore wind farms based on wake effect factors. The system is simple and easy to implement, occupies less memory space and has high accuracy, and is suitable for large-scale offshore wind power research.

In order to achieve the above object, an embodiment of the third aspect of the present invention provides an equivalent device for an offshore wind farm based on a wake effect factor, including a memory, a processor, and a computer program stored on the memory, and the computer program is When executed by the processor, the above-mentioned wake effect factor-based equivalent method for offshore wind farms is implemented.

In the wake effect factor-based offshore wind farm equivalent device according to the embodiment of the present invention, when the computer program corresponding to the above-mentioned wake effect factor-based offshore wind farm equivalent method stored in the memory is executed by the processor, the obtained The equivalent model has high precision, occupies less memory, and is simple and easy to implement, which can be applied to the research of large-scale offshore wind power.

Additional aspects and advantages of the present invention will be set forth, in part, from the following description, and in part will be apparent from the following description, or may be learned by practice of the invention.

Description of drawings

FIG. 1 is a flowchart of an equivalent method for an offshore wind farm based on a wake effect factor according to an embodiment of the present invention;

Figure 2 is a schematic diagram of the Jensen model for wake effects;

FIG. 3 is a schematic diagram of a wake effect between wind turbines according to an example of the present invention;

FIG. 4 is a flowchart of an equivalent method for an offshore wind farm based on a wake effect factor according to a specific embodiment of the present invention;

5 is a structural block diagram of an equivalent system of an offshore wind farm based on a wake effect factor according to an embodiment of the present invention; and

6 is a structural block diagram of an equivalent device for an offshore wind farm based on a wake effect factor according to an embodiment of the present invention.

Detailed ways

The following describes in detail the embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary, and are intended to explain the present invention and should not be construed as limiting the present invention.

The following describes an equivalent method, system, and device for an offshore wind farm based on a wake effect factor according to embodiments of the present invention with reference to the accompanying drawings.

Example 1

FIG. 1 is a flowchart of an equivalent method for an offshore wind farm based on a wake effect factor according to an embodiment of the present invention.

As shown in Fig. 1, the equivalent method for offshore wind farms based on wake effect factors includes the following steps:

S1, obtain the parameters of the offshore wind farm.

Among them, the parameters of the offshore wind farm include the incoming wind speed of the offshore wind farm (including the magnitude and direction of the incoming wind speed), the position information of each wind turbine in the offshore wind farm, the radius of the wind rotor, the attenuation constant and the thrust coefficient.

In this embodiment, the wind turbines in the offshore wind farm are preferably doubly-fed wind turbines.

S2, the wake effect factor of each wind turbine is determined according to the parameters of the offshore wind farm.

Specifically, the wake effect influence degree of each wind turbine can be calculated first according to the parameters of the offshore wind farm, and then the wake effect factor of each wind turbine can be determined according to the wake effect influence degree.

Among them, the influence degree of wake effect of each wind turbine can be calculated by the following formula:

Among them, σ _i is the influence degree of the wake effect, v _in is the wind speed of the incoming flow, v _i is the wind speed at the ith wind turbine, i=1,2,...,n, n is the offshore wind power The number of wind turbines in the farm.

In an embodiment of the present invention, the wake effect factor and the influence degree of the wake effect may have a negative correlation, for example, the wake effect factor and the influence degree of the wake effect are reciprocal of each other.

In another embodiment of the present invention, when the influence degree of the wake effect is greater than the first preset value, the wake effect factor is determined to be 0; when the influence degree of the wake effect is greater than the second preset value and less than or equal to the first When the preset value is used, the wake effect factor is determined to be 1; when the influence degree of the wake effect is greater than the third preset value and less than or equal to the second preset value, the wake effect factor is determined to be 2; when the influence degree of the wake effect is When it is less than or equal to the third preset value, the wake effect factor is determined to be 3.

The first preset value may be 0.95, the second preset value may be 0.7, and the third preset value may be 0.4.

In order to facilitate the understanding of the influence degree of the wake effect and the wake effect factor, the following description is made with reference to Figure 2 and Figure 3:

为该风电机组的为尾流效应影响程度。As shown in Figure 2, for a wind turbine, r is the radius of the rotor (that is, the rotor radius of the wind turbine), x is the horizontal distance along the wind direction, v _in is the incoming wind speed of the offshore wind farm, and v _x is the incoming wind speed. The actual wind speed of the flow wind speed under the wake effect, k is the attenuation constant, C _T is the thrust coefficient, then

is the influence degree of the wake effect of the wind turbine.

In an offshore wind farm, each wind turbine may be affected by the upstream wind turbine, and a single wind turbine may be affected by the wake effect of multiple units. According to the incoming wind speed and the position of each wind turbine, it can be determined that the downstream unit is affected by the upstream unit. As shown in Figure 3, the unit W3 is only affected by the unit W1, and its wake effect factor only includes the unit W1; the unit W4 is affected by the unit W1. For the joint influence of units W1 and W2, the wake effect factor is the sum of the influences of units W1 and W2 alone.

S3, group the wind turbines in the offshore wind power according to the wake effect factor.

Specifically, randomly select one of the n wake effect factors as the first cluster center Y1, and calculate the distance between the remaining n-1 wake effect factors and Y1; select the wake with the largest distance from Y1 The effect factor is used as the second cluster center Y2, and the distances D1(x _j , Y1) and D2 (x _j , Y2 between the n-2 wake effect factors other than Y1 and Y2 and Y1 and Y2 are calculated respectively. ), where j=1,2,...,n-2; select the wake effect factor in min[D1(x _j ,Y1),D2(x _j ,Y2)] as the lower A cluster center until the number of cluster centers reaches m; according to the selected m cluster centers, the k-means algorithm is used to group n wind turbines.

It should be noted that the initialization of the traditional k-means algorithm is random, which may cause the final grouping to be inaccurate. For this, the present invention adopts the maximum and minimum distance method to initialize the grouping data. The maximum and minimum distance initialization method is a tentative algorithm. The idea of the algorithm is to take the object that is farther away from other objects in the data set as the initial point, so as to avoid the situation that the initial point is too close in the random initialization. The guidance of this idea can effectively improve the efficiency and quality of the initialization data set. That is to say, compared with the traditional k-means algorithm, in the case of a better initial solution, the dependence on the initial center point can be effectively overcome, thereby effectively improving the convergence speed and accuracy of the algorithm.

S4, calculate the equivalent parameters of each wind turbine group respectively.

Specifically, after the grouping results are obtained, each wind turbine group is equalized, that is, the entire offshore wind farm is equivalent to m wind turbines, and the capacity weighting method can be used when calculating the equivalent parameters, because the same offshore wind farm In wind farms, wind turbines generally have the same model and capacity, and have similar structures and operating conditions.

Among them, the equivalent parameters may include generator equivalent parameters, transformer equivalent parameters, wind speed equivalent parameters and control equivalent parameters. The specific equivalent relationship is as follows:

Generator Equivalent Parameters:

Among them, S and S _eq respectively represent the generator capacity before and after the equivalence, x _m and x _{m_eq} represent the generator excitation reactance before and after the equivalence, respectively, x ₁ and x _{1_eq} represent the generator stator reactance before and after the equivalence, x ₂ and x _{2_eq} respectively represent the generator rotor reactance before and after the equal value, r ₁ and r _{1_eq} respectively represent the generator stator resistance before and after the equal value, r ₂ and r _{2_eq} respectively represent the generator rotor resistance before and after the equal value, namely S, x _m , x ₁ , and x ₂ are the capacity, excitation reactance, stator reactance, and rotor reactance of a single generator, respectively, and S _eq , x _{m_eq} , x _{1_eq} , and x _{2_eq} are the equivalent capacity, the equivalent excitation reactance, and the equivalent stator, respectively Reactance and Equivalent Rotor Reactance. . It can be seen that the generator capacity is m times the original value after the equivalent value, and the resistance and reactance parameters of the generator are the original 1/m after the equivalent value.

Transformer Equivalent Parameters:

Among them, S _T and S _{T_eq} respectively represent the transformer capacity before and after the equivalence, and Z _T and Z _{T_eq} respectively represent the transformer impedance before and after the equivalence. It can be seen that, similar to the equivalent value of the generator parameters, the capacity of the transformer is m times the original value after the equivalent value, and the impedance of the transformer is the original 1/m after the equivalent value.

Equivalent parameters of wind speed:

In order to make the equivalent wind speed represent the group wind speed to the greatest extent, when calculating the wind speed equivalent parameters, first determine the power of each wind turbine according to the wind speed and wind speed-power curve at each wind turbine, and calculate the average value of the power. Then, according to the average value and the wind speed-power curve, the wind speed equivalent parameters are determined.

Control Equivalent Parameters:

In the control parameters, except that the reference capacity of the power test part is changed to m times the original, that is, S _eq = mS, other parameters remain unchanged, that is, they are the same as the control parameters before the equal value.

S6, establishing an equivalent model of an offshore wind farm according to each wind turbine group and its equivalent parameters.

Among them, the equivalent model of the offshore wind farm refers to the model obtained by simplifying the wind farm under the condition that the dynamic influence of the wind farm on the research system remains unchanged.

Specifically, as shown in Fig. 4, the Jensen model, a wake effect model suitable for large-scale offshore wind power, is selected first, the parameters of the offshore wind farm are obtained, and then the model is analyzed to obtain the influence range of the wake effect and its possible The influence degree within its influence range is classified, and the wake effect influence factor of each unit is obtained. Then, according to the wake effect factor, the improved k-means algorithm is used to group the wind farm, and then the equivalent model of the wind farm is established by the capacity weighting method according to the grouping result.

Therefore, the above-mentioned equivalent model can not only reflect the difference of the operating states of each unit in the offshore wind farm, but also improve the model accuracy, memory occupation and calculation time, which can be applied to the research of large-scale offshore wind power.

Example 2

5 is a structural block diagram of an equivalent system of an offshore wind farm based on a wake effect factor according to an embodiment of the present invention.

As shown in FIG. 5 , the equivalent system 10 of an offshore wind farm based on the wake effect factor includes: an acquisition module 11 , a determination module 12 , a grouping module 13 , a calculation module 14 and a modeling module 15 .

Wherein, the obtaining module 11 is used to obtain the parameters of the offshore wind farm, wherein the parameters of the offshore wind farm include the incoming wind speed of the offshore wind farm, the position information of each wind turbine in the offshore wind farm, the radius of the wind wheel, the attenuation constant and the thrust coefficient; The determination module 12 is used to determine the wake effect factor of each wind turbine according to the parameters of the offshore wind farm; the grouping module 13 is used to group the wind turbines in the offshore wind power according to the wake effect factor; the calculation module 14 is used to calculate each wind turbine respectively. Equivalent parameters of the wind turbine group; the modeling module 15 is used to establish an equivalent model of the offshore wind farm according to each wind turbine group and its equivalent parameters.

It should be noted that the foregoing description of the specific implementation of the wake effect factor-based offshore wind farm equivalent method is also applicable to the specific implementation of the wake effect factor-based offshore wind farm equivalent system in the embodiment of the present invention. It is not repeated here.

The wake effect factor-based equivalent system for offshore wind farms in the embodiment of the present invention groups wind turbines with the wake effect factor as the principle of unit grouping, and then calculates the equivalent parameters of each unit group. The equivalent model of the offshore wind farm based on the wake effect factor is established with the value parameters. The obtained equivalent model has high accuracy, occupies less memory, and has low computational complexity, which is suitable for large-scale offshore wind power research.

Example 3

6 is a structural block diagram of an equivalent device for an offshore wind farm based on a wake effect factor according to an embodiment of the present invention.

As shown in FIG. 6 , the equivalent device 20 for an offshore wind farm based on the wake effect factor includes a memory 21 , a processor 22 and a computer program 23 stored on the memory 21 .

In this embodiment, when the computer program 23 is executed by the processor 22, the above-mentioned method for equivalence of the offshore wind farm based on the wake effect factor is implemented.

In the wake effect factor-based offshore wind farm equivalent device according to the embodiment of the present invention, when the computer program corresponding to the above wake effect factor-based offshore wind farm equivalent method stored in the memory is executed by the processor, it can establish Equivalent model of offshore wind farm, the model has high precision, occupies less memory, and has low computational complexity, and is suitable for large-scale offshore wind power research.

Although the embodiments of the present invention have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and should not be construed as limiting the present invention. Embodiments are subject to variations, modifications, substitutions and variations.

Claims (7)

1. an offshore wind farm equivalent method based on wake effect factor, is characterized in that, comprises the following steps: Acquiring offshore wind farm parameters, wherein the offshore wind farm parameters include the incoming wind speed of the offshore wind farm, the position information of each wind turbine in the offshore wind farm, the radius of the wind rotor, the attenuation constant and the thrust coefficient; Determine the wake effect factor of each wind turbine according to the offshore wind farm parameters; grouping wind turbines in the offshore wind power according to the wake effect factor; Calculate the equivalent parameters of each wind turbine group separately; establishing an equivalent model of an offshore wind farm according to each wind turbine group and its equivalent parameters; The determining of the wake effect factor of each wind turbine according to the offshore wind farm parameters includes: Calculate the wake effect influence degree of each wind turbine according to the offshore wind farm parameters; Determine the wake effect factor of each wind turbine according to the influence degree of the wake effect; The wake effect influence degree of each wind turbine is calculated by the following formula:

Among them, σ _i is the influence degree of the wake effect of the i-th wind turbine, v _in is the incoming wind speed, vi is the wind speed at the _i -th wind turbine, i=1,2,...,n, n is the number of wind turbines in the offshore wind farm; When the influence degree of the wake effect is greater than the first preset value, the wake effect factor is determined to be 0; When the influence degree of the wake effect is greater than the second preset value and less than or equal to the first preset value, determine that the wake effect factor is 1; When the influence degree of the wake effect is greater than the third preset value and less than or equal to the second preset value, determine that the wake effect factor is 2; When the influence degree of the wake effect is less than or equal to the third preset value, the wake effect factor is determined to be 3. 2 . The method for equivalence of an offshore wind farm based on a wake effect factor according to claim 1 , wherein the wake effect factor is negatively correlated with the influence degree of the wake effect. 3 . 3. The wake effect factor-based equivalent method for offshore wind farms according to claim 1, wherein the first preset value is 0.95, the second preset value is 0.7, and the third preset value is 0.7. The default value is 0.4. The method for equivalence of an offshore wind farm based on a wake effect factor according to any one of claims 1 to 3, characterized in that, according to the wake effect factor, the wind turbines in the offshore wind power Grouping, including: Randomly select one of the n wake effect factors as the first cluster center Y1, and calculate the distance between the remaining n-1 wake effect factors and Y1; Select the wake effect factor with the largest distance from Y1 as the second cluster center Y2, and calculate the distances D1(x _j , Y1 between the n-2 wake effect factors other than Y1 and Y2 and Y1 and Y2 respectively) ) and D2(x _j , Y2), where j=1,2,...,n-2; Select the wake effect factor in min[D1(x _j ,Y1),D2(x _j ,Y2)] as the next cluster center in order from large to small, until the number of cluster centers reaches m; According to the selected m cluster centers, the k-means algorithm is used to group n wind turbines. 5. The offshore wind farm equivalent method based on wake effect factor according to claim 1, wherein the equivalent parameters include generator equivalent parameters, transformer equivalent parameters, wind speed equivalent parameters and control etc. value parameter, Wherein, the generator equivalent parameters are:

Among them, S and S _eq respectively represent the generator capacity before and after the equivalence, x _m and x _{m_eq} represent the generator excitation reactance before and after the equivalence, respectively, x ₁ and x _{1_eq} represent the generator stator reactance before and after the equivalence, x ₂ and x _{2_eq} respectively represent the generator rotor reactance before and after the equivalence, r ₁ and r _{1_eq} respectively represent the generator stator resistance before and after the equivalence, and r ₂ and r _{2_eq} respectively represent the generator rotor resistance before and after the equivalence; The equivalent parameters of the transformer are:

Among them, S _T and S _{T_eq} respectively represent the transformer capacity before and after the equivalent value, and Z _T and Z _{T_eq} respectively represent the transformer impedance before and after the equivalent value; When calculating the wind speed equivalent parameters, first determine the power of each wind turbine according to the wind speed and the wind speed-power curve at each wind turbine, calculate the average value of the power, and then according to the average value, the wind speed - the power curve determines the equivalent parameters of the wind speed; In the control equivalence parameters, S _eq =mS, and other control parameters are the same as the control parameters before the equivalence. 6. An offshore wind farm equivalent system based on wake effect factor, characterized in that, comprising: an acquisition module for acquiring offshore wind farm parameters, wherein the offshore wind farm parameters include the incoming wind speed of the offshore wind farm, the position information of each wind turbine in the offshore wind farm, the radius of the wind wheel, the attenuation constant and the thrust coefficient; a determining module for determining the wake effect factor of each wind turbine according to the offshore wind farm parameters; a grouping module, configured to group the wind turbines in the offshore wind power according to the wake effect factor; The calculation module is used to calculate the equivalent parameters of each wind turbine group separately; a modeling module for establishing an equivalent model of an offshore wind farm according to each wind turbine group and its equivalent parameters; The determining of the wake effect factor of each wind turbine according to the offshore wind farm parameters includes: Calculate the wake effect influence degree of each wind turbine according to the offshore wind farm parameters; Determine the wake effect factor of each wind turbine according to the influence degree of the wake effect; The wake effect influence degree of each wind turbine is calculated by the following formula:

Among them, σ _i is the influence degree of the wake effect of the ith wind turbine, v _in is the wind speed of the incoming flow, v _i is the wind speed at the ith wind turbine, i=1,2,...,n, n is the number of wind turbines in the offshore wind farm; When the influence degree of the wake effect is greater than the first preset value, the wake effect factor is determined to be 0; When the influence degree of the wake effect is greater than the second preset value and less than or equal to the first preset value, determine that the wake effect factor is 1; When the influence degree of the wake effect is greater than the third preset value and less than or equal to the second preset value, determine that the wake effect factor is 2; When the influence degree of the wake effect is less than or equal to the third preset value, the wake effect factor is determined to be 3. 7. An equivalent device for an offshore wind farm based on a wake effect factor, comprising a memory, a processor and a computer program stored on the memory, wherein when the computer program is executed by the processor, the The equivalent method for an offshore wind farm based on a wake effect factor according to any one of claims 1-5.

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