CN109802440B - Offshore wind farm equivalence method, system and device based on wake effect factor - Google Patents
Offshore wind farm equivalence method, system and device based on wake effect factor Download PDFInfo
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Abstract
The invention discloses an offshore wind farm equivalent method, system and device based on wake effect factors, wherein the method comprises the following steps: acquiring parameters of an offshore wind farm, wherein the parameters of the offshore wind farm comprise an incoming wind speed of an offshore wind farm, position information of each wind turbine in the offshore wind farm, a wind wheel radius, an attenuation constant and a thrust coefficient; determining a wake effect factor of each wind turbine generator according to the parameters of the offshore wind farm; grouping wind turbine generators in the offshore wind power according to the wake effect factor; respectively calculating equivalent parameters of each wind turbine group; and establishing an equivalent model of the offshore wind farm according to each wind turbine group and equivalent parameters thereof. The method is simple and easy to implement, occupies less memory space, has high accuracy, and is suitable for large-scale offshore wind power research.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to an offshore wind farm equivalent method, system and device based on wake effect factors.
Background
Compared with onshore wind power generation, offshore wind power generation has many obvious advantages, which are mainly shown in that: (1) the offshore wind turbine can reduce occupation of land resources, and the offshore wind turbine has a large continuous space suitable for large-scale wind power engineering and is very suitable for building a large-scale wind power plant; (2) compared with onshore wind power, offshore wind power is close to the traditional power load center, so that the consumption of a power grid is facilitated, and the investment cost and power loss caused by long-distance power transmission are reduced; (3) the offshore wind speed is 20-100% higher than the onshore wind speed, the generating efficiency is correspondingly improved, and under most conditions, the offshore wind speed is stable, so that the wind turbine can more effectively and more fully utilize the wind energy, reduce the fatigue load on the wind turbine, finally improve the service cycle of the wind turbine and generate more electric energy.
The wind power generation system is characterized in that a large number of wind power generation units are distributed in a large offshore wind power plant, under the action of a certain wind direction, the wind speed of the wind power generation unit located in the lower wind direction is often lower than that of the wind power generation unit located in the upper wind direction, the phenomenon is called wake effect, and the wake effect is the main reason for energy loss in the offshore wind power plant. Meanwhile, hundreds of wind turbine generators are often used for simulation of a large offshore wind farm, and if a model of each wind turbine generator is set up in detail, the simulation complexity is greatly increased, so that the calculation time is long, the resource utilization rate is low, and even dimension disaster is caused.
Therefore, equivalent modeling is carried out on the wind power plant in the related technology, the wake effect is considered in the equivalent of the wind power plant, the technology is divided into groups by taking the wind speed as the reference, the whole wind power plant is finally subjected to the equivalent, the real-time wake effect is calculated for each wind power generation set, the calculated amount is large, the complexity of the model is high, and the influence range of the wake effect is not considered. In addition, the technology adopts a k-means algorithm for grouping, although the calculation is simple and easy to implement, the accuracy is not high, and the local optimization is easy to fall into.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the invention aims to provide an offshore wind farm equivalent method based on wake effect factors, which is simple and easy to implement, occupies less memory space, has higher accuracy and can be suitable for large-scale offshore wind power research.
In order to achieve the above object, an embodiment of the first aspect of the present invention provides an offshore wind farm equivalence method based on wake effect factors, including the following steps: acquiring parameters of an offshore wind farm, wherein the parameters of the offshore wind farm comprise an incoming wind speed of the offshore wind farm, position information of each wind turbine in the offshore wind farm, a wind wheel radius, an attenuation constant and a thrust coefficient; determining a wake effect factor of each wind turbine generator according to the offshore wind farm parameters; grouping the wind turbine generators in the offshore wind power according to the wake effect factors; respectively calculating equivalent parameters of each wind turbine group; and establishing an equivalent model of the offshore wind farm according to each wind turbine group and equivalent parameters thereof.
According to the wake effect factor-based offshore wind farm equivalent method, the wind turbines are grouped according to the principle that the wake effect factor is used as the turbine grouping, equivalent parameters of each turbine group are calculated, and an offshore wind farm equivalent model based on the wake effect factor is established according to the grouping result and the equivalent parameters.
In addition, the offshore wind farm equivalent method based on the wake effect factor in the above embodiment 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 influence degree of the wake effect of each wind turbine generator according to the parameters of the offshore wind farm; and determining the wake effect factor of each wind turbine generator according to the influence degree of the wake effect.
Optionally, the influence degree of the wake effect of each wind turbine is calculated by the following formula:
wherein σiIs the influence degree of the wake effect of the ith wind turbine generator set, vinIs the incoming wind speed, viThe wind speed at the ith wind turbine generator set is i ═ 1, 2.. and n is the number of wind turbine generator sets in the offshore wind farm.
Optionally, the wake effect factor is inversely related to the influence degree of the wake effect.
Optionally, when the influence degree of the wake effect is greater than a first preset value, determining that the wake effect factor is 0; when the influence degree of the wake effect is greater than a second preset value and less than or equal to the first preset value, determining that the wake effect factor is 1; when the influence degree of the wake effect is greater than a third preset value and less than or equal to the second preset value, determining that the wake effect factor is 2; and when the influence degree of the wake effect is smaller than or equal to the third preset value, determining that the wake effect factor is 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 the wind turbine generators in the offshore wind power according to the wake effect factor includes: randomly selecting one of the n wake effect factors as a first clustering center Y1, and calculating the distance between the remaining n-1 wake effect factors and Y1; the wake effect factor with the largest distance from Y1 was selected as the second cluster center Y2, and the distances D1 (x) between n-2 wake effect factors other than Y1, Y2 and Y1, Y2 were calculated, respectively (xjY1) and D2 (x)jY2), wherein j is 1, 2.., n-2; from big to small in sequenceSelect min [ D1 (x)j,Y1),D2(xj,Y2)]The wake effect factor in the cluster is used as the next clustering center until the number of the clustering centers reaches m; and clustering the n wind turbines by adopting a k-means algorithm according to the selected m clustering centers.
Optionally, the equivalent parameters include a generator equivalent parameter, a transformer equivalent parameter, a wind speed equivalent parameter and a control equivalent parameter, wherein the generator equivalent parameters are as follows:
wherein S and SeqRespectively representing the generator capacity, x, before and after the equivalencemAnd xm_eqRepresenting the generator excitation reactance, x, before and after equivalence, respectively1And x1_eqRepresenting the generator stator reactance, x, before and after the equivalence, respectively2And x2_eqRepresenting the reactance of the generator rotor before and after equality, r1And r1_eqRepresenting the generator stator resistance, r, before and after the equivalence, respectively2And r2_eqRespectively representing the generator rotor resistance before and after equivalence;
the equivalent parameters of the transformer are as follows:
wherein S isTAnd ST_eqRespectively representing the transformer capacity before and after the equivalence, ZTAnd ZT_eqRespectively representing the transformer impedance before and after the equivalence;
when the wind speed equivalent parameter is calculated, firstly, determining the power of each wind turbine according to the wind speed and the wind speed-power curve of each wind turbine, calculating the average value of the power, and then determining the wind speed equivalent parameter according to the average value and the wind speed-power curve;
s in the control equivalent parametereqmS, the other is the same as the control parameter before equivalence.
In order to achieve the above object, a second aspect of the present invention provides an offshore wind farm equivalence system based on wake effect factors, including: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring offshore wind farm parameters, and the offshore wind farm parameters comprise the incoming wind speed of an offshore wind farm, the position information of each wind turbine in the offshore wind farm, the radius of a wind wheel, an attenuation constant and a thrust coefficient; the determining module is used for determining the wake effect factor of each wind turbine generator according to the offshore wind farm parameters; the grouping module is used for grouping the wind turbine generators in the offshore wind power according to the wake effect factor; the calculation module is used for calculating equivalent parameters of each wind turbine group respectively; and the modeling module is used for establishing an equivalent model of the offshore wind farm according to each wind turbine group and equivalent parameters thereof.
According to the wake effect factor-based offshore wind farm equivalent system, the wind turbines are grouped according to the principle that the wake equivalent factor is used as the turbine grouping, equivalent parameters of each turbine group are calculated, and an offshore wind farm equivalent model based on the wake effect factor is established according to the grouping result and the equivalent parameters.
In order to achieve the above object, a third aspect of the present invention provides a wake effect factor-based offshore wind farm equivalent device, which includes a memory, a processor, and a computer program stored on the memory, and when the computer program is executed by the processor, the computer program implements the above wake effect factor-based offshore wind farm equivalent method.
According to the wake effect factor-based offshore wind farm equivalent device, when a computer program which is stored in a storage of the wake effect factor-based offshore wind farm equivalent device and corresponds to the wake effect factor-based offshore wind farm equivalent method is executed by a processor, an obtained equivalent model is high in precision, small in occupied memory, simple and easy to implement, and applicable to large-scale offshore wind power research.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of an offshore wind farm equivalence method based on wake effect factors, according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a wake effect Jensen model;
FIG. 3 is a schematic illustration of inter-wind turbine wake effects according to an example of the invention;
FIG. 4 is a flow diagram of an offshore wind farm equivalence method based on wake effect factors, according to an embodiment of the invention;
FIG. 5 is a block diagram of a wake effect factor based offshore wind farm equivalent system according to an embodiment of the present invention; and
FIG. 6 is a block diagram of an equivalent device of an offshore wind farm based on wake effect factors, according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The method, system and device for the equivalent of the offshore wind farm based on the wake effect factor according to the embodiments of the present invention are described below with reference to the accompanying drawings.
Example 1
FIG. 1 is a flow chart of an equivalent method of an offshore wind farm based on wake effect factors according to an embodiment of the invention.
As shown in FIG. 1, the offshore wind farm equivalence method based on wake effect factors comprises the following steps:
and S1, obtaining the parameters of the offshore wind farm.
The parameters of the offshore wind farm include the incoming wind speed (including the size and direction of 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.
In this embodiment, the wind turbines in the offshore wind farm are preferably doubly-fed wind turbines.
And S2, determining the wake effect factor of each wind turbine generator according to the parameters of the offshore wind farm.
Specifically, the wake effect influence degree of each wind turbine generator can be calculated according to the parameters of the offshore wind farm, and then the wake effect factor of each wind turbine generator is determined according to the wake effect influence degree.
The influence degree of the wake effect of each wind turbine can be calculated through the following formula:
wherein σiTo the extent of the effect of the wake effect, vinIs the incoming wind speed, viThe wind speed at the ith wind turbine generator set is i ═ 1, 2.. and n is the number of wind turbine generator sets in the offshore wind farm.
In one embodiment of the invention, the wake effect factor and the wake effect influence degree may be in a negative correlation relationship, for example, the wake effect factor and the wake effect influence degree are reciprocal.
In another embodiment of the invention, when the influence degree of the wake effect is greater than a first preset value, determining that the wake effect factor is 0; when the influence degree of the wake effect is greater than a second preset value and less than or equal to a first preset value, determining that the wake effect factor is 1; when the influence degree of the wake effect is greater than a third preset value and less than or equal to a second preset value, determining that the wake effect factor is 2; and when the influence degree of the wake effect is smaller than or equal to a third preset value, determining that the wake effect factor is 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 understand the above wake effect influence degree and wake effect factor, the following description is made with reference to fig. 2 and 3:
as shown in fig. 2, forIn a wind turbine, r is the radius of its rotor (i.e. the rotor radius of the wind turbine), x is the horizontal distance in the wind direction, vinIs the incoming wind speed, v, of an offshore wind farmxK is an attenuation constant, C is the actual wind speed of the incoming wind speed under the wake effectTFor thrust coefficient, there are
The wind turbine generator is the influence degree of the wake effect.
In an offshore wind farm, each wind turbine may be affected by an upstream wind turbine, and a single wind turbine may be affected by the wake effect of multiple turbines. The condition that the downstream unit is influenced by the upstream unit can be determined according to the incoming wind speed and the positions of the wind generation units, as shown in fig. 3, the unit W3 is only influenced by the unit W1, and the wake effect factor of the unit W1; the unit W4 is influenced by the units W1 and W2 together, and the wake effect factor is the sum of the influences of the units W1 and W2 under the independent action.
And S3, grouping the wind turbines in the offshore wind power according to the wake effect factor.
Specifically, one of the n wake effect factors is randomly selected as a first cluster center Y1, and the distances between the remaining n-1 wake effect factors and Y1 are calculated; the wake effect factor with the largest distance from Y1 was selected as the second cluster center Y2, and the distances D1 (x) between n-2 wake effect factors other than Y1, Y2 and Y1, Y2 were calculated, respectively (xjY1) and D2 (x)jY2), wherein j is 1, 2.., n-2; selecting min [ D1 (x) from big to smallj,Y1),D2(xj,Y2)]The wake effect factor in the cluster is used as the next clustering center until the number of the clustering centers reaches m; and clustering the n wind turbines by adopting a k-means algorithm according to the selected m clustering centers.
It should be noted that, the initialization of the conventional k-means algorithm is random, which may cause the final clustering to be inaccurate, and for this point, the present invention adopts the maximum and minimum distance method to initialize the clustering data. The maximum and minimum distance initialization method is a tentative algorithm, and the idea of the algorithm is to take an object which is far away from other objects in a data set as far as possible as an initial point, so that the situation that the initial point is too close to the initial point in randomness initialization can be avoided. That is to say, compared with the conventional k-means algorithm, under the condition of a better initial solution, the method can effectively overcome the dependence on the initial central point, thereby effectively improving the convergence speed and accuracy of the algorithm.
And S4, respectively calculating equivalent parameters of each wind turbine group.
Specifically, after the clustering result is obtained, equating is performed on each wind turbine group, that is, the equivalence of the whole offshore wind farm is m wind turbines, and a capacity weighting method can be adopted when the equivalence parameter is calculated, because each wind turbine generally has the same model and capacity and the structure and the operation condition are similar in the same offshore wind farm.
Wherein, the equivalent parameters can comprise generator equivalent parameters, transformer equivalent parameters, wind speed equivalent parameters and control equivalent parameters, and the specific equivalence relation is as follows:
equivalent parameters of the generator:
wherein S and SeqRespectively representing the generator capacity, x, before and after the equivalencemAnd xm_eqRepresenting the generator excitation reactance, x, before and after equivalence, respectively1And x1_eqRepresenting the generator stator reactance, x, before and after the equivalence, respectively2And x2_eqRepresenting the reactance of the generator rotor before and after equality, r1And r1_eqRepresenting the generator stator resistance, r, before and after the equivalence, respectively2And r2_eqRepresenting the generator rotor resistance before and after the equivalent value, respectively, i.e. S, xm、x1、x2Are respectively asCapacity, excitation reactance, stator reactance and rotor reactance of a single generator, Seq、xm_eq、x1_eq、x2_eqEquivalent capacity, equivalent excitation reactance, equivalent stator reactance and equivalent rotor reactance, respectively. . It can be seen that the capacity of the generator is m times of the original capacity after the equivalent, the resistance reactance parameter of the generator is 1/m of the original resistance reactance parameter after the equivalent,
equivalent parameters of the transformer:
wherein S isTAnd ST_eqRespectively representing the transformer capacity before and after the equivalence, ZTAnd ZT_eqRespectively representing the transformer impedances before and after the equivalent. Therefore, similar to the equivalence of generator parameters, the capacity of the transformer is m times of the original capacity after the equivalence, and the impedance of the transformer is 1/m of the original impedance after the equivalence.
Wind speed equivalent parameters:
in order to enable equivalent wind speed to represent group wind speed to the maximum extent, when wind speed equivalent parameters are calculated, the power of each wind turbine is determined according to the wind speed and the wind speed-power curve of each wind turbine, the average value of the power is calculated, and then the wind speed equivalent parameters are determined according to the average value and the wind speed-power curve.
Controlling equivalent parameters:
the reference capacity of the control parameter except the power test part is changed into m times of the original reference capacity, namely SeqExcept for mS, the other parameters are unchanged, i.e. identical to the control parameters before equivalence.
And S6, establishing an equivalent model of the offshore wind farm according to each wind turbine group and equivalent parameters thereof.
The equivalent model of the offshore wind farm is a model obtained by simplifying the wind farm under the condition of ensuring that the wind farm has no dynamic influence on the research system.
Specifically, as shown in fig. 4, a wake effect model-Jensen model suitable for large-scale offshore wind power is selected, offshore wind farm parameters are obtained, and then the model is analyzed to obtain an influence range of the wake effect and a degree of influence within the influence range, so that a wake effect influence factor of each unit is obtained. And then, according to the wake effect factors, clustering the wind power plants by adopting an improved k-means algorithm, and then establishing a wind power plant equivalent model by adopting a capacity weighting method according to clustering results.
Therefore, the equivalent model can reflect the difference of the running states of all units in the offshore wind power plant, improves the model precision, the occupied memory and the calculation time, and is suitable for the research of large-scale offshore wind power.
Example 2
FIG. 5 is a block diagram of a wake effect factor based offshore wind farm equivalent system, according to an embodiment of the present invention.
As shown in fig. 5, the wake effect factor-based offshore wind farm equivalence system 10 includes: the device comprises an acquisition module 11, a determination module 12, a grouping module 13, a calculation module 14 and a modeling module 15.
The obtaining module 11 is configured to obtain parameters of an offshore wind farm, where the parameters of the offshore wind farm include an incoming wind speed of an offshore wind farm, position information of each wind turbine in the offshore wind farm, a radius of a wind turbine, an attenuation constant, and a thrust coefficient; the determining module 12 is configured to determine a wake effect factor of each wind turbine generator according to the parameter of the offshore wind farm; the grouping module 13 is used for grouping the wind turbine generators in the offshore wind power according to the wake effect factor; the calculation module 14 is used for calculating equivalent parameters of each wind turbine group respectively; the modeling module 15 is used for establishing an equivalent model of the offshore wind farm according to each wind turbine group and equivalent parameters thereof.
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 according to the embodiment of the present invention, and is not repeated here.
According to the wake effect factor-based offshore wind farm equivalent system, the wind turbines are grouped according to the principle that the wake effect factor is used as the turbine grouping, equivalent parameters of each turbine group are calculated, and an offshore wind farm equivalent model based on the wake effect factor is established according to the grouping result and the equivalent parameters, so that the obtained equivalent model is high in precision, small in occupied memory and low in calculation complexity, and is suitable for large-scale offshore wind power research.
Example 3
FIG. 6 is a block diagram of the structure of an offshore wind farm equivalent device based on wake effect factors, according to an embodiment of the invention.
As shown in fig. 6, the wake effect factor based offshore wind farm equivalent 20 comprises a memory 21, a processor 22 and a computer program 23 stored on the memory 21.
In this embodiment, the computer program 23, when executed by the processor 22, implements the wake effect factor-based offshore wind farm equivalent method described above.
According to the wake effect factor-based offshore wind farm equivalent device, the computer program which is stored in the storage and corresponds to the wake effect factor-based offshore wind farm equivalent method is executed by the processor, an offshore wind farm equivalent model can be established, the model is high in precision, small in occupied memory and low in calculation complexity, and the wake effect factor-based offshore wind farm equivalent device is suitable for large-scale offshore wind power research.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (7)
1. An offshore wind farm equivalence method based on wake effect factors is characterized by comprising the following steps:
acquiring parameters of an offshore wind farm, wherein the parameters of the offshore wind farm comprise an incoming wind speed of the offshore wind farm, position information of each wind turbine in the offshore wind farm, a wind wheel radius, an attenuation constant and a thrust coefficient;
determining a wake effect factor of each wind turbine generator according to the offshore wind farm parameters;
grouping the wind turbine generators in the offshore wind power according to the wake effect factors;
respectively calculating equivalent parameters of each wind turbine group;
establishing an equivalent model of the offshore wind farm according to each wind turbine group and equivalent parameters thereof;
determining the wake effect factor of each wind turbine generator according to the parameters of the offshore wind farm comprises the following steps:
calculating the influence degree of the wake effect of each wind turbine generator according to the parameters of the offshore wind farm;
determining a wake effect factor of each wind turbine generator according to the influence degree of the wake effect;
calculating the influence degree of the wake effect of each wind turbine generator by the following formula:
wherein σiIs the influence degree of the wake effect of the ith wind turbine generator set, vinIs the incoming wind speed, viThe wind speed at the ith wind generating set is 1,2, n, n is the number of wind generating sets in the offshore wind farm;
when the influence degree of the wake effect is larger than a first preset value, determining that the wake effect factor is 0;
when the influence degree of the wake effect is greater than a second preset value and less than or equal to the first preset value, determining that the wake effect factor is 1;
when the influence degree of the wake effect is greater than a third preset value and less than or equal to the second preset value, determining that the wake effect factor is 2;
and when the influence degree of the wake effect is smaller than or equal to the third preset value, determining that the wake effect factor is 3.
2. The wake effect factor-based offshore wind farm equivalence method according to claim 1, wherein the wake effect factor is inversely related to the wake effect influence degree.
3. The wake effect factor-based offshore wind farm equivalence method 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 0.4.
4. The wake effect factor-based offshore wind farm equivalence method according to any one of claims 1-3, wherein the clustering wind turbines in the offshore wind power according to the wake effect factor comprises:
randomly selecting one of the n wake effect factors as a first clustering center Y1, and calculating the distance between the remaining n-1 wake effect factors and Y1;
the wake effect factor with the largest distance from Y1 was selected as the second cluster center Y2, and the distances D1 (x) between n-2 wake effect factors other than Y1, Y2 and Y1, Y2 were calculated, respectively (xjY1) and D2 (x)jY2), wherein j is 1, 2.., n-2;
selecting min [ D1 (x) from big to smallj,Y1),D2(xj,Y2)]The wake effect factor in the cluster is used as the next clustering center until the number of the clustering centers reaches m;
and clustering the n wind turbines by adopting a k-means algorithm according to the selected m clustering centers.
5. The wake effect factor-based offshore wind farm equivalence method according to claim 1, wherein the equivalence parameters comprise generator equivalence parameters, transformer equivalence parameters, wind speed equivalence parameters, and control equivalence parameters,
wherein, the equivalent parameters of the generator are as follows:
wherein S and SeqIndividual watchShowing the generator capacity, x, before and after the equivalencemAnd xm_eqRepresenting the generator excitation reactance, x, before and after equivalence, respectively1And x1_eqRepresenting the generator stator reactance, x, before and after the equivalence, respectively2And x2_eqRepresenting the reactance of the generator rotor before and after equality, r1And r1_eqRepresenting the generator stator resistance, r, before and after the equivalence, respectively2And r2_eqRespectively representing the generator rotor resistance before and after equivalence;
the equivalent parameters of the transformer are as follows:
wherein S isTAnd ST_eqRespectively representing the transformer capacity before and after the equivalence, ZTAnd ZT_eqRespectively representing the transformer impedance before and after the equivalence;
when the wind speed equivalent parameter is calculated, firstly, determining the power of each wind turbine according to the wind speed and the wind speed-power curve of each wind turbine, calculating the average value of the power, and then determining the wind speed equivalent parameter according to the average value and the wind speed-power curve;
s in the control equivalent parametereqmS, the other is the same as the control parameter before equivalence.
6. An offshore wind farm equivalent system based on wake effect factors, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring offshore wind farm parameters, and the offshore wind farm parameters comprise the incoming wind speed of an offshore wind farm, the position information of each wind turbine in the offshore wind farm, the radius of a wind wheel, an attenuation constant and a thrust coefficient;
the determining module is used for determining the wake effect factor of each wind turbine generator according to the offshore wind farm parameters;
the grouping module is used for grouping the wind turbine generators in the offshore wind power according to the wake effect factor;
the calculation module is used for calculating equivalent parameters of each wind turbine group respectively;
the modeling module is used for establishing an equivalent model of the offshore wind farm according to each wind turbine group and equivalent parameters thereof;
determining the wake effect factor of each wind turbine generator according to the parameters of the offshore wind farm comprises the following steps:
calculating the influence degree of the wake effect of each wind turbine generator according to the parameters of the offshore wind farm;
determining a wake effect factor of each wind turbine generator according to the influence degree of the wake effect;
calculating the influence degree of the wake effect of each wind turbine generator by the following formula:
wherein σiIs the influence degree of the wake effect of the ith wind turbine generator set, vinIs the incoming wind speed, viThe wind speed at the ith wind generating set is 1,2, n, n is the number of wind generating sets in the offshore wind farm;
when the influence degree of the wake effect is larger than a first preset value, determining that the wake effect factor is 0;
when the influence degree of the wake effect is greater than a second preset value and less than or equal to the first preset value, determining that the wake effect factor is 1;
when the influence degree of the wake effect is greater than a third preset value and less than or equal to the second preset value, determining that the wake effect factor is 2;
and when the influence degree of the wake effect is smaller than or equal to the third preset value, determining that the wake effect factor is 3.
7. A wake effect factor based offshore wind farm equivalent arrangement comprising a memory, a processor and a computer program stored on the memory, wherein the computer program, when executed by the processor, performs the wake effect factor based offshore wind farm equivalent method as claimed in any of the claims 1-5.
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