CN106684922B - A kind of wind turbine group control method and system - Google Patents

Abstract

The present application discloses a wind turbine swarm control method and system. The method includes: pre-creating and initializing a particle swarm suitable for the scale of the wind turbine swarm to obtain a target particle swarm; Penalty function; use the fitness calculation formula to calculate the fitness value of each particle; use the position and fitness value of each particle to iteratively update the speed and position of each particle to obtain the global optimal particle; The optimal particle is used to control the wind turbine group accordingly to control the pitch angle of each wind turbine and the frequency of the variable frequency AC bus. The present application reduces the amount of wind energy loss in the control process of the wind turbine group, and can prevent the output power of the wind turbine group from exceeding the rated power value, which is beneficial to the safe and stable operation of the wind power system.

Description

A wind turbine group control method and system

technical field

The invention relates to the technical field of wind power generation, in particular to a method and system for controlling a wind turbine group.

Background technique

Offshore wind power has the advantages of high wind speed, low turbulence intensity, and stable wind speed and direction. It is the main trend of the development of the wind power industry. If the control of the wind turbine cluster replaces the discrete control of the single machine, it is beneficial to reduce the failure rate, thereby reducing the offshore maintenance work, increasing the effective power generation time, and reducing the total cost of the system. However, when the wind speed distribution in the wind farm is uneven, the control in the wind turbine cluster will easily lead to a large wind energy loss, and the output power is likely to exceed the rated power value. It is a problem that needs to be further solved at present.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a wind turbine group control method and system, in the case of uneven distribution of wind speed, reducing the amount of wind energy loss during the control process of the wind turbine group, and can avoid the output power of the wind turbine group exceeding the rated value power value. Its specific plan is as follows:

A method for controlling a wind turbine group, applied to a wind turbine group, wherein the rotational speed of each wind turbine group in the wind turbine group is consistent during a power control process, and the method includes:

A particle swarm suitable for the scale of the wind turbine swarm is pre-created and initialized to obtain a target particle swarm; wherein, the vector expression of the position of the i-th particle in the target particle swarm is:

In the formula, M represents the number of wind turbines in the wind turbine group, N represents the number of particles in the target particle group, β _i,j represents the pitch angle of the jth wind turbine in the ith particle, f _{i ,bus} represents the frequency of the variable-frequency AC bus in the i-th particle;

The penalty function determination formula is used to determine the penalty function of each wind turbine; wherein, the penalty function determination formula is:

In the formula, P _i,k represents the output power of the kth wind turbine in the ith particle, P _N represents the rated power value of the wind turbine, and P _PENi,k represents the kth wind turbine in the ith particle. penalty function;

Use the fitness calculation formula to calculate the fitness value of each particle; wherein, the fitness calculation formula is:

In the formula, P _i represents the fitness value of the ith particle in the target particle swarm, and D represents the preset penalty factor;

Using the position and fitness value of each particle, iteratively update the velocity and position of each particle to obtain the global optimal particle; wherein, the vector expression of the position of the global optimal particle is:

In the formula, β _gj represents the optimal pitch angle of the jth wind turbine in the wind turbine group, and f _gbus represents the optimal frequency of the variable frequency AC bus;

According to the global optimal particle, corresponding control is carried out on the wind turbine group to control the pitch angle of each wind turbine group and the frequency of the variable-frequency AC bus.

Optionally, the process of using the penalty function determination formula to determine the penalty function of each wind turbine includes:

Use the output power calculation formula to calculate the output power of each wind turbine; wherein, the output power calculation formula is:

In the formula, P _i,k represents the output power of the k-th wind turbine in the i-th particle, ρ represents the air density, A represents the swept area of the wind turbine blade, C _k (V _k ,β _i,k ,f _{i ,bus} ) represents the power coefficient of the kth wind turbine, and V _k represents the real-time wind speed of the kth wind turbine;

Using the output power of each wind turbine and the penalty function determination formula, the penalty function of each wind turbine is determined.

Optionally, the calculation formula of the power coefficient C _k (V _k ,β _i,k ,f _i,bus ) of the kth wind turbine is:

Among them, the index S is:

In the formula, V _k represents the real-time wind speed of the kth wind turbine, R represents the blade radius of the wind turbine, n _p represents the number of pole pairs of the motor, n _g represents the gearbox ratio, and K ₁ to K ₆ are based on the wind turbine blades in advance. type-determined coefficients.

Optionally, the wind turbine group is a variable-speed wind turbine group that is centrally controlled by a variable frequency transformer, or a variable-speed wind turbine group that is centrally controlled by a high-voltage DC transmission converter, or a variable-speed wind turbine that is centrally controlled by a frequency-divided AC transmission converter. fleet.

Optionally, the process of using the position and fitness value of each particle to iteratively update the speed and position of each particle to obtain the global optimal particle includes:

Step A1: Determine the initial position of each particle as the current optimal position of each particle i=1,2,...,N, and screen out the particle with the largest fitness value from all the current particles, and determine the position of the particle as the current global optimal position in the target particle swarm

Step A2: Utilize the current optimal position of each particle and the current global optimal position in the target particle swarm Update the speed and position of each particle to get the new position of each particle after this round of updates;

Step A3: Calculate the fitness value corresponding to the new position of each particle, and use the preset update principle to update the current optimal positions of all particles; wherein, the preset update principle is: when the new If the fitness value of the position is greater than the current optimal position of the particle, use the new position of the particle to replace and update the current optimal position of the particle;

Step A4: Screen out the particle with the largest fitness value from all the current particles, and determine whether the fitness value of the particle is greater than the current global optimal position The corresponding fitness value, if so, use the current position of the particle to calculate the current global optimal position in the target particle swarm make a replacement update;

Step A5: Re-enter Step A2 until the preset number of iterations is reached, and use the current global optimal position obtained after the iteration The corresponding particle is determined as the global optimal particle.

Optionally, the process of updating the speed and position of each particle to obtain the new position of each particle after the current round of updating includes:

The iterative update formula is used to update the velocity and position of each particle, and in the process of updating by the iterative update formula, if the value of any vector element in the position vector of any particle exceeds the preset element value Constraint range, then use the boundary value of the element’s numerical constraint range to replace and update the value of the vector element to obtain the new position of each particle after the current round of update; wherein, the iterative update formula is:

In the formula, are inertia weights, c ₁ and c ₂ are learning factors, and r ₁ and r ₂ are random numbers.

The present invention also discloses a wind turbine group control system, which is applied to the wind turbine group, wherein the rotational speed of each wind turbine group in the wind turbine group is consistent during the power control process, and the system includes:

The particle swarm creation module is used to pre-create and initialize a particle swarm suitable for the scale of the wind turbine swarm to obtain a target particle swarm; wherein, the vector expression of the position of the i-th particle in the target particle swarm is:

In the formula, M represents the number of wind turbines in the wind turbine group, N represents the number of particles in the target particle group, β _i,j represents the pitch angle of the jth wind turbine in the ith particle, f _{i ,bus} represents the frequency of the variable-frequency AC bus in the i-th particle;

The penalty function determination module is used to determine the penalty function of each wind turbine by using the penalty function determination formula; wherein, the penalty function determination formula is:

In the formula, P _i,k represents the output power of the kth wind turbine in the ith particle, P _N represents the rated power value of the wind turbine, and P _PENi,k represents the kth wind turbine in the ith particle. penalty function;

The fitness value calculation module is used to calculate the fitness value of each particle by using the fitness calculation formula; wherein, the fitness calculation formula is:

In the formula, P _i represents the fitness value of the ith particle in the target particle swarm, and D represents the preset penalty factor;

The iterative update module is used to iteratively update the speed and position of each particle by using the position and fitness value of each particle to obtain the global optimal particle; wherein, the vector expression of the position of the global optimal particle is: :

In the formula, β _gj represents the optimal pitch angle of the jth wind turbine in the wind turbine group, and f _gbus represents the optimal frequency of the variable frequency AC bus;

The wind turbine group control module is configured to carry out corresponding control on the wind turbine group according to the global optimal particle, so as to control the pitch angle of each wind turbine group and the frequency of the variable-frequency AC bus.

Optionally, the penalty function determination module specifically includes:

The output power calculation sub-module is used to calculate the output power of each wind turbine by using the output power calculation formula; wherein, the output power calculation formula is:

In the formula, P _i,k represents the output power of the k-th wind turbine in the i-th particle, ρ represents the air density, A represents the swept area of the wind turbine blade, C _k (V _k ,β _i,k ,f _{i ,bus} ) represents the power coefficient of the kth wind turbine, and V _k represents the real-time wind speed of the kth wind turbine;

The penalty function determination submodule is used for determining the penalty function of each wind turbine by using the output power of each wind turbine and the penalty function determination formula.

Optionally, the output power calculation submodule includes:

The power coefficient calculation unit is used to calculate the power coefficient of each wind turbine by using the power coefficient calculation formula; wherein, the power coefficient calculation formula is:

Among them, the index S is:

In the formula, V _k represents the real-time wind speed of the kth wind turbine, R represents the blade radius of the wind turbine, n _p represents the number of pole pairs of the motor, n _g represents the gearbox ratio, and K ₁ to K ₆ are based on the wind turbine blades in advance. type-determined coefficients.

Optionally, the wind turbine group is a variable-speed wind turbine group that is centrally controlled by a variable frequency transformer, or a variable-speed wind turbine group that is centrally controlled by a high-voltage DC transmission converter, or a variable-speed wind turbine that is centrally controlled by a frequency-divided AC transmission converter. fleet.

In the present invention, the wind turbine group control method includes: creating and initializing a particle group suitable for the scale of the wind turbine group in advance to obtain the target particle group; using the above penalty function determination formula to determine the penalty function of each wind turbine group; using the above The fitness calculation formula calculates the fitness value of each particle; uses the position and fitness value of each particle to iteratively update the speed and position of each particle to obtain the global optimal particle; The wind turbine group is controlled accordingly.

It can be seen that the present invention can maximize the output power of the wind turbine group by using the particle swarm optimization algorithm with penalty function, and can ensure that the output power of the wind turbine group does not exceed the rated power value, thereby improving the utilization rate of wind energy and reducing the control of the wind turbine group. The amount of wind energy loss in the process can be avoided, and the output power of the wind turbine can be prevented from exceeding the rated power value, which is conducive to the safe and stable operation of the wind power system.

Description of drawings

In order to illustrate the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that are used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only It is an embodiment of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without creative efforts.

FIG. 1 is a flowchart of a method for controlling a wind turbine group disclosed in an embodiment of the present invention;

FIG. 2 is a schematic structural diagram of a wind turbine group control system disclosed in an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

An embodiment of the present invention discloses a method for controlling a wind turbine group, which is applied to a wind turbine group, wherein the rotational speed of each wind turbine group in the wind turbine group is consistent during the power control process. Referring to FIG. 1 , the method includes:

Step S11: Pre-create and initialize a particle swarm suitable for the scale of the wind turbine swarm to obtain a target particle swarm; wherein, the vector expression of the position of the i-th particle in the target particle swarm is:

In the formula, M represents the number of wind turbines in the wind turbine group, N represents the number of particles in the target particle group, β _i,j represents the pitch angle of the jth wind turbine in the ith particle, and f _i,bus represents the ith wind turbine. The frequency of the variable-frequency AC bus in i particles;

Step S12: Determine the penalty function of each wind turbine by using the penalty function determination formula; wherein, the penalty function determination formula is:

In the formula, P _i,k represents the output power of the kth wind turbine in the ith particle, P _N represents the rated power value of the wind turbine, and P _PENi,k represents the kth wind turbine in the ith particle. penalty function;

Step S13: Calculate the fitness value of each particle by using the fitness calculation formula; wherein, the fitness calculation formula is:

In the formula, P _i represents the fitness value of the ith particle in the target particle swarm, and D represents the preset penalty factor;

Step S14: Using the position and fitness value of each particle, iteratively update the speed and position of each particle to obtain the global optimal particle; wherein, the vector expression of the position of the global optimal particle is:

In the formula, β _gj represents the optimal pitch angle of the jth wind turbine in the wind turbine cluster, and f _gbus represents the optimal frequency of the variable frequency AC bus;

Step S15: According to the global optimal particle, corresponding control is carried out on the wind turbine group.

The specific process of the above step S15 may include: assigning f _gbus to the frequency regulator of the wind turbine group, so that the frequency regulator performs corresponding frequency adjustment according to f _gbus , and assigning β _g1 , β _g2 , . . . , β _gM to To the pitch angle controllers of the corresponding wind turbines, so that each pitch angle controller performs corresponding pitch angle control processing according to the optimal pitch angle obtained by each.

It should be noted that the wind turbine group in this embodiment may be a variable-speed wind turbine group centrally controlled by a variable frequency transformer, or a variable-speed wind turbine group centrally controlled by a high-voltage DC transmission converter, or a frequency-divided AC transmission converter. Centrally controlled variable speed wind turbines. In this embodiment, the rotational speed of each wind turbine in the wind turbine group is consistent during the power control process. In the above power control process, it is necessary to perform unified control on all the wind turbines in the wind turbine group, and not for any wind turbine. for individual control.

It can be seen that by using the particle swarm optimization algorithm with penalty function in the embodiment of the present invention, the output power of the wind turbine group can be maximized, and the output power of the wind turbine group can be ensured not to exceed the rated power value, thereby improving the utilization rate of wind energy and reducing wind power. The amount of wind energy loss in the process of fleet control, and is conducive to the safe and stable operation of the wind power system.

The embodiment of the present invention discloses a specific wind turbine group control method, which includes the following steps:

Step S21: Pre-create and initialize a particle swarm suitable for the scale of the wind turbine swarm to obtain a target particle swarm; wherein, the vector expression of the position of the i-th particle in the target particle swarm is:

In the formula, M represents the number of wind turbines in the wind turbine group, N represents the number of particles in the target particle group, β _i,j represents the pitch angle of the jth wind turbine in the ith particle, and f _i,bus represents the ith wind turbine. The frequency of the variable frequency AC bus in i particles.

Step S22: Calculate the output power of each wind turbine by using the output power calculation formula; wherein, the output power calculation formula is:

In the formula, P _i,k represents the output power of the k-th wind turbine in the i-th particle, ρ represents the air density, A represents the swept area of the wind turbine blade, C _k (V _k ,β _i,k ,f _{i ,bus} ) represents the power coefficient of the kth wind turbine, and V _k represents the real-time wind speed of the kth wind turbine.

In this embodiment, the calculation formula of the power coefficient C _k (V _k ,β _i,k ,fi _,bus ) of the kth wind turbine is:

Among them, the index S is:

In the formula, V _k represents the real-time wind speed of the kth wind turbine, R represents the blade radius of the wind turbine, n _p represents the number of pole pairs of the motor, n _g represents the gearbox ratio, and K ₁ to K ₆ are based on the wind turbine blades in advance. type-determined coefficients.

Step S23: Determine the penalty function of each wind turbine by using the output power of each wind turbine and the penalty function determination formula. Among them, the penalty function determination formula is:

In the formula, P _i,k represents the output power of the kth wind turbine in the ith particle, P _N represents the rated power value of the wind turbine, and P _PENi,k represents the kth wind turbine in the ith particle. penalty function.

Step S24: Determine the initial position of each particle as the current optimal position of each particle i=1,2,...,N, and screen out the particle with the largest fitness value from all the current particles, and determine the position of the particle as the current global optimal position in the target particle swarm

Step S25: Use the current optimal position of each particle and the current global optimal position in the target particle swarm Update the speed and position of each particle to get the new position of each particle after this round of updates.

Specifically, the above process of updating the speed and position of each particle to obtain the new position of each particle after the current round of updates may include:

Use the iterative update formula to update the speed and position of each particle, and in the process of using the iterative update formula to update, if the value of any vector element in the position vector of any particle exceeds the preset element value constraint range , then the value of the vector element is replaced and updated by the boundary value of the numerical constraint range of the element, and the new position of each particle after the current round of update is obtained; wherein, the iterative update formula is:

In the formula, are inertia weights, c ₁ and c ₂ are learning factors, and r ₁ and r ₂ are random numbers.

Step S26: Calculate the fitness value corresponding to the new position of each particle, and use the preset update principle to update the current optimal positions of all particles; wherein, the preset update principle is: when the new position of any particle is If the fitness value is greater than the current optimal position of the particle, the current optimal position of the particle is replaced and updated with the new position of the particle.

Step S27: Screen out the particle with the largest fitness value from all the current particles, and determine whether the fitness value of the particle is greater than the current global optimal position The corresponding fitness value, if so, use the current position of the particle to calculate the current global optimal position in the target particle swarm Do a replacement update.

Step S28: Re-enter step S25 until the preset number of iterations is reached, and use the current global optimal position obtained after the iteration ends. The corresponding particle is determined as the global optimal particle.

Step S29: According to the global optimal particle, corresponding control is carried out on the wind turbine group.

Correspondingly, the embodiment of the present invention also discloses a wind turbine group control system, which is applied to a wind turbine group, wherein the rotational speed of each wind turbine group in the wind turbine group is consistent during the power control process. Referring to FIG. 2 , the system include:

The particle swarm creation module 11 is used to pre-create and initialize a particle swarm suitable for the scale of the wind turbine swarm to obtain a target particle swarm; wherein, the vector expression of the position of the i-th particle in the target particle swarm is:

In the formula, M represents the number of wind turbines in the wind turbine group, N represents the number of particles in the target particle group, β _i,j represents the pitch angle of the jth wind turbine in the ith particle, and f _i,bus represents the ith wind turbine. The frequency of the variable-frequency AC bus in i particles;

The penalty function determination module 12 is used to determine the penalty function of each wind turbine by using the penalty function determination formula; wherein, the penalty function determination formula is:

In the formula, P _i,k represents the output power of the kth wind turbine in the ith particle, P _N represents the rated power value of the wind turbine, and P _PENi,k represents the kth wind turbine in the ith particle. penalty function;

The fitness value calculation module 13 is used to calculate the fitness value of each particle by using the fitness calculation formula; wherein, the fitness calculation formula is:

In the formula, P _i represents the fitness value of the ith particle in the target particle swarm, and D represents the preset penalty factor;

The iterative update module 14 is used to iteratively update the speed and position of each particle by using the position and fitness value of each particle to obtain the global optimal particle; wherein, the vector expression of the position of the global optimal particle is:

In the formula, β _gj represents the optimal pitch angle of the jth wind turbine in the wind turbine cluster, and f _gbus represents the optimal frequency of the variable frequency AC bus;

The wind turbine group control module 15 is configured to carry out corresponding control on the wind turbine group according to the global optimal particle.

In this embodiment, the aforementioned penalty function determination module may specifically include an output power calculation submodule and a penalty function determination submodule; wherein,

The output power calculation sub-module is used to calculate the output power of each wind turbine by using the output power calculation formula; wherein, the output power calculation formula is:

In the formula, P _i,k represents the output power of the k-th wind turbine in the i-th particle, ρ represents the air density, A represents the swept area of the wind turbine blade, C _k (V _k ,β _i,k ,f _{i ,bus} ) represents the power coefficient of the kth wind turbine, and V _k represents the real-time wind speed of the kth wind turbine;

The penalty function determination sub-module is used to determine the penalty function of each wind turbine by using the output power of each wind turbine and the penalty function determination formula.

It can be understood that the above-mentioned output power calculation sub-module needs to include:

The power coefficient calculation unit is used to calculate the power coefficient of each wind turbine by using the power coefficient calculation formula; wherein, the power coefficient calculation formula is:

Among them, the index S is:

In the formula, V _k represents the real-time wind speed of the kth wind turbine, R represents the blade radius of the wind turbine, n _p represents the number of pole pairs of the motor, n _g represents the gearbox ratio, and K ₁ to K ₆ are based on the wind turbine blades in advance. type-determined coefficients.

It should be noted that, the wind turbine group in this embodiment may specifically be a variable-speed wind turbine group that is centrally controlled by a variable frequency transformer, or a variable-speed wind turbine group that is centrally controlled by a high-voltage DC transmission converter, or a frequency-divided AC power transmission converter. The variable speed wind turbine group controlled by the centralized controller.

For more detailed working processes of the above-mentioned modules, reference may be made to the corresponding contents disclosed in the foregoing embodiments, which will not be repeated here.

It can be seen that by using the particle swarm optimization algorithm with penalty function in the embodiment of the present invention, the output power of the wind turbine group can be maximized, and the output power of the wind turbine group can be ensured not to exceed the rated power value, thereby improving the utilization rate of wind energy and reducing wind power. The amount of wind energy loss during the control of the fleet, and can prevent the output power of the wind turbine from exceeding the rated power value, which is conducive to the safe and stable operation of the wind power system.

Finally, it should also be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply these entities or that there is any such actual relationship or sequence between operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The method and system for controlling a wind turbine group provided by the present invention have been described in detail above. The principles and implementations of the present invention are described with specific examples in this paper. The descriptions of the above embodiments are only used to help understand the present invention. method and its core idea; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation and application scope. Invention limitations.

Claims (10)

1. A method for controlling a wind turbine group is characterized by being applied to the wind turbine group, wherein the rotating speed of each wind turbine group in the wind turbine group is kept consistent in the power control process, and the method comprises the following steps:

a particle swarm which is suitable for the scale of the wind turbine group is created and initialized in advance to obtain a target particle swarm; wherein a vector expression of a position of an ith particle in the target particle population is:

wherein M represents the number of wind generating sets in the wind turbine group, N represents the number of particles in the target particle group, β_i,jRepresenting the pitch angle, f, of the jth wind turbine generator in the ith particle_i,busRepresenting the frequency of a variable frequency alternating current bus in the ith particle;

determining a penalty function of each wind turbine generator by using a penalty function determination formula; wherein the penalty function determination formula is:

in the formula, P_i,kRepresents the output power, P, of the kth wind turbine generator in the ith particle_NRepresenting rated power value, P, of wind turbine_PENi,kA penalty function representing the kth wind turbine in the ith particle;

calculating the fitness value of each particle by using a fitness calculation formula; wherein the fitness calculation formula is as follows:

in the formula, P_iRepresenting the fitness value of the ith particle in the target particle swarm, wherein D represents a preset penalty factor;

iteratively updating the speed and the position of each particle by using the position and the fitness value of each particle to obtain a global optimal particle; wherein the vector expression of the position of the globally optimal particle is:

in the formula, β_gjRepresenting an optimal pitch angle, f, of a jth wind turbine group in said wind turbine group_gbusRepresenting the optimal frequency of the variable frequency alternating current bus;

and performing corresponding control on the wind turbine groups according to the global optimal particles so as to control the pitch angle of each wind turbine group and the frequency of the variable-frequency alternating-current bus.

2. The wind farm control method according to claim 1, wherein the process of determining the penalty function for each wind turbine using the penalty function determination formula comprises:

calculating the output power of each wind turbine generator by using an output power calculation formula; wherein, the output power calculation formula is as follows:

in the formula, P_i,kRepresenting the output power of the kth wind turbine generator set in the ith particle, rho representing the air density, A representing the swept area of the wind turbine blade, C_k(V_k,β_i,k,f_i,bus) Represents the power coefficient, V, of the kth wind turbine_kRepresenting the real-time wind speed of the kth wind power generation unit;

and determining the penalty function of each wind turbine generator by using the output power of each wind turbine generator and the penalty function determination formula.

3. The wind farm control method according to claim 2, wherein the power coefficient C of the kth wind farm_k(V_k,β_i,k,f_i,bus) The calculation formula of (2) is as follows:

wherein the index S is:

in the formula, V_kRepresenting the real-time wind speed of the kth wind turbine, R representing the blade radius of the wind turbine, n_pRepresenting the number of pole pairs, n, of the motor_gRepresenting gear box ratio, K₁To K₆Coefficients are determined in advance based on the wind turbine airfoil.

4. The wind turbine generator set control method according to claim 1, wherein the wind turbine generator set is a variable speed wind turbine generator set centrally controlled by a variable frequency transformer, or a variable speed wind turbine generator set centrally controlled by a high voltage direct current transmission converter, or a variable speed wind turbine generator set centrally controlled by a frequency division alternating current transmission converter.

5. The wind farm control method according to any one of claims 1 to 4, wherein the process of iteratively updating the speed and the position of each particle by using the position and the fitness value of each particle to obtain a globally optimal particle comprises:

step A1: determining an initial position of each particle as a current optimal position of each particlei 1, 2.. N, and screening out the particle with the largest fitness value from all current particles, and determining the position of the particle as the current global optimal position in the target particle swarm

Step A2: using the current optimal position of each particleAnd a current global optimal position in the target particle swarmFor each oneUpdating the speed and the position of the particles to obtain a new position of each particle after the current round of updating;

step A3: calculating the fitness value corresponding to the new position of each particle, and updating the current optimal positions of all the particles by using a preset updating principle; wherein, the preset updating principle is as follows: when the fitness value of the new position of any particle is larger than the fitness value of the current optimal position of the particle, replacing and updating the current optimal position of the particle by using the new position of the particle;

step A4: screening out the particles with the largest fitness value from all the current particles, and judging whether the fitness value of the particle is larger than the current global optimal position or notCorresponding fitness value, if yes, the current position of the particle is utilized to carry out the current global optimal position in the target particle swarmCarrying out replacement updating;

step A5: re-entering the step A2 until reaching the preset iteration number, and obtaining the current global optimal position after the iteration is finishedThe corresponding particle is determined as the global optimal particle.

6. The wind farm control method according to claim 5, wherein the process of updating the speed and the position of each particle to obtain the new position of each particle after the current round of updating comprises:

updating the speed and the position of each particle by using an iterative updating formula, and in the updating process by using the iterative updating formula, if the value of any vector element in the position vector of any particle exceeds a preset element value constraint range, replacing and updating the value of the vector element by using the boundary value of the element value constraint range to obtain the new position of each particle after the current round of updating; wherein the iterative update formula is:

in the formula,is an inertial weight, c₁And c₂Is a learning factor, r₁And r₂Is a random number.

7. The utility model provides a wind turbine group control system which characterized in that is applied to the wind turbine group, wherein, every wind turbine group in the wind turbine group all keeps unanimous at the rotational speed of power control in-process, the system includes:

the particle swarm creating module is used for creating and initializing a particle swarm which is adaptive to the scale of the wind turbine group in advance to obtain a target particle swarm; wherein a vector expression of a position of an ith particle in the target particle population is:

wherein M represents the number of wind generating sets in the wind turbine group, N represents the number of particles in the target particle group, β_i,jRepresenting the pitch angle, f, of the jth wind turbine generator in the ith particle_i,busRepresenting the frequency of a variable frequency alternating current bus in the ith particle;

the penalty function determining module is used for determining a penalty function of each wind turbine generator by using a penalty function determining formula; wherein the penalty function determination formula is:

in the formula, P_i,kRepresents the output power, P, of the kth wind turbine generator in the ith particle_NRepresenting rated power value, P, of wind turbine_PENi,kA penalty function representing the kth wind turbine in the ith particle;

the fitness value calculation module is used for calculating the fitness value of each particle by using a fitness calculation formula; wherein the fitness calculation formula is as follows:

in the formula, P_iRepresenting the fitness value of the ith particle in the target particle swarm, wherein D represents a preset penalty factor;

the iterative update module is used for carrying out iterative update on the speed and the position of each particle by utilizing the position and the fitness value of each particle to obtain a global optimal particle; wherein the vector expression of the position of the globally optimal particle is:

in the formula, β_gjRepresenting an optimal pitch angle, f, of a jth wind turbine group in said wind turbine group_gbusRepresenting the optimal frequency of the variable frequency alternating current bus;

and the wind turbine generator group control module is used for correspondingly controlling the wind turbine generator group according to the global optimal particles so as to control the pitch angle of each wind turbine generator and the frequency of the variable-frequency alternating-current bus.

8. The wind farm control system according to claim 7, wherein the penalty function determining module specifically comprises:

the output power calculation submodule is used for calculating the output power of each wind turbine generator by using an output power calculation formula; wherein, the output power calculation formula is as follows:

in the formula, P_i,kRepresenting the output power of the kth wind turbine generator set in the ith particle, rho representing the air density, A representing the swept area of the wind turbine blade, C_k(V_k,β_i,k,f_i,bus) Represents the power coefficient, V, of the kth wind turbine_kRepresenting the real-time wind speed of the kth wind power generation unit;

and the penalty function determining submodule is used for determining the penalty function of each wind turbine generator by utilizing the output power of each wind turbine generator and the penalty function determining formula.

9. The wind farm control system according to claim 8, wherein the output power calculation sub-module comprises:

the power coefficient calculation unit is used for calculating the power coefficient of each wind turbine generator by using a power coefficient calculation formula; wherein, the power coefficient calculation formula is as follows:

wherein the index S is:

in the formula, V_kRepresenting the real-time wind speed of the kth wind turbine, R representing the blade radius of the wind turbine, n_pRepresenting the number of pole pairs, n, of the motor_gRepresenting gear box ratio, K₁To K₆Coefficients are determined in advance based on the wind turbine airfoil.

10. The wind turbine generator set control system according to claim 7, wherein the wind turbine generator set is a variable speed wind turbine generator set centrally controlled by a variable frequency transformer, or a variable speed wind turbine generator set centrally controlled by a high voltage direct current transmission converter, or a variable speed wind turbine generator set centrally controlled by a frequency division alternating current transmission converter.