CN113839398A - A variable droop coefficient control method for doubly-fed wind turbines participating in primary frequency regulation of power grid - Google Patents

Abstract

The invention relates to a variable droop coefficient control method for a doubly-fed wind turbine to participate in primary frequency regulation of a power grid, comprising the following steps: step 1) collecting real-time wind speed, and monitoring the power grid frequency deviation signal in real time; step 2) adding a self-defined variable droop characteristic unit to the grid The traditional rotor active controller is used to set the droop control error coefficient of the doubly-fed fan participating in the primary frequency regulation of the power grid under different wind speeds; step 3) optimize the droop control error coefficient based on the gray wolf optimization algorithm; step 4) according to the grid frequency deviation signal , Set the optimized droop control adjustment coefficient to obtain additional active power increments and participate in frequency regulation according to the active power increments. The beneficial effects are: avoid the secondary drop of frequency caused by excessive response of the wind turbine due to the setting of the adjustment coefficient is too small; improve the control accuracy, have better adaptive ability, can make full use of the real-time available capacity of the unit, and improve the frequency response capability of the unit .

Description

A variable droop coefficient control method for doubly-fed wind turbines participating in primary frequency regulation of power grid

technical field

The invention belongs to the technical field of wind power generation, and in particular relates to a variable droop coefficient control method for a doubly-fed fan to participate in primary frequency regulation of a power grid.

Background technique

With mature technology, short construction period and low cost, wind power generation has gradually become one of the new energy sources vigorously developed by all countries in the world. As a renewable energy source, wind farms have certain value in terms of energy conservation and emission reduction, and optimization of power supply structure. However, the wind in nature is unstable, and the wind speed is high and small, and has strong randomness and uncontrollability. Therefore, the large-scale grid connection of wind power is bound to have a great impact on the frequency stability of the system.

Double-fed wind turbines are the mainstream models of wind power generation. Since the rotor of the doubly-fed wind turbine is connected to the power grid through the converter, the rotor speed of the wind turbine is completely decoupled from the system frequency and cannot respond to the change of the system frequency. Therefore, the integration of large-scale wind power into the power grid will weaken the frequency regulation capability of the system and affect the stability of the system. In addition, the frequency regulation capability of the wind turbine is closely related to its current wind speed. In the low wind speed section, the load shedding reserve of the wind turbine is less, and the frequency regulation capability is limited. Excessive use of the load shedding reserve energy and rotor kinetic energy of the wind turbine will easily cause the fan to stall and withdraw from operation. In the case of high wind speed, the load shedding reserve of the wind turbine is relatively sufficient, and the frequency regulation power that can be provided is more, and the frequency regulation capability is strong.

At present, some domestic and foreign experts and scholars have studied the control method of the doubly-fed wind turbine participating in the primary frequency regulation of the power grid based on the variable droop control coefficient. However, in the existing research, some ignore the pulsation and uncertainty of natural wind, and only carry out the simulation check under the fixed wind speed. Some methods do not consider the control law of the DFIG itself, and simply use a fixed droop coefficient. If the droop factor is set too small, it will cause the wind turbine to respond excessively, resulting in a secondary drop in the system frequency; if the droop factor is set too large, the frequency response capability of the wind turbine will not be fully utilized. Some just select a few wind speed points, obtain the value of the corresponding wind speed adjustment difference coefficient, and fit the obtained data into a variable droop control curve, the control accuracy is insufficient, and there is still a lot of room for improvement.

SUMMARY OF THE INVENTION

In view of the above-mentioned shortcomings of the prior art, the solution is to fit a small amount of data into a droop control curve, the control accuracy is insufficient, and the setting of the adjustment coefficient is too small, resulting in a secondary drop of the frequency caused by the excessive response of the wind turbine, and the setting of the adjustment coefficient is too large. , resulting in the problem that the frequency response capability of the wind turbine cannot be fully exerted. The present invention proposes a variable droop coefficient control method for the doubly-fed wind turbine to participate in the primary frequency regulation of the power grid, which is specifically implemented by the following scheme:

The variable droop coefficient control method of the doubly-fed wind turbine participating in the primary frequency regulation of the power grid includes the following steps:

Step 1) collect the real-time wind speed, and simultaneously monitor the grid frequency deviation signal Δf in real time;

Step 2) adding a self-defined variable droop characteristic unit to the traditional rotor active power controller, and setting the droop control difference coefficient of the doubly-fed wind turbine participating in the primary frequency regulation of the power grid under different wind speeds through the self-defined variable droop characteristic unit;

Step 3) optimizing the droop control adjustment coefficient based on the gray wolf optimization algorithm, so that the doubly-fed fan can automatically select the optimal droop control adjustment coefficient according to the current wind speed;

Step 4) Obtain an additional active power increment according to the grid frequency deviation signal Δf and the adjusted and optimized droop control adjustment coefficient, and participate in frequency regulation according to the active power increment.

The further design of the variable droop coefficient control method of the DFIG participating in the primary frequency regulation of the power grid is that in the step 2), the custom variable droop characteristic unit completes the droop control difference adjustment of the DFIG participating in the primary frequency regulation of the power grid according to the formula (1). the setting of the coefficients,

In formula (1), Δf ₀ is the allowable value of frequency deviation, which is set to 0.2 Hz, f _N is the rated frequency of the grid at 50 Hz, P _del ' is the total reserve power for load shedding of n DFIGs in the wind farm, and P _WN is the wind power The rated active power of the wind farm; according to the formula (2), calculate the total reserve power P _del ' for load shedding of n sets of double-fed wind turbines in the wind farm:

In the formula, P _del is the reserve power of a single DFIG for load shedding, ρ is the air density, d is the load shedding coefficient, C _p,max is the maximum wind energy capture coefficient when the maximum wind power is tracked, and v is the wind speed.

The further design of the variable droop coefficient control method for the doubly-fed wind turbine to participate in the primary frequency regulation of the power grid is that, in the step 3), optimizing the droop coefficient of the droop control based on the gray wolf optimization algorithm specifically includes the following steps:

Step 3-1) For different wind speeds, a group of gray wolves is randomly defined based on the adjustment coefficient formula to generate a group of gray wolves, see formula (3), this step uses the most initial search for the local possible optimal solution range in the gray wolf optimization algorithm;

是灰狼个体与猎物之间的距离，t是迭代次数，

和

是系数向量，

是猎物经t次迭代的位置，

是灰狼经t次迭代的位置；in,

is the distance between the individual gray wolf and the prey, t is the number of iterations,

and

is the coefficient vector,

is the position of the prey after t iterations,

is the position of the gray wolf after t iterations;

Step 3-2) The control requires the optimized objective function to be the smallest within the constraints to achieve the best power point tracking, and construct the objective function according to formula (4):

Among them, f(x) is the frequency overshoot, Δf _max is the maximum frequency deviation in the frequency response process;

The constraints for constructing the objective function according to equations (5) to (12) are:

v _in <v < v _out (5)

where v is the wind speed, v _in is the cut-in wind speed, and v _out is the cut-out wind speed;

Δf ₀ ≤0.2Hz (6)

In the formula, Δf ₀ is the allowable value of frequency deviation;

ω _min ≤ω _ref ≤ω _max (7)

In the formula, ω _min is the minimum value of the rotation speed, ω _ref is the reference value of the rotation speed, and ω _max is the maximum value of the rotation speed;

β _min ≤β _ref ≤β _max (8)

where β _min is the minimum value of the pitch angle, β _ref is the reference value of the pitch angle, and β _max is the maximum value of the pitch angle;

In the formula, P _m is the mechanical power of the double-fed fan, C _p is the wind energy capture coefficient, C _{p, max} is the maximum wind energy capture coefficient when the maximum wind power is tracked, and v _n is the rated wind speed;

In the formula, ω _r is the rotational speed;

In the formula, P _G is the output power of the double-fed fan, K _C is the MPPT coefficient, ω _r,n is the rated speed;

In the formula, f is the frequency, t' is the time, and this constraint ensures that the frequency response process will not have a secondary drop in frequency;

According to the simultaneous equations of equation (13), based on the gray wolf optimization algorithm, the optimal adjustment coefficient under the corresponding wind speed is found, so that the frequency overshoot f(x) is the smallest under the constraints of various constraints.

Step 3-3) Construct the generalized objective function according to formula (14):

F(x)=f(x)+δ(t)H(x) (14)

In the formula, f(x) is the original objective function, δ(t)H(x) is the penalty term, δ(t) is the penalty intensity, and H(x) is the penalty factor; Steps 3-4) calculate each grayscale separately. The penalty factor of all the constraints of the individual wolf, and the fitness value of each individual gray wolf is calculated according to the formula (14), and the optimal fitness value and corresponding position are recorded;

Step 3-5) Judging whether the penalty factor H(x) meets the accuracy requirements or whether it reaches the maximum number of iterations, if so, the algorithm ends, and the optimal solution is output; otherwise, step 3-6) is performed;

作为决策层，按照式(15)计算其他个体与

的距离，并根据式(16)-(17)更新每个灰狼个体的位置，重新返回步骤3-4)；Step 3-6) Record the positions of the first three gray wolves with fitness values as

As the decision-making layer, according to Eq. (15), calculate other individuals and

distance, and update the position of each individual gray wolf according to formula (16)-(17), and return to step 3-4);

分别表示α，β和δ与狼群中其他个体的距离；

分别表示α，β和δ的当前位置；

为系数向量，t为迭代次数。in the formula

represent the distances of α, β and δ from other individuals in the wolf pack, respectively;

represent the current positions of α, β and δ, respectively;

is the coefficient vector, and t is the number of iterations.

Equation (16) defines the direction and distance of the individual ω toward α, β and δ in the wolf pack, respectively, and Equation (17) represents the final position of the individual ω in the wolf pack.

4. The variable droop coefficient control method of the doubly-fed wind turbine participating in the primary frequency regulation of the power grid according to claim 1, wherein the coefficient in the step 3-1) is calculated by the formula (18):

在迭代过程中从2线性递减至0，

和

分别是[0,1]内的随机向量。In the formula,

Decrease linearly from 2 to 0 in the iterative process,

and

are random vectors in [0,1] respectively.

The beneficial effects of the present invention

Based on the grey wolf optimization algorithm, the invention optimizes the droop control coefficient of the doubly-fed fan participating in the primary frequency regulation of the power grid, and can adjust the droop control droop coefficient according to different wind speeds, so as to avoid the setting of the droop coefficient being too small. , resulting in a secondary drop in frequency due to excessive response of the wind turbine; avoid the failure to give full play to the frequency response capability of the wind turbine due to the setting of the adjustment coefficient is too large. At the same time, the adjustment coefficient is optimized based on the gray wolf optimization algorithm, which improves the control accuracy and has better adaptive ability. It can make full use of the real-time available capacity of the unit, and improve the frequency response capability of the unit. Insufficient control precision.

Description of drawings

Figure 1 is a block diagram of the variable droop coefficient control of the doubly-fed wind turbine.

Figure 2 shows the relationship between the down-adjustment coefficient and wind speed of the gray wolf optimization algorithm.

Figure 3 is the simulation model diagram built in PSCAD.

Figure 4 is the simulation effect diagram obtained when the wind speed is 11m/s and the droop control adjustment coefficients are set in PSCAD to be 2%, 3.47% and 5% respectively.

Figure 5 is the simulation effect diagram obtained when the wind speed is 13m/s and the droop control adjustment coefficients are set to 2% and 5% respectively in PSCAD, and the droop control link is not added.

Figure 6 is a graph of the pitch angle change when the wind speed is 13m/s, the droop control adjustment coefficients are set to 2% and 5% in PSCAD, and the droop control link is not added.

Detailed ways

The technical solutions of the present invention are further described below with reference to the accompanying drawings.

The method for controlling the variable droop coefficient of the doubly-fed wind turbine participating in the primary frequency regulation of the power grid of the present invention includes the following steps:

Step 1) Collect real-time wind speed, and at the same time conduct real-time monitoring of grid frequency deviation signal Δf.

Step 2) A custom variable droop characteristic unit is added to the traditional rotor active power controller, and the droop control error coefficient of the doubly-fed wind turbine participating in the primary frequency regulation of the power grid at different wind speeds is set by the self-defined variable droop characteristic unit.

Step 3) Optimizing the droop control adjustment coefficient based on the gray wolf optimization algorithm, so that the doubly-fed fan can automatically select the optimal droop control adjustment coefficient according to the current wind speed.

Step 4) Obtain an additional active power increment according to the grid frequency deviation signal Δf and the adjusted and optimized droop control adjustment coefficient, and participate in frequency regulation according to the active power increment.

In step 2), a self-defined variable droop characteristic unit is added on the basis of the traditional rotor active controller, and the coefficient of droop control is automatically set according to the current wind speed. The control block diagram of the variable droop coefficient of the DFIG proposed by the present invention is shown in Figure 1, and the specific steps are as follows:

Step 2-1) When simulating the droop characteristics of the traditional synchronous generator, ΔP=KΔf of the doubly-fed wind turbine. In the traditional synchronous generator, there are:

In the formula, K _G is the unit regulated power of the traditional synchronous generator, K _G ^* is the per-unit value of the unit regulated power of the traditional synchronous generator, P _GN is the rated active power of the traditional synchronous generator, f _N is the rated frequency 50Hz, and δ _G is The traditional synchronous generator adjustment coefficient, Δf is the system frequency variation.

Step 2-2) Therefore, simulating the expression of the traditional synchronous generator can obtain the following expression in the doubly-fed wind turbine:

In the formula, K is the droop control coefficient, _δw is the adjustment coefficient of the double-fed wind turbine, and P _WN is the rated active power of the double-fed wind turbine.

Steps 2-3) thus have:

It can be seen from the above formula that the droop control coefficient K is related to the adjustment coefficient _δw of the doubly-fed wind turbine.

Step 2-4) According to the definition formula of the traditional synchronous generator adjustment coefficient, the adjustment coefficient of the variable droop control of the doubly-fed fan is defined as:

In the formula, Δf ₀ is the allowable value of frequency deviation, which is set to 0.2 Hz, f _N is the rated frequency of the power grid of 50 Hz, considering the equivalent value of a single machine, P _del ' is the total reserve power for load shedding of n sets of DFIGs in the wind farm, P _WN is the rated active power of the wind farm.

In the formula, P _del is the reserve power for load shedding of a single DFIG, ρ is the air density, d is the load shedding coefficient, and the load shedding level in this paper is taken as 20%. C _p,max is the maximum wind energy capture coefficient when the maximum wind power is tracked, and v is the wind speed.

In step 3), optimizing the adjustment coefficient of the droop control based on the gray wolf optimization algorithm specifically includes the following steps:

Step 3-1) For different wind speeds, a group of gray wolves is randomly defined based on the adjustment coefficient formula to generate a group of gray wolves, see formula (3), this step uses the most initial search for the local possible optimal solution range in the gray wolf optimization algorithm;

是灰狼个体与猎物之间的距离，t是迭代次数，

和

是系数向量，

是猎物经t次迭代的位置，

是灰狼经t次迭代的位置。in,

is the distance between the individual gray wolf and the prey, t is the number of iterations,

and

is the coefficient vector,

is the position of the prey after t iterations,

is the position of the gray wolf after t iterations.

Step 3-2) The control requires the optimized objective function to be the smallest within the constraints to achieve the best power point tracking, and construct the objective function according to formula (4):

Among them, f(x) is the frequency overshoot, Δf _max is the maximum frequency deviation in the frequency response process;

The constraints for constructing the objective function according to equations (5) to (12) are:

v _in <v < v _out (5)

where v is the wind speed, v _in is the cut-in wind speed, and v _out is the cut-out wind speed;

Δf ₀ ≤0.2Hz (6)

In the formula, Δf ₀ is the allowable value of frequency deviation;

ω _min ≤ω _ref ≤ω _max (7)

In the formula, ω _min is the minimum value of the rotation speed, ω _ref is the reference value of the rotation speed, and ω _max is the maximum value of the rotation speed;

β _min ≤β _ref ≤β _max (8)

where β _min is the minimum value of the pitch angle, β _ref is the reference value of the pitch angle, and β _max is the maximum value of the pitch angle;

In the formula, P _m is the mechanical power of the double-fed fan, C _p is the wind energy capture coefficient, C _{p, max} is the maximum wind energy capture coefficient when the maximum wind power is tracked, and v _n is the rated wind speed;

In the formula, ω _r is the rotational speed;

In the formula, P _G is the output power of the double-fed fan, K _C is the MPPT coefficient, ω _r,n is the rated speed;

In the formula, f is the frequency, and t' is the time. This constraint ensures that the frequency response process will not have a secondary drop in frequency.

According to the simultaneous equations of equation (13), based on the gray wolf optimization algorithm, the optimal adjustment coefficient under the corresponding wind speed is found, so that the frequency overshoot f(x) is the smallest under the constraints of various constraints.

Step 3-3) Construct the generalized objective function according to formula (14):

F(x)=f(x)+δ(t)H(x) (14)

In the formula, f(x) is the original objective function, δ(t)H(x) is the penalty term, δ(t) is the penalty intensity, and H(x) is the penalty factor. Step 3-4) Calculate the penalty factors of all constraints of each individual gray wolf, and calculate the fitness value of each individual gray wolf according to formula (14), and record the optimal fitness value and corresponding position.

Step 3-5) Determine whether the penalty factor H(x) meets the precision requirement or whether it reaches the maximum number of iterations, if so, the algorithm ends and the optimal solution is output; otherwise, step 3-6) is executed.

作为决策层，按照式(15)计算其他个体与

的距离，并根据式(16)-(17)更新每个灰狼个体的位置，重新返回步骤3-4)。Step 3-6) Record the positions of the first three gray wolves with fitness values as

As the decision-making layer, according to Eq. (15), calculate other individuals and

and update the position of each individual gray wolf according to equations (16)-(17), and return to step 3-4).

分别表示α，β和δ与狼群中其他个体的距离；

分别表示α，β和δ的当前位置；

为系数向量，t为迭代次数。in the formula

represent the distances of α, β and δ from other individuals in the wolf pack, respectively;

represent the current positions of α, β and δ, respectively;

is the coefficient vector, and t is the number of iterations.

Equation (16) defines the direction and distance of the individual ω toward α, β and δ in the wolf pack, respectively, and Equation (17) represents the final position of the individual ω in the wolf pack.

In step 3-1), the coefficient is calculated by formula (18):

在迭代过程中从2线性递减至0，

和

分别是[0,1]内的随机向量。In the formula,

Decrease linearly from 2 to 0 in the iterative process,

and

are random vectors in [0,1] respectively.

The relationship between the optimized adjustment coefficient and wind speed is shown in Figure 2.

In order to verify the effectiveness of the frequency modulation strategy, the present invention uses PSCAD software to build a simulation model, and the model is shown in FIG. 3 . In this model, the wind farm consists of 10 wind turbines with a rated capacity of 2.5MW, passing through a 35kV collector line, a 35/110kV step-up transformer connected to a 110kV system, and a rated wind speed of 11m/s. The 110kV system simulates the P-f droop characteristics, initially connects to a 20MW load, and runs the simulation to 6s, then connects to a 5MW load. The input frequency of the simulated load decreases, and the fan participates in frequency regulation. Set the doubly-fed wind turbine to run at 20% load reduction.

The present invention also provides the following two specific embodiments to select two typical representative wind speeds of 11m/s and 13m/s respectively. Since the frequency response time scale of the present invention is about 18s, it is assumed that the wind speed is constant during the frequency response process.

Example 1:

When the wind speed is 11m/s, in order to analyze the influence of the droop coefficient on the frequency response performance of the doubly-fed wind turbine, take _δw as 2%, 3.47%, and 5% respectively. Among them, 2% is the setting value that is too small; 3.47% is the setting value obtained by the method of the present invention; 5% is the setting value that is too large, and the obtained simulation is shown in FIG. 4 .

It can be seen from Figure 4 that when _δw is set to a small value (2%), the grid frequency drops twice during the frequency regulation process. When _δw is set too large (5%), although the grid frequency does not drop twice during the frequency regulation process, the absolute value of the maximum deviation of the dynamic frequency of the grid is larger than the absolute value of the maximum deviation of the dynamic frequency when _δw is set to 3.47%. , the frequency dynamic response effect is worse than that of the method of the present invention. It can be seen that when the variable droop control coefficient method of the present invention is used to participate in frequency regulation, the absolute value of the maximum deviation of the dynamic frequency of the power grid can be reduced, which is more conducive to reducing frequency fluctuation and maintaining the stability of the power grid frequency.

Example 2:

When the wind speed is 13m/s, the pitch angle control works to reserve spare capacity. In order to analyze the influence of the droop coefficient on the frequency response performance of the doubly-fed wind turbine, take _δw as 2%, 5%, and do not add droop control for comparison. Among them, 2% is the setting value obtained by the present invention; 5% is the setting value that is too large, and the obtained simulation is shown in FIG. 5 .

It can be seen from Fig. 5 that after adopting the variable droop coefficient setting method proposed by the present invention, the excessive response of the doubly-fed wind turbine can be avoided, and the dynamic response of the frequency can be effectively improved. Similar to the wind speed of 8m/s, when _δw is set too large (5%), the method proposed in the present invention has better frequency response capability.

Combining with Figure 5 and Figure 6, it can be seen that when the wind speed is 13m/s, the pitch angle control participates in frequency modulation. When the setting value obtained by the present invention is adopted, the pitch angle response is faster, and the improvement effect of the frequency response capability is more obvious.

The above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. The equivalent replacement or modification of the inventive concept thereof shall be included within the protection scope of the present invention.

Claims (4)

1. a doubling-fed fan participates in the variable droop coefficient control method of primary frequency regulation of power grid, is characterized in that, comprises the steps: Step 1) collect the real-time wind speed, and simultaneously monitor the grid frequency deviation signal Δf in real time; Step 2) adding a self-defined variable droop characteristic unit to the traditional rotor active power controller, and setting the droop control difference coefficient of the doubly-fed wind turbine participating in the primary frequency regulation of the power grid under different wind speeds through the self-defined variable droop characteristic unit; Step 3) optimizing the droop control adjustment coefficient based on the gray wolf optimization algorithm, so that the doubly-fed fan can automatically select the optimal droop control adjustment coefficient according to the current wind speed; Step 4) Obtain an additional active power increment according to the grid frequency deviation signal Δf and the adjusted and optimized droop control adjustment coefficient, and participate in frequency regulation according to the active power increment. 2. The variable droop coefficient control method of the DFIG according to claim 1 participating in the primary frequency regulation of the power grid, it is characterized in that, in the described step 2), the self-defined variable droop characteristic unit completes the DFIG to participate in the power grid according to formula (1). The droop of the primary frequency modulation controls the setting of the error coefficient,

In formula (1), Δf ₀ is the allowable value of frequency deviation, which is set to 0.2 Hz, f _N is the rated frequency of the grid at 50 Hz, P _del ' is the total reserve power for load shedding of n DFIGs in the wind farm, and P _WN is the wind power the rated active power of the field; Calculate the total reserve power P _del ′ for load shedding of n sets of DFIGs in the wind farm according to formula (2):

In the formula, P _del is the reserve power of a single DFIG for load shedding, ρ is the air density, d is the load shedding coefficient, C _p,max is the maximum wind energy capture coefficient when the maximum wind power is tracked, and v is the wind speed. 3. The variable droop coefficient control method of the doubly-fed fan according to claim 1 participating in the primary frequency regulation of the power grid, is characterized in that, In the step 3), optimizing the adjustment coefficient of the droop control based on the gray wolf optimization algorithm specifically includes the following steps: Step 3-1) according to different wind speeds, randomly define a group of gray wolves based on the adjustment coefficient formula, see formula (3) ), this step uses the initial search range of local possible optimal solutions in the gray wolf optimization algorithm;

in,

is the distance between the individual gray wolf and the prey, t is the number of iterations,

and

is the coefficient vector,

is the position of the prey after t iterations,

is the position of the gray wolf after t iterations; Step 3-2) The control requires the optimized objective function to be the smallest within the constraints to achieve the best power point tracking, and construct the objective function according to formula (4):

Among them, f(x) is the frequency overshoot, Δf _max is the maximum frequency deviation in the frequency response process; The constraints for constructing the objective function according to equations (5) to (12) are: v _in <v < v _out (5) where v is the wind speed, v _in is the cut-in wind speed, and v _out is the cut-out wind speed; Δf ₀ ≤0.2Hz (6) In the formula, Δf ₀ is the allowable value of frequency deviation; ω _min ≤ω _ref ≤ω _max (7) In the formula, ω _min is the minimum value of the rotation speed, ω _ref is the reference value of the rotation speed, and ω _max is the maximum value of the rotation speed; β _min ≤β _ref ≤β _max (8) where β _min is the minimum value of the pitch angle, β _ref is the reference value of the pitch angle, and β _max is the maximum value of the pitch angle;

In the formula, P _m is the mechanical power of the double-fed fan, C _p is the wind energy capture coefficient, C _{p, max} is the maximum wind energy capture coefficient when the maximum wind power is tracked, and v _n is the rated wind speed;

In the formula, ω _r is the rotational speed;

In the formula, P _G is the output power of the double-fed fan, K _C is the MPPT coefficient, ω _r,n is the rated speed;

In the formula, f is the frequency, t' is the time, and this constraint ensures that the frequency response process will not have a secondary drop in frequency; According to the simultaneous equations of equation (13), based on the gray wolf optimization algorithm, find the optimal adjustment coefficient under the corresponding wind speed, so that the frequency overshoot f(x) is the smallest under the constraints of various constraints;

Step 3-3) Construct the generalized objective function F(x) according to formula (14): F(x)=f(x)+δ(t)H(x) (14) In the formula, f(x) is the original objective function, δ(t)H(x) is the penalty term, δ(t) is the penalty intensity, and H(x) is the penalty factor; Step 3-4) respectively calculate the penalty factor of all the constraints of each individual gray wolf, and calculate the fitness value of each individual gray wolf according to formula (14), and record the optimal fitness value and corresponding position; Step 3-5) Judging whether the penalty factor H(x) meets the accuracy requirements or whether it reaches the maximum number of iterations, if so, the algorithm ends, and the optimal solution is output; otherwise, step 3-6) is performed; Step 3-6) Record the positions of the first three gray wolves with fitness values as

As the decision-making layer, according to Eq. (15), calculate other individuals and

distance, and update the position of each individual gray wolf according to formula (16)-(17), and return to step 3-4);

in the formula

represent the distances of α, β and δ from other individuals in the wolf pack, respectively;

represent the current positions of α, β and δ, respectively;

is the coefficient vector, and t is the number of iterations;

Equation (16) defines the direction and distance of the individual ω toward α, β and δ in the wolf pack, respectively, and Equation (17) represents the final position of the individual ω in the wolf pack. 4. The variable droop coefficient control method of the doubly-fed wind turbine according to claim 1 participating in the primary frequency regulation of the power grid, it is characterized in that, the coefficient in described step 3-1) is calculated by such as formula (18):

In the formula,

Decrease linearly from 2 to 0 in the iterative process,

and

are random vectors in [0,1] respectively.

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