Offshore wind plant phase modifier capacity configuration optimization algorithm

Abstract

The invention discloses an optimization algorithm for capacity configuration of a phase modifier of an offshore wind power plant, and the optimization process is divided into two stages, the first stage aims at improving the short-circuit ratio of the offshore wind power plant, and the second stage aims at suppressing overvoltage. The method comprises the following steps: firstly, establishing objective functions and constraint conditions of two stage targets; and solving the target function by adopting an eagle group intelligent optimization algorithm to obtain the capacity of the phase modifier which should be configured for each sub-wind power plant in the offshore wind power plant.

Description

Technical Field

[0001] The invention relates to the technical field of wind farm capacity configuration, and in particular to an offshore wind farm phase regulator capacity configuration optimization algorithm. Background Art

[0002] The existing offshore wind farms are connected to the grid on a large scale, which improves the utilization rate of clean energy, but the voltage and frequency support capabilities of the large grid are weakened. In addition, when there are faults in the sending and receiving power stations of offshore wind farms, it is easy to cause large fluctuations in the grid voltage, causing transient overvoltage, resulting in overvoltage of the grid-connected equipment, affecting its performance.

[0003] Phase regulators can provide reactive power support, thereby improving the voltage support capacity of the power grid. Large-capacity phase regulators are expensive and complex to control, while distributed, small-capacity phase regulators have control flexibility, and their investment and maintenance costs are lower than those of centralized large-capacity phase regulators. However, the installation capacity and location of distributed phase regulators are closely related to the voltage support capacity and investment cost of offshore wind farms.

[0004] Therefore, there is still room for improvement in the prior art. Summary of the invention

[0005] The purpose of the present invention is to provide an offshore wind farm phase regulator capacity configuration optimization algorithm, aiming to solve the technical problems that the installation capacity, installation location and voltage support capacity of the offshore wind farm and investment cost of the existing distributed phase regulator cannot be reasonably configured.

[0006] To achieve the above-mentioned purpose, the technical solution of the present invention is: an offshore wind farm phase regulator capacity configuration optimization algorithm, wherein the offshore wind farm is composed of 6 sub-wind farms, each sub-wind farm is composed of 16 offshore wind turbines, and each sub-wind farm is configured with multiple phase regulators; the method is divided into two stages, the first stage aims to improve the short-circuit ratio of the offshore wind farm, and the second stage aims to suppress overvoltage; then the objective functions and constraints of the two stage objectives are established; finally, the Eagle swarm intelligent optimization algorithm is used to solve the objective function to obtain the capacity of the phase regulators that should be configured for each sub-wind farm in the offshore wind farm.

[0007] Furthermore, with the goal of improving the short-circuit ratio of offshore wind farms, the first objective function is to minimize the capacity of the phase regulator, which is expressed as:

[0008]

[0009] In the formula, κ i represents the number of phase regulators configured in the i-th wind farm, S i The capacity of a single distributed phase regulator configured for the i-th wind farm.

[0010] Furthermore, the constraints are the short-circuit ratio of the sub-wind farm, the short-circuit ratio of the offshore wind farm, the short-circuit ratio balance of the offshore wind farm, the number of phase regulators required, and the power flow constraint; among which:

[0011] (1) The short-circuit ratio constraint of an offshore wind farm is expressed as:

[0012] R SCRi >R SCRi,min (2)

[0013] In the formula, R SCRi represents the short-circuit ratio of the ith offshore wind farm, R SCRi,MIN is the minimum short-circuit ratio of the ith offshore wind farm, R SCRi It is expressed as:

[0014]

[0015] In the formula, S ac,i is the short-circuit capacity of the i-th wind farm, P RE i is the active power of the i-th wind farm, P RE j is the active power of the j-th wind farm, Z eqii is the equivalent self-impedance of the i-th wind farm, Z eqij is the equivalent mutual impedance between the i-th wind farm and the j-th wind farm.

[0016] (2) The constraint on the short-circuit ratio of an offshore wind farm is expressed as:

[0017]

[0018] In the formula, R' SCRmin is the short-circuit ratio of the offshore wind farm, and N is the number of sub-wind farms.

[0019] (3) The short-circuit ratio balance constraint of the offshore wind farm is expressed as:

[0020]

[0021] In the formula, R SCRav is the average short-circuit ratio of all wind farms, R SCRD is the maximum value of the short-circuit ratio of the sub-wind farm; where R SCRav It is expressed as:

[0022]

[0023] (4) The constraint condition on the number of phase regulators is expressed as:

[0024] 0≤κ i ≤κ i,max (7)

[0025] In the formula, κ i,max It represents the upper limit of the number of phase regulators in the i-th wind farm.

[0026] (6) The power flow constraint is expressed as:

[0027]

[0028] Where P i represents the active power of the ith node in the power grid, Q i represents the reactive power of the ith node in the power grid; n is the number of nodes in the power grid; U i is the voltage of the ith node, U j represents the voltage of the jth node, G ij represents the real part of the element in the i-th row and j-th column of the grid admittance matrix, B ij represents the imaginary part of the element in the i-th row and j-th column of the grid admittance matrix, θ ij Represents the phase difference between the voltage at the i-th node and the voltage at the j-th node.

[0029] Furthermore, with the goal of suppressing overvoltage, the second objective function for correcting the capacity of the phase regulator is expressed as:

[0030]

[0031] Where N * represents the number of sub-wind farms that need to be equipped with phase regulators after the first stage of optimization, κ i * It indicates the number of phase regulators that need to be configured in the i-th wind farm after the first stage of optimization. i is the number of phase regulators configured for the i-th wind farm after the second stage optimization. ρ is the penalty coefficient.

[0032] Furthermore, the constraints include power flow constraints, transient time domain solution constraints, overvoltage constraints, and constraints on the number of phase regulators configured in each sub-wind farm.

[0033] The power flow constraint is shown in formula (8), and the constraint on the number of phase regulators configured in each sub-wind farm is shown in formula (7).

[0034] The transient time domain solution constraint can be expressed as:

[0035]

[0036] In the formula, x is the state variable of the power grid, y is the algebraic variable of the power grid, f(x, y) represents the differential equation, g(x, y) represents the algebraic equation, and t represents time. The differential equation group represents the dynamic response process of each component in the power grid system, such as phase regulators, offshore wind turbines, thermal power generators, filters, etc. The algebraic equation group represents the power balance, current balance, etc. of each network node.

[0037] The overvoltage constraint can be expressed as

[0038] U min (t)≤U(t)≤U max (t) (11)

[0039] Where U(t) represents the output voltage of the sub-wind farm, U min (t) represents the minimum output voltage of the sub-wind farm, U max (t) represents the maximum value of the output voltage of the sub-wind farm.

[0040] Furthermore, the swarm intelligence optimization algorithm Sky Eagle algorithm is used to solve the optimal solution of equation (1) and equation (9).

[0041] The hunting behaviors of the Sky Eagle Algorithm are divided into four types, namely, extended exploration, narrowed search range, expanded development, and narrowed development range. Among them, the diffusion search behavior is the vertical dive and high-altitude soaring behavior of the Sky Eagle, which can be expressed as:

[0042]

[0043] Where, X 1 is the solution of the tth iteration, which is also the solution of the current iteration number, X best (t) is the optimal solution up to the tth iteration, and the optimal solution is the solution closest to the prey position of the eagle; T is the maximum number of iterations, t is the current number of iterations; X M (t) represents the average value of all eagle positions at the tth iteration, that is, the average value of all solutions, which can be expressed as formula (13); rand is a random number between 0 and 1;

[0044]

[0045] Where N is the total number of eagles, X i (t) represents the position of the tth iteration of the i-th eagle in the eagle population.

[0046] The behavior of the Skyhawk shrinking its range indicates the flight behavior of the Skyhawk's short-range gliding attack, which can be expressed as:

[0047] X 2 =X best (t)×Levy(D)+X R(t)+(yx)×rand (14)

[0048] In the formula, Levy (D) represents the flight distribution function, which can be expressed as formula (15), D represents the dimensional space, X R (t) represents the random solution of a random eagle in the eagle group at the tth iteration, and R is a positive integer between 1 and N; x and y represent the shape parameters of the eagle during spiral flight, which can be expressed as formula (16):

[0049]

[0050] y=r×cos(θ),x=r×sin(θ) (16)

[0051] Where s is a fixed coefficient, usually 0.01; μ and υ are random numbers, usually ranging from 0 to 1; β is a fixed coefficient, usually 1.5; σ can be expressed as (17); r and θ are expressed as (19)-(20) respectively;

[0052]

[0053] r=r 1 +U×D 1 (18)

[0054]

[0055] Where Γ is a fixed function symbol, representing the Tao function; r 1 is a constant between 1 and 20, indicating the number of searches by the Eagle. U is a constant with a value of 0.00565. 1 is an integer ranging from 1 to D; ω is a constant, usually 0.005.

[0056] The behavior of expanding development refers to the behavior of Skyhawk approaching and attacking the target, which is expressed as:

[0057] X 3 =(X best (t)-X M (t))×α-rand+((UB-LB)×UB+LB)×δ (20)

[0058] Where α and δ are constants, and the common value is 0.1. UB is the maximum value of the solution domain, and LB is the minimum value of the solution domain.

[0059] The behavior of narrowing the development range refers to the behavior of the eagle attacking the prey according to the random action route of the prey, which is expressed as:

[0060] X 4 =QF×Xbest -(G 1 ×X(t)×rand)-G 2 ×Levy(D)+rand×G 1 (twenty one)

[0061] In the formula, the function QF can be expressed as formula (22), G 1 represents the random motion of the prey, expressed as formula (23), G 2 represents the flight process of the eagle in the process of tracking prey, that is, the flight speed of the eagle, expressed as formula (24);

[0062]

[0063] G 1 =2×rand-1 (23)

[0064]

[0065] Furthermore, the chaotic map is used to initialize the eagle population, which is expressed as:

[0066]

[0067] Among them, X i The position of the i-th eagle, X i+1 Indicates the position of the (i+1)th eagle.

[0068] Furthermore, the specific implementation process of the Tianying optimization algorithm is as follows:

[0069] Step 1: Initialize parameters and initialize the population of the Sky Eagle swarm;

[0070] Step 2: Calculate the fitness of each individual in the eagle population, and find the optimal solution and average solution of the current population. Step 3: Determine whether the number of iterations is greater than 2 / 3 of the total number of iterations. If the current number of iterations is greater than 2 / 3 of the total number of iterations, determine the random variable. If the random variable is greater than 0.5, use formula (14) to update the position of the eagle population; if the random variable is less than 0.5, use formula (12) to update the position of the eagle population; if the current number of iterations is less than 2 / 3 of the total number of iterations, determine the random variable. If the random variable is greater than 0.5, use formula (21) to update the position of the eagle population; if the random variable is less than 0.5, use formula (20) to update the position of the eagle population; Step 4: If the number of iterations reaches the maximum, the optimization process ends and the current optimal solution is obtained.

[0071] Beneficial effects: The present invention provides reactive power for offshore wind farms through phase regulators, but the output power of wind farms is characterized by volatility and randomness. The capacity design of phase regulators is crucial. If the capacity design of phase regulators is small, they cannot provide sufficient reactive power support. If the capacity design of phase regulators is large, the utilization rate of the equipment is low, and the manufacturing cost and maintenance cost are high, which reduces the economic benefits of wind farms. The present invention proposes the optimization objective function and constraint conditions of phase regulator capacity in two stages. The objective function of the first stage is to reduce the capacity of phase regulators, and the minimum capacity of phase regulators is taken as the objective function, thereby reducing the construction cost of phase regulators. The objective function of the second stage suppresses overvoltage and corrects the capacity of phase regulators. The present invention uses the intelligent swarm optimization Sky Eagle algorithm to solve the objective function, thereby optimizing the capacity of phase regulators, improving the short-circuit ratio of wind farms, and realizing peak shaving and valley filling of new energy wind farms. Compared with the prior art, the present invention takes into account the overvoltage problem of phase regulators in the reactive regulation stage, and can suppress the breakdown of power electronic equipment caused by overvoltage. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is the offshore wind farm configuration of the present invention.

[0073] Figure 2 It is the flow chart of the Skyhawk optimization algorithm of the present invention. DETAILED DESCRIPTION

[0074] In order to make the objectives, technical solutions and advantages of the present invention more clear and explicit, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0075] like Figure 1 As shown, the present invention discloses an offshore wind farm consisting of 6 sub-wind farms, each of which consists of 16 offshore wind turbines, and each sub-wind farm is equipped with multiple phase regulators; the method is divided into two stages, the first stage aims to improve the short-circuit ratio of the offshore wind farm, and the second stage aims to suppress overvoltage; then the objective functions and constraints of the two stage objectives are established; finally, the Eagle swarm intelligent optimization algorithm is used to solve the objective function to obtain the capacity of the phase regulators that should be configured for each sub-wind farm in the offshore wind farm.

[0076] Furthermore, with the goal of improving the short-circuit ratio of offshore wind farms, the first objective function is to minimize the capacity of the phase regulator, which is expressed as:

[0077]

[0078] In the formula, κ i represents the number of phase regulators configured in the i-th wind farm, S i The capacity of a single distributed phase regulator configured for the i-th wind farm.

[0079] Furthermore, the constraints are the short-circuit ratio of the sub-wind farm, the short-circuit ratio of the offshore wind farm, the short-circuit ratio balance of the offshore wind farm, the number of phase regulators required, and the power flow constraint; among which:

[0080] (1) The short-circuit ratio constraint of an offshore wind farm is expressed as:

[0081] R SCRi >R SCRi,min (2)

[0082] In the formula, R SCRi represents the short-circuit ratio of the ith offshore wind farm, R SCRi,MIN is the minimum short-circuit ratio of the ith offshore wind farm, R SCRi It can be expressed as:

[0083]

[0084] In the formula, S ac,i is the short-circuit capacity of the i-th wind farm, P RE i is the active power of the i-th wind farm, P RE j is the active power of the j-th wind farm, Z eqii is the equivalent self-impedance of the i-th wind farm, Z eqij is the equivalent mutual impedance between the i-th wind farm and the j-th wind farm.

[0085] (2) The constraint on the short-circuit ratio of an offshore wind farm is expressed as:

[0086]

[0087] In the formula, R' SCRmin is the short-circuit ratio of the offshore wind farm, and N is the number of sub-wind farms.

[0088] (3) The short-circuit ratio balance constraint of the offshore wind farm is expressed as:

[0089]

[0090] In the formula, R SCRav is the average short-circuit ratio of all wind farms, R SCRD is the maximum value of the short-circuit ratio of the sub-wind farm; where R SCRav It is expressed as:

[0091]

[0092] (4) The constraint condition of the number of phase regulators is expressed as:

[0093] 0≤κ i ≤κ i,max (7)

[0094] In the formula, κ i,max It represents the upper limit of the number of phase regulators in the i-th wind farm.

[0095] (7) The power flow constraint can be expressed as:

[0096]

[0097] Where P i represents the active power of the ith node in the power grid, Q i represents the reactive power of the ith node in the power grid; n is the number of nodes in the power grid; U i is the voltage of the ith node, U j represents the voltage of the jth node, G ij represents the real part of the element in the i-th row and j-th column of the grid admittance matrix, B ij represents the imaginary part of the element in the i-th row and j-th column of the grid admittance matrix, θ ij Represents the phase difference between the voltage at the i-th node and the voltage at the j-th node.

[0098] Furthermore, the second objective function of correcting the capacity of the phase regulator is expressed as:

[0099]

[0100] Where N * represents the number of sub-wind farms that need to be equipped with phase regulators after the first stage of optimization, κ i * It indicates the number of phase regulators that need to be configured in the i-th wind farm after the first stage of optimization; K i is the number of phase regulators configured for the ith sub-wind farm after the second stage of optimization; ρ is the penalty coefficient.

[0101] Furthermore, the constraints include power flow constraints, transient time domain solution constraints, overvoltage constraints, and constraints on the number of phase regulators configured in each sub-wind farm.

[0102] The power flow constraint is shown in formula (8), and the constraint on the number of phase regulators configured in each sub-wind farm is shown in formula (7).

[0103] The transient time domain solution constraint is expressed as:

[0104]

[0105] In the formula, x is the state variable of the power grid, y is the algebraic variable of the power grid, f(x, y) represents the differential equation, g(x, y) represents the algebraic equation, and t represents time; the differential equation group represents the dynamic response process of each component in the power grid system, such as phase regulators, offshore wind turbines, thermal power generators, filters, etc. The algebraic equation group represents the power balance, current balance, etc. of each network node.

[0106] The overvoltage constraint is expressed as:

[0107] U min (t)≤U(t)≤U max (t) (11)

[0108] Where U(t) represents the output voltage of the sub-wind farm, U min (t) represents the minimum output voltage of the sub-wind farm, U max (t) represents the maximum value of the output voltage of the sub-wind farm.

[0109] Furthermore, the swarm intelligence optimization algorithm Sky Eagle algorithm is used to solve the optimal solution of equation (1) and equation (9).

[0110] The hunting behaviors of the Sky Eagle Algorithm are divided into four types: extended exploration, narrowed search range, expanded development, and narrowed development range. Among them, the diffusion search behavior is the vertical dive and high-altitude soaring behavior of the Sky Eagle, which can be expressed as:

[0111]

[0112] Where, X 1 is the solution of the tth iteration, which is also the solution of the current iteration number, X best (t) is the optimal solution up to the tth iteration, and the optimal solution is the solution closest to the prey position of the eagle; T is the maximum number of iterations, t is the current number of iterations; X M (t) represents the average value of all eagle positions at the tth iteration, that is, the average value of all solutions, expressed as formula (13); rand is a random number between 0 and 1.

[0113]

[0114] Where N is the total number of eagles, X i (t) represents the position of the tth iteration of the i-th eagle in the eagle population.

[0115] The behavior of the Skyhawk shrinking its range indicates the flight behavior of the Skyhawk's short-range gliding attack, which can be expressed as:

[0116] X 2 =X best (t)×Levy(D)+X R(t)+(yx)×rand (14)

[0117] In the formula, Levy (D) represents the flight distribution function, which is expressed as formula (15), D represents the dimensional space, X R (t) represents the random solution of a random eagle in the eagle group at the tth iteration, and R is a positive integer between 1 and N. x and y represent the shape parameters of the eagle during spiral flight, which can be expressed as (16)

[0118]

[0119] y=r×cos(θ),x=r×sin(θ) (16)

[0120] Where s is a fixed coefficient, usually 0.01; μ and υ are random numbers, usually ranging from 0 to 1; β is a fixed coefficient, usually 1.5; σ can be expressed as (17). r and θ are expressed as (19)-(20) respectively.

[0121]

[0122] r=r 1 +U×D 1 (18)

[0123]

[0124] Where Γ is a fixed function symbol, representing the Tao function; r 1 is a constant between 1 and 20, indicating the number of searches by the Eagle. U is a constant with a value of 0.00565. 1 is an integer ranging from 1 to D; ω is a constant, usually 0.005.

[0125] The behavior of expanding development refers to the behavior of Skyhawk approaching and attacking the target, which is expressed as:

[0126] X 3 =(X best (t)-X M (t))×α-rand+((UB-LB)×UB+LB)×δ (20)

[0127] Where α and δ are constants, and the common value is 0.1. UB is the maximum value of the solution domain, and LB is the minimum value of the solution domain.

[0128] The behavior of narrowing the development range refers to the behavior of the eagle attacking the prey according to the random action route of the prey, which is expressed as:

[0129] X 4 =QF×Xbest -(G 1 ×X(t)×rand)-G 2 ×Levy(D)+rand×G 1 (twenty one)

[0130] In the formula, the function QF is expressed as formula (22), G 1 represents the random motion of the prey, expressed as formula (23), G 2 represents the flight process of the eagle in the process of tracking prey, that is, the flight speed of the eagle, which is expressed as formula (24).

[0131]

[0132] G 1 =2×rand-1 (23)

[0133]

[0134] Furthermore, the chaotic map is used to initialize the eagle population, which is expressed as:

[0135]

[0136] Where, X i The position of the i-th eagle, X i+1 Indicates the position of the (i+1)th eagle.

[0137] like Figure 2 As shown in Figure 2, the specific implementation process of the Tianying optimization algorithm is as follows:

[0138] Step 1: Initialize parameters and initialize the population of the Sky Eagle swarm;

[0139] Step 2: Calculate the fitness of each individual in the eagle group and find the optimal solution and average solution of the current population.

[0140] Step 3: Determine whether the number of iterations is greater than 2 / 3 of the total number of iterations. If the current number of iterations is greater than 2 / 3 of the total number of iterations, determine the random variable. If the random variable is greater than 0.5, use formula (14) to update the position of the eagle population; if the random variable is less than 0.5, use formula (12) to update the position of the eagle population; if the current number of iterations is less than 2 / 3 of the total number of iterations, determine the random variable. If the random variable is greater than 0.5, use formula (21) to update the position of the eagle population; if the random variable is less than 0.5, use formula (20) to update the position of the eagle population;

[0141] Step 4: If the number of iterations reaches the maximum, the optimization process ends and the current optimal solution is obtained.

[0142] The present invention provides reactive power to offshore wind farms through phase regulators, and uses the intelligent swarm optimization Sky Eagle algorithm to optimize the capacity of phase regulators, thereby improving the short-circuit ratio of wind farms and achieving peak shaving and valley filling of new energy wind farms.

[0143] The above are only preferred implementation modes of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. It should be pointed out that for technicians in this technical field, any modification or equivalent replacement of the technical solution of the present invention without creative work does not depart from the protection scope of the technical solution of the present invention.

Claims

1. An offshore wind farm phase regulator capacity configuration optimization algorithm, characterized in that: The offshore wind farm consists of 6 sub-wind farms, each of which consists of 16 offshore wind turbines, and each sub-wind farm is equipped with multiple phase regulators; the method is divided into two stages, the first stage aims to improve the short-circuit ratio of the offshore wind farm, and the second stage aims to suppress overvoltage; then the objective functions and constraints of the two stage objectives are established; finally, the Eagle swarm intelligent optimization algorithm is used to solve the objective function to obtain the capacity of the phase regulators that should be configured for each sub-wind farm in the offshore wind farm.

2. The offshore wind farm phase regulator capacity configuration optimization algorithm according to claim 1 is characterized in that: With the goal of improving the short-circuit ratio of offshore wind farms, the first objective function is to minimize the capacity of the phase regulator, which can be expressed as: In the formula, κ i represents the number of phase regulators configured in the i-th wind farm, S i The capacity of a single distributed phase regulator configured for the i-th wind farm.

3. The offshore wind farm phase regulator capacity configuration optimization algorithm according to claim 2 is characterized in that: With the goal of improving the short-circuit ratio, the constraints are the short-circuit ratio of the sub-wind farm, the short-circuit ratio of the offshore wind farm, the short-circuit ratio balance of the offshore wind farm, the phase-shifting quantity requirement and the power flow constraint; among which: (1) The constraint on the short-circuit ratio of an offshore wind farm is expressed as: R SCRi >R SCRi,min (2) In the formula, R SCRi represents the short-circuit ratio of the ith offshore wind farm, R SCRi,MIN is the minimum short-circuit ratio of the ith offshore wind farm, R SCRi It is expressed as: In the formula, S ac,i is the short-circuit capacity of the i-th wind farm, P RE i is the active power of the i-th wind farm, P RE j is the active power of the j-th wind farm, Z eqii is the equivalent self-impedance of the i-th wind farm, Z eqij is the equivalent mutual impedance between the i-th wind farm and the j-th wind farm; (2) The constraint on the short-circuit ratio of an offshore wind farm is expressed as: In the formula, R' SCRmin is the short-circuit ratio of the offshore wind farm, N is the number of sub-wind farms; (3) The short-circuit ratio balance constraint of the offshore wind farm is expressed as: In the formula, R SCRav is the average short-circuit ratio of all wind farms, R SCRD is the maximum value of the short-circuit ratio of the sub-wind farm; where R SCRav It can be expressed as: (4) The constraint condition for the number of phase regulators is expressed as: 0≤κ i ≤κ i,max (7) In the formula, κ i,max represents the upper limit of the number of phase regulators in the i-th wind farm; (5) The power flow constraint can be expressed as: Where P i represents the active power of the ith node in the power grid, Q i represents the reactive power of the ith node in the power grid; n is the number of nodes in the power grid; U i is the voltage of the ith node, U j represents the voltage of the jth node, G ij represents the real part of the element in the i-th row and j-th column of the grid admittance matrix, B ij represents the imaginary part of the element in the i-th row and j-th column of the grid admittance matrix, θ ij Represents the phase difference between the voltage at the i-th node and the voltage at the j-th node.

4. The offshore wind farm phase regulator capacity configuration optimization algorithm according to claim 3 is characterized in that: With the goal of suppressing overvoltage, the second objective function of correcting the capacity of the phase regulator is expressed as: Where N * represents the number of sub-wind farms that need to be equipped with phase regulators after the first stage of optimization, κ i * It indicates the number of phase regulators that need to be configured in the i-th wind farm after the first stage of optimization; K i is the number of phase regulators configured for the i-th wind farm after the second stage of optimization, and ρ is the penalty coefficient.

5. The offshore wind farm phase regulator capacity configuration optimization algorithm according to claim 4 is characterized in that: The goal is to suppress overvoltage, and the constraints are power flow constraints, transient time domain solution constraints, overvoltage constraints, and the number of phase regulators configured in each sub-wind farm; Among them, the power flow constraint is shown in formula (8), and the constraint on the number of phase regulators configured in each sub-wind farm is shown in formula (7); The transient time domain solution constraint is expressed as: In the formula, x is the state variable of the power grid, y is the algebraic variable of the power grid, f(x, y) represents the differential equation, g(x, y) represents the algebraic equation, and t represents time; the differential equation group represents the dynamic response process of each component in the power grid system, including phase regulators, offshore wind turbines, thermal power generators and filters; the algebraic equation group represents the power balance and current balance of each network node; The overvoltage constraint is expressed as: U min (t)≤U(t)≤U max (t) (11) Where U(t) represents the output voltage of the sub-wind farm, U min (t) represents the minimum output voltage of the sub-wind farm, U max (t) represents the maximum value of the output voltage of the sub-wind farm.

6. The offshore wind farm phase regulator capacity configuration optimization algorithm according to claim 5, characterized in that: The swarm intelligence optimization algorithm Sky Eagle algorithm is used to solve the optimal solution of equation (1) and equation (9); The hunting behaviors of the Sky Eagle Algorithm are divided into four types, namely, extended exploration, narrowed search range, expanded development, and narrowed development range. Among them, the diffusion search behavior is the vertical dive and high-altitude soaring behavior of the Sky Eagle, which is expressed as: Where X1 is the solution of the tth iteration, which is also the solution of the current iteration number, and X best (t) is the optimal solution up to the tth iteration, and the optimal solution is the solution closest to the prey position of the eagle; T is the maximum number of iterations, t is the current number of iterations; X M (t) represents the average value of all eagle positions at the tth iteration, that is, the average value of all solutions, which can be expressed as formula (13); rand is a random number between 0 and 1; Where N is the total number of eagles, X i (t) represents the position of the tth iteration of the i-th eagle in the eagle group; The behavior of the Skyhawk shrinking its range indicates the flight behavior of the Skyhawk's short-range gliding attack, which can be expressed as: X2=X best (t)×Levy(D)+X R (t)+(y-x)×rand (14) In the formula, Levy (D) represents the flight distribution function, which can be expressed as formula (15), D represents the dimensional space, X R (t) represents the random solution of a random eagle in the eagle group at the tth iteration, and R is a positive integer between 1 and N; x and y represent the shape parameters of the eagle during spiral flight, which can be expressed as formula (16) y=r×cos(θ),x=r×sin(θ) (16) Where s is a fixed coefficient, usually 0.01; μ and υ are random numbers, usually ranging from 0 to 1; β is a fixed coefficient, usually 1.5; σ can be expressed as (17); r and θ can be expressed as (19)-(20) respectively; r=r1+U×D1 (18) In the formula, r1 is a constant between 1 and 20, indicating the number of times the Skyhawk searches, U is a constant with a value of 0.00565, D1 is an integer with a value range of 1 to D; ω is a constant with a value of 0.005; The behavior of expanding development refers to the behavior of Skyhawk approaching and attacking the target, which is expressed as: X3=(X best (t)-X M (t))×α-rand+((UB-LB)×UB+LB)×δ (20) In the formula, α and δ are constants, and the common value is 0.

1. UB is the maximum value of the solution domain, and LB is the minimum value of the solution domain. The behavior of narrowing the development range refers to the behavior of the eagle attacking the prey according to the random action route of the prey, which is expressed as: X4=QF×X best -(G1×X(t)×rand)-G2×Levy(D)+rand×G1 (21) In the formula, the function QF can be expressed as formula (22), G1 represents the random movement of the prey, expressed as formula (23), G2 represents the flight process of the eagle in the process of tracking the prey, that is, the flight rate of the eagle, expressed as formula (24); G1=2×rand-1 (23) 7. The offshore wind farm phase regulator capacity configuration optimization algorithm according to claim 6, characterized in that: The chaotic map is used to initialize the eagle population, which is expressed as: Among them, Xi is the position of the i-th eagle, X i+1 Indicates the position of the (i+1)th eagle.

8. The offshore wind farm phase regulator capacity configuration optimization algorithm according to claim 7, characterized in that: The specific implementation process of the Tianying optimization algorithm is as follows: Step 1: Initialize parameters and initialize the population of the Sky Eagle swarm; Step 2: Calculate the fitness of each individual in the eagle group and find the optimal solution and average solution of the current population. Step 3: Determine whether the number of iterations is greater than 2 / 3 of the total number of iterations. If the current number of iterations is greater than 2 / 3 of the total number of iterations, determine the random variable. If the random variable is greater than 0.5, use formula (14) to update the position of the Sky Eagle population. If the random variable is less than 0.5, use formula (12) to update the position of the eagle population; if the current number of iterations is less than 2 / 3 of the total number of iterations, judge the random variable. If the random variable is greater than 0.5, use formula (21) to update the position of the eagle population; If the random variable is less than 0.5, then the position of the eagle population is updated using formula (20); Step 4: If the number of iterations reaches the maximum, the optimization process ends and the current optimal solution is obtained.

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