Black-start overvoltage suppression method for network-forming double-fed fan

Abstract

The invention discloses a black-start overvoltage suppression method for a network-forming type double-fed fan, and relates to the technical field of control, and the method comprises the steps: determining a phase pre-synchronization PI controller in the network type control of a double-fed fan; determining an objective function and constraint conditions; a traditional grey wolf algorithm is improved, a nonlinear convergence factor is adopted, and a grey wolf position updating formula is designed; setting PI parameters by adopting an improved grey wolf algorithm; and performing simulation verification on the improved algorithm effect. According to the invention, no-load switching-on overvoltage of the network-forming double-fed fan in the black start process can be effectively suppressed, and smooth completion of black start is ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of power grid control, and in particular relates to a method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine. Background Art

[0002] The grid-type doubly-fed wind turbine can establish voltage and frequency independently and can serve as a black start power supply during the black start process. However, the grid-type doubly-fed wind turbine is prone to overvoltage when it is switched on at no load, which can easily threaten the safety of the equipment and affect the frequency stability of the system. If the output phase of the unit can be controlled to be consistent with the grid phase before the unit is switched on at no load, the overvoltage value will be reduced. Therefore, a new control method needs to be designed to achieve a greater reduction in overvoltage. Summary of the invention

[0003] In order to solve the problems in the prior art, the present invention provides a method for suppressing overvoltage during black start of a grid-type doubly-fed wind turbine, which can optimize the kp and ki parameters in the PI controller and reduce the no-load closing overvoltage of the grid-type doubly-fed wind turbine.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The present invention provides a method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine, which specifically comprises the following steps:

[0006] S1. Determine the phase pre-synchronization PI controller in the grid control of the doubly fed wind mechanism;

[0007] S2, determine the objective function and constraints;

[0008] S3, improve the traditional gray wolf algorithm, adopt nonlinear convergence factor and design the gray wolf position update formula;

[0009] S4, use the improved grey wolf algorithm to adjust the PI parameters;

[0010] S5. Conduct simulation verification on the effect of the improved algorithm.

[0011] Preferably, in step S1, the specific method for determining the phase presynchronization PI controller in the grid type control of the doubly fed wind mechanism includes:

[0012] S11, the output phase angle θ of the grid-type doubly fed wind turbine and the reference phase angle θ ref The difference is input into the PI controller;

[0013] S12, PI controller optimizes the two parameters kp and ki by improving the grey wolf algorithm, where kp is the proportional coefficient and ki is the integral coefficient.

[0014] Preferably, in step S2, the objective function is determined by using a fitness function, and the objective function is established by a weighted sum of the square of the phase difference and the square of the frequency difference.

[0015] Preferably, in step S2, in order to ensure the stability of system operation, the following constraints are set:

[0016] Node voltage constraints:

[0017] U imin ≤U i ≤U imax (i=1,2,3,......,n)

[0018] In the formula, U imin , U imax are the voltage U at the i-th node i Lower and upper limits; n is the total number of nodes;

[0019] Branch current constraints:

[0020] I i ≤I imax (i=1,2,3,......,d)

[0021] In the formula, I imax is the i-th branch I i The upper limit of ; d is the total number of branches;

[0022] System frequency constraints

[0023] |ff N |≤0.5

[0024] Where, f is the system frequency, f N is the rated frequency of the system.

[0025] Preferably, the step S3 includes the following steps:

[0026] S31. Design an expression for the nonlinear convergence factor:

[0027] a=e t / T -1

[0028] In the formula, a is the convergence factor, t is the current number of iterations, and T is the maximum number of iterations;

[0029] S32. Improvements are made based on the traditional gray wolf position update formula as follows.

[0030]

[0031] Where, X 1 , X 2 , X 3are the positions of wolf α, wolf β, and wolf δ respectively, and m 1 、m 2 、m 3 is the weight coefficient.

[0032] Preferably, the weight coefficient m 1 、m 2 、m 3 It is expressed as follows:

[0033]

[0034] Where Fitness(α), Fitness(β), and Fitness(δ) represent the fitness values ​​of α wolf, β wolf, and δ wolf respectively.

[0035] Preferably, in step S4, the specific steps of using the improved grey wolf algorithm to adjust the PI parameters include:

[0036] S41, initialize the parameters of the Grey Wolf algorithm, and set the upper and lower limits of kp and ki in the PI regulator;

[0037] S42, calculating the fitness of all the gray wolf individuals in the initial population, and selecting the three best gray wolves according to the calculated fitness values, setting them as α wolf, β wolf, and δ wolf, which respectively represent the optimal solution, the second optimal solution, and the third optimal solution of the current population;

[0038] S43, iteratively update the population to ensure that the updated individual positions do not exceed the boundary, recalculate the fitness, and stop the iteration if the maximum number of iterations is reached, and output the optimal solution.

[0039] Preferably, in step 5, the improved algorithm effect is simulated and verified, which specifically includes the following steps:

[0040] S51, the grid-type double-fed wind turbine no-load closing voltage waveform obtained by simulation, the PI parameters obtained by improving the Grey Wolf algorithm are applied to the PI regulator, and the no-load closing overvoltage after phase pre-synchronization control is the smallest;

[0041] S52. The system frequency waveform obtained by simulation shows that after the grid-type double-fed wind turbine is switched on, the system frequency fluctuation under the control of the improved grey wolf algorithm is minimal.

[0042] Compared with the prior art, the present invention has the following beneficial effects: the present invention can effectively suppress the no-load closing overvoltage of the grid-type doubly fed wind turbine during the black start process, and effectively reduce the system frequency fluctuation, thereby ensuring the smooth completion of the black start. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a block diagram of the PI control pre-synchronization based on the improved grey wolf algorithm of the present invention;

[0044] Figure 2 This is a no-load closing voltage waveform diagram of the grid-type doubly-fed wind turbine described in the present invention;

[0045] Figure 3 It is a frequency waveform diagram of the system described in the present invention. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0047] This embodiment provides a method for suppressing overvoltage during black start of a grid-connected doubly-fed wind turbine, comprising the following steps:

[0048] Step 1: Determine the phase presynchronization PI controller in the grid control of the doubly fed wind power mechanism.

[0049] Phase angle pre-synchronization control is performed before the grid-type double-fed wind turbine is closed at no-load. When the phase is synchronized, the circuit is closed. Therefore, the PI controller that controls phase synchronization is selected for parameter setting. It is necessary to collect the output phase angle θ and the grid-side phase angle θ of the grid-type double-fed wind turbine. ref The difference is input into the PI controller. The specific implementation steps are as follows:

[0050] S11, Figure 1 This is the PI control pre-synchronization block diagram based on the improved grey wolf algorithm. The output phase angle θ of the grid-type double-fed wind turbine and the reference phase angle θ ref The difference is input into the PI controller. m , P e are the given value and actual output value of active power respectively, J is the moment of inertia, D P is the damping coefficient and ω is the angular frequency.

[0051] S12, PI controller can be expressed by formula (1), kp is the proportional coefficient, ki is the integral coefficient, and the two parameters kp and ki are optimized by the improved grey wolf algorithm.

[0052]

[0053] Step 2: Determine the objective function and constraints. The specific method is as follows:

[0054] S21. Determine the objective function, i.e., the fitness function, and establish the objective function by taking the weighted sum of the square of the phase difference and the square of the frequency difference, as shown in formula (2).

[0055] Fitness=n 1 (θ-θ ref ) 2 +n2 (ff N ) 2 (2)

[0056] Where n 1 、n 2 is the weighting coefficient, f is the system frequency, f N is the rated frequency of the system.

[0057] S22. Determine the constraints. To ensure the stability of the system operation, set the following constraints:

[0058] (1) Node voltage constraints

[0059] U imin ≤U i ≤U imax (i=1,2,3,......,n) (3)

[0060] In the formula, U imin , U imax are the voltage U at the i-th node i Lower and upper bounds; n is the total number of nodes.

[0061] (2) Branch current constraints

[0062] I i ≤I imax (i=1,2,3,......,d)(4)

[0063] In the formula, I imax is the i-th branch I i The upper limit of ; d is the total number of branches.

[0064] (3) System frequency constraints

[0065] ff N |≤0.5 (5)

[0066] Step 3:

[0067] The traditional gray wolf algorithm is improved by adopting a nonlinear convergence factor and designing the gray wolf position update formula. The specific implementation steps are as follows:

[0068] S31. The traditional grey wolf algorithm adopts a linear convergence factor decreasing from 2 to 0. This paper designs an expression of a nonlinear convergence factor as shown in formula (6), where a is the convergence factor, t is the current number of iterations, and T is the maximum number of iterations.

[0069] a=e t / T -1(6)

[0070] S32. Improvements are made based on the traditional gray wolf position update formula as follows.

[0071]

[0072] Where, X 1 , X 2 , X 3 are the positions of wolf α, wolf β, and wolf δ respectively, and m 1 、m 2 、m 3 is the weight coefficient, which can be expressed by formula (8).

[0073]

[0074] In the formula, Fitness(α), Fitness(β), and Fitness(δ) represent the fitness values ​​of α wolf, β wolf, and δ wolf respectively. Based on the traditional linear convergence factor, a nonlinear expression is designed for the convergence factor to improve the accuracy; based on the traditional gray wolf position update formula, an improvement is made to highlight the optimal solution.

[0075] Step 4: Use the improved grey wolf algorithm to adjust the PI parameters. The specific implementation steps are as follows:

[0076] S41, initialize the parameters of the gray wolf algorithm, set the upper and lower limits of kp and ki to 0-100, the maximum number of iterations to 200, and set the population size of the gray wolf algorithm to 100. Randomly generate a set of PI parameters in the search space as the initial population of the gray wolf algorithm.

[0077] S42. During the iteration process, the position of the wolf pack at the next iteration can be expressed by equations (9)-(12).

[0078] X(t+1)=X p (t)-H·D (9)

[0079] D=|C×X p (t)-X(t)| (10)

[0080] C=2ρ 1 (11)

[0081] H=a(2ρ 2 -1) (12)

[0082] Where X P (t) represents the current prey position, X(t) represents the current wolf pack position, C is the swing factor, ρ 1 , 2 are all random numbers between 0 and 1, D is the distance between the wolf and its prey, and the convergence factor a is expressed by formula (6).

[0083] S43, calculate the fitness of all the gray wolf individuals in the initial population, select the three best gray wolves according to the calculated fitness values ​​and set them as α wolf, β wolf, and δ wolf, which represent the optimal solution, suboptimal solution and third optimal solution of the current population respectively, and the remaining wolves are ω wolf; iteratively update the population to ensure that the updated individual position does not exceed the boundary, recalculate the fitness, stop the iteration if the maximum number of iterations is reached, and output the optimal solution. Among them, the distance between α wolf, β wolf, δ wolf and ω wolf can be expressed by formula (13).

[0084] D α =|C 1 ·X α (t)-X(t)|

[0085] D β =|C 2 ·X β (t)-X(t)|

[0086] D δ =|C 3 ·X s (t)-X(t)| (13)

[0087] Where, X α (t), X β (t), X δ (t) represent the positions of α wolf, β wolf, and δ wolf respectively, C 1 , C 2 , C 3 is a random value. The position of ω wolf is updated according to formula (13), and the update formula is expressed by formula (14) and formula (7).

[0088] X 1 =X α (t)-H 1 ·D α

[0089] X 2 =X β (t)-H 2 ·D β

[0090] X 3 =X δ (t)-H 3 ·D δ (14)

[0091] Step 5: Simulate and verify the effect of the improved algorithm. Input the kp and ki values ​​obtained by the improved Grey Wolf algorithm into the PI regulator for simulation verification. Observe the no-load closing voltage and system frequency waveform of the grid-type double-fed wind turbine. Compare the waveforms under the traditional Grey Wolf algorithm to verify the effectiveness of the improved Grey Wolf algorithm. The specific verification method is as follows:

[0092] S51, the simulated no-load closing voltage waveform of the grid-type double-fed wind turbine is as follows Figure 2 As shown, by comparing the traditional Grey Wolf algorithm and the algorithm without intelligent algorithm, it can be seen that when the PI parameters obtained by the improved Grey Wolf algorithm are applied to the PI regulator, the no-load closing overvoltage after phase pre-synchronization control is the smallest. Compared with the conventional Grey Wolf algorithm, the overvoltage suppression effect is more obvious.

[0093] S52, the system frequency waveform obtained by simulation is as follows Figure 3 As shown, by comparing the traditional Grey Wolf algorithm and the algorithm without intelligent algorithm, it can be seen that after the grid-type double-fed wind turbine is closed, the system frequency fluctuation under the control of the improved Grey Wolf algorithm is the smallest, which is significantly better than the system frequency under the control of the conventional Grey Wolf algorithm.

[0094] The present invention provides a black start overvoltage suppression method for a grid-type doubly fed wind turbine based on an improved grey wolf algorithm, which can optimize the kp and ki parameters in a PI controller, reduce the no-load closing overvoltage of the grid-type doubly fed wind turbine, and effectively reduce system frequency fluctuations, and has good practical engineering application value.

[0095] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine, characterized in that: The following steps are involved: S1. Determine the phase pre-synchronization PI controller in the grid control of the doubly fed wind mechanism; S2, determine the objective function and constraints; S3, improve the traditional gray wolf algorithm, adopt nonlinear convergence factor and design the gray wolf position update formula; S4, use the improved grey wolf algorithm to adjust the PI parameters; S5. Conduct simulation verification on the effect of the improved algorithm.

2. A method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine according to claim 1, characterized in that: In step S1, the specific method of determining the phase pre-synchronization PI controller in the grid type control of the doubly fed wind mechanism includes: S11, the output phase angle θ of the grid-type doubly fed wind turbine and the reference phase angle θ ref The difference is input into the PI controller; S12, PI controller optimizes the two parameters kp and ki by improving the grey wolf algorithm, where kp is the proportional coefficient and ki is the integral coefficient.

3. A method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine according to claim 1, characterized in that: In step S2, the objective function is determined by using a fitness function, and the objective function is established by weighted sum of the square of the phase difference and the square of the frequency difference.

4. A method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine according to claim 1, characterized in that: In step S2, in order to ensure the stability of system operation, the following constraints are set: Node voltage constraints: U imin ≤U i ≤U imax (i=1,2,3,......,n) In the formula, U imin , U imax are the voltage U at the i-th node i Lower and upper limits; n is the total number of nodes; Branch current constraints: I i ≤I imax (i=1,2,3,......,d) In the formula, I imax is the i-th branch I i The upper limit of ; d is the total number of branches; System frequency constraints |f-f N |≤0.5 Where, f is the system frequency, f N is the rated frequency of the system.

5. A method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine according to claim 1, characterized in that: The step S3 includes the following steps: S31. Design an expression for the nonlinear convergence factor: a=e t / T -1 In the formula, a is the convergence factor, t is the current number of iterations, and T is the maximum number of iterations; S32. Improvements are made based on the traditional gray wolf position update formula as follows. Where X1, X2, and X3 are the positions of α wolf, β wolf, and δ wolf respectively, and m1, m2, and m3 are weight coefficients.

6. A method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine according to claim 5, characterized in that: The weight coefficients m1, m2, and m3 are expressed by the following formula: Where Fitness(α), Fitness(β), and Fitness(δ) represent the fitness values ​​of α wolf, β wolf, and δ wolf respectively.

7. A method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine according to claim 1, characterized in that: In step S4, the specific steps of using the improved grey wolf algorithm to adjust the PI parameters include: S41, initialize the parameters of the Grey Wolf algorithm, and set the upper and lower limits of kp and ki in the PI regulator; S42, calculating the fitness of all the gray wolf individuals in the initial population, and selecting the three best gray wolves according to the calculated fitness values, setting them as α wolf, β wolf, and δ wolf, which respectively represent the optimal solution, the second optimal solution, and the third optimal solution of the current population; S43, iteratively update the population to ensure that the updated individual positions do not exceed the boundary, recalculate the fitness, and stop the iteration if the maximum number of iterations is reached, and output the optimal solution.

8. A method for suppressing overvoltage during black start of a grid-connected double-fed wind turbine according to claim 1, characterized in that: In step 5, the improved algorithm effect is simulated and verified, which specifically includes the following steps: S51, the grid-type double-fed wind turbine no-load closing voltage waveform obtained by simulation, the PI parameters obtained by improving the Grey Wolf algorithm are applied to the PI regulator, and the no-load closing overvoltage after phase pre-synchronization control is the smallest; S52. The system frequency waveform obtained by simulation shows that after the grid-type double-fed wind turbine is switched on, the system frequency fluctuation under the control of the improved grey wolf algorithm is minimal.

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