Method and device for determining frequency modulation exit time of fan, and computer device

Abstract

The embodiment of the application discloses a method for determining the exit time of frequency modulation of a fan, which comprises the following steps: establishing a system model comprising a hydroelectric generator model, a wind turbine model and a frequency response model based on a state space method; obtaining a piecewise linearization model based on a preset transformation and rewriting method; determining system frequency expressions of a frequency modulation stage and a speed recovery stage according to the piecewise linearization model, and determining the lowest frequency values corresponding to the two stages and the time corresponding to the lowest frequency values according to the two system frequency expressions; establishing an optimization model based on the time range of the system frequency modulation stage with the second lowest frequency value corresponding to the second frequency drop as the target; and determining the optimal exit time according to the optimization model. By establishing the system model and the optimization model, the optimal exit time of the fan frequency modulation is determined, so that the influence on the normal operation of the system can be reduced when the fan exits the frequency modulation at the optimal exit time, and the safe operation of the system is facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power frequency modulation, and in particular to a method and device for determining the exit time of wind turbine frequency modulation, and a computer device. BACKGROUND

[0002] With the development of new energy industry, wind power is becoming a hot trend in the world. The principle of wind power is to convert wind energy into mechanical energy, and then convert the mechanical energy into electrical energy. Wind turbines used to realize wind power generation need to have certain frequency regulation capability to improve the frequency stability of the power system, so wind turbines will actively participate in frequency regulation.

[0003] A common way for wind turbines to participate in frequency regulation is virtual inertia control. When wind turbines participate in frequency modulation through virtual inertia control, they need to exit frequency modulation after a period of time and enter the speed recovery stage. In this process, there will be a power drop, which will cause a secondary frequency drop. In the prior art, due to improper selection of the exit time of wind turbine frequency modulation, the minimum frequency during the secondary frequency drop may be lower than the minimum frequency during the initial frequency drop, which will have a greater impact on the normal operation of the system, which will be detrimental to the safe operation of the system.

[0004] Therefore, determining the optimal exit time of wind turbine frequency modulation has become a problem to be solved at the present stage. SUMMARY

[0005] Therefore, it is necessary to propose a method and device for determining the exit time of wind turbine frequency modulation and a computer device to reduce the impact of wind turbine exit frequency modulation on the safe operation of the system.

[0006] In a first aspect, the present application provides a method for determining the exit time of wind turbine frequency modulation, comprising:

[0007] establishing a system model including a hydroelectric generator model, a wind turbine model and a frequency response model based on a state space method;

[0008] transforming the system model based on a preset transformation method, and rewriting a segmented linearization model with the exit time of wind turbine frequency modulation as a boundary;

[0009] determining a first expression of system frequency in the frequency modulation stage and a second expression of system frequency in the speed recovery stage according to the segmented linearization model;

[0010] determining a first time corresponding to the first minimum frequency corresponding to the first frequency drop in the frequency modulation stage according to the first expression, and determining a second minimum frequency corresponding to the second frequency drop in the speed recovery stage and a second time corresponding to the second minimum frequency according to the second expression;

[0011] A second frequency minimum value corresponding to a frequency quadratic drop is taken as a target, and an optimization model is established based on a time range of a frequency modulation stage of the system;

[0012] An optimal exit time is determined according to the optimization model.

[0013] Optionally, a third expression of the system model is:

[0014]

[0015] Wherein, T 11 ∈R n×n , T 12 ∈R n×m , n is a state variable number, m is an algebraic variable number, Δx is an n-dimensional column vector composed of state variables, Δz is an m-dimensional column vector composed of algebraic variables, J 11 , J 12 , J 21 , J 22 , d1 and d2 are preset matrices of corresponding dimensions, and Δu is an input quantity.

[0016] Parameters in the water turbine model, the wind turbine model and the frequency response model are distributed in T 11 , T 12 , Δx, Δz, J 11 , J 12 , J 21 , J 22 , d1, d2 and Δu in the third expression.

[0017] Optionally, the system model is transformed based on the preset transformation method, and a segmented linearization model is obtained by rewriting based on a wind turbine frequency modulation exit time as a boundary, and the segmented linearization model specifically comprises:

[0018] The system model is transformed by using the elimination method to obtain a transformed system model.

[0019] The transformed system model is rewritten based on a wind turbine frequency modulation exit time as a boundary to obtain a segmented linearization model.

[0020] Optionally, the segmented linearization model specifically comprises:

[0021]

[0022] Wherein, A1 and b1 are a system matrix and an input matrix corresponding to a frequency modulation stage, A2 and b2 are a system matrix and an input matrix corresponding to a speed recovery stage, t0 is a time when a load disturbance occurs, t e is a wind turbine frequency modulation exit time, and t1 is a time when a time range studied ends.

[0023] Optionally,

[0024] Setting The first expression of the system frequency in the frequency modulation stage is:

[0025]

[0026] Wherein, f n is the rated frequency, e n ∈R n×1 , and e n The nth bit is 1, and the rest are 0, A1 is the system matrix corresponding to the frequency modulation stage, V1 is the matrix composed of the right eigenvectors of matrix A1, Λ1 is the diagonal matrix composed of the eigenvalues of matrix A1, t0 is the time when the load disturbance occurs, is The value at t0, and has b1 is the input matrix corresponding to the frequency modulation stage, and Δu is the input quantity;

[0027] Setting The second expression of the system frequency in the speed recovery stage is:

[0028]

[0029] Wherein, A2 is the system matrix corresponding to the speed recovery stage, V2 is the matrix composed of the right eigenvectors of matrix A2, Λ2 is the diagonal matrix composed of the eigenvalues of matrix A2, t e is the time when the frequency modulation of the fan is exited, is The value at t e , and has b2 is the input matrix corresponding to the speed recovery stage.

[0030] Optionally, the first time corresponding to the first frequency minimum value corresponding to the frequency drop once in the frequency modulation stage is determined according to the first expression, and the second frequency minimum value and the second time corresponding to the second frequency minimum value corresponding to the frequency drop twice in the speed recovery stage are determined according to the second expression, specifically comprising:

[0031] Derive the first expression to obtain the first derivative expression corresponding to the first expression;

[0032] Based on the first derivative expression, the first time corresponding to the first frequency minimum value corresponding to the frequency drop once in the frequency modulation stage is calculated;

[0033] Derive the second expression to obtain the second derivative expression corresponding to the second expression;

[0034] Based on the second derivative expression, a second time corresponding to a second frequency minimum value corresponding to a frequency second drop in the speed recovery stage is calculated;

[0035] The second time is substituted into the second expression to calculate the second frequency minimum value corresponding to the frequency second drop in the speed recovery stage.

[0036] Optionally,

[0037] The objective function of the optimization model includes:

[0038]

[0039] The optimization model further includes a constraint condition, and the constraint condition specifically includes:

[0040]

[0041] Wherein, f n is the rated frequency, e n ∈R n×1 , and e n The nth bit is 1, and the rest are 0, A2 is the system matrix corresponding to the speed recovery stage, V2 is the matrix composed of the right eigenvectors of the matrix A2, Λ2 is the diagonal matrix composed of the eigenvalues of the matrix A2, t n1 is a first time corresponding to a first frequency minimum value corresponding to a frequency first drop in the frequency modulation stage, t n2 is a second time corresponding to a second frequency minimum value corresponding to a frequency second drop in the speed recovery stage, t e is the time when the fan frequency modulation exits, is The value at t e , and has b2 is the input matrix corresponding to the speed recovery stage, and Δu is the input quantity.

[0042] Optionally, the determination of the optimal exit time according to the optimization model specifically includes:

[0043] A time interval [t n1 +Δt, t n1 +i] is set, and the exit time t e is sequentially taken in the time interval according to the increment Δt;

[0044] The value of the exit time t e is substituted into the equality constraint in the constraint condition to obtain the second time t n2 corresponding to the second frequency minimum value corresponding to the frequency second drop in the speed recovery stage.

[0045] The te and the t e corresponding to the t n2 is substituted into the objective function, to obtain a corresponding objective function value f n2 ;

[0046] The objective function values f n2 are compared, to determine the optimal exit time within the time interval.

[0047] In a second aspect, an embodiment of the present application provides a device for determining a wind turbine frequency modulation exit time, the device comprising:

[0048] A first modeling module is configured to establish a system model comprising a hydroelectric generator model, a wind turbine model, and a frequency response model based on a state space method;

[0049] A transformation and rewriting module is configured to transform the system model based on a preset transformation method, and rewrite the system model to obtain a piecewise linearization model based on the wind turbine frequency modulation exit time as a boundary;

[0050] A determination expression module is configured to determine a first expression of a system frequency in a frequency modulation stage and a second expression of a system frequency in a speed recovery stage according to the piecewise linearization model;

[0051] A calculation module is configured to determine a first time corresponding to a first frequency minimum value corresponding to a first frequency drop in the frequency modulation stage according to the first expression, and determine a second frequency minimum value corresponding to a second frequency drop in the speed recovery stage and a second time corresponding to the second frequency minimum value according to the second expression;

[0052] A second modeling module is configured to establish an optimization model based on a time range of the system frequency modulation stage, with the second frequency minimum value corresponding to the second frequency drop as a target;

[0053] A determination module is configured to determine an optimal exit time according to the optimization model.

[0054] In a third aspect, an embodiment of the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to cause the processor to perform the following steps:

[0055] A system model comprising a hydroelectric generator model, a wind turbine model, and a frequency response model is established based on a state space method;

[0056] The system model is transformed based on a preset transformation method, and a piecewise linearization model is rewritten based on the wind turbine frequency modulation exit time as a boundary;

[0057] determine a first expression of system frequency in the frequency modulation stage and a second expression of system frequency in the speed recovery stage according to the piecewise linearization model;

[0058] determine a first time corresponding to the first minimum frequency value corresponding to the first frequency drop in the frequency modulation stage according to the first expression, and determine a second minimum frequency value corresponding to the second frequency drop in the speed recovery stage and a second time corresponding to the second minimum frequency value according to the second expression;

[0059] establish an optimization model based on the time range of the system frequency modulation stage, with the target of improving the second minimum frequency value corresponding to the second frequency drop;

[0060] determine the optimal exit time according to the optimization model.

[0061] The embodiment of the present application has the following beneficial effects:

[0062] The present application provides a method for determining the exit time of wind turbine frequency modulation, which comprises the following steps: establishing a system model comprising a hydroelectric generator model, a wind turbine model and a frequency response model based on a state space method; transforming the system model based on a preset transformation method, and rewriting the transformed system model to obtain a piecewise linearization model with the exit time of wind turbine frequency modulation as a boundary; determining a first expression of system frequency in the frequency modulation stage and a second expression of system frequency in the speed recovery stage according to the piecewise linearization model; determining a first time corresponding to the first minimum frequency value corresponding to the first frequency drop in the frequency modulation stage and a second minimum frequency value corresponding to the second frequency drop in the speed recovery stage and a second time corresponding to the second minimum frequency value according to the two system frequency expressions; establishing an optimization model based on the time range of the system frequency modulation stage, with the target of improving the second minimum frequency value corresponding to the second frequency drop; and determining the optimal exit time according to the optimization model. By establishing the system model and the optimization model, the optimal exit time of wind turbine frequency modulation is determined, so that the wind turbine can exit the frequency modulation at the optimal exit time, thereby reducing the impact on the normal operation of the system and being conducive to the safe operation of the system. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0064] wherein:

[0065] Figure 1 a flowchart of the method for determining the exit time of wind turbine frequency modulation in the embodiments of the present application;

[0066] Figure 2 FIG. 1 is a flowchart of a method for determining the exit time of the frequency modulation of the wind turbine according to an embodiment of the present application;

[0067] Figure 3 FIG. 2 is a flowchart of a method for determining the optimal exit time according to the established optimization model according to an embodiment of the present application;

[0068] Figure 4 FIG. 3 is a structure diagram of a simulation model according to an embodiment of the present application;

[0069] Figure 5 FIG. 4 is a system frequency curve of the wind turbine at different exit times of the frequency modulation according to an embodiment of the present application;

[0070] Figure 6 FIG. 5 is an internal structure diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0072] In the embodiments of the present application, the exit time of the frequency modulation of the wind turbine is taken as a demarcation line to divide the frequency modulation of the wind turbine into two stages: a frequency modulation stage and a speed recovery stage. The frequency drop in the frequency modulation stage is a frequency first drop, and the frequency drop in the speed recovery stage is a frequency second drop.

[0073] Please refer to Figure 1 FIG. 1 is a flowchart of a method for determining the exit time of the frequency modulation of the wind turbine according to an embodiment of the present application. The method comprises:

[0074] Step 101: establishing a system model comprising a hydroelectric generator model, a wind turbine model and a frequency response model based on a state space method;

[0075] Step 102: transforming the system model based on a preset transformation method, and rewriting to obtain a piecewise linearization model with the exit time of the frequency modulation of the wind turbine as a demarcation line;

[0076] Step 103: determining a first expression of the system frequency in the frequency modulation stage and a second expression of the system frequency in the speed recovery stage according to the piecewise linearization model;

[0077] In step 104, a first time instant corresponding to a first frequency minimum value of a first frequency drop in the frequency modulation stage is determined according to a first expression, and a second frequency minimum value and a second time instant corresponding to the second frequency minimum value of a second frequency drop in the speed recovery stage are determined according to a second expression;

[0078] In step 105, an optimization model is established based on a time range of the system frequency modulation stage, with the second frequency minimum value corresponding to the second frequency drop as a target;

[0079] In step 106, an optimal exit time instant is determined according to the optimization model.

[0080] In a feasible implementation, the third expression of the system model in step 101 is a combination of expressions of a hydroelectric unit model, a wind turbine model and a frequency response model. The expressions of the hydroelectric unit model, the wind turbine model and the frequency response model will be introduced respectively in the embodiments of the present application.

[0081] Specifically, the hydroelectric unit model is introduced first, which includes a regulation system model, an electro-hydraulic servo system model and a prime mover model. The regulation system model is:

[0082]

[0083] wherein, K W is a frequency deviation amplification factor; T1 is a time constant; K P1 , K I1 and K D1 are regulation system proportion, integral and differential coefficients respectively; b P is a regulation coefficient; Δx1, Δx2, Δx3, Δx D1 and Δx I1 are intermediate variables; and Δf is a frequency change amount unit value.

[0084] The electro-hydraulic servo system model is:

[0085]

[0086] wherein, K P2 is an electro-hydraulic servo system proportion coefficient; T2 and T OC are time constants; and Δx4, Δx5, Δx6 and Δx7 are intermediate variables.

[0087] The prime mover model is:

[0088]

[0089] wherein, T W is a water hammer effect time constant; and ΔP h is a water turbine output power change amount unit value.

[0090] Secondly, the wind turbine model is introduced, which includes:

[0091]

[0092] where Δω is the wind turbine speed variation; ΔP m is the wind turbine mechanical power variation unit; ΔP w is the wind turbine output power variation unit; P wn is the wind turbine rated power; k p and k d are the proportional term and differential term coefficients of the wind turbine virtual inertia control; J w is the wind turbine rotational inertia; a, b, c, d and e are linearization coefficients.

[0093] where the specific expressions of the linearization coefficients a, b, c, d and e are:

[0094]

[0095] where ρ is the air density; R is the wind wheel radius; v0 is the wind speed; C p is the wind energy utilization coefficient, which is usually expressed by an empirical formula; k m is the maximum power tracking coefficient; ω0 is the wind turbine speed.

[0096] Finally, the frequency response model is introduced, which includes:

[0097]

[0098] where ΔP d is the load power variation unit; H s is the system equivalent inertia time constant; D s is the system equivalent damping coefficient; k h is the proportion of hydropower; k w is the proportion of wind power.

[0099] The specific calculation formulas of H s , D s , k h and k w are respectively:

[0100]

[0101] where H h is the hydropower unit inertia time constant; N h is the number of hydropower units; P hn is the rated power of hydropower units; D h is the damping coefficient of hydropower units; N wThe number of wind turbines.

[0102] The third expression of the system model is obtained by synthesizing and arranging the expressions of the above three models: the hydroelectric generator model, the wind turbine model and the frequency response model.

[0103]

[0104] Wherein, T 11 ∈R n×n , T 12 ∈R n×m , n is the number of state variables, m is the number of algebraic variables, Δx is an n-dimensional column vector composed of state variables, Δz is an m-dimensional column vector composed of algebraic variables, J 11 , J 12 , J 21 , J 22 , d1, d2 are preset matrices of corresponding dimensions, and Δu is an input variable.

[0105] It can be understood that the parameters in the hydroelectric generator model, the wind turbine model and the frequency response model are distributed in T 11 , T 12 , Δx, Δz, J 11 , J 12 , J 21 , J 22 , d1, d2 and Δu in the third expression of the system model. Δx specifically includes the unitary value of the change rate of the output power of the hydraulic turbine ΔP h , the intermediate variables Δx I1 , Δx4, Δx5 and Δx7, the change amount of the speed of the wind turbine Δω, and the unitary value of the frequency change Δf; Δz specifically includes the intermediate variables Δx1, Δx D1 , Δx2, Δx3 and Δx6, the unitary value of the change amount of the output power of the wind turbine ΔP w ; Δu = ΔP d ; T 11 , T 12 , J 11 , J 12 , J 21 , J 22 , d1 and d2 contain the remaining parameters other than the above-mentioned parameters.

[0106] It should be noted that the specific steps of rewriting the piecewise linearization model based on the preset transformation method in step 102 include:

[0107] Step 1021, using the elimination method to eliminate and transform the system model to obtain the transformed system model;

[0108] Step 1022, taking the fan frequency modulation exit time as the demarcation line, rewriting the transformed system model to obtain a segmented linearization model.

[0109] Wherein, step 1021, the transformed system model is:

[0110] Wherein A is a system matrix, b is an input matrix, and the specific calculation formula of A and b is:

[0111]

[0112] It can be understood that when the fan participates in frequency modulation, the virtual inertia control parameters in the wind turbine model: the proportional term coefficient k p of the fan virtual inertia control and the differential term coefficient k d of the fan virtual inertia control are not 0; when the fan exits frequency modulation and enters the speed recovery stage, the proportional term coefficient k p of the fan virtual inertia control and the differential term coefficient k d of the fan virtual inertia control are 0. And since k p and k d are included in the calculation formula of A and b, A and b are different in the frequency modulation stage and the speed recovery stage.

[0113] Based on this, in step 1022, the transformed system model is segmented and rewritten with the fan frequency modulation exit time as the demarcation line to obtain a segmented linearization model:

[0114]

[0115] Wherein, A1 and b1 are the system matrix and input matrix corresponding to the frequency modulation stage, A2 and b2 are the system matrix and input matrix corresponding to the speed recovery stage, t0 is the load disturbance occurrence time, t e is the fan frequency modulation exit time, and t1 is the end time of the time range studied.

[0116] In a feasible implementation, in step 103, the first expression of the system frequency in the frequency modulation stage and the second expression of the system frequency in the speed recovery stage are determined according to the segmented linearization model, specifically,

[0117] In the frequency modulation stage: in order to facilitate calculation, set to obtain the first expression of the system frequency in the frequency modulation stage:

[0118] Wherein, f n is the rated frequency, and f n = 50 Hz, e n ∈R n×1 , and e nThe nth bit is 1, and the rest of the bits are 0, A1 is the system matrix corresponding to the frequency modulation stage, V1 is the matrix composed of the right eigenvectors of the matrix A1, Λ1 is the diagonal matrix composed of the eigenvalues of the matrix A1, t0 is the time when the load disturbance occurs, is The value at t0, and b1 is the input matrix corresponding to the frequency modulation stage, and Δu is the input quantity.

[0119] In the speed recovery stage: in order to facilitate calculation, set Get the second expression of the system frequency in the speed recovery stage:

[0120] Wherein, A2 is the system matrix corresponding to the speed recovery stage, V2 is the matrix composed of the right eigenvectors of the matrix A2, Λ2 is the diagonal matrix composed of the eigenvalues of the matrix A2, t e is the time when the frequency modulation of the fan is exited, is The value at t e , and b2 is the input matrix corresponding to the speed recovery stage.

[0121] It can be understood that the two system frequency expressions obtained according to step 103 can be used to calculate the first time corresponding to the first frequency minimum value corresponding to the frequency drop once in the frequency modulation stage, and the second frequency minimum value and the second time corresponding to the second frequency minimum value corresponding to the frequency drop twice in the speed recovery stage in step 104. The total steps include:

[0122] i. Derive the derivative expression of the system frequency from the system frequency expression;

[0123] ii. Calculate the time corresponding to the frequency minimum value in the corresponding stage based on the derivative expression;

[0124] iii. Substitute the time corresponding to the frequency minimum value into the frequency expression to obtain the frequency minimum value.

[0125] In step ii, the time corresponding to the frequency minimum value in the corresponding stage is calculated based on the derivative expression by Newton method or dichotomy.

[0126] Specifically, please refer to Figure 2 , the flowchart for calculating according to the first expression and the second expression in the embodiments of the application, the calculation steps of the frequency modulation stage are:

[0127] Step 1041, derive the first derivative expression corresponding to the first expression by differentiating the first expression;

[0128] The first derivative expression is:

[0129] Step 1042, based on the first derivative expression, the first time corresponding to the first frequency minimum value in the frequency drop stage is calculated;

[0130] The equation corresponding to the first derivative expression equal to zero is The first time t corresponding to the first frequency minimum value is obtained by solving the equation by Newton method or dichotomy method. n1 And the initial value is given when solving the equation, because in the process of frequency modulation of the fan, the frequency reaches the minimum value within 3-5s after the load disturbance occurs, so the initial value is selected within this time range.

[0131] The calculation steps of the speed recovery stage are:

[0132] Step 1043, the second derivative expression corresponding to the second expression is obtained by differentiating the second expression;

[0133] The second derivative expression is:

[0134] Step 1044, based on the second derivative expression, the second time corresponding to the second frequency minimum value in the frequency drop stage is calculated;

[0135] Similarly, the second time t corresponding to the second frequency minimum value in the frequency drop stage is obtained by solving the equation corresponding to the second derivative expression equal to zero by Newton method or dichotomy method. n2

[0136] Step 1045, the second time is substituted into the second expression to calculate the second frequency minimum value corresponding to the frequency drop in the speed recovery stage.

[0137] The second time t corresponding to the second frequency minimum value is substituted into the second expression to obtain the second frequency minimum value f corresponding to the frequency drop. n2 n2

[0138] After obtaining the time and frequency minimum value corresponding to the frequency minimum value in the two stages, step 105 of establishing an optimization model can be executed, and the optimization model is established to improve the second frequency minimum value corresponding to the frequency drop as the target.

[0139] Based on this target, the objective function of the optimization model is:

[0140]

[0141] Wherein, the variables t n2 and t e ​​​The equation that corresponds to the second derivative expression of the system frequency during the aforementioned speed recovery phase being equal to zero needs to be satisfied.

[0142] Considering the time range of primary frequency regulation, which is typically within 30 seconds, constraints are established for the optimization model to ensure the wind turbine meets the time range of primary frequency regulation, i.e., the time range of the system frequency regulation phase.

[0143]

[0144] It is understandable that by observing the objective function and constraints of the optimization model, it is clear that: [the objective function and constraints...] In t e value of time The objective function f is proportional to the input Δu. n Since Δu is a constant, the solution obtained when solving the optimization model depends only on the specific system parameters and is independent of the input quantity Δu. Therefore, when solving the optimization model, Δu can be directly set to 1.

[0145] Based on the optimization model and Δu = 1, proceed to step 106: determine the optimal exit time according to the established optimization model. Please refer to [link / reference]. Figure 3 The diagram below illustrates the method for determining the optimal exit time based on the established optimization model in this embodiment of the application, and specifically includes the following steps:

[0146] Step 1061, set the time interval [t] n1 +Δt,t n1 +i], Exit time t e The values ​​are taken sequentially according to the increment Δt within the time interval.

[0147] Define a time interval with a relatively small time interval, here the time interval is [t n1 +Δt,t n1 +i], in this embodiment, Δt=0.5, i=30.

[0148] Among them, t e The value of is continuously updated within the time interval, for example, t e We can take t separately n1 +Δt、t n1 +2Δt、t n1 +3Δt、......、t n1 +30.

[0149] Step 1062, exit time t e Substitute the value into the equality constraints in the constraint conditions Solving for the second time t corresponding to the second minimum frequency value corresponding to the second frequency drop, we obtain the second time t. n2 .

[0150] Update t in step 1061 e The value of is substituted into the constraints of the optimization model, specifically into the equality constraints. In this case, the equation will be transformed into a univariate nonlinear equation. Solving this univariate nonlinear equation will yield the second time t corresponding to the second minimum frequency value corresponding to the second frequency drop. n2 The value of .

[0151] It is understandable that the second time t corresponds to the second minimum frequency value during the second frequency drop calculated in step 104. n2 With the t obtained here n2 The values ​​have different functions. The former indicates the second time t corresponding to the second lowest frequency value during the second frequency drop. n2 The calculation method is used to establish the constraints of the optimization model. The latter is based on the actual value obtained by the optimization model under actual conditions, which is of great significance for determining the optimal exit time.

[0152] Step 1063, t e and t e The corresponding t n2 Substituting into the objective function, we obtain the corresponding objective function value f. n2 .

[0153] In step 1062, each t e The corresponding solution will yield a t. n2 , for each group t e and its corresponding t n2 Substituting the value of f into the objective function of the optimization model, we can solve for the corresponding objective function value f. n2 .

[0154] Similarly, the lowest value f of the second frequency during the second frequency drop calculated in step 104 above. n2 The f obtained here n2 The meanings are also different; the former is to indicate the second lowest frequency value f during the second frequency drop. n2 The calculation method is used to establish the objective function of the optimization model, which is the actual value obtained based on the optimization model.

[0155] Step 1064, compare the objective function value f n2 Determine the optimal exit time within the time interval.

[0156] The multiple sets of objective function values ​​f obtained by comparison n2 The objective function value fn2 The greater the value, the greater the influence of the frequency secondary drop on the target function value f e The smaller the influence of the frequency secondary drop, the greater the target function value f n2 When the target function value reaches the maximum value, the exit time t e corresponding to the target function value can be determined as the optimal exit time within the time interval [t n1 + Δt, t n1 + i].

[0157] The embodiment of the application determines the optimal exit time of the fan by establishing a system model and an optimization model, so that the fan has the smallest possible influence on the normal operation of the system when it exits the frequency modulation at the time, which is of great significance to improving the safety of system operation.

[0158] In a feasible implementation manner, the method provided by the embodiment of the application is simulated and verified. Specifically, a simulation model is built on a Matlab / Simulink platform. Please refer to Figure 4 , which is a simulation model structure diagram built in the embodiment of the application. Specifically:

[0159] In the simulation model, G1-G4 are hydroelectric generating units, and a wind farm is connected to the bus 10. The specific parameters include hydroelectric generating unit parameters and wind turbine parameters. Please refer to Table 1, hydroelectric generating unit parameters, and Table 2, wind turbine parameters.

[0160] Table 1 Hydroelectric generating unit parameters

[0161]

[0162] Table 2 Wind turbine parameters

[0163]

[0164] In the simulation model, a load sudden increase disturbance is set, the disturbance size is ΔP d = 0.02pu, and the disturbance occurs at t0= 20s.

[0165] Based on the above set parameters, the simulation model is simulated, and the system frequency curve of the fan exiting the frequency modulation at different times can be obtained. Please refer to Figure 5 , which is the system frequency curve of the fan exiting the frequency modulation at different times in the embodiment of the application.

[0166] It can be understood that, from the system frequency curve, when the fan determines the optimal exit time according to the method proposed in the embodiment of the application and exits the frequency modulation at the time, the second frequency minimum value corresponding to the frequency secondary drop can be effectively improved, thereby reducing the influence of the fan exiting the frequency modulation on the normal operation of the system, which is conducive to the safe operation of the system.

[0167] Figure 6 An internal structural diagram of a computer device in one embodiment is shown. This computer device can specifically be a terminal or a server. Figure 6 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a method for determining the wind turbine frequency regulation exit time. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement the method for determining the wind turbine frequency regulation exit time. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0168] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0169] Any combination of the technical features in the above embodiments can be made, and for the sake of brevity, not all possible combinations are described above, however, as long as the combination of the technical features does not exist in contradiction, it shall be considered within the scope of the present disclosure.

[0170] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it shall not be understood as a limitation on the patent scope of the present application. It shall be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these shall be within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for determining the time of frequency regulation shutdown of a wind turbine, characterized in that, include: A system model including a hydropower unit model, a wind turbine model, and a frequency response model is established based on the state-space method. Based on a preset transformation method, the system model is transformed, and a piecewise linearized model is obtained by rewriting the model with the wind turbine frequency regulation exit time as the boundary. The first expression for the system frequency during the frequency modulation stage and the second expression for the system frequency during the speed recovery stage are determined based on the piecewise linearization model. Based on the first expression, determine the first moment corresponding to the first minimum frequency value during the frequency modulation phase, which corresponds to the first frequency drop during the frequency modulation phase; and based on the second expression, determine the second minimum frequency value and the second moment corresponding to the second minimum frequency value during the speed recovery phase, which correspond to the second frequency drop during the speed recovery phase. With the goal of increasing the minimum value of the second frequency corresponding to the second frequency drop, an optimization model is established based on the time range of the system frequency modulation stage. Based on the optimization model, the optimal exit time is determined.

2. The method according to claim 1, characterized in that, The third expression of the system model is: Among them, T 11 ∈R n×n T 12 ∈R n×m Where n is the number of state variables, m is the number of algebraic variables, Δx is the n-dimensional column vector composed of state variables, Δz is the m-dimensional column vector composed of algebraic variables, and J 11 J 12 J 21 J 22 d1 and d2 are preset matrices of corresponding dimensions, and Δu is the input quantity; The parameter distributions in the hydropower unit model, wind turbine model, and frequency response model are defined in the third expression T. 11 T 12 , Δx, Δz, J 11 J 12 J 21 J 22 , d1, d2, Δu.

3. The method according to claim 2, characterized in that, The system model is transformed using a preset transformation method, and a piecewise linearized model is obtained by rewriting it with the wind turbine frequency regulation exit time as the boundary. Specifically, this includes: The system model is transformed by elimination to obtain the transformed system model. Using the moment when the wind turbine frequency regulation is discontinued as the dividing line, the transformed system model is rewritten to obtain a piecewise linearized model.

4. The method according to claim 3, characterized in that, The piecewise linearization model specifically includes: Where A1 and b1 are the system matrix and input matrix corresponding to the frequency regulation stage, A2 and b2 are the system matrix and input matrix corresponding to the speed recovery stage, t0 is the time when the load disturbance occurs, and t e t1 is the time when the wind turbine frequency regulation is terminated, and t1 is the end time of the time range under study.

5. The method according to claim 4, characterized in that, set up The first expression for the system frequency during the frequency modulation phase is: Among them, f n For the rated frequency, e n ∈R n×1 , and e n In this matrix, the nth bit is 1, and all other bits are 0. A1 is the system matrix corresponding to the frequency modulation stage, V1 is the matrix formed by the right eigenvectors of matrix A1, Λ1 is the diagonal matrix formed by the eigenvalues ​​of matrix A1, and t0 is the time when the load disturbance occurs. for The value at time t0, and has b1 is the input matrix corresponding to the frequency modulation stage, and Δu is the input quantity; set up The second expression for the system frequency during the speed recovery phase is: Where A2 is the system matrix corresponding to the speed recovery phase, V2 is the matrix formed by the right eigenvectors of matrix A2, Λ2 is the diagonal matrix formed by the eigenvalues ​​of matrix A2, and t e This is the time when the wind turbine frequency regulation is discontinued. for In t e The value at time, and has b2 is the input matrix corresponding to the speed recovery phase.

6. The method according to claim 5, characterized in that, The step of determining the first moment corresponding to the first minimum frequency value during the frequency modulation phase based on the first expression, and determining the second minimum frequency value and the second moment corresponding to the second minimum frequency value during the speed recovery phase based on the second expression, specifically includes: Taking the derivative of the first expression yields the first derivative expression corresponding to the first expression; Based on the first derivative expression, the first moment corresponding to the first minimum frequency value corresponding to a single frequency drop during the frequency modulation phase is calculated. Taking the derivative of the second expression yields the second derivative expression. Based on the second derivative expression, the second moment corresponding to the second minimum frequency value corresponding to the second frequency drop during the speed recovery phase is calculated. Substituting the second moment into the second expression, the second minimum frequency value corresponding to the second frequency drop during the speed recovery phase is calculated.

7. The method according to claim 6, characterized in that: The objective function of the optimization model includes: The optimization model also includes constraints, which specifically include: Among them, f n For the rated frequency, e n ∈R n×1 , and e n In the matrix, the nth bit is 1, and all other bits are 0. A2 is the system matrix corresponding to the speed recovery stage, V2 is the matrix formed by the right eigenvectors of matrix A2, Λ2 is the diagonal matrix formed by the eigenvalues ​​of matrix A2, and t n1 t represents the first moment corresponding to the first lowest frequency value during a frequency drop within the frequency modulation phase. n2 t represents the second time point corresponding to the second lowest frequency value during the second frequency drop within the speed recovery phase. e This is the time when the wind turbine frequency regulation is discontinued. for In t e The value at time, and has b2 is the input matrix corresponding to the speed recovery stage, and Δu is the input quantity.

8. The method according to claim 7, characterized in that, Determining the optimal exit time based on the optimization model specifically includes: Set time interval [t] n1 +Δt,t n1 +i], the exit time t e Within the time interval, values ​​are taken sequentially according to the increment Δt. The exit time t e Substitute the value into the equality constraint in the constraint condition Solving for the second time t corresponding to the second minimum frequency value during the second frequency drop within the speed recovery phase yields the solution. n2 ; t e and the t e The corresponding t n2 Substituting these values ​​into the objective function yields the corresponding objective function value f. n2 ; Compare the objective function value f n2 Determine the optimal exit time within the time interval.

9. A device for determining the timing of frequency regulation shutdown of a fan, characterized in that, The device includes: The first modeling module is used to establish a system model based on the state-space method, including a hydropower unit model, a wind turbine model, and a frequency response model. The transformation and rewriting module is used to transform the system model based on a preset transformation method, and rewrite it to obtain a piecewise linearized model with the wind turbine frequency regulation exit time as the boundary. The expression determination module is used to determine the first expression for the system frequency during the frequency modulation stage and the second expression for the system frequency during the speed recovery stage based on the piecewise linearization model. The calculation module is used to determine the first moment corresponding to the first minimum frequency value during the frequency modulation phase based on the first expression, and to determine the second minimum frequency value and the second moment corresponding to the second minimum frequency value during the speed recovery phase based on the second expression. The second modeling module is used to establish an optimization model based on the time range of the system frequency modulation stage with the goal of increasing the minimum value of the second frequency corresponding to the second frequency drop. The determination module is used to determine the optimal exit time based on the optimization model.

10. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as claimed in any one of claims 1 to 8.

Images