Wind power active support frequency modulation parameter optimization method for suppressing frequency drop

Abstract

The invention provides a wind power active support frequency modulation parameter optimization method for suppressing frequency drop. The method comprises the following steps: establishing a doubly-fed fan grid-connected power generation system model; frequency drop characteristics when the doubly-fed fan participates in power grid frequency modulation are determined, the doubly-fed fan participates in power grid frequency modulation through a comprehensive inertia control strategy, and the frequency drop characteristics comprise a frequency primary drop characteristic, a frequency secondary drop characteristic and a system frequency characteristic when the doubly-fed fan quits power grid frequency modulation; frequency modulation parameters needing to be optimized are selected based on the frequency drop characteristics when the doubly-fed fan participates in power grid frequency modulation; the optimal value of the frequency modulation parameter is obtained through searching by using an improved particle swarm algorithm, and the constraint condition of the improved particle swarm algorithm comprises a rotor safety and power output safety coupling item of the doubly-fed fan. The frequency modulation parameter obtained by using the method provided by the invention can effectively suppress the frequency secondary drop problem when the doubly-fed fan participates in power grid frequency modulation.

Description

Technical Field

[0001] The present application belongs to the field of wind power generation intelligent control technology, relates to wind power grid-connected frequency modulation optimization technology, and specifically provides a wind power active support frequency modulation parameter optimization method for suppressing frequency drops. Background Art

[0002] Doubly-fed wind turbines are the most commonly used variable speed constant frequency wind turbines. They are connected to the grid through a converter, and their speed is decoupled from the grid frequency. They cannot release shaft kinetic energy and automatically provide inertial response like synchronous machines. Therefore, when the grid system experiences frequency fluctuations, in order to use doubly-fed wind turbines to participate in the frequency regulation of the power system and provide active support for the grid, comprehensive inertial control is generally used as the frequency regulation strategy for doubly-fed wind turbines to participate in grid frequency regulation.

[0003] When the above strategy is used to control the doubly fed wind turbine to participate in grid frequency regulation, in the later stage of frequency regulation, the doubly fed wind turbine must absorb system active power to restore the speed, and cause the problem of secondary frequency drop. In order to alleviate the problem of secondary frequency drop of the grid when the doubly fed wind turbine is in the process of speed recovery as much as possible, it is necessary to comprehensively consider the primary frequency drop, secondary frequency drop and system frequency after restoring the maximum power point tracking (MPPT) mode when the doubly fed wind turbine participates in grid frequency regulation, so as to reasonably set multiple control parameters of the doubly fed wind turbine participating in grid frequency regulation.

[0004] However, existing research on control strategies for wind turbines participating in frequency regulation generally focuses on their power changes or energy storage control. These methods can achieve the purpose of suppressing the secondary frequency drop of the system to a certain extent, but have not conducted in-depth research on the dynamic characteristics of the primary and secondary frequency drops of wind turbines participating in grid frequency regulation, and have not optimized the value range of the droop coefficient and inertia response coefficient in combination with the operating status of the wind turbine. Therefore, the frequency support function of the wind turbine itself cannot be fully utilized. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present application provides a method for optimizing wind power active support frequency modulation parameters for suppressing frequency drop, the method comprising the following steps:

[0006] S1, establish a system model of double-fed wind turbine grid-connected power generation;

[0007] S2, analyzing the frequency drop characteristics of the doubly-fed wind turbine when participating in grid frequency modulation, wherein the doubly-fed wind turbine participates in grid frequency modulation through a comprehensive inertia control strategy, and the frequency drop characteristics include a primary frequency drop characteristic, a secondary frequency drop characteristic, and a system steady-state frequency characteristic after the doubly-fed wind turbine exits grid frequency modulation;

[0008] S3, selecting frequency modulation parameters that need to be optimized based on the frequency drop characteristics of the double-fed wind turbine when participating in grid frequency modulation;

[0009] S4, using an improved particle swarm algorithm to search for the optimal value of the frequency modulation parameter, wherein the constraint conditions of the improved particle swarm algorithm include the rotor safety and power output safety coupling items of the doubly fed wind turbine.

[0010] Furthermore, the system model of the doubly fed wind turbine grid-connected power generation includes a doubly fed wind turbine, a rotor-side converter, a grid-side converter and a phase-locked loop; the rotor-side converter and the grid-side converter are connected back-to-back through a DC bus capacitor; the doubly fed wind turbine converts the captured wind energy into electrical energy, and its rotor side is connected to the grid through the back-to-back connected rotor-side converter and grid-side converter, and its stator side is directly connected to the grid; the phase-locked loop is used to synchronize the frequency of the doubly fed wind turbine with the frequency of the grid system.

[0011] Furthermore, the doubly-fed wind turbine participates in the grid frequency regulation through a comprehensive inertia control strategy, specifically, the doubly-fed wind turbine provides an electromagnetic power increment for the grid based on the following formula:

[0012]

[0013] Among them, ΔP W is the electromagnetic power increment provided by the double-fed wind turbine to the power grid, K W1 is the droop coefficient, K W2 is the inertia response coefficient, Δf is the system frequency f of the power grid and the system frequency reference value f ref The frequency deviation between .

[0014] Furthermore, the frequency primary drop characteristics include a time domain expression of the system frequency of the power grid during the frequency primary drop stage, a time of the lowest point of the system frequency primary drop, and a maximum value of the system frequency primary drop deviation;

[0015] The time domain expression of the system frequency of the power grid in the first frequency drop stage is as follows:

[0016]

[0017] Among them, ΔP L is the load increment in the power grid system, T J is the inertia time constant of the synchronous unit, T G K is the speed governor action time constant of the synchronous unit, G Adjust power for equivalent units of synchronous units;

[0018] The moment when the system frequency drops to the lowest point is determined by the following formula:

[0019]

[0020] Among them, t nadir1 is the moment when the system frequency drops to the lowest point, and t0 is the moment when the doubly fed wind turbine starts to participate in the grid frequency regulation;

[0021] The maximum value of the system frequency drop deviation is determined by the following formula:

[0022] Δf max1 =a5-a1 exp[a2(θ-a4) / a3]cosθ,

[0023] Where Δf max1 It is the maximum value of the system frequency drop deviation.

[0024] Furthermore, the secondary frequency drop characteristics include a time domain expression of the system frequency of the power grid during the secondary frequency drop stage, a time of the lowest point of the secondary frequency drop, and a maximum value of the secondary frequency drop deviation;

[0025] The time domain expression of the system frequency of the power grid in the secondary frequency drop stage is as follows:

[0026]

[0027] Among them, f1 is the system frequency at the time t1 when the double-fed wind turbine exits the grid frequency regulation, P u The system power shortage caused by the speed recovery of the doubly-fed wind turbine;

[0028] The moment when the frequency drops to the lowest point for the second time is determined by the following formula:

[0029]

[0030] Among them, t nadir2 is the moment when the frequency drops to the lowest point for the second time;

[0031] The maximum value of the secondary drop deviation of the frequency is determined by the following formula:

[0032] Δf max2 =1-f1+b5-b1 exp[b2(θ1-b4) / b3]cosθ1,

[0033] Where Δf max2 It is the maximum value of the secondary frequency drop deviation.

[0034] Furthermore, the system steady-state frequency characteristic after the double-fed wind turbine exits the grid frequency regulation is determined by the following formula:

[0035]

[0036] Where Δf sis the steady-state frequency deviation value of the power grid system after the doubly-fed wind turbine returns to the maximum power point tracking mode, P W0 It is the initial value of the electromagnetic power output of the doubly fed wind turbine at time t0.

[0037] Preferably, the frequency modulation parameters to be optimized include K W1 , K W2 , t1 and ΔP d , where ΔP d It is the difference between the mechanical power and electromagnetic power of the doubly fed wind turbine when it exits the grid frequency regulation at time t1.

[0038] Preferably, the objective function of the improved particle swarm algorithm is:

[0039] min{max{Δf max1 |,Δf max2}};

[0040] The constraints of the improved particle swarm algorithm include:

[0041]

[0042] Among them, H W is the doubly fed wind turbine rotor inertia time constant, ω r (t0) is the initial speed of the doubly fed wind turbine, ω min is the lower speed limit of the doubly fed wind turbine, K W1,max K W1 The upper limit of K W2,max K W2 The upper limit of K W1,max , K W2,max Both are coupling items of rotor safety and power output safety of doubly-fed wind turbines.

[0043] Preferably, K is determined by the following steps: W1,max With K W2,max :

[0044] The kinetic energy calculation factor k of the doubly fed wind turbine at the current speed is determined based on the following formula: m :

[0045]

[0046] Among them, ω * r ,ω * r_max and ω * r_min are the actual values ​​of the rotor speed of the doubly fed wind turbine ω r , upper limit value ω r_max and the lower limit ω r_minThe standard value of the unit speed is the synchronous speed ω of the doubly fed wind turbine. rn ;

[0047] The power calculation factor k of the doubly fed wind turbine at the current speed is determined based on the following formula: c :

[0048]

[0049] Among them, k opt is the overspeed load reduction curve coefficient, Its per unit value, ΔP e is the frequency modulation active power increment, is its per unit value, K is the coefficient of frequency modulation active power increment, P w , P rated are the actual value and rated value of the active output of the doubly-fed wind turbine, It is the per unit value of the lower limit of the active output of the doubly-fed wind turbine;

[0050] K is determined based on the following formula W1,max With K W2,max :

[0051]

[0052] Among them, ε is the frequency modulation participation coefficient, and K0, K1, K2 and n are all constants.

[0053] Preferably, the improved particle swarm algorithm uses the following formula to adaptively update the inertia weight factor in the particle swarm algorithm:

[0054]

[0055] Among them, w is the inertia weight factor, w d 、w u is the initial value and final value of w, k is the current number of iterations, k max is the total number of iterations;

[0056] The improved particle swarm algorithm uses the following formula to adaptively update the learning factor in the particle swarm algorithm:

[0057]

[0058] Among them, c1 and c2 are learning factors, c 1d 、c 1u is the initial and final value of c1, c 2d 、c 2u are the initial and final values ​​of c2.

[0059] The optimization method provided in the present application performs time-domain analytical analysis on the primary drop, secondary drop and steady-state recovery characteristics of the system frequency during the process of the wind power system participating in the grid frequency regulation, and determines the optimal combination of frequency regulation control parameters (including exit frequency regulation time, droop coefficient, inertia response coefficient, etc.) under different wind power penetration rates based on the analysis results, so as to ensure that the wind power system can provide stable frequency support under different operating conditions; at the same time, in the process of parameter optimization, the safety requirements of the operating state of the doubly fed wind turbine are taken into consideration, and the value range of the droop coefficient and the inertia response coefficient is more reasonably constrained by means of coupling terms including rotor rotation safety and power output safety, thereby improving the convergence speed and solution accuracy of the algorithm, so that the optimal frequency regulation control parameters suitable for different wind power penetration rates can be found quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flow chart of a method for optimizing wind power active support frequency modulation parameters for suppressing frequency drop provided by the present application;

[0061] Figure 2 A schematic diagram of the architecture of a system model for grid-connected power generation of a doubly-fed wind turbine provided according to an embodiment of the present application;

[0062] Figure 3 A schematic diagram of the relationship between the mechanical power of a doubly-fed wind turbine and the rotor speed of the wind turbine at different wind speeds provided in an embodiment of the present application;

[0063] Figure 4 The schematic diagram of controlling the double-fed wind turbine using the integrated inertia control strategy;

[0064] Figure 5 The schematic diagram of the power variation curve of the double-fed wind turbine adopting the comprehensive inertia control strategy for grid frequency regulation;

[0065] Figure 6 This is a schematic diagram of system frequency changes during the process of double-fed wind turbine participating in grid frequency regulation;

[0066] Figure 7 A schematic diagram of the three-dimensional relationship between the maximum value of the primary drop deviation of the system frequency and the inertial response coefficient of the droop coefficient of the doubly-fed wind turbine provided according to an embodiment of the present application;

[0067] Figure 8 A schematic diagram of the three-dimensional relationship between the maximum value of the secondary drop deviation of the system frequency and the system frequency and the power shortage in the system when the doubly-fed wind turbine exits frequency modulation according to an embodiment of the present application;

[0068] Fig. 9 A schematic diagram of the three-dimensional relationship between the system steady-state frequency deviation and the droop coefficient and inertial response coefficient of the doubly-fed wind turbine provided according to an embodiment of the present application;

[0069] Fig.10 A schematic diagram of the three-dimensional relationship between the maximum value of the system frequency change rate and the droop coefficient and inertial response coefficient of the doubly-fed wind turbine provided according to an embodiment of the present application;

[0070] Fig.11 ω of the doubly-fed wind turbine at different speeds provided in the embodiment of the present application r -ε relationship diagram;

[0071] Fig.12 This is a schematic diagram of the architecture of a 3-machine 9-node power system model provided according to specific embodiment 1 of the present application;

[0072] Fig.13 According to the specific embodiment 1 of the present application, different K W1 Schematic diagram of the corresponding system frequency response curve;

[0073] Fig.14 According to the specific embodiment 1 of the present application, different K W1 Schematic diagram of the corresponding doubly-fed wind turbine speed curve;

[0074] Fig.15 According to the specific embodiment 1 of the present application, different K W2 Schematic diagram of the corresponding system frequency response curve;

[0075] Fig.16 According to the specific embodiment 1 of the present application, different K W2 Schematic diagram of the corresponding doubly-fed wind turbine speed curve;

[0076] Fig.17 A schematic diagram of a system frequency response curve corresponding to different t1 provided according to the specific embodiment 1 of the present application;

[0077] Fig.18 A schematic diagram of an algorithm convergence curve provided according to specific embodiment 1 of the present application;

[0078] Fig.19 A schematic diagram of a system frequency response curve corresponding to different frequency modulation parameter combinations provided according to specific embodiment 1 of the present application;

[0079] Fig. 20 A schematic diagram of a doubly-fed wind turbine speed curve corresponding to different frequency modulation parameter combinations provided in specific embodiment 1 of the present application;

[0080] Fig.21 This is a schematic diagram of the electromagnetic power curves of a doubly-fed wind turbine corresponding to different frequency modulation parameter combinations provided in specific embodiment 1 of the present application. DETAILED DESCRIPTION

[0081] Hereinafter, the present application will be further described based on preferred embodiments with reference to the accompanying drawings.

[0082] The present application provides a method for optimizing wind power active support frequency regulation parameters for suppressing frequency drop, which is used to determine the optimal frequency regulation parameters of a double-fed wind turbine participating in the frequency regulation of a power grid system, such as Figure 1 As shown, the method comprises the following steps:

[0083] S1, establish a system model of double-fed wind turbine grid-connected power generation;

[0084] S2, analyzing the frequency drop characteristics of the doubly-fed wind turbine when participating in grid frequency modulation, wherein the doubly-fed wind turbine participates in grid frequency modulation through a comprehensive inertia control strategy, and the frequency drop characteristics include a primary frequency drop characteristic, a secondary frequency drop characteristic, and a system steady-state frequency characteristic after the doubly-fed wind turbine exits grid frequency modulation;

[0085] S3, selecting frequency modulation parameters that need to be optimized based on the frequency drop characteristics of the double-fed wind turbine when participating in grid frequency modulation;

[0086] S4, using an improved particle swarm algorithm to search for the optimal value of the frequency modulation parameter, wherein the constraint conditions of the improved particle swarm algorithm include the rotor safety and power output safety coupling items of the doubly fed wind turbine.

[0087] The specific implementation of the method is described in detail below with reference to the accompanying drawings.

[0088] <1. Establishing a system model for doubly-fed wind turbine power generation>

[0089] Step S1 is used to establish a system model of a doubly-fed induction generator (DFIG) grid-connected power generation, thereby providing a basis for analyzing the time domain variation characteristics of the system frequency when the doubly-fed induction generator participates in grid frequency regulation and extracting frequency regulation parameters.

[0090] Figure 2 FIG. 4 shows a schematic diagram of a system model architecture of a double-fed wind turbine grid-connected power generation system in a specific embodiment. Figure 2 As shown, the doubly-fed wind turbine grid-connected power generation system includes a doubly-fed wind turbine, a rotor-side converter, a grid-side converter and a phase-locked loop.

[0091] Specifically, the main structure of a doubly fed wind turbine includes an impeller, a gearbox, a stator, a rotor, and a transmission system connecting the above parts. The impeller rotates by capturing wind energy, converts the wind energy into mechanical energy through the gearbox, drives the rotor to rotate relative to the stator, and converts the mechanical energy into electrical energy according to the principle of electromagnetic induction.

[0092] Furthermore, the electric energy output by the doubly-fed wind turbine needs to be connected to the grid through a converter, such as Figure 2 As shown, the rotor side is connected to the grid through the rotor-side converter and the grid-side converter connected back to back, and the stator side is directly connected to the grid. The rotor-side converter and the grid-side converter are connected back to back through the DC bus capacitor.

[0093] The phase-locked loop (PLL) is connected between the doubly fed wind turbine and the power grid. It consists of a phase detector, a low-pass filter and a voltage-controlled oscillator, etc., and is used to synchronize the frequency of the doubly fed wind turbine with the frequency of the power grid.

[0094] In the specific process of controlling the converter switching devices, the rotor-side converter can adopt a power outer loop control plus a current inner loop control method, and the grid-side converter can adopt a DC voltage control plus a current inner loop control method, combined with the phase command output by the phase-locked loop, to generate SPWM signals for controlling each switching device of the rotor-side converter and the grid-side converter respectively.

[0095] <2. Analysis of frequency drop characteristics when doubly-fed wind turbine participates in grid frequency regulation>

[0096] In an embodiment of the present application, after the system model of the doubly fed wind turbine connected to the grid is established through step S1, the frequency drop characteristics when it participates in the grid frequency modulation are further analyzed through step S2 to select appropriate frequency modulation parameters to optimize the frequency modulation effect and achieve effective suppression of the frequency drop.

[0097] Specifically, in an embodiment of the present application, the doubly-fed wind turbine participates in grid frequency modulation through a comprehensive inertia control strategy, and its frequency drop characteristics when participating in grid frequency modulation include a primary frequency drop characteristic, a secondary frequency drop characteristic, and a system frequency characteristic when exiting grid frequency modulation. Step S2 is described in detail below.

[0098] 2.1 Comprehensive inertia control strategy

[0099] When the power grid is in a stable operating state, the doubly fed wind turbine generally operates in the maximum power point tracking (MPPT) mode. Figure 3 The relationship between mechanical power and wind turbine rotor speed at different wind speeds v (v is the wind speed entering the impeller scanning surface) (pitch angle β = 0°), and the fitting curve P of the MPPT point at each wind speed MPPT As shown by the dotted line in the figure, its expression is:

[0100]

[0101] Among them, ω r is the angular velocity of the doubly fed fan, ρ is the air density, R W is the impeller radius, C pmax is the wind energy utilization coefficient C p The maximum value of λopt is the optimum tip speed ratio.

[0102] The rotor side of the doubly fed wind turbine is connected to the power grid via a rotor side converter. In order to enable it to quickly respond to changes in system frequency, a certain frequency control strategy needs to be adopted. In an embodiment of the present application, the doubly fed wind turbine adopts a comprehensive inertia control strategy to participate in system frequency regulation. Under this control strategy, the doubly fed wind turbine has both system frequency regulation capability and inertia response characteristics.

[0103] Figure 4 shows the schematic diagram of the integrated inertia control strategy. Figure 4 As shown in the figure, when the wind turbine is operating normally in MPPT mode, its electromagnetic power P W and mechanical power P m Phase balance, when the power grid system has load disturbance, the system frequency f and the system frequency reference value f ref The frequency deviation Δf is generated. At this time, the double-fed wind turbine adopts comprehensive inertial control to participate in the system frequency regulation, by providing an additional electromagnetic power increment ΔP to the system. W To achieve active support for the system frequency, specifically, the electromagnetic power increment ΔP W There are the following expressions:

[0104]

[0105] In the above formula, K W1 is the droop coefficient, K W2 is the inertial response coefficient.

[0106] Figure 5 The power variation curve of the double-fed wind turbine using the comprehensive inertia control strategy for grid frequency regulation is shown in Figure 1. Figure 5 As shown in the figure, the process of doubly fed wind turbines participating in grid frequency regulation can be divided into three periods:

[0107] The first stage: from t0 to t1, the doubly-fed wind turbine actively performs system frequency modulation. At this time, the rotor releases kinetic energy and converts it into electrical energy, providing active support for the power grid system.

[0108] The second stage: from t1 to t2, the doubly fed wind turbine exits the system frequency regulation, and at this time absorbs electrical energy from the grid and converts it into rotor kinetic energy;

[0109] In the third stage, after time t2, the doubly fed wind turbine returns to MPPT mode operation.

[0110] Specifically, at time t0, the system experiences a power disturbance, resulting in system frequency fluctuations. The doubly-fed wind turbine participates in system frequency modulation, and the shaft kinetic energy released by it is converted into the increased electromagnetic power of the wind turbine itself. Due to the continuous fluctuation of the system frequency, the increased electromagnetic power of the doubly-fed wind turbine will also change continuously, which can be expressed as ΔP W (t) = P W(t)-P W0 , where P W (t) is the electromagnetic power of the wind turbine at time t, P W0 is the initial electromagnetic power of the doubly fed wind turbine. At the same time, the release of the shaft kinetic energy of the doubly fed wind turbine will cause its rotor speed to decrease, that is, its mechanical power P m (t) continues to decrease.

[0111] Since the mechanical frequency of the doubly-fed wind turbine is continuously reduced during the frequency modulation of the power grid system, when the mechanical frequency lower limit is reached, it will no longer be able to provide active support for the system frequency, that is, at time t1, the doubly-fed wind turbine exits the frequency modulation of the system and enters the rotor speed recovery process. During this process, the doubly-fed wind turbine absorbs energy from the power grid. In the embodiment of the present application, the doubly-fed wind turbine adopts a constant output electromagnetic power strategy in its speed recovery stage, and its power relationship satisfies the expression: P W (t1) = P m (t1)-ΔP d , where ΔP d It is the difference between the mechanical power and electromagnetic power of the doubly fed wind turbine at the time of exiting frequency modulation (time t1).

[0112] As the speed of the doubly fed wind turbine continues to increase, after the speed of the wind turbine rotor gradually increases to the rated state, the doubly fed wind turbine returns to the maximum power point tracking operation mode at time t2, thus completing a complete system frequency regulation process.

[0113] Figure 6 The figure shows the changes of system frequency in a complete double-fed wind turbine system frequency regulation process. Figure 6 As shown in the figure, the system provides system frequency support through the doubly fed wind turbine from t0 to t1, and its frequency first drops and then rebounds, so there is a frequency minimum value, which can be called the first frequency drop stage; from t1 to t2, the system frequency drops and rebounds again because the doubly fed wind turbine exits frequency regulation and absorbs power from the power grid, which can be called the second frequency drop stage; after t2, the doubly fed wind turbine operates in MPPT mode, and the steady-state frequency deviation value of the system is Δf s , after which the system frequency relies on synchronous units to maintain stability.

[0114] Obviously, although the participation of the doubly fed wind turbine in the grid frequency regulation can provide active support for the system in some periods, it is at the expense of reducing the kinetic energy of the wind turbine. The process of restoring the speed to return to MPPT will inevitably cause a secondary drop in the system frequency and a system frequency deviation after restoring the steady state. Therefore, it is necessary to reasonably select the control parameters to comprehensively control the maximum deviation Δf of the system frequency drop. max1 , the maximum deviation of the second drop Δf max2And the steady-state frequency deviation value Δf after the system frequency stabilizes s Ideal suppression.

[0115] To this end, in step S2, the system frequency drop characteristics during the complete process of the doubly fed wind turbine participating in the grid frequency regulation are analyzed to determine reasonable control parameters and objective functions, thereby ensuring optimal control of the doubly fed wind turbine participating in the grid frequency regulation.

[0116] Specifically, corresponding to the aforementioned frequency primary drop stage, frequency secondary drop stage, and frequency steady-state stage after restoring MPPT, the frequency drop characteristics to be analyzed include frequency primary drop characteristics, frequency secondary drop characteristics, and system steady-state frequency characteristics after exiting grid frequency modulation.

[0117] 2.2 Time domain analysis of frequency drop characteristics

[0118] For the power grid system with double-fed wind turbines, the original load in the system is set to P L0 , at time t0, the load is cut into the system, making the load increment in the system ΔP L , which causes the system frequency to drop. The synchronous units and doubly fed wind turbines in the power system support the system frequency to alleviate the fluctuation of the system frequency. At this stage, the state of the synchronous units in the system can be expressed by the following formula:

[0119]

[0120] Among them, P W0 is the initial value of the electromagnetic power output of the doubly fed wind turbine at time t0, P G0 is the initial value of the electromagnetic power output of the synchronous unit at time t0, ΔP G The additional power of the synchronous unit, ΔP W To increase the power of the doubly-fed wind turbine, ω g is the speed of the synchronous unit, T J is the inertia time constant of the synchronous unit. When using the per-unit value for calculation, the synchronous unit speed ω in the above formula can be g Replaced with the system frequency f, the initial value of the system frequency f0=f(t0).

[0121] When the doubly-fed wind turbine operates in MPPT mode, the power of the doubly-fed wind turbine and the synchronous unit in the system satisfies the following formula:

[0122] P G0 +P W0 =P L0 (4);

[0123] The output process of the synchronous unit in the system can be simplified into the following first-order inertia link:

[0124]

[0125] Among them, T G K is the speed governor action time constant of the synchronous unit, G Adjust power for equivalent units of synchronous units.

[0126] The doubly fed wind turbine adopts typical integrated inertia control and participates in the dynamic trajectory of the system frequency during the power system frequency regulation process. Substituting equations (3) and (5) into equation (2), and combining the initial conditions, the following differential equation is obtained:

[0127]

[0128] Where f” and f’ are the second-order derivative and first-order derivative of the system frequency f, respectively.

[0129] By solving equation (6), we can obtain the time domain expression of the system frequency in the first frequency drop stage:

[0130]

[0131] From equation (7) and setting f'(t) = 0, we can get the time t when the frequency drops to the lowest point nadir1 Determined by the following formula:

[0132]

[0133] Substituting equation (7) into equation (8) and subtracting it from the frequency reference value, we can obtain the maximum frequency drop deviation Δf as shown in the following equation: max1 :

[0134] Δf max1 =a5-a1 exp[a2(θ-a4) / a3]cosθ (9).

[0135] Studies have shown that the droop coefficient K W1 Corresponding to the inertia time constant T of the synchronous unit J , inertial response coefficient K W2 Corresponding to the equivalent unit regulation power K of the synchronous unit G Therefore, for a system with certain parameters, the droop coefficient K of the wind turbine is W1 , inertial response coefficient K W2 and initial load change ΔP L The size of will affect the maximum value of the system frequency drop deviation Δf max1 .

[0136] 2.3 Time domain analysis of the secondary frequency drop characteristics

[0137] At time t1, the doubly fed wind turbine exits the system frequency support and absorbs power from the grid to restore the rotor speed. At this time, the electromagnetic power of the doubly fed wind turbine is P W0 +ΔP W Change to P m1 -ΔP d In the speed recovery stage, the wind power output is approximately constant, and the system power shortage P caused by the speed recovery of the double-fed wind turbine can be defined as u for:

[0138] P u =(P L0 +ΔP L )-P G1 -(P m1 -ΔP d ) (10),

[0139] Among them, P m1 is the mechanical power of the doubly fed wind turbine at time t1, P G1 is the electromagnetic power output of the synchronous unit at time t1, and f1 is the system frequency at time t1.

[0140] Ignoring the output change rate of the synchronous unit during the speed recovery process, the differential equation with initial conditions shown in formula (11) can be obtained from the derivation process of the doubly fed wind turbine participating in the frequency regulation stage:

[0141]

[0142] Solving equation (11), we can obtain the time domain expression of the system frequency in the speed recovery stage (i.e., the second frequency drop stage):

[0143]

[0144] From formula (12), we can get the time t when the frequency drops to the lowest point for the second time: nadir2 :

[0145]

[0146] Substituting equation (13) into equation (12) and subtracting it from the frequency standard value, we can obtain the maximum value of the frequency secondary drop deviation Δf as shown in the following equation: max2 :

[0147] Δf max2 =1-f1+b5-b1 exp[b2(θ1-b4) / b3]cosθ1(14).

[0148] 2.4 System Steady-State Frequency Analysis after the Doubly-Fed Wind Turbine Restores the MPPT Mode

[0149] The magnitude of the secondary drop in system frequency is related to the system frequency value f1 at the time when wind power exits frequency regulation (time t1) and the system power shortage P u After the speed recovery process of the doubly fed wind turbine is completed, the doubly fed wind turbine returns to the MPPT mode, and only the synchronous units in the system will participate in the grid frequency regulation. According to the final value theorem, the system steady-state frequency deviation value Δf s for:

[0150]

[0151] Where L[f(t)] is the Laplace transform of frequency, and s is the Laplace operator.

[0152] According to the above formula, the steady-state frequency deviation value Δf of the power grid system after the doubly fed wind turbine returns to the maximum power point tracking mode is s for:

[0153]

[0154] Where P W_max is the maximum output power of the doubly fed wind turbine.

[0155] When the system frequency drops to the maximum, the doubly fed wind turbine outputs the maximum power, which can be determined according to formula (16):

[0156] P W_max =K W1 Δf max2 (17);

[0157] According to equations (16) and (17), the system steady-state frequency deviation expression after the system frequency recovers can be determined as:

[0158]

[0159] <3. Select the frequency modulation control parameters to be optimized>

[0160] After analyzing the frequency drop characteristics of the double-fed wind turbine in the process of frequency modulation of the power grid system by step S2, appropriate frequency modulation control parameters for optimization can be determined based on the above analysis results in step S3. Specifically, the frequency modulation control parameters of the double-fed wind turbine can be used as independent variables, and simulation can be performed using equations (7) to (9), (12) to (14) and (16) obtained in step S2 to analyze the influence of each frequency modulation control parameter on the frequency response index.

[0161] Figure 7 In a specific embodiment, the maximum value of the system frequency drop deviation Δf obtained by simulation is shown. max1 And the droop coefficient K of the double-fed wind turbine W1 , Inertia response coefficient KW2 The three-dimensional relationship of the synchronous machine set in this embodiment is T J =13.8s, speed regulator action time constant T G =5s, synchronous unit equivalent unit adjustment power K G =15, load disturbance ΔP in the system L =0.1pu; Figure 8 It shows the maximum value of the secondary drop deviation of the system frequency Δf max2 The system frequency f1 and the power shortage P in the system when the double-fed wind turbine exits the frequency regulation u The three-dimensional relationship Fig. 9 shows the system steady-state frequency deviation Δf s And the droop coefficient K of the double-fed wind turbine W1 , Inertia response coefficient K W2 The three-dimensional relationship between the three Fig.10 It shows the maximum value of system frequency change rate RoCoF when the double-fed wind turbine participates in system frequency support max And the droop coefficient K of the double-fed wind turbine W1 , Inertia response coefficient K W2 three-dimensional relationship.

[0162] Table 1 below lists the droop coefficient K of the doubly fed wind turbine in this embodiment: W1 , Inertia response coefficient K W2 And the relationship between f1 and related indicators of power grid system frequency regulation.

[0163] Table 1K W1 , K W2 The relationship between f1 and frequency modulation index

[0164] FM parameters <![CDATA[Δf max1 ]]> <![CDATA[Δf max2 ]]> <![CDATA[Δf s ]]> Frequency change rate <![CDATA[K W1 ]]> Negative correlation Less impact Negative correlation Negative correlation <![CDATA[K W2 ]]> Negative correlation Positive correlation Unrelated Negative correlation <![CDATA[f1]]> Unrelated Negative correlation Negative correlation Unrelated

[0165] pass Figures 6 to 10 As can be seen from Table 1:

[0166] Doubly fed wind turbine droop coefficient K W1 The larger the value, the smaller the maximum value of the primary frequency drop deviation, but it will cause the wind turbine to release more shaft kinetic energy, resulting in an increase in the system power shortage when exiting the frequency regulation. However, the released shaft kinetic energy is related to the frequency deviation, and considering the difference in energy absorption during the speed recovery process under different wind power penetration rates, it can be concluded from the above that the droop coefficient has little effect on the maximum value of the secondary frequency drop deviation. At the same time, K W1 The larger the K, the smaller the steady-state frequency deviation. After the system frequency stabilizes, the frequency is closer to the system frequency reference value. However, if the K is too large, W1 This will make the frequency change rate smaller, resulting in an increase in the time consumed by the wind turbine to participate in frequency regulation. Therefore, it is necessary to comprehensively consider the above frequency regulation indicators to determine the droop coefficient K of the wind turbine.W1 The value of .

[0167] Wind turbine inertia response coefficient K W2 The larger the value, the smaller the maximum value of the first frequency drop deviation, and the slower the system frequency changes. However, the total shaft system kinetic energy released in the frequency support stage is large, which will also cause the maximum value of the second frequency drop deviation to increase. At the same time, appropriately increase K W2 It helps to improve the overall frequency regulation effect, but too large a value may cause the capacity of the converter to exceed the limit and the frequency change rate to slow down. Therefore, it is necessary to comprehensively consider the above indicators to determine the appropriate wind turbine inertia response coefficient K. W2 The value of

[0168] In order to analyze the parameter ΔP d For Δf max2 and dω r The influence of / dt can be combined with equation (10) and equation (12) to obtain the following equation:

[0169]

[0170] From the above formula, we know that ΔP d The larger the Δf max2 The larger the value, the more ΔP is expected to be. d Smaller, yet smaller ΔP d This will make the difference between the electromagnetic power and mechanical power of the wind turbine output smaller, the rotor speed recovery slower, and the time required for the wind turbine to return to MPPT mode longer. Therefore, selecting a suitable ΔP d It is crucial.

[0171] Through the above analysis, we can find that K W1 , K W2 , f1(or t1), ΔP d In the process of doubly-fed wind turbines participating in system frequency regulation, the frequency regulation index is affected in different degrees and directions. Therefore, in the embodiment of the present application, K is selected. W1 , K W2 , t1, ΔP d As the frequency regulation parameter of the doubly fed wind turbine that needs to be optimized.

[0172] <IV. Frequency modulation parameter optimization based on improved particle swarm algorithm>

[0173] 4.1 Determine the objective function and constraints for frequency modulation parameter optimization

[0174] Through the analysis above, the goal of doubly fed wind turbines participating in system frequency support is to improve the frequency support capability of doubly fed wind turbines for system frequency regulation while minimizing the impact of secondary frequency drop. The frequency regulation parameter setting of wind turbines can be regarded as an optimization problem. The optimization goal of this optimization problem is to increase the lowest points of the primary and secondary frequency drops as much as possible, that is, the objective function of frequency regulation parameter optimization is shown in formula (20):

[0175] min{max{Δf max1 |,Δf max2}}(20).

[0176] In some embodiments, the constraint condition for optimizing the frequency modulation parameters is as shown in formula (21):

[0177]

[0178] Among them, H W is the doubly fed wind turbine rotor inertia time constant, ω r (t0) is the initial speed of the doubly fed wind turbine, ω min is the lower speed limit of the doubly fed wind turbine, K W1,max K W1 The upper limit of K W2,max K W2 The upper limit of the value.

[0179] The setting of constraints also has a direct impact on the results of frequency modulation parameter optimization. For example, in some existing parameter optimization methods, K W1,max , K W2,max It is often selected as a constant based on empirical values. However, as the operating state of the wind turbine changes, its rotor kinetic energy and output power change in real time and are coupled with each other, causing the parameter optimization space to also be in dynamic change. Unreasonable constraints will not only increase the size of the parameter optimization space, but also significantly increase the time for parameter optimization. In addition, if effective constraints are not imposed in combination with the actual operating state of the doubly fed wind turbine, the frequency modulation parameters obtained by optimization will exceed the performance limit of the doubly fed wind turbine, affecting the safe operation of the wind turbine.

[0180] Therefore, in the embodiments of the present application, K W1,max , K W2,max are the coupling items of rotor safety and power output safety of the doubly-fed wind turbine, and need to be determined in combination with the actual operating state of the doubly-fed wind turbine. Specifically, in some embodiments, K W1,max , K W2,max By introducing the kinetic energy calculation factor k from the aspects of rotor safety and power output safety respectively m and power calculation factor k c To determine, the specific steps include:

[0181] First, k m It can be expressed as (22):

[0182]

[0183] k m It is used to characterize the proportion of the rotor available kinetic energy of the doubly fed wind turbine at the current speed to its maximum available kinetic energy. (22) In the formula, E, E max 、E min They respectively represent the actual value, maximum and minimum value of the rotor kinetic energy of the doubly fed wind turbine.

[0184] Considering Formula (22) can be written as formula (23):

[0185]

[0186] Among them, ω r ,ω r_max and ω r_min They represent the actual value, upper limit and lower limit of the rotor speed of the doubly fed wind turbine respectively. The superscript * represents the per-unit value, and its reference value is the synchronous speed ω of the doubly fed wind turbine. rn ,Therefore, the primary frequency regulation capability of the doubly-fed wind turbine was quantified from the perspective of wind turbine speed safety.

[0187] Then, k c It can be expressed as (24):

[0188]

[0189] k c It is used to characterize the proportion of the current remaining capacity of the converter of the doubly fed wind turbine to the maximum active output within its power constraint range at the current speed. (24) In the formula, P w , P w_max , P w_min They represent the actual value, maximum value and minimum value of the active output of the doubly fed wind turbine respectively, and the active output of the doubly fed wind turbine can be further expressed as the sum of the overspeed load reduction control power of the doubly fed wind turbine and the incremental active power it generates for frequency regulation:

[0190]

[0191] (25) where k opt Represents the overspeed load reduction curve coefficient, ΔP e To obtain the frequency modulation active power increment, substitute (25) into (24) and use the rated capacity P of the unit as rated and ω rn Standardize it as the reference value, and we can get:

[0192]

[0193] Formula (26) quantifies the primary frequency regulation capability of the doubly fed wind turbine converter from the perspective of capacity security.

[0194] Furthermore, considering k m With k c , the speed and electromagnetic power of the doubly fed wind turbine are reversely coupled during the frequency regulation process. To ensure that they participate in frequency regulation within a safe range, the smaller value of the two is taken to represent the actual frequency regulation capability of the unit, that is,

[0195] ε=min{k m ,k c},0≤ε≤1 (27),

[0196] Among them, ε is the FM participation coefficient, Fig.11 The ω value of the double-fed wind turbine participating in system frequency regulation at different speeds is shown. r -ε curve. Fig.11 As shown, when there is no load disturbance in the power system, k m With k c They intersect at point A0, and the curve of ε is as follows Fig.11 As shown by the black dotted line; when the frequency changes, k c Curve according to ΔP e The size and sign of k m The curve remains unchanged. When the speed of the double-fed wind turbine is high, k c The curve determines the value of ε. The double-fed wind turbine converter is under heavy load at this time. The frequency modulation depth of the wind turbine should be reduced as much as possible to avoid the sudden increase of active power to impact the converter. At low speed, k m The curve determines the size of ε, which will continue to decrease as the fan speed decreases. r When approaching the lower limit, ε takes a value close to zero, so that the unit no longer participates in the frequency regulation response. Therefore, by adjusting the droop coefficient and inertia response coefficient of the doubly fed wind turbine through ε, the risk of unit speed and power exceeding the limit can be greatly reduced.

[0197] It should be noted that ε is the frequency regulation participation coefficient. When the speed of the double-fed wind turbine reaches Fig.11 ω in r_maxWhen , the speed of the wind turbine has reached the set maximum value. Continuing to participate in frequency regulation will cause the power of the converter to exceed the limit, but ε does not control the unit to exit frequency regulation in time. In addition, the maximum value point A0 in the ε curve is small, and the frequency regulation capability of wind power cannot be fully utilized. Therefore, ε needs to be further optimized: when the unit speed is too low or too high, the frequency regulation capability of the wind power frequency regulation unit is weak, and its frequency regulation participation should be reduced as much as possible; when the speed is moderate, the frequency regulation participation of the unit should be maintained at a large value, so that the rotor kinetic energy can be fully utilized and the frequency stability of the system can be improved, which is in line with the curve characteristics of the S-shaped function.

[0198] Specifically, the LOGISTIC function can be used to optimize it, which is expressed as:

[0199]

[0200] Where K0, K1, K2 and n are all constants.

[0201] Obviously, K obtained by (28) W1,max and K W2,max It can not only reflect the coupling characteristics between the real-time changing speed and power output of the doubly fed wind turbine, but also perform appropriate optimization based on a narrow restriction range, using it as the constraint range for the droop parameters and the inertia response coefficient, which can take into account both the operating safety and frequency regulation capability of the wind turbine, thereby obtaining a more ideal optimization result.

[0202] 4.2 Frequency modulation parameter optimization based on improved particle swarm algorithm

[0203] The core idea of ​​the particle swarm optimization (PSO) algorithm for parameter optimization is to regard the solution space of the problem as a multidimensional superbody and search for the global optimal solution in it. At the same time, particles adjust their states through information exchange and cooperation to move towards the optimal solution. Each particle represents a potential optimal solution to the optimization problem and is characterized by a position vector and a velocity vector. The position vector represents a possible solution to the problem (i.e., a set of K W1 , K W2 , t1, ΔP d The velocity vector determines the direction and distance that the particle moves.

[0204] In this application, an improved particle swarm algorithm is used to perform K W1 , K W2 , t1, ΔP d Preferably, the improved particle swarm algorithm can be performed by the following steps:

[0205] Set the total number of particles to N, and use X nDenote the position vector of the nth particle, V n Represents its velocity vector, then the position and velocity of the nth particle in a space of dimension m can be expressed as follows:

[0206]

[0207] Obviously, when the frequency modulation parameter to be optimized is K W1 , K W2 , t1, ΔP d When m=4, the X of each particle n That is, a set of optional K W1 , K W2 , t1, ΔP d Get the value.

[0208] According to the position vector of each particle, the objective function value is calculated by formula (20) to determine the individual optimal position and the group optimal position of each iterative search. Each particle in the space dynamically adjusts its position vector and velocity vector under the constraint of formula (21) by tracking the previous individual optimal position and group optimal position.

[0209] Specifically, using P n Denote the individual optimal position of the nth particle, and use P f represents the best position of the total population in the previous iteration. The two best positions can be expressed as:

[0210]

[0211] The update formula of the velocity vector and position vector of each particle in space can be expressed as:

[0212]

[0213] Among them, c1 and c2 are learning factors, r1 and r2 are random numbers uniformly distributed in [0,1], w is the inertia weight factor, and k is the current number of iterations.

[0214] The traditional PSO algorithm uses a fixed inertia weight factor and a learning factor. A larger inertia weight factor will prevent the algorithm from falling into the local optimum at the beginning of the iteration, which is beneficial to the global search of particles. A smaller inertia weight factor will make the algorithm converge faster in the later stages of the iteration, which is convenient for the local search of particles. For the learning factor, a larger c1 and a smaller c2 will be beneficial to the global search of particles at the beginning of the iteration, and a smaller c1 and a larger c2 will be beneficial to the local search of particles in the later stages of the iteration, thereby making the algorithm converge quickly. For this reason, the improved particle swarm algorithm of the present application performs adaptive dynamic adjustment of the inertia weight factor and the learning factor during the iterative optimization process, that is, the factor adaptive mutation process, and its mutation equation can be expressed as:

[0215]

[0216] Among them, w d 、w u is the initial and final value of the inertia weight factor, c 1d 、c 1u is the initial value and final value of the learning factor c1, c 2d 、c 2u is the initial and final value of the learning factor c2, k max is the total number of iterations.

[0217] <Specific Example 1>

[0218] This embodiment uses Fig.12 The 3-machine 9-node power system model with double-fed wind turbines shown in the figure is simulated, and the frequency modulation parameters of the double-fed wind turbines in the system are optimized by the wind power active support frequency modulation parameter optimization method for suppressing frequency drop provided in this application to verify its effectiveness. Among them, the system rated frequency is 50Hz; SG1 and SG2 are synchronous units, simulating thermal power units, with a rated capacity of 100MW, and an inertia time constant T J =13.8s, the synchronous machine speed regulator adjustment coefficient is 0.015, and the speed regulator action time constant is set to 6.0s; the wind farm contains a certain number of double-fed wind turbines, each of which has a rated capacity of 1.5MW, of which the rated wind speed of the wind turbine is 10m / s, and the initial wind speed of the system is set to 9m / s (0.9pu), and the equivalent time constant H W =5.04s; Load 1 is set to 117MW, 20MW can be cut off in parallel; Load 2 is 95MW; Load 3 is 35MW; Load 4 is 2.5MW. The line length and transformer ratio parameters in the system are shown in Table 3-2.

[0219] When t0=10s, the system cuts in a load of 20MW at load 1, and the total system load increases, causing the system frequency to drop. The wind turbines participate in frequency support through comprehensive inertia control, thereby improving the frequency stability of the power system.

[0220] Under the condition of 50 wind turbines and a wind power penetration rate of 27.27%, the initial setting of wind turbine exit frequency regulation time is t1 = 17s. Fig.13 , Fig.14 K W2 =10, different K W1 The corresponding system frequency response curve and doubly-fed wind turbine speed curve; Fig.15 , Fig.16 K W1 =10, different K W2The corresponding system frequency response curve and doubly-fed wind turbine speed curve; Fig.17 Shows K W1 =20, K W2 When =30, the system frequency response curve corresponding to different t1.

[0221] From the above figures, we can see that in the inertial response coefficient K W2 When the larger droop coefficient K W1 It can slow down the primary frequency drop and the secondary frequency drop at the same time, but the excessive droop coefficient K W1 It will also cause the wind turbine to release too much rotor kinetic energy during the frequency modulation process, which is not conducive to the speed recovery process of the wind turbine. W1 When a certain value is given, the larger inertial response coefficient K W2 It can effectively slow down the primary frequency drop, but it will also aggravate the secondary frequency drop, making the lowest point of the secondary frequency drop lower than the primary drop, and an excessively large inertia response coefficient will still cause a sharp increase in the rotor kinetic energy released by the wind turbine. When the frequency modulation exit time is too short, the secondary frequency drop will be aggravated. This is because when the exit time is too short, the smaller the frequency at the time of exiting the frequency modulation, the more serious the secondary frequency drop. At the same time, when the frequency modulation exit time is too long, the more rotor kinetic energy released by the wind turbine will be, which will increase the system power shortage accordingly and aggravate the secondary frequency drop.

[0222] Taking into account the system frequency characteristics, the droop coefficient K W1 The search range is set to [15,40], and the inertial response coefficient K W2 The search range of is set to [15,30]; the search range of the exit frequency modulation time is set to [14s,18s], and the improved particle swarm algorithm described above is used for particle swarm optimization. Fig.18 The algorithm convergence curve is shown when the wind power penetration rate is 27.27%. Table 2 below lists the optimization results of the frequency modulation parameters.

[0223] Table 2 Optimization results of improved particle swarm algorithm when wind power penetration rate is 27.27%

[0224] parameter Optimal location <![CDATA[t1 / s]]> <![CDATA[K W1 ]]> <![CDATA[K W2 ]]> Optimization results 0.1062 15.5 38 28

[0225] Figures 19 to 21 The system frequency response curve, the doubly-fed wind turbine speed curve and the doubly-fed wind turbine electromagnetic power curve when the above optimal frequency modulation parameter combination is adopted are respectively shown, and the comparison with other optional frequency modulation parameter combinations is shown. It can be seen that the optimal droop coefficient K obtained by the method provided by this application W1 , Inertia response coefficient K W2and exit frequency modulation time t1, which can better solve the problem of system frequency drop once, and at the same time ensure that the lowest point of the second frequency drop is higher than the lowest point of the first drop. By comparing the simulation results of this group of parameters with the simulation results of its edge parameters, the optimal characteristics of the optimization results of the present application method can be seen. For example, Fig. 20 It can be seen that the frequency modulation parameters obtained by the method of the present application can ensure that the speed of the wind turbine is within an appropriate range (0.7pu~1.1pu); Fig.21 It can be seen that the frequency modulation parameters obtained by the method of this application can ensure that the fan has sufficient frequency modulation capability to support the stability of the system frequency.

[0226] The above is a detailed introduction to the specific implementation methods of the present application. For those skilled in the art, several improvements and modifications may be made to the present application without departing from the principles of the present application. These improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for optimizing wind power active support frequency modulation parameters for suppressing frequency drop, characterized in that: The following steps are involved: S1, establish a system model of double-fed wind turbine grid-connected power generation; S2, analyzing the frequency drop characteristics of the doubly-fed wind turbine when participating in grid frequency modulation, wherein the doubly-fed wind turbine participates in grid frequency modulation through a comprehensive inertia control strategy, and the frequency drop characteristics include a primary frequency drop characteristic, a secondary frequency drop characteristic, and a system steady-state frequency characteristic after the doubly-fed wind turbine exits grid frequency modulation; S3, selecting frequency modulation parameters that need to be optimized based on the frequency drop characteristics of the double-fed wind turbine when participating in grid frequency modulation; S4, using an improved particle swarm algorithm to search for the optimal value of the frequency modulation parameter, wherein the constraint conditions of the improved particle swarm algorithm include the rotor safety and power output safety coupling items of the doubly fed wind turbine.

2. The wind power active support frequency modulation parameter optimization method for suppressing frequency drop according to claim 1 is characterized in that: The system model of the double-fed wind turbine grid-connected power generation includes a double-fed wind turbine, a rotor-side converter, a grid-side converter and a phase-locked loop; The rotor-side converter and the grid-side converter are connected back-to-back via a DC bus capacitor; The doubly-fed wind turbine converts captured wind energy into electrical energy, and its rotor side is connected to the grid through a rotor-side converter and a grid-side converter connected back to back, and its stator side is directly connected to the grid; The phase-locked loop is used to synchronize the frequency of the double-fed wind turbine with the frequency of the power grid system.

3. The wind power active support frequency modulation parameter optimization method for suppressing frequency drop according to claim 1 is characterized in that: The doubly-fed wind turbine participates in the grid frequency regulation through a comprehensive inertia control strategy, specifically, the doubly-fed wind turbine provides an electromagnetic power increment for the grid based on the following formula: Among them, ΔP W is the electromagnetic power increment provided by the double-fed wind turbine to the power grid, K W1 is the droop coefficient, K W2 is the inertia response coefficient, Δf is the system frequency f of the power grid and the system frequency reference value f ref The frequency deviation between .

4. The method for optimizing wind power active support frequency modulation parameters for suppressing frequency drop according to claim 3 is characterized in that: The frequency primary drop characteristics include the time domain expression of the system frequency of the power grid during the primary frequency drop stage, the time of the lowest point of the system frequency primary drop, and the maximum value of the system frequency primary drop deviation; The time domain expression of the system frequency of the power grid in the first frequency drop stage is as follows: Among them, ΔP L is the load increment in the power grid system, T J is the inertia time constant of the synchronous unit, T G K is the speed governor action time constant of the synchronous unit, G Adjust power for equivalent units of synchronous units; The moment when the system frequency drops to the lowest point is determined by the following formula: Among them, t nadir1 is the moment when the system frequency drops to the lowest point, and t0 is the moment when the doubly fed wind turbine starts to participate in the grid frequency regulation; The maximum value of the system frequency drop deviation is determined by the following formula: Δf max1 =a5-a1 exp[a2(θ-a4) / a3]cosθ, Where Δf max1 It is the maximum value of the system frequency drop deviation.

5. The wind power active support frequency modulation parameter optimization method for suppressing frequency drop according to claim 4 is characterized in that: The frequency secondary drop characteristics include the time domain expression of the system frequency of the power grid in the frequency secondary drop stage, the moment of the lowest point of the frequency secondary drop and the maximum value of the frequency secondary drop deviation; The time domain expression of the system frequency of the power grid in the secondary frequency drop stage is as follows: Among them, f1 is the system frequency at the time t1 when the double-fed wind turbine exits the grid frequency regulation, P u The system power shortage caused by the speed recovery of the doubly-fed wind turbine; The moment when the frequency drops to the lowest point for the second time is determined by the following formula: Among them, t nadir2 It is the moment when the frequency drops to the lowest point for the second time; The maximum value of the secondary drop deviation of the frequency is determined by the following formula: Δf max2 =1-f1+b5-b1 exp[b2(θ1-b4) / b3]cosθ1, Where Δf max2 It is the maximum value of the secondary frequency drop deviation.

6. The method for optimizing wind power active support frequency modulation parameters for suppressing frequency drop according to claim 5, characterized in that: The system steady-state frequency characteristics after the double-fed wind turbine exits the grid frequency regulation are determined by the following formula: Where Δf s is the steady-state frequency deviation value of the power grid system after the doubly-fed wind turbine returns to the maximum power point tracking mode, P W0 It is the initial value of the electromagnetic power output of the doubly fed wind turbine at time t0.

7. The method for optimizing wind power active support frequency modulation parameters for suppressing frequency drop according to claim 6, characterized in that: The frequency modulation parameters to be optimized include K W1 , K W2 , t1 and ΔP d , where ΔP d It is the difference between the mechanical power and electromagnetic power of the doubly fed wind turbine when it exits the grid frequency regulation at time t1.

8. The method for optimizing wind power active support frequency modulation parameters for suppressing frequency drop according to claim 6, characterized in that: The objective function of the improved particle swarm algorithm is: min{max{Δf max1 |,Δf max2 }}; The constraints of the improved particle swarm algorithm include: Among them, H W is the doubly fed wind turbine rotor inertia time constant, ω r (t0) is the initial speed of the doubly fed wind turbine, ω min is the lower speed limit of the doubly fed wind turbine, K W1,max K W1 The upper limit of K W2,max K W2 The upper limit of K W1,max , K W2,max Both are coupling items of rotor safety and power output safety of doubly-fed wind turbines.

9. The method for optimizing wind power active support frequency modulation parameters for suppressing frequency drop according to claim 8, characterized in that: Determine K by the following steps W1,max With K W2,max : The kinetic energy calculation factor k of the doubly fed wind turbine at the current speed is determined based on the following formula: m : Among them, ω * r ,ω * r_max and ω * r_min are the actual values ​​of the rotor speed of the doubly fed wind turbine ω r , upper limit value ω r_max and the lower limit ω r_min The standard value of the unit speed is the synchronous speed ω of the doubly fed wind turbine. rn ; The power calculation factor k of the doubly fed wind turbine at the current speed is determined based on the following formula: c : Among them, k opt is the overspeed load reduction curve coefficient, Its per unit value, ΔP e is the frequency modulation active power increment, is its per unit value, K is the coefficient of frequency modulation active power increment, P w , P rated are the actual value and rated value of the active output of the doubly-fed wind turbine, It is the per unit value of the lower limit of the active output of the doubly-fed wind turbine; K is determined based on the following formula W1,max With K W2,max : Among them, ε is the frequency modulation participation coefficient, and K0, K1, K2 and n are all constants.

10. The method for optimizing wind power active support frequency modulation parameters for suppressing frequency drop according to claim 6, characterized in that: The improved particle swarm algorithm uses the following formula to adaptively update the inertia weight factor in the particle swarm algorithm: Among them, w is the inertia weight factor, w d 、w u is the initial value and final value of w, k is the current number of iterations, k max is the total number of iterations; The improved particle swarm algorithm uses the following formula to adaptively update the learning factor in the particle swarm algorithm: Among them, c1 and c2 are learning factors, c 1d 、c 1u is the initial and final value of c1, c 2d 、c 2u are the initial and final values ​​of c2.

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