Fan set transmission chain dynamic load and power grid frequency response collaborative optimization control strategy

Abstract

The invention relates to the technical field of wind power plants, and discloses a cooperative optimization control strategy for dynamic load of a transmission chain of a fan set and frequency response of a power grid to realize frequency stabilization and dynamic load relief of the transmission chain. The method comprises the steps that firstly, a dynamic model of a wind turbine generator is established and linearized, a power system frequency regulation model is constructed and participates in power grid frequency regulation, and meanwhile the power system frequency regulation model further comprises a thermal power generating unit model; subsequently, a hybrid controller is designed for simultaneously considering frequency response and transmission chain load fluctuation suppression.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farms, and in particular to a coordinated optimization control strategy for a wind turbine transmission chain dynamic load and a power grid frequency response. Background Art

[0002] Grid frequency fluctuations will affect the speed regulation of wind turbines to maintain synchronous operation with the grid. Therefore, problems such as electromagnetic torque disturbances caused by grid-side faults or auxiliary frequency support provided by wind farms will cause serious load fluctuations in the transmission chain and tower of wind turbines. Although these unbalanced load fluctuations usually do not exceed the ultimate strength of the wind turbine structure. However, considering the huge mechanical loads and inertial loads that the wind turbine transmission chain originally bears, or defects or cracks in the structure itself, if it operates under unbalanced load fluctuations for a long time, it will cause serious fatigue damage to the structure. This damage will significantly reduce the service life of the wind turbine, and may even cause serious problems such as blade breakage, failure of key components of the transmission chain, and tower collapse, posing a major threat to the safe operation of wind turbines. Therefore, how to effectively identify and distribute the unit load and suppress the torsional oscillation of the transmission chain is an urgent problem to be solved in the active optimization control of the dynamic load of the wind turbine. Summary of the invention

[0003] In view of the above problems, the present invention proposes a control strategy that considers the coordinated optimization of the dynamic load of the wind turbine transmission chain and the frequency response of the power grid to achieve frequency stability and transmission chain dynamic load relief. First, the dynamic model of the wind turbine is established and linearized, and then the power system frequency regulation model is constructed to make it participate in the power grid frequency regulation. At the same time, the power system frequency regulation model also includes a thermal power unit model; then, a hybrid controller is designed to simultaneously consider the frequency response and transmission chain load fluctuation suppression.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A wind turbine transmission chain dynamic load and power grid frequency response coordinated optimization control strategy includes the following steps:

[0006] Step 1: Establish the wind turbine dynamics model: including transmission chain modeling and double-fed asynchronous generator modeling;

[0007] Step 2: Linearization of the torque model of the wind turbine generator: Select a certain equilibrium working point of the nonlinear dynamic system, and apply disturbance to each state variable of the system. By performing Taylor expansion on the equilibrium point and retaining the first-order term of the Taylor expansion, the linearized model of the nonlinear system near the working point is obtained.

[0008] Step 3: Establish the power system frequency regulation model of the wind turbine group: including the wind turbine group model based on frequency regulation control, the thermal power unit model based on frequency regulation control and the wind-thermal hybrid power system model;

[0009] Step 4: Design of optimization controller for wind turbine transmission chain dynamic load and system frequency response: including PSOPI controller design, active disturbance rejection controller design and hybrid controller design.

[0010] Furthermore, the step 1 is specifically as follows:

[0011] 1.1) Transmission chain modeling:

[0012] Using the dual mass block modeling method, the expression is as follows:

[0013]

[0014]

[0015]

[0016] Among them, M s Indicates the torque of the low-speed shaft of the transmission chain; M g Represents the generator torque; ω g Indicates the generator speed; η g Indicates the gear speed ratio; J r Represents the rotor moment of inertia; J g Generator moment of inertia; K Ts represents the spindle viscous friction constant; B Ts Represents the spindle spring constant; M rot Indicates the rotor torque of the fan unit; Indicates the integral symbol; Represents the first derivative of the wind rotor speed; Represents the first derivative of the generator speed;

[0017] 1.2) Double-fed asynchronous generator modeling:

[0018] The torque model of the wind turbine generator is expressed as follows:

[0019]

[0020] Among them, M g_ref Indicates the generator reference torque; Unit power reference value; τ g Generator time constant; ω f Represents the generator speed after filtering; τ f Represents the sampling filter time constant;

[0021] The calculation of the wind turbine generator torque is expressed as:

[0022]

[0023] Among them, k opt Indicates the control parameters of torque control.

[0024] Furthermore, the step 2 is specifically as follows:

[0025] Assume that the wind turbine is currently working at time t = k, and the wind speed v is v 0 , and assuming that the wind speed v 0 The short working cycle is constant in the short working cycle (the short working cycle is the working cycle of the wind turbine when it is discontinuously running or intermittently used); based on the above assumption, at t = k, the measured power output of the wind turbine, generator speed, generator filter speed, pitch angle, rotor torque and generator torque are defined as P g0 ,ω g0 ,ω f0 , β 0 , M rot0 and M g0 ;

[0026] The incremental state space equation of the fan unit transmission chain at time t = k:

[0027]

[0028]

[0029]

[0030] Δω r Indicates the change in wind wheel speed; Δω g Indicates the change in generator speed; Δω s Indicates the difference between the change in wind rotor speed and the change in generator speed; ΔM rot Indicates the change in the wind turbine rotor torque; ΔM s Indicates the change in torque of the main shaft of the fan drive chain; ΔM g Indicates the torque change of the wind turbine generator;

[0031] The incremental state space equation of the fan unit transmission chain main shaft torque at time t = k is:

[0032]

[0033] The incremental state space equation of the wind turbine generator torque at time t = k is:

[0034]

[0035] The change in active reference value of the wind turbine model;

[0036] The incremental state space equation of the wind turbine pitch angle at time t = k is:

[0037]

[0038] Among them, Δt is the control period of the fan group; k p Represents the proportional gain coefficient in PI control; k i Indicates the integral gain coefficient in PI control; k a1 , k a2 Represents different gain coefficients of the pitch controller; ω g_rated Indicates the rated speed of the generator; θ ref The reference pitch angle is obtained by looking up the table based on the generator speed.

[0039] Furthermore, the step 3 is specifically as follows:

[0040] 3.1) Fan unit model based on frequency modulation control

[0041] The wind turbine group adjusts the frequency through derating control, and changes the rotor speed and generator torque to adjust the power at the maximum power tracking point P mppt The output power is changed between the rated power and the derated power. When the derated factor is assumed to be γ, the positive reserve power of the wind farm is Determined by γ for frequency adjustment;

[0042]

[0043]

[0044]

[0045]

[0046] in, Indicates the active reference value of the wind turbine model; Indicates the power reference value deviation of the fan group; p out Indicates the active output of the unit model; Δp out Indicates the power output deviation of the fan unit; P dem Indicates the power demand of the wind turbine under frequency regulation control; P avai Indicates the maximum available power of the fan unit; ΔP dem Indicates the change in power demand of the wind turbine under frequency regulation control; ΔM g0 Indicates the measurable change in the generator torque of the wind turbine; Δω g0 Indicates the measurable change in the generator speed of the wind turbine group;

[0047] Combined with the generator torque linearization model in step 2, the generator torque increment under the wind turbine participating in frequency regulation control is expressed as:

[0048]

[0049] P ref Indicates reference power; P ref0 Indicates the measurable reference power;

[0050] 3.2) Thermal power unit model based on frequency modulation control

[0051] The transfer function of the reheat gas turbine is expressed as:

[0052]

[0053] Among them, T TR Represents the reheat time constant of the thermal power unit, K TR It represents the power fraction generated by the high voltage section of the thermal power unit, T TT Indicates the time constant of the main air inlet and steam box of the thermal power unit;

[0054] The mathematical model of the speed governor of a thermal power unit is expressed as:

[0055]

[0056] Among them, T TG Indicates the time constant of the speed governor of the thermal power unit;

[0057] 3.3) Wind-thermal hybrid power system model

[0058] The system frequency fluctuation characteristics satisfy the following equation:

[0059]

[0060] Where H represents the equivalent inertia constant of the power system; D represents the equivalent damping coefficient of the power system load; ΔP e It represents the power deviation of all power sources in the power system; Δf represents the frequency deviation of the power system; s represents a complex variable, which represents the frequency in the Laplace transform domain.

[0061] Furthermore, the step 4 is specifically as follows:

[0062] 4.1) PSOPI controller design

[0063] 4.1.1) PI controller

[0064] The PI controller is expressed as:

[0065]

[0066] Among them, k p is the integral gain coefficient; k i is the differential gain coefficient; represents the Laplace integral operator symbol;

[0067] Establish transfer function to analyze the dynamic characteristics of fan unit;

[0068] have to:

[0069]

[0070]

[0071]

[0072] Among them, K 1 =1 / ω g0 , K 3 =P avai Δt, K 4 =ω g0 , K 5 =M g0 , K 6 =5×10 6 , represents the transfer function of generator torque and generator speed; represents the transfer function between the generator speed and the transmission chain main shaft torque; Represents the transfer function between the wind wheel rotor speed and the transmission chain main shaft torque;

[0073] The relationship between the transmission chain main shaft torque and the reference power is expressed as:

[0074]

[0075] The relationship between the generator speed and the reference power is expressed as:

[0076]

[0077] The comprehensive output characteristics of the fan unit without a PI controller are expressed as:

[0078]

[0079] G wt1 (s) represents the transfer function of the power output deviation of the wind turbine group and the reference power deviation; represents the transfer function of the generator speed and the reference power;

[0080] Since the power reference is modified by the PI controller, the expression of the power reference of the wind turbine under the PI controller is obtained:

[0081]

[0082] G PI (s) represents the PI controller transfer function;

[0083] The incremental expression of the reference power of the wind turbine under the PI controller is:

[0084]

[0085] The relationship between the fan unit rotor speed and the reference power is expressed as:

[0086]

[0087] According to transfer functions (26), (28) and (29), the comprehensive output characteristics of the fan group under PI control are expressed as:

[0088]

[0089] G wt2 (s) represents the comprehensive output characteristic transfer function of the fan group under PI control, Represents the transfer function between the wind rotor speed and the reference power;

[0090] When the power reference is modified by PI control, the relationship between shaft torque and system frequency is expressed as:

[0091]

[0092] 4.1.2) PSO optimization algorithm

[0093] In the PSO algorithm, the particle velocity and position are updated as follows:

[0094]

[0095] Where x and v are the position and velocity of the particle; lbest is the local best position of the particle, gbest is the global best position of the particle; c 1 and c 2 is the acceleration coefficient; r 1 and r 2 is a random number in (0,1); i represents the number of particles; d represents the dimension; k represents the number of iterations;

[0096] 4.1.33) Cost Function

[0097] Assume that Δf 2 and The smaller the integral of is, the smaller the fluctuation of system frequency and spindle load is; the cost function is expressed as:

[0098]

[0099] Δf 2 It represents the square of the frequency deviation of the power system; It represents the square of the torque deviation of the main shaft of the transmission chain; represents the square of the frequency deviation of the power system at time i; It represents the square of the torque deviation of the main shaft of the transmission chain at time i;

[0100] In order to improve the generalization ability of the controller model, Δf 2 and The data is normalized as follows:

[0101]

[0102] Δf norm Indicates the normalized value of the normal frequency deviation of the power system; ΔT s_norm Represents the normalized value of the transmission chain main shaft torque, Δf min Indicates the minimum value of the normal frequency deviation of the power system, Δf max Indicates the maximum value of the normal frequency deviation of the power system, ΔM s_min Indicates the minimum value of the transmission chain main shaft torque, ΔM s_max Indicates the maximum value of the main shaft torque of the transmission chain;

[0103] Therefore, the normalized equation of the cost function is as follows:

[0104]

[0105] The variance entropy is incorporated into the cost function to account for power system frequency f fluctuations and main shaft torque M s Fluctuation, mathematical expression is as follows:

[0106] C 2 =-∑p(ΔT s,i )·log 2 p(ΔT s,i )-p(Δf i )·log 2 p(Δf i )#(36)

[0107] Where p(x) represents the probability density function of the data set; the specific calculation formula is as follows:

[0108]

[0109] Similarly, C 2 Considering the dynamic load of the transmission chain main shaft and the discreteness of the system frequency fluctuation, the normalization is performed, which is specifically expressed as:

[0110] C 2 =-∑p(ΔT s_norm,i )·log 2 p(ΔT s_norm,i )-p(Δf norm,i )·log 2 p(Δf norm,i )#(38)

[0111] At this point, the new multi-objective cost function is expressed as:

[0112] minC=(1-α)C 1 +αC 2 #(39)

[0113] Among them, α represents C 1 and C 2 The weight factor ranges from 0 to 1;

[0114] 4.2) Design of Active Disturbance Rejection Controller

[0115] 4.2.1) Wind turbine drive system model based on ADRC

[0116] The dual-mass transmission chain model is used as the controlled object in the ADRC system. Based on step 1, the main shaft torque equation of the wind turbine transmission chain is expressed as:

[0117]

[0118] Among them, θ s Indicates the torsion angle of the transmission system; Represents the torsional angular velocity of the transmission system; θ s and The mathematical formula is:

[0119]

[0120] Among them, θ r represents the integral of the rotor angular velocity; θ g The integral of the generator angular velocity;

[0121] Based on equations (39) and (40), the double-mass block model of the wind turbine transmission chain is reconstructed as θ s The specific expression is as follows:

[0122]

[0123] in, represents the torsional angular acceleration of the main shaft, and w represents the unknown disturbance of the wind turbine model;

[0124] In order to decouple the controller from the state and assign the unknown disturbances caused by the uncertainty of the aerodynamic parameters and the grid anomaly to the generator as a combination of disturbances; the expression of the transmission chain system model (41) is modified as follows:

[0125]

[0126] represents the system state equation; u represents the system input; b represents the adjustable parameter;

[0127] Considering the nonlinearity of the fan group, equation (43) is reformulated into the following nonlinear form:

[0128]

[0129] Among them, x 1 Indicates the spindle torsion angle; x 2 Represents x 1 The derivative of x 2 represents the spindle torsional angular acceleration; b 0 represents the input control gain, f(x 1 ,x 2 ,M rot ) represents the nonlinear state equation of the fan system;

[0130] Considering that the expected value of the torsion angle of the transmission system is set to 0, the resulting tracking error system is expressed as follows:

[0131]

[0132] Among them, x 3 represents the new state variable of the system, and x 3 is considered continuous;

[0133] Based on equation (45), the obtained tracking error system is expanded to the new system in the following way, which is specifically expressed as:

[0134]

[0135] in, represents the system extended state variable; fh(w) represents the expanded state estimate;

[0136] 4.2.2) Active Disturbance Rejection Controller

[0137] The wind turbine transmission chain tracking error system includes a tracking differentiator TD, an extended state observer ESO and a nonlinear state error feedback controller NLSEFC;

[0138] 4.2.2.1) TD

[0139] The mathematical expression of TD is:

[0140]

[0141] Among them, v 0 Represents the target value of control, where v 0 =θ s , take v 0 The target value of v is 0; 1 , v 2 Respectively represent v 0 The expected trajectory and its derivative; r represents the sampling period; h 0 represents the filter factor; fhan represents the inverse function;

[0142] 4.2.2.2)ESO

[0143] The relationship between input and output in ESO is as follows:

[0144]

[0145] Among them, z 1 , z 2 , z 3 represents the output signal of the extended state observer; β 1 , β 2 , β 3 , α 1 , α 2 represents the design parameters of the extended state sensor, the nonlinear equation fal(ε,α 1 ,δ) is:

[0146]

[0147] δ represents a constant that affects the filtering effect; α represents a constant between 0 and 1; ε represents an error;

[0148] 4.2.2.3) NLSEFC

[0149] A nonlinear state error feedback controller is established in the ADRC structure. The estimated state of the ESO (z 1 ,z 2 ) and TD output signal (v 1 ,v 2 ) are combined to realize the control input of the system, which is specifically expressed as:

[0150]

[0151] Among them, e 1 , e 2 Represents the differential signal error; k 1 , k 2 denote the proportional and integral parameters respectively;

[0152] Finally, according to the tracking error closed-loop system (45), the ADRC nonlinear controller can be derived as:

[0153]

[0154] 4.3) Hybrid Controller Design

[0155] The PSOPI-ADRC hybrid controller is proposed to increase the spindle speed fluctuation value Δω s As input for frequency regulation and fatigue load relief;

[0156] The reference power fluctuation caused by random wind speed is compensated by introducing the PSOPI controller, that is, the fluctuation of the main shaft speed Δω is adopted s As input for power compensation;

[0157] By introducing the fluctuation error Δθ of the spindle torsion angle s As the input of ADRC, the torque compensation of the generator is realized.

[0158] The beneficial effects of the present invention are as follows:

[0159] The introduction of the PSOPI controller can compensate for the reference power fluctuation caused by random wind speed, using the fluctuation Δω of the main shaft speed s As the input of power compensation, it not only reduces the impact of frequency regulation on fatigue load, but also suppresses frequency fluctuation within the tolerance range of the power system; on the other hand, ADRC tracks the main shaft torsion angle Δθ s The fluctuation error is reduced, achieving load relief by alleviating the generator reference torque fluctuation. BRIEF DESCRIPTION OF THE DRAWINGS

[0160] Figure 1 This is a model diagram of a fan unit based on frequency modulation control according to the present invention;

[0161] Figure 2 The control model diagram of the speed governor of a thermal power unit of the present invention;

[0162] Figure 3 This is a block diagram of a frequency regulation model for a wind-fire hybrid power system of the present invention;

[0163] Figure 4This is a block diagram of a linearized model of a wind turbine group considering power system frequency regulation of the present invention;

[0164] Figure 5 It is the block diagram of the PI controller of the present invention;

[0165] Figure 6 It is a flow chart of optimizing PI controller parameters based on particle swarm algorithm of the present invention;

[0166] Figure 7 It is the ADRC block diagram of the present invention;

[0167] Figure 8 This is a block diagram of the hybrid control strategy of the present invention. DETAILED DESCRIPTION

[0168] The present invention is further described below in conjunction with the accompanying drawings.

[0169] A control strategy considering the coordinated optimization of the dynamic load of the wind turbine transmission chain and the frequency response of the power grid includes the following steps:

[0170] Step 1: Establish the wind turbine dynamics model

[0171] 1.3) Transmission chain modeling

[0172] The transmission chain system is an important component connecting the fan impeller and the generator. Its function is to transfer the kinetic energy captured by the fan impeller to the low-speed shaft of the gearbox, and then transfer it to the high-speed shaft after speed change and connect to the engine, thereby converting mechanical energy into electrical energy. Since the low-speed shaft is directly connected to the fan impeller, the load it bears is large and highly volatile, which can better reflect the dynamic load state of the entire transmission chain. Therefore, the dynamic load and fatigue load of the low-speed shaft in the fan unit transmission chain should be focused on.

[0173] There are two main ways to model the wind turbine transmission chain: single mass block modeling and dual mass block modeling. However, since the single mass block model feeds back the first-order dynamic characteristics of the transmission chain, it cannot describe the transient process between the wind turbine impeller and the transmission chain, especially ignoring the torque and friction characteristics of the low-speed shaft of the wind turbine transmission chain, and cannot accurately calculate the dynamic load and fatigue load of the transmission chain. Therefore, the dual mass block modeling method is used, and the expression is as follows:

[0174]

[0175]

[0176]

[0177] Among them, M s Indicates the torque of the low-speed shaft of the transmission chain; M g Represents the generator torque; ωg Indicates the generator speed; η g Indicates the gear speed ratio; J r Represents the rotor moment of inertia; J g Generator moment of inertia; K Ts represents the spindle viscous friction constant; B Ts Indicates the spindle spring constant.

[0178] 1.4) Modeling of double-fed asynchronous generator

[0179] Since the invention considers the wind turbine group to participate in the grid frequency regulation control, the dynamic response characteristics of the system frequency are relatively slow compared with the dynamic characteristics of the voltage and power angle. Therefore, when the power system frequency is in the long-term behavior under small disturbances, the dynamic characteristics of the voltage and power angle are often ignored. Therefore, the generator torque model can be expressed as follows:

[0180]

[0181] Among them, M g_ref Indicates the generator reference torque; Unit power reference value; τ g Generator time constant; ω f Represents the generator speed after filtering; τ f Represents the sampling filter time constant.

[0182] The calculation of the wind turbine generator torque is expressed as:

[0183]

[0184] Among them, k opt Indicates the control parameters of torque control.

[0185] Step 2: Linearization of wind turbine generator torque model

[0186] Small signal analysis is a commonly used method in power system analysis. When applying the small signal analysis method, a certain equilibrium operating point of the nonlinear dynamic system is usually selected, and a small disturbance is applied to each state variable of the system. Subsequently, by performing Taylor expansion on the equilibrium point and retaining the first-order terms of the Taylor expansion, a linearized model of the nonlinear system near the operating point is obtained.

[0187] First, assume that the wind turbine is currently operating at time t = k. Since the wind speed v is a variable that can be estimated or measured, the value of the wind speed v at this time is v 0 , and assuming that the wind speed v 0 Therefore, based on the above assumptions, at t = k, the measurable wind turbine power output, generator speed, generator filter speed, pitch angle, rotor torque and generator torque can be defined as Pg0 ,ω g0 ,ω f0 , β 0 , M rot0 and M g0 Based on the above small signal method, the incremental linear model of each system of the wind turbine group can be derived.

[0188] The incremental state space equation of the fan unit transmission chain at time t = k:

[0189]

[0190]

[0191]

[0192] The incremental state space equation of the fan unit transmission chain main shaft torque at time t = k is:

[0193]

[0194] The incremental state space equation of the wind turbine generator torque at time t = k is:

[0195]

[0196] Wherein Δt is the control period of the fan group, and Δt=0.0125s is taken.

[0197] The incremental state space equation of the wind turbine pitch angle at time t = k is:

[0198]

[0199] Step 3: Establish the power system frequency regulation model for wind turbines

[0200] 3.1) Fan unit model based on frequency modulation control

[0201] Generally, the incremental control model of the wind turbine unit is used in the frequency modulation control-based wind turbine unit model. The model changes the frequency according to the change of the grid load, and then controls the power change of each unit according to the change of the frequency to stabilize the frequency. The wind turbine unit has the ability to adjust the frequency through derating control. By changing the rotor speed and generator torque, the power P at the maximum power tracking point can be adjusted. mppt When the output power changes between the rated power and the derated power, and the derated factor is assumed to be γ, the forward reserve power P of the wind farm pr+ Determined by γ for frequency adjustment.

[0202]

[0203] Where γ represents the derating factor, which ranges from 0 to 1 and is set to 0.15. The normalization process can be expressed by formulas (12) and (13):

[0204]

[0205]

[0206]

[0207] in, Indicates the active reference value of the wind turbine model; Indicates the power reference value deviation of the fan group; P out Indicates the active output of the unit model; ΔP out Indicates the power output deviation of the fan unit; P dem Indicates the power demand of the wind turbine under frequency regulation control; P avai represents the maximum available power of the fan group. Therefore, the fan group model based on frequency modulation control, such as Figure 1 shown.

[0208] Combined with the generator torque linearization model in step 2, the generator torque increment under the wind turbine participating in frequency regulation control can be expressed as:

[0209]

[0210] 3.2) Thermal power unit model based on frequency modulation control

[0211] As a part of the power system frequency regulation model, the thermal power unit model covers key parts such as the governor, reheat gas turbine and steam turbine, and can accurately describe the behavior and response of the thermal power unit in the system frequency regulation.

[0212] The transfer function of the reheat gas turbine can be expressed as:

[0213]

[0214] Among them, T TR Represents the reheating time constant of the thermal power unit, T TR =7s; K TR Indicates the power fraction generated by the high-voltage section of the thermal power unit, K TR =0.3pu; T TT It represents the time constant of the main air inlet and steam box of the thermal power unit, and takes T TT =0.3s.

[0215] The mathematical model of the speed governor of a thermal power unit can be expressed as:

[0216]

[0217] Among them, T TG It represents the time constant of the thermal power unit speed regulator, and takes T TG =0.2s.

[0218] Considering the stability regulation of the system, a transient droop compensation controller needs to be added to the speed regulator of the thermal power unit. The specific control model is as follows: Figure 2 As shown, the model parameter B 1 =0.452, K T1 =0.3, R T1 =0.05:

[0219] 3.3) Wind-thermal hybrid power system model

[0220] As for the power system, the power system frequency response will induce high excitation current in wind turbines and grid-side transformers, which will lead to torque vibration of the wind turbine drive chain, generate failure loads, and cause damage to wind farm operating equipment and economic losses. Therefore, in order to achieve smooth operation of the power system and suppress torque vibration of the wind turbine drive chain, the system frequency response needs to be maintained in a relatively stable range, and the fluctuation should be minimized. The degree of fluctuation of the system frequency is related to both the generator set and the load characteristics. The system frequency fluctuation characteristics satisfy the following equation:

[0221]

[0222] Where H represents the equivalent inertia constant of the power system, taking H = 5.994s; D represents the equivalent damping coefficient of the power system load, taking D = 1s; ΔP e represents the power deviation of all power sources in the power system; Δf represents the frequency deviation of the power system. So far, the power system frequency regulation model block diagram of the wind turbine group is as follows: Figure 3 As shown:

[0223] Therefore, the linearization model block diagram of the fan group model considering frequency modulation control is as follows: Figure 4 shown.

[0224] Step 4: Design of controller for optimizing dynamic load and system frequency response of fan drive train

[0225] 4.1) PSOPI controller design

[0226] (1) PI controller

[0227] Generator torque fluctuation caused by frequency regulation (FR) control and maximum power point tracking (MPPT) control In MPPT mode, the wind turbine controller fine-tunes the generator torque to track the maximum power, so the torque variation is small. However, under FR control, the generator torque fluctuates with the frequency change, so its fluctuation is significant. In addition, since the transmission chain system of the wind turbine is flexibly coupled, the increase in the generator torque fluctuation will aggravate the speed difference Δω between the generator and the rotor. s , and the change of the spindle torque ΔM s Directly depends on the speed difference Δω s Δω caused by FR control s The increase is the main reason for the increase in the dynamic load of the spindle.

[0228] Therefore, a PI controller is designed by changing Δω s As a feedback signal, it is used to adjust the power reference value to reduce the impact of FR control on the torque fluctuation of the transmission chain. Since the introduction of PI control changes the dynamic characteristics of the fan unit, it is necessary to consider the response of the PI control scheme to the system frequency. The PI control block diagram is shown in Figure 5.

[0229] Where K 1 =1 / ω g0 , K 3 =P avai Δt, K 4 =ω g0 , K 5 =M g0 , K 6 =5×10 6 ,

[0230] The PI controller can be expressed as:

[0231]

[0232] Among them, k p is the integral gain coefficient; k i is the differential gain coefficient.

[0233] When considering the impact of PI control on system frequency, it is necessary to establish a comprehensive transfer function to analyze the dynamic characteristics of the fan unit.

[0234] We can get:

[0235]

[0236]

[0237]

[0238] The relationship between the transmission chain main shaft torque and the reference power can be expressed as:

[0239]

[0240] The relationship between the generator speed and the reference power can be expressed as a transfer function:

[0241]

[0242] The comprehensive output characteristics of the fan unit without a PI controller can be expressed as:

[0243]

[0244] Since the power reference can be modified by the PI controller, the expression of the power reference of the wind turbine under the PI controller can be obtained:

[0245]

[0246] Therefore, the incremental expression of the reference power of the wind turbine under the PI controller is:

[0247]

[0248] The relationship between the fan unit rotor speed and the reference power can be expressed as:

[0249]

[0250] Therefore, according to transfer functions (26), (28) and (29), the comprehensive output characteristics of the fan group under PI control can be expressed as:

[0251]

[0252] In order to further clarify the influence of power and frequency coupling, it is necessary to derive the relationship between the transmission chain main shaft torque and the system frequency. By analyzing this relationship, we can better understand how the change of shaft torque affects the behavior of system frequency.

[0253] When the power reference can be modified by PI control, the relationship between shaft torque and system frequency can be expressed as:

[0254]

[0255] When different parameters are given Bode plot, improve kp This will result in a change in the shaft resonance frequency and a reduction in amplitude. Therefore, increasing k p It is helpful to reduce the fatigue of the shaft. Similarly, changing k within a certain range i The impact on the main axis is small.

[0256] (2) PSO optimization algorithm

[0257] In the previous PI control performance analysis, the appropriate k p and k i The parameter values ​​can effectively optimize the main shaft load and the frequency of the fan unit. Therefore, to further determine the PI control parameters, it is necessary to use an optimization algorithm to search for the optimal controller parameters. Particle Swarm Optimization (PSO) algorithm is a powerful global optimization method. Its purpose is to facilitate the exchange of information and promote competition between individuals in a complex search space to find the optimal value. The algorithm is essentially a multi-objective optimization algorithm used to solve the global optimal solution in multi-dimensional space. In the PSO algorithm, the speed and position of the particle are updated as shown in the following formula:

[0258]

[0259] Where x and v are the position and velocity of the particle; lbest is the local best position of the particle, gbest is the global best position of the particle; c 1 and c 2 is the acceleration coefficient; r 1 and r 2 is a random number in (0,1); i represents the number of particles; d represents the dimension; k represents the number of iterations.

[0260] (3) Cost function

[0261] Considering the participation of wind turbines in frequency regulation introduces frequent changes in generator torque and increases the dynamic load of wind turbines. The system frequency deviation and the dynamic load of the transmission chain main shaft can be directly calculated using Δf 2 and Therefore, it can be simply assumed that Δf 2 and The smaller the integral of is, the smaller the fluctuation of system frequency and spindle load is. Therefore, the traditional cost function can be simply expressed as:

[0262]

[0263] In order to improve the generalization ability of the controller model, Δf 2 and The data is normalized as follows:

[0264]

[0265] Therefore, the normalized equation of the traditional cost function is as follows:

[0266]

[0267] Considering the uncertainty and discreteness of the dynamic load of the transmission chain main shaft and the system frequency. Although the traditional cost function C 1 The total fluctuation of the dynamic load and frequency of the main shaft is realized, but it is still weak in evaluating the uncertainty and discreteness of the power system frequency and fatigue load. To solve this problem, the variance entropy is incorporated into the cost function to consider the fluctuation of the power system frequency f and the main shaft torque M s This innovative improvement overcomes the original cost function C 1 Then, the variance entropy can be expressed mathematically as follows:

[0268] C 2 =-∑p(ΔT s,i )log 2 p(ΔT s,i )-p(Δf i )·log 2 p(Δf i )#(36)

[0269] Where p(x) represents the probability density function of the data set. The specific calculation formula is as follows:

[0270]

[0271] Similarly, C 2 Considering the dynamic load of the transmission chain main shaft and the discreteness of the system frequency fluctuation, the normalization process can be expressed as:

[0272] C 2 =-∑p(ΔT s_norm,i )·log 2 p(ΔT s_norm,i )-p(Δf norm,i )·log 2 p(Δf norm,i )#(38)

[0273] At this point, the new multi-objective cost function can be expressed as:

[0274] minC=(1-α)C 1 +αC 2 #(39)

[0275] Among them, α represents C 1 and C 2The weight factor ranges from 0 to 1. The balance between the total amount and the discreteness of the dynamic load and system frequency fluctuations is determined by α in the cost function. In other words, a larger α means that the discreteness of the spindle dynamic load and frequency is more dominant than the total fluctuation. On the contrary, a smaller α means that the total fluctuation is more important in the proposed cost function.

[0276] So far, the proposed PSOPI controller algorithm flow is as follows Figure 6 shown.

[0277] 4.2) Design of Active Disturbance Rejection Controller

[0278] Wind speed is a typical external disturbance in wind turbines, while model uncertainty and measurement errors are ubiquitous internal disturbances. Considering the excellent ability of ADRC in suppressing disturbances and tracking reference signals, the present invention adopts ADRC to collaboratively solve the challenge of maintaining constant torsion angle control of the main shaft of the wind turbine. ADRC control strategy is as follows Figure 7 shown.

[0279] (1) Fan unit transmission system model based on ADRC

[0280] The two-mass block transmission chain model is used as the controlled object in the ADRC system, based on step 1. The main shaft torque equation of the wind turbine transmission chain can be expressed as:

[0281]

[0282] Among them, θ s Indicates the torsion angle of the transmission system; Represents the torsional angular velocity of the transmission system. s and The mathematical formula can be expressed as:

[0283]

[0284] Among them, θ r represents the integral of the rotor angular velocity; θ g Integral of the generator angular velocity.

[0285] Based on formulas (39) and (40), the dual-mass model of the wind turbine transmission chain can be reconstructed as θ s The specific expression is as follows:

[0286]

[0287] in, represents the main shaft torsional angular acceleration, and w represents the unknown disturbance of the wind turbine model.

[0288] The change of angular acceleration of the main shaft of the fan drive chain will cause the fluctuation of the drive shaft torque, thus causing vibration in the drive system. s It can also be expressed as the vibration amplitude of the main shaft.

[0289] Then, in order to decouple the controller from the state and distribute the unknown disturbances caused by the uncertainty of the aerodynamic parameters and the grid anomaly to the generator as a combination of disturbances, denoted as w, the expression of the transmission chain system model (41) is modified as follows:

[0290]

[0291] Considering the nonlinearity of the wind turbine group, formula (43) can be restated as follows:

[0292]

[0293] Among them, x 1 Indicates the spindle torsion angle θ s ;x 2 Represents x 1 The derivative of x 2 Indicates the spindle torsional angular acceleration

[0294] Because θ s It is represented by the vibration amplitude of the main shaft, so the dynamic load optimization of the transmission chain will be achieved by suppressing the torsion angle of the main shaft. Therefore, considering setting the expected value of the torsion angle of the transmission system to 0, the resulting tracking error system can be expressed as follows:

[0295]

[0296] Among them, x 3 represents the new state variable of the system, represents w, and x 3 is considered continuous.

[0297] Based on formula (45), the obtained tracking error system can be extended to a new system in the following way, which can be specifically expressed as:

[0298]

[0299] in, Represents system extended state variables.

[0300] (2) Design of Active Disturbance Rejection Controller

[0301] An active disturbance rejection controller (ADRC) control method is adopted to achieve accurate control of the wind turbine drive train system under nonlinear uncertainties. Figure 7The block diagram of ADRC applied to the tracking error system of the wind turbine transmission chain is depicted, which includes a tracking differentiator (TD), an extended state observer (ESO) and a nonlinear state error feedback controller (NLSEFC).

[0302] 1) TD

[0303] The purpose of TD is to facilitate the smooth conversion process of the reference signal, effectively converting the input error signal into a smoother signal. This helps prevent the output from being affected by too many initial conditions and avoids oversaturation. TD also plays a vital role in obtaining a smooth and differential signal of the error signal, which is crucial to the subsequent controller design in the wind turbine drive train system.

[0304] The mathematical expression of TD is:

[0305]

[0306] Among them, v 0 Represents the target value of control, where v 0 =θ s , take v 0 The target value of v is 0; 1 , v 2 Respectively represent v 0 The expected trajectory and its derivative; r represents the sampling period; h 0 Represents the filtering factor.

[0307] 2) ESO

[0308] ESO is a robust control technique that evaluates external and internal disturbances and subsequently compensates for them to achieve the desired response. The relationship between inputs and outputs in ESO is shown below:

[0309]

[0310] Among them, z 1 , z 2 , z 3 represents the output signal of the extended state observer; β 1 , β 2 , β 3 , α 1 , α 2 represents the design parameters of the extended state sensor, the nonlinear equation fal(ε,α 1 ,δ) is:

[0311]

[0312] 3) NLSEFC

[0313] A nonlinear state error feedback controller is established in the ADRC structure, which uses the estimated state of the ESO (z 1 ,z 2 ) and TD output signal (v 1 ,v 2 ) are combined to realize the control input of the system, which is specifically expressed as:

[0314]

[0315] Among them, e 1 , e 2 Represents the differential signal error; k 1 , k 2 denote the proportional and integral parameters respectively.

[0316] Finally, according to the tracking error closed-loop system formula (45), the active disturbance rejection nonlinear controller can be derived as:

[0317]

[0318] 4.3) Hybrid Controller Design

[0319] PSOPI control has achieved frequency regulation and fatigue load reduction. However, PSOPI has strong frequency regulation capability but is weak in dynamic load suppression of the transmission chain. Meanwhile, ADRC shows outstanding performance in suppressing the torque of the wind turbine. Inspired by the advantages of PSOPI and ADRC control, a hybrid control strategy called PSOPI-ADRC is proposed. The overall block diagram is shown in Figure 8 shown.

[0320] Different from the traditional controller design, the proposed hybrid controller increases the spindle speed fluctuation value Δω s As input, it is used for frequency regulation and fatigue load reduction. The reasons for the hybrid controller are summarized as follows: On the one hand, the introduction of the PSOPI controller can compensate for the reference power fluctuation caused by random wind speed. Specifically, the fluctuation Δω of the main shaft speed is used s As the input of power compensation. The compensation power not only reduces the impact of frequency regulation on fatigue load, but also suppresses frequency fluctuation within the tolerance range of the power system. On the other hand, ADRC tracks the main shaft torsion angle Δθ s The fluctuation error of the main shaft torsion angle is introduced to achieve load relief of the generator reference torque fluctuation. Since ADRC performs well in reducing fatigue loads.s As the input of ADRC, the torque compensation of the generator is realized.

[0321] The above embodiments are only preferred embodiments of the present invention and are not limitations of the technical solutions of the present invention. Any technical solution that can be implemented on the basis of the above embodiments without creative work should be deemed to fall within the scope of protection of the patent of the present invention.

Claims

1. A coordinated optimization control strategy for wind turbine transmission chain dynamic load and grid frequency response, characterized in that: The steps include: Step 1: Establish the wind turbine dynamics model: including transmission chain modeling and double-fed asynchronous generator modeling; Step 2: Linearization of the torque model of the wind turbine generator: Select a certain equilibrium working point of the nonlinear dynamic system, and apply disturbance to each state variable of the system. By performing Taylor expansion on the equilibrium point and retaining the first-order term of the Taylor expansion, the linearized model of the nonlinear system near the working point is obtained. Step 3: Establish the power system frequency regulation model of the wind turbine group: including the wind turbine group model based on frequency regulation control, the thermal power unit model based on frequency regulation control and the wind-thermal hybrid power system model; Step 4: Design of optimization controller for wind turbine transmission chain dynamic load and system frequency response: including PSOPI controller design, active disturbance rejection controller design and hybrid controller design.

2. A wind turbine transmission chain dynamic load and grid frequency response coordinated optimization control strategy according to claim 1, characterized in that: The step 1 is specifically as follows: 2.1) Transmission chain modeling: Using the dual mass block modeling method, the expression is as follows: Among them, M s Indicates the torque of the low-speed shaft of the transmission chain; M g Represents the generator torque; ω g Indicates the generator speed; η g Indicates the gear speed ratio; J r Indicates the rotor moment of inertia; J g Generator moment of inertia; K Ts represents the spindle viscous friction constant; B Ts Represents the spindle spring constant; M rot Indicates the rotor torque of the fan unit; represents the integral symbol; Represents the first derivative of the wind rotor speed; Represents the first derivative of the generator speed; 2.2) Double-fed asynchronous generator modeling: The torque model of the wind turbine generator is expressed as follows: Among them, M g-ref Indicates the generator reference torque; Unit power reference value; τ g Generator time constant; ω f Represents the generator speed after filtering; τ f Represents the sampling filter time constant; The calculation of the wind turbine generator torque is expressed as: Among them, k opt Indicates the control parameters of torque control.

3. The wind turbine transmission chain dynamic load and grid frequency response coordinated optimization control strategy according to claim 1 is characterized in that: The step 2 is specifically as follows: Assume that the wind turbine is currently operating at time t=k, the wind speed v is v0, and assume that the wind speed v0 is constant within a short working cycle; the short working cycle is the working cycle of the wind turbine when it is discontinuously running or intermittently used; Based on the above assumptions, at t = k, the measured wind turbine power output, generator speed, generator filter speed, pitch angle, rotor torque and generator torque are defined as P g0 ,ω g0 ,ω f0 ,β0,M rot0 and M g0 ; The incremental state space equation of the fan unit transmission chain at time t = k: Δω r Indicates the change in wind wheel speed; Δω g Indicates the change in generator speed; Δω s Indicates the difference between the change in wind rotor speed and the change in generator speed; ΔM rot Indicates the change in the wind turbine rotor torque; ΔM s Indicates the change in torque of the main shaft of the fan drive chain; ΔM g Indicates the torque change of the wind turbine generator; The incremental state space equation of the fan unit transmission chain main shaft torque at time t = k is: The incremental state space equation of the wind turbine generator torque at time t = k is: The change in active reference value of the wind turbine model; The incremental state space equation of the wind turbine pitch angle at time t = k is: Among them, Δt is the control period of the fan group; k p Represents the proportional gain coefficient in PI control; k i Indicates the integral gain coefficient in PI control; k a1 , k a2 Represents different gain coefficients of the pitch controller; ω g_rated Indicates the rated speed of the generator; θ ref The reference pitch angle is obtained by looking up the table based on the generator speed.

4. The wind turbine transmission chain dynamic load and grid frequency response coordinated optimization control strategy according to claim 1, characterized in that: The step 3 is as follows: 4.1) Fan unit model based on frequency modulation control The wind turbine group adjusts the frequency through derating control, and changes the rotor speed and generator torque to adjust the power at the maximum power tracking point P mppt The output power changes between the rated power and the derated power. When the derated factor is assumed to be γ, the forward reserve power P of the wind farm is pr+ Determined by γ for frequency adjustment; in, Indicates the active reference value of the wind turbine model; Indicates the power reference value deviation of the fan group; P out Indicates the active output of the unit model; ΔP out Indicates the power output deviation of the fan unit; P dem Indicates the power demand of the wind turbine under frequency regulation control; P avai Indicates the maximum available power of the fan unit; ΔP dem Indicates the change in power demand of the wind turbine under frequency regulation control; ΔM g0 Indicates the measurable change in the generator torque of the wind turbine; Δω g0 Indicates the measurable change in the generator speed of the wind turbine group; Combined with the generator torque linearization model in step 2, the generator torque increment under the wind turbine participating in frequency regulation control is expressed as: P ref Indicates reference power; P ref0 Indicates the measurable reference power; 4.2) Thermal power unit model based on frequency modulation control The transfer function of the reheat gas turbine is expressed as: Among them, T TR Represents the reheat time constant of the thermal power unit, K TR It represents the power fraction generated by the high voltage section of the thermal power unit, T TT Indicates the time constant of the main air inlet and steam box of the thermal power unit; The mathematical model of the speed governor of a thermal power unit is expressed as: Among them, T TG Indicates the time constant of the speed governor of the thermal power unit; 4.3) Wind-thermal hybrid power system model The system frequency fluctuation characteristics satisfy the following equation: Where H represents the equivalent inertia constant of the power system; D represents the equivalent damping coefficient of the power system load; ΔP e It represents the power deviation of all power sources in the power system; Δf represents the frequency deviation of the power system; s represents a complex variable, which represents the frequency in the Laplace transform domain.

5. The wind turbine transmission chain dynamic load and grid frequency response coordinated optimization control strategy according to claim 1, characterized in that: The step 4 is specifically as follows: 5.1) PSOPI controller design 5.1.1) PI controller The PI controller is expressed as: in, k p is the integral gain coefficient; k i is the differential gain coefficient; represents the Laplace integral operator symbol; Establish transfer function to analyze the dynamic characteristics of fan unit; have to: Where K1 = 1 / ω g0 , K3=P avai Δt, K4 = ω g0 , K5=M g0 , K6=5×10 6 , represents the transfer function of generator torque and generator speed; represents the transfer function between the generator speed and the transmission chain main shaft torque; Represents the transfer function between the wind wheel rotor speed and the transmission chain main shaft torque; The relationship between the transmission chain main shaft torque and the reference power is expressed as: The relationship between the generator speed and the reference power is expressed as: The comprehensive output characteristics of the fan unit without a PI controller are expressed as: G wt1 (s) represents the transfer function of the power output deviation of the wind turbine group and the reference power deviation; represents the transfer function of the generator speed and the reference power; Since the power reference is modified by the PI controller, the expression of the power reference of the wind turbine under the PI controller is obtained: G PI (s) represents the PI controller transfer function; The incremental expression of the reference power of the wind turbine under the PI controller is: The relationship between the fan unit rotor speed and the reference power is expressed as: According to transfer functions (26), (28) and (29), the comprehensive output characteristics of the fan group under PI control are expressed as: G wt2 (s) represents the comprehensive output characteristic transfer function of the fan group under PI control, Represents the transfer function between the wind rotor speed and the reference power; When the power reference is modified by PI control, the relationship between shaft torque and system frequency is expressed as: 5.1.2) PSO optimization algorithm In the PSO algorithm, the particle velocity and position are updated as follows: in, x and v are the position and velocity of the particle; lbest is the local best position of the particle, gbest is the global best position of the particle; c1 and c2 are acceleration coefficients; r1 and r2 are random numbers in (0,1); i represents the number of particles; d represents the dimension; k represents the number of iterations; 5.1.3) Cost Function Assume that Δf 2 and The smaller the integral of is, the smaller the fluctuation of system frequency and spindle load is; the cost function is expressed as: Δf 2 It represents the square of the frequency deviation of the power system; It represents the square of the torque deviation of the main shaft of the transmission chain; represents the square of the frequency deviation of the power system at time i; It represents the square of the torque deviation of the main shaft of the transmission chain at time i; In order to improve the generalization ability of the controller model, Δf 2 and The data is normalized as follows: Δf norm Indicates the normalized value of the normal frequency deviation of the power system; ΔT s_norm Represents the normalized value of the transmission chain main shaft torque, Δf min Indicates the minimum value of the normal frequency deviation of the power system, Δf max Indicates the maximum value of the normal frequency deviation of the power system, ΔM s_min Indicates the minimum value of the transmission chain main shaft torque, ΔM s_max Indicates the maximum value of the main shaft torque of the transmission chain; Therefore, the normalized equation of the cost function is as follows: The variance entropy is incorporated into the cost function to account for power system frequency f fluctuations and main shaft torque M s Fluctuation, mathematical expression is as follows: C2=-∑p(ΔT s,i )·log2p(ΔT s,i )-p(Δf i )·log2p(Δf i ) #(36) Where p(x) represents the probability density function of the data set; the specific calculation formula is as follows: Similarly, C2 is normalized considering the discreteness of the dynamic load of the transmission chain main shaft and the system frequency fluctuation, which is specifically expressed as: C2=-∑p(ΔT s-norm,i )·log2p(ΔT s-norm,i )-p(Δf norm,i )·log2p(Δf norm,i )#(38) At this point, the new multi-objective cost function is expressed as: minC=(1-α)C1+αC2 #(39) Among them, α represents the weight factor of C1 and C2, ranging from 0 to 1; 5.2) Design of Active Disturbance Rejection Controller 5.2.1) Wind turbine drive system model based on ADRC The dual-mass transmission chain model is used as the controlled object in the ADRC system. Based on step 1, the main shaft torque equation of the wind turbine transmission chain is expressed as: in, θ s Indicates the torsion angle of the transmission system; Indicates the torsional angular velocity of the transmission system; θ s and The mathematical formula is: Among them, θ r represents the integral of the rotor angular velocity; θ g The integral of the generator angular velocity; Based on equations (39) and (40), the double-mass block model of the wind turbine transmission chain is reconstructed as θ s The specific expression is as follows: in, represents the torsional angular acceleration of the main shaft, and w represents the unknown disturbance of the wind turbine model; In order to decouple the controller from the state and assign the unknown disturbances caused by the uncertainty of the aerodynamic parameters and the grid anomaly to the generator as a combination of disturbances; the expression of the transmission chain system model (41) is modified as follows: represents the system state equation; u represents the system input; b represents the adjustable parameter; Considering the nonlinearity of the fan group, equation (43) is reformulated into the following nonlinear form: Among them, x1 represents the spindle torsion angle; x2 represents the derivative of x1; x2 represents the spindle torsion angle acceleration; b0 represents the input control gain, f(x1,x2,M rot ) represents the nonlinear state equation of the fan system; Considering that the expected value of the torsion angle of the transmission system is set to 0, the resulting tracking error system is expressed as follows: Among them, x3 represents the new state variable of the system, and x3 is considered to be continuous; Based on equation (45), the obtained tracking error system is expanded to the new system in the following way, which is specifically expressed as: in, represents the system extended state variable; fh(w) represents the expanded state estimate; 5.2.2) Active Disturbance Rejection Controller The wind turbine transmission chain tracking error system includes a tracking differentiator TD, an extended state observer ESO and a nonlinear state error feedback controller NLSEFC; 5.2.2.1) TD The mathematical expression of TD is: Among them, v0 represents the target value of control, where v0 = θ s , take the target value of v0 as 0; v1, v2 represent the expected trajectory of v0 and its derivative respectively; r represents the sampling period; h0 represents the filter factor; fhan represents the inverse function; 5.2.2.2)ESO The relationship between input and output in ESO is as follows: Where z1, z2, z3 represent the output signals of the extended state observer; β1, β2, β3, α1, α2 represent the design parameters of the extended state sensor. The mathematical expression of the nonlinear equation fal(ε, α1, δ) is: δ represents a constant that affects the filtering effect; α represents a constant between 0 and 1; ε represents an error; 5.2.2.3) NLSEFC A nonlinear state error feedback controller is established in the ADRC structure. The control input of the system is realized by combining the estimated state of ESO (z1, z2) with the output signal of TD (v1, v2), which is specifically expressed as: Where, e1, e2 represent the differential signal error; k1, k2 represent the proportional and integral parameters respectively; Finally, according to the tracking error closed-loop system (45), the ADRC nonlinear controller can be derived as: 5.3) Hybrid Controller Design The PSOPI-ADRC hybrid controller is proposed to increase the spindle speed fluctuation value Δω s As input for frequency regulation and fatigue load relief; The reference power fluctuation caused by random wind speed is compensated by introducing the PSOPI controller, that is, the fluctuation of the main shaft speed Δω is adopted s As input for power compensation; By introducing the fluctuation error Δθ of the spindle torsion angle s As the input of ADRC, the torque compensation of the generator is realized.

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