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CN111963372B - Tracking control method for optimal rotating speed of wind driven generator - Google Patents

Tracking control method for optimal rotating speed of wind driven generator Download PDF

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Publication number
CN111963372B
CN111963372B CN202010903671.4A CN202010903671A CN111963372B CN 111963372 B CN111963372 B CN 111963372B CN 202010903671 A CN202010903671 A CN 202010903671A CN 111963372 B CN111963372 B CN 111963372B
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wind
speed
error
uncertainty
driven generator
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CN111963372A (en
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荆丰梅
孙寒冰
刘伟杰
于丰玮
王凯
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Beijing Institute of Petrochemical Technology
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03DWIND MOTORS
    • F03D7/00Controlling wind motors 
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05BINDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00Control
    • F05B2270/10Purpose of the control system
    • F05B2270/101Purpose of the control system to control rotational speed (n)
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05BINDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00Control
    • F05B2270/70Type of control algorithm
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/72Wind turbines with rotation axis in wind direction

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Abstract

Optimum wind driven generatorA rotating speed tracking control method belongs to the technical field of control. The invention aims to solve the problem that the wind energy capture efficiency of a wind driven generator is low in a low wind speed area. The method comprises the steps of firstly establishing a dynamic equation of the wind power generation system aiming at the wind power generation system; then tracking error variable e-omega of expected wind wheel speed to wheel speedr‑ωrefMake a constraint Pl(t)<e(t)<Pr(t), converting the error variable, finally selecting a Lyapunov function, deriving the Lyapunov function V for time t based on the converted error epsilon and the corresponding derivative, finally obtaining a preset performance controller of the wind driven generator, and controlling according to the preset performance controller of the wind driven generator. The method is mainly used for tracking and controlling the optimal rotating speed of the wind driven generator.

Description

Tracking control method for optimal rotating speed of wind driven generator
Technical Field
The invention relates to a method for tracking and controlling the rotating speed of a wind driven generator. Belongs to the technical field of control.
Background
In recent years, wind power generation has received much attention due to the shortage of energy and the harmful effects of fossil fuels. Wind power generators can be generally classified into a constant speed type and a variable speed type, and the variable speed type has advantages of high efficiency and low conversion cost compared with the constant speed type at various wind speeds. However, these advantages must depend on advanced control methods, and Maximum Power Point Tracking (MPPT) control is an important issue, aiming at controlling the rotation speed of the wind turbine to track the optimal rotation speed at the rated wind speed, so as to capture more wind energy.
The current commonly used control methods for maximum power tracking include an optimal torque method, a power curve method, a tip speed ratio method and the like, wherein the tip speed ratio method is widely used for theoretical research. The method mainly maintains the tip speed ratio of the wind driven generator at an optimal value when the wind speed changes, so that the maximum wind energy capture rate is maintained. However, due to the highly nonlinear and cross-coupled system dynamics, and the dynamic changes of natural wind speed, power grid requirements and system operation conditions, large model uncertainty and external disturbance are introduced to the wind power generation system model, and the design difficulty of the controller is further increased. The conventional control method often has the problems of serious buffeting of a wind wheel, low wind energy capturing efficiency and the like, and reduces the safety, reliability and economy of a wind power generation system.
Disclosure of Invention
The invention aims to solve the problem that the wind energy capturing efficiency of a wind driven generator in a low wind speed area is low, and further provides a wind driven generator optimal rotating speed tracking control method.
A tracking control method for the optimal rotating speed of a wind driven generator comprises the following steps:
s1, establishing a dynamic equation of the wind power generation system aiming at the wind power generation system;
s2 wheel speed omegarTracking desired rotor speed omegarefThe error variable of (2) is e ═ ωrrefAnd (4) carrying out constraint:
Pl(t)<e(t)<Pr(t) (1)
upper bound of error Pr(t) and lower bound Pl(t) is as follows
Figure BDA0002660643400000011
Wherein 0 is not less than delta1≤1,0≤δ2≤1,δ1And delta2Sign () is a sign function for a design parameter; ρ (t) is a performance function;
converting the error variable to obtain a converted error epsilon (t)
Figure BDA0002660643400000021
Figure BDA0002660643400000022
s3, selecting a Lyapunov function, deriving the Lyapunov function V for time t based on the converted error epsilon and the corresponding derivative, finally obtaining a preset performance controller of the wind driven generator, and controlling according to the preset performance controller of the wind driven generator.
Further, said desired rotor speed ωrefIs composed of
ωref=λoptv/R (4)
Wherein v is wind speed, λoptFor optimal tip speed ratio, R is the wind wheel radius.
Further, the kinetic equation of the wind power generation system established in step s1 is as follows:
Figure BDA0002660643400000023
Figure BDA0002660643400000024
Figure BDA0002660643400000025
in the formula, ωrIs the rotational angular velocity of the wind wheel, JrMoment of inertia of low-speed shaft, BrDamping coefficient for low-speed shaft, JgMoment of inertia of high-speed shaft, BgIs a high-speed shaft damping coefficient, TaFor pneumatic torque, TgFor the electromagnetic torque of the generator, ngIs the gear ratio of the gearbox, and F is the system uncertainty and disturbance.
Further, the system uncertainty and disturbance F are bounded, and can be expressed as:
Figure BDA0002660643400000026
in the formula,
Figure BDA0002660643400000027
for system uncertainty, d is the unknown interference and δ is the upper bound of uncertainty.
Further, the pneumatic torque TaThe following were used:
Figure BDA0002660643400000028
wherein rho is air density, R is wind wheel radius, v is wind speed, and wind energy utilization coefficient CpIs a non-linear function of the tip speed ratio λ and the blade pitch angle β.
Further, the performance function ρ (t) is as follows
Figure BDA0002660643400000029
Where ρ is0ρ (0) is an initial value; rhoThe maximum allowable value of the tracking error at the steady state; t is0Is a preset convergence time.
Further, the preset performance controller of the wind driven generator is
Figure BDA00026606434000000210
Figure BDA0002660643400000031
Figure BDA0002660643400000032
Wherein, TgFor generator electromagnetic torque, TaFor pneumatic torque, ngIs the transmission ratio of the gear box,
Figure BDA0002660643400000033
Brdamping coefficient for low-speed shafts, BgIs a high-speed shaft damping coefficient, omegarIs the angular velocity, omega, of the wind wheel rotationrefTo expect a rotor speed, delta is an upper bound of uncertainty,
Figure BDA0002660643400000034
Jrmoment of inertia of low-speed shaft, JgThe moment of inertia of the high-speed axis, and the converted error.
Further, the step s3 of selecting the lyapunov function, and deriving the lyapunov function V with respect to the time t based on the converted error e and the corresponding derivative to obtain the preset performance controller of the wind turbine generator includes the steps of:
under the condition of not considering uncertainty and interference, the error epsilon after conversion is obtained by derivation of time t
Figure BDA0002660643400000035
Wherein
Figure BDA0002660643400000036
When uncertainty and interference F are considered, the Lyapunov function V is derived over time t
Figure BDA0002660643400000037
Where δ represents the upper bound of uncertainty;
finally obtaining a preset performance controller of the wind driven generator as
Figure BDA0002660643400000038
Further, in the derivation of the post-conversion error e with respect to time t without considering uncertainty and interference, the tracking error e is first derived without considering uncertainty and interference
Figure BDA0002660643400000039
The post-conversion error epsilon is then derived over time t
Figure BDA00026606434000000310
The above formula is arranged to obtain
Figure BDA0002660643400000041
Wherein
Figure BDA0002660643400000042
Has the advantages that:
the wind power generator can well solve the problem that the existing wind power generator has low wind energy capturing efficiency in a low wind speed area, and has high capturing efficiency even in the low wind speed area.
Compared with the existing control method, the existing control method does not consider the overshoot problem of control, and an explicit mathematical relationship is difficult to establish between the parameter of the designed performance function and the actual error convergence rate. The invention improves the traditional preset performance control method, adopts an improved performance function and a new error transformation method, can limit the overshoot of the system, and can obtain the required steady-state precision within the specified time. The embodiment shows that the preset performance control method for improving the performance function can obtain the required steady-state precision within the specified time and has better robustness.
Drawings
FIG. 1 is a wind speed curve used in the simulation in the example;
FIG. 2 is a graph of the tracking error of the rotational speed in the example.
Detailed Description
The first embodiment is as follows:
before describing the present embodiment, first, parameters related to the present embodiment will be described:
ωr-wind wheel rotational angular velocity; j. the design is a squarerThe moment of inertia of the low speed shaft; b isr-damping coefficient of the low speed shaft; j. the design is a squareg-the moment of inertia of the high speed shaft; b isg-high speed shaft damping coefficient; t isa-a pneumatic torque; t isg-the generator electromagnetic torque; n isg-the gear ratio of the gearbox; F-System uncertainty and interference; omegaref-a desired value of the rotational angular velocity of the wind wheel; e- ωrref-a tracking error; ρ is air density; r is the wind wheel radius; v-wind speed; cp-a wind energy utilization factor; λ -tip speed ratio; lambda [ alpha ]opt-an optimal tip speed ratio; β -blade pitch angle; δ — the upper bound of uncertainty.
The method for tracking and controlling the optimal rotating speed of the wind driven generator in the embodiment comprises the following steps:
1. establishing a kinetic equation of the wind power generation system:
Figure BDA0002660643400000051
Figure BDA0002660643400000052
Figure BDA0002660643400000053
introducing a system disturbance force F, assuming the low speed shaft is completely rigid, a dynamic model of the wind power generation system can be expressed as:
Figure BDA0002660643400000054
in the formula, ωrIs the rotational angular velocity of the wind wheel, JrMoment of inertia of low-speed shaft, BrDamping coefficient for low-speed shaft, JgMoment of inertia of high-speed shaft, BgIs a high-speed shaft damping coefficient, TaFor pneumatic torque, TgFor the electromagnetic torque of the generator, ngF is the system uncertainty and disturbance, which is typically unknown, for the gear ratio of the gearbox.
Pneumatic torque Ta
Figure BDA0002660643400000055
Wherein rho is air density, R is wind wheel radius, v is wind speed, and wind energy utilization coefficient CpIs a non-linear function of the tip speed ratio λ and the blade pitch angle β;
assuming F is bounded, it can be expressed as:
Figure BDA0002660643400000056
in the formula,
Figure BDA0002660643400000057
for system uncertainty, d is the unknown interference and δ is the upper bound of uncertainty.
The control aim of the wind turbine generator below the rated wind speed is to capture wind energy to the maximum extent. If the output power of the wind power system is maximized, C needs to be enabledp(λ, β) is at a maximum. Due to Cp(λ, β) is a function of λ and β as variables, by adjusting the generator torque T while keeping the pitch angle β constant (usually around 0 °)gIndirectly varying wind wheel speed omegarSo that it better tracks the optimum tip speed ratio lambdaopt
Desired rotor speed ωrefIs composed of
ωref=λoptv/R (14)
Where v is the wind speed.
Tracking of maximum power using tip speed ratio typically requires an estimate of wind speed. The commonly used estimation method comprises observation through an observer, Kalman filtering, Newton-Raphson algorithm and the like, and has good estimation effect. The present invention assumes that the estimated value of the wind speed is equal to the true value.
The object of the invention can thus be expressed as: control of generator torque T by designing a predetermined performance controller to improve performance functiongTo make the wind wheel rotate at a speed omegarFaster tracking of desired rotor speed ωref. The error variable is defined as e ═ ωrref
2. Improving the performance function and error transformation:
to achieve the desired control objective, a performance function is introduced as a preset performance boundary. The definition of the performance function is as follows:
definition 1: for a smoothing function ρ (t) R+→ R if satisfied
(1) ρ (t) is a monotonically decreasing positive function.
(2)limt→∞ρ(t)=ρ>0。
Then this function is called the performance function.
Conventional performance functions are typically designed in an exponential fashion.
ρ(t)=(ρ0)e-kt (15)
Where ρ is0,ρAnd k is a preset normal number.
It is clear that the convergence speed of the conventional performance function depends on the exponential term e-kt. However, it is difficult to establish a clear mathematical relationship between the parameter k and the convergence speed, and it is difficult to determine the specific error convergence time. Furthermore, the choice of the parameter k is not clear. Thus, the present invention performs the conventional performance functionAn improvement is made. The improved performance function is
Figure BDA0002660643400000061
Where ρ is0,ρ,T0Are predefined design parameters. Rho0ρ (0) as an initial value; rhoThe maximum allowable value of the tracking error at the steady state; t is0Is a preset convergence time.
By means of an improved performance function, we can preset the convergence time T0And can visually summarize the preset convergence time T0The smaller the error precision reaches rho under the same performance requirementThe faster the convergence speed of (c).
The wind turbine preset performance controller is then derived based on the proposed improved performance function. The constraint inequality adopted is
Pl(t)<e(t)<Pr(t) (17)
Upper bound of error Pr(t) and lower bound Pl(t) is as defined below
Figure BDA0002660643400000062
Wherein 0 is not less than delta1≤1,0≤δ2Less than or equal to 1 as design parameter. sign (·) is a sign function.
It is difficult to directly solve the tracking control problem under the constraint, so an error conversion mode is needed to convert the constrained problem into the unconstrained stable control problem. Conventional error transformation function Sii) The definition is as follows:
definition 2: if a function S existsii) The following properties are satisfied:
(1)Sii) Smooth and strictly monotonic increase
(2)
Figure BDA0002660643400000063
(3)
Figure BDA0002660643400000071
This function is called the error transformation function.
Because the invention improves the traditional performance function and can not use the traditional error transformation function, a corresponding new error transformation method needs to be designed. The invention employs a post-conversion error ε (t) of
Figure BDA0002660643400000072
The following theorem can be obtained:
theorem 1: if the post-conversion error ε (t) is bounded, the tracking error e (t) may be constrained to an upper bound Pr(t) and lower bound Pl(t) between.
And (3) proving that: inverse transform of equation (19) ∈ (t)
Figure BDA0002660643400000073
Formula (20) can further give
Figure BDA0002660643400000074
Since ε (t) is bounded, there is a constant εM∈R+So that | Epsilon (t) | is less than or equal to epsilonMI.e. -epsilonM≤ε(t)≤εM(ii) a Therefore, the above formula (21) can be changed to
Figure BDA0002660643400000075
It is noted that
Figure BDA0002660643400000076
Can obtain
Figure BDA0002660643400000077
Finally have
Pl(t)<e(t)<Pr(t) (24)
After the syndrome is confirmed.
Next we will use the converted error epsilon (t) instead of the tracking error e (t) for the derivation of the controller. Theorem 1 shows that when the transformed error ε (t) is bounded, the tracking error e (t) is limited to a pre-set performance bound (17). By being Pl(t) and Pr(t) selecting proper design parameters, and ensuring the transient performance and the steady-state performance of the tracking error e (t).
3. Preset performance controller design
The derivative of the tracking error e from equation (11) is, without taking uncertainty and interference into account
Figure BDA0002660643400000081
Derivation of the post-conversion error epsilon over time t
Figure BDA0002660643400000082
Wherein
Figure BDA0002660643400000083
The above formula is finished to obtain
Figure BDA0002660643400000084
Wherein,
Figure BDA0002660643400000085
for ease of description and presentation, all time variables t are omitted. Hereinafter, e (t), ε (t), θ (t), ρ (t), and the like are abbreviated as e, ε, θ, ρ.
Selecting the Lyapunov function as
Figure BDA0002660643400000086
Then the Lyapunov function V is derived over time t
Figure BDA0002660643400000087
To this end, the predetermined performance controller of the wind turbine is designed as
Figure BDA0002660643400000088
Wherein c is1Is a positive constant. At this time, the derivative of the Lyapunov function V with respect to time t
Figure BDA0002660643400000089
Is negative, so the system asymptotically stabilizes.
When uncertainty and interference F are considered, the Lyapunov function V is derived over time t
Figure BDA00026606434000000810
In the formula, δ represents an upper bound of uncertainty.
The preset performance controller of the wind driven generator is designed to
Figure BDA00026606434000000811
Examples
In order to verify the effectiveness of the control method designed by the invention, the control method is applied to a wind turbine model for simulation verification, and the influence caused by model uncertainty and interference is considered. The parameters of the 5MW wind turbine used are shown in Table 1.
TABLE 1 aerogenerator parameters
Figure BDA0002660643400000091
The invention superposes two sinusoidal signals with different frequencies as the input wind speed. The wind speed curve used in the simulation is shown in fig. 1.
To verify the effectiveness of the designed controller, white noise was added as a random disturbance to the system, and the improved performance function controller parameters are shown in table 2.
TABLE 2 improved Performance function controller parameters
Figure BDA0002660643400000092
Simulation analysis: the rotational speed tracking error curve is shown in fig. 2.
As can be seen from fig. 2, the tracking error can obtain the required steady-state accuracy at the 4 th second of the preset convergence time, and the random disturbance of the system can cause the tracking error curve to slightly fluctuate but still be controlled within the preset performance function range. The preset performance control method for improving the performance function is proved to be capable of obtaining required steady-state precision within a specified time and have better robustness.
Comparison with the prior art solution
If the control requirements of tracking the rotating speed of the wind driven generator under the influence of rated wind speed, model uncertainty, unknown interference and the like are to be realized, the invention also comprises a scheme based on neural network control, a traditional preset performance control scheme and the like besides the algorithm, and the two schemes are briefly introduced below and compared with the algorithm of the invention.
a. Neural network based scheme
The neural network is mainly used for processing the uncertainty of the wind driven generator model or the unknown external disturbance problem, the disturbance is estimated by using the neural network, and a relatively good control scheme is obtained by applying some common control methods, such as PID control, sliding mode control, backstepping control, self-adaptive control and the like. For example, in the "non-singular fast terminal sliding mode control of mechanical arm neural network", a Radial Basis Function Neural Network (RBFNN) is used to approximate unknown dynamic characteristics.
However, compared with the algorithm of the present invention, the above scheme cannot meet the requirement of system rapidity because of too large calculation amount. The algorithm can preset the convergence speed and the convergence time by introducing a preset performance method and error conversion, and is closer to the actual engineering requirement.
b. Scheme based on traditional preset performance control
In the case of system uncertainty and external interference, an Adaptive neural network based scheduled performance control for a neural network under input management scheme is designed, and the scheme is compared with a PD (proportional plus derivative) controller in a simulation manner, so that the effectiveness of the scheduled performance control method is proved. A novel preset performance control method adopting Quasi-timing is researched in Quasi fixed-time fault-complete control for nonlinear mechanical systems with enhanced performance, and the outstanding advantage that fixed convergence time can be specified in advance is found. According to an unknown pure feedback system, a controller is designed by using a preset performance control method, and a general feedback control scheme without an approximate state is provided. The scheme can make the output error converge to a preset arbitrary small value, and avoid the complexity problem caused by the traditional backstepping method.
However, compared with the algorithm of the present invention, the above scheme does not consider the overshoot problem of the control, and it is difficult to establish a definite mathematical relationship between the parameters of the designed performance function and the actual error convergence rate. The algorithm of the invention improves the traditional preset performance control method, adopts an improved performance function and a new error transformation method, can limit the overshoot of the system, and can obtain the required steady-state precision within the specified time.
It should be noted that the detailed description is only for explaining and explaining the technical solution of the present invention, and the scope of protection of the claims is not limited thereby. It is intended that all such modifications and variations be included within the scope of the invention as defined in the following claims and the description.

Claims (8)

1. The optimal rotating speed tracking control method of the wind driven generator is characterized by comprising the following steps of:
s1, establishing a dynamic equation of the wind power generation system aiming at the wind power generation system;
s2 wheel speed omegarTracking desired rotor speed omegarefThe error variable of (2) is e ═ ωrrefAnd (4) carrying out constraint:
Pl(t)<e(t)<Pr(t) (1)
upper bound of error Pr(t) and lower bound Pl(t) is as follows
Figure FDA0003228500470000011
Wherein 0 is not less than delta1≤1,0≤δ2≤1,δ1And delta2Sign () is a sign function for a design parameter; ρ (t) is a performance function; the performance function ρ (t) is as follows
Figure FDA0003228500470000012
Where ρ is0ρ (0) is an initial value; rhoThe maximum allowable value of the tracking error at the steady state; t is0Is a preset convergence time;
converting the error variable to obtain a converted error epsilon (t)
Figure FDA0003228500470000013
Figure FDA0003228500470000014
s3, selecting a Lyapunov function, deriving the Lyapunov function V for time t based on the converted error epsilon and the corresponding derivative, finally obtaining a preset performance controller of the wind driven generator, and controlling according to the preset performance controller of the wind driven generator.
2. Method for optimal speed tracking control of wind turbines according to claim 1, wherein said desired rotor speed ωrefIs composed of
ωref=λoptv/R (4)
Wherein v is wind speed, λoptFor optimal tip speed ratio, R is the wind wheel radius.
3. The method for tracking and controlling the optimal rotating speed of the wind turbine generator as claimed in claim 1, wherein the step s1 is implemented by establishing the following dynamic equations of the wind turbine generator system:
Figure FDA0003228500470000017
Figure FDA0003228500470000015
Figure FDA0003228500470000016
in the formula, ωrIs the rotational angular velocity of the wind wheel, JrMoment of inertia of low-speed shaft, BrDamping coefficient for low-speed shaft, JgMoment of inertia of high-speed shaft, BgIs a high-speed shaft damping coefficient, TaFor pneumatic torque, TgFor the electromagnetic torque of the generator, ngIs the gear ratio of the gearbox, and F is the system uncertainty and disturbance.
4. A method according to claim 3, wherein the system uncertainty and disturbance F are bounded and can be expressed as:
Figure FDA0003228500470000021
in the formula,
Figure FDA0003228500470000022
for system uncertainty, d is the unknown interference and δ is the upper bound of uncertainty.
5. The method as claimed in claim 4, wherein the aerodynamic torque T is a torque value of a rotor of the wind turbineaThe following were used:
Figure FDA0003228500470000023
wherein rho is air density, R is wind wheel radius, v is wind speed, and wind energy utilization coefficient CpIs a non-linear function of the tip speed ratio λ and the blade pitch angle β.
6. The method as claimed in claim 1, wherein the predetermined performance controller of the wind turbine is a predetermined performance controller
Figure FDA0003228500470000024
Figure FDA0003228500470000025
Figure FDA0003228500470000026
Wherein, TgFor generator electromagnetic torque, TaFor pneumatic torque, ngIs the transmission ratio of the gear box,
Figure FDA0003228500470000027
Brdamping coefficient for low-speed shafts, BgIs a high-speed shaft damping coefficient, omegarIs the angular velocity, omega, of the wind wheel rotationrefTo expect a rotor speed, delta is an upper bound of uncertainty,
Figure FDA0003228500470000028
Jrmoment of inertia of low-speed shaft, JgIs the moment of inertia of the high-speed axis, ε is the error after conversion, c1Is a positive constant.
7. The method as claimed in claim 6, wherein the step s3 of selecting the lyapunov function, deriving the lyapunov function V with respect to time t based on the converted error e and the corresponding derivative to obtain the preset performance controller of the wind turbine comprises the steps of:
under the condition of not considering uncertainty and interference, the error epsilon after conversion is obtained by derivation of time t
Figure FDA0003228500470000029
Figure FDA00032285004700000210
Figure FDA00032285004700000211
Wherein
Figure FDA0003228500470000031
When uncertainty and interference F are considered, the Lyapunov function V is derived over time t
Figure FDA0003228500470000032
Where δ represents the upper bound of uncertainty;
finally obtaining a preset performance controller of the wind driven generator as
Figure FDA0003228500470000033
8. The method as claimed in claim 7, wherein the derivation of the post-conversion error e with respect to time t without considering uncertainty and disturbance is performed by first deriving the tracking error e without considering uncertainty and disturbance
Figure FDA0003228500470000034
The post-conversion error epsilon is then derived over time t
Figure FDA0003228500470000035
The above formula is arranged to obtain
Figure FDA0003228500470000036
Figure FDA0003228500470000037
Figure FDA0003228500470000038
Wherein
Figure FDA0003228500470000039
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