CN111878308A - Wind turbine cluster prediction group control method - Google Patents
Wind turbine cluster prediction group control method Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/028—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
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Abstract
A wind turbine group control method based on predictive control is characterized in that preliminary calculation of the aerodynamic performance of a wind turbine is carried out according to an individual three-dimensional parameterized geometric model of a wind turbine cluster and the relative position of the wind turbine in the cluster; measuring the wind speed of the wind turbine cluster in the windward side to the outer side by a laser wind measuring radar system; by measuring wind speed information, considering the influence of the tower and the wake of the upstream wind turbine in the cluster on wind conditions, predicting the incoming flow wind speed on the swept surface of the wind wheel in the cluster at a given time step; and determining a group control strategy of the wind turbine cluster according to the wind speed prediction model and the wind turbine pneumatic prediction model in time step by using a model prediction optimization control system positioned at the cloud. The invention meets the control requirement of the wind turbine cluster on the problem of multi-input and multi-output nonlinear optimization under the complex working environment, improves the working efficiency of the wind turbine cluster, increases the power generation power, effectively reduces the load fluctuation of the wind turbine cluster, and improves the safety and the reliability of the system.
Description
Technical Field
The invention relates to a technology in the field of wind power generation, in particular to a wind turbine group control method based on predictive control.
Background
In a wind turbine cluster, the distance between the wind turbines is short, so that the obvious wind turbine cluster effect is achieved, and the trail of the upwind wind turbine can obviously influence the aerodynamic characteristics of the downwind wind turbine, so that the difficulty in adjustment and control is increased. At present, PID control aiming at a single wind turbine is widely adopted, although a wind speed feedforward control algorithm design based on a laser wind measuring radar (LiDAR) exists, the wind speed feedforward control algorithm design is still a classical control loop based on the PID, only the measured wind speed is used as a feedforward signal to increase compensation in the control loop, and the PID control method for increasing the feedforward compensation control to the wind turbine can not meet the control requirement of the wind turbine cluster on the problem of multi-input and multi-output band constraint nonlinear optimization under the complex working environment. At present, no better solution is available for the optimal control problem of the wind turbine cluster.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a wind turbine group control method based on predictive control, so as to realize the purpose of group control of a wind turbine cluster, meet the control requirement of the wind turbine cluster on the problem of multi-input and multi-output band constraint nonlinear optimization under a complex working environment, improve the working efficiency of the wind turbine cluster, increase the power generation power, effectively reduce the load fluctuation of the wind turbine cluster and improve the safety and reliability of the system.
The invention is realized by the following technical scheme:
the invention relates to a wind turbine group control method based on predictive control, which carries out preliminary calculation of the aerodynamic performance of a wind turbine according to an individual three-dimensional parameterized geometric model of a wind turbine cluster and the relative position of the wind turbine in the cluster; measuring the wind-up outside near-field wind speed of a wind turbine cluster by a laser Detection and Ranging (LiDAR) system; by measuring wind speed information, considering the influence of the tower and the wake of the upstream wind turbine in the cluster on wind conditions, predicting the incoming flow wind speed on the swept surface of the wind wheel in the cluster at a given time step; and determining a group control strategy of the wind turbine cluster according to the wind speed prediction model and the wind turbine pneumatic prediction model in time step by using a model prediction optimization control system positioned at the cloud.
The individual three-dimensional parameterized geometric model comprises geometric shape models of blades, cabins and towers of a single wind turbine and a wind turbine aerodynamic performance prediction model.
The wind turbine aerodynamic performance prediction model comprises: wind speed on swept surface of wind turbine
And acquiring the aerodynamic performance of the wind turbine at the current moment according to the operating parameters of the wind turbine and the aerodynamic performance of the wind turbine at the previous moment.
The wind turbine operation parameters comprise the rotating speed omega and the pitch angle of each blade
Wind wheel yaw angle thetayaw。
The aerodynamic performance comprises aerodynamic distribution, a near-tail influence characteristic and a far-field tail characteristic on each blade of the wind turbine.
The wind speed prediction model obtains wind speed vector information on a vertical plane at a given distance h in front of a wind wheel in the wind direction on a cluster of the wind turbine through a LiDAR wind measurement system
Calculating the time t when the wind speed on the current LiDAR measuring surface reaches the wind wheel swept surface and the corresponding wind speed by considering the influence of the tower on the upstream wind condition
The optimized group control strategy is as follows: the operation data of each wind turbine is analyzed through a cloud computing center, namely the rotating speed and the pitch angle of each blade of each wind turbine in a cluster are used as input variables, the output power of the wind turbine cluster is improved, the load fluctuation of each wind turbine component in the cluster is reduced as far as possible, and the wind turbine cluster prediction control method based on the time stepping pneumatic performance prediction model is established.
The time step wind turbine aerodynamic prediction model is used for establishing a time step wind turbine aerodynamic performance and free trail prediction model according to a computational fluid mechanics theory, inputting corresponding wind turbine blade geometry and wind turbine operation parameters to complete wind turbine aerodynamic performance prediction model setting, combining the influence of upwind wind turbine trail characteristics on downwind wind turbine aerodynamic characteristics, and predicting wind turbine blade surface pressure distribution and trail development characteristics based on the model.
The wind speed and the wind direction on different distance planes in front of the swept surface of the individual wind turbines are measured in real time by adopting a LiDAR wind measuring technology at the near-field wind speed at the windward side of the wind turbine cluster. Based on the calculation, the average wind speed on each plane is calculated
Degree of turbulence
Coefficient of wind shear
The incoming wind speed on the swept surface of the wind wheel is obtained by the following modes: establishing a physical model of the wind turbine tower to the shadow effect of the surrounding tower by adopting a semi-infinite length dipole model based on a potential flow theory; obtaining the wind speed on the swept surface of the wind wheel at the next time step by calculation based on the wind information basic parameters obtained by the wind speed data parallel to the swept surface of the wind turbine at different distances and the tower shadow model
The wind information basic parameters comprise: mean wind speed
Degree of turbulence
Coefficient of wind shear
The group control strategy for the wind turbine cluster is obtained by the following steps: and establishing a wind turbine group control analysis and calculation module located in a cloud calculation center, and collecting the operation parameters of each wind turbine in the cluster. For the upwind wind turbines in the cluster, the influence of the upwind wind turbine trail on the aerodynamic performance of the downwind wind turbines is predicted according to the LiDAR system measurement result and the upwind wind turbine trail development characteristics; and establishing a wind turbine model prediction control method based on a time stepping pneumatic performance prediction model by taking the rotating speed of each wind turbine in the cluster and the pitch angle of each blade as input variables through an optimization algorithm to improve the output power of the wind turbine cluster and reduce the load fluctuation of each wind turbine component in the cluster as much as possible as a control target.
The model prediction optimization control system comprises: the system comprises a cloud computing center, a LiDAR information collection system and wind turbine control systems, wherein all units are connected to the cloud computing center, the LiDAR information collection system collects the wind speed of the upwind outer side near field and transmits the wind speed to the cloud computing center, and the cloud computing center transmits control information to the wind turbine control systems in the cluster based on a control strategy of the wind turbine cluster. Namely, the wind speed on the swept surface of each wind wheel of the wind turbine in the cluster is used as external input, and the pitch angle of each blade of the wind turbine in the cluster
Wind wheel rotation speed omega and wind wheel yaw angle thetayawFor controlling variable, the performance of the wind turbine cluster at a time step is taken as system feedback to improve the output power of the wind turbine cluster and to make the output power as much as possibleAnd reducing the load fluctuation of each wind turbine component in the cluster as a control target, and calculating and outputting control signals for each wind turbine in the cluster.
The performance of the wind turbine cluster in a time step comprises the following steps: the wind turbine cluster outputs power and loads of all parts.
Technical effects
The invention integrally solves the problem of predictive control of the wind turbine cluster. Compared with the prior art that only a single wind turbine is subjected to predictive control, in general, if only each wind turbine in a cluster is subjected to predictive control, due to the wind power cluster effect, the result is often not the optimal solution of the whole cluster, and if a LiDAR system is additionally arranged on each wind turbine in the cluster, the cost is high, certain loss is brought to the performance of the wind turbines, and only if a collective prediction model is adopted for control, the optimal control of the wind turbine cluster can be obtained, so that the power generation power of the cluster reaches the maximum, and the load fluctuation of the cluster is reduced. According to the wind turbine cluster control method based on the prediction control, the wind turbine cluster control method based on the prediction control can effectively meet the requirement of the wind turbine cluster on multi-input multi-output strong coupling nonlinear optimization control under the environment of uncontrolled wind resources, the working efficiency of the system is effectively improved, the power generation power is increased, the load fluctuation of the wind turbine cluster is effectively reduced, and the safety and the reliability of the system are improved.
Drawings
FIG. 1 is a schematic view of chord length, twist angle and airfoil profile spanwise distribution on a wind turbine blade;
FIG. 2 is a schematic view of a multi-body motion coordinate system of a wind turbine;
FIG. 3 is a schematic view of a wind turbine tower shadow effect model;
FIG. 4 is a schematic diagram illustrating the influence of wind turbine tower shadow effect on induced velocity;
FIG. 5(a) is a schematic diagram of a lifting surface method with a free trail;
FIG. 5(b) is a schematic diagram of a vortex structure with a free wake;
FIG. 6 is a schematic diagram of a group control system based on model prediction;
FIG. 7 is a schematic diagram of a model prediction module.
Detailed Description
As shown in FIG. 1, the present embodiment relates to a wind turbine group control method based on predictive control, which includes the following steps:
establishing a three-dimensional geometric model of a wind turbine cluster: establishing geometric shape models of blades, cabins, towers and the like of a single wind turbine, determining relative geometric positions of the wind turbines, and calculating the aerodynamic performance of a wind turbine cluster, wherein the specific process comprises the following steps:
1.1) with key parameters affecting the aerodynamic performance of the blade: radius of wind wheel RrotorThe spanwise distribution c (r) of the chord length of the blade and the spanwise distribution theta of the twist angletws(r) and the spanwise distribution of the airfoil define the geometry of the blade (as shown in FIG. 1).
1.2) with key parameters affecting the aerodynamic performance of a wind turbine: wind wheel inclination angle thetatiltAngle of wind wheel cone thetaconeThe distance L between the sweeping surface of the wind wheel and the center of the towersweepHigh speed L between wind wheel center and groundsweepRadius of tower along radius height distribution Rtower(y) defines the wind turbine geometry.
1.3) defining the relation of a multi-body motion coordinate system of the wind turbine according to the motion relation among the components when the wind turbine operates: geodetic coordinate system, tower bottom coordinate system, tower top coordinate system, nacelle coordinate system, hub coordinate system and blade coordinate system, as shown in fig. 2. In fig. 2: the coordinate system E is an inertial coordinate system, the coordinate system 0 is arranged at the tower bottom, the coordinate systems 1 and 2 are arranged at the top end of the tower, the coordinate systems 3 and 4 are arranged at the shaft ends of the motor, and the coordinate systems 5, 6, 7, 8 and 9 are arranged at the center of the hub.
1.4) the parametric modeling of the blades should also take into account the tower influence, which is simplified to a semi-infinite line dipole based on a tower coordinate system schematic (as shown in FIG. 3), as a function of the ambient velocity potential:
wherein: phi is half infinite linear dipole velocity potential, V∞For incoming wind speed, the tower is aligned with the surrounding areaThe induced velocity of (a) is a gradient of velocity potential:
wherein:
is the tower effect on the induced velocity of the surroundings.
1.5) future flow velocity V∞And tower influence induced velocity
The superposition results in the local velocity around the tower (as shown in fig. 4).
Secondly, establishing a time-stepping wind turbine cluster aerodynamic performance prediction model: based on the wind speed on the swept surface of each wind turbine
The method comprises the following steps of obtaining the aerodynamic performance of the wind turbine at the current moment by the geometric models of the wind turbine blades, the engine room and the tower, the wind turbine operation parameters and the aerodynamic performance of the wind turbine at the previous moment, and specifically comprises the following steps: the blade part model is characterized in that grids are divided on a lifting surface of the blade, attached vortexes distributed in the spanwise direction are arranged in the chordwise direction 1/4 of the grids to represent the lifting force distribution on the blade, two vortex lines which are separated from two ends of the attached vortexes in the chordwise direction of the blade towards the downstream are called as free vortexes on the blade to represent the change of the upper ring volume of the blade in the spanwise direction, and control points are arranged at the chordwise and spanwise central positions of a grid front edge 3/4. (as shown in FIG. 5a, each ray in the figure consists of a free wake in multiple horseshoe vortices).
The current aerodynamic performance of the wind turbine comprises the following steps: aerodynamic force distribution, near trail influence characteristics and far field trail characteristics on each blade of the wind turbine, wherein:
the free vortex comprises: the blade tip vortex generated by the free vortex, the near-field wake part and the following free vortex on the blade, and the shedding vortex arranged along the spanwise direction of the blade in the near-field wake part of the blade represents the change of the ring volume on the blade in time when the blade is subjected to an unsteady working condition (as shown in fig. 5 b).
The change of the upper ring amount of the blade along the spanwise direction is obtained by solving the boundary condition of the object plane on the control point of the blade, which satisfies the local speed, and the boundary condition is as follows:
wherein: phi is the velocity potential at the control point,
for the speed of movement at the control point of the blade,
is a unit vector of the normal direction on the control point.
The induction speed of the vortex to the control point is calculated by the Biot-Savart theorem to obtain:
wherein:
the induction speed of the vortex section infinitesimal pair control points is taken as the vortex section annular quantity,
in the form of a vortex segment vector, the vortex segment vector,
the vortex center to control point vector.
By the aid of a lifting surface method with free trails, not only can pressure distribution on blades of a current wind turbine be obtained, but also a trail development rule of an upwind wind direction wind turbine can be obtained, and further wind speed information parameters of a wind wheel surface of a downwind wind turbine are obtained through calculation.
The far-field wake characteristic is solved in a time stepping-based free wake numerical mode. Describing the control point of the blade tip vortex by adopting a Lagrange mode, wherein the position of the blade is represented by an azimuth angle psi, when the azimuth angle is psi, the life angle zeta represents the trailing track generated by the blade at the azimuth angle psi-zeta, and psi phi can be known by definition0+Ωt,ζ＝Ω(t-t0). Wake tip vortex controlThe Lagrange control equation for the movement of a point in the local flow field can be written as
In the formula
Which represents the flow rate of the natural incoming flow,
showing the induced speed of the attached vortex, the free vortex and the wake vortex on the blade to the control point,
for speed variations induced at the control points by other factors (such as wind shear, tower effects, turbulence, etc.). Therefore, the far-field tail control equation can be written as
Writing the discrete control equation as
Wherein L is a differential three-step three-order precision operator
The solution of the trail difference control equation can be solved by adopting a prediction correction method.
Establishing a model for developing and calculating the wind speed information measuring surface to the wind wheel swept surface: for the wind wheel in the wind direction on the wind turbine cluster, wind speed vector information on the vertical plane at a given distance h in front of the wind wheel is obtained through a LiDAR wind measuring system
Meter for considering influence of tower on upstream wind conditionCalculating the time t when the wind speed on the current LiDAR measuring surface reaches the swept surface of the wind wheel and the corresponding wind speed
The wind speed vector information
Obtained by the following method: measuring distance from wind wheel swept surface z in real time by using LiDAR (light detection and ranging) laser radar wind measurement technologyjWind speed vectors in the 1.5D, 1D, 0.7D, 0.4D, 0.2D, and 0.15D (j is 1,2 … 5) planes
Based on the calculation, the average wind speed on each plane is calculated
Degree of turbulence
Coefficient of wind shear
The wind speed parameter on the swept surface of the wind wheel is weighted and determined according to a one-dimensional normal function, taking the average wind speed as an example:
wherein: the subscript j +1 represents the wind speed parameter over the swept surface of the wind wheel; reversely solving the basic wind speed on the swept surface of the wind wheel according to the wind speed parameters on the swept surface of the wind wheel
And the tower shadow effect induced speed is superposed to obtain the wind speed on the swept surface of the wind wheel
Then the wind speed
To be from the current time
After which time the wind wheel sweeps the wind speed over the surface.
The weighting of the one-dimensional normal function means that: taking a one-dimensional normal distribution function as a weight function:
wherein: z is the horizontal distance between the measuring surface and the swept surface of the wind wheel, mu is a central value, and sigma is a variance; in this example, μ is 0.4D and σ is 40.
Deploying a wind turbine group control analysis and calculation module in the cloud computing center, analyzing and calculating the operation data of each wind turbine, taking the rotating speed and the pitch angle of each blade of each wind turbine in the cluster as input variables, improving the output power of the wind turbine cluster, reducing the load fluctuation of each wind turbine component in the cluster as a control target, determining a group control algorithm for group control of the wind turbine cluster, and establishing a wind turbine cluster prediction control method based on a time stepping pneumatic performance prediction model, wherein the method specifically comprises the following steps: calculating the wind speed on the swept surface of each wind turbine through the development rule of the windward trail of the wind turbine predicted by the LiDAR wind measurement system and the wind turbine aerodynamic prediction model, and controlling the pitch angle of each blade of each wind turbine by taking the wind speed on the swept surface as external input
And controlling variables, and determining a control method of the wind turbine cluster by taking the power maximization and the load fluctuation minimization of the wind turbine cluster as control targets.
The control targets of the maximization of the power and the minimization of the load fluctuation of the wind turbine cluster are as follows: setting a satisfaction function as an optimization target of the blade:
wherein: p is the total generated power of the wind turbine cluster, dL is the derivative of the total load of the wind turbine cluster to time, namely the load fluctuation condition, A1 and A2 are weight coefficients, and ξ is the wind turbine cluster control variable. The value of the objective function at the time step of t + Deltat is less than the time tThe objective function value of (2) is a control objective. Fig. 7 shows a specific model predictive control flow chart.
The method for controlling and determining the cluster of the wind turbine comprises the following steps: under the condition of the known wind turbine parameterized model in the cluster, the pitch angle of each blade of each wind turbine is used
Is a control variable, noted ξ. Taking a method of optimizing by a method of decision-making and optimization as an example, an optimization model is set as Minimize: F (ξ); g (ξ), the optimal control strategy within the feasible domain is found in the steepest descent method. It is to be understood that the present invention is not limited to the specific embodiments, optimization strategies and decision-making methods described above, and that various changes or modifications may be made by one skilled in the art within the scope of the claims without departing from the spirit of the invention.
Establishing a local wind turbine performance communication and control module: the wind turbine group control analysis and calculation module is used for keeping communication with the group control analysis and calculation module and executing the algorithm result obtained by the wind turbine group control analysis and calculation module.
Compared with the prior art, the method considers the cluster prediction control of the wind turbine, not only considers the influence of the incoming flow wind speed of the wind turbine, but also considers the influence of the free trail of the upwind wind turbine on the downwind wind turbine. Each blade pitch angle of each wind turbine with the aim of maximizing power and minimizing load fluctuation
And controlling the variables to obtain a predictive control strategy for the wind turbine cluster. It is proved by the fact that the obtained control strategy is the optimal control strategy only by taking the wind turbine cluster into overall consideration.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (7)
1. A wind turbine group control method based on predictive control is characterized in that preliminary calculation of the aerodynamic performance of a wind turbine is carried out according to an individual three-dimensional parameterized geometric model of a wind turbine cluster and the relative position of the wind turbine in the cluster; measuring the wind speed of the wind turbine cluster in the windward side to the outer side by a laser wind measuring radar system; by measuring wind speed information, considering the influence of the tower and the wake of the upstream wind turbine in the cluster on wind conditions, predicting the incoming flow wind speed on the swept surface of the wind wheel in the cluster at a given time step; and determining a group control strategy of the wind turbine cluster according to the wind speed prediction model and the wind turbine pneumatic prediction model in time step by using a model prediction optimization control system positioned at the cloud.
2. The wind turbine group control system based on the predictive control as claimed in claim 1, wherein the individual three-dimensional parameterized geometric model comprises a geometric shape model of a blade, a nacelle and a tower of a single wind turbine and a wind turbine aerodynamic performance prediction model;
the wind turbine aerodynamic performance prediction model comprises: wind speed on swept surface of wind turbine
Acquiring the pneumatic performance of the wind turbine at the current moment according to the operating parameters of the wind turbine and the pneumatic performance of the wind turbine at the previous moment;
the wind speed prediction model obtains wind speed vector information on a vertical plane at a given distance h in front of a wind wheel in the wind direction on a cluster of the wind turbine through a LiDAR wind measurement system
Calculating the time t when the wind speed on the current LiDAR measuring surface reaches the wind wheel swept surface and the corresponding wind speed by considering the influence of the tower on the upstream wind condition
3. The wind turbine group control system based on predictive control as claimed in claim 2, wherein the wind turbine operation parameters include rotation speed ω and pitch angle of each blade
Wind wheel yaw angle thetayaw；
The aerodynamic performance comprises aerodynamic distribution, a near-tail influence characteristic and a far-field tail characteristic on each blade of the wind turbine.
4. The wind turbine group control system based on predictive control as claimed in claim 1, wherein the optimized group control strategy is: the operation data of each wind turbine is analyzed through a cloud computing center, namely the rotating speed and the pitch angle of each blade of each wind turbine in a cluster are used as input variables, the output power of the wind turbine cluster is improved, the load fluctuation of each wind turbine component in the cluster is reduced as far as possible, and the wind turbine cluster prediction control method based on the time stepping pneumatic performance prediction model is established.
5. The wind turbine group control system based on predictive control as claimed in claim 1, wherein the time step wind turbine aerodynamic prediction model is established based on a computational fluid dynamics theory, a wind turbine blade geometric and wind turbine operation parameters are input to complete the wind turbine aerodynamic performance prediction model setting, and the wind turbine blade surface pressure distribution and the development characteristics of the trail thereof are predicted based on the model in combination with the influence of the upwind wind turbine trail characteristics on the downwind wind turbine aerodynamic characteristics.
6. The cluster control system of wind turbines based on predictive control as claimed in claim 1, wherein the near field wind speed of the cluster of wind turbines from the windward side to the outer side is measured in real time by LiDAR technique to measure the wind speed and wind direction of planes parallel to the individual wind turbines at different distances in front of the swept surface, based on which the average wind speed of each plane is calculated
Degree of turbulence
Coefficient of wind shear
7. The wind turbine cluster control system based on predictive control as claimed in claim 1, wherein the incoming wind speed on the swept surface of the wind turbine is obtained by: establishing a physical model of the wind turbine tower to the shadow effect of the surrounding tower by adopting a semi-infinite length dipole model based on a potential flow theory; obtaining the wind speed on the swept surface of the wind wheel at the next time step by calculation based on the wind information basic parameters obtained by the wind speed data parallel to the swept surface of the wind turbine at different distances and the tower shadow model
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