CN112682256A - Fan combined load shedding method based on TMD and variable pitch optimization control - Google Patents

Fan combined load shedding method based on TMD and variable pitch optimization control Download PDF

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CN112682256A
CN112682256A CN202011446990.3A CN202011446990A CN112682256A CN 112682256 A CN112682256 A CN 112682256A CN 202011446990 A CN202011446990 A CN 202011446990A CN 112682256 A CN112682256 A CN 112682256A
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fan
tmd
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optimization
pitch angle
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CN112682256B (en
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曾凡春
江灿安
刘碧峰
孙晓刚
韩健
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Beijing Huaneng Xinrui Control Technology Co Ltd
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Abstract

The invention provides a combined fan load shedding method based on TMD and variable pitch optimization control, which comprises the following steps: establishing a fan mechanism model, a TMD mechanism model and a structural vibration mechanism model; constructing an electromagnetic torque model and a pitch angle controller model of the fan, and selecting a proper control target according to the load of the fan; selecting a specific optimization scheme to realize optimization of the parameters by taking parameters of a TMD (transition mode data) and parameters of a variable pitch controller as optimization variables; and verifying the effectiveness of the fan combined load shedding method based on TMD and variable pitch optimization control based on fan load simulation software or an actual experiment platform. The invention improves the defects of the existing fan load shedding mode, can effectively reduce the load of the fan under different working conditions under the condition of ensuring the stable tracking of the power, ensures the stability of the fan tower frame to a certain extent, improves the safety performance of the fan, prolongs the service life of the fan and improves the output electric energy quality of the fan.

Description

Fan combined load shedding method based on TMD and variable pitch optimization control
Technical Field
The invention belongs to the technical field of wind power generation, and particularly relates to a combined fan load shedding method based on TMD and variable pitch optimization control.
Background
Wind generators operate in the natural environment for a long time, not only obtaining energy from environmental wind, but also suffering from adverse effects of environmental loads such as strong wind, acid corrosion, earthquakes, and the like. The fan can receive the combined action of various loads such as pneumatic load, inertial load, control load and the like in the operation process, and a tower frame of the fan can generate larger vibration response, so that not only can the relevant structures generate fatigue load and the service life be shortened, but also the output power of the fan fluctuates to a certain degree.
Therefore, an economical and effective method is needed to be found for reducing the structural vibration of the fan, reducing the load of the fan, improving the adaptability of the fan in different environments and prolonging the service life of the fan.
The prior art means include physical means and control means. The physical means is mainly to install a Tuned Mass Damper (TMD), the TMD consists of a Mass, a spring and a damping unit, and the energy of the vibration part of the fan is absorbed through the interaction with the fan structure, so that the vibration of the fan is reduced. The control means is mainly independent variable pitch control, the vibration of the tower of the fan can be directly influenced by variable pitch, the aggravation of the vibration of the tower caused by environmental load is counteracted through proper variable pitch action, but the variable pitch also influences the capture of wind energy, partial power output can be sacrificed, and the output power of the fan cannot meet the control requirement if the control strategy is improper.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art and provides a combined load shedding method of a fan based on TMD and variable pitch optimization control.
The invention provides a combined fan load shedding method based on TMD and variable pitch optimization control, which comprises the following steps:
establishing a fan mechanism model, a TMD mechanism model and a structural vibration mechanism model;
constructing an electromagnetic torque model and a pitch angle controller model of the fan, and selecting a proper control target according to the load of the fan;
selecting a specific optimization scheme to realize optimization of the parameters by taking parameters of a TMD (transition mode data) and parameters of a variable pitch controller as optimization variables;
and verifying the effectiveness of the fan combined load shedding method based on TMD and variable pitch optimization control based on fan load simulation software or an actual experiment platform. .
In some optional embodiments, the wind turbine mechanism model comprises an aerodynamic model represented by the following equation (1):
Figure BDA0002825052100000021
wherein, ProtIs the pneumatic power, TrotIs the aerodynamic torque, ρ is the air density, R is the rotor radius of the rotor, v is the average wind speed through the rotor, CP(λ, β) is the aerodynamic power coefficient, β is the actual pitch angle, λ is the tip speed ratio, λ is represented as ωrotR/v，ωrotIs the rotor speed of the rotor, CPRepresented by the following formula (2):
Figure BDA0002825052100000022
in some optional embodiments, the fan mechanism model comprises a transmission model, which is represented by the following formula (3):
Figure BDA0002825052100000023
wherein J is the equivalent moment of inertia of the transmission model, JrotAnd JgenRespectively, the rotational inertia of the rotor of the wind wheel and the rotational inertia of the generator, N is the gear ratio of the gear box, BdmpFor low-speed shaft damping coefficient, TgenIs the electromagnetic torque of the generator.
In some optional embodiments, the wind turbine mechanism model comprises a pitch model, which is represented by the following equation (4):
Figure BDA0002825052100000031
wherein, beta*For pitch angle reference, τbThe pitch angle is the first order inertia time constant.
In some optional embodiments, the wind turbine mechanism model comprises a generator model, which is represented by the following formula (5):
Figure BDA0002825052100000032
wherein, T*For electromagnetic torque reference, τgIs a first-order inertia time constant of electromagnetic torque, PgenIs the output power of the generator, eta is the output efficiency of the generator, omegagenIs the generator speed.
In some alternative embodiments, the TMD mechanism model is represented by the following formula (6):
Figure BDA0002825052100000033
wherein, FtmdIs the resultant force of a spring and a damper in TMD to a fan structure, xtmdIs a telescopic displacement of TMD, KtmdAs spring rate, BtmdIs the damping coefficient.
In some alternative embodiments, the structural vibration mechanism model is represented by the following formula (7):
Figure BDA0002825052100000034
wherein, JtIs the moment of inertia, theta, of the wind turbine towertAngular displacement of the top of the tower in the front-rear direction of the fan tower, FwAxial thrust of wind at the tower top, htIs the height of the fan tower, mtIs the total mass of the fan, DtIs the distance from the center of mass of the fan to the base, mtmdIs the quality of TMD, BtIs the damping coefficient of the fan tower, KtFor the stiffness of the fan tower, DtmdThe distance from the mass center of the TMD to the base of the fan is set;
the structural vibration mechanism model is simplified to the following formula (8):
Figure BDA0002825052100000041
in some optional embodiments, the building an electromagnetic torque model and a pitch angle controller model of the wind turbine, and selecting a suitable control target according to a load of the wind turbine includes:
when the rated wind speed is lower than the rated wind speed, the electromagnetic torque model is expressed as the following formula (9):
Figure BDA0002825052100000042
wherein, T*Given value, max, representing electromagnetic torque under OTC controlThe index represents the maximum value of the variable, and the opt subscript represents the optimal value of the variable for maximizing wind energy capture;
when the wind speed is higher than the rated wind speed, the electromagnetic torque model is expressed by the following formula (10):
Figure BDA0002825052100000043
wherein the rated superscript represents the nominal value of the parameter;
when the wind speed is lower than the rated wind speed, the pitch angle in the pitch angle model is set to be zero degree;
the pitch angle model is expressed by the following equation (11) when the rated wind speed is not less than the rated wind speed:
Figure BDA0002825052100000044
wherein, U(s) is the output of the controller in the complex frequency domain, E(s) is the deviation between the output of the rotating speed of the generator and the rated rotating speed in the complex frequency domain, and KIIs the integral constant, K, of a PID controllerPProportional gain, K, for PID controllersG(β) is a pitch angle dependent gain scheduling coefficient, expressed as the following equation (12):
Figure BDA0002825052100000045
wherein, betakA value representing a pitch angle in a particular state, the particular state being: output power P of generatorgenThe output power P is equal to the actual pitch angle beta when the partial derivative value of the actual pitch angle beta at this moment is 0genTwice the value of the partial derivative for the actual pitch angle β.
In some optional embodiments, the selecting a specific optimization scheme to achieve optimization of the parameters with the parameters of the TMD and the parameters of the pitch controller as optimization variables includes:
the steps of using the particle swarm optimization algorithm to optimize parameters and reduce the load of the fan are as follows:
step 1: selecting and setting particle swarm parameters, and setting a particle fitness function according to an optimization target;
step 2: initializing particle swarm parameters according to the parameter range of the TMD and the parameter range of the variable pitch controller, and randomly initializing the positions of primary particles;
and step 3: calculating to obtain a fitness function of each particle;
and 4, step 4: updating the optimal solution of the particle individual;
and 5: updating a particle global optimal solution;
step 6: updating the position and the speed of each particle according to a state transition equation;
and 7: calculating to obtain a fitness function of each particle;
judging whether a stop condition is met: if the stopping condition is met, the optimization process is exited, and the particle global optimal solution is used as the optimal parameter finished by the optimization process; if the stop condition is not satisfied, the process returns to step 4.
The stop condition includes: the number of iterations satisfies the requirement and the error satisfies the convergence condition.
In some optional embodiments, the step of reducing the wind turbine load using a particle swarm algorithm to optimize the parameter comprises:
the particle swarm optimization parameters comprise the position of the TMD, the mass of the TMD, a damping coefficient, a rigidity coefficient, a gain parameter of the pitch angle controller and an integral time parameter, and the parameter space has 6 dimensions;
the position for the ith generation of particles is denoted Xi＝(xi1,xi2,…,xi6)TThe corresponding particle velocity is denoted Vi＝(vi1,vi2,…,vi6)T；
In the iterative optimization process, the individual optimal solution is represented as Pi＝(pi1,pi2,…,pi6)TThe global optimal solution is represented as Pg＝(pg1,pg2,…,pg6)T；
The initial population of particles is randomly generated within an allowable range, each timePosition x of particleidAnd velocity vidThe update is made according to the following state transition equation (13):
Figure BDA0002825052100000061
where d denotes the d-th dimension of the particle, k denotes the kth generation of the iteration, c1And c2Is an acceleration factor, r1And r2Is a random number from 0 to 1, w is the inertial weight;
the objective function and the constraint condition of the particle swarm algorithm are expressed as the following formulas (14) to (21):
Figure BDA0002825052100000062
s.t.0≤β≤βmax (15)
Figure BDA0002825052100000063
Figure BDA0002825052100000064
Figure BDA0002825052100000065
Figure BDA0002825052100000066
Figure BDA0002825052100000067
Figure BDA0002825052100000068
wherein, w1As a weight of power fluctuation, w2And the weight of the tower top displacement is adopted, the power is deviated from a rated value, the power is deviated from the rated value, the DeltaX is deviated from the rated value of the tower top displacement, min appearing in the superscript and the subscript represents the minimum value allowed by the parameter, and max appearing in the superscript and the subscript represents the maximum value allowed by the parameter.
The invention improves the defects of the existing fan load shedding mode, improves the defect that the existing TMD application does not carry out parameter optimization and design according to the state of the fan under the actual variable pitch control, also improves the defect that the existing variable pitch control application does not carry out parameter optimization and design under the state of TMD coupling fan, and simultaneously improves the defects of the existing fan load shedding mode.
Drawings
FIG. 1 is a flowchart of a combined wind turbine load shedding method based on TMD and pitch optimization control according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the vibration of a fan and TMD coupling structure according to another embodiment of the present invention;
FIG. 3 is a flow chart of particle swarm optimization according to another embodiment of the present invention;
FIG. 4 is a time domain diagram of a simulated input wind according to another embodiment of the present invention;
FIG. 5 is a graph of simulated generator output power comparison according to another embodiment of the present invention;
fig. 6 is a diagram comparing the front and rear displacements of the top of the simulated fan tower according to another embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a wind turbine combined load shedding method S100 based on TMD and pitch optimization control includes the following steps:
and S110, establishing a fan mechanism model, a TMD mechanism model and a structural vibration mechanism model. Through the step, the coupling relation between the TMD and the fan and the effect of the TMD on the load shedding of the fan can be qualitatively analyzed. Step S110 specifically includes the following steps:
and establishing a fan mechanism model according to a fan mechanism, wherein the fan mechanism model comprises a pneumatic model, a transmission model, a variable pitch model and a generator model. The method specifically comprises the following steps:
establishing a pneumatic model according to the pneumatic characteristics of the fan, which specifically comprises the following steps:
according to the aerodynamic theory, the aerodynamic power P captured by the rotor of the fanrotAnd the aerodynamic torque TrotIt can be expressed as the following formula (1), that is, the pneumatic model can be expressed as the following formula (1):
Figure BDA0002825052100000071
where ρ is the air density, R is the rotor radius of the rotor, v is the average wind speed through the rotor, CP(λ, β) is the aerodynamic power coefficient, β is the actual pitch angle, λ is the tip speed ratio, λ may be expressed as ωrotR/v，ωrotIs the rotational speed of the rotor of the wind wheel, CPRepresented by the following formula (2):
Figure BDA0002825052100000081
establishing a transmission model, specifically comprising:
in a transmission system, the flexible characteristics and friction of a high-speed shaft and a low-speed shaft are ignored, a single mass model is established, and a dynamic equation of the single mass model is obtained according to a rotation theorem.
That is, assuming that the high-speed shaft and the low-speed shaft of the fan are both rigid shafts, a dynamic equation of the single-mass model of the transmission system can be obtained, which is expressed as the following formula (3), that is, the transmission model can be expressed as the following formula (3):
Figure BDA0002825052100000082
wherein J is the equivalent moment of inertia of the transmission model, JrotAnd JgenRespectively, the rotational inertia of the rotor of the wind wheel and the rotational inertia of the generator, N is the gear ratio of the gear box, BdmpFor low-speed shaft damping coefficient, TgenIs the electromagnetic torque of the generator.
Establishing a variable pitch model, which specifically comprises the following steps:
the variable pitch system is a nonlinear servo mechanism, the variable pitch servo mechanism is equivalent to a first-order inertia link for modeling, and in a linear operation interval of the variable pitch system, a dynamic equation of a variable pitch model can be expressed as the following formula (4), that is, the variable pitch model can be expressed as the following formula (4):
Figure BDA0002825052100000083
wherein, beta*For the pitch angle reference, typically the output of the pitch controller, τbThe pitch angle is the first order inertia time constant.
Establishing a generator model, which specifically comprises the following steps:
neglecting the converter model, taking the generator as a torque source, modeling it as a first-order inertia link, and its dynamic equation can be expressed as the following formula (5), that is, the generator model can be expressed as the following formula (5):
Figure BDA0002825052100000091
wherein, T*For electromagnetic torque reference, τgIs a first-order inertia time constant of electromagnetic torque, PgenIs the output power of the generator, eta is the output efficiency of the generator, omegagenIs the generator speed.
Establishing a TMD mechanism model, which specifically comprises the following steps:
TMD is composed of mass and springAnd damping, and thus, Newton's second law can be applied to construct its dynamic equation. The force generated by the TMD on the fan structure is composed of damping force and elastic deformation force, the damping force is in direct proportion to the speed of the TMD piston relative to the rigid body, the elastic force is in direct proportion to the telescopic displacement of the spring, and the damping can be matched with the main structure to absorb shock by adjusting the mass, the rigidity and the damping of the system. Let the telescopic displacement of TMD be xtmdThe resultant force F of the spring and the damper to the fan structure in the TMD can be obtained according to the characteristics of the spring and the dampertmdIt can be expressed as the following formula (6), that is, the TMD mechanism model can be expressed as the following formula (6):
Figure BDA0002825052100000092
wherein, KtmdAs spring rate, BtmdIs the damping coefficient.
Establishing a structural vibration mechanism model, which specifically comprises the following steps:
the structural vibration of the wind turbine tower is the combined action result of the dynamic characteristics of the wind turbine and the dynamic characteristics of the TMD, so that a dynamic model of tower structural vibration caused by the coupling of the wind turbine and the TMD can be established by utilizing the Kane dynamic equation. According to the established dynamic model, the damping effect of the TMD on the fan can be qualitatively explained.
The structural vibration diagram of the fan coupled with the TMD is shown in fig. 2, then the motion equation of the fan tower and the TMD can be expressed as the following formula (7) according to the Kane kinetic equation, that is, the structural vibration mechanism model can be expressed as the following formula (7):
Figure BDA0002825052100000093
wherein, JtIs the moment of inertia, theta, of the wind turbine towertAngular displacement of the top of the tower in the front-rear direction of the fan tower, FwAxial thrust of wind at the tower top, htHeight of the tower of the fan, mtIs the total mass of the fan, DtIs the distance from the center of mass of the fan to the base, mtmdBeing TMDMass, BtDamping coefficient for wind turbine tower, KtFor the stiffness of the wind turbine tower, DtmdIs the distance from the mass center of the TMD to the base of the fan.
Because the angular displacement of the fan in the front-back direction of the tower top is small when the fan operates, the motion equation (7) can be simplified to a certain extent according to the assumption of small angles, and is expressed as the following formula (8), that is, the structural vibration mechanism model can be simplified to the following formula (8):
Figure BDA0002825052100000101
s120, building an electromagnetic torque model and a pitch angle controller model of the fan, and selecting a proper control target according to the load of the fan.
The control unit of the fan mainly comprises an electromagnetic torque controller and a pitch angle controller. The electromagnetic torque controller is equivalent to a torque source, the product of the torque source and the rotating speed of the generator represents the output power of the generator, but the electromagnetic torque controller is equivalent to a counter torque for a fan rotor, and the rotating speed of the fan rotor is limited. The change of the pitch angle can change the windward angle of the fan blade and the pneumatic power of the blade, thereby achieving the purpose of controlling the output power of the fan. When the fan works under the working condition below the rated Power, the control target of the wind turbine generator is mainly Maximum Power Tracking (MPPT), and captures the energy in the wind as much as possible, at this time, the pitch angle is generally set to zero degree, the electromagnetic Torque operates under the control strategy of MPPT, and this embodiment mainly takes an Optimal Torque Controller (OTC) under MPPT as an example for explanation. The output reference torque value of the torque controller under the OTC control is in direct proportion to the square of the rotating speed of the generator, and the proportionality coefficient is the optimal modulus and can be calculated according to the pneumatic model. When the wind turbine works in a working condition above the rated power, the control target of the wind turbine generator is power tracking rated power, and the generator parameter is allowed to operate under the rated power, at this time, the embodiment applies a Gain Scheduling Proportional Integral (GSPI) controller, adjusts the pitch angle, controls the rotating speed of the generator to operate under the rated rotating speed, and sets the reference torque output of the electromagnetic torque as the ratio of the rated power to the rotating speed of the generator, thereby ensuring the output of the constant power.
Because the purpose of this embodiment is to reduce the fan load, guarantee not to cause the influence to the normal operating of fan simultaneously, consequently, the control objective can be expressed as two parts, firstly, the absolute difference of fan output power and rated power is as little as possible, secondly, the load of fan is as little as possible. Therefore, an optimization method is needed to find a balance point between two targets.
This step will be specifically described below.
When the wind speed is below the rated wind speed, the fan operates in the MPPT state, the electromagnetic torque controller outputs the optimal torque, and the given value T of the electromagnetic torque is controlled under the OTC control*It can be expressed as the following expression (9), that is, the electromagnetic torque model can be expressed as the following expression (9):
Figure BDA0002825052100000111
where the max superscript represents the maximum value of the variable and the opt subscript represents the optimum value of the variable for maximizing wind energy capture.
When the wind speed is above the rated wind speed, the wind turbine operates in a constant power tracking state, the electromagnetic torque controller outputs a torque value capable of enabling the output power to be rated according to the rotating speed of the generator, and the given value of the electromagnetic torque can be expressed as the following formula (10), that is, the electromagnetic torque model can be expressed as the following formula (10):
Figure BDA0002825052100000112
wherein the rated superscript represents the nominal value of the parameter.
Below the rated wind speed, the pitch angle is generally set to zero degrees in the pitch angle model.
When the wind speed is higher than the rated wind speed, the pitch angle controller mainly uses a GSPI controller, the input of the controller is the deviation between the generator rotation speed and the rated wind speed, and the output is the pitch angle reference value, the output u(s) of the controller in the complex frequency domain can be expressed as the following formula (11), that is, the pitch angle model can be expressed as the following formula (11):
Figure BDA0002825052100000113
wherein E(s) is the deviation of the rotating speed output of the generator and the rated rotating speed in a complex frequency domain, KIIs the integral constant, K, of a PID controllerPProportional gain, K, for PID controllersG(β) is a pitch angle dependent gain scheduling coefficient, which can be expressed as the following equation (12):
Figure BDA0002825052100000121
wherein, betakA value representing a pitch angle in a particular state, the particular state being: output power P of generatorgenThe output power P is equal to the actual pitch angle beta when the partial derivative value of the actual pitch angle beta at this moment is 0genFor twice the value of the partial derivative of the actual pitch angle β, reference may be made specifically to the second formula of formula (12).
And S130, selecting a specific optimization scheme to realize parameter optimization by taking the parameters of the TMD and the parameters of the variable pitch controller as optimization variables. The optimization scheme can be optimization algorithms such as a particle swarm algorithm, a genetic algorithm, an ant colony algorithm and the like, and can be selected by a person skilled in the art according to actual needs. The particle swarm optimization is explained below as an example.
Referring to fig. 3, the steps of using the parameters of the TMD and the parameters of the pitch controller as optimization variables and using the particle swarm optimization to optimize the parameters to reduce the load of the wind turbine are as follows:
step 1: selecting and setting particle swarm parameters, wherein the parameters can comprise the number of the population particles, the iteration times, the particle dimensions, the particle flight range and speed and the like, and setting a particle fitness function according to an optimization target;
step 2: initializing particle swarm parameters according to the parameter range of the TMD and the parameter range of the variable pitch controller, and randomly initializing the positions of primary particles;
and step 3: calculating to obtain a fitness function of each particle;
and 4, step 4: updating the optimal solution of the particle individual;
and 5: updating a particle global optimal solution;
step 6: updating the position and the speed of each particle according to a state transition equation;
and 7: calculating to obtain a fitness function of each particle;
judging whether a stop condition is met: if the stopping condition is met, the optimization process is exited, and the global optimal solution of the particles is used as the optimal parameter for completing the optimization process; if the stop condition is not satisfied, the process returns to step 4.
The stop conditions include: the number of iterations satisfies the requirement and the error satisfies the convergence condition. The error here refers to a difference between the optimal value of the current generation particle and the optimal value of the previous generation particle, that is, when the optimal value of the current generation particle and the optimal value of the previous generation particle are not changed basically, the optimal value is considered to be found, and the stop condition is satisfied.
Specifically, the particle swarm optimization parameters may be the position of the TMD, the mass of the TMD, the damping coefficient, the stiffness coefficient, the gain parameter of the pitch angle controller, and the integration time parameter, which are 6 dimensions of the parameter space, and thus, the position of the ith generation particle may be represented as Xi＝(xi1,xi2,…,xi6)TThe corresponding particle velocity can be expressed as Vi＝(vi1,vi2,…,vi6)T。
In the iterative optimization process, the position where each particle has the maximum fitness is called an individual optimal solution, which can be represented as Pi＝(pi1,pi2,…,pi6)TThe position with the greatest fitness for all particles is called the global optimal solution and can be denoted as Pg＝(pg1,pg2,…,pg6)T。
The initial population of particles is randomly generated within an allowed range, the position x of each particleidAnd velocity vidThe update is made according to the following state transition equation (13):
Figure BDA0002825052100000131
where d denotes the d-th dimension of the particle, k denotes the kth generation of the iteration, c1And c2Is an acceleration factor, r1And r2Is a random number from 0 to 1 and w is the inertial weight.
According to the object of the present embodiment, the objective function and the constraint condition of the particle swarm algorithm are expressed as the following equations (14) to (21):
Figure BDA0002825052100000132
s.t.0≤β≤βmax (15)
Figure BDA0002825052100000133
Figure BDA0002825052100000134
Figure BDA0002825052100000135
Figure BDA0002825052100000136
Figure BDA0002825052100000137
Figure BDA0002825052100000141
wherein，w1As a weight of power fluctuation, w2And the weight of the tower top displacement is adopted, the power is deviated from a rated value, the power is deviated from the rated value, the DeltaX is deviated from the rated value of the tower top displacement, min appearing in the superscript and the subscript represents the minimum value allowed by the parameter, and max appearing in the superscript and the subscript represents the maximum value allowed by the parameter.
And S140, verifying the effectiveness of the fan combined load shedding method based on TMD and variable pitch optimization control based on fan load simulation software or an actual experiment platform.
In this step, verification may be based on using high fidelity simulation software or on-site wind turbine actual operation testing. This example uses fan load simulation software (Fatigue, Aerodynamics, Structures, and Turbulence, FAST) for validation purposes.
The IEC standard turbulent wind with the average wind speed of 12m/s is taken as simulation input wind, the simulation time is 60s, and the sampling time is 0.00625 s. Since there was a large disturbance just when the blower was started, the first 10s were removed for analysis.
And (3) jointly optimizing parameters of the TMD and parameters of the pitch controller by using a particle swarm optimization according to the wind condition, substituting the optimized parameters into the model, and performing simulation verification, wherein simulation results are shown in figures 4 to 6.
Fig. 4 is a time domain diagram of simulated input wind, and fig. 5 is a comparison diagram of output power of a simulated generator under a conventional control strategy and the method of the present embodiment, from which it can be seen that the maximum absolute deviation of power under the conventional control strategy is 1139.6kW, the maximum absolute deviation of power under the method of the present embodiment is 510.9kW, and the absolute deviation of power is reduced by 55.2%, and the method of the present embodiment can effectively suppress deviation and fluctuation of the power output of the generator. Fig. 6 is a comparison diagram of the front-rear displacement of the tower top of the simulation fan under the traditional control strategy and the method of the embodiment, and it can be seen from the diagram that the maximum absolute deviation of the front-rear displacement of the tower top under the traditional control strategy is 0.636m, the maximum absolute deviation of the front-rear displacement of the tower top under the embodiment is 0.566m, and the maximum absolute deviation of the front-rear displacement of the tower top is reduced by 11%.
In the embodiment, by comparing different effects of the traditional control strategy and the method provided by the embodiment on load control, the effectiveness of the fan combined load shedding method based on TMD and variable pitch optimization control is verified.
The method of the embodiment improves the defect that parameter optimization and design are not performed according to the state of the fan under actual variable pitch control by using the existing TMD, also improves the defect that parameter optimization and design are not performed under the state of the TMD coupled fan by using the existing variable pitch control, simultaneously improves the defect of the load shedding mode of the existing fan, is a coupling soft and hard combination method of mechanical structure design and control strategy optimization, can effectively reduce the load of the fan under different working conditions under the condition of ensuring stable power tracking, ensures the stability of a fan tower frame to a certain extent, improves the safety performance of the fan, prolongs the service life of the fan and improves the output power quality of the fan.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.

Claims (10)

1. A combined wind turbine load shedding method based on TMD and variable pitch optimization control is characterized by comprising the following steps:
establishing a fan mechanism model, a TMD mechanism model and a structural vibration mechanism model;
constructing an electromagnetic torque model and a pitch angle controller model of the fan, and selecting a proper control target according to the load of the fan;
selecting a specific optimization scheme to realize optimization of the parameters by taking parameters of a TMD (transition mode data) and parameters of a variable pitch controller as optimization variables;
and verifying the effectiveness of the fan combined load shedding method based on TMD and variable pitch optimization control based on fan load simulation software or an actual experiment platform.
2. The method of claim 1, wherein the fan mechanism model comprises an aerodynamic model represented by the following equation (1):
Figure FDA0002825052090000011
wherein, ProtIs the pneumatic power, TrotIs the aerodynamic torque, ρ is the air density, R is the rotor radius of the rotor, v is the average wind speed through the rotor, CP(λ, β) is the aerodynamic power coefficient, β is the actual pitch angle, λ is the tip speed ratio, λ is represented as ωrotR/v，ωrotIs the rotor speed of the rotor, CPRepresented by the following formula (2):
Figure FDA0002825052090000012
3. the method of claim 2, wherein the fan mechanism model comprises a transmission model, the transmission model being represented by the following equation (3):
Figure FDA0002825052090000021
wherein J is the equivalent moment of inertia of the transmission model, JrotAnd JgenRespectively, the rotational inertia of the rotor of the wind wheel and the rotational inertia of the generator, N is the gear ratio of the gear box, BdmpFor low-speed shaft damping coefficient, TgenIs the electromagnetic torque of the generator.
4. The method of claim 3, wherein the wind turbine mechanism model comprises a pitch model, the pitch model being represented by equation (4):
Figure FDA0002825052090000022
wherein, beta*For pitch angle reference, τbThe pitch angle is the first order inertia time constant.
5. The method of claim 4, wherein the wind turbine model comprises a generator model represented by the following equation (5):
Figure FDA0002825052090000023
wherein, T*For electromagnetic torque reference, τgIs a first-order inertia time constant of electromagnetic torque, PgenIs the output power of the generator, eta is the output efficiency of the generator, omegagenIs the generator speed.
6. The method according to claim 5, wherein the TMD mechanism model is represented by the following formula (6):
Figure FDA0002825052090000024
wherein, FtmdIs the resultant force of a spring and a damper in TMD to a fan structure, xtmdIs a telescopic displacement of TMD, KtmdAs spring rate, BtmdIs the damping coefficient.
7. The method according to claim 6, wherein the structural vibration mechanism model is represented by the following formula (7):
Figure FDA0002825052090000031
wherein, JtIs the moment of inertia, theta, of the wind turbine towertAngular displacement of the top of the tower in the front-rear direction of the fan tower, FwAxial thrust of wind at the tower top, htIs the height of the fan tower, mtIs the total mass of the fan, DtIs the distance from the center of mass of the fan to the base, mtmdIs the quality of TMD, BtIs the damping coefficient of the fan tower, KtFor the stiffness of the fan tower, DtmdThe distance from the mass center of the TMD to the base of the fan is set;
the structural vibration mechanism model is simplified to the following formula (8):
Figure FDA0002825052090000032
8. the method according to claim 7, wherein the building of the electromagnetic torque model and the pitch angle controller model of the wind turbine and the selection of the appropriate control target according to the load of the wind turbine comprise:
when the rated wind speed is lower than the rated wind speed, the electromagnetic torque model is expressed as the following formula (9):
Figure FDA0002825052090000033
wherein, T*The method comprises the following steps of (1) representing a given value of electromagnetic torque under OTC control, wherein a max subscript represents the maximum value of a variable, and an opt subscript represents the optimal value of the variable when wind energy capture is maximum;
when the wind speed is higher than the rated wind speed, the electromagnetic torque model is expressed by the following formula (10):
Figure FDA0002825052090000034
wherein the rated superscript represents the nominal value of the parameter;
when the wind speed is lower than the rated wind speed, the pitch angle in the pitch angle model is set to be zero degree;
the pitch angle model is expressed by the following equation (11) when the rated wind speed is not less than the rated wind speed:
Figure FDA0002825052090000041
wherein, U(s) is the output of the controller in the complex frequency domain, E(s) is the deviation between the output of the rotating speed of the generator and the rated rotating speed in the complex frequency domain, and KIIs the integral constant, K, of a PID controllerPProportional gain, K, for PID controllersG(β) is a pitch angle dependent gain scheduling coefficient, expressed as the following equation (12):
Figure FDA0002825052090000042
wherein, betakA value representing a pitch angle in a particular state, the particular state being: output power P of generatorgenThe output power P is equal to the actual pitch angle beta when the partial derivative value of the actual pitch angle beta at this moment is 0genTwice the value of the partial derivative for the actual pitch angle β.
9. The method according to claim 8, wherein the selecting a specific optimization scheme to achieve optimization of the parameters with the parameters of the TMD and the parameters of the pitch controller as optimization variables comprises:
the steps of using the particle swarm optimization algorithm to optimize parameters and reduce the load of the fan are as follows:
step 1: selecting and setting particle swarm parameters, and setting a particle fitness function according to an optimization target;
step 2: initializing particle swarm parameters according to the parameter range of the TMD and the parameter range of the variable pitch controller, and randomly initializing the positions of primary particles;
and step 3: calculating to obtain a fitness function of each particle;
and 4, step 4: updating the optimal solution of the particle individual;
and 5: updating a particle global optimal solution;
step 6: updating the position and the speed of each particle according to a state transition equation;
and 7: calculating to obtain a fitness function of each particle;
judging whether a stop condition is met: if the stopping condition is met, the optimization process is exited, and the particle global optimal solution is used as the optimal parameter finished by the optimization process; if the stop condition is not met, returning to the step 4;
the stop condition includes: the number of iterations satisfies the requirement and the error satisfies the convergence condition.
10. The method of claim 9, wherein the step of using a particle swarm algorithm to optimize the parameter to reduce the wind turbine load comprises:
the particle swarm optimization parameters comprise the position of the TMD, the mass of the TMD, a damping coefficient, a rigidity coefficient, a gain parameter of the pitch angle controller and an integral time parameter, and the parameter space has 6 dimensions;
the position for the ith generation of particles is denoted Xi＝(xi1,xi2,…,xi6)TThe corresponding particle velocity is denoted Vi＝(vi1,vi2,…,vi6)T；
In the iterative optimization process, the individual optimal solution is represented as Pi＝(pi1,pi2,…,pi6)TThe global optimal solution is represented as Pg＝(pg1,pg2,…,pg6)T；
The initial population of particles is randomly generated within an allowed range, the position x of each particleidAnd velocity vidThe update is made according to the following state transition equation (13):
Figure FDA0002825052090000051
where d denotes the d-th dimension of the particle, k denotes the kth generation of the iteration, c1And c2Is an acceleration factor, r1And r2Is a random number from 0 to 1, w is the inertial weight;
the objective function and the constraint condition of the particle swarm algorithm are expressed as the following formulas (14) to (21):
Figure FDA0002825052090000052
s.t.0≤β≤βmax (15)
Figure FDA0002825052090000053
Figure FDA0002825052090000054
Figure FDA0002825052090000055
Figure FDA0002825052090000056
Figure FDA0002825052090000057
Figure FDA0002825052090000061
wherein, w1As a weight of power fluctuation, w2The weight of tower top displacement is taken as the reference, the power is deviated from the rated value, the X is deviated from the rated value, min appeared in the superscript and the subscript represents the minimum value allowed by the parameter, max appeared in the superscript and the subscript represents the parameterThe maximum value allowed.
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