A wind turbine nonlinear model predictive control method and system

Abstract

This invention discloses a nonlinear model predictive control method and system for wind turbine generator sets. The method includes the following steps: constructing a nonlinear wind turbine model; wherein the nonlinear wind turbine model includes a nonlinear aerodynamic model, a wind turbine drivetrain model, a wind turbine blade-tower coupling model, and an actuator response model; based on the nonlinear wind turbine model, using wind speed disturbance, rotational speed, pitch angle, tower displacement estimation, and tower velocity estimation as input variables, predicting the state of the wind turbine generator set; wherein the tower displacement estimation and tower velocity estimation are obtained through a nonlinear observer; the input variables of the nonlinear observer are the measured tower acceleration, wind speed disturbance, rotational speed, and pitch angle. This invention can effectively reduce the load on the wind turbine generator set's blade root and tower, which not only helps to reduce the weight of various major components and reduce the cost of the unit, but also improves the stability of the unit's operation and extends its service life.

Description

Technical Field

[0001] This invention relates to the field of wind power technology, specifically to a nonlinear model predictive control method and system for wind turbine generator sets. Background Technology

[0002] During wind turbine operation, wind speed disturbances cause fluctuations in the dynamic load of the unit, thus affecting the stability of the unit's operation. Furthermore, with the continuous increase in wind turbine capacity and blade flexibility, wind turbine systems are becoming increasingly difficult to accurately describe using mathematical models. Traditional industrial standard PID control methods rely heavily on the model parameters and theoretical assumptions of the controlled object, resulting in poor control performance, long settling times, and large overshoot when input disturbances change significantly and rapidly. Therefore, how to incorporate the calculation of measurable disturbances into the unit control system and add feedforward compensation for wind speed disturbances will be a major bottleneck in improving the control stability of wind turbines.

[0003] The wind turbine control system plays a crucial role in ensuring the stability of the overall turbine's dynamics, optimizing power output, and mitigating fatigue loads. Currently, the main control technologies include:

[0004] 1) Traditional PID control methods linearize the unit model and design control parameters near the operating point to achieve torque and pitch control;

[0005] 2) Intelligent control methods, representative ones include fuzzy control and neural network control. Fuzzy control uses language rules to represent experience and knowledge for control, which can overcome the influence of nonlinear factors to a certain extent. Neural networks use wind speed, rotational speed, power, etc. as inputs and torque, pitch angle, etc. as outputs to construct a BP neural network to achieve the control target.

[0006] The methods described above can achieve control over torque and pitch to some extent, but traditional PID control methods have poor resistance to input disturbances and may exhibit oscillations. Intelligent control methods require a large amount of data as training samples, the calculation process is relatively complex, and the interpretability is poor. Summary of the Invention

[0007] In view of the technical problems existing in the prior art, the present invention provides a nonlinear model predictive control method and system for wind turbine generators that can effectively reduce the load on the blade root and tower of wind turbine generators, thereby reducing the cost of the generators, while also improving the stability of the generator operation and extending the service life of the generators.

[0008] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0009] A nonlinear model predictive control method for wind turbine generators includes the following steps:

[0010] Construct a nonlinear wind turbine model; the nonlinear wind turbine model includes a nonlinear aerodynamic model, a wind turbine drive train model, a wind turbine blade-tower coupling model, and an actuator response model;

[0011] Based on a nonlinear wind turbine model, the state of the wind turbine is predicted using wind speed disturbance, rotational speed, pitch angle, tower displacement estimation, and tower velocity estimation as input variables. The tower displacement estimation and tower velocity estimation are obtained through a nonlinear observer. The input variables of the nonlinear observer are the measured tower acceleration, wind speed disturbance, rotational speed, and pitch angle.

[0012] Preferably, the wind turbine drive train model is as follows:

[0013]

[0014] In the formula, θ is the torsion angle of the drive shaft, and ω r ω is the rotor speed. g N is the generator speed. g J is the gearbox transmission ratio. r J is the equivalent rotational inertia on the rotor side (including rotor, hub, and main shaft). g K represents the equivalent moment of inertia on the generator side (including the gearbox, high-speed shaft, and generator). θ B is the equivalent torsional stiffness coefficient of the transmission chain system. θ T is the equivalent damping coefficient of the transmission chain system. r For aerodynamic torque, T g This represents the generator torque.

[0015] Preferably, the wind turbine blade-tower coupling model is as follows:

[0016]

[0017] In the formula, m bld For the total blade mass, m twr equivalent mass of the tower, d twr c is the equivalent damping coefficient of the tower. twr R is the equivalent stiffness coefficient of the tower. bs R is the distance from the center of gravity of the blade to the center of gravity at the top of the tower. bt φ is the distance from the center of thrust to the center of gravity at the top of the tower. b d represents the angle of the blade flapping. b c is the blade damping coefficient. b This represents the blade stiffness coefficient.

[0018] Where d twr and c twr The calculation formula is:

[0019] m Te=0.25m T +m N +m H +3m B

[0020] d twr =4πm Te d s f0

[0021] c twr =m Te (2πf0) 2

[0022] Where m T For the mass of the tower, m N For cabin mass, m H For the hub mass, m B For the mass of a single blade, d s Here, f is the structural damping ratio, and f0 is the frequency of the tower's forward and backward movement.

[0023] Preferably, the nonlinear aerodynamic model includes:

[0024] Nonlinear aerodynamic torque T r expression:

[0025]

[0026] Nonlinear thrust F t expression:

[0027]

[0028] Among them, the wind energy utilization coefficient C p (β,λ) and air thrust coefficient C t (β,λ) is obtained by looking up a table, where β is the blade pitch angle and λ is the tip speed ratio. The expression is:

[0029]

[0030] v rel For relative wind speed, the expression is:

[0031]

[0032] Where v w This refers to wind speed.

[0033] Preferably, the actuator response model is:

[0034]

[0035] in It is a natural frequency. is the damping coefficient, and u is the pitch angle change rate control quantity.

[0036] Preferably, the nonlinear wind turbine model is:

[0037]

[0038] Preferably, in model predictive control, the optimal control problem of wind turbines is described as follows:

[0039]

[0040] in:

[0041]

[0042] Constraints:

[0043]

[0044] x(t0)=x0

[0045]

[0046] The objective function is a quadratic expression, with weights independent of the system state x and input u, but allowed to depend on external perturbations d, where the objective function is set as follows:

[0047]

[0048] Among them W ω W is the weight for the speed deviation. T W is the weight of the tower's forward and backward oscillation velocity. P Weighted by the rated power deviation. W is the pitch rate weight. M W is the weight for torque variation. θ This represents the pitch angle weight.

[0049] Preferably, in model predictive control, the constraint set H(x(t),u(t),d(t)) is set as follows:

[0050] The speed ω(t) limit is within the rated speed ω rated Within 114%

[0051] ω(t)≤1.14ω rated

[0052] The pitch angle limit is within the feasible range:

[0053] θ min ≤θ yip (r)≤θ max

[0054] The pitch angle and generator torque variation rate are limited to feasible ranges:

[0055]

[0056] When implementing control, a tip speed ratio limit is added to increase the power coefficient without changing the speed limit.

[0057] λ min (v0(t))≤λ(t)≤λ max (v0(t)).

[0058] Preferably, the ordinary differential equation of the nonlinear observer is:

[0059]

[0060] Where A, B, C, and D are state matrices:

[0061]

[0062] The present invention further discloses a nonlinear model predictive control system for wind turbine generators, including a memory and a processor connected to each other. The memory stores a computer program, which executes the steps of the method described above when run by the processor.

[0063] Compared with the prior art, the advantages of the present invention are as follows:

[0064] The nonlinear model predictive control method for wind turbine generators of the present invention mainly includes mechanism modeling and nonlinear model predictive control methods, which can effectively reduce the load on the blade root and tower of the wind turbine generator. This not only helps to reduce the weight of various major components and reduce the cost of the generator, but also improves the stability of the generator operation and extends the service life of the generator.

[0065] This invention implements unit torque and pitch control by writing algorithm logic into the main control program. It observes the algorithm state variables based on existing data acquisition equipment such as sensors, without adding additional hardware costs. Based on nonlinear model predictive control, the unit can predict future states according to the model and current measurements. Adding feedforward compensation control can reduce the extreme load caused by sudden wind speed changes and ensure safe operation of the unit. By reducing pitch frequency and tower vibration, the stability of generator output power is improved, which is beneficial to improving the power generation quality of the unit. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of the transmission chain model in this invention.

[0067] Figure 2 This is a block diagram of the model predictive control structure of the present invention.

[0068] Figure 3 A schematic diagram illustrating the estimation of tower displacement and velocity using the nonlinear observer in this invention. Detailed Implementation

[0069] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0070] The nonlinear model predictive control method for wind turbine generators provided in this embodiment of the invention specifically includes the following steps:

[0071] A nonlinear wind turbine model is constructed, which consists of a nonlinear aerodynamic model, a transmission chain model, a blade-tower model, and an actuator response model.

[0072] like Figure 1 As shown, the wind turbine drive train model is as follows:

[0073]

[0074] In the formula, θ is the torsion angle of the drive shaft, and ω r ω is the rotor speed. g N is the generator speed. g J is the gearbox transmission ratio. r J is the equivalent rotational inertia on the rotor side (including rotor, hub, and main shaft). g K represents the equivalent moment of inertia on the generator side (including the gearbox, high-speed shaft, and generator). θ B is the equivalent torsional stiffness coefficient of the transmission chain system. θ T is the equivalent damping coefficient of the transmission chain system. r For aerodynamic torque, T g T represents the generator torque. ls =K θ θ represents low-speed shaft torsion. This refers to the high-speed shaft torque.

[0075] Model (1) can be transformed into:

[0076]

[0077] Discretizing model (1) yields:

[0078]

[0079] The wind turbine blade-tower coupling model is as follows:

[0080]

[0081] In the formula, m bld For the total blade mass, m twr equivalent mass of the tower, dtwr c is the equivalent damping coefficient of the tower. twr R is the equivalent stiffness coefficient of the tower. bs R is the distance from the center of gravity of the blade to the center of gravity at the top of the tower. bt φ is the distance from the center of thrust to the center of gravity at the top of the tower. b d represents the angle of the blade flapping. b c is the blade damping coefficient. b This represents the blade stiffness coefficient.

[0082] Where d twr and c twr The calculation formula is:

[0083]

[0084] Where m t For the mass of the tower, m N For cabin mass, m H For the hub mass, m B For the mass of a single blade, d s Here, f is the structural damping ratio, and f0 is the frequency of the tower's forward and backward movement.

[0085] Aerodynamic models include:

[0086] Nonlinear aerodynamic torque T r expression:

[0087]

[0088] Nonlinear thrust F t expression:

[0089]

[0090] Among them, the wind energy utilization coefficient C p (β,λ) and air thrust coefficient C t (β,λ) is obtained by looking up a table, where β is the blade pitch angle and λ is the tip speed ratio. The expression is:

[0091]

[0092] v rel For relative wind speed, the expression is:

[0093]

[0094] Where v w This refers to wind speed.

[0095] The response model of the pitch angle actuator is as follows:

[0096]

[0097] in It is a natural frequency. is the damping coefficient, and u is the pitch angle change rate control quantity.

[0098] The nonlinear wind turbine model, composed of the aforementioned transmission chain model, blade-tower model, nonlinear aerodynamic model, and actuator response model, is as follows:

[0099]

[0100] The state variables in model (11) are defined as follows:

[0101]

[0102] Transforming (11) into state-space form:

[0103]

[0104] Specifically, generator torque losses and generator torque T must be considered during modeling. loss The losses include mechanical transmission losses T loss,mec Torque loss T caused by power loss loss,el The calculation is performed by looking up a table.

[0105] Mechanical transmission loss T loss,mec The calculation is as follows (unit: kNm):

[0106] Shaft input torque 0 200 2000 4000 60000 Shaft loss torque 86 86 92 107 112

[0107] The mechanical transmission loss T is obtained by dividing the shaft loss torque by the transmission ratio. loss,mec .

[0108] Torque loss T caused by power loss loss,el The calculation is as follows (unit: MW):

[0109] Generator input power 0 1 2 3 4 5 6 7 Generator power loss 0.05 0.09 0.13 0.17 0.21 0.25 0.29 0.33

[0110] The generator's power loss divided by its speed equals the torque T generated by electrical losses. loss,el Considering the torque loss of the generator, T is used in model (1). loss The torque T applied to the generator g .

[0111] Based on the nonlinear wind turbine model constructed using the above steps, wind speed disturbance, rotational speed, pitch angle, tower displacement estimation, and tower velocity estimation are used as input variables to predict the state of the wind turbine unit; the corresponding model predictive control is as follows: Figure 2 As shown, the specific implementation steps are as follows:

[0112] The optimal control problem of wind turbine units is described as follows:

[0113]

[0114] in:

[0115]

[0116] Constraints:

[0117]

[0118] x(t0)=x0

[0119]

[0120] The objective function is a quadratic expression, with weights independent of the system state x and input u, but allowed to depend on external perturbations d, where the objective function is set as follows:

[0121]

[0122] Among them W ω W is the weight for the speed deviation. T W is the weight of the tower's forward and backward oscillation velocity. P Weighted by the rated power deviation. W is the pitch rate weight. M W is the weight for torque variation. θ This represents the pitch angle weight. Specifically, the pitch angle weight W... θ Effective below the rated wind speed.

[0123] The constraint set H(x(t),u(t),d(t)) is defined as follows:

[0124] 1. The speed ω(t) limit is within the rated speed ω rated Within 114%

[0125] ω(t)≤1.14ω rated

[0126] 2. The pitch angle limit is within the feasible range:

[0127] θ min ≤θ tip (t)≤θ max

[0128] 3. The pitch angle and generator torque variation rate are limited to feasible ranges:

[0129]

[0130] 4. If the tip speed ratio is simply tracked completely during control, the load on some shafts may increase significantly. Therefore, a tip speed ratio limit is added to improve the power coefficient without changing the speed limit.

[0131] λ min (v0(t))≤λ(t)≤λ max (v0(t))

[0132] Predicting the time domain T f The time step was set to 10 seconds, and the differential equations were solved using the fourth-order explicit Runge-Kutta method. The time step was set to 0.2 seconds to match the anemometer's update frequency, resulting in 50 segments. After each optimization, one segment was selected for system feedforward control.

[0133] Model predictive controllers require full-state vectors at the start of the optimization range. In practical applications, the displacement and velocity of the tower's forward and backward swaying cannot be directly measured; therefore, it is necessary to construct a system such as... Figure 3 The observer shown is used to reconstruct x T and

[0134] The nonlinear observer consists of a static nonlinear observer for aerodynamic thrust and a linear Luneburg observer, used to monitor the dynamic state variable x of the tower's forward and backward motion. T and Make an estimate.

[0135] The filtered measured outputs of the propeller pitch angle and rotational speed are used to set the forward and backward oscillation speed to zero through nonlinear equations, thereby ignoring the influence of the tower motion and estimating the aerodynamic thrust. Then through Compared with the filtered tower acceleration To estimate

[0136] The ordinary differential equation of the observer is:

[0137]

[0138] Where A, B, C, and D are state matrices:

[0139]

[0140] Where u L For aerodynamic thrust, L yL Let k be the stater gain matrix. T Let c be the aerodynamic constant. Te Where is the aerodynamic damping constant, m is the tower mass, and T is the aerodynamic damping constant. e is the dynamic time constant.

[0141] The nonlinear model predictive control method for wind turbine generators of the present invention mainly includes mechanism modeling and nonlinear model predictive control methods, which can effectively reduce the load on the blade root and tower of the wind turbine generator. This not only helps to reduce the weight of various major components and reduce the cost of the generator, but also improves the stability of the generator operation and extends the service life of the generator.

[0142] This invention implements unit torque and pitch control by writing algorithm logic into the main control program. It observes the algorithm state variables based on existing data acquisition equipment such as sensors, without adding additional hardware costs. Based on nonlinear model predictive control, the unit can predict future states according to the model and current measurements. Adding feedforward compensation control can reduce the extreme load caused by sudden wind speed changes and ensure safe operation of the unit. By reducing pitch frequency and tower vibration, the stability of generator output power is improved, which is beneficial to improving the power generation quality of the unit.

[0143] This invention can effectively reduce the extreme tower and blade root loads during sudden wind speed changes; it can effectively reduce power fluctuations and improve power generation quality; the algorithm has strong versatility and can be quickly expanded and ported to units of different power levels and specifications.

[0144] This invention also discloses a computer-readable storage medium storing a computer program thereon, which, when run by a processor, executes the steps of the method described above. This invention further discloses a nonlinear model predictive control system for a wind turbine generator, comprising an interconnected memory and a processor, wherein the memory stores a computer program, which, when run by a processor, executes the steps of the method described above. The medium and system of this invention, corresponding to the methods described above, also possess the advantages described above.

[0145] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0146] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A nonlinear model predictive control method for wind turbine generator sets, characterized in that, Including the following steps: Construct a nonlinear wind turbine model; the nonlinear wind turbine model includes a nonlinear aerodynamic model, a wind turbine drive train model, a wind turbine blade-tower coupling model, and an actuator response model; Based on a nonlinear wind turbine model, the state of the wind turbine is predicted using wind speed disturbance, rotational speed, pitch angle, tower displacement estimation, and tower velocity estimation as input variables. The tower displacement and tower velocity are estimated using a nonlinear observer; the input variables of the nonlinear observer are the measured tower acceleration, wind speed disturbance, rotational speed, and blade pitch angle. The wind turbine transmission chain model is as follows: In the formula The torsion angle of the drive shaft. The rotor speed is For generator speed, This refers to the gearbox transmission ratio. The equivalent rotational inertia on the rotor side. This is the equivalent moment of inertia on the generator side. This is the equivalent torsional stiffness coefficient of the transmission chain system. This is the equivalent damping coefficient of the transmission chain system. For aerodynamic torque, This refers to the generator torque. The wind turbine blade-tower coupling model is as follows: In the formula, Total blade mass equivalent mass of the tower This is the equivalent damping coefficient of the tower. The equivalent stiffness coefficient of the tower is... This is the distance from the center of gravity of the blade to the center of gravity at the top of the tower. This is the distance from the center of thrust to the center of gravity at the top of the tower. For the angle of the blade's waving, This is the blade damping coefficient. This refers to the blade stiffness coefficient; in and The calculation formula is: in For the tower mass, For cabin quality, For the quality of the wheel hub, Mass of a single blade The structural damping ratio, The frequency of the tower's forward and backward movement.

2. The nonlinear model predictive control method for wind turbine generators according to claim 1, characterized in that, The nonlinear aerodynamic model includes: Nonlinear aerodynamic torque expression: Nonlinear thrust expression: Among them, wind energy utilization coefficient and air thrust coefficient This was obtained by looking up a table. The pitch angle is the propeller angle. The expression for the tip speed ratio is: For relative wind speed, the expression is: in This refers to wind speed.

3. The nonlinear model predictive control method for wind turbine generators according to claim 2, characterized in that, The actuator response model is as follows: in It is a natural frequency. It is the damping coefficient. It is the control quantity for the rate of change of pitch angle.

4. The nonlinear model predictive control method for wind turbine generators according to any one of claims 1-3, characterized in that, In model predictive control, the optimal control problem for wind turbines is described as follows: in: Constraints: The objective function is a quadratic expression, and its weights are independent of the system state. and input However, it allows reliance on external disturbances. The objective function is set as follows: in As the weight for speed deviation, As the weight of the tower's forward and backward oscillation velocity, Weighted by the rated power deviation. As the pitch rate weight, As the weight for torque variation, This represents the pitch angle weight.

5. The nonlinear model predictive control method for wind turbine generator sets according to claim 4, characterized in that, In model predictive control, constraint set The settings are as follows: rotational speed Limit at rated speed Within 114% The pitch angle limit is within the feasible range: The rates of change of pitch angle and generator torque are limited to feasible ranges: When implementing control, a tip speed ratio limit is added to increase the power coefficient without changing the speed limit. 。 6. The nonlinear model predictive control method for wind turbine generator sets according to any one of claims 1-3, characterized in that, The ordinary differential equation of the nonlinear observer is: Where A, B, C, and D are state matrices: 。 7. A nonlinear model predictive control system for a wind turbine generator, comprising an interconnected memory and a processor, wherein the memory stores a computer program, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-6.

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