Direct-driven wind driven generator side and grid side decoupling prediction control method and system

Abstract

The invention discloses a direct-driven wind driven generator side and grid side decoupling prediction control method, and relates to the technical field of wind turbine generators, and the method comprises the steps: building a dual-PWM converter topological structure; collecting a machine side control signal and a network side control signal, and designing a generalized predictive controller; establishing a generalized virtual prediction model; outputting a control signal to a switching mechanism by using the generalized predictive controller, and selecting an actual control signal of an optimal controller output system according to an error switching strategy; outputting a corresponding generalized virtual prediction model output value according to the actual control signal, and calculating an output error as the input of a generalized prediction controller by using the set expected prediction value and the prediction model output value; the MPC control modules are arranged, the output errors, the machine side control signals and the grid side control signals are input to the corresponding MPC control modules, torque control is carried out on the motor side PWM converter, impedance control is carried out on the grid side PWM converter, and therefore the transient performance and the steady-state performance of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine generator sets, and in particular to a direct-drive wind turbine generator side and a grid side decoupling predictive control method and system. Background Art

[0002] The penetration rate of wind turbines is getting higher and higher, and the overall inertia of the system is getting lower and lower. The limited synchronous generators cannot provide the required active power for frequency regulation, which seriously affects the stability of the power grid frequency. Considering the limited regulation ability of synchronous generators, frequency deviation may directly lead to load shedding and even cause the entire system to collapse. This urgently requires wind turbines to participate in frequency regulation. Therefore, this application proposes a decoupling predictive control method for direct-drive wind turbine generator side and grid side. Summary of the invention

[0003] In view of one or more of the above-mentioned existing problems, the present invention proposes a decoupling predictive control method and system for a direct-drive wind turbine generator side and a grid side.

[0004] According to a first aspect of the present application, a direct-drive wind turbine generator side and grid side decoupling predictive control method is provided, the control method comprising the following steps:

[0005] S100. Establishing a dual PWM converter topology for a direct-drive wind turbine, the topology comprising a motor-side PWM converter and a grid-side PWM converter;

[0006] S200. Collect the machine-side control signal of the motor-side PWM converter and the grid-side control signal of the grid-side PWM converter according to the topological structure of the wind turbine, and design a generalized predictive controller C i (i=1,2);

[0007] S300. Establishing a generalized virtual prediction model M based on the topological structure i (i=1,2), the generalized virtual prediction model includes a machine-side nonlinear virtual prediction model and a grid-side nonlinear virtual prediction model;

[0008] S400. Using generalized predictive controller C i (i=1,2) Calculate the control signal u corresponding to the output i (k) (i = 1, 2) to the switching mechanism, which uses the switching mechanism to select the optimal controller according to the error switching strategy to output the actual control signal u(k) of the system, where k refers to a certain moment;

[0009] S500. Output the corresponding generalized virtual prediction model M according to the actual control signal u(k) i (i=1,2) output value, using the expected prediction value set and the corresponding generalized virtual prediction model M i(i=1,2) output value calculates the output error e at time k+1 i (k+1) as the generalized predictive controller C i Input of (i=1,2);

[0010] S600. An MPC control module is set according to the dual PWM converter topology structure of the direct-drive wind turbine. The MPC control module includes a motor-side MPC control module and a grid-side MPC control module, and the output error e i (k+1)(i=1,2), the machine-side control signal and the grid-side control signal are input to the corresponding MPC control module to respectively perform torque control on the motor-side PWM converter and impedance control on the grid-side PWM converter.

[0011] As a feasible preferred method, S300. establish a generalized virtual prediction model M according to the topological structure i (i=1,2), the generalized virtual prediction model includes a machine-side nonlinear virtual prediction model and a grid-side nonlinear virtual prediction model, and specifically includes the following steps:

[0012] According to the two established generalized virtual prediction models, the prediction model with the minimum variance output of each model is obtained as follows:

[0013] in, is the system's predicted output value vector for the next moment, Y m,p is the output value vector of the system at the previous moment, ΔU m is the control increment of the prediction model, expressed as U m (k)-U m (k-1) is calculated, U m (k) is the actual control signal of the mth prediction model, and the value range of m is 1 and 2. 2×2 is the parameter matrix in the system, E m,1 is the error compensation term of the nonlinear part of the mth model, which is obtained by the error between the predicted value and the actual value.

[0014] As an achievable preferred method, step S400 uses a switching mechanism to select the optimal controller to output the actual control signal u(k) of the system according to the error switching strategy, including: selecting the prediction model corresponding to the minimum performance index as the optimal model according to the error performance index formula, and using the control action generated by the optimal controller corresponding to the selected optimal model as the actual control signal u(k) of the system, wherein the error performance index formula is as follows:

[0015] Among them, J m (k) is the error performance index, Ψ(k)=[ωT (k),||ω(k)||] T , ω(k) is the system regression vector, ||ω(k)|| is the bi-norm composed of the system regression vector, e m (k) is the output error of the virtual model in the mth subset at time k.

[0016] As an achievable preferred method, step S500 outputs the corresponding generalized virtual prediction model M according to the actual control signal u(k) i (i=1,2) output value, using the expected prediction value set and the corresponding generalized virtual prediction model M i (i=1,2) output value calculates the output error e at time k+1 i (k+1) as the generalized predictive controller C i The input of (i=1,2) is calculated as follows:

[0017] y(k+1) represents the output signal of the system at time k+1, which represents the torque control of the motor-side PWM converter and the impedance control of the grid-side PWM converter.

[0018] As an achievable preferred method, step S200 collects the motor side PWM converter signal according to the wind turbine topology structure, including the following steps:

[0019] a. Establish the voltage equation of the permanent magnet synchronous generator in the dq coordinate system:

[0020]

[0021] In the formula, u sd is the d-axis component of the dq-axis of the generator stator voltage, u sq is the q-axis component of the dq-axis of the generator stator voltage, i sd The d-axis component of the generator stator current dq-axis, i sq is the q-axis component of the dq-axis of the generator stator current, R S Its stator resistance ω r is the generator rotor speed; L d is the generator shaft d-axis inductance and L q is the q-axis inductance of the generator shaft;

[0022] b. Decouple the formula in step a to obtain the machine-side control signal of the motor-side PWM converter:

[0023]

[0024] As an achievable preferred method, step S200 collects the motor side PWM converter signal according to the wind turbine topology structure, including the following steps: step S200 collects the grid side control signal of the grid side PWM converter according to the wind turbine topology structure, including the voltage equation of the grid side converter in the dq coordinate system is:

[0025]

[0026] After decoupling the power of the above formula, the control equation of the grid-side converter is obtained as follows:

[0027]

[0028] Where U d is the d-axis component of the dq-axis of the grid-side voltage, U q is the q-axis component of the dq-axis of the grid-side voltage, R is the grid-side resistance, K iP , K iI are the proportional gain and integral gain of the current inner loop PI regulator respectively; i dref 、i qref i d and i q The given reference value, L is the grid-side inductance, ω e is the grid angular frequency, e d and e q are the equivalent potentials of the d-axis and q-axis respectively.

[0029] The beneficial effects of the present invention are:

[0030] The invention provides a direct-drive wind turbine generator side and grid side decoupling predictive control method. The invention establishes a nonlinear virtual predictive model on the generator side and the grid side, and designs a generalized predictive controller. The error between the expected predicted output and the corresponding i-th virtual model predicted output is used as the output of the controller, thereby calculating the control action u i (k)(i=1,2); then the performance index is calculated by the switching mechanism based on the error between the actual output of the system and the output of each virtual prediction model, and the prediction model corresponding to the minimum performance index is selected as the optimal model to output the actual control signal. Finally, the output error, the machine-side control signal, and the grid-side control signal are input into the corresponding MPC control module to respectively perform torque control on the motor-side PWM converter and impedance control on the grid-side PWM converter, thereby improving the transient performance and steady-state performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the structure of the dual PWM converter in the direct-drive wind turbine generator side and the grid side decoupling predictive control method;

[0032] Figure 2It is a schematic diagram of the control principle of the machine-side converter in the direct-drive wind turbine generator side and grid-side decoupling predictive control method;

[0033] Figure 3 It is a schematic diagram of the control principle of the grid-side converter in the direct-drive wind turbine generator side and grid-side decoupling predictive control method;

[0034] Figure 4 This is the control principle diagram of the decoupling predictive control method for the direct-drive wind turbine generator side and the grid side.

[0035] The above drawings show clear embodiments of the present disclosure, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present disclosure in any way, but to illustrate the concepts of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0036] The technical solution of the application is further described in detail below with reference to the accompanying drawings.

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0039] The following is combined with Figure 1-4 , some embodiments of the present invention are described in detail. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0040] The present application provides a direct-drive wind turbine generator side and grid side decoupling predictive control method, the control method comprising the following steps:

[0041] S100. Establishing a dual PWM converter topology for a direct-drive wind turbine, the topology comprising a motor-side PWM converter and a grid-side PWM converter;

[0042] It should be noted that, as shown in the figure, the dual PWM converter topology includes generator-side and grid-side PWM converters, and frequency decoupling is achieved through the intermediate bus capacitor. The current emitted by the generator is controlled by the dual PWM converter to control the unstable frequency on the generator side to a stable frequency to achieve stable grid connection.

[0043] The machine side realizes torque control by controlling the stator current, which is achieved by controlling the stator current. By changing its speed, the maximum power tracking of the machine side system is performed. The task of the grid side is to control the grid side current, stabilize the intermediate bus voltage, decouple the active and reactive power of the grid side, and optimize the grid-connected power. Therefore, this topology makes the motor's starting and braking performance more flexible, can realize the reversible flow of energy, and can work in four quadrants. This feature improves the working stability of the frequency converter and the system working efficiency.

[0044] S200. Collect the machine-side control signal of the motor-side PWM converter and the grid-side control signal of the grid-side PWM converter according to the topological structure of the wind turbine, and design a generalized predictive controller C i (i=1,2);

[0045] The machine-side converter of the present application adopts a dual closed-loop control of a power outer loop and a current inner loop; the grid-side converter adopts a dual closed-loop control of a voltage outer loop and a current inner loop.

[0046] Specifically, collecting the motor side PWM converter signal according to the wind turbine topology structure includes the following steps:

[0047] a. Establish the voltage equation of the permanent magnet synchronous generator in the dq coordinate system:

[0048]

[0049] In the formula, u sd is the d-axis component of the dq-axis of the generator stator voltage, u sq is the q-axis component of the dq-axis of the generator stator voltage, i sd The d-axis component of the generator stator current dq-axis, i sq is the q-axis component of the dq-axis of the generator stator current, R S Its stator resistance ωr is the generator rotor speed; L d is the generator shaft d-axis inductance and L q is the q-axis inductance of the generator shaft;

[0050] b. Decouple the formula in step a to obtain the machine-side control signal of the motor-side PWM converter:

[0051]

[0052] As shown in the figure, the maximum power corresponding to the maximum wind energy tracking is used as the outer loop power reference value on the q-axis of the generator side. Compared with the actual active power of the generator, a deviation is obtained, and the active current reference value is obtained through the proportional integral controller. Then the electromagnetic torque is controlled. After the deviation is compared with the q-axis feedback current, u' is obtained through PI adjustment. sq , and the decoupled Δu sq Add together to get the q-axis modulation voltage u sq , d-axis reactive current reference value The deviation from the actual d-axis current is adjusted by PI to obtain u′ sd , and then decoupled to obtain Δu sd Add together to get the d-axis modulation voltage u sd In this way, two modulation voltages are obtained, which are transformed into dq voltages and SVPWM modulation method is used to generate PWM waves to control the machine-side converter.

[0053] Step S200 collects the motor side PWM converter signal according to the wind turbine topology structure, including the following steps: Step S200 collects the grid side control signal of the grid side PWM converter according to the wind turbine topology structure, including the voltage equation of the grid side converter in the dq coordinate system is:

[0054]

[0055] After decoupling the power of the above formula, the control equation of the grid-side converter is obtained as follows:

[0056]

[0057] Where U d is the d-axis component of the dq-axis of the grid-side voltage, U q is the q-axis component of the dq-axis of the grid-side voltage, R is the grid-side resistance, K iP , K iI are the proportional gain and integral gain of the current inner loop PI regulator respectively; i dref 、i qref i d and i q The given reference value, L is the grid-side inductance, ω e is the grid angular frequency, e d and e q are the equivalent potentials of the d-axis and q-axis respectively.

[0058] As shown in the figure, the present application adopts a dual closed-loop control method of voltage outer loop and current inner loop. The deviation between the given DC voltage of the d-axis and the actual DC voltage is output as the d-axis current setting value i after PI adjustment. dref , and then get u′ through PI adjustment gd , decoupling operation to obtain the d-axis voltage control quantity ud q-axis reactive current reference value i qref =0, and the actual q-axis current i q The deviation is adjusted by PI to get u′ gq , and then decoupled with e q -ω e Li d Add together to get the q-axis modulation voltage u q In this way, two modulation voltages are obtained, which are transformed and then SVPWM modulation method is used to generate PWM waves to control the grid-side converter.

[0059] The experimental results show that by adopting this control strategy, the system can track the maximum power point of wind energy and operate safely and stably, which verifies the effectiveness of this control strategy.

[0060] S300. Establishing a generalized virtual prediction model M based on the topological structure i (i=1,2), the generalized virtual prediction model includes a machine-side nonlinear virtual prediction model and a grid-side nonlinear virtual prediction model; specifically, it also includes:

[0061] According to the established virtual prediction model, the prediction model with the minimum variance output of each model is obtained as follows:

[0062] in, is the system's predicted output value vector for the next moment, Y m,p is the output value vector of the system at the previous moment, ΔU m is the control increment of the prediction model, expressed as U m (k)-U m (k-1) is calculated, U m (k) is the actual control signal of the mth prediction model, and the value range of m is 1 and 2. 2×2 is the parameter matrix in the system, E m,1 is the error compensation term of the nonlinear part of the mth model, which is obtained by the error between the predicted value and the actual value.

[0063] S400. Using generalized predictive controller C i (i=1,2) Calculate the control signal u corresponding to the output i (k)(i=1,2) to the switching mechanism, and the optimal controller is selected according to the error switching strategy to output the actual control signal u(k) of the system, where k refers to a certain moment; specifically, it includes:

[0064] According to the error performance index formula, the prediction model corresponding to the minimum performance index is selected as the optimal model, and the control action generated by the optimal controller corresponding to the selected optimal model is used as the actual control signal u(k) of the system. The error performance index formula is as follows:

[0065]

[0066] Among them, J m (k) is the error performance index, Ψ(k)=[ω T (k),||ω(k)||] T , ω(k) is the system regression vector, ||ω(k)|| is the bi-norm composed of the system regression vector, e m (k) is the output error of the virtual model in the mth subset at time k.

[0067] Step S500 outputs the corresponding generalized virtual prediction model M according to the actual control signal u(k) i (i=1,2) output value, using the expected prediction value set and the corresponding generalized virtual prediction model M i (i=1,2) output value calculates the output error e at time k+1 i (k+1) as the generalized predictive controller C i The input of (i=1,2) is calculated as follows:

[0068] y(k+1) represents the output signal of the system at time k+1, which represents the torque control of the motor-side PWM converter and the impedance control of the grid-side PWM converter. is a decreasing sequence of positive scalars.

[0069] S500. Output the corresponding generalized virtual prediction model M according to the actual control signal u(k) i (i=1,2) output value, using the expected prediction value set and the corresponding generalized virtual prediction model M i (i=1,2) output value calculates the output error e at time k+1 i (k+1) as the generalized predictive controller C i (i=1,2) input; the calculation feedback formula is as follows:

[0070] y(k+1) represents the output signal of the system at time k+1, that is, it represents the torque control of the motor-side PWM converter and the impedance control of the motor-side PWM converter.

[0071] S600. An MPC control module is set according to the dual PWM converter topology structure of the direct-drive wind turbine. The MPC control module includes a motor-side MPC control module and a grid-side MPC control module, and the output error e i (k+1)(i=1,2), the machine-side control signal and the grid-side control signal are input to the corresponding MPC control module to respectively perform torque control on the motor-side PWM converter and impedance control on the grid-side PWM converter.

[0072] As shown in the figure, the generalized predictive controller used in the present application can resist interference and accurately track the parameters of direct-drive wind power generation. By establishing nonlinear virtual prediction models on the machine side and the grid side, singular values ​​are identified and filtered, and accurate parameter estimates are obtained for use in generalized predictive control. The generalized predictive controller based on the error switching strategy of the present application can converge to the vicinity of the new true value more quickly after a parameter jump occurs in the system, thereby reducing the transient error of the system and improving the stability of the system. r(k) in the figure is the expected tracking signal for setting the expected prediction value, which makes y(k+1) transition to the set value in a smooth manner; M i (i=1,2) is the virtual prediction model corresponding to the ith subset, C i (i=1,2) is a generalized predictive controller, which uses the expected predicted output and the corresponding i-th virtual model predicted output error as the controller output to calculate the control action u i (k)(i=1,2); the switching mechanism calculates the performance index based on the error between the actual output of the system and the output of the virtual prediction model of each subset, and selects the prediction model corresponding to the minimum performance index as the optimal model according to the calculation result. The control signal us(k) corresponding to the optimal model is used as the actual control signal u(k) of the system, and then the predicted output y m,p (k+1), in this application, m is 1 and 2, and then subtracted from y(k+1) to obtain e i (k+1), where e i (k+1) is the output error of the virtual model in the i-th (i=1,2) subset at time k, as shown in the figure, that is, e 1 (k+1) and e 2 (k+1) as well as the machine-side control signal and the grid-side control signal are input into the corresponding MPC control module to respectively control the torque of the motor-side PWM converter and the impedance of the grid-side PWM converter, thereby accelerating the convergence speed of the parameter estimation value in the controller and overcoming the multi-model degradation problem: and selecting the optimal controller at each moment as the controller of the nonlinear system through the error switching strategy, thereby improving the transient performance and steady-state performance of the system.

[0073] The present application also provides a memory, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned direct-drive wind turbine generator side and grid side decoupling predictive control method when running. Based on such an understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0074] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

[0076] The above are only some embodiments of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the creative concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A direct-drive wind turbine generator side and grid side decoupling predictive control method, characterized in that: The control method comprises the following steps: S100. Establishing a dual PWM converter topology for a direct-drive wind turbine, the topology comprising a motor-side PWM converter and a grid-side PWM converter; S200. Collect the machine-side control signal of the motor-side PWM converter and the grid-side control signal of the grid-side PWM converter according to the topological structure of the wind turbine, and design a generalized predictive controller C i (i=1, 2); S300. Establishing a generalized virtual prediction model M based on the topological structure i (i=1, 2), the generalized virtual prediction model includes a machine-side nonlinear virtual prediction model and a grid-side nonlinear virtual prediction model; S400. Using generalized predictive controller C i (i=1, 2) Calculate the control signal u corresponding to the output i (k) (i=1, 2) to the switching mechanism, which uses the switching mechanism to select the optimal controller according to the error switching strategy to output the actual control signal u(k) of the system, where k refers to a certain moment; S500. Output the corresponding generalized virtual prediction model M according to the actual control signal u(k) i (i=1, 2) output value, using the expected prediction value set and the corresponding generalized virtual prediction model M i (i=1, 2) output value calculates the output error e at time k+1 i (k+1) as the generalized predictive controller C i Input of (i=1, 2); S600. An MPC control module is set according to the dual PWM converter topology structure of the direct-drive wind turbine. The MPC control module includes a motor-side MPC control module and a grid-side MPC control module, and the output error e i (k+1)(i=1, 2), the machine-side control signal and the grid-side control signal are input to the corresponding MPC control module to respectively perform torque control on the motor-side PWM converter and impedance control on the grid-side PWM converter.

2. The direct-drive wind turbine generator side and grid side decoupling predictive control method according to claim 1, characterized in that: S300. Establishing a generalized virtual prediction model M based on the topological structure i (i=1, 2), the generalized virtual prediction model includes a machine-side nonlinear virtual prediction model and a grid-side nonlinear virtual prediction model, and specifically includes the following steps: According to the established virtual prediction model, the prediction model with the minimum variance output of each model is obtained as follows: in, is the system's predicted output value vector for the next moment, Y m，p is the output value vector of the system at the previous moment, ΔU m is the control increment of the prediction model, expressed as U m (k)-U m (k-1) is calculated, U m (k) is the actual control signal of the mth prediction model, and the value range of m is 1 and 2. 2×2 is the parameter matrix in the system, E m，1 is the error compensation term of the nonlinear part of the mth model, which is obtained by the error between the predicted value and the actual value.

3. The direct-drive wind turbine generator side and grid side decoupling predictive control method according to claim 1, characterized in that: Step S400 uses the switching mechanism to select the optimal controller to output the actual control signal u(k) of the system according to the error switching strategy, including: selecting the prediction model corresponding to the minimum performance index as the optimal model according to the error performance index formula, and taking the control action generated by the optimal controller corresponding to the selected optimal model as the actual control signal u(k) of the system, wherein the error performance index formula is as follows: Among them, J m (k) is the error performance index, Ψ(k)=[ω T (k),||ω(k)||] T , ω(k) is the system regression vector, ||ω(k)|| is the bi-norm composed of the system regression vector, e m (k) is the output error of the virtual model in the mth subset at time k.

4. The direct-drive wind turbine generator side and grid side decoupling predictive control method according to claim 1, characterized in that: Step S500 outputs the corresponding generalized virtual prediction model M according to the actual control signal u(k) i (i=1, 2) output value, using the expected prediction value set and the corresponding generalized virtual prediction model M i (i=1, 2) output value calculates the output error e at time k+1 i (k+1) as the generalized predictive controller C i The input of (i=1, 2) is calculated as follows: y(k+1) represents the output signal of the system at time k+1, which represents the torque control of the motor-side PWM converter and the impedance control of the grid-side PWM converter. is a decreasing sequence of positive scalars.

5. The direct-drive wind turbine generator side and grid side decoupling predictive control method according to claim 3, characterized in that: Step S200 collects the motor side PWM converter signal according to the wind turbine topology structure, including the following steps: a. Establish the voltage equation of the permanent magnet synchronous generator in the dq coordinate system: In the formula, u sd is the d-axis component of the dq-axis of the generator stator voltage, u sq is the q-axis component of the dq-axis of the generator stator voltage, i sd The d-axis component of the generator stator current dq-axis, i sq is the q-axis component of the dq-axis of the generator stator current, R S Its stator resistance ω r is the generator rotor speed; L d is the generator shaft d-axis inductance and L q is the q-axis inductance of the generator shaft; b. Decouple the formula in step a to obtain the machine-side control signal of the motor-side PWM converter:

6. The direct-drive wind turbine generator side and grid side decoupling predictive control method according to claim 3, characterized in that: Step S200 collects the motor side PWM converter signal according to the wind turbine topology structure, including the following steps: Step S200 collects the grid side control signal of the grid side PWM converter according to the wind turbine topology structure, including the voltage equation of the grid side converter in the dq coordinate system is: After decoupling the power of the above formula, the control equation of the grid-side converter is obtained as follows: Among them, U d is the d-axis component of the dq-axis of the grid-side voltage, U q is the q-axis component of the dq-axis of the grid-side voltage, R is the grid-side resistance, K iP , K iI are the proportional gain and integral gain of the current inner loop PI regulator respectively; i dref 、i qref i d and i q The given reference value, L is the grid-side inductance, ω e is the grid angular frequency, e d and e q are the equivalent potentials of the d-axis and q-axis respectively.

7. A memory, characterized in that: A computer program is stored in the memory, wherein the computer program is configured to execute the direct-drive wind turbine generator side and grid side decoupling predictive control method according to any one of claims 1 to 6 when running.

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