Variable pitch control optimization method for offshore wind turbine generator

Abstract

According to the variable-pitch control optimization method for the offshore wind turbine generator, flapping matrixes of three blades are converted into d-axis and q-axis signal components under a rotating coordinate system through extended Collmann transformation in combination with a dynamic compensation angle technology, a compensation angle delta is dynamically calculated through a pneumatic unbalance factor (delta a) and a mass unbalance factor (delta m), and the compensation angle delta is calculated through a dynamic balance factor (delta m). And the adaptability of a load unbalance state is obviously improved. And then, a robust controller based on a product perturbation model is adopted to generate a control signal in a dq domain, and the robustness of the system to complex wind conditions (such as turbulence and wind shearing) on the sea is enhanced by minimizing weighted norms of a sensitivity function and a complementary sensitivity function and optimizing parameters by using a DK iterative algorithm. And finally, the control signals are mapped back to the independent control quantity of each blade through anti-Collman transformation, and the blade angle is adjusted in real time by combining a closed-loop P I execution system.

Description

Technical Field

[0001] The present invention relates to the field of wind power generation, and in particular to a method for optimizing pitch control of an offshore wind turbine. Background Art

[0002] With the development of renewable energy technology, especially the growing demand in the field of offshore wind power, higher requirements are placed on the stability, reliability and efficiency of large offshore wind turbines in complex environments.

[0003] Existing pitch control systems often struggle to simultaneously meet the demands of multi-objective coordinated control, including power regulation, load optimization, and safety protection. Especially at wind speeds above rated, aerodynamic imbalances significantly increase blade flapping loads and tower overturning loads, posing challenges to the safe operation of turbines. Summary of the Invention

[0004] A first aspect of the present disclosure provides a method for optimizing pitch control of an offshore wind turbine, comprising the following steps:

[0005] The flapping matrices M1, M2, and M3 of the three blades of the offshore wind turbine are collected and converted into the signal components x of the d-axis and q-axis in the rotating coordinate system by extended Coleman transformation. d with x q ;

[0006] The signal components are input into the designed dq domain controller to generate the control signal u d with u q ;

[0007] The control signal is mapped back to the control quantities u1, u2 and u3 corresponding to each blade using an inverse Coleman transform, and the blade angles of the three blades of the offshore wind turbine are adjusted based on the control quantities.

[0008] In combination with the first aspect, the waving matrix is ​​converted into the signal components x of the d-axis and the q-axis in the rotating coordinate system by the extended Coleman transform. d with x q Through the following formula:

[0009]

[0010] where θ r is the rotor position angle.

[0011] In combination with the first aspect, the method further includes adding a compensation angle to the Coleman transform to convert the waving matrix into a signal component x of the d-axis and the q-axis in the rotating coordinate system. d with x q :

[0012]

[0013] Where δ is the compensation angle.

[0014] In combination with the first aspect, the compensation angle δ is dynamically calculated by the following formula:

[0015] δ=k1*Δa+k2*Δm,

[0016] Where Δa is the aerodynamic imbalance factor, Δm is the mass imbalance factor, k1∈[0.2,0.5], k2∈[0.1,0.3] are weighting coefficients.

[0017] In combination with the first aspect, the dq domain controller is a robust controller based on a product perturbation model, and its transfer function satisfies:

[0018] k s =argmin‖W_s(S)+W_T(T)‖,

[0019] Among them, S is the sensitivity function, T is the complementary sensitivity function, W_s and W_T are frequency weighting functions.

[0020] In conjunction with the first aspect, the design process of the dq domain controller includes:

[0021] Establish a product perturbation model including mass imbalance perturbation Δm and aerodynamic imbalance perturbation Δa;

[0022] The closed-loop bandwidth is regulated using a second-order reference model;

[0023] The controller parameters are solved using the DK iterative algorithm.

[0024] In combination with the first aspect, the inverse Coleman transform satisfies:

[0025] [u1;u2;u3]=T(θ_r)^-1·[u d ;u q ;u0],

[0026] Where T(θ_r) is the transformation matrix and u_0 is the zero-sequence component.

[0027] In combination with the first aspect, adjusting the blade angles of the three blades of the offshore wind turbine based on the control amount includes:

[0028] Input the control variables u1, u2, and u3 into the closed-loop execution system to generate the pitch angle command:

[0029] β i ^cmd=β θ +k p ·(u i -β i ^fb)+ki ·∫(u i -β i ^fb)dt,

[0030] Among them, β θ is the reference pitch angle at rated wind speed, β i ^fb is the real-time pitch angle feedback value of blade i, k p ∈[0.8,1.2],k i ∈[0.05,0.15] is the PI control parameter;

[0031] β is performed by the pitch servo drive i ^cmd command.

[0032] According to a second aspect of the present disclosure, an electronic device is provided, comprising:

[0033] one or more processors;

[0034] A storage unit is used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the offshore wind turbine pitch control optimization method.

[0035] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the offshore wind turbine pitch control optimization method can be implemented.

[0036] Beneficial effects: The present disclosure provides a method for optimizing pitch control of offshore wind turbines. By extending the Coleman transform and combining the dynamic compensation angle technology, the flapping matrices of the three blades are converted into d-axis and q-axis signal components in a rotating coordinate system, wherein the compensation angle δ is dynamically calculated through the aerodynamic imbalance factor (Δa) and the mass imbalance factor (Δm), which significantly improves the adaptability to the load imbalance state. Subsequently, a robust controller based on the product perturbation model is used to generate a control signal in the dq domain. By minimizing the weighted norm of the sensitivity function and the complementary sensitivity function, and optimizing the parameters using the DK iterative algorithm, the robustness of the system to complex offshore wind conditions (such as turbulence and wind shear) is enhanced. Finally, the control signal is mapped back to the independent control quantity of each blade through the inverse Coleman transform, and the blade angle is adjusted in real time in combination with the closed-loop PI execution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The present invention is a flowchart of a method for optimizing pitch control of an offshore wind turbine according to an embodiment of the present invention.

[0038] Figure 2 The electronic device disclosed herein. DETAILED DESCRIPTION

[0039] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present disclosure.

[0040] The terms used in the embodiments of the present disclosure are for the purpose of describing specific embodiments only and are not intended to limit the embodiments of the present disclosure. The singular forms "a," "the," and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0041] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0042] like Figure 1 FIG. 1 is a flow chart of a method for optimizing pitch control of an offshore wind turbine according to an embodiment of the present disclosure, including:

[0043] S101: Collect the flapping matrices M1, M1, and M3 of the three blades of the offshore wind turbine, and convert the flapping matrices into signal components x of the d-axis and q-axis in the rotating coordinate system through the extended Coleman transform. d with x q ;

[0044] S102: Input the signal component into the designed dq domain design controller to generate a control signal u d with u q ;

[0045] S103: Use inverse Coleman transformation to map the control signal back to control quantities u1, u2 and u3 corresponding to each blade, and adjust the blade angles of the three blades of the offshore wind turbine based on the control quantities.

[0046] Specifically, strain sensors are installed at the roots of the three blades to measure the blade root flapping moment load signals M1, M2, and M3 (unit: kN·m) in real time.

[0047] Optionally, high-frequency noise is filtered out by a low-pass filter (cut-off frequency 2 Hz), and the sampling frequency is ≥ 50 Hz.

[0048] Bring the above parameters into Matlab to implement the extended Coleman transform and output the signal component x d with x q .

[0049] The calculation formula is:

[0050]

[0051] where θ r is the rotor position angle.

[0052] Furthermore, the dq domain controller is a robust controller based on a product perturbation model.

[0053] The product perturbation model is constructed, and the mass imbalance perturbation Δm (±5% blade mass) and aerodynamic imbalance perturbation Δa (±10% wind speed variance) are defined.

[0054] Its state equation is: G(s)=G0(s)(1+W m (s)△ m )(1+W a (s)△ a ),

[0055] Where W m (s), W a (s) is the perturbation weight function.

[0056] Its transfer function satisfies:

[0057] k s =argmin‖W_s(S)+W_T(T)‖,

[0058] Among them, S is the sensitivity function, T is the complementary sensitivity function, W_s and W_T are frequency weighting functions.

[0059] The closed-loop bandwidth is regulated by a second-order reference model, and the controller parameters are solved by the DK iterative algorithm.

[0060] Furthermore, the signal component is input into the designed dq domain controller to generate a control signal u d with u q .

[0061] The dq domain design controller generates the outer loop current reference value according to the signal components obtained by the above transformation, and the inner loop current controller generates the control signal u according to the outer loop current reference value. d with u q .

[0062] Furthermore, the control signal is mapped back to the control quantities u1, u2 and u3 corresponding to each blade using an inverse Coleman transform, and the blade angles of the three blades of the offshore wind turbine are adjusted based on the control quantities.

[0063] The inverse Coleman transform satisfies:

[0064] [u1;u2;u3]=T(θ_r)^-1·[u d ;u q ;u0],

[0065] Where T(θ_r) is the transformation matrix and u_0 is the zero-sequence component.

[0066] The above control signal u d with u q Substitute into MATLAB to generate the pitch angle command:

[0067] β i ^cmd=β θ +k p ·(u i -β i ^fb)+k i ·∫(u i -β i ^fb)dt,

[0068] Among them, β θ is the reference pitch angle at rated wind speed, β i ^fb is the real-time pitch angle feedback value of blade i, k p ∈[0.8,1.2],k i ∈[0.05,0.15] is the PI control parameter;

[0069] β is performed by the pitch servo drive i ^cmd command.

[0070] Optionally, the method further includes adding a compensation angle to the Coleman transform to convert the waving matrix into the signal components x of the d-axis and q-axis in the rotating coordinate system. d with x q :

[0071]

[0072] Where δ is the compensation angle.

[0073] Among them, the aerodynamic imbalance factor Δa is calculated based on the wind speed distribution variance of the blade surface pressure sensor data (formula: σ ui is the standard deviation of wind speed at blade i).

[0074] Mass imbalance factor Δm: The mass deviation is obtained by the blade mass distribution detector (formula: m i is the mass of blade i, For average quality.

[0075] Compensation angle generation: δ = k1*Δa + k2*Δm, where k1∈[0.2,0.5], k2∈[0.1,0.3] are weighting coefficients.

[0076] Beneficial effects: By extending the Coleman transform and combining it with the dynamic compensation angle technology, the flapping matrices of the three blades are converted into d-axis and q-axis signal components in the rotating coordinate system, where the compensation angle δ is dynamically calculated through the aerodynamic imbalance factor (Δa) and the mass imbalance factor (Δm), which significantly improves the adaptability to the load imbalance state. Subsequently, a robust controller based on the product perturbation model is used to generate control signals in the dq domain. By minimizing the weighted norm of the sensitivity function and the complementary sensitivity function and optimizing the parameters using the DK iterative algorithm, the system's robustness to complex offshore wind conditions (such as turbulence and wind shear) is enhanced. Finally, the control signal is mapped back to the independent control quantity of each blade through the inverse Coleman transform, and the blade angle is adjusted in real time in combination with the closed-loop PI execution system.

[0077] The electronic device 200 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 200 may include but is not limited to a processor 201 and a memory 202. Those skilled in the art will appreciate that Figure 2 This is merely an example of the electronic device 200 and does not constitute a limitation of the electronic device 200. The electronic device 200 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0078] The processor 201 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0079] The memory 202 can be an internal storage unit of the electronic device 200, such as a hard disk or memory of the electronic device 200. The memory 202 can also be an external storage device of the electronic device 200, such as a plug-in hard disk equipped on the electronic device 200, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. Furthermore, the memory 202 can also include both an internal storage unit of the electronic device 200 and an external storage device. The memory 202 is used to store the computer program 203 and other programs and data required by the electronic device. The memory 202 can also be used to temporarily store data that has been output or is about to be output.

[0080] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure 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 make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present disclosure, and should all be included in the scope of protection of the present disclosure.

Claims

1. A method for optimizing pitch control of an offshore wind turbine, characterized in that: The following steps are involved: The flapping matrices M1, M2, and M3 of the three blades of the offshore wind turbine are collected and converted into the signal components x of the d-axis and q-axis in the rotating coordinate system by extended Coleman transformation. d with x q ; The signal components are input into the designed dq domain controller to generate the control signal u d with u q ; The control signal is mapped back to the control quantities u1, u2 and u3 corresponding to each blade using an inverse Coleman transform, and the blade angles of the three blades of the offshore wind turbine are adjusted based on the control quantities.

2. The method according to claim 1, characterized in that The waving matrix is ​​converted into the signal components x of the d-axis and q-axis in the rotating coordinate system by the extended Coleman transform. d with x q Through the following formula: where θ r is the rotor position angle.

3. The method according to claim 2, characterized in that The method further includes adding a compensation angle to the Coleman transform to convert the waving matrix into a signal component x of the d-axis and the q-axis in a rotating coordinate system. d with x q : Where δ is the compensation angle.

4. The method according to claim 3, characterized in that The compensation angle δ is dynamically calculated by the following formula: δ=k1*Δa+k2*Δm, Where Δa is the aerodynamic imbalance factor, Δm is the mass imbalance factor, k1∈[0.2,0.5], k2∈[0.1,0.3] are weighting coefficients.

5. The method according to claim 4, characterized in that The dq domain controller is a robust controller based on a product perturbation model, and its transfer function satisfies: k s =argmin‖W_s(S)+W_T(T)‖, Among them, S is the sensitivity function, T is the complementary sensitivity function, W_s and W_T are frequency weighting functions.

6. The method according to claim 5, characterized in that The design process of the dq domain controller includes: Establish a product perturbation model including mass imbalance perturbation Δm and aerodynamic imbalance perturbation Δa; The closed-loop bandwidth is regulated using a second-order reference model; The controller parameters are solved using the DK iterative algorithm.

7. The method according to claim 1, characterized in that The inverse Coleman transform satisfies: [u1;u2;u3]=T(θ_r)^-1·[u d ;u q ;u0], Where T(θ_r) is the transformation matrix and u_0 is the zero-sequence component.

8. The method according to claim 1, characterized in that The adjusting the blade angles of the three blades of the offshore wind turbine generator system based on the control amount includes: Input the control variables u1, u2, and u3 into the closed-loop execution system to generate the pitch angle command: b i ^cmd=β θ +k p ·(u i -b i ^fb)+k i ·∫(u i -b i ^fb)dt, Among them, β θ is the reference pitch angle at rated wind speed, β i ^fb is the real-time pitch angle feedback value of blade i, k p ∈[0.8,1.2],k i ∈[0.05,0.15] is the PI control parameter; β is performed by the pitch servo drive i ^cmd command.

9. An electronic device, characterized in that: include: one or more processors; A storage unit for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the offshore wind turbine pitch control optimization method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the offshore wind turbine pitch control optimization method according to any one of claims 1 to 8 can be implemented.

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