Data-driven performance optimization method based on generator torque gain control

Abstract

The present invention relates to the field of torque control technology, and in particular to a data-driven performance optimization method based on generator torque gain control. Step 1: Input J(k) to the MPPT control system, and update the optimal torque gain K online through the MPPT control system. opt , where k represents the kth step and is a positive integer, and J(k) represents the performance function value at the kth step; Step 2: Introduce #imgabs0# into the MPPT control system. The MPPT control system introduces #imgabs1# and K opt Fusion is performed and the #imgabs2# output is used to adjust the torque gain in real time. Among them, ω m Represents the generator speed. Through the above technical solution, the present invention has the advantages of being model-free, simple to optimize, and capable of adjusting the torque gain in real time.

Description

Technical Field

[0001] The present invention relates to a wind turbine torque gain control technology, and in particular to a data-driven performance optimization method based on turbine torque gain control. Background Art

[0002] Generators are important power generation equipment and come in a variety of types, including wind turbines and hydroelectric generators. Traditional generator torque control methods rely on static parameters, which are optimized under steady-state conditions. However, in practical applications, these parameters are often insufficient. For example, when using wind turbines, wind turbulence and inaccurate wind speed estimations can make existing control methods ineffective.

[0003] The power coefficient is the ratio of the power produced by a wind turbine operating under conventional torque control to the power available in the wind. Therefore, in order to maximize the power output, the power factor must be Maximize; for variable speed and pitch wind turbines, the power coefficient is highly efficient Usually the tip speed ratio (TSR) and blade pitch (BP) angle unimodal function of .

[0004] Torque drive of generator set It is a commonly used control variable for power maximization; in traditional control, under equilibrium As long as the appropriate torque gain is selected, the control law can make the power coefficient Maximize the dimensionless tip speed ratio Is the interpretation of torque control law and torque gain The key to optimal selection.

[0005] However, the performance of model-based controllers may be limited by model uncertainties and inaccurate wind estimates. The aerodynamic characteristics of wind turbines are complex, nonlinear, and time-varying, affected by factors such as wind speed and direction, wind shear, air density, blade surface wear, and the accumulation of ice, dust, and insects. Therefore, in modern wind turbines, any wind estimate used for control purposes is usually obtained from the rotor speed through knowledge of the wind turbine aerodynamic characteristics, which may not be known precisely and may change with operation.

[0006] Furthermore, there are limitations to maximizing energy capture based on power diagrams. First, the accuracy of simulation results based on computational fluid dynamics (CFD) is problematic; the aerodynamic interaction between wind and wind turbine blades is quite complex, and the wind field around the wind turbine has a significant random component due to the presence of turbulence. Second, wind measurements are often inaccurate; due to the large rotor size of most utility-grade wind turbines, the larger the area swept by the rotor's rotation, the greater the impact of wind changes. In addition, the characteristics of the modeled wind turbine change over time, and the aerodynamics and associated power diagrams will change due to reasons such as surface wear, dirt, corrosion, or ice accumulation. Summary of the Invention

[0007] In order to solve the problems in the related art, the present application provides a data-driven performance optimization method based on generator torque gain control, which solves the problem of insufficient energy capture and efficiency caused by existing torque control methods under variable and uncertain wind conditions.

[0008] The technical solution is as follows:

[0009] The data-driven performance optimization method based on generator torque gain control is characterized by comprising the following steps:

[0010] Step 1: Input J(k) to the MPPT control system and update the optimal torque gain online through the MPPT control system , where k represents the k-th step, and k is a positive integer, and J(k) represents the performance function value at the k-th step;

[0011] Step 2: Introduce to MPPT control system , through the MPPT control system and Fusion is performed and output is used to adjust the torque gain in real time ,in, Indicates the generator speed.

[0012] Through the above technical solution, a new feedback loop is constructed through the MPPT control system to form a system that can achieve real-time online update. The DOO algorithm can estimate the optimal performance direction, which makes DDO technology attractive for control under motor speed fluctuations and can adjust the torque gain in real time to improve wind energy capture efficiency;

[0013] DDO technology is intelligent, easy to use, and model-free, minimizing the need for modification and interference with the original system control logic. Driven by real-time data, it is insensitive to unmodeled uncertainties and exhibits real-time performance, thus correcting suboptimal issues caused by model inaccuracies and environmental changes in traditional controllers.

[0014] Preferably, the step 1 comprises the following steps:

[0015] Step 11: Input J(k) into the gradient observer in the MPPT control system, and input the calculated data information into the generator torque control system in the MPPT control system for optimization search to obtain ,in, represents the kth step of the normalized power of the Nth cycle;

[0016] Step 12: Get the Perform outer slow cycle optimization and obtain 、 and ,according to Get output , output The formula is:

[0017] ,

[0018] according to Getting Input ,enter The formula is:

[0019] ,

[0020] in, Indicates the torque gain currently used by the system. represents the current optimal gain, It represents the optimal normalized power value in the current record, and q represents the forgetting factor.

[0021] Preferably, in step 11, the MPPT control system is input and , h is measured by the high-pass filter in the MPPT control system;

[0022] Obtained by gradient observer calculation , The formula is:

[0023] Formula 1;

[0024] Obtained by gradient observer calculation , The formula is:

[0025] Formula 2;

[0026] Obtained by gradient observer calculation , The formula is:

[0027] Formula 3;

[0028] Obtained by gradient observer calculation , The formula is:

[0029] Formula 4;

[0030] in, 、 , and h represent the amplitude of the disturbance, the integral gain, and the pole of the first-order high-pass filter, respectively. z represents the z domain. 、 、 and and represent the control torque gain input value at the kth step, the state value after filtering of the performance function, the state value after demodulation, and the state value after integrator respectively.

[0031] Preferably, it can be derived from Formula 1: ;

[0032] From formula 3, we can get: ;

[0033] Will 、 、 and After inputting into the gradient observer, we get .

[0034] Preferably, the step 12 includes the following steps:

[0035] Step 1201: Set i=0, k=0, initialize ;

[0036] Step 1202: Input , update after calculation , after judgment, ;

[0037] Step 1203: Input the measured 、 、 and , update after calculation , after judgment, and , where i represents the i-th sampling, represents the rotor power, Indicates wind speed, represents the air density, represents the yaw error;

[0038] Step 1204: According to the information obtained in step 1203 calculate ,according to Update Input .

[0039] Preferably, in step 1202: input Afterwards, we obtain 、 、 and , The calculation formula is:

[0040] ;

[0041] Preferably, in step 1202: , and judge ;

[0042] when When Already in The boundary of is disturbed, find the boundary point ,make , and output ;

[0043] when When , and output .

[0044] Preferably, in step 1203, The judgment is as follows;

[0045] when When the new optimal operating point is found, , ;

[0046] when When , .

[0047] In summary, the beneficial effect of the data-driven performance optimization method based on generator torque gain control is to construct a new feedback loop through the MPPT control system to form a method that can achieve real-time online update. The DOO algorithm can estimate the optimal performance direction, which makes DDO technology attractive for control under motor speed fluctuations and can adjust the torque gain in real time, thereby improving wind energy capture efficiency. DDO technology is intelligent, easy to use and model-free, which can minimize the modification and interference of the original system control logic. Because it is driven by real-time data, it is insensitive to unmodeled uncertainties and has real-time performance, which can correct sub-optimization problems caused by model inaccuracies and environmental changes in traditional controllers.

[0048] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0050] Figure 1 Schematic diagram of a data-driven performance optimization method based on generator torque gain control;

[0051] Figure 2 Schematic diagram of the feedback loop framework in the data-driven performance optimization method based on generator torque gain control;

[0052] Figure 3 Schematic diagram of the fast cycle algorithm in the data-driven performance optimization method based on generator torque gain control;

[0053] Figure 4 Schematic diagram of a single-input single-output system in a data-driven performance optimization method based on generator torque gain control;

[0054] Figure 5 The data driven performance optimization method based on generator torque gain control has an extreme value Schematic diagram of;

[0055] In the figure, 1. MPPT control system; 2. Generator torque control system. DETAILED DESCRIPTION

[0056] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0057] In one possible embodiment, as shown in the attached Figure 1-5 As shown, the data-driven performance optimization method based on generator torque gain control includes the following steps:

[0058] Step 1: Input J(k) to the MPPT control system 1 and update the optimal torque gain online through the MPPT control system 1 , where k represents the k-th step, and k is a positive integer, and J(k) represents the performance function value at the k-th step;

[0059] Step 2: Introduce to MPPT control system 1 , MPPT control system 1 will and Fusion is performed and output is used to adjust the torque gain in real time ,in, Indicates the generator speed.

[0060] Input real-time data into a single-input single-output system After that, get the factory output data , and the system input / output Has a local maximum ; Establish a system dynamics model in a single-input single-output system:

[0061] ,

[0062] Where x is a (n-dimensional) state variable, let the function 、 are differentiable in their parameters;

[0063] To ensure that the concept of steady state is well defined, the following assumptions can be imposed:

[0064] Assumption 1 There exists a differentiable function , if , ;

[0065] Assumption 2 For each constant u, there exists a corresponding equilibrium point in the system dynamics model Satisfies global asymptotic stability, and u satisfies consistent distribution:

[0066] Assumption 1 implies that the steady-state characteristics are well-defined and a differentiable function:

[0067] ;

[0068] Assumption 2 ensures that the steady-state properties are stable and attractive in some isouniform way (regardless of u), and are unique.

[0069] At the expense of abandoning any attempt to achieve a global analysis, one can simply ask for the equilibrium point Local (in x) results can be obtained by applying the local uniqueness and local stability properties of . This route also allows the analysis of multivalued steady-state properties.

[0070] Assumption 3 Considering l defined in Assumption 2, let There is a steady-state characteristic and there is a unique Maximize g:

[0071] ;

[0072] .

[0073] Step 1 includes the following steps:

[0074] Step 11: Input J(k) into the gradient observer in the MPPT control system 1, and input the calculated data information into the generator torque control system 2 in the MPPT control system 1 for optimization search to obtain ,in, represents the kth step of the normalized power of the Nth cycle;

[0075] Step 12: Get the Perform outer slow cycle optimization and obtain 、 and ,according to Get output , output The formula is:

[0076] ,

[0077] according to Getting Input ,enter The formula is:

[0078] ,

[0079] in, Indicates the torque gain currently used by the system. represents the current optimal gain, It represents the optimal normalized power value in the current record, and q represents the forgetting factor.

[0080] In step 11, input to the MPPT control system 1 and , h is measured by the high-pass filter in the MPPT control system 1, and is calculated by the gradient observer , The formula is:

[0081] Formula 1;

[0082] Obtained by gradient observer calculation , The formula is:

[0083] Formula 2;

[0084] Obtained by gradient observer calculation , The formula is:

[0085] Formula 3;

[0086] Obtained by gradient observer calculation , The formula is:

[0087] Formula 4;

[0088] in, 、 , and h represent the amplitude of the disturbance, the integral gain, and the pole of the first-order high-pass filter, respectively. z represents the z domain. 、 、 and and represent the control torque gain input value at the kth step, the state value after filtering of the performance function, the state value after demodulation, and the state value after integrator respectively.

[0089] From formula 1, we can deduce that: ;

[0090] From formula 3, we can get: ;

[0091] Will 、 、 and After inputting into the gradient observer, we get .

[0092] Step 12 includes the following steps:

[0093] Step 1201: Set i=0, k=0, initialize ;

[0094] Step 1202: Input , update after calculation , after judgment, ;

[0095] Step 1203: Input the measured 、 、 and , update after calculation , after judgment, and , where i represents the i-th sampling, represents the rotor power, Indicates wind speed, represents the air density, represents the yaw error;

[0096] Step 1204: According to the information obtained in step 1203 calculate ,according to Update Input .

[0097] In step 1202: input Afterwards, we obtain 、 、 and , The calculation formula is:

[0098] ,

[0099] In step 1202: , and judge ;

[0100] when When Already in The boundary of is disturbed, find the boundary point ,make , and output ;

[0101] when When , and output .

[0102] In step 1203, The judgment is as follows;

[0103] when When the new optimal operating point is found, , ;

[0104] when When , .

[0105] Those skilled in the art will readily appreciate other embodiments of the present invention upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not invented herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the appended claims.

[0106] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A data-driven performance optimization method based on generator torque gain control, characterized in that: The following steps are involved: Step 1: Input J(k) to the MPPT control system and update the optimal torque gain online through the MPPT control system , where k represents the k-th step, and k is a positive integer, and J(k) represents the performance function value at the k-th step; The step 1 comprises the following steps: Step 11: Input J(k) into the gradient observer in the MPPT control system, and input the calculated data information into the generator torque control system in the MPPT control system for optimization search to obtain ,in, represents the kth step of the normalized power of the Nth cycle; Step 12: Get the value from step 11 Perform outer slow cycle optimization and obtain 、 and ,according to Get output , output The formula is: ， according to Getting Input ,enter The formula is: ， in, Indicates the torque gain currently used by the system. represents the current optimal gain, represents the optimal normalized power value in the current record, and q represents the forgetting factor; Step 2: Introduce to MPPT control system , through the MPPT control system and Fusion is performed and output is used to adjust the torque gain in real time ,in, Indicates the generator speed.

2. The data driven performance optimization method based on generator torque gain control according to claim 1, characterized in that: In step 11, input to the MPPT control system and , h is measured by the high-pass filter in the MPPT control system; Obtained by gradient observer calculation , The formula is: Formula 1; Obtained by gradient observer calculation , The formula is: Formula 2; Obtained by gradient observer calculation , The formula is: Formula 3; Obtained by gradient observer calculation , The formula is: Formula 4; in, 、 , and h represent the amplitude of the disturbance, the integral gain, and the pole of the first-order high-pass filter, respectively. z represents the z domain. 、 、 and They respectively represent the control torque gain input value at the kth step, the state value after filtering of the performance function, the state value after demodulation, and the state value after the integrator.

3. The data driven performance optimization method based on generator torque gain control according to claim 2, characterized in that: From formula 1, we can get: ; From formula 3, we can get: ; Will 、 、 and After inputting into the gradient observer, we get .

4. The data driven performance optimization method based on generator torque gain control according to claim 1, characterized in that: The step 12 includes the following steps: Step 1201: Set i=0, k=0, initialize ; Step 1202: Input , update after calculation , after judgment, ; Step 1203: Input the measured 、 、 and , update after calculation , after judgment, and , where i represents the i-th sampling, represents the rotor power, Indicates wind speed, represents the air density, represents the yaw error; Step 1204: According to the information obtained in step 1203 calculate ,according to Update Input .

5. The data driven performance optimization method based on generator torque gain control according to claim 4, characterized in that: In step 1202: input Afterwards, we obtain 、 、 and , The calculation formula is: 。 6. The data driven performance optimization method based on generator torque gain control according to claim 5, characterized in that: In the step 1202: , and judge ; when When Already in The boundary of is disturbed, find the boundary point ,make , and output ; when When , and output .

7. The data driven performance optimization method based on generator torque gain control according to claim 5, characterized in that: In the step 1203, The judgment is as follows; when When the new optimal operating point is found, , ; when When , .

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