An optimal tracking rotor control method for reducing load of fan transmission chain

Abstract

The application discloses an optimal tracking rotor control method for reducing the load of a fan transmission chain. When the traditional OTR method is applied to improve the tracking performance of the fan, the fan transmission chain is affected by the high-frequency wind speed in the aerodynamic torque, which causes the frequent fluctuation of the electromagnetic torque and the significant increase of the load. The traditional OTR method is improved, a first-order filter is introduced into the aerodynamic torque loop in the OTR method to filter out the high-frequency wind speed in the turbulent wind speed, the filtering parameters are obtained from the response surface model constructed by the offline traversal filtering parameters and the turbulent wind condition, and then the response surface model is called online according to the current wind condition to periodically guide the improved OTR method filtering parameters, so that the load growth of the transmission chain is reduced in the maximum power point tracking process. The application overcomes the phenomenon that the high-frequency turbulent component in the turbulent wind speed causes the significant growth of the load of the fan transmission chain, and can significantly reduce the load fluctuation of the transmission chain compared with the traditional OTR method, and prolong the service time of the fan.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine control technology, and in particular, it is an optimal tracking rotor control method for reducing the load on the wind turbine drive train. Background Technology

[0002] The transformation and upgrading of the energy structure, represented by wind power, is the core and key. To maximize wind energy utilization, variable-speed constant-frequency wind turbines typically employ Maximum Power Point Tracking (MPPT) control mode when operating below rated speed. Currently, in practical engineering applications, the Optimal Torque (OT) method is widely used in large-scale wind turbines due to its advantages such as not requiring real-time wind speed measurement and its strong practicality.

[0003] With the increasing size of wind turbines, large wind turbines with weak tracking capabilities are no longer able to track rapidly changing turbulent winds. To address this, some scholars have proposed an improved approach: adjusting the electromagnetic torque to accelerate the dynamic process of wind energy capture. Current improvement methods can be categorized as using acceleration information, aerodynamic torque information, and unbalanced torque to adjust the electromagnetic torque command, thereby amplifying the unbalanced torque during acceleration and deceleration to enhance wind energy capture capability.

[0004] However, current improvement strategies, such as the Optimally Tracking Rotor (OTR) method, only focus on improving the wind turbine's energy capture efficiency, without considering the negative impact of such methods on the load of the wind turbine's drivetrain. The reason for this is that these methods aggressively adjust the electromagnetic torque to capture the high and low frequency wind energy contained in turbulent wind speeds. Considering the increasing size of wind turbines, which have slow mechanical dynamics, they can no longer respond to rapidly changing turbulent wind speeds. Forcibly adjusting the electromagnetic torque to capture the energy contained in the high frequency wind energy in turbulent wind speeds would incur a huge load burden on the wind turbine, thus reducing its service life. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the prior art by providing an optimal tracking rotor control method for reducing the load on the wind turbine drivetrain. This method utilizes an online-to-offline response surface model of wind speed characteristics and filtering parameters based on turbulent wind conditions to guide control parameter design. This model then filters the aerodynamic module, reducing the increase in drivetrain load caused by high-frequency components of turbulent wind speeds during maximum power point tracking. This method exhibits good adaptability to turbulent wind conditions, simple logic, and broad engineering application potential.

[0006] The technical solution to achieve the objective of this invention is as follows: On the one hand, an optimal tracking rotor control method for reducing the load on the wind turbine drive train is provided, the method comprising the following steps:

[0007] Step 1: Offline iterate through the mapping relationship between different wind speed characteristics and filter parameters τ, and construct a response surface model;

[0008] Step 2: Initialize the filter parameter τ and set the optimization period T. s ;

[0009] Step 3: Obtain wind speed information, rotational speed information, and electromagnetic torque T. e Based on these data, wind speed characteristics and aerodynamic torque T are calculated. a ;

[0010] Step 4: Based on the response surface model established in Step 1, optimize the filtering parameters τ online according to the current wind speed characteristics;

[0011] Step 5: Based on the filtering parameter τ obtained in Step 4, adjust the aerodynamic torque T. a Optimized and updated to aerodynamic torque T alpf And update the electromagnetic torque T e ;

[0012] Step 6, determine the current optimization period T s Is the process complete? If so, then use the electromagnetic torque command T calculated in step 5. e Send the order to the maximum power point tracking controller and return to step 2.

[0013] Furthermore, step 1, which involves offline traversal of the mapping relationship between different wind speed characteristics and the filtering parameter τ, and construction of a response surface model, specifically includes:

[0014] Step 1-1: Obtain the structural parameters and environmental parameters of the wind turbine. The structural parameters include the moment of inertia J. t Blade radius R, rated power P N Rated speed ω N The environmental parameters include air density ρ.

[0015] Steps 1-2: Establish a maximum power point tracking control model for the wind turbine using the improved optimal tracking rotor OTR method, and fit the filter parameter τ under different wind speed characteristics.

[0016] Steps 1-3: Based on steps 1-2, construct a response surface model using different wind speed characteristics and the filtering parameter τ. Using the least squares method, the functional relationship can be obtained as follows:

[0017]

[0018] In the formula, ω represents the average wind speed, TI represents the turbulence intensity level, and ω represents the average wind speed. eff The equivalent turbulence frequency.

[0019] Furthermore, in step 3, the aerodynamic torque T is calculated. a The specific process includes:

[0020] Calculate the fan acceleration 'a' based on the rotational speed information;

[0021] The aerodynamic torque T of the fan is calculated according to the following formula. a :

[0022] T a =J t a+T e (2)

[0023] Furthermore, in step 5, the electromagnetic torque T is updated. e The formula is:

[0024] T e '=T e +G(T alpf -T e (3)

[0025] In the formula, T e ' represents the updated electromagnetic torque, and G is the control parameter.

[0026] On the other hand, an optimal tracking rotor control system for reducing wind turbine drivetrain load is provided, the system comprising sequentially executed:

[0027] The first module is used to build response surface models with different wind speed characteristics and filtering parameters offline;

[0028] The second module is used to initialize the filter parameters τ and set the optimization period T. s ;

[0029] The third module is used to acquire wind speed information, rotational speed information, and electromagnetic torque T. e Based on these data, wind speed characteristics and aerodynamic torque T are calculated. a ;

[0030] The fourth module is used to optimize the filtering parameters τ online based on the response surface model established in the first module and the current wind speed characteristics.

[0031] The fifth module is used to filter the aerodynamic torque T based on the filtering parameters obtained from the fourth module. a Optimized and updated to aerodynamic torque T alpf And update the electromagnetic torque T e ;

[0032] The sixth module is used to determine the current optimization period T.s Is it finished? If finished, update the electromagnetic torque command T in module 5. e The order is sent to the maximum power point tracking controller, and the process returns to execute the second module.

[0033] Compared with the prior art, the significant advantages of this invention are:

[0034] 1) This invention implements an MPPT control strategy that takes into account the reduction of drive train load, which significantly reduces the wind turbine drive train load growth caused by the high-frequency components in turbulent wind speed.

[0035] 2) The design logic of this invention is simple and easy to apply in engineering. Only the control instructions need to be changed, and no additional hardware devices are required. It exhibits good adaptability to different turbulent wind conditions.

[0036] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0037] Figure 1 This is a flowchart of the optimal tracking rotor control method for reducing wind turbine drive train load according to the present invention.

[0038] Figure 2 This is a control block diagram of the optimal tracking rotor control method for reducing wind turbine drive train load according to the present invention.

[0039] Figure 3 This is a torque trajectory comparison diagram between the optimal tracking rotor control method for reducing wind turbine drivetrain load in one embodiment and the traditional OTR method. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0042] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0043] In one embodiment, combined Figure 1 An optimal tracking rotor control method for reducing the load on the wind turbine drive train is provided, the method comprising the following steps:

[0044] Step 1: Offline traversal of the mapping relationship between different wind speed characteristics and filter parameters τ, and construction of a response surface model; specifically including:

[0045] Step 1-1: Obtain the structural parameters and environmental parameters of the wind turbine. The structural parameters include the moment of inertia J. t Blade radius R, rated power P N Rated speed ω N The environmental parameters include air density ρ.

[0046] Steps 1-2: Establish a maximum power point tracking control model for the wind turbine using the improved optimal tracking rotor OTR method, and fit the filter parameter τ under different wind speed characteristics.

[0047] Steps 1-3: Based on steps 1-2, construct a response surface model using different wind speed characteristics and the filtering parameter τ. Using the least squares method, the functional relationship can be obtained as follows:

[0048]

[0049] In the formula, ω represents the average wind speed, TI represents the turbulence intensity level, and ω represents the average wind speed. eff The equivalent turbulence frequency;

[0050] Step 2: Initialize the filter parameter τ and set the optimization period T. s ;

[0051] Preferably, the initial filter parameter τ is 0.8, and the optimization period T is... s Take 10 minutes.

[0052] Step 3: Obtain wind speed information, rotational speed information, and electromagnetic torque T. e Based on these data, wind speed characteristics and aerodynamic torque T are calculated.a Among these methods, a lidar anemometer is used to obtain wind speed information at the wind turbine hub; the aerodynamic torque T is calculated. a The specific process includes:

[0053] Calculate the fan acceleration 'a' based on the rotational speed information;

[0054] The aerodynamic torque T of the fan is calculated according to the following formula. a :

[0055] T a =J t a+T e (5)

[0056] Step 4: Based on the response surface model established in Step 1, optimize the filtering parameters τ online according to the current wind speed characteristics;

[0057] Step 5: Based on the filtering parameter τ obtained in Step 4, adjust the aerodynamic torque T. a Optimized and updated to aerodynamic torque T alpf And update the electromagnetic torque T e ;

[0058] Here, the electromagnetic torque T is updated. e The formula is:

[0059] T e '=T e +G(T alpf -T e (6)

[0060] In the formula, T e ' represents the updated electromagnetic torque, and G is the control parameter.

[0061] Step 6, determine the current optimization period T s Is the process complete? If so, then use the electromagnetic torque command T calculated in step 5. e Send the order to the maximum power point tracking controller and return to step 2.

[0062] In one embodiment, an optimal tracking rotor control system for reducing wind turbine drivetrain load is provided, the system comprising sequentially executing:

[0063] The first module is used to build response surface models with different wind speed characteristics and filtering parameters offline;

[0064] The second module is used to initialize the filter parameters τ and set the optimization period T. s ;

[0065] The third module is used to acquire wind speed information, rotational speed information, and electromagnetic torque T. eBased on these data, wind speed characteristics and aerodynamic torque T are calculated. a ;

[0066] The fourth module is used to optimize the filtering parameters τ online based on the response surface model established in the first module and the current wind speed characteristics.

[0067] The fifth module is used to filter the aerodynamic torque T based on the filtering parameters obtained from the fourth module. a Optimized and updated to aerodynamic torque T alpf And update the electromagnetic torque T e ;

[0068] The sixth module is used to determine the current optimization period T. s Is it finished? If finished, update the electromagnetic torque command T in module 5. e The order is sent to the maximum power point tracking controller, and the process returns to execute the second module.

[0069] Specific limitations regarding the optimal tracking rotor control system for reducing wind turbine drivetrain load can be found in the limitations of the optimal tracking rotor control method for reducing wind turbine drivetrain load mentioned above, and will not be repeated here. Each module in the aforementioned optimal tracking rotor control system for reducing wind turbine drivetrain load can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0070] This invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0071] Step 1: Offline iterate through the mapping relationship between different wind speed characteristics and filter parameters τ, and construct a response surface model;

[0072] Step 2: Initialize the filter parameter τ and set the optimization period T. s ;

[0073] Step 3: Obtain wind speed information, rotational speed information, and electromagnetic torque T. e Based on these data, wind speed characteristics and aerodynamic torque T are calculated. a ;

[0074] Step 4: Based on the response surface model established in Step 1, optimize the filtering parameters τ online according to the current wind speed characteristics;

[0075] Step 5: Based on the filtering parameter τ obtained in Step 4, adjust the aerodynamic torque T. a Optimized and updated to aerodynamic torque T alpfAnd update the electromagnetic torque T e ;

[0076] Step 6, determine the current optimization period T s Is the process complete? If so, then use the electromagnetic torque command T calculated in step 5. e Send the order to the maximum power point tracking controller and return to step 2.

[0077] For specific limitations on each step, please refer to the limitations of the optimal tracking rotor control method for reducing the load on the wind turbine drive train mentioned above, which will not be repeated here.

[0078] This invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the following steps:

[0079] Step 1: Offline iterate through the mapping relationship between different wind speed characteristics and filter parameters τ, and construct a response surface model;

[0080] Step 2: Initialize the filter parameter τ and set the optimization period T. s ;

[0081] Step 3: Obtain wind speed information, rotational speed information, and electromagnetic torque T. e Based on the wind speed information, the wind speed characteristics and aerodynamic torque T are calculated. a ;

[0082] Step 4: Based on the response surface model established in Step 1, optimize the filtering parameters τ online according to the current wind speed characteristics;

[0083] Step 5: Based on the filtering parameters calculated in Step 4, adjust the aerodynamic torque T... a Updated to pneumatic torque T alpf And recalculate the electromagnetic torque T e ;

[0084] Step 6, determine the current optimization period T s Is the process complete? If so, then use the electromagnetic torque command T calculated in step 5. e Send the order to the maximum power point tracking controller and return to step 2.

[0085] For specific limitations on each step, please refer to the limitations of the optimal tracking rotor control method for reducing the load on the wind turbine drive train mentioned above, which will not be repeated here.

[0086] As a specific example, the invention will be further verified and illustrated in one embodiment.

[0087] The simulation model uses FAST (Fatigue, Aerodynamics, Structures, and Turbulence), an open-source professional wind turbine simulation software provided by the National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy. The wind turbine model corresponds to the 5MW Baseline model developed by NREL, and its relevant parameters are shown in Table 1 below.

[0088] Table 1. Main parameters of NREL 5MW Baseline wind turbine

[0089]

[0090] First, 10-minute timescale turbulent wind speed sequences conforming to the Kaimal power spectrum were generated using NREL TurSim. The turbulence characteristics were as follows: average wind speed fluctuated between 4 and 7 m / s (step size 1 m / s), turbulence intensity was at level AC, and equivalent frequency fluctuated between 0.1 and 0.5 (step size 0.05). By arranging and combining different average wind speeds, turbulence intensities, and equivalent frequencies, a total of 120 turbulent wind speed sequences were formed.

[0091] The wind turbine was modeled using the professional wind turbine simulation software FAST. The response surface model of different turbulent wind speed characteristics with respect to the filter parameters, constructed above, was used to fit the sample data. The functional relationship between wind speed characteristics and filter parameters is as follows:

[0092]

[0093] In the formula, ω represents the average wind speed, TI represents the turbulence intensity level, and ω represents the average wind speed. eff The equivalent turbulence frequency.

[0094] Because the high-frequency components of turbulent wind speed cause fluctuations in electromagnetic torque, a first-order filter is introduced into the estimated aerodynamic torque. Wind speed information at the hub of a lidar anemometer is used, and the current wind speed characteristics are calculated based on this information. Furthermore, based on these wind speed characteristics, an offline-constructed response surface model of the turbulent wind speed characteristics with respect to the filter parameters is invoked to optimize the current aerodynamic torque and update the electromagnetic torque command. The working principle is as follows: Figure 2 As shown.

[0095] A set of turbulent wind speed sequences was generated using NREL TurSim to simulate the wind conditions. The traditional OTR method and the optimal tracking rotor control method of this invention for reducing the wind turbine drivetrain load were simulated in the professional simulation software FAST. The drivetrain load of a large wind turbine was calculated to verify the effectiveness of the method. The simulation results of the two maximum power point tracking control strategies are shown in Table 2.

[0096] Table 2 Comparison of loads before and after the OTR method improvement

[0097]

[0098] As can be seen from the table above, the method of this invention reduces the load by 13.27% compared to the traditional OTR method. Figure 3 As shown, due to the effect of the high-frequency component in turbulent wind speed, the electromagnetic torque under the traditional OTR method fluctuates drastically with the high-frequency wind speed, resulting in a significant increase in the transmission chain load. This invention, however, can significantly reduce the frequent fluctuations in electromagnetic torque caused by high-frequency wind speed, significantly reduce load growth, and extend the unit's service life.

[0099] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this invention should be included within the protection scope of this invention.

Claims

1. An optimal tracking rotor control method for reducing the load on the wind turbine drive train, characterized in that, The method includes the following steps: Step 1: Offline iterate through the mapping relationship between different wind speed characteristics and filter parameters τ, and construct a response surface model; Step 2: Initialize the filter parameter τ and set the optimization period T. s ; Step 3: Obtain wind speed information, rotational speed information, and electromagnetic torque T. e Based on these data, wind speed characteristics and aerodynamic torque T are calculated. a ; Step 4: Based on the response surface model established in Step 1, optimize the filtering parameters τ online according to the current wind speed characteristics; Step 5: Based on the filtering parameter τ obtained in Step 4, adjust the aerodynamic torque T. a Optimized and updated to aerodynamic torque T alpf And update the electromagnetic torque T e ; Step 6, determine the current optimization period T s Is the process complete? If so, then use the electromagnetic torque command T calculated in step 5. e Send the order to the maximum power point tracking controller and return to step 2. Step 1, which involves offline traversal of the mapping relationship between different wind speed characteristics and the filter parameter τ, and construction of a response surface model, specifically includes: Step 1-1: Obtain the structural parameters and environmental parameters of the wind turbine. The structural parameters include the moment of inertia J. t Blade radius R, rated power P N Rated speed ω N The environmental parameters include air density ρ. Steps 1-2: Establish a maximum power point tracking control model for the wind turbine using the improved optimal tracking rotor OTR method, and fit the filter parameter τ under different wind speed characteristics. Steps 1-3: Based on steps 1-2, construct a response surface model using different wind speed characteristics and the filtering parameter τ. Using the least squares method, the functional relationship can be obtained as follows: In the formula, ω represents the average wind speed, TI represents the turbulence intensity level, and ω represents the turbulence intensity level. eff The equivalent turbulence frequency.

2. The optimal tracking rotor control method for reducing wind turbine drive train load according to claim 1, characterized in that, In step 2, the filter parameter τ is initialized to 0.8, and the optimization period T is... s Take 10 minutes.

3. The optimal tracking rotor control method for reducing wind turbine drive train load according to claim 1, characterized in that, In step 3, the aerodynamic torque T is calculated. a The specific process includes: Calculate the fan acceleration 'a' based on the rotational speed information; The aerodynamic torque T of the fan is calculated according to the following formula. a : T a =J t a+T e (2)。 4. The optimal tracking rotor control method for reducing wind turbine drive train load according to claim 1, characterized in that, In step 3, a lidar anemometer is used to obtain wind speed information at the wind turbine hub.

5. The optimal tracking rotor control method for reducing wind turbine drive train load according to claim 1, characterized in that, In step 5, update the electromagnetic torque T. e The formula is: T e '=T e +G(T alpf -T e ) (3) In the formula, T e ' represents the updated electromagnetic torque, and G is the control parameter.

6. An optimal tracking rotor control system for reducing wind turbine drivetrain load based on the method of any one of claims 1 to 5, characterized in that, The system includes sequential execution of: The first module is used to build response surface models with different wind speed characteristics and filtering parameters offline; The second module is used to initialize the filter parameters τ and set the optimization period T. s ; The third module is used to acquire wind speed information, rotational speed information, and electromagnetic torque T. e Based on these data, wind speed characteristics and aerodynamic torque T are calculated. a ; The fourth module is used to optimize the filtering parameters τ online based on the response surface model established in the first module and the current wind speed characteristics. The fifth module is used to filter the aerodynamic torque T based on the filtering parameters obtained from the fourth module. a Optimized and updated to aerodynamic torque T alpf And update the electromagnetic torque T e ; The sixth module is used to determine the current optimization period T. s Is it finished? If finished, update the electromagnetic torque command T in module 5. e The order is sent to the maximum power point tracking controller, and the process returns to execute the second module.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

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