Wind power gear box planetary gear train meshing and lubricating performance collaborative optimization method

Abstract

The invention provides a wind power gear box planetary gear train meshing and lubricating performance collaborative optimization method. The method comprises the following steps that a planetary-level sliding bearing wind power gear box system friction dynamics coupling model is established; determining the tooth profile and the tooth direction of the planetary gear train and the axial modification range of the sliding bearing; the collaborative modification amount of the gear and the sliding bearing is designed based on the modification range; substituting into a dynamic coupling model for calculation, and constructing a data set based on a cross validation method according to a system simulation calculation result; and according to the data set, constructing a radial basis function proxy model used for reflecting a working condition-gear and sliding bearing modification parameter-system dynamic response mapping relation, and through particle swarm optimization, obtaining an optimal gear and sliding bearing collaborative modification amount. According to the method, gear and sliding bearing modification is considered during modification optimization, so that the meshing performance of the gear pair is improved, and the lubricating performance of the sliding bearing is also improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train of a wind power gearbox. Background Art

[0002] Wind turbines are essential equipment for large-scale, low-cost development of wind resources. Gearboxes are the key transmission device in wind turbines, transmitting megawatt-level power from the hub to the generator. As wind turbines develop toward ultra-high power outputs exceeding 20MW, the "sliding-instead-rolling" design of planetary gear bearings can significantly increase torque density, reduce wind turbine failure rates caused by rolling bearings under high load conditions, and support the development of larger wind turbines. Because the coupled deformation of wind turbine gearbox systems under low-speed and heavy-load conditions is complex, coupled with the non-torsional loads on the main shaft and the dynamic bending moments generated by the meshing of the helical gear pairs in the planetary gear train, these can significantly alter the dynamic clearance of planetary-grade sliding bearings. This can easily lead to contact wear on the sliding bearing edges and uneven loading in the gear pair meshing, increasing the risk of fatigue failure of planetary-grade sliding bearings and gears, and reducing the operating life of the gearbox system. Therefore, research on the coordinated modification optimization of sliding bearings and gears is of great significance for guiding system design optimization and improving system operating life.

[0003] Currently, research on the optimization of sliding bearing-gear systems can be roughly divided into those focused solely on gear shaping and those focused solely on sliding bearing shaping. Optimization methods focused solely on gear shaping can only improve the meshing performance of the gearbox gear transmission system, but fail to account for the rough edge peak contact phenomenon of the sliding bearing caused by the non-torsional load on the main shaft end and the meshing bending moment of the helical gear pair. Optimization methods focused solely on sliding bearing shaping can only improve the lubrication performance of planetary-grade sliding bearings, but fail to mitigate the impact forces generated during the meshing phase of the gear pair, or reduce the adverse effects caused by manufacturing and installation errors. Therefore, a new optimization method is urgently needed that can collaboratively modify the sliding bearings and gears of wind turbine gearboxes, while simultaneously improving the meshing performance of the gear pair and the lubrication performance of the sliding bearings in the gearbox transmission system. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention proposes a method for collaboratively optimizing the meshing and lubrication performance of the planetary gear system of a wind turbine gearbox to solve the technical problem that there is no method in the existing technology for collaboratively modifying the sliding bearings and gears of the wind turbine gearbox to simultaneously improve the meshing performance of the gear pairs and the lubrication performance of the sliding bearings of the gearbox transmission system.

[0005] The technical solution adopted in the present invention is as follows: In a first aspect, a method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train in a wind turbine gearbox is provided, comprising the following steps: Establish a friction dynamics coupling model for planetary-grade sliding bearing wind turbine gearbox systems; Determine the tooth profile and tooth direction of the planetary gear train and the axial modification range of the sliding bearing; Design the coordinated modification amount of gears and sliding bearings based on the modification range; The coordinated modification of gears and sliding bearings is incorporated into the friction dynamics coupling model of a planetary-grade sliding bearing wind turbine gearbox system to obtain system simulation results. A data set is constructed based on the cross-validation method based on the calculation results. Constructing a radial basis function proxy model based on the data set to reflect the mapping relationship between working conditions, gear and sliding bearing modification parameters, and system dynamic response; The particle swarm optimization algorithm is used to search for the optimal modification parameters within the given modification range, and the Pareto solution of the modification amount of the planetary-grade sliding bearing wind turbine gearbox gear and sliding bearing is obtained. The optimal gear and sliding bearing collaborative modification amount in the Pareto solution set is selected as the optimization result.

[0006] Furthermore, the friction dynamics coupling model of the planetary-grade sliding bearing wind turbine gearbox system is as follows: In the above formula, is the system mass matrix, The system damping matrix, The system stiffness matrix, is the exciting force matrix, is the oil film force matrix of the sliding bearing; represents the generalized displacement vector in the global coordinate system, represents the first-order derivative of the generalized displacement vector with respect to time, represents the second derivative of the generalized displacement vector with respect to time.

[0007] Furthermore, the tooth profile and tooth direction of the planetary gear train and the axial modification range of the sliding bearing are determined, including: The formula specified in the national standard is used to calculate the first theoretical modification amount of the planetary gear train tooth profile and tooth guide. The modification amount of the left and right tooth surfaces of each gear is kept consistent. The preset percentage of the first theoretical modification amount is used as the upper and lower limits of the modification range of the planetary gear train tooth profile and tooth guide respectively. The second theoretical modification amount in the axial direction of the sliding bearing is calculated using a quadratic modification curve, and preset percentages of the second theoretical modification amount are used as the upper and lower limits of the axial modification range of the sliding bearing.

[0008] Furthermore, the coordinated modification amount of the gear and the sliding bearing is designed based on the modification range, including: The modification amounts within the modification range of gears and sliding bearings are randomly sampled by the Monte Carlo sampling method. Random sampling is performed simultaneously within the respective modification ranges of multiple modification parameters to obtain multiple combinations of the axial coordinated modification amounts of gears and sliding bearings. Each combination corresponds to a coordinated modification amount of gears and sliding bearings.

[0009] Furthermore, the multiple shaping parameters include: 、 、 、 、 、 、 、 、 、 ; in, is the tooth profile modification amount, is the tooth profile modification length, is the tooth direction drum adjustment amount; When the subscript n is s1, it indicates the sun gear of the first-stage planetary gear train; when the subscript n is p1, it indicates the planet gear of the first-stage planetary gear train; when the subscript n is r1, it indicates the inner ring gear. is the axial modification amount of the planetary gear sliding bearing.

[0010] Furthermore, the system simulation calculation results include: minimum oil film thickness of the sliding bearing, eccentric load coefficient of the tooth surface of the inner ring gear-planet gear meshing pair, and dynamic response results of the vibration acceleration of the low-speed inner ring gear.

[0011] Furthermore, when constructing a radial basis function proxy model to reflect the mapping relationship between working conditions, gear and sliding bearing modification parameters, and system dynamic response, The optimization parameters are the tooth profile modification of the planetary gear train and the axial modification of the sliding bearing. The optimization targets are the lubrication performance of the low-speed sliding bearing of the wind turbine gearbox with planetary sliding bearings, the meshing performance of the gear pair, and the low-speed vibration acceleration. The constraints include: the minimum oil film thickness of the low-speed sliding bearing after optimization must be greater than the minimum oil film thickness before optimization; the tooth surface eccentric load coefficient of the optimized inner ring gear-planetary gear meshing pair must be smaller than the tooth surface eccentric load coefficient before optimization; the effective value of the vibration acceleration of the low-speed inner ring gear after optimization is smaller than that before optimization, and the effective value of the vibration acceleration of the high-speed inner ring gear after optimization is smaller than that before optimization.

[0012] Furthermore, the particle swarm optimization algorithm is used to search for the optimal modification parameters within the given modification range, and the Pareto solution set of the modification amount of the planetary-grade sliding bearing wind turbine gearbox gear and the sliding bearing is obtained, including: Randomly initialize the position and velocity of particles in the search space and record their individual optimal solutions; Evaluate particle performance through a multi-objective fitness function, extract non-dominated solutions and store them in an external archive; In each iteration, particles update their speed and position based on their individual optimal solution and the guidance solution in the external archive. If the new solution is better, the record is updated and the external archive is adjusted according to the dominance relationship. The iteration is performed until the termination condition is met, and finally a well-distributed Pareto solution set is output.

[0013] Furthermore, the solution closest to the origin is selected from the Pareto solution set as the optimal solution.

[0014] It can be seen from the above technical solution that the beneficial technical effects of the present invention are as follows: During the modification optimization, both gear and sliding bearing modification were considered, improving both the meshing performance of the gear pair and the lubrication performance of the sliding bearing. A proxy model approach was used to collaboratively optimize the meshing and lubrication performance of the planetary gear train in the gearbox. A mapping relationship between optimization parameters and optimization targets was established, and the fitting accuracy was calculated. This improved the friction dynamics coupling model of the planetary sliding bearing wind turbine gearbox system, which was found to be inefficient due to slow model calculations under low-speed and heavy-load conditions, significantly reducing the time required for optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0016] Figure 1 For the embodiment of the present invention; Figure 2 Schematic diagram of gear tooth profile and tooth modification according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the axial modification of a sliding bearing according to an embodiment of the present invention; Figure 4 Schematic diagram of the fitting accuracy of parameters of the radial basis function proxy model under different load conditions according to an embodiment of the present invention; Figure 5 Schematic diagram of the Pareto set solved by the particle swarm optimization algorithm for the vibration acceleration of the planetary gear sliding bearing wind turbine gearbox system, the gear pair meshing eccentric load coefficient, and the minimum film thickness dynamic response of the sliding bearing under different gear and sliding bearing modification parameters according to an embodiment of the present invention; Figure 6 Schematic diagram of the minimum oil film thickness of the sliding bearings before and after the coordinated modification of the gear and planetary gear sliding bearings under different load conditions according to an embodiment of the present invention; Figure 7 Schematic diagram of the eccentric load coefficient of the tooth surface of the inner ring gear-planet gear meshing pair according to an embodiment of the present invention; Figure 8 Schematic diagram of the comparison of vibration acceleration of the low-speed internal gear ring according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following embodiments of the technical solution of the present invention will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore only examples and are not intended to limit the scope of protection of the present invention.

[0018] It should be noted that, unless otherwise specified, the technical or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0019] Example This embodiment provides a method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train in a wind turbine gearbox, comprising the following steps: Step 1: Establish a friction dynamics coupling model for planetary-grade sliding bearing wind turbine gearbox system The friction dynamics coupling model of the planetary-grade sliding bearing wind turbine gearbox system constructed in this step is as follows: (1) In the above formula (1), is the system mass matrix, The system damping matrix, The system stiffness matrix, is the exciting force matrix, is the oil film force matrix of the sliding bearing; represents the generalized displacement vector in the global coordinate system, represents the first-order derivative of the generalized displacement vector with respect to time, represents the second derivative of the generalized displacement vector with respect to time.

[0020] Step 2: Determine the tooth profile and tooth direction of the planetary gear train and the axial modification range of the sliding bearing Step 21: Calculate the modification range of the planetary gear tooth profile and tooth direction In a specific implementation, the gear modification amount is calculated based on the ISO21771 standard tooth profile modification and tooth direction modification calculation formula, and 50% and 150% of the theoretical modification amount are used as the lower limit and upper limit of each gear modification amount respectively to obtain the range of each gear modification amount, and the modification amount of the left and right tooth surfaces of each gear remains consistent.

[0021] like Figure 2 The figure shows the schematic diagram of tooth profile and tooth direction modification. The tooth profile modification amount calculation formula includes: Maximum tooth top modification amount for: (2) In the above formula (2), is the circumferential force tangential to the gear pitch circle, is the effective tooth width.

[0022] Trimming length for: (3) In the above formula (3), is the base circle pitch, is the gear end face overlap.

[0023] Connect the starting point of the shaping to the maximum shaping amount The curve is the shaping curve, and the shaping curve is expressed by the following formula: (4) In the above formula (4), is the relative coordinate value of the meshing position, is the distance between the starting point of the modification and the upper limit point of the meshing of a single pair of teeth, is the tooth addendum modification index, and its preferred value is 2.

[0024] The calculation formula of tooth profile modification includes: Tooth direction bulging amount for: (5) (6) In the above formulas (5) and (6), is the gear tooth error, is the tooth modification coefficient, is the tooth width.

[0025] Step 22: Calculate the axial modification range of the sliding bearing like Figure 3 The figure shows the axial shaping method of the sliding bearing, which uses a quadratic shaping curve. The shaping amount is calculated as follows: (7) In the above formula (7), is the depth of shaping, is the distance between the modified part and the bearing center, is the axial modification coefficient of the planetary gear sliding bearing, and its preferred value is 0.0016.

[0026] The modification range of the planetary gear sliding bearing is determined based on 50% and 150% of the theoretical modification amount.

[0027] Step 3: Design the coordinated modification amount of gears and sliding bearings based on the modification range In some embodiments, the modification amount within the modification amount range of the gear and the sliding bearing is randomly sampled by the Monte Carlo sampling method, and random sampling is performed simultaneously in the modification amount range of each of the ten modification parameters to obtain 、 、 、 、 、 、 、 、 、 Such a combination of gear and sliding bearing axial collaborative modification, similarly, under seven working conditions, a total of 560 groups of modification combinations are obtained ( is the tooth profile modification amount, is the tooth profile modification length, is the tooth direction drum correction amount, They are the sun gear, planet gear and inner ring gear of the first stage planetary gear train respectively. is the axial modification amount of the planetary gear sliding bearing). The multiple combinations of axial coordinated modification amounts of the gears and the sliding bearings obtained in this step are multiple. The gears include an internal gear ring, planetary gears, and a sun gear. Each combination is a coordinated modification amount combination consisting of the tooth profile and tooth guide modification amounts of the internal gear ring, planetary gears, and sun gear, and the modification amount of the sliding bearing.

[0028] Step 4: Bring the gear and sliding bearing collaborative modification amount into the planetary sliding bearing wind turbine gearbox system friction dynamics coupling model for calculation to obtain the system simulation calculation results; construct a data set based on the calculation results using the cross-validation method The designed gear and sliding bearing collaborative modification values ​​are substituted into the system friction dynamics coupling model (Equation (1)) in Step 1 for calculation. The system simulation results are obtained, including the minimum oil film thickness of the sliding bearing, the eccentric load coefficient of the tooth surface of the inner ring gear-planet gear meshing pair, and the dynamic response of the low-speed inner ring gear vibration acceleration. Based on the calculation results, the cross-validation method is then used to filter the data of the calculation results to obtain the training data set and the test data set.

[0029] Step 5: Based on the training data set in step 4, a radial basis function proxy model is constructed to reflect the mapping relationship between working conditions, gear and sliding bearing modification parameters, and system dynamic response. The optimization parameters are the tooth profile modification of the planetary gear train and the axial modification of the sliding bearing. The optimization objectives are the lubrication performance of the low-speed sliding bearing of the planetary-grade sliding bearing wind turbine gearbox, the meshing performance of the gear pair, and the low-speed vibration acceleration. The constraints are: ① The minimum oil film thickness of the low-speed sliding bearing after optimization must be greater than the minimum oil film thickness before optimization; ② The tooth surface eccentricity coefficient of the optimized inner ring-planetary gear meshing pair must be less than the tooth surface eccentricity coefficient before optimization; ③ The effective value of the vibration acceleration of the low-speed inner ring after optimization is less than that before optimization, and ④ The effective value of the vibration acceleration of the high-speed inner ring after optimization is less than that before optimization. Based on the above requirements, the corresponding modification parameter optimization function is set as follows: (8) (9) In the above formula, To optimize the goal, , Corresponding to step 3 、 、 、 、 、 、 、 、 、 ; are the tooth profile modification of the planetary gear train and the axial modification of the sliding bearing. Since the optimization goal is to find the maximum value of the minimum oil film thickness of the sliding bearing, the optimization solution becomes The minimum value of They are the oil film thickness of the low-speed sliding bearing before optimization, the eccentric load coefficient of the low-speed inner gear ring-planet gear tooth surface, the vibration acceleration of the low-speed inner gear ring, and the vibration acceleration of the high-speed inner gear ring; is the feasible domain of optimization.

[0030] In some embodiments, in order to comprehensively consider the impact of design variables on optimization objectives under different working conditions and obtain design variables that are optimal under different working conditions, the optimization objectives under different working conditions are weighted, and the expression is: (10) In the above formula (10), is the weight coefficient under the nth working condition, is the optimization objective function under the nth working condition.

[0031] The radial basis function proxy model expression constructed in this step is as follows: (11) In the above formula (11), , , is the weight coefficient corresponding to different radial basis functions, is the radial basis function, For unknown points With sample points The Euclidean distance.

[0032] Substituting the sample point data into formula (11) we can obtain: (12) make , ; Basis functions As a positive definite function, the weighted coefficient matrix can be directly inverted as follows: (13) The radial basis function selected in this embodiment is: (14) In the above formula (14), For unknown points With sample points The Euclidean distance, is the shape parameter, >0.

[0033] In some embodiments, the prediction accuracy of the constructed radial basis function proxy model can be evaluated using the test data set in step 4, and the certainty coefficient can be selected. As an evaluation index of the prediction accuracy of the proxy model, the closer it is to 1, the higher the accuracy. Its theoretical calculation formula is as follows (15) In the above formula (15), 、 are the calculated and predicted values ​​of the sample points, respectively. is the mean of the sample; A value greater than 0.9 is considered qualified, and the closer it is to 1, the higher the prediction accuracy of the proxy model.

[0034] like Figure 4 Shown is the proxy model , the accuracy of the proxy model is high under different working conditions.

[0035] Step 6: Use the particle swarm optimization algorithm to find the optimal solution in the given modification parameter modification range to obtain the Pareto solution of the modification amount of the planetary-grade sliding bearing wind turbine gearbox gear and sliding bearing. First, the position and velocity of the particles are randomly initialized in the search space, and their individual optimal solutions are recorded. The particle performance is evaluated through a multi-objective fitness function, and non-dominated solutions are extracted and stored in an external archive. Subsequently, the particles update their velocity and position in each iteration based on the individual optimal solution and the guided solution in the external archive. If the new solution is better, the record is updated, and the external archive is adjusted according to the dominance relationship. The iteration is carried out until the termination condition is met, and a well-distributed Pareto solution set is finally output. Since this embodiment has ten optimization parameters and three optimization objectives, it belongs to multi-objective optimization; therefore, in order to ensure the calculation accuracy, the population size in the particle swarm optimization algorithm of this paper is set to 500, the maximum number of iterations is set to 1000, the care weight is set to 0.45, the learning factor is set to 2, and the fitness error threshold is set to 0.01.

[0036] like Figure 5 Shown is the Pareto solution set obtained based on the particle swarm optimization algorithm.

[0037] Step 7: Select the optimal gear and sliding bearing collaborative modification amount in the Pareto solution set as the optimized result In this embodiment, the solution closest to the origin is selected from the Pareto solution set as the optimal solution, and finally a set of gear and sliding bearing modification results are obtained.

[0038] To verify the effect of the optimization method of this embodiment, the modification amount is substituted into the system model in step 1 for calculation, and the improvement effects of the meshing performance of the gear pairs and the lubrication performance of the sliding bearings of the wind turbine gearbox system before and after optimization are compared as follows: Figure 6 Comparison of the minimum oil film thickness of the planetary gear sliding bearing before the gearbox system model modification optimization under different load conditions and the gearbox system model calculated based on the optimal gear and sliding bearing coordinated modification amount.

[0039] Figure 7 Comparison of the eccentric load coefficient of the tooth surface of the ring gear-planet gear meshing pair before the gearbox system model modification optimization under different load conditions and the gearbox system model calculated based on the optimal gear and sliding bearing coordinated modification amount.

[0040] Figure 8 Comparison of the X-direction vibration acceleration of the low-speed internal gear ring before the gearbox system model modification optimization under different load conditions and the gearbox system model calculated based on the optimal gear and sliding bearing coordinated modification amount.

[0041] From the comparison of the calculation results of the gearbox system model before and after optimization, it can be seen that after the coordinated shape modification optimization of the gears and sliding bearings, the minimum oil film thickness of the planetary gear sliding bearings increases under different load conditions, the gear pair off-load coefficient decreases, the off-load degree decreases, and the vibration acceleration of the system components decreases; after the coordinated shape modification optimization, the lubrication performance of the sliding bearings and the meshing performance of the gear pairs are both improved.

[0042] The technical solution described in this embodiment simultaneously considers the modification of gears and sliding bearings during modification optimization, improving both the meshing performance of the gear pair and the lubrication performance of the sliding bearings. A proxy model approach is employed to collaboratively optimize the meshing and lubrication performance of the planetary gear train in the gearbox. A mapping relationship between optimization parameters and optimization objectives is established, and the fitting accuracy is calculated. This approach addresses the low efficiency of multi-component collaborative modification optimization, which is often caused by slow model calculations under low-speed and heavy-load conditions in the friction-dynamic coupling model of a planetary-grade sliding bearing wind turbine gearbox system. This significantly reduces the time required for optimization.

[0043] 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 make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train in a wind turbine gearbox, characterized in that: The following steps are involved: Establish a friction dynamics coupling model for planetary-grade sliding bearing wind turbine gearbox systems; Determine the tooth profile and tooth direction of the planetary gear train and the axial modification range of the sliding bearing; Designing the coordinated modification amount of the gear and the sliding bearing based on the modification range; The coordinated modification of gears and sliding bearings is incorporated into the friction dynamics coupling model of a planetary-grade sliding bearing wind turbine gearbox system to obtain system simulation results. A data set is constructed based on the cross-validation method based on the calculation results. Constructing a radial basis function proxy model based on the data set to reflect the mapping relationship between working conditions, gear and sliding bearing modification parameters, and system dynamic response; The particle swarm optimization algorithm is used to search for the optimal modification parameters within the given modification range, and the Pareto solution of the modification amount of the planetary-grade sliding bearing wind turbine gearbox gear and sliding bearing is obtained. The optimal gear and sliding bearing collaborative modification amount in the Pareto solution set is selected as the optimization result.

2. The method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train of a wind turbine gearbox according to claim 1, characterized in that: The friction dynamics coupling model of the planetary-grade sliding bearing wind turbine gearbox system is as follows: In the above formula, is the system mass matrix, The system damping matrix, The system stiffness matrix, is the exciting force matrix, is the oil film force matrix of the sliding bearing; represents the generalized displacement vector in the global coordinate system, represents the first-order derivative of the generalized displacement vector with respect to time, represents the second derivative of the generalized displacement vector with respect to time.

3. The method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train of a wind turbine gearbox according to claim 1, characterized in that: Determine the planetary gear train tooth profile and tooth direction as well as the axial modification range of the sliding bearing, including: The formula specified in the national standard is used to calculate the first theoretical modification amount of the planetary gear train tooth profile and tooth guide. The modification amount of the left and right tooth surfaces of each gear is kept consistent. The preset percentage of the first theoretical modification amount is used as the upper and lower limits of the modification range of the planetary gear train tooth profile and tooth guide respectively. The second theoretical modification amount in the axial direction of the sliding bearing is calculated using a quadratic modification curve, and preset percentages of the second theoretical modification amount are used as the upper and lower limits of the axial modification range of the sliding bearing.

4. The method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train of a wind turbine gearbox according to claim 1, characterized in that: Design the coordinated modification amount of gears and sliding bearings based on the modification range, including: The modification amounts within the modification range of gears and sliding bearings are randomly sampled by the Monte Carlo sampling method. Random sampling is performed simultaneously within the respective modification ranges of multiple modification parameters to obtain multiple combinations of the axial coordinated modification amounts of gears and sliding bearings. Each combination corresponds to a coordinated modification amount of gears and sliding bearings.

5. The method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train of a wind turbine gearbox according to claim 4, characterized in that: The various shaping parameters include: 、 、 、 、 、 、 、 、 、 ； in, is the tooth profile modification amount, is the tooth profile modification length, is the tooth direction drum adjustment amount; When the subscript n is s1, it indicates the sun gear of the first-stage planetary gear train; when the subscript n is p1, it indicates the planet gear of the first-stage planetary gear train; when the subscript n is r1, it indicates the inner ring gear. is the axial modification amount of the planetary gear sliding bearing.

6. The method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train of a wind turbine gearbox according to claim 1, characterized in that: The system simulation calculation results include: the minimum oil film thickness of the sliding bearing, the eccentric load coefficient of the tooth surface of the inner ring gear-planet gear meshing pair, and the dynamic response result of the vibration acceleration of the low-speed inner ring gear.

7. The method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train of a wind turbine gearbox according to claim 1, characterized in that: When constructing a radial basis function proxy model to reflect the mapping relationship between working conditions, gear and sliding bearing modification parameters, and system dynamic response, The optimization parameters are the tooth profile modification of the planetary gear train and the axial modification of the sliding bearing. The optimization targets are the lubrication performance of the low-speed sliding bearing of the wind turbine gearbox with planetary sliding bearings, the meshing performance of the gear pair, and the low-speed vibration acceleration. The constraints include: the minimum oil film thickness of the low-speed sliding bearing after optimization must be greater than the minimum oil film thickness before optimization; the tooth surface eccentric load coefficient of the optimized inner ring gear-planetary gear meshing pair must be smaller than the tooth surface eccentric load coefficient before optimization; the effective value of the vibration acceleration of the low-speed inner ring gear after optimization is smaller than that before optimization, and the effective value of the vibration acceleration of the high-speed inner ring gear after optimization is smaller than that before optimization.

8. The method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train of a wind turbine gearbox according to claim 1, characterized in that: The particle swarm optimization algorithm is used to search for the optimal modification parameters within the given modification range, and the Pareto solution set of the modification amount of the planetary-grade sliding bearing wind turbine gearbox gear and sliding bearing is obtained, including: Randomly initialize the position and velocity of particles in the search space and record their individual optimal solutions; Evaluate particle performance through a multi-objective fitness function, extract non-dominated solutions and store them in an external archive; In each iteration, particles update their speed and position based on their individual optimal solution and the guidance solution in the external archive. If the new solution is better, the record is updated and the external archive is adjusted according to the dominance relationship. The iteration is performed until the termination condition is met, and finally a well-distributed Pareto solution set is output.

9. The method for collaboratively optimizing the meshing and lubrication performance of a planetary gear train of a wind turbine gearbox according to claim 1, characterized in that: The solution closest to the origin is selected from the Pareto solution set as the optimal solution.

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