Method for analyzing lubricating contact risk of sliding bearing of wind power gear box

Abstract

The invention provides a wind power gear box sliding bearing lubrication contact risk analysis method, which comprises the following steps: screening out a key working condition from a plurality of wind power working conditions, and obtaining an envelope sampling load spectrum containing a plurality of load grades in combination with the key working condition; constructing a wind turbine generator transmission chain multi-flexible dynamic model; the envelope sampling load spectrum containing the multiple load grades serves as an input load of the transmission chain multi-flexible-body dynamic model, the transmission chain multi-flexible-body dynamic model is solved, and the sliding bearing contact pressure and the sliding bearing contact time are obtained; and calculating the lubricating contact risk of the planet gear sliding bearing according to the sliding bearing contact pressure and the sliding bearing contact time. According to the method, the wind power working condition calculation number can be greatly reduced, key core working conditions can be rapidly identified and extracted, and the effect similar to full working condition simulation can be rapidly achieved; the method can effectively represent the potential contact risk of the sliding bearing under different working conditions, and is suitable for rapidly evaluating the influence of the working condition change on the lubricating contact of the sliding bearing of the wind generating set transmission chain.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power generation technology, in particular to a wind turbine gearbox sliding bearing lubrication contact risk analysis method. BACKGROUND

[0002] The wind turbine gearbox is a key transmission component of megawatt wind turbines, and its performance directly affects the power output and service life of the unit. At present, wind turbine gearbox bearings mostly use rolling bearings, but with the development of large-scale wind turbines, rolling bearings face problems such as size exceeding the limit, increased raceway wear, and rising failure rate. In contrast, sliding bearings have smaller radial size, stable running characteristics, and advantages such as high load capacity and low cost while significantly reducing the volume and weight of the gearbox. "Replacing rolling bearings with sliding bearings" has become an important technical trend, but the mapping relationship between the lubrication state of sliding bearings and the operating conditions of wind turbines is not clear, and the edge contact failure risk of sliding bearings is high under complex system coupling deformation. Therefore, it is of great significance to carry out transient lubrication contact risk analysis of wind turbine gearbox sliding bearings to guide system optimization design.

[0003] In the actual working process of a wind turbine, there are more than a thousand operating conditions, and the dynamic response of the wind turbine gearbox differs significantly under different operating conditions, which will affect the lubrication state of the sliding bearing. However, if full-condition analysis is performed, the calculation cost is extremely high, so there is an urgent need for an efficient analysis method that can consider the influence of various operating conditions on the transient lubrication characteristics of the sliding bearing of the wind turbine gearbox. SUMMARY

[0004] To solve the technical problem that there is no method in the prior art that can quickly analyze the transient lubrication characteristics of the sliding bearing of the wind turbine gearbox under various operating conditions, the present application proposes a wind turbine gearbox sliding bearing lubrication contact risk analysis method.

[0005] The technical solution adopted by the present application is as follows: In a first aspect, a wind turbine gearbox sliding bearing lubrication contact risk analysis method is provided, comprising: selecting key operating conditions from a plurality of wind power operating conditions, and obtaining an envelope sampling load spectrum containing a plurality of load levels in combination with the key operating conditions; constructing a multi-flexible-body dynamics model of the wind turbine transmission chain; using the envelope sampling load spectrum containing a plurality of load levels as the input load of the transmission chain multi-flexible-body dynamics model, solving the transmission chain multi-flexible-body dynamics model to obtain the sliding bearing contact pressure and the sliding bearing contact time; calculating the planetary gear sliding bearing lubrication contact risk according to the sliding bearing contact pressure and the sliding bearing contact time.

[0006] Further, the key working conditions are screened from the plurality of wind power working conditions, including: The global load of the wind turbine is calculated to obtain original time series data in 7 dimensions corresponding to the plurality of wind power working conditions. The original time series data are enveloped and clustered in sequence to screen the key working conditions.

[0007] Further, the original time series data in 7 dimensions include axial force, lateral force and normal force at the hub, input torque, pitching moment and overturning moment at the hub, and hub rotating speed.

[0008] Further, the original time series data are enveloped and clustered in sequence to screen the key working conditions, including: The convex hull algorithm is used to envelope the original time series data to construct a high-dimensional envelope space, and the original time series data are divided into envelope space boundary points and internal points in the space. The clustering algorithm is used to find representative points in the boundary points and internal points, and all the representative points are taken as a key working condition set.

[0009] Further, before the representative points are found by the clustering algorithm, the principal component analysis is performed on the original time series data in 7 dimensions, the variance of each component is taken as a spatial distance calculation weight, and then the K-means algorithm weighted by the principal component analysis is used to cluster the boundary point set and the internal point set, respectively.

[0010] Further, a multi-flexible-body dynamics model of the wind turbine transmission chain is constructed, including: The sliding bearing transient average flow equation is constructed and solved to obtain the sliding bearing oil film pressure; the rough peak contact pressure, sliding bearing oil film force and rough peak contact force, and oil film bending moment and rough peak contact bending moment combined bending moment are obtained according to the sliding bearing oil film pressure; The finite element substructure condensation method is used to model the substructure of the flexible component, and the master nodes are set at the key positions based on the topological structure of the transmission chain. According to the connection relationship between the components of the transmission chain and the master nodes, and combined with the sliding bearing oil film force and the rough peak contact force, and the oil film bending moment and the rough peak contact bending moment combined bending moment, the multi-flexible-body dynamics model of the transmission chain is constructed.

[0011] Further, when the master nodes are set at the key positions based on the topological structure of the transmission chain, the master nodes include the gear box body and the transmission shaft bearing support, the planetary carrier pin-shaft and planetary gear assembly, the transmission shaft-gear assembly, and further include the large-size ring gear tooth width mid-section pitch circle position.

[0012] Further, when the multi-flexible-body dynamics model of the transmission chain is solved, the Newton iteration method is used as the solving method, and the simulation step is set to 10-4 s, the convergence tolerance of the sliding bearing is 10 -5 .

[0013] Further, the sliding bearing lubrication contact risk of the planetary gear is calculated according to the sliding bearing contact pressure and the sliding bearing contact time, and the calculation is performed according to the following formula: In the above formula, is the sliding bearing contact risk; is the dimensionless maximum contact pressure of the sliding bearing, wherein is the maximum contact pressure of the sliding bearing under each working condition, is the maximum contact pressure of the sliding bearing in the key working condition; is the sliding bearing contact time ratio, wherein is the key working condition in the envelope sampling load spectrum, is the time required for each component to complete at least one rotation under each working condition, is the time when the sliding bearing rough peak contact occurs when each component completes at least one rotation.

[0014] In a second aspect, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the wind turbine gearbox sliding bearing lubrication contact risk analysis method provided in the first aspect.

[0015] From the above technical solution, the beneficial technical effects of the present application are as follows: The envelope sampling load spectrum calculation method can greatly reduce the number of wind power working condition calculations, quickly identify and extract key core working conditions, and quickly achieve an effect similar to full working condition simulation; the sliding bearing lubrication contact risk calculation method proposed can effectively represent the potential contact risk of the sliding bearing under different working conditions, and is suitable for quickly evaluating the influence of working condition changes on the lubrication contact of the sliding bearing of the wind turbine generator set transmission chain. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0017] Figure 1 is a schematic diagram of the load spectrum calculation process in the embodiments of the present application; Figure 2This is a schematic diagram illustrating the simplification process of the 7-dimensional original time-series data in an embodiment of the present invention; Figure 3 This is a graph showing the contact risk curves of sliding bearings under different operating conditions in an embodiment of the present invention. Figure 4 This is a schematic diagram of the lubrication contact risk analysis method for sliding bearings in wind turbine gearboxes in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0019] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0020] Example This embodiment provides a method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes, applicable to various sliding bearings inside gearboxes. The analysis method includes the following steps: Step S1: Select key operating conditions from various wind power operating conditions, and combine the key operating conditions to obtain an envelope sampling load spectrum containing multiple load levels. This step includes the following sub-steps: Step S11: Calculate the global load of the entire wind turbine generator set to obtain raw time-series data in 7 dimensions. The raw time-series data corresponds to various wind power operating conditions. In a specific implementation, OpenFAST software can be used for the calculations in this step. OpenFAST software includes multiple functional modules, each of which can run independently or be coupled for simulation, covering the entire chain of analysis from wind field generation and aerodynamic calculation to structural dynamics and control systems.

[0021] First, wind field data conforming to the IEC 61400-1 standard is generated using the TurbSim module. The wind field data is a turbulent wind sequence with a wind speed range of 0~30m / s and a sampling frequency of 20Hz. Secondly, the wind field data is mapped to the wind turbine coordinate system using the InflowWind module; Next, the blade parameters are input into the AeroDyn module to calculate the aerodynamic loads and transmit them to the ElastoDyn module, while the ServoDyn module is used to simulate the control strategy. Finally, based on the wind turbine coordinate system, aerodynamic loads, and simulation control strategy, the overall dynamics of the wind turbine are solved, and the time-series load data of 6 degrees of freedom, such as torque, bending moment, and axial force at the hub, as well as the rotational speed, are output.

[0022] According to the boundary conditions of the whole machine model set in DLC1.2 normal power generation condition in IEC61400-1 standard, the forces in three directions at the hub (axial force F) can be obtained. x Lateral force F y Normal force F z Three-directional torque (input torque M) x Pitch moment M y Overturning moment M z ) and hub speed (n r Time series data in 7 dimensions, such as Figure 1 As shown; the simulation frequency of the whole machine model is set to 20Hz, and each set of working conditions is calculated for 600s, resulting in a total of 1,296,000 sets of working conditions.

[0023] Step S12: Perform envelope and clustering on the original time series data sequentially to filter out key operating conditions. In this step, when calculating the envelope sampling load spectrum of the wind turbine generator drivetrain, to preserve the correlation and global distribution characteristics between the original time-series loads in multiple dimensions, the 7-dimensional space of the time-series data such as forces in three directions, moments in three directions, and rotational speed is first enveloped. Then, key samples are extracted through cluster sampling, ultimately achieving compression of the number of operating conditions while ensuring consistency in the distribution of operating conditions. Figure 2 As shown; specifically as follows: Step S12-1: Use the convex hull algorithm to envelop the original time series data in 7 dimensions, construct a high-dimensional envelope space, and divide the original time series data into boundary points and interior points within this space. The principle of the convex hull algorithm is based on a given set of points in the Euclidean plane (or space). Its convex hull is the containment The minimum convex set of all points. Geometrically represented as... A subset of a convex hull is a convex polygon (or a high-dimensional convex polyhedron) whose vertices satisfy the following conditions: the line connecting any two points within the convex hull remains within the convex hull; and there is no smaller convex set containing it. The mathematical definition of a convex hull is the set of convex combinations: (1) In the above formula, For the original point set, Let be any point inside the convex hull. For point The weighting coefficients in a convex combination depend on the specific convex hull construction algorithm and the point set. (Distribution of data). After enveloping the original time series data in the previous step using the convex hull algorithm, a 7-dimensional envelope space is obtained. Within this space, the original time series data is divided into boundary points and interior points.

[0024] Step S12-2: Use a clustering algorithm to find representative points among the boundary points and interior points, and use all representative points as the set of key working conditions. In this step, the clustering algorithm is illustrated using K-means as an example. K-means clustering is a partition-based unsupervised learning algorithm whose goal is to divide a given set of n samples into k clusters, minimizing the variance of samples within each cluster. The clustering algorithm is used to find representative points among the boundary points and interior points, including: First, define the objective function optimized by the K-means algorithm as: (2) In the above formula, For the first The sample set of each cluster satisfies Furthermore, no two clusters intersect; It is a spatial dimension; It belongs to The data points, its first Each component is denoted as ; For the first The centroid (center point) of each cluster, its th... Each component is denoted as Then we have: (3) Secondly, a greedy iterative strategy is used to optimize the objective function. The optimization process is as follows: Initialize the centroid: randomly select An initial centroid .

[0025] Sample allocation: For the first Each sample in the next iteration Assign it to the nearest centroid using the following formula. Cluster: (4) In the above formula, Indicates the number of clusters. Election Order The smallest cluster index. If the cluster number corresponding to the smallest value is... Then the sample He was assigned to the first Clusters.

[0026] Centroid Update: Based on the new cluster partitioning, recalculate the centroid of each cluster using the following formula: (5) Iteration termination condition: When the cluster partition no longer changes or the objective function... The algorithm terminates when the decrease falls below a preset threshold.

[0027] In some embodiments, considering the different contributions of data from each dimension to the response of the transmission chain system, before using a clustering algorithm to find representative points, principal component analysis is first performed on the original time-series data of the seven dimensions. The variance of each component is used as the weight for calculating the spatial distance; the larger the variance, the greater the influence on the clustering distance. Then, the boundary point set and the internal point set are clustered using the K-means algorithm with weights from principal component analysis. The objective function of the K-means algorithm after introducing principal component analysis is: (6) In the above formula, , representing the cluster center of the boundary points; , representing the internal point cluster center; and These are the weighting coefficients derived from principal component analysis, used to adjust the importance of each dimension in distance calculation.

[0028] Step S12-3: Obtain the envelope sampling load spectrum containing multiple load levels in the key operating condition set. In a specific implementation, by calculating the normalized variance of the original time-series data across seven dimensions for different cluster numbers, it was found that the variance was smallest when the number of clusters was 21, indicating the lowest dispersion among the clusters and the best clustering effect. Therefore, the number of clusters was chosen to be 21. From each cluster, the original data point closest to the centroid was selected as the representative point. (7) In the above formula, For the first Each cluster contains a set of original data points.

[0029] In this step, through envelope and clustering, the 1,296,000 sets of original 7-dimensional time-series load conditions are simplified into 21 sets of 7-dimensional speed-load load conditions, which are then arranged in ascending order according to the input torque to obtain an envelope sampling load spectrum containing 21 load levels. As shown in Table 1 below: Step S2: Construct a multi-flexible body dynamics model of the wind turbine drive train. This step includes the following sub-steps: Step S21: Construct and solve the transient average flow equation of the sliding bearing to obtain the sliding bearing oil film pressure; based on the sliding bearing oil film pressure, obtain the rough peak contact pressure, sliding bearing oil film force, rough peak contact force, oil film bending moment, and the combined bending moment of the rough peak contact moment. The transient average flow equation for a sliding bearing is: (8) In the above formula, The outer radius of the pin is... This refers to the horizontal position of the pin relative to the inner hole. For bearing circumference factor; This refers to the position of the pin relative to the inner hole in the vertical direction. Axial flow factor; For oil film thickness, For oil film pressure, This refers to the viscosity of the lubricating oil. The linear velocity of the outer surface of the pin is... For contact factor, Shear flow factor; To achieve comprehensive roughness, For time.

[0030] The sliding bearing oil film pressure can be obtained by solving equation (8) using the finite difference method. Then, the rough peak contact pressure can be calculated using the GW (Greenwood-Williamson) model based on the sliding bearing oil film pressure. By integrating the sliding bearing oil film pressure and the rough peak contact pressure along the circumference and width directions, the sliding bearing oil film force and rough peak contact force, as well as the oil film bending moment, rough peak contact bending moment and combined bending moment can be obtained.

[0031] Step S22: Use the finite element substructure condensation method to model the flexible component into a substructure, and set master nodes at key locations based on the transmission chain topology. The finite element substructure condensation method is used to model the core components such as the box body, planetary carrier, and internal gear ring. The modal synthesis method is then used for substructure condensation, and the free vibration equations of the condensed components are obtained as follows: (9) In the above formula, and These are the mass matrix and stiffness matrix of the component after condensation, respectively. This is the displacement vector after the component has been condensed. This is the acceleration vector after the component has condensed.

[0032] Where the displacement vector It can also be divided into interface node j and internal node i, then equation (9) can be rewritten as: In the above formula, and These are the displacement vectors for internal nodes and interface nodes, respectively; and These are the interface and constraint mode matrices, respectively. These are interface mode coordinates; and These are the modal matrix and modal coordinates of the condensed component, respectively. 0 represents the identity matrix and the zero matrix.

[0033] Based on the transmission chain topology, master nodes are set at key locations, including the gearbox housing and drive shaft bearing support, the planet carrier pin-planet gear assembly, and the drive shaft-gear assembly; the meshing surface nodes on both sides of the gear teeth are defined as slave nodes.

[0034] In some embodiments, a main node is added at the pitch circle position of the cross section of the large-size internal gear ring to refine the meshing extrusion deformation effect.

[0035] Step S23: Based on the connection relationships between the components of the transmission chain and the main nodes, combined with the sliding bearing oil film force and rough peak contact force, as well as the combined bending moment of the oil film and rough peak contact moments, construct a multi-flexible body dynamic model of the transmission chain. Based on the local coordinate system of the main nodes of each component such as the spindle, housing, and gear pair, the generalized displacement matrix of the system is defined as follows (12): In the above formula, , , , , , , , These are the generalized displacement vectors of the sun gear, planet carrier, internal gear ring, planet gears, hub, main shaft, housing, and generator, respectively.

[0036] Based on the connection relationships between the components of the transmission chain and the node numbers in equation (12), the mass matrix, stiffness matrix, damping matrix, and excitation force matrix of each component are assembled to establish a multi-flexible body dynamic model of the transmission chain, as shown in the following expression: (13) In the above formula, The system quality matrix, Here is the system stiffness matrix. Here is the system damping matrix. The excitation force matrix is... For the oil film force matrix of a sliding bearing, Input torque vector to the system, This is the system load vector. This represents the second derivative of the generalized displacement vector with respect to time. This represents the first derivative of the generalized displacement vector with respect to time. This represents the generalized displacement vector.

[0037] Step S3: Use the envelope sampling load spectrum containing multiple load levels as the input load for the multi-body dynamics model of the transmission chain. Solve the multi-body dynamics model of the transmission chain to obtain the sliding bearing contact pressure and sliding bearing contact time. Calculate the lubrication contact risk of the planetary gear sliding bearing based on the sliding bearing contact pressure and sliding bearing contact time. In this step, when solving the multi-flexible body dynamics model (13) of the transmission chain, the Newton iteration method is used, and the solution parameters are set to a simulation step size of 10. -4 s, the convergence tolerance of the sliding bearing is 10 -5 Then, the envelope sampling load spectrum containing multiple load levels is used as input to the multi-flexible body dynamics model of the transmission chain, the rough peak contact force of the sliding bearing is extracted, and a set is constructed to calculate the contact risk of the sliding bearing.

[0038] The risk of contact in sliding bearings is calculated using the following formula: (14) In the above formula, Risk of contact in sliding bearings; The dimensionless maximum contact pressure of the sliding bearing is where This represents the maximum contact pressure of the sliding bearing under various operating conditions. This represents the maximum contact pressure of the sliding bearing under 21 operating conditions. This represents the percentage of contact time in the sliding bearing, where... For the 21 working conditions in the envelope sampling load spectrum ( =1,2,…,21), The time required for each component to complete at least one rotation under various working conditions. This refers to the time during which the sliding bearing experiences rough peak contact when each component completes at least one revolution; each component includes planetary gears, sun gears, etc. In this embodiment, the calculated sliding bearing contact risk for 21 operating conditions is as follows: Figure 3 As shown.

[0039] The envelope sampling load spectrum calculation method proposed in this embodiment can significantly reduce the number of wind power operating condition calculations, quickly identify and extract key core operating conditions, and quickly achieve an effect similar to full-condition simulation. The proposed sliding bearing lubrication contact risk calculation method can effectively characterize the potential contact risk of sliding bearings under different operating conditions and is suitable for quickly assessing the impact of operating condition changes on the lubrication contact of sliding bearings in the wind turbine generator drive chain.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes, characterized in that, include: Key operating conditions are selected from various wind power operating conditions, and envelope sampling load spectra containing multiple load levels are obtained by combining key operating conditions. Construct a multi-flexible body dynamics model for the wind turbine drive train; The envelope sampling load spectrum containing multiple load levels is used as the input load for the multi-flexible body dynamics model of the transmission chain. The multi-flexible body dynamics model of the transmission chain is solved to obtain the sliding bearing contact pressure and sliding bearing contact time. Calculate the lubrication contact risk of the planetary gear sliding bearing based on the sliding bearing contact pressure and sliding bearing contact time.

2. The method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes according to claim 1, characterized in that, Key operating conditions were selected from various wind power operating conditions, including: The global load of the wind turbine generator set is calculated to obtain raw time-series data in seven dimensions, which correspond to various wind power operating conditions. The original time-series data are sequentially enveloped and clustered to filter out key operating conditions.

3. The method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes according to claim 2, characterized in that, The raw time-series data for the seven dimensions include axial force, lateral force, and normal force at the hub, as well as input torque, pitching moment, overturning moment, and hub speed at the hub.

4. The method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes according to claim 2, characterized in that, The original time-series data is sequentially subjected to envelope and clustering processes to identify key operating conditions, including: The convex hull algorithm is used to enclose the original time series data and construct a high-dimensional envelope space. Within this space, the original time series data is divided into boundary points and interior points of the envelope space. Clustering algorithms are used to find representative points among boundary points and interior points, and all representative points are used as the set of key operating conditions.

5. The method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes according to claim 4, characterized in that, Before using clustering algorithms to find representative points, principal component analysis is first performed on the original time series data in 7 dimensions. The variance of each component is used as the weight for spatial distance calculation. Then, the K-means algorithm with weights from principal component analysis is used to cluster the boundary point set and the internal point set respectively.

6. The method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes according to claim 1, characterized in that, Constructing a multi-flexible-body dynamics model of the wind turbine drivetrain, including: The transient average flow equation of the sliding bearing is constructed and solved to obtain the oil film pressure of the sliding bearing; based on the oil film pressure of the sliding bearing, the rough peak contact pressure, the oil film force and the rough peak contact force, as well as the oil film bending moment, the rough peak contact bending moment and the combined bending moment are obtained. The finite element substructure condensation method is used to model the substructure of the flexible component, and the master node is set at the key position based on the transmission chain topology. Based on the connection relationships between the components of the transmission chain and the main nodes, combined with the sliding bearing oil film force and rough peak contact force, as well as the combined bending moment of the oil film and the rough peak contact moment, a multi-flexible body dynamic model of the transmission chain is constructed.

7. The method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes according to claim 1, characterized in that, When a master node is set at a key location based on the transmission chain topology, the master node includes the gearbox housing and the transmission shaft bearing support, the planetary carrier pin-planet gear assembly, and the transmission shaft-gear assembly; it also includes the pitch circle position of the mid-section of the large-size internal gear ring.

8. The method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes according to claim 1, characterized in that, When solving the multi-body dynamics model of the transmission chain, the Newton-Raphson iteration method was used, and the solution parameters set included a simulation step size of 10. -4 s, the convergence tolerance of the sliding bearing is 10 -5 .

9. The method for analyzing the lubrication contact risk of sliding bearings in wind turbine gearboxes according to claim 1, characterized in that, The risk of lubrication contact in the planetary gear sliding bearing is calculated based on the sliding bearing contact pressure and sliding bearing contact time using the following formula: In the above formula, Risk of contact in sliding bearings; The dimensionless maximum contact pressure of the sliding bearing is where This represents the maximum contact pressure of the sliding bearing under various operating conditions. This refers to the maximum contact pressure of the sliding bearing under critical operating conditions. This represents the percentage of contact time in the sliding bearing, where... This is a key operating condition in the envelope sampling load spectrum. The time required for each component to complete at least one rotation under various working conditions. This refers to the time during which the sliding bearing experiences rough peak contact when each component completes at least one revolution.

10. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the wind turbine gearbox sliding bearing lubrication contact risk analysis method according to any one of claims 1-9.

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