Method for analyzing reliability of sliding bearing of wind power gear box

Abstract

The invention provides a method for analyzing reliability of a sliding bearing of a wind power gear box, which comprises the following steps of: selecting parameters of a complete machine, determining environmental parameters according to a mounting position, establishing a dynamical model of the complete machine of a wind driven generator, importing and solving the environmental parameters, and obtaining environmental loads such as wind wave flow and the like; the method comprises the following steps of: constructing a dynamical model of the sliding bearing wind power gear box, importing the dynamical model into the constructed dynamical model of the sliding bearing wind power gear box for solving, extracting required data, preprocessing the data, eliminating abnormal data, retaining reliable data, improving the reliability of the data, and further processing the data, so that the method is suitable for analyzing the reliability of the sliding bearing of the wind power gear box. And finally, based on a fuzzy reliability theory, calculating the reliability of the sliding bearing of the wind power gear box, and judging whether the sliding bearing has wear failure or not. The method has high comprehensive performance and generalization ability, and is suitable for similar application scenes.

Description

Technical Field

[0001] The invention relates to the field of wind turbines, and in particular to a reliability analysis method for a wind turbine gearbox sliding bearing. Background Art

[0002] As the diameter of the wind rotor increases, the power generation of wind turbines also increases. According to the current development trend of the wind power industry, wind turbines are developing in the direction of large-scale, intelligent and high-tower. In particular, with the development of large-scale wind turbines, for wind turbines greater than 10 megawatts, if rolling bearings continue to be used, the radial size of the bearings may be too large, which will increase the wear of the bearing balls, raceways and inner and outer rings, causing fatigue peeling and other problems, which limits the reliability and economy of wind power gearboxes. With the implementation of the grid parity policy, reducing the cost per kilowatt-hour of wind turbines has become the main competitive advantage in seizing the wind turbine market. For this reason, wind power gearboxes urgently need to develop new bearings to replace traditional rolling bearings. Sliding bearings are an ideal choice for solving the problems of rolling bearing failure rate and processing cost due to their high stability, good tolerance to material defects and external impurities, and low processing cost. Compared with rolling bearings, sliding bearings do not have the impact vibration caused by roller movement, and have better vibration reduction performance and smoother running characteristics.

[0003] The transition from sliding to rolling can improve the torque density of wind turbine gearboxes, reduce their volume and weight, reduce manufacturing costs, and extend their service life, while solving the problem of dependence on imported rolling bearings. Therefore, sliding bearings are considered to be one of the most promising paths to break through the technical bottleneck of wind turbine gearboxes.

[0004] At present, the application and research of sliding bearings in wind turbine gearboxes are still in their infancy, especially in the research on the reliability analysis of sliding bearings. However, sliding bearings have the characteristics of small size, high operating stability, low processing cost, and strong load-bearing capacity, and are expected to overcome the high failure rate and high processing cost of rolling bearings in traditional wind turbines. Using sliding bearings as key components of wind turbine gearboxes can not only reduce the dependence on external rolling bearings, but also effectively reduce costs and achieve a leapfrog development of wind turbine bearing technology. Therefore, in-depth research on the reliability of sliding bearings in wind turbine gearboxes has important practical significance. Summary of the invention

[0005] The purpose of the present invention is to provide a reliability analysis method and a method of using a wind turbine gearbox sliding bearing, by selecting the whole machine parameters and determining the environmental parameters according to the installation position, establishing a wind turbine whole machine dynamics model, importing and solving the environmental parameters, obtaining environmental loads such as wind waves and currents, and importing them into the constructed sliding bearing wind turbine gearbox dynamics model for solving, extracting the required data, pre-processing the data, eliminating abnormal data, retaining reliable data, improving the reliability of the data, and further processing the data to make it suitable for wind turbine gearbox sliding bearing reliability analysis. Finally, based on the fuzzy reliability theory, the reliability of the wind turbine gearbox sliding bearing is calculated, and it is determined whether it has wear failure. The method has high comprehensive performance and generalization ability, and is suitable for similar application scenarios.

[0006] In order to achieve the above technical features, the purpose of the present invention is achieved as follows: A wind power gearbox sliding bearing reliability analysis method comprises the following steps: 1) Determine the parameters of the whole machine: Determine the basic parameters of the whole machine based on the machine model selection, installation location, design requirements, and installation and maintenance cost factors; 2) Fitting and selection of wind speed distribution function: Using the measured data of the wind field at the installation location, fit, calculate and select the distribution function and model under the conditions of average wind speed and pulsating wind speed; 3) Selection of wave spectrum and calculation of effective wave height and spectrum peak period: According to the measured data of sea waves at the installation location, the wave spectrum is fitted, estimated and selected, and the effective wave height and spectrum peak period under the corresponding wind speed are calculated; 4) Modeling and solving of wind turbine whole machine dynamics simulation model: Construct a wind turbine whole machine dynamics simulation model, import the environmental parameters of the installation location, solve the model, and obtain the required environmental load data; 5) Modeling and solving the dynamic model of the wind turbine gearbox with sliding bearings: Establish the corresponding dynamic model of the wind turbine gearbox with sliding bearings, use the environmental load data obtained by the dynamic model of the whole machine as the excitation source, import it through the interface, and solve the model dynamically; 6) Data extraction: After solving the model dynamically, extract relevant data to obtain the data required for reliability analysis of wind turbine gearbox sliding bearings based on fuzzy reliability theory; 7) Data preprocessing and anomaly detection: After extracting the data, preprocess the data: divide the data into segments, calculate the change rate of each segment, set the data change rate threshold, and judge whether the change rate of each data is within the normal threshold range, so as to identify abnormal data and reliable data; 8) Abnormal data elimination and iterative processing: After eliminating the abnormal data, re-segment the remaining data and calculate the change rate of each segment. Repeat the above operation until all data are reliable. 9) Reliability analysis and failure judgment: Based on the processed reliable data and fuzzy reliability theory, the reliability analysis of the sliding bearings of the wind turbine gearbox is carried out to determine whether they have wear failure.

[0007] Preferably, the wind turbine parameters include: rated power, rotor arrangement, rotor configuration, control method, total gearbox transmission ratio, cut-in wind speed, rated wind speed and cut-out wind speed key indicator parameters.

[0008] Preferably, the installation location environmental parameters include: wind speed spectrum, wind speed level, wave spectrum, effective wave height and spectrum peak period environmental parameters, which are used to describe the specific environmental conditions of the location of the wind turbine.

[0009] Preferably, the whole machine dynamics model is constructed: the modules required to construct the whole machine dynamics model include: a pneumatic module, a hydrodynamic module, a structural module, a servo module, a mooring module and a control module, which are used to simulate the dynamic behavior of the wind turbine under different working conditions.

[0010] Preferably, a sliding bearing wind turbine gearbox dynamic model is constructed: the components required to construct the sliding bearing wind turbine gearbox dynamic model include: a sun gear, a planetary gear, a planetary carrier, an inner ring gear, a housing, a rolling bearing and a sliding bearing. The components are constrained by stiffness and damping to simulate in detail the mechanical interactions between the components inside the sliding bearing wind turbine gearbox.

[0011] Preferably, the data required for reliability analysis of the wind turbine gearbox sliding bearing constructed include: key data of the sliding bearing radial load, bearing diameter, bearing effective width, sliding speed and bearing initial fit clearance.

[0012] Preferably, the data obtained in 6) is preprocessed, and the specific steps are as follows: a. Process the data to be processed in segments; b. Calculate the change rate of each segment of data to be processed k i ; c. Set the data change rate k i The normal threshold range is used to determine the change rate of each data. k i Whether it is within the normal threshold range, so as to distinguish abnormal data from reliable data; e. Eliminate abnormal data that exceeds the normal threshold range; f. Calculate the data change rate of the remaining data after removing abnormal data k i The arithmetic mean ; g. Set a new normal threshold range for the data change rate, and then determine whether the data change rate is within the normal threshold range, so as to further distinguish abnormal data from reliable data; h. Remove the abnormal data that has been identified again, and repeat the above steps until all data are reliable.

[0013] Preferably, based on the wind turbine gearbox sliding bearing data preprocessed in 7), the fuzzy reliability theory is used to calculate the reliability of the sliding bearing and determine whether the sliding bearing has suffered wear failure.

[0014] Beneficial effects of the present invention: 1. High reliability analysis accuracy and generalization ability: By selecting the whole machine parameters and determining the environmental parameters of the installation location, based on the joint simulation dynamics model, the required data is solved, and combined with the fuzzy reliability theory, the reliability of the wind turbine gearbox sliding bearing is calculated to determine whether the sliding bearing has worn out. This method has a high reliability analysis accuracy and strong generalization ability.

[0015] 2. Data preprocessing improves the reliability of results: Through data preprocessing, abnormal data are eliminated, reliable data are retained, and the interference of abnormal data on the reliability analysis results of sliding bearings is reduced, the reliability and accuracy of the results are improved, and the practicality and generalization ability of the method are enhanced.

[0016] 3. Reduce analysis cost and complexity: By selecting the parameters of the whole machine and determining the environmental parameters of the installation location, the required data is solved based on the joint simulation dynamics model, and the reliability analysis of the wind turbine gearbox sliding bearing is carried out in combination with the fuzzy reliability theory, which reduces the cost of reliability analysis of the wind turbine gearbox sliding bearing and the complexity of the implementation method.

[0017] 4. Support for sliding bearing modeling methods: A method for modeling wind turbine gearboxes with sliding bearings is proposed, which provides certain technical support for the application and research of sliding bearings in wind turbine gearboxes.

[0018] 5. Filling the research gap: It provides a reference method for reliability analysis of sliding bearings in wind turbine gearboxes, making up for the lack of research in this field. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0020] Figure 1 The figure is the flowchart of the joint simulation.

[0021] Figure 2 Flowchart of data preprocessing.

[0022] Figure 3A flowchart of the overall idea.

[0023] Figure 4 It is the membership function graph of the half normal distribution. DETAILED DESCRIPTION

[0024] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0025] Embodiment 1: like Figure 1-4 As shown, the present invention provides a reliability analysis method for a wind turbine gearbox sliding bearing. The method first determines the parameters of the whole machine according to factors such as machine type selection, installation location, design requirements, and installation and maintenance costs. Then, by determining environmental parameters such as wind speed spectrum, wind speed level, wave spectrum, effective wave height, spectrum peak period, etc. at the installation location, the environmental parameters are imported based on the joint simulation dynamics model, and the relevant data are solved and extracted. Subsequently, the acquired data is preprocessed, and the specific steps include segmenting the data, calculating the change rate of each segment of data, setting a change rate threshold, determining abnormal data and reliable data, eliminating abnormal data, segmenting again, recalculating the change rate of each segment of data, setting a change rate threshold, determining abnormal data and reliable data, and eliminating abnormal data until all data are reliable data. Finally, based on the processed reliable data and fuzzy reliability theory, the reliability of the wind turbine gearbox sliding bearing is calculated to determine whether the sliding bearing has wear failure.

[0026] Embodiment 2: In this embodiment, the present invention provides a wind turbine gearbox sliding bearing reliability analysis method, comprising the following steps: Step 1): Determine the parameters of the whole machine based on factors such as model selection, installation location, design requirements, and installation and maintenance costs; Step 2): According to the installation location, determine the environmental parameters including: wind speed spectrum, wind speed level, wave spectrum, effective wave height, spectrum peak period, etc.; Step 3): Construct a wind turbine dynamics model and import the environmental parameters into it for solution to obtain the required environmental load data such as wind, wave and current; Step 4): Construct a sliding bearing wind turbine gearbox dynamics model, and import environmental load data such as wind, wave and current obtained from the wind turbine dynamics model through the interface as the excitation source of the model; Step 5): Solve the constructed sliding bearing wind turbine gearbox dynamics model, and process and extract various data required for the wind turbine gearbox sliding bearing reliability analysis method based on fuzzy reliability theory; Step 6): Preprocessing the acquired data includes: segmenting the data, calculating the change rate of each segment of data, setting a data change rate threshold and judging whether the change rate of each data is within the normal threshold range, thereby distinguishing abnormal data from reliable data; Step 7): Remove abnormal data, re-segment the data, calculate the change rate of each segment of data, and repeat the above steps until all data are reliable; Step 8): Based on the processed reliable data and fuzzy reliability theory, calculate the reliability of the wind turbine gearbox sliding bearing to determine whether it has worn out or not.

[0027] In the above, the present invention determines the parameters of the whole machine through factors such as machine type selection, installation location, design requirements, installation and maintenance costs, and determines the environmental parameters according to the installation location, including wind speed spectrum, wind speed level, wave spectrum, effective wave height and spectrum peak period. On this basis, the above environmental parameters are imported into the model through the interface and solved by using the established wind turbine whole machine dynamics model, so as to obtain environmental loads such as wind waves and currents. Subsequently, a sliding bearing wind turbine gearbox dynamics model is established, and environmental loads such as wind waves and currents are imported as excitation sources through the interface to solve to obtain the required data. Next, the acquired data is preprocessed, and the specific steps include: data segmentation processing, calculating the change rate of each segment of data, setting the change rate threshold, and judging abnormal data and reliable data based on this, eliminating abnormal data, re-segmenting the data, calculating the change rate of each segment of data, and repeating the above steps until all data are reliable data. After eliminating the abnormal data, the reliability of the data is further improved. Finally, based on the processed reliable data and fuzzy reliability theory, the reliability of the wind turbine gearbox sliding bearing is calculated, and it is judged whether it has wear failure. By preprocessing the data and removing abnormal data, the present invention improves the reliability of the data and has high reliability and generalization ability. Using fuzzy reliability theory, the present method can calculate the reliability of the wind turbine gearbox sliding bearing, has high prediction accuracy and generalization ability, and is suitable for similar application scenarios.

[0028] Embodiment 3: The purpose of the present invention is to provide a wind turbine gearbox sliding bearing reliability analysis method, comprising the following steps: 1) Determination of whole machine parameters: Determine the whole machine parameters based on the machine model selection, installation location, whole machine power and economy, installation and maintenance costs. The whole machine parameters of the wind turbine generator include: rated power, wind rotor layout, wind rotor configuration, control method, total gearbox ratio, cut-in, rated and cut-out wind speed and other key parameters.

[0029] 2) Selection of average wind speed: Wind speed frequency is an important parameter that reflects the statistical characteristics of wind. The percentage of the duration of the same wind speed in a certain period of time to the total measurement duration is usually called wind speed frequency. The models usually used to fit the wind speed frequency distribution include Rayleigh distribution, logarithmic distribution, Г distribution, two-parameter Weibull distribution, three-parameter Weibull distribution and Pearson distribution. Among them, Rayleigh distribution and two-parameter Weibull distribution are simple probability models that can better fit the actual wind speed frequency distribution.

[0030] Weibull distribution probability density function: ; Distribution function: ; In the formula, and represent the shape parameter and size parameter of the Weibull distribution respectively. When is called Rayleigh distribution, is the wind speed.

[0031] In IEC 61400-1, the 10-minute average wind speed at hub height is assumed to conform to the following Rayleigh distribution: ; In the formula, Indicates the 10-minute average wind speed at the hub height. Represents the annual average wind speed.

[0032] In IEC 61400-1, the average wind speed variation with height should be given by the exponential: ; In the formula, Indicates the height of the wheel hub from the ground. Indicates the desired height above the ground. Represents the wind shear coefficient, which is an empirical value.

[0033] 3) Selection of pulsating wind speed: The contribution of kinetic energy of vortices of different sizes to the kinetic energy of pulsating wind can be described by the turbulence power spectrum density. Commonly used longitudinal turbulence power spectra include Davenport spectrum, Harris spectrum, Kaimal spectrum, Karman spectrum and Simiu spectrum.

[0034] In IEC 61400-1, two turbulence models are given for generating fluctuating wind: the Mann turbulence model and the Kaimal turbulence model. For the Kaimal turbulence model: ; In the formula, represents the velocity component subscript, indicating the direction ( Vertically, Horizontally, vertical); express Power spectrum of velocity components; express Standard deviation of velocity components; express The integral scale parameter of the velocity component; Indicates frequency.

[0035] The Kaimal power spectrum can be used with the following exponential coherence function to calculate the longitudinal velocity component at each point in space (the Kaimal turbulence model does not define transverse and vertical coherence functions): ; 4) Determination of waves: At present, there are many studies on the spectrum of ocean waves, because it is easier to observe and analyze, and it is sufficient to study some related theoretical and application problems. In the study of random waves, the wave spectrum density is S w ( ω ) (abbreviated as wave spectrum) is defined as the distribution of fixed point wave surface heights of a stationary process. Many wave spectra have been proposed so far, most of which have the form obtained by Neumann spectrum in 1953.

[0036] A, Neumann spectrum: ; B, Pierson-Moskowitz spectrum: ; C, ISSC spectrum: ; D, light-sensitive spectrum: ; E, normalized spectrum: ; F, normalized spectrum: ; In the formula, represents the wave spectral density, H represents the effective wave height, T represents the peak period of the spectrum, g represents the acceleration of gravity, represents the spectrum peak frequency, represents the random frequency, represents a dimensionless constant, Represents the peak enhancement factor.

[0037] 5) Calculate the effective wave height H , peak period T : According to the GL2012 standard, the corresponding external wind and wave combined loads under the wind and wave combined conditions are calculated, and the effective wave height H and peak period T The calculation method is as follows: ; ; ; ; ; ; In the formula, Assuming infinity, the value is m; Indicates the duration of the storm; represents the acceleration due to gravity; etc. represents the 10-minute average wind speed at the hub height, in m / s; Indicates the height of the wheel hub above sea level in meters.

[0038] 6) Construction and solution of wind turbine whole machine dynamics simulation model: Construct the whole machine dynamics model of the wind turbine, and import and solve the environmental parameters through the interface. The modules required to build the whole machine dynamics model include: pneumatic module, hydrodynamic module, structural module, servo module, mooring module and control module, and obtain environmental load data such as wind, wave and current through solution.

[0039] 7) Import environmental loads such as wind, waves and currents into the constructed sliding bearing wind turbine gearbox simulation dynamics model and solve it: Construct a sliding bearing wind turbine gearbox dynamics model, and import environmental loads such as wind, waves and currents into the model for solution. The components required to construct the sliding bearing wind turbine gearbox dynamics model include: sun gear, planetary gear, planetary carrier, inner ring, housing, rolling bearing and sliding bearing, etc. The components are constrained by stiffness and damping, etc. The environmental loads such as wind, waves and currents obtained from the wind turbine dynamics model are imported as excitation sources through the interface and solved to obtain the required data.

[0040] 8) Preprocess the data required for acquisition: The specific steps include segmenting the data to be processed; calculating the change rate of each segment of data; calculating the arithmetic mean of the change rate of each segment of data; setting the normal threshold range of the data change rate; and judging whether the change rate of each segment of data is within the normal threshold range, distinguishing abnormal data from reliable data; after removing the abnormal data, re-segmenting the remaining data, and calculating the change rate of each segment, and repeating the above steps until all data are reliable data (the change rate of all data is within the normal threshold range). All in Inside).

[0041] a. Divide the data to be processed into part; b. Calculate the rate of change of each segment of data ; ; In the formula, Indicates The length of the segment data; Indicates The change of the value of the segment data.

[0042] c. Calculate the change rate of each segment of the data to be processed , the calculation method is as follows: ; d. Setting data changes Normal threshold range: , and judge the change rate of each data accordingly Whether it is within the normal threshold range, determine abnormal data and reliable data, specifically, determine the rate of change exist The data segment within the range is credible data, and the change rate is determined Greater than or less than The data segment is abnormal data; e. Eliminate abnormal data. Specifically, the eliminated abnormal data can be divided into part.

[0043] f. Calculate the data change rate of the remaining data after removing abnormal data The arithmetic mean Specifically, the following formula is used to calculate the elimination The new arithmetic mean of the rate of change of the reliable data after the abnormal data segment; ; g. Set a new normal threshold range for data change rate , and based on this, determine whether the change rate of each data is within the normal threshold range, determine abnormal data and reliable data, specifically, determine the change exist The data segment within the range is credible data, and the change rate is determined Greater than or less than The data segment is abnormal data; f. Repeat the above steps e to g until all the remaining data after eliminating the corresponding abnormal data are reliable data (the change rate of the remaining data is All in In the range It is the arithmetic mean of the latest data change rate of the remaining data after removing new abnormal data each time.

[0044] 9) Calculate the reliability of sliding bearings and determine whether wear failure occurs: After data preprocessing is completed, reliability analysis of wind turbine gearbox sliding bearings is performed based on the processed reliable data and fuzzy reliability theory to determine whether the bearings have worn out.

[0045] a. Calculation Sliding bearings usually fail mainly due to wear, and the factors that affect the performance of sliding bearings are mainly the bearing pressure. , sliding speed and Value, where The value is calculated as follows: ; In the formula, Sliding bearing radial load, unit is N; Bearing diameter, in mm; The effective width of the bearing, in mm; For permission Value, unit is MPa·m / s.

[0046] b. Calculate the amount of wear Considering the boundary wear condition, according to Archard wear theory, the wear volume of sliding bearing is related to the sliding distance l For a linear relationship, the wear coefficient K Estimated wear for: ; In the formula, is the wear coefficient, in mm 3 / (N·m).

[0047] c. Calculate the actual gap , to determine whether the bearing has worn out. The actual clearance of the sliding bearing can be determined by the initial clearance of the bearing. Radial wear Due to factors such as environment and manufacturing errors, the allowable clearance of different sliding bearings is random. Assume that it obeys the normal distribution. , when the actual gap Exceeding the allowable clearance When the sliding bearing is considered to have worn out, the actual clearance The calculation method is as follows.

[0048] ; Due to the allowable clearance As a boundary condition for judging wear failure, it is also obviously ambiguous, and the mean value of the allowable clearance is The ambiguity at is the largest, so a smaller descending half-normal distribution function is used to represent it. When the membership degree is 0.5, the allowable gap mean is As the corresponding value, the membership function is: ; In the formula, is the actual fit clearance, in mm; is the allowable gap variance; is the mean value of the allowable clearance, in mm; is the fuzzy upper bound, and .

[0049] d. Calculate the reliability of wind turbine gearbox sliding bearings According to the reliability theory, the reliability of the sliding bearing is calculated as follows by solving the transcendental function integral formula: ; In the formula, express is the standard normal distribution function; Indicates the mean value of the allowable clearance, in mm; Indicates the actual gap mean value, in mm; represents the allowable gap variance; represents the actual gap variance.

[0050] For comparison, the wear volume and maximum wear Interference model, wear and maximum wear All of them are normally distributed and independent of each other, and the reliability is: ; In the formula, represents the variance of the maximum wear amount; Indicates the variance of actual wear amount.

Claims

1. A wind turbine gearbox sliding bearing reliability analysis method, characterized in that: The following steps are involved: 1) Determination of machine parameters: Determine the basic parameters of the machine based on the machine model, installation location, design requirements, and installation and maintenance cost factors; 2) Fitting and selection of wind speed distribution function: Using the measured data of the wind field at the installation location, fit, calculate and select the distribution function and model under the conditions of average wind speed and pulsating wind speed; 3) Selection of wave spectrum and calculation of effective wave height and spectrum peak period: According to the measured data of sea waves at the installation location, the wave spectrum is fitted, estimated and selected, and the effective wave height and spectrum peak period under the corresponding wind speed are calculated; 4) Modeling and solving of wind turbine whole machine dynamics simulation model: Construct a wind turbine whole machine dynamics simulation model, import the environmental parameters of the installation location, solve the model, and obtain the required environmental load data; 5) Modeling and solving the dynamic model of the wind turbine gearbox with sliding bearings: Establish the corresponding dynamic model of the wind turbine gearbox with sliding bearings, use the environmental load data obtained by the dynamic model of the whole machine as the excitation source, import it through the interface, and solve the model dynamically; 6) Data extraction: After solving the model dynamically, extract relevant data and obtain the data required for reliability analysis of wind turbine gearbox sliding bearings based on fuzzy reliability theory; 7) Data preprocessing and anomaly detection: After extracting the data, preprocess the data: divide the data into segments, calculate the change rate of each segment, set the data change rate threshold, and judge whether the change rate of each data is within the normal threshold range, so as to identify abnormal data and reliable data; 8) Abnormal data elimination and iterative processing: After eliminating the abnormal data, re-segment the remaining data and calculate the change rate of each segment. Repeat the above operation until all data are reliable. 9) Reliability analysis and failure judgment: Based on the processed reliable data and fuzzy reliability theory, the reliability analysis of the sliding bearings of the wind turbine gearbox is carried out to determine whether they have wear failure.

2. A wind turbine gearbox sliding bearing reliability analysis method according to claim 1, characterized in that: The overall parameters of a wind turbine include: rated power, rotor layout, rotor configuration, control method, total gearbox transmission ratio, cut-in wind speed, rated wind speed and cut-out wind speed key indicator parameters.

3. A wind turbine gearbox sliding bearing reliability analysis method according to claim 1, characterized in that: The installation location environmental parameters include: wind speed spectrum, wind speed level, wave spectrum, effective wave height and spectrum peak period environmental parameters, which are used to describe the specific environmental conditions of the location where the wind turbine is located.

4. A wind turbine gearbox sliding bearing reliability analysis method according to claim 1, characterized in that: Construction of the whole machine dynamics model: The modules required for constructing the whole machine dynamics model include: a pneumatic module, a hydrodynamic module, a structural module, a servo module, a mooring module and a control module, which are used to simulate the dynamic behavior of the wind turbine under different working conditions.

5. A wind turbine gearbox sliding bearing reliability analysis method according to claim 1, characterized in that: Construction of the dynamic model of the sliding bearing wind turbine gearbox: The components required to construct the dynamic model of the sliding bearing wind turbine gearbox include: sun gear, planetary gear, planetary carrier, inner ring gear, housing, rolling bearing and sliding bearing. The components are constrained by stiffness and damping to simulate in detail the mechanical interactions between the internal components of the sliding bearing wind turbine gearbox.

6. A wind turbine gearbox sliding bearing reliability analysis method according to claim 1, characterized in that: The data required for reliability analysis of wind turbine gearbox sliding bearings include key data such as sliding bearing radial load, bearing diameter, bearing effective width, sliding speed and bearing initial fit clearance.

7. A wind turbine gearbox sliding bearing reliability analysis method according to claim 1, characterized in that: Preprocess the data obtained in 6). The specific steps are as follows: a. Process the data to be processed in segments; b. Calculate the change rate of each segment of data to be processed k i ; c. Set the data change rate k i The normal threshold range is used to determine the change rate of each data. k i Whether it is within the normal threshold range, so as to distinguish abnormal data from reliable data; e. Eliminate abnormal data that exceeds the normal threshold range; f. Calculate the data change rate of the remaining data after removing abnormal data k i The arithmetic mean ; g. Set a new normal threshold range for the data change rate, and then determine whether the data change rate is within the normal threshold range, so as to further distinguish abnormal data from reliable data; h. Remove the abnormal data that has been identified again, and repeat the above steps until all data are reliable.

8. A wind turbine gearbox sliding bearing reliability analysis method according to claim 1, characterized in that: Based on the wind turbine gearbox sliding bearing data preprocessed in 7), the fuzzy reliability theory is used to calculate the reliability of the sliding bearing and determine whether the sliding bearing has worn out.

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