Comprehensive performance analysis method for high-power sliding bearing wind power gear box

Abstract

The invention provides a comprehensive performance analysis method for a high-power sliding bearing wind power gear box, and the method comprises the steps: building a multi-body dynamic model of the high-power sliding bearing wind power gear box through comprehensively considering the structural characteristics and internal and external excitation of the sliding bearing wind power gear box; the invention provides a sliding bearing wind power gear box comprehensive performance analysis method based on a network analytic hierarchy process and a fuzzy comprehensive analysis method. A multi-dimensional comprehensive performance analysis system with practicability and economy as principles, a target layer, criterion layer and index layer system as a framework and main shaft bearing supporting force, reliability, uniform load performance, vibration characteristics and power density as key elements is established, and a network analytic hierarchy process-fuzzy comprehensive evaluation method is adopted as an analysis tool. And analyzing the performance of the sliding bearing wind power gear box.

Description

Technical Field

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

[0002] In recent years, my country is actively developing environmentally friendly, clean and pollution-free renewable energy. Wind energy has become one of the most promising green energy sources due to its advantages of being clean, pollution-free, widely distributed, with huge reserves and unlimited renewable energy. As the main component of a wind turbine, the wind turbine gearbox converts the low speed generated by the wind wheel into the high speed required by the generator through the speed-increasing effect to ensure efficient power generation. It is also a key component of power transmission to achieve the conversion of wind energy to electrical energy. In addition, the wind turbine gearbox can change the transmission direction of power, provide braking capabilities, and increase the braking function of the unit. It is an indispensable component for the efficient and safe operation of wind turbines. The planetary gear system in the wind turbine gearbox has the advantages of high power density and compact structure. In the planetary gear system, the planetary gear system is often supported by rolling bearings, but with the continuous increase in the power of the wind turbine gearbox, the contradiction between low cost and high power density design under high load has become increasingly prominent. To this end, some wind turbine gearbox companies have successively applied low-cost, simple and reliable sliding bearings to the planetary gear train of wind turbine gearboxes, realizing "sliding instead of rolling" to increase the torque density of the gearbox, reduce the volume of the gearbox, reduce the weight of the gearbox, reduce the cost of the gearbox, increase the service life of the gearbox, and solve the problem of relying on imported rolling bearings. At present, there is a lack of comprehensive performance analysis of high-power sliding bearing wind turbine gearboxes. Therefore, it is of great significance to study the comprehensive performance analysis method of high-power sliding bearing wind turbine gearboxes.

[0003] In the past, most wind turbine gearboxes were supported by rolling bearings, and few used the "sliding instead of rolling" method. Moreover, there is a lack of research on the quantitative analysis of the comprehensive performance of wind turbine gearboxes with sliding bearings. Therefore, it is of great significance to develop a comprehensive performance analysis method for high-power sliding bearing wind turbine gearboxes. Summary of the invention

[0004] The present invention provides a comprehensive performance analysis method for a high-power sliding bearing wind turbine gearbox. By establishing a rigid-flexible coupling multi-body dynamics model of a high-power sliding bearing wind turbine gearbox, five main performance indicators and 13 secondary performance indicators of the sliding bearing wind turbine gearbox are considered, and a comprehensive performance analysis method for a sliding bearing wind turbine gearbox based on an analytic network process (ANP) and a fuzzy comprehensive evaluation method is proposed.

[0005] In order to realize the above technical features, the object of the present invention is achieved as follows: a comprehensive performance analysis method of a high-power sliding bearing wind turbine gearbox comprises the following steps:

[0006] Step 1: Determine the wind turbine gearbox parameters based on model selection, installation location, design requirements, installation and maintenance cost requirements;

[0007] Step 2, constructing a comprehensive performance analysis method for a wind turbine gearbox with a sliding bearing based on a network analytic hierarchy process and a fuzzy comprehensive analysis method;

[0008] Step 3, comprehensively considering the structural characteristics and internal and external excitations of the wind turbine gearbox with sliding bearings, and establishing a multi-body dynamics model of a high-power wind turbine gearbox with sliding bearings;

[0009] Step 4: Establish a multi-dimensional comprehensive performance analysis system based on the principles of practicality and economy, with the target layer, criterion layer, and indicator layer system as the framework, and with the main shaft bearing support force, reliability, load balancing performance, vibration characteristics, and power density as key elements;

[0010] Step 5, using network hierarchy analysis method and fuzzy comprehensive analysis method as tools to comprehensively analyze the performance of the sliding bearing wind turbine gearbox.

[0011] The characteristics of the multi-body dynamics model of the high-power sliding bearing wind turbine gearbox include: the structure and operation mechanism of the sliding bearing wind turbine gearbox, the dynamics model of the sliding bearing wind turbine gearbox, and the sliding bearing model of the wind turbine gearbox.

[0012] The comprehensive performance modeling of the sliding bearing wind turbine gearbox includes: the comprehensive performance analysis system of the sliding bearing wind turbine gearbox: reliability, load balancing factor, power density;

[0013] The network hierarchy analysis model for comprehensive performance analysis of wind turbines includes: constructing a network hierarchy analysis supermatrix, calculating unweighted, weighted, and limit supermatrices;

[0014] The fuzzy comprehensive evaluation method for the comprehensive performance of sliding bearing wind turbine gearbox includes: analysis factors and levels, fuzzy membership analysis matrix, and fuzzy membership calculation.

[0015] The system obtained in step 3 is processed, and the specific steps are as follows:

[0016] Step 3.1, obtaining data of the sliding bearing wind turbine gearbox;

[0017] Step 3.2, through the analysis of the established system, the primary indicators are considered to be independent of each other and only affected by the secondary indicators. The secondary indicators have a unidirectional or bidirectional influence on each other;

[0018] Step 3.3, taking the target layer as the control layer of the network analysis hierarchy process, and the criterion layer and the indicator layer as the network layer, construct the analysis indicator network model of the network analysis hierarchy process.

[0019] The present invention has the following beneficial effects:

[0020] 1. The relative importance of the first-level indicators of the comprehensive performance analysis of the sliding bearing wind turbine gearbox of the present invention is from large to small: power density, reliability, load-sharing performance, main shaft bearing support force and vibration characteristics. The weights of power density and reliability are both above 30%, indicating that the key to improving the comprehensive performance of the sliding bearing wind turbine gearbox lies in power density and reliability.

[0021] 2. The present invention uses the ANP-fuzzy comprehensive evaluation method to comprehensively consider the mutual influence relationship between various indicators, and determines that the bearing support force along the z direction, the bearing time-varying reliability, the low-speed level load factor, the intermediate level load factor, the vibration acceleration along the y direction, the unit megawatt weight and the torque density are the key points of the comprehensive performance analysis of the sliding bearing wind turbine gearbox. Research on this type of key indicators can effectively improve the comprehensive performance of wind turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0023] Figure 1 Schematic diagram of the variables of the differential equation for fluid dynamic lubrication.

[0024] Figure 2 ANP model for comprehensive performance of wind turbine gearbox with sliding bearings. DETAILED DESCRIPTION

[0025] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0026] The purpose of the present invention is to provide a comprehensive performance analysis method for a high-power sliding bearing wind turbine gearbox, comprising the following steps:

[0027] 1) Sliding bearing wind turbine gearbox structure and operation mechanism. Wind turbine gearbox mainly includes three-stage transmission structure: low speed stage, intermediate stage and high speed stage. The low speed stage and intermediate stage are planetary helical gear transmission, which are mainly composed of sun gear, planetary gear, planetary carrier, gear ring and other components. The high speed stage is parallel shaft helical gear transmission, which is mainly composed of large gear, small gear and other components. The low speed and high torque mechanical energy from the main shaft end is transmitted to the planetary gear system through the low speed stage planetary carrier. The planetary gear in the planetary gear system meshes with the sun gear, and transmits the motion and force to the intermediate stage planetary carrier through the low speed shaft; similar to the low speed stage transmission mode, the motion and force of the intermediate stage planetary gear system is transmitted to the high speed large gear through the intermediate stage sun gear and intermediate shaft, and finally transmitted to the output shaft through the small gear meshing with the large gear. At this time, the low speed and high torque mechanical energy has been converted into high speed and low torque mechanical energy to drive the generator to generate electricity.

[0028] 2) As a typical rigid-flexible coupling system, the wind turbine gearbox with sliding bearings needs to consider the influence of flexible components on the dynamic characteristics of the system. Therefore, according to the GL 2012 standard, the rigid body and flexible body in the system are modeled.

[0029] The gearbox and gears are considered as rigid bodies with six degrees of freedom, and the bearings are equivalent to spring-damper models with six degrees of freedom considering coupling effects. A 6×6 matrix is ​​used to represent their stiffness; the planet carrier and gear shaft are considered as flexible bodies. Based on the Lagrange multiplier method and the modal synthesis method, the differential equation of motion of the flexible body is established (the calculation formula is as follows), and the modal reduction model of the flexible body is established through finite element software.

[0030]

[0031] Where M is the mass matrix; K is the stiffness matrix; D is the modal damping matrix; f g is generalized gravity.

[0032] The gear model is established by the slicing method. According to GB / T 3480, the finite element method is used to obtain the meshing stiffness of the gear teeth in the unit tooth width direction. Since the number of slices contacted by the gears during the meshing process is different, the change of the gear meshing stiffness with the meshing phase can be simulated, so as to obtain the distribution of the load along the tooth width direction. The components are constrained by stiffness-damping, and the topological relationship diagram is shown in the figure. Figure 2 shown.

[0033] 3) Wind turbine gearbox sliding bearing model. The planetary gear sliding bearing of the wind turbine gearbox is often in low-speed and heavy-load conditions. The oil film of the bearing will generate heat under shear force and pressure during operation. It is necessary to consider the influence of thermal elastic deformation on the bearing, and use the coupled Reynolds equation, temperature control equation and temperature viscosity equation to calculate the distribution of oil film pressure. The discrete solution is performed through the iterative calculation method, and the oil film thickness, load-bearing capacity, friction power consumption, stiffness and damping of the bearing are calculated according to the formula.

[0034] The planetary gear sliding bearing of the wind turbine gearbox is in a state of mixed friction under heavy load and low speed conditions. Considering the influence of surface roughness on the film area of ​​the lubricating fluid, the generalized average Reynolds equation flow factor of laminar flow is expanded and the Reynolds equation is established:

[0035]

[0036] Where: F0, F1, F2 are the dynamic viscosity coefficients of the lubricant; p h is the oil film pressure; h is the oil film thickness at any point; is the pressure flow factor of the x and z axes; is the shear factor; R q is the surface roughness; θ is the density of lubricating oil; U p is the planet shaft speed.

[0037] Figure 1 This is a schematic diagram of the variables of the differential equation for fluid dynamic lubrication, where u, v, and w represent the rotation speeds in the x, y, and z directions, respectively. During the operation of the sliding bearing, the oil film is subjected to shear force and pressure to generate heat, and the three-dimensional temperature control equation of the planetary gear bearing is:

[0038]

[0039] Where: T is temperature; c is specific heat capacity; ρ is density; η is dynamic viscosity of lubricant.

[0040] The temperature and viscosity of lubricating oil are inversely proportional. The temperature-viscosity equation can reflect the changes in lubricating oil viscosity and temperature. Its basic expression is:

[0041]

[0042] Where: η, η0 are the bearing temperature T and T0 dynamic viscosity of the lubricating oil respectively.

[0043] Formulas (2), (3) and (4) are coupled and the distribution of oil film pressure is iteratively calculated by the difference method.

[0044] The formula for oil film thickness h is:

[0045]

[0046] Where: C R is the radial clearance; e is the eccentricity; γ is the attitude angle; φ is the angular coordinate; Δh is the value of the oil film thickness at each point minus the minimum oil film thickness; The star shaft and bearing alignment angle.

[0047] According to formula (5), the oil film thickness at each point is calculated and the minimum oil film thickness is determined. In order to determine the stability of the bearing operation, it is required to satisfy hmin>[hmin], where [hmin] is the minimum allowable oil film thickness, and its expression is:

[0048] [h min ]=S b (R a1 +R a2 +y h )(6);

[0049] Where: S b is the margin factor; R a1 R is the roughness of the journal surface; a2 is the surface roughness of the bearing in contact with the journal; h The offset is caused by the deflection of the journal and the installation accuracy.

[0050] After finding the oil film pressure and oil film thickness, the bearing's load capacity, friction power consumption, oil film stiffness and damping are calculated using calculation formulas.

[0051] The calculation formula for the total bearing capacity P is:

[0052]

[0053] Where: P x , P y is the x and y direction components of the radial bearing load capacity; B is the bearing width; r is the bearing radius; φ a ,φ b are the starting angle and ending angle of the bearing respectively.

[0054] Usually dimensionless bearing characteristic C p The load-bearing capacity of the bearing can be expressed as shown in formula (10).

[0055]

[0056] Where ψ is the bearing clearance ratio, ω is the bearing speed, and p m is the average bearing pressure.

[0057] The friction resistance generated by the rotation of the radial bearing on the journal is caused by shear stress and pressure. The total friction force F generated by the rotation of the bearing is μ for:

[0058]

[0059] Frictional resistance is defined as the dimensionless friction coefficient C μ And the friction factor μ are:

[0060]

[0061] The specific friction coefficient C of the bearing is obtained by using formulas (11), (12) and (13): μ for:

[0062]

[0063] The friction energy N formula is:

[0064]

[0065] The stiffness and damping of the oil film can reflect whether the bearing is running stably. ii Refers to the derivative of the oil film bearing capacity with respect to the eccentricity, the formula is as follows:

[0066]

[0067] The oil film damping Cii is the derivative of the oil film bearing capacity with respect to velocity, and the formula is as follows:

[0068]

[0069] During the operation of the sliding bearing, in order to prevent the dynamic characteristics of the bearing from taking effect and causing the bearing to become unstable, the oil film stiffness and oil film damping are calculated according to formulas (16) and (17), and the stability K of the bearing operation state is determined according to formula (18): eq , when K eq When <0, the system is unstable and the bearing needs to be recalculated.

[0070]

[0071] 4) Comprehensive performance analysis of sliding bearing wind turbine gearboxes. In view of the lack of comprehensive performance analysis indicators and analysis methods for sliding bearing wind turbine gearboxes, this paper determines the comprehensive performance indicators based on the practicality and economy of sliding bearing wind turbine gearboxes, including 5 primary indicators and 13 secondary indicators, and establishes the analysis framework shown in Table 1.

[0072] Table 1 Comprehensive performance analysis framework of sliding bearing wind turbine gearbox

[0073]

[0074] (1) Reliability. Bearings are standardized mechanical parts, and their life generally follows a three-parameter Weibull distribution. Based on the life distribution function, the bearing reliability function can be derived as follows:

[0075]

[0076] Where: L his the basic rated life; γ and β are the position parameters and shape parameters.

[0077] Fuzzy language is used to describe and analyze the uncertainty and operating status of component performance degradation. Considering the strength degradation of components, the fuzzy reliability function of components is:

[0078]

[0079] Where: S i is the standard deviation of the allowable stress, Φ(·) is the standard normal distribution function; is the mean stress; r i (t) is the static strength.

[0080] (2) Load sharing coefficient. The load sharing of a planetary transmission system means that during the power distribution process, the meshing forces between the gear pairs are the same and the loads on the planetary gears are equal. However, in practice, due to various factors, the system will have uneven load distribution. Therefore, the closer the load sharing coefficient is to 1, the more uniform the load distribution is and the stronger the system's carrying capacity is. In order to quantify the load sharing characteristics of a planetary gear transmission system for a wind turbine, the load sharing coefficient is defined as:

[0081]

[0082] Where: R 31 and R 32 is the load-sharing factor of the low-speed and medium-speed planetary transmission systems; F RPi-max and F SPi-max F is the maximum dynamic meshing force of a single internal gear ring planetary gear and sun gear planetary gear transmission; RP-max and F SP-max It is the maximum dynamic meshing force of the planetary gear and sun gear transmission at each stage.

[0083] (3) Power density. In a gearbox, the gearbox torque density is used to measure its transmission capacity. A higher torque density means that the gearbox can withstand a greater torque load, improving transmission efficiency and reliability. Torque density R51 refers to the torque per unit volume and is usually used to analyze the performance and efficiency of a mechanical system. The calculation formula is:

[0084]

[0085] Where: T is the gearbox torque; V is the gearbox volume.

[0086] Unit MW weight R 52 Used to measure the power generation capacity of wind turbines. A higher unit megawatt weight means that the wind turbine gearbox can withstand a greater power generation capacity. The specific calculation formula is:

[0087]

[0088] Where: P is the generator power; V is the gearbox density.

[0089] 5) ANP model for comprehensive performance analysis of wind turbines. It includes: constructing ANP supermatrix, solving judgment matrix, calculating unweighted, weighted and limit supermatrix. ANP is a complex decision-making system for internal dependencies and feedback relationships. Different from the hierarchical structure adopted by the multi-index system, ANP adopts a network structure to accurately describe the connection between objective things by combining qualitative analysis with quantitative analysis. ANP consists of two parts: the control layer and the network layer. The control layer is a typical hierarchical structure, while the elements in the network layer influence and dominate each other. ANP is mainly divided into problem description and analysis, construction of ANP hierarchical structure, construction of judgment matrix, verification of consistency of judgment matrix, and determination of the weights of the basic elements of supermatrix and weighted supermatrix to the overall goal.

[0090] (1) Construct the ANP supermatrix. The target layer is the control layer of ANP, the criterion layer and the indicator layer are the network layer, and the ANP analysis indicator network model is constructed. Suppose the control layer elements of ANP are: P1, P2, ..., P m ,The first-level indicators of the network layer are: R1, R2, ..., R5, where R i It also contains secondary indicators

[0091] To control the layer element P S (s=1,2,…,m) as the criterion, with R j The element R in (j=1,2,…,4) jk (k=1,2,…,n j ) is the secondary criterion, and R i The elements in the jk The nine-level scaling method is used to construct the corresponding judgment matrix L, and the scoring scale of the nine-level scaling method is shown in Table 2.

[0092]

[0093] Table 2 Judgment Matrix Nine-Level Scaling Method Scoring Scale

[0094]

[0095] (2) Solve the judgment matrix. Since the construction of the judgment matrix is ​​subjective, it is necessary to test the consistency of the judgment matrix. It is necessary to calculate the consistency ratio CR of the judgment matrix, which is calculated as follows:

[0096]

[0097] Where: CI is the consistency index; RI is the random consistency index, and its value is shown in Table 3; n is the order of the judgment matrix; λ max is the maximum eigenvalue of the judgment matrix. When n=0,1, CI=0, and the judgment matrix has complete consistency; when n≥2, if CR<0.1, it can be considered that the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be adjusted.

[0098] Table 3 Average random consistency index RI

[0099]

[0100] (3) Calculate the unweighted, weighted, and limit supermatrices:

[0101] The judgment matrix is ​​formed based on the survey results. After passing the consistency test, the judgment matrix can be identified as the unweighted supermatrix W ij , which is expressed as follows:

[0102]

[0103] Where: W ij The column vector is the influencing factor of the factor layer in the analysis index system R j Medium Impact Factor The influence ranking vector.

[0104] Criteria level index R in the reuse index system i R j (i, j = 1, 2, 3, 4, 5) are compared for importance. After pairwise comparison, the weighted matrix A of the criterion layer index can be obtained, which is expressed as follows:

[0105]

[0106] Use the weighted matrix A to transform the unweighted super matrix W ij Weighted, get the weighted super matrix It is expressed as follows:

[0107]

[0108] Where:

[0109] Further stability processing is performed on the weighted supermatrix, and the normalized result is the limit supermatrix W∞, whose column vector is the comprehensive performance analysis factor index of the sliding bearing wind turbine gearbox The weight vector W' is expressed as follows:

[0110] W'=(W 11 , W 12 , …, Wini , …, W NnN ) T (31);

[0111] Each index R ij In the criterion level indicator R i The normalized weight vector in is W i ', which means as follows:

[0112] W i '=(W i1 'W i2 ' ... W ini ') Τ (32);

[0113] 6) Fuzzy comprehensive evaluation method for comprehensive performance of wind turbine gearbox with sliding bearing. It includes: evaluation factors and grades, fuzzy membership evaluation matrix, and fuzzy membership calculation. Fuzzy comprehensive evaluation is a method that uses fuzzy mathematics methods to comprehensively evaluate multi-objective problems with random analysis. Its theoretical basis is fuzzy mathematics theory. Through fuzzy mathematics methods, the fuzzy objects to be examined and the corresponding fuzzy concepts are regarded as a fuzzy set, and appropriate membership functions are established. Through fuzzy set theory related operations and transformations, the fuzzy objects are quantitatively analyzed.

[0114] (1) Evaluation factors and levels

[0115] Factors are the attributes and performance of the evaluation target, and levels are the degree of influence of different factor values ​​on the target. According to the complexity of the target, the factor set U = (u1, ..., u m ) and the evaluation set V = (v1, ..., v n ).

[0116] (2) Fuzzy membership evaluation matrix

[0117] First, the factor concentration u i (i=1,2,…,m) make single factor evaluation and get u i Evaluation level v j The membership degree of (j=1, 2, ..., n) is r ij , get u i Judgement set i =(r i1 ,…,r in ), the total evaluation matrix R can be obtained in the same way, that is, a fuzzy relationship R from U→V.

[0118]

[0119] Evaluation indicators are divided into qualitative indicators and quantitative indicators. The membership matrix of qualitative indicators mainly adopts the combination of expert scoring method, public participation survey method and field research. The membership of quantitative indicators is generally calculated using membership functions. Commonly used membership functions include rectangular or semi-rectangular distribution, normal distribution, trapezoidal distribution, Cauchy distribution, parabolic distribution, etc.

[0120] (3) Fuzzy membership calculation

[0121] Since each factor in the evaluation factor set U has different status and role relative to the evaluation target, it is necessary to assign different weights to each factor, which can be expressed as a fuzzy subset A on U = (a1, a2, ..., a n ) is called the weight distribution set. The weight vector A represents the relative importance of each factor in the evaluation.

[0122] Each row in the membership matrix R represents the degree of membership of a single indicator to each level of fuzzy subsets, and the comprehensive evaluation needs to obtain the degree of membership of the entire evaluation index system to each level of fuzzy subsets, that is, the final result vector B P =(b1,b2,…,b n The process of obtaining B when R and W2 are known is called fuzzy transformation. That is, B = W2 * R (* is the operator symbol). There are many types of operators, and ordinary matrix multiplication is usually used.

[0123] The membership matrix R ij The calculation of adopts the membership function method. In order to eliminate the phenomenon that the numerical difference between each level is small, but the evaluation level is different, and the membership function can smoothly transition between each level, it needs to be fuzzy processed and calculated. The simplest ordinary matrix multiplication is used for secondary comprehensive evaluation to calculate the final fuzzy comprehensive evaluation result vector B P .

[0124] B i =ω Bi R i (34);

[0125] B P =ωR B (35);

[0126] Where: B i is the set of comments under the ith criterion of the criterion layer; ω Bi is the weight set of each indicator in the indicator layer relative to the ith criterion in the criterion layer; R i is the membership matrix of each indicator in the indicator layer, B P is the final fuzzy comprehensive evaluation result vector; ω is the weight set of each criterion in the criterion layer relative to the target layer; R B Fuzzy evaluation membership matrix of each criterion in the target layer.

[0127] In order to quantitatively reflect the influence of each level on the comprehensive performance, each level is assigned a value, and the comprehensive performance evaluation value P is obtained by calculation. The higher P is, the stronger the comprehensive performance is. The calculation formula is as follows:

[0128]

[0129] Where: P is the comprehensive performance evaluation value; C i Assign values ​​to 4 levels, specifically C1=100, C2=75, C3=50, C3=25; b j For R B Elements corresponding to the 4 levels.

[0130] 7) Comprehensive performance analysis of wind turbine gearbox with sliding bearing based on ANP-fuzzy comprehensive evaluation method. Through the analysis system established by the previous analysis, the primary indicators are considered to be independent of each other and are only affected by the secondary indicators. The secondary indicators have a unidirectional or bidirectional influence on each other. The target layer in Table 2 is the control layer of ANP, and the criterion layer and indicator layer are the network layer. The ANP analysis indicator network model is constructed, as shown in Figure 2 Based on the ANP model for comprehensive performance analysis of wind turbines, the paper uses expert scoring to determine the corresponding weights of indicators at all levels: the first-level indicators are considered independent of each other, and they are scored according to the nine-level scaling method to obtain the judgment matrix of the first-level indicators of the control layer, as shown in Table 4.

[0131] Table 4 Judgment matrix and weight vector of first-level indicators of control layer

[0132]

[0133] From formula (32), we can get: λmax=5.24,CI=0.059,RI=1.12,CR=0.053<0.1, and the judgment matrix passes the consistency test. The weight of the network layer indicators is determined based on the importance of the first-level indicators, and the mutual influence relationship between the network layer elements under the first-level indicators needs to be considered. The same method as the weight calculation of the first-level indicators of the control layer is used to construct the judgment matrix of the second-level indicators of the network layer, and the consistency test is performed on it, taking reliability as an example, as shown in Table 5.

[0134] Table 5 Reliability judgment matrix and weight vector

[0135]

[0136] From equations (26) and (27), we can get: max =3.05,CI=0.046,RI=0.58,CR=0.046<0.1, the judgment matrix passed the consistency test.

[0137] The remaining secondary indicators of the network layer are calculated in turn to obtain the comprehensive performance indicator weights of the sliding bearing wind turbine gearbox.

[0138] Table 6 Weights for comprehensive performance analysis of sliding wind turbine gearbox

[0139]

[0140] According to the performance level, an evaluation set is established, as shown in Table 7.

[0141] Table 7 Evaluation of membership levels

[0142]

[0143] Then construct a membership matrix to represent the membership of each risk factor to each level in the evaluation set. Let the risk factor U i The membership vector of the evaluation set V is V i = {V i1 , V i2 , V i3 , V i4}Thus, a membership matrix is ​​obtained.

[0144] According to formula (34), the simplest ordinary matrix multiplication is used to perform fuzzy transformation and calculate the fuzzy comprehensive evaluation result vector B.

[0145] B = WR2;

[0146] Then, formula (35) is used to perform a secondary comprehensive analysis to obtain the final fuzzy comprehensive evaluation result vector B P .

[0147] B P =BW;

[0148] Finally, according to formula (36), the comprehensive performance analysis value P of the sliding bearing wind turbine gearbox is calculated.

[0149] Corresponding to the comprehensive performance rating table 8, the performance of the wind turbine gearbox with sliding bearings is analyzed.

[0150] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Comprehensive performance analysis method of high-power sliding bearing wind turbine gearbox, characterized in that: The following steps are involved: Step 1: Determine the wind turbine gearbox parameters based on model selection, installation location, design requirements, installation and maintenance cost requirements; Step 2, constructing a comprehensive performance analysis method for a wind turbine gearbox with a sliding bearing based on a network analytic hierarchy process and a fuzzy comprehensive analysis method; Step 3, comprehensively considering the structural characteristics and internal and external excitations of the wind turbine gearbox with sliding bearings, and establishing a multi-body dynamics model of a high-power wind turbine gearbox with sliding bearings; Step 4: Establish a multi-dimensional comprehensive performance analysis system based on the principles of practicality and economy, with the target layer, criterion layer, and indicator layer system as the framework, and with the main shaft bearing support force, reliability, load balancing performance, vibration characteristics, and power density as key elements; Step 5, using network hierarchy analysis method and fuzzy comprehensive analysis method as tools to comprehensively analyze the performance of the sliding bearing wind turbine gearbox.

2. According to the comprehensive performance analysis method of high-power sliding bearing wind turbine gearbox according to claim 1, it is characterized in that: The characteristics of the multi-body dynamics model of the high-power sliding bearing wind turbine gearbox include: the structure and operation mechanism of the sliding bearing wind turbine gearbox, the dynamics model of the sliding bearing wind turbine gearbox, and the sliding bearing model of the wind turbine gearbox.

3. The comprehensive performance analysis method of a high-power sliding bearing wind turbine gearbox according to claim 1 is characterized in that: The comprehensive performance modeling of the sliding bearing wind turbine gearbox includes: the comprehensive performance analysis system of the sliding bearing wind turbine gearbox: reliability, load balancing factor, power density; The network hierarchy analysis model for comprehensive performance analysis of wind turbines includes: constructing a network hierarchy analysis supermatrix, calculating unweighted, weighted, and limit supermatrices; The fuzzy comprehensive evaluation method for the comprehensive performance of sliding bearing wind turbine gearbox includes: analysis factors and levels, fuzzy membership analysis matrix, and fuzzy membership calculation.

4. The comprehensive performance analysis method of a high-power sliding bearing wind turbine gearbox according to claim 1 is characterized in that: The system obtained in step 3 is processed, and the specific steps are as follows: Step 3.1, obtaining data of the sliding bearing wind turbine gearbox; Step 3.2, through the analysis of the established system, the primary indicators are considered to be independent of each other and only affected by the secondary indicators. The secondary indicators have a unidirectional or bidirectional influence on each other; Step 3.3, taking the target layer as the control layer of the network analysis hierarchy process, and the criterion layer and the indicator layer as the network layer, construct the analysis indicator network model of the network analysis hierarchy process.

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