BEM-based wind power main shaft bearing load spectrum prediction method and system

Abstract

The invention discloses a BEM-based wind power main shaft bearing load spectrum prediction method and system. The method comprises the steps of obtaining wind flow field actually measured wind speed data of a wind turbine generator based on a preset acquisition frequency; feature analysis is carried out based on a Weibull probability density function, and probability distribution of effective wind speed on the vertical height of the blade wind wheel surface of the wind turbine generator is determined; the load of the wind speed acting on the blades of the wind generating set is obtained through the improved blade element-momentum theory, and the aerodynamic load of the blades of the wind generating set at the effective wind speed is obtained; and carrying out wind power main shaft bearing load spectrum prediction according to the blade aerodynamic load-hub-main shaft-bearing transmission path to obtain a wind power main shaft bearing load spectrum prediction result. The method can improve the calculation precision and efficiency of the wind driven generator main shaft bearing load spectrum. The BEM-based wind power main shaft bearing load spectrum prediction method and system can be widely applied to the technical field of wind turbine generator aerodynamics.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine aerodynamics, and particularly to a method and system for predicting the load spectrum of a wind power main shaft bearing based on BEM. Background Art

[0002] The blade is the core component of a wind power generation unit that converts the kinetic energy of the wind into mechanical energy. The aerodynamics of the wind turbine blade is the basic theory and technology for studying the interaction between the wind turbine blade and the air flow. Mastering and proficiently applying this technology is crucial for the design, performance optimization, power generation efficiency, and structural stability of wind turbines. The aerodynamic load of the wind turbine blade mainly comes from the pressure distribution of the wind on the blade surface. The direction, speed, turbulence of the wind, and the rotation angle of the blade will all affect this distribution, resulting in load changes. Based on different calculation requirements, common aerodynamic load calculation methods such as the momentum - blade element theory, numerical simulation methods, and aeroelastic analysis mainly target the case where the inflow wind speed is determined. Existing blade aerodynamic load methods mostly consider aspects such as the equivalent inflow wind speed at the blade, blade optimization design, and optimization algorithms to improve the blade aerodynamic load efficiency. With the development of large - scale wind turbines, the wind speed difference between the highest and lowest points of the blade wind wheel surface cannot be ignored, and the load transfer mechanism between the wind flow field wind speed, blade aerodynamic load, wind power main shaft, and main shaft bearing is not clear enough. The real - time wind speed in an actual wind farm is affected by factors such as terrain, meteorological conditions, turbulence, wind shadow effect, and surface roughness, showing significant spatio - temporal non - uniformity, manifested as differences in wind speed and direction at different times and positions. Summary of the Invention

[0003] In order to solve the above - mentioned technical problems, the purpose of the present invention is to provide a method and system for predicting the load spectrum of a wind power main shaft bearing based on BEM, which can obtain the distribution of the load spectrum of the wind power main shaft bearing and the statistical characteristics of the bearing load frequency, thereby improving the calculation accuracy and efficiency of the load spectrum of the wind turbine main shaft bearing.

[0004] The first technical solution adopted by the present invention is: A method for predicting the load spectrum of a wind power main shaft bearing based on BEM, comprising the following steps:

[0005] Obtain the measured wind speed data of the wind flow field of the wind turbine based on a preset acquisition frequency, and the measured wind speed data of the wind flow field includes real - time full - period wind speed data, maximum wind speed data, and minimum wind speed data;

[0006] Conduct characteristic analysis on the measured wind speed data of the wind flow field of the wind turbine based on the Weibull probability density function to determine the probability distribution of the effective wind speed on the vertical height of the blade wind wheel surface of the wind turbine;

[0007] Based on the probability distribution of the effective wind speed at the vertical height of the wind turbine blade wind wheel surface, the load exerted by the wind speed on the wind turbine blade is obtained through the improved blade element - momentum theory (BEM), and the aerodynamic load of the wind turbine blade under the effective wind speed is obtained.

[0008] Based on the aerodynamic load of the wind turbine blade under the effective wind speed, the load spectrum of the wind power main shaft bearing is predicted according to the transmission path of the blade aerodynamic load - hub - main shaft - bearing, and the prediction result of the load spectrum of the wind power main shaft bearing is obtained.

[0009] Furthermore, the step of analyzing the characteristics of the measured wind speed data of the wind flow field of the wind turbine based on the Weibull probability density function to determine the probability distribution of the effective wind speed at the vertical height of the wind turbine blade wind wheel surface specifically includes:

[0010] Perform data fitting on the measured wind speed data of the wind flow field of the wind turbine to construct a wind profile equation.

[0011] Perform real - time speculation on the wind profile equation to obtain the wind speed data at the vertical height of the wind turbine blade surface.

[0012] Perform characteristic statistics on the measured wind speed data of the wind flow field of the wind turbine through the Weibull probability density function to obtain the probability distribution of the wind speed at the vertical height of the wind turbine blade surface.

[0013] Determine the effective wind speed range of the wind turbine blade according to the calibrated cut - in wind speed, cut - out wind speed and extreme wind speed in the wind turbine.

[0014] Combining the wind speed data at the vertical height of the wind turbine blade surface, the probability distribution of the wind speed at the vertical height of the wind turbine blade surface, and the effective wind speed range of the wind turbine blade, determine the probability distribution of the effective wind speed at the vertical height of the wind turbine blade wind wheel surface.

[0015] Furthermore, the expression of the Weibull probability density function is specifically as follows:

[0016]

[0017] In the above formula, f(v) represents the probability of observing the wind speed v, k represents the dimensionless Weibull shape parameter, c represents the Weibull scale parameter, and v represents the wind speed.

[0018] Furthermore, the step of obtaining the load exerted by the wind speed on the wind turbine blade through the improved blade element - momentum theory (BEM) based on the probability distribution of the effective wind speed at the vertical height of the wind turbine blade wind wheel surface to obtain the aerodynamic load of the wind turbine blade under the effective wind speed specifically includes:

[0019] Modify the blade element - momentum theory to construct an improved blade element - momentum theory.

[0020] Based on the improved blade element - momentum theory, differential processing of the blade elements of the wind turbine is carried out to obtain a number of annular units, and the annular units are represented as blade elements;

[0021] Integrate a number of annular units in the blade unfolding direction according to a preset acting force to obtain the load acting on the wind turbine rotor;

[0022] Based on the probability distribution of the effective wind speed at the vertical height of the wind turbine blade rotor plane, conduct momentum theory analysis on the load acting on the rotor to obtain the aerodynamic load of the wind turbine blade under the effective wind speed.

[0023] Furthermore, the step of correcting the blade element - momentum theory to construct the improved blade element - momentum theory specifically includes:

[0024] Based on the tip - loss coefficient, conduct tip - loss correction on the blade element - momentum theory to obtain the first - corrected blade element - momentum theory;

[0025] Based on the hub - loss coefficient, conduct hub - loss correction on the first - corrected blade element - momentum theory to obtain the second - corrected blade element - momentum theory;

[0026] Based on the change in angle of attack thickness and the change in angle of attack width, conduct angle - of - attack correction on the second - corrected blade element - momentum theory to obtain the third - corrected blade element - momentum theory;

[0027] According to the thrust correction coefficient, select the Glauert correction method, Wilson correction method or empirical correction method to conduct axial - coefficient correction processing on the third - corrected blade element - momentum theory to construct the improved blade element - momentum theory.

[0028] Furthermore, the step of predicting the load spectrum of the wind power main shaft bearing based on the aerodynamic load of the wind turbine blade under the effective wind speed according to the transmission path of the blade aerodynamic load - hub - main shaft - bearing to obtain the prediction result of the load spectrum of the wind power main shaft bearing specifically includes:

[0029] Based on the aerodynamic load of the wind turbine blade under the effective wind speed, obtain the axial force received by the main shaft bearing of the wind turbine blade and the torque received by the main shaft bearing of the wind turbine blade;

[0030] Combine the axial force received by the main shaft bearing of the wind turbine blade and the torque received by the main shaft bearing of the wind turbine blade, and conduct prediction of the load spectrum of the wind power main shaft bearing according to the transmission path of the blade aerodynamic load - hub - main shaft - bearing to obtain the prediction result of the load spectrum of the wind power main shaft bearing.

[0031] Furthermore, the expression of the axial force received by the main shaft bearing of the wind turbine blade is specifically as follows:

[0032]

[0033] In the above formula, F t represents the thrust received by the main shaft bearing, η 1 represents the load transfer efficiency coefficient between the blade and the hub, η 2 represents the load transfer efficiency coefficient between the hub and the main shaft, η 3 represents the load transfer efficiency coefficient between the main shaft and the main shaft bearing, v represents the wind speed, K represents the number of blades, ρ represents the air density, S y represents the blade area, dS represents the blade area integral element, C D represents the drag coefficient, C L represents the lift coefficient, and φ represents the angle between the inflow velocity and the wind turbine rotation plane.

[0034] Furthermore, the expression for the torque on the main shaft bearing of the wind turbine blade is specifically as follows:

[0035]

[0036] In the above formula, M represents the torque received by the main shaft bearing, δ 1 represents the torque transfer efficiency coefficient between the blade and the hub, δ 2 represents the torque transfer efficiency coefficient between the hub and the main shaft, δ 3 represents the torque transfer coefficient between the main shaft and the main shaft bearing, K represents the number of blades; r i represents the distance from the blade integral element to the blade rotation center, ρ represents the air density, S y represents the blade area, dS represents the blade area integral element, C D represents the drag coefficient, C L represents the lift coefficient, and φ represents the angle between the inflow velocity and the wind turbine rotation plane.

[0037] The second technical solution adopted by the present invention is: a BEM-based load spectrum prediction system for a wind power main shaft bearing, including:

[0038] The first module is used to obtain the measured wind speed data of the wind flow field of the wind turbine based on a preset acquisition frequency, and the measured wind speed data of the wind flow field includes real-time full-time wind speed data, maximum wind speed data, and minimum wind speed data;

[0039] The second module is used to perform characteristic analysis on the measured wind speed data of the wind flow field of the wind turbine based on the Weibull probability density function to determine the probability distribution of the effective wind speed at the vertical height of the wind turbine blade wind wheel surface;

[0040] The third module is used to obtain the load of wind speed on the wind turbine blades based on the probability distribution of the effective wind speed at the vertical height of the wind turbine blade rotor surface through the improved blade element-momentum theory (BEM), and obtain the aerodynamic load of the wind turbine blades under the effective wind speed;

[0041] The fourth module is used to predict the load spectrum of the wind turbine main shaft bearing based on the aerodynamic load of the wind turbine blades under the effective wind speed and the transmission path of the blade aerodynamic load-hub-main shaft-bearing, and obtain the prediction result of the load spectrum of the wind turbine main shaft bearing.

[0042] The beneficial effects of the method and system of the present invention are as follows: the present invention obtains the measured wind speed data of the wind flow field of the wind turbine set based on a preset acquisition frequency, further performs characteristic analysis on the measured wind speed data of the wind flow field of the wind turbine set based on the Weibull probability density function, and uses the wind profile function to infer the wind speed range in the vertical direction of the blade wind wheel, and then obtains the load of the wind speed on the blade of the wind turbine set based on the probability distribution of the effective wind speed at the vertical height of the blade wind wheel surface of the wind turbine set through the improved blade element-momentum theory (BEM), and refines the calculation method of the real-time aerodynamic load of the blade. The effective wind speed and wind speed frequency statistical characteristics obtained in the vertical direction of the blade are then integrated into the aerodynamic load calculation of the blade, thereby obtaining the real-time aerodynamic load of the blade and the statistical characteristics of the load frequency. Finally, based on the probability distribution of the effective wind speed at the vertical height of the wind turbine blade rotor surface, the load of the wind speed on the wind turbine blade is obtained through the improved blade element-momentum theory (BEM), thereby obtaining the load spectrum distribution of the wind turbine main shaft bearing and the statistical characteristics of the bearing load frequency, thereby improving the calculation accuracy and efficiency of the wind turbine main shaft bearing load spectrum. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of the steps of a method for predicting a wind turbine main shaft bearing load spectrum based on BEM of the present invention;

[0044] Figure 2 It is a structural block diagram of a wind turbine main shaft bearing load spectrum prediction system based on BEM of the present invention;

[0045] Figure 3 It is a schematic diagram of the differential of a blade element of a wind turbine blade plane provided by a specific embodiment of the present invention;

[0046] Figure 4 is a schematic diagram of blade element momentum analysis provided by a specific embodiment of the present invention;

[0047] Figure 5 is a schematic diagram of the blade element velocity vector relationship provided by a specific embodiment of the present invention;

[0048] Figure 6It is a schematic diagram of the framework for predicting the load spectrum of a wind power main shaft bearing provided by a specific embodiment of the present invention. Specific Embodiments

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. For the step numbers in the following embodiments, they are only set for the convenience of elaboration and explanation, and no limitation is imposed on the order between steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0050] Refer to Figure 1 , the present invention provides a method for predicting the load spectrum of a wind power main shaft bearing based on BEM. The method includes the following steps:

[0051] S100. Obtain the measured wind speed data of the wind flow field of the wind turbine based on a preset acquisition frequency. The measured wind speed data of the wind flow field includes real-time full-time wind speed data, maximum wind speed data, and minimum wind speed data;

[0052] Specifically, adopt a 10-minute acquisition frequency to collect the measured wind speed data of the wind flow field of the wind turbine. The wind speed data includes real-time full-time wind speed data, maximum wind speed, and minimum wind speed;

[0053] Specifically, the collected wind speed, its expression is:

[0054]

[0055] In the above formula, represents the longitudinal average wind speed at this point, u, v, and w respectively represent the longitudinal, transverse, and vertical upward pulsating wind speed components, which are functions of time t, y and z respectively represent the transverse and vertical coordinates of the pulsating wind speed simulation point. The wind speed at a certain point in the wind field can be decomposed into the linear superposition of the average wind speed and pulsating wind speed in the longitudinal, transverse, and vertical 3 directions.

[0056] S200. Conduct characteristic analysis on the measured wind speed data of the wind flow field of the wind turbine based on the Weibull probability density function, and determine the probability distribution of the effective wind speed on the vertical height of the blade wind wheel surface of the wind turbine;

[0057] In this embodiment, the effective wind speed data and the statistical characteristics of the wind speed frequency in the vertical direction of the blade wind wheel swept surface are obtained through the wind profile equation, the statistical characteristics of the wind speed frequency, the cut-in wind speed, the cut-out wind speed, and the safety wind speed calibrated by the target wind turbine, and a calculation method for the main shaft bearing load spectrum of the wind turbine based on BEM (blade element - momentum theory) is constructed. More specifically, by analyzing the relationship between the ground roughness and the wind shear index, a real-time wind speed profile equation is established, and the effective wind speed data in the vertical direction of the blade wind wheel of the target wind turbine is determined based on the fan data such as the vertical height of the blade wind wheel of the target unit, the cut-out wind speed, the cut-in wind speed, and the safety wind speed; subsequently, with the aid of the statistical characteristics of the wind speed frequency, the probability distribution of the measured wind speed within a certain period of time is fitted, and then the probability distribution of the effective wind speed at the vertical height of the blade wind wheel surface is inferred.

[0058] S210. Perform data fitting on the measured wind speed data of the wind flow field of the wind turbine to construct a wind profile equation;

[0059] S220. Perform real-time inference on the wind profile equation to obtain the wind speed data at the vertical height of the blade surface of the wind turbine;

[0060] In this embodiment, specifically, the measured wind flow field data collected at a frequency of 10 minutes is preliminarily processed and fitted into a wind profile to realize the inference of the wind speed data at the vertical height of the blade surface of the wind turbine from the measured ground wind speed. The expression of the wind profile is as follows:

[0061]

[0062] In the above formula, v 1 represents the wind speed at height h 1 , with the unit of m / s, v 2 represents the wind speed at height h 2 , with the unit of m / s, d represents the influence coefficient of the ground profile line, and z 0 represents the roughness length, which is an index to measure the magnitude of the ground friction force.

[0063] Furthermore, when the obstacles on the ground are relatively discrete and low, and the height is in the range of 30 - 50m, d is selected as 0; otherwise, d adopts 70% - 80% of the obstacle height, and the above formula is used to fit the wind profile;

[0064] When the height range is greater than 50m, d = 0, and the wind profile adopts an exponential form, and its expression is:

[0065]

[0066] In the above formula, α represents the wind shear index, and its expression and parameters are shown in Table 1 and the following formula:

[0067] α = 0.04ln Z0 +0.003(ln Z 0 ) 2 +0.24

[0068] Table 1 Parameter data table

[0069]

[0070] In summary, the larger the α, the faster the wind speed increases. A small α value indicates that the wind speed increases slowly with height, and the α value is related to the ground roughness.

[0071] S230. Characteristically statistic the measured wind speed data of the wind flow field of the wind turbine through the Weibull probability density function to obtain the probability distribution of the wind speed at the vertical height of the blade surface of the wind turbine;

[0072] In this embodiment, the statistical characteristics of the wind speed frequency are to statistically analyze the distribution of the wind speed, that is, the expression of the different proportions of wind speeds of different magnitudes. The means used to fit the probability distribution of the measured wind speed within a certain period of time is the Weibull probability density function, as shown below:

[0073]

[0074] In the above formula, f(v) represents the probability of observing the wind speed v, k represents the dimensionless Weibull shape parameter, c represents the Weibull scale parameter, which has reference value in units of wind speed, and v represents the wind speed.

[0075] The cumulative probability function corresponding to the Weibull distribution is expressed as:

[0076]

[0077] The expressions of the variance and mathematical expectation of the Weibull function are shown in the following formula:

[0078]

[0079] Taking the natural logarithm twice of the above formula to determine the Weibull distribution function, we get:

[0080] ln{-ln[1 - F(v)]} = k ln(v) - k ln c

[0081] In the above formula, f(v) represents the probability of observing the wind speed v, k represents the dimensionless Weibull shape parameter (or factor), c represents the Weibull scale parameter, which has reference value in units of wind speed.

[0082] For most wind speed conditions, the k value is between 1.5 and 3.0. When k = 2, it is a special form of the Weibull distribution, the Rayleigh distribution. The probability density function of the Rayleigh distribution can be simplified as:

[0083]

[0084] The parameters k and c are closely related to the average value of the wind speed v m and the relationship is shown in the following formula:

[0085]

[0086] Γ(·) is a function of. By calculating the scale and shape parameters, two wind speeds meaningful for wind energy estimation can be obtained, namely the most probable wind speed and the wind speed carrying the maximum energy. The most probable wind speed represents the most common wind speed in a given wind probability distribution, and the expression of the most probable wind speed is shown in the following formula:

[0087]

[0088] In the above formula, k is the dimensionless Weibull shape parameter (or factor), and c is the Weibull scale parameter.

[0089] The wind speed carrying the maximum energy represents the wind speed with the maximum wind energy, and the expression is shown in the following formula:

[0090]

[0091] In the above formula, v MaxE represents the wind speed carrying the maximum energy.

[0092] S240. Determine the effective wind speed range of the wind turbine blade according to the calibrated cut-in wind speed, cut-out wind speed and extreme wind speed in the wind turbine;

[0093] S250. Combine the wind speed data at the vertical height of the wind turbine blade surface, the probability distribution of the wind speed at the vertical height of the wind turbine blade surface, and the effective wind speed range of the wind turbine blade to determine the probability distribution of the effective wind speed at the vertical height of the wind turbine blade wind wheel surface.

[0094] In this embodiment, the wind turbine data such as the vertical height of the wind turbine blade wind wheel, cut-out wind speed, cut-in wind speed, and safety wind speed of the calibrated wind turbine are used to determine the effective wind speed data in the vertical direction of the wind turbine blade wind wheel of the target wind turbine. First, query the calibrated cut-in wind speed v in and cut-out wind speed v out of the target wind turbine, extreme wind speed (safety wind speed), determine the effective wind speed range of the wind turbine blade, and then use the real-time data of the organized air flow field. After fitting and organizing, the wind speed data at each point in the vertical direction of the wind turbine blade wind wheel is deduced with the help of the wind profile expression. Finally, with the help of the Weibull distribution, the statistical characteristics of the wind speed frequency of the wind turbine blade wind wheel are calculated using the wind speed data at each point in the vertical direction of the wind wheel, and the mathematical expectation and variance of each wind speed within the effective wind speed range are obtained.

[0095] S300. Based on the probability distribution of the effective wind speed at the vertical height of the wind turbine blade airfoil surface, the load acting on the wind turbine blade by the wind speed is obtained through the improved blade element - momentum theory (BEM), and the aerodynamic load of the wind turbine blade under the effective wind speed is obtained.

[0096] S310. Modify the blade element - momentum theory to construct an improved blade element - momentum theory.

[0097] In this embodiment, the blade element - momentum theory is modified according to tip loss correction, hub loss correction, angle of attack correction, and thrust coefficient correction. Specifically, first, the tip loss and hub loss are corrected, and their expressions are:

[0098]

[0099] Further, the angle of attack is corrected. First is the blade thickness correction, and its expression is:

[0100]

[0101] Secondly is the blade width correction, and its expression is:

[0102]

[0103] In the above formula, t max represents the maximum thickness of the blade element airfoil, Δα 1 represents the change in the angle of attack caused by the influence of the blade width on the air flow direction, and Δα 2 represents the change in the angle of attack caused by the influence of the blade thickness on the air flow direction. The angle of attack after considering the change in the angle of attack correction is, and its expression is:

[0104] α = f - β + Δα 1 +Δα 2

[0105] Finally, the axial coefficient is corrected. When the wind turbine blade part enters the vortex ring state, the momentum equation is no longer applicable, and the momentum - blade element theory needs to be corrected with an empirical formula. The value of a in

[0106] will be corrected: When 0.2 < a ≤ 0.3539, the Glauert correction method is used for correction, and its expression is:

[0107]

[0108] Among them,

[0109] When a > 0.3539, the Wilson correction method is used for correction, and its expression is:

[0110]

[0111] When the axial induction factor a > 0.5, the blade bears a high load and is in a disturbed wake state. In this state, the momentum theory is no longer applicable, and an empirical formula needs to be used for correction. Its expression is:

[0112] C r = 4a(1 - a) = 0.6 + 0.61a + 0.79a 2

[0113] In the above formula, a represents the axial coefficient.

[0114] S320. Based on the improved blade element - momentum theory, perform blade element differential processing on the wind turbine blade to obtain a number of annular units, and the annular units are represented as blade elements;

[0115] S330. Integrate a number of annular units in the blade deployment direction according to a preset acting force to obtain the load acting on the wind wheel;

[0116] Specifically, the blade element differential theory is a calculation model for calculating the axial load under different wind speeds, wind wheel rotation speeds, and pitch angle configurations. Differentiate the pipe flow model, and perform differential processing on the blade. Each section is a d of a height r annular unit. These units are called blade elements (the blade elements are independent of each other). The acting force remains unchanged on the annular unit, and integrating the acting force in the blade deployment direction can obtain the load acting on the wind wheel. As Figure 3 shown, the momentum theory synthesizes and decomposes the wind speed and force on the blade element to obtain the blade element velocity vector relationship, as Figure 5 shown.

[0117] S340. Based on the probability distribution of the effective wind speed on the vertical height of the wind wheel surface of the wind turbine blade, perform momentum theory analysis on the load acting on the wind wheel to obtain the aerodynamic load of the wind turbine blade under the effective wind speed.

[0118] In this embodiment, the actual inflow wind speed v of the wind wheel is obtained by reverse synthesis of the wind speed direction and the rotation direction of the wind wheel. φ is the angle between the inflow velocity v and the wind wheel rotation plane, and the expression is shown as:

[0119] f = α + θ

[0120] In the above formula, α represents the angle of attack, and θ represents the pitch angle.

[0121] The drag dF D and lift dF L received by the blade element can be obtained by the following formula:

[0122]

[0123] In the above formula, C d represents the drag coefficient, C l represents the lift coefficient, c represents the chord length of the blade element profile, and ρ represents the local atmospheric density.

[0124] According to Figure 4 shown, the axial thrust dF a received by the blade element is:

[0125]

[0126] In the above formula, B represents the number of blades of the wind turbine blade, C d represents the drag coefficient, C l represents the lift coefficient, c represents the chord length of the blade element profile, and ρ represents the local atmospheric density.

[0127] The axial torque dM of the blade element is:

[0128]

[0129] To sum up, combining the blade element theory and the momentum theory, the axial induction factor a and the tangential induction factor b, the angular velocity w of the wind turbine rotor, are integrated and processed, and the combined equation is:

[0130]

[0131] C n and C t respectively represent the normal force coefficient and the tangential force coefficient of the blade element, and their expressions are as follows:

[0132]

[0133] C d and C l respectively represent the drag coefficient and the lift coefficient.

[0134] S400. For the aerodynamic load of the wind turbine blade based on the effective wind speed, predict the load spectrum of the wind power main shaft bearing according to the transmission path of the aerodynamic load of the blade - hub - main shaft - bearing, and obtain the prediction result of the load spectrum of the wind power main shaft bearing.

[0135] S410. Based on the aerodynamic load of the wind turbine blade under the effective wind speed, obtain the axial force received by the main shaft bearing of the wind turbine blade and the torque received by the main shaft bearing of the wind turbine blade;

[0136] S420. Combine the axial force on the main shaft bearing of the wind turbine blade and the torque on the main shaft bearing of the wind turbine blade, and predict the load spectrum of the wind power main shaft bearing according to the transmission path of the blade aerodynamic load - hub - main shaft - bearing, so as to obtain the prediction result of the load spectrum of the wind power main shaft bearing.

[0137] In this embodiment, for the statistical characteristic analysis of the load distribution of the wind power main shaft bearing and the bearing load frequency, according to the transmission path of the blade aerodynamic load - hub - main shaft - bearing, where the expression of the axial force on the main shaft bearing of the wind turbine blade is specifically as follows:

[0138]

[0139] In the above formula, F t represents the thrust on the main shaft bearing, η 1 represents the load transfer efficiency coefficient between the blade and the hub, η 2 represents the load transfer efficiency coefficient between the hub and the main shaft, η 3 represents the load transfer efficiency coefficient between the main shaft and the main shaft bearing, v represents the wind speed, K represents the number of blades, ρ represents the air density, S y represents the blade area, dS represents the blade area integral element, C D represents the drag coefficient, C L represents the lift coefficient, and φ represents the angle between the inflow velocity and the wind turbine rotation plane.

[0140] The expression of the torque on the main shaft bearing of the wind turbine blade is specifically as follows:

[0141]

[0142] In the above formula, M represents the torque on the main shaft bearing, δ 1 represents the torque transfer efficiency coefficient between the blade and the hub, δ 2 represents the torque transfer efficiency coefficient between the hub and the main shaft, δ 3 represents the torque transfer coefficient between the main shaft and the main shaft bearing, K represents the number of blades; r i represents the distance from the blade integral element to the blade rotation center, ρ represents the air density, S y represents the blade area, dS represents the blade area integral element, C D represents the drag coefficient, C L represents the lift coefficient, and φ represents the angle between the inflow velocity and the wind turbine rotation plane.

[0143] To sum up, as Figure 6As shown in the figure, in the embodiment of the present invention, the acquisition frequency is 10 minutes, and the measured wind speed data of the wind flow field of the wind turbine is collected. Subsequently, the effective wind speed data and the statistical characteristics of the wind speed frequency in the vertical direction of the blade wind wheel swept surface are obtained through the wind profile equation, the statistical characteristics of the wind speed frequency, the measured wind speed indexes such as the cut-in wind speed, cut-out wind speed, and safety wind speed calibrated by the target wind turbine, and the blade aerodynamic design parameters of the target wind turbine. Finally, the blade aerodynamic load is accurately calculated by correcting the tip loss, hub loss, angle of attack, and thrust coefficient of the blade element-momentum theory, and according to the load transfer path of the blade hub-main shaft-bearing, the load spectrum distribution of the wind power main shaft bearing and the statistical characteristics of the bearing load frequency are obtained, thereby improving the calculation accuracy and efficiency of the wind power generator main shaft bearing load spectrum.

[0144] Referring to Figure 2 , a prediction system for the load spectrum of a wind power main shaft bearing based on BEM, comprising:

[0145] The first module 201 is used to obtain the measured wind speed data of the wind flow field of the wind turbine based on a preset acquisition frequency, and the measured wind speed data of the wind flow field includes real-time full-time wind speed data, maximum wind speed data, and minimum wind speed data;

[0146] The second module 202 is used to perform characteristic analysis on the measured wind speed data of the wind flow field of the wind turbine based on the Weibull probability density function, and determine the probability distribution of the effective wind speed at the vertical height of the blade wind wheel surface of the wind turbine;

[0147] The third module 203 is used to obtain the load acting on the blade of the wind turbine by the wind speed based on the probability distribution of the effective wind speed at the vertical height of the blade wind wheel surface of the wind turbine through the improved blade element-momentum theory (BEM), and obtain the blade aerodynamic load of the wind power generation unit under the effective wind speed;

[0148] The fourth module 204 is used to predict the load spectrum of the wind power main shaft bearing based on the blade aerodynamic load of the wind power generation unit under the effective wind speed, and obtain the prediction result of the load spectrum of the wind power main shaft bearing according to the blade aerodynamic load-hub-main shaft-bearing transfer path.

[0149] The content in the above method embodiments is applicable to the system embodiments of the present invention. The functions specifically implemented by the system embodiments of the present invention are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0150] The above is a specific description of the preferred embodiments of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A wind turbine main shaft bearing load spectrum prediction method based on BEM, characterized in that: The following steps are involved: Acquire the wind speed data of the wind flow field of the wind turbine generator set based on the preset acquisition frequency, wherein the wind speed data of the wind flow field includes the real-time full-time wind speed data, the maximum wind speed data and the minimum wind speed data; Based on the Weibull probability density function, the wind speed data of the wind flow field of the wind turbine is analyzed to determine the probability distribution of the effective wind speed at the vertical height of the wind turbine blade rotor surface; Based on the probability distribution of effective wind speed at the vertical height of the wind turbine blade rotor surface, the load of wind speed on the wind turbine blade is obtained through the improved blade element-momentum theory (BEM), and the aerodynamic load of the wind turbine blade under effective wind speed is obtained. Based on the aerodynamic load of wind turbine blades under effective wind speed, the load spectrum of wind turbine main shaft bearing is predicted according to the transmission path of blade aerodynamic load-hub-main shaft-bearing, and the prediction result of wind turbine main shaft bearing load spectrum is obtained.

2. According to the BEM-based wind turbine main shaft bearing load spectrum prediction method of claim 1, it is characterized in that: The step of performing characteristic analysis on the wind flow field measured wind speed data of the wind turbine generator set based on the Weibull probability density function to determine the probability distribution of the effective wind speed at the vertical height of the wind turbine rotor blade surface specifically includes: Perform data fitting on the measured wind speed data of the wind flow field of the wind turbine and construct the wind contour equation; The wind contour equation is estimated in real time to obtain the wind speed data at the vertical height of the wind turbine blade surface; The characteristic statistics of the wind speed data measured in the wind flow field of the wind turbine are performed through the Weibull probability density function to obtain the probability distribution of the wind speed at the vertical height of the wind turbine blade surface; Determine the effective wind speed range of the wind turbine blades according to the calibrated cut-in wind speed, cut-out wind speed and extreme wind speed in the wind turbine set; The probability distribution of the effective wind speed at the vertical height of the wind turbine blade surface is determined by combining the wind speed data at the vertical height of the wind turbine blade surface, the probability distribution of the wind speed at the vertical height of the wind turbine blade surface and the effective wind speed range of the wind turbine blade.

3. The method for predicting the load spectrum of a wind turbine main shaft bearing based on BEM according to claim 2 is characterized in that: The expression of the Weibull probability density function is specifically as follows: In the above formula, f(v) represents the probability of observing wind speed v, k represents the dimensionless Weibull shape parameter, c represents the Weibull scale parameter, and v represents the wind speed.

4. The method for predicting the load spectrum of a wind turbine main shaft bearing based on BEM according to claim 3 is characterized in that: The step of obtaining the load of wind speed on the wind turbine blades based on the probability distribution of the effective wind speed at the vertical height of the wind turbine blade rotor surface by using the improved blade element-momentum theory (BEM) to obtain the aerodynamic load of the wind turbine blades under the effective wind speed specifically includes: Modify the blade element-momentum theory and construct an improved blade element-momentum theory; Based on the improved blade element-momentum theory, blade element differential processing is performed on the blades of the wind turbine to obtain a number of annular units, which are represented as blade elements. According to the preset force, several annular units are integrated in the direction of blade expansion to obtain the load acting on the wind wheel; Based on the probability distribution of effective wind speed at the vertical height of the wind turbine blade rotor surface, the momentum theory analysis of the load acting on the wind rotor is carried out to obtain the aerodynamic load of the wind turbine blade under the effective wind speed.

5. The method for predicting the load spectrum of a wind turbine main shaft bearing based on BEM according to claim 4 is characterized in that: The step of correcting the blade element-momentum theory and constructing an improved blade element-momentum theory specifically includes: Based on the tip loss coefficient, the blade element-momentum theory is corrected by the tip loss to obtain the first corrected blade element-momentum theory; Based on the hub loss coefficient, the hub loss correction is performed on the first corrected blade element-momentum theory to obtain the second corrected blade element-momentum theory; Based on the change in the angle of attack thickness and the change in the angle of attack width, the second corrected blade element-momentum theory is corrected in angle of attack to obtain a third corrected blade element-momentum theory; According to the thrust correction coefficient, the Glauert correction method, the Wilson correction method or the empirical correction method is selected to perform axial coefficient correction processing on the blade element-momentum theory after the third correction, and an improved blade element-momentum theory is constructed.

6. The method for predicting the load spectrum of a wind turbine main shaft bearing based on BEM according to claim 5, characterized in that: The step of predicting the load spectrum of the wind turbine main shaft bearing based on the aerodynamic load of the wind turbine blades at the effective wind speed according to the aerodynamic load-hub-main shaft-bearing transmission path to obtain the prediction result of the load spectrum of the wind turbine main shaft bearing specifically includes: Based on the aerodynamic load of the wind turbine blades at the effective wind speed, the axial force on the main shaft bearing of the wind turbine blades and the torque on the main shaft bearing of the wind turbine blades are obtained; Combining the axial force on the main shaft bearing of the wind turbine blade and the torque on the main shaft bearing of the wind turbine blade, the load spectrum of the wind turbine main shaft bearing is predicted according to the blade aerodynamic load-hub-main shaft-bearing transmission path, and the prediction result of the wind turbine main shaft bearing load spectrum is obtained.

7. A method for predicting wind turbine main shaft bearing load spectrum based on BEM according to claim 6, characterized in that: The expression of the axial force on the main shaft bearing of the wind turbine blade is specifically as follows: In the above formula, F t represents the thrust on the main shaft bearing, η1 represents the load transfer efficiency coefficient between the blade and the hub, η2 represents the load transfer efficiency coefficient between the hub and the main shaft, η3 represents the load transfer efficiency coefficient between the main shaft and the main shaft bearing, v represents the wind speed, K represents the number of blades, ρ represents the air density, S y represents the leaf area, dS represents the leaf area integral element, C D represents the drag coefficient, C L represents the lift coefficient, and φ represents the angle between the inflow velocity and the rotor rotation plane.

8. The method for predicting the load spectrum of a wind turbine main shaft bearing based on BEM according to claim 7, characterized in that: The expression of the torque borne by the main shaft of the wind turbine blade is specifically as follows: In the above formula, M represents the torque on the main shaft bearing, δ1 represents the torque transmission efficiency coefficient between the blade and the hub, δ2 represents the torque transmission efficiency coefficient between the hub and the main shaft, δ3 represents the torque transmission coefficient between the main shaft and the main shaft bearing, and K represents the number of blades; r i represents the distance between the blade integral element and the blade rotation center, ρ represents the air density, S y represents the leaf area, dS represents the leaf area integral element, C D represents the drag coefficient, C L represents the lift coefficient, and φ represents the angle between the inflow velocity and the rotor rotation plane.

9. A wind turbine main shaft bearing load spectrum prediction system based on BEM, characterized in that: Includes the following modules: The first module is used to obtain the wind flow field measured wind speed data of the wind turbine generator set based on a preset acquisition frequency, wherein the wind flow field measured wind speed data includes real-time full-time wind speed data, maximum wind speed data and minimum wind speed data; The second module is used to perform characteristic analysis on the wind speed data of the wind flow field of the wind turbine based on the Weibull probability density function, and determine the probability distribution of the effective wind speed at the vertical height of the wind turbine blade rotor surface; The third module is used to obtain the load of wind speed on the wind turbine blades based on the probability distribution of the effective wind speed at the vertical height of the wind turbine blade rotor surface through the improved blade element-momentum theory (BEM), and obtain the aerodynamic load of the wind turbine blades under the effective wind speed; The fourth module is used to predict the load spectrum of the wind turbine main shaft bearing based on the aerodynamic load of the wind turbine blades under the effective wind speed and the transmission path of the blade aerodynamic load-hub-main shaft-bearing, and obtain the prediction result of the load spectrum of the wind turbine main shaft bearing.

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