Life assessment method and device for pitch bearing of wind turbine generator

Abstract

The present disclosure provides a method and device for evaluating the life of a pitch bearing of a wind turbine generator. The life evaluation method includes: obtaining the probability density of the pitch driving torque in M historical time periods, where M is a positive integer; obtaining the angle cumulative value of the pitch angle in each of the M historical time periods; determining the equivalent load of the pitch bearing according to the pitch driving torque, its probability density in the M historical time periods, and the angle cumulative values in the M historical time periods; and determining the consumed life of the pitch bearing according to the equivalent load of the pitch bearing.

Description

Technical Field

[0001] The present disclosure relates to the field of wind power generation, and more particularly, to a method and device for evaluating the life of a pitch bearing of a wind turbine generator set. Background Art

[0002] The pitch bearing of a wind turbine generator set is an important component connecting the blade and the hub, and is also used to transfer the load of the blade to the pitch system on the hub. The pitch system is an important safety system of the wind turbine generator set. If a failure occurs, it may bring a devastating disaster. Therefore, during the operation of the wind turbine generator set, the life evaluation of the pitch bearing becomes particularly important and crucial.

[0003] In the related art, it is usually necessary to install load sensors, and based on the load of the pitch bearing collected by the load sensors, the life of the pitch bearing of the in-service wind turbine generator set is evaluated. However, this solution has a long installation cycle time and high capital consumption, and it requires a large capital cost and time cost to achieve full coverage of each unit in the wind farm. Summary of the Invention

[0004] Therefore, it is crucial for how to achieve the online life evaluation of the pitch bearing at low cost to reasonably estimate the load of the pitch bearing by using the parameters that can be directly obtained currently.

[0005] In one general aspect, there is provided a method for evaluating the life of a pitch bearing of a wind turbine generator set. The life evaluation method includes: obtaining the probability density of the pitch driving torque in M historical time periods, where M is a positive integer; obtaining the angle cumulative value of the pitch angle in each of the M historical time periods; determining the equivalent load of the pitch bearing according to the pitch driving torque, its probability density in the M historical time periods, and the angle cumulative values of the M historical time periods; and determining the consumed life of the pitch bearing according to the equivalent load of the pitch bearing.

[0006] Optionally, the obtaining the probability density of the pitch driving torque in M historical time periods includes: determining the occurrence frequency and the corresponding distribution parameters of the pitch driving torque in different pitch motion states according to the operation data of the M historical time periods, where the pitch motion states include a positive state, a constant state, and a negative state; and determining the probability density according to the frequency and the corresponding distribution parameters.

[0007] Optionally, determining the occurrence frequency and corresponding distribution parameters of the pitch driving torque in different pitch motion states according to the operation data of the M historical periods includes: for each historical period, determining the product of the correlation coefficient matrix, the first transfer function, and the operation data column vector to obtain a first column vector, where the number of rows of the correlation coefficient matrix is equal to the sum of the number of the occurrence frequency and the corresponding distribution parameters, and the operation data column vector is composed of multiple operation data; determining the sum of the first column vector and the first correlation coefficient column vector, and multiplying the sum by the second transfer function to obtain a second column vector; determining the sum of the second column vector and the second correlation coefficient column vector as the output vector, where the output vector includes the occurrence frequency and the corresponding distribution parameters, and the correlation coefficient matrix, the first correlation coefficient column vector, the second correlation coefficient column vector, the first transfer function, and the second transfer function are obtained through testing or training.

[0008] Optionally, before determining the occurrence frequency and corresponding distribution parameters of the pitch driving torque in different pitch motion states according to the operation data of the M historical periods, it further includes: collecting the operation data of the wind turbine generator set in the M historical periods; where the operation data includes output power, impeller speed, generator torque, x-direction component of nacelle acceleration, y-direction component of nacelle acceleration, and pitch angle.

[0009] Optionally, the number of the pitch driving torques is multiple, and determining the equivalent load of the pitch bearing according to the pitch driving torque, its probability density in the M historical periods, and the angle cumulative value in the M historical periods includes: for each historical period, determining the product of the m-th power of each pitch driving torque, the corresponding probability density of the pitch driving torque, and the angle cumulative value, and summing the products corresponding to the multiple pitch driving torques respectively to obtain the reference load of the corresponding historical period, where m is the Wahl coefficient of the material of the pitch bearing; determining the average value of the reference loads of the multiple historical periods; determining the 1 / m-th power of the average value of the reference loads as the equivalent load of the pitch bearing.

[0010] Optionally, the multiple pitch driving torques are obtained through the following steps: taking values at set step lengths for the pitch driving torque change interval to obtain the multiple pitch driving torques.

[0011] Optionally, the life evaluation method further includes: obtaining the estimated wind resource parameters of multiple machine positions in a target future period, where the estimated wind resource parameters include the estimated wind speed; determining the probability density of the pitch driving torque at multiple estimated wind speeds and the estimated angle accumulation value of the pitch angle within the target future period according to the estimated wind resource parameters; determining the estimated equivalent load of the pitch bearing in the target future period according to the multiple estimated wind speeds, the pitch driving torque, its probability density at the multiple estimated wind speeds, and the estimated angle accumulation values at the multiple estimated wind speeds; determining the estimated consumed life of the pitch bearing according to the estimated equivalent load; and determining the estimated remaining life of the pitch bearing according to the designed life, the consumed life, and the estimated consumed life of the pitch bearing.

[0012] Optionally, the estimated wind resource parameters further include turbulence intensity, wind shear, and air density.

[0013] Optionally, the step of determining the estimated equivalent load of the pitch bearing in the target future period according to the multiple estimated wind speeds, the pitch driving torque, its probability density at the multiple estimated wind speeds, and the estimated angle accumulation values at the multiple estimated wind speeds includes: determining the probability density of the multiple estimated wind speeds; for each pitch driving torque at each of the estimated wind speeds, determining the product of the probability density of the estimated wind speed, the m-th power of the pitch driving torque, the probability density of the pitch driving torque, and the estimated angle accumulation value, and summing all the products to obtain an estimated reference load, where m is the material Wahl coefficient of the pitch bearing; and determining the 1 / m-th power of the estimated reference load as the estimated equivalent load.

[0014] In another general aspect, a life evaluation device for a pitch bearing of a wind turbine generator is provided. The life evaluation device includes: a first acquisition unit configured to acquire the probability density of the pitch driving torque in M historical periods, where M is a positive integer; the first acquisition unit is further configured to acquire the angle accumulation value of the pitch angle within each of the M historical periods; an equivalent unit configured to determine the equivalent load of the pitch bearing according to the pitch driving torque, its probability density in the M historical periods, and the angle accumulation values in the M historical periods; and a first calculation unit configured to determine the consumed life of the pitch bearing according to the equivalent load of the pitch bearing.

[0015] Optionally, the first acquisition unit is further configured to: collect the operation data of the wind turbine generator set in the M historical time periods; determine the occurrence frequency and corresponding distribution parameters of the pitch driving torque in different pitch motion states according to the operation data of the M historical time periods, where the pitch motion states include a forward state, a constant state, and a negative state; determine the probability density according to the frequency and the corresponding distribution parameters.

[0016] Optionally, the first acquisition unit is further configured to: for each historical time period, determine the product of the correlation coefficient matrix, the first transfer function, and the operation data column vector to obtain a first column vector, where the number of rows of the correlation coefficient matrix is equal to the sum of the occurrence frequency and the corresponding distribution parameters, and the operation data column vector is composed of multiple operation data; determine the sum of the first column vector and the first correlation coefficient column vector, and multiply the sum by the second transfer function to obtain a second column vector; determine the sum of the second column vector and the second correlation coefficient column vector as the output vector, where the output vector includes the occurrence frequency and the corresponding distribution parameters, and the correlation coefficient matrix, the first correlation coefficient column vector, the second correlation coefficient column vector, the first transfer function, and the second transfer function are obtained through testing or training.

[0017] Optionally, the first acquisition unit is further configured to: collect the operation data of the wind turbine generator set in the M historical time periods; where the operation data includes output power, impeller speed, generator torque, x-direction component of nacelle acceleration, y-direction component of nacelle acceleration, and pitch angle.

[0018] Optionally, the number of the pitch driving torques is multiple, and the equivalent unit is further configured to: for each historical time period, determine the product of the m-th power of each pitch driving torque, the probability density of the corresponding pitch driving torque, and the angle cumulative value, and sum the products corresponding to the multiple pitch driving torques to obtain the reference load of the corresponding historical time period, where m is the material Wall coefficient of the pitch bearing; determine the average value of the reference loads of the multiple historical time periods; determine the 1 / m-th power of the average value of the reference loads as the equivalent load of the pitch bearing.

[0019] Optionally, the multiple pitch driving torques are obtained through the following steps: take values at a set step length for the pitch driving torque change interval to obtain multiple pitch driving torques.

[0020] Optionally, the life evaluation device further includes: a second acquisition unit configured to acquire the estimated wind resource parameters of a plurality of machine positions in a target future period, where the estimated wind resource parameters include the estimated wind speed; a determination unit configured to determine, according to the estimated wind resource parameters, the probability density of the pitch driving torque at a plurality of estimated wind speeds and the estimated angle accumulation value of the pitch angle within the target future period; an estimation unit configured to determine the estimated equivalent load of the pitch bearing in the target future period according to the plurality of estimated wind speeds, the pitch driving torque, its probability density at the plurality of estimated wind speeds, and the estimated angle accumulation value at the plurality of estimated wind speeds; a second calculation unit configured to determine the estimated consumed life of the pitch bearing in the target future period according to the estimated equivalent load; the second calculation unit is further configured to determine the estimated remaining life of the pitch bearing according to the design life of the pitch bearing, the consumed life, and the estimated consumed life.

[0021] Optionally, the estimated wind resource parameters further include turbulence intensity, wind shear, and air density.

[0022] Optionally, the estimation unit is further configured to determine the probability density of the plurality of estimated wind speeds; for each pitch driving torque at each of the estimated wind speeds, determine the product of the probability density of the estimated wind speed, the m-th power of the pitch driving torque, the probability density of the pitch driving torque, and the estimated angle accumulation value, and sum all the products to obtain an estimated reference load, where m is the material Wahl coefficient of the pitch bearing; determine the 1 / m-th power of the estimated reference load as the estimated equivalent load.

[0023] In another general aspect, there is provided a computer-readable storage medium, which when the instructions in the computer-readable storage medium are run by at least one processor, cause the at least one processor to execute the life evaluation method as described above.

[0024] In another general aspect, there is provided a computer device, including: at least one processor; at least one memory storing computer-executable instructions, where the computer-executable instructions, when run by the at least one processor, cause the at least one processor to execute the life evaluation method as described above.

[0025] The present disclosure utilizes the operating data that is inherently collected during the operation of the unit to achieve the life assessment of the pitch bearing, eliminating the need for additional data collection and thus eliminating the need to configure additional data collection sensors. This can reduce the product cost and save the time cost of life assessment. Within an acceptable accuracy range, the present disclosure realizes real-time online assessment of the consumed life and prediction of the remaining life in the future. It can perform predictive maintenance and prediction of failure events on the premise of ensuring the safe operation of the wind turbine generator set, thereby reducing the unplanned downtime of the wind turbine generator set and improving its economic benefits.

[0026] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flowchart showing a method for life assessment of a pitch bearing of a wind turbine generator set according to an embodiment of the present disclosure.

[0028] Figure 2 is a schematic flowchart showing an online life assessment system for a pitch bearing according to an embodiment of the present disclosure.

[0029] Figure 3 is a schematic flowchart showing a remaining life prediction system for a pitch bearing according to an embodiment of the present disclosure.

[0030] Figure 4 is a block diagram showing a life assessment device for a pitch bearing of a wind turbine generator set according to an embodiment of the present disclosure.

[0031] Figure 5 is a block diagram showing a life assessment device for a pitch bearing of a wind turbine generator set according to another embodiment of the present disclosure.

[0032] Figure 6 is a block diagram showing a computer device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The following detailed description is provided to assist the reader in obtaining a comprehensive understanding of the methods, devices, and / or systems described herein. However, various changes, modifications, and equivalents of the methods, devices, and / or systems described herein will be apparent after understanding the disclosure of the present application. For example, the order of operations described herein is merely exemplary and is not limited to those set forth herein, but may be changed as will be apparent after understanding the disclosure of the present application, except for operations that must occur in a specific order. In addition, descriptions of features known in the art may be omitted for greater clarity and conciseness.

[0034] The features described herein can be implemented in various forms and should not be construed as limited to the examples described herein. On the contrary, the examples described herein are provided only to illustrate some of the many viable ways of implementing the methods, devices, and / or systems described herein, which will be apparent after understanding the disclosure of the present application.

[0035] As used herein, the term "and / or" includes any one of the associated listed items and any combination of any two or more of them.

[0036] Although terms such as "first", "second", and "third" may be used herein to describe various components, elements, regions, layers, or parts, these components, elements, regions, layers, or parts should not be limited by these terms. On the contrary, these terms are only used to distinguish one component, element, region, layer, or part from another. Thus, a first component, first element, first region, first layer, or first part as referred to in the examples described herein may also be referred to as a second component, second element, second region, second layer, or second part without departing from the teachings of the examples.

[0037] In the specification, when an element (such as a layer, region, or substrate) is described as "on" another element, "connected to" or "coupled to" another element, the element may be directly "on" the other element, directly "connected to" or "coupled to" the other element, or there may be one or more other elements in between. On the contrary, when an element is described as "directly on" another element, "directly connected to" or "directly coupled to" another element, there may be no other elements in between.

[0038] The terms used herein are only for describing various examples and are not intended to limit the disclosure. Unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. The terms "comprising", "including", and "having" specify the presence of the recited features, quantities, operations, components, elements, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof.

[0039] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in a general dictionary) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and should not be interpreted in an idealized or overly formal manner.

[0040] In addition, in the description of the examples, when a detailed description of related structures or functions that are considered to be well-known will cause an ambiguous interpretation of the present disclosure, such a detailed description will be omitted.

[0041] Figure 1 is a flowchart showing a method for evaluating the life of a pitch bearing of a wind turbine according to an embodiment of the present disclosure. The life evaluation method can be implemented relying on a life evaluation system, and the life evaluation system can further include an on-line life evaluation system for evaluating the consumed life of the pitch bearing, and a remaining life prediction system for predicting the future remaining life of the pitch bearing. Figure 2 is a schematic flowchart showing an on-line life evaluation system of a pitch bearing according to an embodiment of the present disclosure. Figure 3 is a schematic flowchart showing a remaining life prediction system of a pitch bearing according to an embodiment of the present disclosure.

[0042] Referring to Figure 1 , in step S101, the probability density of the pitch driving torque in M historical periods is obtained, where M is a positive integer. Since the pitch driving torque affects the wear of the pitch bearing, this parameter is selected to implement the life evaluation of the pitch bearing. At the same time, the cumulative operating duration of the wind turbine often reaches several years. By dividing the cumulative operating duration into M historical periods according to a certain step size (for example, 10 minutes), data processing can be performed for each historical period respectively to ensure that the duration of a single historical period used in the evaluation of different wind turbines is the same, so as to ensure the universality of the strategy.

[0043] Optionally, the pitch driving torque usually varies within a certain range. For example, for a wind turbine with a rated capacity less than 5 MW, its pitch driving torque is generally in the range of [-200 KNm, 200 KNm]. Multiple specific values can be selected from it, for example, taking values according to a set step size, to obtain multiple specific pitch driving torques. That is to say, the multiple selected pitch driving torques can form an arithmetic sequence, and then in step S101, the probability density of each pitch driving torque in each historical period is obtained.

[0044] Referring to Figure 2 , step S101 specifically includes: according to the operation data of M historical periods, determining the occurrence frequency and corresponding distribution parameters of the pitch driving torque in different pitch motion states (it should be understood that the corresponding here means that the distribution parameters correspond to the occurrence frequency and also correspond to the pitch motion state), and the pitch motion states include a positive state, a constant state, and a negative state; determining the probability density according to the frequency and the corresponding distribution parameters. According to an embodiment of the present disclosure, the operation data of the wind turbine is provided by, for example Figure 2Collected by the data acquisition system shown. Accordingly, before performing step S101, it further includes the step of collecting the operation data of the wind turbine generator set in M historical time periods. The operation data is specifically SCADA (Supervisory Control And Data Acquisition) data, including output power, impeller speed, generator torque, x-direction component of nacelle acceleration, y-direction component of nacelle acceleration, and pitch angle, which can fully reflect the operation of the unit, and these conditions are related to the pitch driving torque. Therefore, as Figure 2 shown, the pitch driving torque distribution evaluation module can determine the distribution parameters of the pitch driving torque accordingly. In addition, since these are the data that will be collected during the operation of the unit itself and no additional data collection is required, there is no need to configure additional data collection sensors, which can reduce the product cost and save the time cost of life evaluation, improving the economic performance.

[0045] According to the positive, negative, and constant three states of blade pitching, three different pitch movement states can be obtained. And in each historical time period, the pitch driving torque may appear in these three states simultaneously. Therefore, the occurrence frequencies and corresponding distribution parameters of the pitch driving torque in the three pitch movement states can be determined, so as to more accurately describe the pitch driving torque.

[0046] As an example, for a historical time period, the pitch movement usually only has the above three states, so the sum of the occurrence frequencies of the three is 1, and the occurrence frequencies are specifically:

[0047]

[0048] Based on this, the pitch driving torque L i The probability density within a certain historical time period t can be described as:

[0049] p t (L i ) = f 1,t * f t,1 (L i ) + (1 - f 1,t - f 2,t ) * f t,3 (L i ) + f 2,t * f t,2 (L i )

[0050] Among them, f t,1 (L i ), f t,3 (L i ), f t,2 (L iare the pitch driving torques L respectively i The probability density under three pitch motion states. It should be noted that since both the probability density and the occurrence frequency are mainly represented by the letter f, for the sake of distinction, the subscript t is placed in front of and behind the number respectively. Taking the probability density obeying the normal distribution as an example, the probability density can be represented by the mathematical expectation μ and the standard deviation σ of the variable (here is the pitch driving torque L i ) that is, it can be expressed as:

[0051]

[0052] Among them, is the distribution parameter of the pitch driving torque L i under three pitch motion states within the historical period t. The subscript f t,1 , f t,2 , f t,3 represent the positive, negative, and constant states respectively. That is to say, in this example, the probability density p i of the pitch driving torque L t (L i ) can be obtained using f 1,t , f 2,t , these 8 parameters.

[0053] Optionally, when determining the occurrence frequency and the corresponding distribution parameters of the pitch driving torque under different pitch motion states based on the operation data, for the convenience of data calculation, the operation data of each historical period collected can be formed into a multi-dimensional vector, denoted as the operation data column vector. For example, for the aforementioned multiple operation data, the output power Pwr, the impeller speed r, the generator torque T, the x-direction component Ax of the nacelle acceleration, the y-direction component Ay of the nacelle acceleration, and the pitch angle Pa can form a 6-dimensional vector [Pwr t , r t , T t , Ax t , Ay t , Pa t , where the subscript t represents a certain historical period. The calculation targets, that is, the occurrence frequency and the distribution parameters, can also form a multi-dimensional vector. For the aforementioned example, it is

[0054] Correspondingly, the above-mentioned occurrence frequency and the corresponding distribution parameters can be determined using the following equation.

[0055]

[0056] Specifically, it is to first determine the correlation coefficient matrix a for each historical period i,j, the product of the first transfer function G0 and the column vector of operating data, to obtain a first column vector, where the number of rows of the correlation coefficient matrix is equal to the sum of the occurrence frequencies and the corresponding distribution parameters, and the column vector of operating data is composed of multiple operating data. In this example, the number of rows of the correlation coefficient matrix can be 8. That is, the product of a row of coefficients in the correlation coefficient matrix a i,j and the first transfer function G0 can be used as a weight to calculate the weighted sum of multiple operating data, so as to converge multiple operating data into one data. Finally, as many weighted sums as the number of parameters to be output are obtained. Then, determine the sum of the first column vector (composed of the aforementioned multiple weighted sums) and the first correlation coefficient column vector b i and multiply it by the second transfer function G1 to obtain a second column vector. Next, determine the sum of the second column vector and the second correlation coefficient column vector c i which is equivalent to making multiple changes to the aforementioned weighted sums and is used as the output vector. The output vector includes the occurrence frequencies and the corresponding distribution parameters, completing the data conversion. According to the embodiments of the present disclosure, the correlation coefficient matrix, the first correlation coefficient column vector, the second correlation coefficient column vector, the first transfer function, and the second transfer function are obtained through testing or training. It should be understood that the subscript i of each correlation coefficient here represents the serial number of the specific coefficient and has nothing to do with the subscript i of the pitch driving torque.

[0057] Return reference Figure 1 , in step S102, obtain the angle cumulative value of the pitch angle in each historical period among M historical periods. The angle cumulative value is obtained by the pitch experience accumulation module as shown in Figure 2 . Substantially, pitching is that the blade rotates through a certain angle under the action of the pitch driving torque. Therefore, the pitch driving torque accumulates in the dimension of the pitch angle, which can comprehensively reflect the load borne by the pitch bearing. By obtaining the above angle cumulative value, it can be used as the basis for evaluating the life of the pitch bearing.

[0058] Optionally, within one historical period, the blade may first pitch forward and then pitch backward, resulting in the cancellation of the pitch angle. Therefore, the angle cumulative value of the pitch angle can be gradually counted according to the set frequency. As an example, the following equation can be used:

[0059]

[0060] where i = 1, 2,..., N, N = 600, indicating that the sampling frequency of the data is 1 Hz, and φ i is the pitch angle at the i-th time within the historical period.

[0061] In step S103, determine the equivalent load of the pitch bearing according to the pitch driving torque, its probability density in M historical periods, and the angle cumulative values in M historical periods. The equivalent load of the pitch bearing is obtained by the Figure 2The pitch bearing's consumed life assessment module shown above is obtained. As described above, by accumulating the pitch driving torque in the dimension of the pitch angle, the equivalent load of the pitch bearing can be obtained.

[0062] Optionally, step S103 specifically includes: for each historical period, determining the product of the m-th power of each pitch driving torque, the corresponding probability density of the pitch driving torque, and the angle cumulative value, and summing the products corresponding to each of the multiple pitch driving torques to obtain the reference load for the corresponding historical period, where m is the Wahl coefficient of the material of the pitch bearing; determining the average value of the reference loads for multiple historical periods; determining the 1 / m-th power of the average value of the reference loads as the equivalent load of the pitch bearing.

[0063] For the pitch system of an in-service operating unit that has experienced M historical periods, the probability densities of N pitch driving torques can be summarized to obtain the following history matrix:

[0064]

[0065] Among them, taking the pitch driving torque L1 as an example, the column of data below it is the probability density of the pitch driving torque L1 in each historical period.

[0066] The angle cumulative values of the pitch angles for M historical periods can be summarized to obtain the following history matrix:

[0067]

[0068] Similar to the previous history matrix, taking the pitch driving torque L1 as an example, the column of data below it is the angle cumulative value of the pitch angle corresponding to the pitch driving torque L1 in each historical period. The difference is that since the magnitude of the angle cumulative value is fixed within a historical period and does not change with the pitch driving torque, the angle cumulative values in each row (i.e., each historical period) are the same.

[0069] The equivalent load of the pitch bearing is:

[0070]

[0071] In step S104, according to the equivalent load of the pitch bearing, determine the consumed life of the pitch bearing. The consumed life is obtained by the pitch bearing consumed life assessment module shown as Figure 2 above. Specifically, first determine the ratio of the equivalent load of the pitch bearing to the design equivalent load, and then determine the product of this ratio and the design life of the pitch bearing as the consumed life of the pitch bearing. It can be expressed as the following equation:

[0072]

[0073] Among them, is the design equivalent load of the pitch bearing, lifetime design is the design life of the pitch bearing.

[0074] Optionally, the life assessment method according to an embodiment of the present disclosure may further include: obtaining estimated wind resource parameters of a plurality of machine positions in a target future period, the estimated wind resource parameters including an estimated wind speed; determining the probability density of the pitch driving torque at a plurality of estimated wind speeds and the estimated angle accumulation value of the pitch angle within the target future period according to the estimated wind resource parameters; determining the estimated equivalent load of the pitch bearing in the target future period according to the plurality of estimated wind speeds, the pitch driving torque, its probability density at the plurality of estimated wind speeds, and the estimated angle accumulation values at the plurality of estimated wind speeds; determining the estimated consumed life of the pitch bearing according to the estimated equivalent load; and determining the estimated remaining life of the pitch bearing according to the design life, the consumed life, and the estimated consumed life of the pitch bearing.

[0075] For the pitch bearing of an operating wind turbine generator, the consumed life can be obtained through the pitch bearing online life assessment system as shown in Figure 2 . For the remaining life, although it is impossible to obtain the future operation data of the wind turbine generator in advance, the subsequent operation of the unit is affected by the wind resource conditions of the future wind farm. By obtaining the estimated wind resource parameters of a plurality of machine positions in the target future period, the estimated equivalent load of the pitch bearing in the target future period can be estimated based on this, and then the estimation of the consumed life in the future target period can be completed. Finally, by combining it with the design life and the consumed life, the estimated remaining life of the pitch bearing at the end of the target future period can be obtained. Since the current wind resource analysis technology already has the ability to predict the wind resource parameters in the next few years, the data acquisition system and the wind resource statistical analysis module as shown in Figure 3 can be used to obtain the estimated wind resource parameters of the future target period. Therefore, no additional data acquisition is required, and there is no need to configure additional data acquisition sensors, which can reduce the product cost and save the time cost of life assessment, improving the economic performance.

[0076] Specifically, the pitch bearing remaining life prediction system may use the estimated wind resource parameters as input. First, the pitch driving torque distribution estimation module as shown in Figure 3 estimates the probability density of the pitch driving torque (which can be the same as the pitch driving torque in step S101) at a plurality of estimated wind speeds, and the pitch experience cumulative angle prediction module as shown in Figure 3 estimates the estimated angle accumulation value of the pitch angle within the target future period. The estimation method can refer to step S101 and is realized by using a transfer function and a correlation coefficient.

[0077] As an example, the estimated wind speed v is the annual average wind speed. The estimated wind resource parameters also include the turbulence intensity ti, the wind shear α, and the air density ρ. For a wind farm, each estimated wind speed v may occur with different attribute values (such as the turbulence intensity ti, the wind shear α, etc.). The inputs of the pitch driving torque distribution estimation module and the pitch experience cumulative angle prediction module (i.e., the estimated wind resource parameters) are a single estimated wind speed v k and its attribute values, and the air density ρ is added as a constant attribute value to the estimated wind resource parameters, that is, [v k , ti k , α k , ρ k is the input vector, where the subscript k represents the serial number of the estimated wind speed.

[0078] For the pitch driving torque distribution estimation module, the division of the pitch motion state under a single estimated wind speed is the same as described above. The three pitch motion states are divided according to the positive, negative, and constant states of the blade pitch. The output can still be the distribution parameters of the three pitch motion states and the corresponding occurrence frequencies respectively. Then, using the distribution parameters and occurrence frequencies of the pitch driving torque under the estimated wind speed, the probability density distribution of the pitch driving torque under the estimated wind speed is determined. For the pitch driving torque L i The probability density at a certain estimated wind speed v is:

[0079] p v (L i ) = f 1,v * f v,1 (L i ) + (1 - f 1,v - f 2,v ) * f v,3 (L i ) + f 2,v * f v,2 (L i )

[0080] Under a single estimated wind speed, the pitch driving torque may simultaneously appear in three states, and the corresponding occurrence frequencies are f 1,v , 1 - f 1,v - f 2,v , f 2,v . The pitch driving torques in the three states each have their own probability density, which are f v,1 (L i ), f v,3 (L i ), f v,2 (L i ). Similar to p t (L i) Since both the probability density and the occurrence frequency are mainly represented by the letter f, for the sake of distinction here, the subscript v is placed in front of and behind the number respectively. In addition, within the reference historical period t, the pitch driving torque L i The probability density under three pitch motion states. Here, at the estimated wind speed v, the pitch driving torque L i The probability density f under three pitch motion states v,1 (L i )、f v,3 (L i )、f v,2 (L i ) may also follow a normal distribution, so it can also be represented by the mathematical expectation μ and the standard deviation σ of the pitch driving torque L i , that is, the distribution parameters are The above-mentioned occurrence frequency and the corresponding distribution parameters can be determined by using the following equation.

[0081]

[0082] Among them, F1 and F0 are transfer functions, and a m,n , b m , c n are correlation coefficients. The transfer functions and correlation coefficients can be obtained through training in the simulation database. Substitute the obtained occurrence frequency and distribution parameters into the aforementioned expression of p v (L i ), and the p v (L i ) can be obtained.

[0083] For the pitch experience cumulative angle prediction module, when predicting the predicted angle accumulation value of the pitch angle at a single estimated wind speed, its prediction equation is as follows:

[0084]

[0085] Among them, Q1 and Q0 are transfer functions, and a1, a2, a3, a4, b1, c1 are correlation coefficients. The transfer functions and correlation coefficients can be obtained through training in the simulation database.

[0086] After determining the distribution parameters of the pitch driving torque at multiple estimated wind speeds and the predicted angle accumulation value of the pitch angle in the target future period, a method similar to the aforementioned step S103 can be adopted. From the pitch bearing consumed life evaluation module shown in Figure 3 , the predicted equivalent load and the predicted consumed life are estimated, and finally, from the pitch bearing remaining life prediction module shown in Figure 3 , the predicted remaining life is estimated.

[0087] Optionally, the steps of progressively determining the estimated equivalent load, the estimated consumed life, and the estimated remaining life include: determining the probability density of a plurality of estimated wind speeds; for each pitch driving torque at each estimated wind speed, determining the product of the probability density of the estimated wind speed, the m-th power of the pitch driving torque, the probability density of the pitch driving torque, and the estimated angle accumulation value, and summing all the products to obtain an estimated reference load, where m is the Wahl coefficient of the material of the pitch bearing; determining the 1 / m-th power of the estimated reference load as the estimated equivalent load.

[0088] For the pitch system of a wind turbine generator for a target future time period T, the pitch driving torque is divided into N, and the estimated equivalent load of the pitch bearing is:

[0089]

[0090] where f(v) is the probability density of the estimated wind speed, which is a Rayleigh distribution and is only related to the annual average wind speed.

[0091] Then the estimated remaining life of the pitch bearing in the target future time period T is:

[0092] l remain = lifetime design -l cost -l pred

[0093] where l cost is the consumed life, obtained from the foregoing step S104, and l pred is the estimated consumed life within the target future time period T, satisfying:

[0094]

[0095] According to the life assessment method of the pitch bearing of a wind turbine generator according to an embodiment of the present disclosure, by using the operation data that will be collected during the operation of the unit itself and the estimated wind resource parameters that can be predicted based on the current technology, the life assessment of the pitch bearing is realized, without the need for additional data collection, and thus there is no need to configure additional data collection sensors, which can reduce the product cost and save the time cost of life assessment. Within the acceptable range of accuracy, the present disclosure realizes real-time online assessment of the consumed life and prediction of the remaining life in the future, and can perform predictive maintenance and prediction of failure events on the premise of ensuring the safe operation of the wind turbine generator, thereby reducing the unplanned downtime of the wind turbine generator and improving its economic benefits.

[0096] Figure 4 is a block diagram showing a life assessment device for a pitch bearing of a wind turbine generator according to an embodiment of the present disclosure.

[0097] Refer to Figure 4, the life evaluation device 400 of the pitch bearing of a wind turbine includes a first acquisition unit 401, an equivalent unit 402, and a first calculation unit 403, corresponding to the on-line life evaluation system of the pitch bearing as shown in Figure 2 shown.

[0098] The first acquisition unit 401 can acquire the probability density of the pitch driving torque in M historical periods, where M is a positive integer. Since the pitch driving torque affects the wear of the pitch bearing, this parameter is selected to realize the life evaluation of the pitch bearing. At the same time, the cumulative operation duration of the wind turbine often reaches several years. By dividing the cumulative operation duration into M historical periods according to a certain step size, data processing can be performed for each historical period respectively to ensure that the duration of a single historical period used in the evaluation of different wind turbines is the same, so as to ensure the universality of the strategy.

[0099] Optionally, the pitch driving torque usually varies within a certain range. Multiple specific values can be selected from it, for example, taking values according to a set step size to obtain multiple specific pitch driving torques. That is to say, the selected multiple pitch driving torques can form an arithmetic sequence, and then the probability density of each pitch driving torque in each historical period is acquired.

[0100] Referring to Figure 2 , the first acquisition unit 401 can specifically determine the occurrence frequency and corresponding distribution parameters of the pitch driving torque in different pitch motion states according to the operation data of M historical periods (it should be understood that the corresponding here means that the distribution parameters correspond to the occurrence frequency and also correspond to the pitch motion state). The pitch motion states include a positive state, a constant state, and a negative state; according to the frequency and the corresponding distribution parameters, the probability density is determined. According to an embodiment of the present disclosure, the operation data of the wind turbine is acquired by the data acquisition system as shown in Figure 2 shown. Correspondingly, before determining the occurrence frequency and the corresponding distribution parameters, the first acquisition unit 401 can also acquire the operation data of the wind turbine in M historical periods. The operation data is specifically SCADA data, including output power, impeller speed, generator torque, x-direction component of nacelle acceleration, y-direction component of nacelle acceleration, and pitch angle, which can fully reflect the operation of the unit. And these conditions are related to the pitch driving torque. Therefore, the pitch driving torque distribution evaluation module as shown in Figure 2 can determine the distribution parameters of the pitch driving torque accordingly. In addition, since these are the data that will be acquired during the operation of the unit itself and no additional data acquisition is required, there is no need to configure additional data acquisition sensors, which can reduce the product cost and save the time cost of life evaluation, and improve the economic performance.

[0101] According to the positive, negative and constant states of blade pitch, three different pitch motion states can be obtained. In each historical period, the pitch drive torque may appear in these three states at the same time. Therefore, the occurrence frequency and corresponding distribution parameters of the pitch drive torque in the three pitch motion states can be determined, so as to more accurately describe the pitch drive torque.

[0102] Optionally, the first acquisition unit 401 may first determine the product of the correlation coefficient matrix, the first transfer function, and the operation data column vector for each historical period to obtain a first column vector, wherein the number of rows of the correlation coefficient matrix is ​​equal to the sum of the frequency of occurrence and the number of corresponding distribution parameters, and the operation data column vector is composed of multiple operation data. Then determine the sum of the first column vector and the first correlation coefficient column vector, and multiply it with the second transfer function to obtain the second column vector, and determine the sum of the second column vector and the second correlation coefficient column vector, which is equivalent to making multiple changes to the aforementioned weighted sum as the output vector. The output vector includes the frequency of occurrence and the corresponding distribution parameters, and the data conversion is completed. According to an embodiment of the present disclosure, the correlation coefficient matrix, the first correlation coefficient column vector, the second correlation coefficient column vector, the first transfer function, and the second transfer function are obtained by testing or training.

[0103] The first acquisition unit 401 can also acquire the angle accumulation value of the pitch angle in each of the M historical periods. The angle accumulation value is calculated as follows: Figure 2 The pitch experience accumulation module shown in the figure is obtained. Pitch is essentially the blade turning a certain angle under the action of the pitch drive torque. Therefore, the accumulation of the pitch drive torque in the dimension of the pitch angle can fully reflect the load borne by the pitch bearing. By obtaining the above angle accumulation value, it can be used as a basis for evaluating the life of the pitch bearing.

[0104] The equivalent unit 402 can determine the equivalent load of the pitch bearing according to the pitch drive torque and its probability density in M ​​historical time periods and the angle accumulation value in M ​​historical time periods. The equivalent load of the pitch bearing is given by Figure 2 The variable pitch bearing has been consumed by the life assessment module. As mentioned above, by accumulating the variable pitch drive torque in the dimension of the variable pitch angle, the equivalent load of the variable pitch bearing can be obtained.

[0105] Optionally, the equivalent unit 402 can be specifically executed as follows: for each historical time period, determine the mth power of each pitch drive torque, the probability density of the corresponding pitch drive torque, and the product of the angle cumulative value, and sum the corresponding products of multiple pitch drive torques to obtain the reference load of the corresponding historical time period, where m is the material Wall coefficient of the pitch bearing; determine the average value of the reference load for multiple historical time periods; determine the 1 / mth power of the reference load average value as the equivalent load of the pitch bearing.

[0106] The first calculation unit 403 can determine the consumed life of the pitch bearing according to the equivalent load of the pitch bearing. The consumed life is obtained by the pitch bearing consumed life evaluation module as shown in Figure 2 . Specifically, the ratio of the equivalent load of the pitch bearing to the design equivalent load can be determined first, and then the product of this ratio and the design life of the pitch bearing can be determined as the consumed life of the pitch bearing.

[0107] Figure 5 FIG. is a block diagram showing a life evaluation device for a pitch bearing of a wind turbine according to another embodiment of the present disclosure.

[0108] Referring to Figure 5 , the life evaluation device 500 for the pitch bearing of the wind turbine includes a first acquisition unit 501, an equivalent unit 502, a first calculation unit 503, a second acquisition unit 504, a determination unit 505, an estimation unit 506, and a second calculation unit 507. Among them, the first acquisition unit 501, the equivalent unit 502, and the first calculation unit 503 correspond to the pitch bearing online life evaluation system as shown in Figure 2 , and their executed actions are the same as those of the first acquisition unit 401, the equivalent unit 402, and the first calculation unit 403, which will not be elaborated here. The second acquisition unit 504, the determination unit 505, the estimation unit 506, and the second calculation unit 507 correspond to the pitch bearing remaining life prediction system as shown in Figure 3 .

[0109] The second acquisition unit 504 can acquire the estimated wind resource parameters at multiple machine positions in the target future period. The estimated wind resource parameters include the estimated wind speed. As an example, the estimated wind speed is the annual average wind speed. The estimated wind resource parameters also include the turbulence intensity, wind shear, and air density. For a wind farm, each estimated wind speed may occur and has different attribute values (turbulence intensity, wind shear, etc.). The inputs of the pitch driving torque distribution estimation module and the pitch experience cumulative angle prediction module (i.e., the estimated wind resource parameters) are a single estimated wind speed and its attribute values, and the air density is added as a constant attribute value to the estimated wind resource parameters.

[0110] The determination unit 505 can determine the probability density of the pitch driving torque at multiple estimated wind speeds and the estimated angle accumulation value of the pitch angle within the target future period according to the estimated wind resource parameters. The determination method can refer to the first acquisition unit 501 and be realized by using the transfer function and the correlation coefficient.

[0111] The estimation unit 506 can determine the estimated equivalent load of the pitch bearing in the target future period according to the multiple estimated wind speeds, the pitch driving torque, its probability density at the multiple estimated wind speeds, and the estimated angle accumulation values at the multiple estimated wind speeds.

[0112] Optionally, the prediction unit 506 may specifically execute: determining the probability density of multiple predicted wind speeds; for each pitch driving torque at each predicted wind speed, determining the product of the probability density of the predicted wind speed, the m-th power of the pitch driving torque, the probability density of the pitch driving torque, and the predicted angle accumulation value, and summing all the products to obtain a predicted reference load, where m is the Wahl coefficient of the material of the pitch bearing; determining the 1 / m-th power of the predicted reference load as the predicted equivalent load.

[0113] The second calculation unit 507 may determine the predicted consumed life of the pitch bearing in the target future period according to the predicted equivalent load. The determination method may refer to the method by which the first calculation unit 503 determines the consumed life.

[0114] The second calculation unit 507 may also determine the predicted remaining life of the pitch bearing according to the design life, the consumed life, and the predicted consumed life of the pitch bearing. Here, the consumed life is obtained by the first calculation unit 503. Specifically, the difference obtained by subtracting the consumed life and the predicted consumed life from the design life is the predicted remaining life.

[0115] For the pitch bearing of an operating wind turbine generator, the consumed life can be obtained through the pitch bearing online life assessment system as shown in Figure 2 . For the remaining life, although it is impossible to obtain the future operation data of the wind turbine generator in advance, the subsequent operation of the unit is affected by the wind resource conditions of the future wind farm. By obtaining the predicted wind resource parameters of multiple machine positions in the target future period, the predicted equivalent load of the pitch bearing in the target future period can be estimated based on this, and then the prediction of the consumed life in the future target period can be completed. Finally, by combining it with the design life and the consumed life, the predicted remaining life of the pitch bearing at the end of the target future period can be obtained. Since the current wind resource analysis technology already has the ability to predict the wind resource parameters in the next few years, the predicted wind resource parameters in the future target period can be obtained by using the data acquisition system and the wind resource statistical analysis module as shown in Figure 3 . Therefore, there is no need for additional data collection, and there is no need to configure additional data collection sensors, which can reduce the product cost and save the time cost of life assessment, improving the economic performance.

[0116] The method for evaluating the service life of a pitch bearing of a wind turbine according to an embodiment of the present disclosure can be written as a computer program and stored on a computer-readable storage medium. When the instructions corresponding to the computer program are executed by a processor, the method for evaluating the service life of the pitch bearing of the wind turbine as described above can be implemented. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disc memory, hard disk drive (HDD), solid state drive (SSD), cartridge memory (such as, multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, and any other device configured to store the computer program and any associated data, data files, and data structures in a non-transitory manner and to provide the computer program and any associated data, data files, and data structures to a processor or computer such that the processor or computer can execute the computer program. In one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner by one or more processors or computers.

[0117] Figure 6 is a block diagram showing a computer device according to an embodiment of the present disclosure.

[0118] Referring to Figure 6 , the computer device 600 includes at least one memory 601 and at least one processor 602. A set of computer-executable instructions is stored in the at least one memory 601. When the set of computer-executable instructions is executed by the at least one processor 602, the method for evaluating the service life of the pitch bearing of the wind turbine according to an exemplary embodiment of the present disclosure is executed.

[0119] As an example, the computer device 600 can be a PC computer, a tablet device, a personal digital assistant, a smart phone, or other devices capable of executing the above instruction set. Here, the computer device 600 does not have to be a single electronic device, and can also be any assembly of devices or circuits capable of executing the above instructions (or instruction sets) individually or jointly. The computer device 600 can also be a part of an integrated control system or a system manager, or can be configured as a portable electronic device that is interconnected with a local or remote device (e.g., via wireless transmission).

[0120] In the computer device 600, the processor 602 can include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller, or a microprocessor. By way of example and not limitation, the processor can also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.

[0121] The processor 602 can run the instructions or code stored in the memory 601, where the memory 601 can also store data. The instructions and data can also be sent and received via the network interface device over the network, where the network interface device can use any known transmission protocol.

[0122] The memory 601 can be integrated with the processor 602. For example, RAM or flash memory can be arranged within an integrated circuit microprocessor, etc. In addition, the memory 601 can include a separate device, such as an external disk drive, a storage array, or other storage devices that can be used by any database system. The memory 601 and the processor 602 can be operatively coupled, or can communicate with each other, for example, through an I / O port, a network connection, etc., so that the processor 602 can read the files stored in the memory.

[0123] In addition, the computer device 600 can also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.). All components of the computer device 600 can be connected to each other via a bus and / or a network.

[0124] The present disclosure utilizes the operation data that is itself collected during the operation of the unit, as well as the estimated wind resource parameters that can be predicted based on the current technology, to achieve the life assessment of the pitch bearing. No additional data collection is required, and thus no additional data collection sensors need to be configured, which can reduce the product cost and save the time cost of life assessment. Within an acceptable range of accuracy, the present disclosure realizes real-time online assessment of the consumed life and prediction of the remaining life in the future. Under the premise of ensuring the safe operation of the wind turbine generator, predictive operation and maintenance and prediction of failure events can be carried out, thereby reducing the unplanned downtime of the wind turbine generator and improving its economic benefits.

[0125] The specific embodiments of the present disclosure have been described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that these embodiments can be modified and varied without departing from the principles and spirit of the present disclosure as defined by the claims and their equivalents, and such modifications and variations should also be within the protection scope of the claims of the present disclosure.

Claims

1. A method for evaluating the life of a pitch bearing of a wind turbine generator set, characterized in that, The described life assessment method includes: Obtaining the probability density of the pitch driving torque in M historical periods, where M is a positive integer; Obtaining the angle cumulative value of the pitch angle within each of the M historical periods; Determining the equivalent load of the pitch bearing according to the pitch driving torque, its probability density in the M historical periods, and the angle cumulative values of the M historical periods, where the equivalent load of the pitch bearing is obtained by accumulating the pitch driving torque in the dimension of the pitch angle; Determining the consumed life of the pitch bearing according to the equivalent load of the pitch bearing; Among them, obtaining the probability density of the pitch driving torque in M historical periods includes: Determining the occurrence frequency and corresponding distribution parameters of the pitch driving torque under different pitch motion states according to the operation data of the M historical periods; Determining the probability density according to the frequency and the corresponding distribution parameters.

2. The lifespan assessment method according to claim 1, wherein The pitch motion states include a forward state, a constant state, and a negative state.

3. The life evaluation method according to claim 2, wherein The determining the occurrence frequency and corresponding distribution parameters of the pitch driving torque under different pitch motion states according to the operation data of the M historical periods includes: For each historical period, determining the product of the correlation coefficient matrix, the first transfer function, and the column vector of operation data to obtain a first column vector, where the number of rows of the correlation coefficient matrix is equal to the sum of the number of the occurrence frequency and the corresponding distribution parameters, and the column vector of operation data is composed of multiple operation data; Determining the sum of the first column vector and the first correlation coefficient column vector, and multiplying it by the second transfer function to obtain a second column vector; Determining the sum of the second column vector and the second correlation coefficient column vector as the output vector, and the output vector includes the occurrence frequency and the corresponding distribution parameters. Among them, the correlation coefficient matrix, the first correlation coefficient column vector, the second correlation coefficient column vector, the first transfer function, and the second transfer function are obtained through testing or training.

4. The life evaluation method according to claim 2, wherein Before determining the occurrence frequency and corresponding distribution parameters of the pitch driving torque under different pitch motion states according to the operation data of the M historical periods, it further includes: Collecting the operation data of the wind turbine generator set in the M historical periods; where the operation data includes output power, impeller speed, generator torque, x-direction component of nacelle acceleration, y-direction component of nacelle acceleration, and pitch angle.

5. The life evaluation method according to claim 1, wherein The number of the pitch driving torques is multiple, and among them, determining the equivalent load of the pitch bearing according to the pitch driving torque, its probability density in the M historical periods, and the angle cumulative values of the M historical periods includes: For each historical period, determining the product of the m-th power of each pitch driving torque, the corresponding probability density of the pitch driving torque, and the angle cumulative value, and summing the products corresponding to the multiple pitch driving torques to obtain the reference load of the corresponding historical period, where m is the material Wall coefficient of the pitch bearing; Determining the average value of the reference loads of the M historical periods; Determine the 1 / m power of the average value of the reference load as the equivalent load of the pitch bearing.

6. The life evaluation method according to claim 5, wherein The multiple pitch driving torques are obtained through the following steps: For the pitch driving torque change range, take values at a set step size to obtain the multiple pitch driving torques.

7. The life evaluation method according to any one of claims 1 to 6, characterized in that, It further includes: Obtain the predicted wind resource parameters of multiple machine positions in a target future time period, and the predicted wind resource parameters include predicted wind speed; According to the predicted wind resource parameters, determine the probability density of the pitch driving torque at multiple predicted wind speeds and the predicted angle accumulation value of the pitch angle within the target future time period; According to the multiple predicted wind speeds, the pitch driving torque, its probability density at the multiple predicted wind speeds, and the predicted angle accumulation values at the multiple predicted wind speeds, determine the predicted equivalent load of the pitch bearing in the target future time period; According to the predicted equivalent load, determine the predicted consumed life of the pitch bearing in the target future time period; According to the design life, the consumed life, and the predicted consumed life of the pitch bearing, determine the predicted remaining life of the pitch bearing.

8. The life evaluation method according to claim 7, wherein The predicted wind resource parameters further include turbulence intensity, wind shear, and air density.

9. The lifespan assessment method according to claim 7, wherein The step of determining the predicted equivalent load of the pitch bearing in the target future time period according to the multiple predicted wind speeds, the pitch driving torque, its probability density at the multiple predicted wind speeds, and the predicted angle accumulation values at the multiple predicted wind speeds includes: Determine the probability density of the multiple predicted wind speeds; For each pitch driving torque at each predicted wind speed, determine the product of the probability density of the predicted wind speed, the m power of the pitch driving torque, the probability density of the pitch driving torque, and the predicted angle accumulation value, and sum all the products to obtain a predicted reference load, where m is the material Wahl coefficient of the pitch bearing; Determine the 1 / m power of the predicted reference load as the predicted equivalent load.

10. A life evaluation device for a pitch bearing of a wind turbine generator, characterized in that, The life evaluation device includes: A first acquisition unit configured to: acquire the probability density of the pitch driving torque in M historical time periods, where M is a positive integer; The first acquisition unit is further configured to acquire the angle accumulation value of the pitch angle in each of the M historical time periods; An equivalent unit configured to: determine the equivalent load of the pitch bearing according to the pitch driving torque, its probability density in the M historical time periods, and the angle accumulation values in the M historical time periods, where the equivalent load of the pitch bearing is obtained by accumulating the pitch driving torque in the dimension of the pitch angle; A first calculation unit configured to: determine the consumed life of the pitch bearing according to the equivalent load of the pitch bearing; The first acquisition unit is further configured to: According to the operation data of the M historical time periods, determine the occurrence frequency and corresponding distribution parameters of the pitch driving torque in different pitch motion states; Determine the probability density according to the frequency and the corresponding distribution parameters.

11. The life evaluation device according to claim 10, characterized in that, The pitch motion states include a forward state, a constant state, and a negative state.

12. The life evaluation device according to claim 11, wherein The first acquisition unit is further configured to: For each historical period, determine the product of the correlation coefficient matrix, the first transfer function, and the column vector of operating data to obtain a first column vector, where the number of rows of the correlation coefficient matrix is equal to the sum of the occurrence frequency and the corresponding distribution parameters, and the column vector of operating data consists of multiple operating data; Determine the sum of the first column vector and the first correlation coefficient column vector, and multiply it by the second transfer function to obtain a second column vector; Determine the sum of the second column vector and the second correlation coefficient column vector as the output vector, and the output vector includes the occurrence frequency and the corresponding distribution parameters; where the correlation coefficient matrix, the first correlation coefficient column vector, the second correlation coefficient column vector, the first transfer function, and the second transfer function are obtained through testing or training.

13. The life evaluation device according to claim 11, wherein The first obtaining unit is further configured to: Collect the operating data of the wind turbine generator set in the M historical periods; where the operating data includes output power, impeller speed, generator torque, x-direction component of nacelle acceleration, y-direction component of nacelle acceleration, and pitch angle.

14. The life evaluation device according to claim 10, characterized in that The number of the pitch driving torques is multiple, and the equivalent unit is further configured to: For each historical period, determine the product of the m-th power of each pitch driving torque, the probability density of the corresponding pitch driving torque, and the angle cumulative value, and sum the products corresponding to the multiple pitch driving torques respectively to obtain the reference load of the corresponding historical period, where m is the material Wall coefficient of the pitch bearing; Determine the average value of the reference loads of the M historical periods; Determine the 1 / m-th power of the average value of the reference loads as the equivalent load of the pitch bearing.

15. The life evaluation device according to claim 14, wherein, The multiple pitch driving torques are obtained through the following steps: Take values at a set step size for the pitch driving torque change interval to obtain multiple pitch driving torques.

16. The life evaluation device according to any one of claims 10 to 15, characterized in that It further includes: A second obtaining unit, configured to: obtain the estimated wind resource parameters of multiple machine positions in a target future period, and the estimated wind resource parameters include estimated wind speed; A determining unit, configured to: determine the probability density of the pitch driving torque at multiple estimated wind speeds and the estimated angle accumulation value of the pitch angle within the target future period according to the estimated wind resource parameters; An estimating unit, configured to: determine the estimated equivalent load of the pitch bearing in the target future period according to the multiple estimated wind speeds, the pitch driving torque, its probability density at the multiple estimated wind speeds, and the estimated angle accumulation values at the multiple estimated wind speeds; A second calculating unit, configured to: determine the estimated consumed life of the pitch bearing according to the estimated equivalent load; The second calculating unit is further configured to: determine the estimated remaining life of the pitch bearing according to the design life, the consumed life, and the estimated consumed life of the pitch bearing.

17. The life evaluation device according to claim 16, wherein The estimated wind resource parameters further include turbulence intensity, wind shear, and air density.

18. The life evaluation device according to claim 16, wherein The estimating unit is further configured to: Determine the probability density of the multiple estimated wind speeds; For each of the pitch driving torques at each of the predicted wind speeds, determine the product of the probability density of the predicted wind speed, the m-th power of the pitch driving torque, the probability density of the pitch driving torque, and the predicted angle accumulation value, and sum all the products to obtain the predicted reference load, where m is the material Woll coefficient of the pitch bearing; Determine the 1 / m-th power of the predicted reference load as the predicted equivalent load.

19. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are run by at least one processor, cause the at least one processor to execute the life assessment method according to any one of claims 1 to 9.

20. A computer device, characterized in that, Comprising: At least one processor; At least one memory storing computer-executable instructions, wherein, when the computer-executable instructions are run by the at least one processor, cause the at least one processor to execute the life assessment method according to any one of claims 1 to 9.

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