A wind turbine tower fatigue life prediction method and system

Abstract

The present application relates to wind turbine tower life prediction technical field, and disclose a kind of wind turbine tower fatigue life prediction method and system, comprising the following steps: S1, the height, diameter, wall thickness, material data of wind turbine tower are obtained.The wind turbine tower fatigue life prediction method, according to the height, diameter, wall thickness of tower, the fatigue damage degree of tower in ideal state is calculated, the load borne by tower is calculated according to temperature and wind speed in the environment where wind turbine is located, and the life change of tower in actual environment, i.e.predicted life, when predicted life is less than tower life threshold, determine that tower life is in damaged state, the predicted life of tower is calculated using prediction model and the environment where tower is located, avoid depending on the experience level of experimenter, leading to low prediction accuracy, improve the accuracy of prediction result.

Description

Technical Field

[0001] This invention relates to the field of wind turbine tower life prediction technology, specifically a method and system for predicting the fatigue life of wind turbine towers. Background Technology

[0002] Fatigue life prediction for wind turbine towers is crucial for the wind power industry. This is because the tower, as a key structural component supporting the entire wind turbine, bears enormous forces and wind loads. Wind turbines are typically located in areas with abundant wind resources, where wind speeds are often high. Therefore, the tower needs to be able to withstand strong winds and dynamic loads for extended periods. Accurately predicting the tower's fatigue life ensures its structural integrity within its design life, thereby guaranteeing the safe operation of the wind turbine. Furthermore, precise prediction of tower fatigue life allows for the development of more rational operating strategies and maintenance plans, extending the overall service life of the wind turbine.

[0003] However, the commonly used prediction method is to simplify predictions based on historical data and empirical rules. This method can be implemented quickly, but it depends on the experience level of the practitioners, which leads to low prediction accuracy and large deviations in the prediction results. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for predicting the fatigue life of wind turbine towers. This method avoids reliance on the experience level of experienced personnel, which leads to low prediction accuracy, and improves the accuracy of prediction results, thus solving the aforementioned problems.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting the fatigue life of a wind turbine tower, comprising the following steps:

[0008] S1. Obtain the height of the wind turbine tower. ,diameter Wall thickness Material Data, and simultaneously, temperature data of the environment where the wind turbine generator is located. Wind speed data ;

[0009] S2, according to material Data calculation of the fatigue damage degree of the tower under ideal conditions. ;

[0010] S3, Based on temperature data Wind speed data Calculate the load borne by the tower of the wind turbine in the environment. ;

[0011] S4. Based on the height of the wind turbine tower ,diameter Wall thickness And the degree of fatigue damage under ideal conditions Establish a fatigue life prediction model for towers ;

[0012] S5. Tower fatigue life prediction model With the load borne by the tower The predicted lifespan of the final tower is obtained by combining these factors. ;

[0013] S6. Predict the final lifespan of the tower. With tower life threshold In comparison, when the predicted lifespan of the final tower... Less than the tower life threshold When the tower's lifespan is below the threshold, it is determined to be in a damaged state.

[0014] Preferably, the wind turbine tower material The data is derived from the maximum stress value of the material. Fatigue strength of materials Fatigue life of the material under maximum stress level and the total number of fatigue cycles of the material at the maximum stress level. composition.

[0015] Preferably, the degree of fatigue damage of the tower under ideal conditions The algorithm expression is as follows:

[0016]

[0017] In the formula, This represents the ratio of the material's maximum stress level to its fatigue strength, i.e., the maximum stress the material experiences in actual operation. This represents the ratio between the fatigue life of the material and the total number of cycles, indicating the specific proportion of fatigue cycles the material has undergone. This involves assessing the fatigue life state of the material based on the contribution of each fatigue stress to the material's damage under maximum stress, which is essentially the degree of fatigue damage to the tower under ideal conditions. .

[0018] Preferably, the load borne by the tower The algorithm expression is as follows:

[0019]

[0020] In the formula, Indicates the wind speed coefficient. Indicates the temperature coefficient. , The value is a constant, and its value is determined by the environment in which the wind turbine is located. This represents the nonlinear effect of wind on the tower structure; that is, as wind speed increases, the load on the tower increases quadratically. This indicates the specific degree of influence of temperature on the tower. The sum of these two factors represents the impact of wind speed and temperature on the tower load within the tower's environment, which is equivalent to the load borne by the tower. .

[0021] Preferably, the tower fatigue life prediction model The algorithm expression is as follows:

[0022]

[0023] In the formula, This represents an empirical coefficient, a constant derived from fatigue tests of the tower material in a laboratory setting. This indicates a reference value for wind speed. This represents the average observed wind speed in the area where the wind turbine is installed. This represents the average temperature observed in the area where the wind turbine is installed. Indicates the temperature reference value. This indicates the reference height of the wind turbine tower, which is the average height of a conventional wind turbine tower. This indicates the reference diameter of the wind turbine tower, which is the average diameter of a conventional wind turbine tower. This indicates the reference wall thickness of the wind turbine tower, i.e., the average wall thickness of a conventional wind turbine tower. Indicates the wind speed influencing factor. Indicates the influence factor of temperature. Indicates the influencing factor of tower height. This indicates the influence factor of tower diameter. This indicates the influence factor of tower wall thickness.

[0024] Preferably, the predicted lifetime The algorithm expression is as follows:

[0025]

[0026] In the formula, Indicates the first The fatigue damage level of the tower under ideal conditions is calculated, which represents the cumulative damage level of the tower. This indicates the total cumulative damage to the tower due to fatigue under specific natural conditions. This indicates that the predicted lifespan of the tower is derived by considering the effects of temperature and wind speed on the tower structure under natural environmental conditions. .

[0027] Preferably, the empirical coefficient The steps to obtain it are as follows:

[0028] A1. Establish a multiple regression model;

[0029] A2. Input multiple sets of experimental data into the multiple regression model; A3. Obtain the empirical coefficients from the fitting results. .

[0030] A wind turbine tower fatigue life prediction system, based on the aforementioned wind turbine tower fatigue life prediction method, includes:

[0031] The data acquisition module is used to collect data on the height, diameter, wall thickness, material, temperature, and wind speed of the wind turbine tower.

[0032] The data processing module is used to receive data on the height, diameter, wall thickness, material, temperature and wind speed of the wind turbine tower, calculate the corresponding degree of fatigue damage, the load borne by the tower, and establish a fatigue life prediction model for the tower based on the height, diameter, wall thickness and fatigue damage under ideal conditions. The fatigue life prediction model is then combined with the load borne by the tower to obtain the final predicted life of the tower.

[0033] The data analysis module receives the predicted lifespan of the final tower from the data processing module, compares the received predicted lifespan of the final tower with the tower lifespan threshold, and determines whether the tower is in a damaged state.

[0034] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the wind turbine tower fatigue life prediction method.

[0035] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for predicting the fatigue life of a wind turbine tower.

[0036] Compared with the prior art, the present invention provides a method and system for predicting the fatigue life of wind turbine towers, which has the following advantages:

[0037] This invention calculates the fatigue damage degree of the tower under ideal conditions based on its height, diameter, and wall thickness. Simultaneously, it calculates the load borne by the tower based on the temperature and wind speed of the wind turbine's environment. A tower fatigue life prediction model is established based on the tower's height, diameter, wall thickness, and the fatigue damage degree under ideal conditions. This prediction model is then combined with the load borne by the tower to calculate the tower's lifespan variation in the actual environment, i.e., the predicted lifespan. When the predicted lifespan is less than the tower's lifespan threshold, the tower is considered to be in a damaged state. By using the prediction model and the tower's environment to calculate the predicted lifespan, the invention avoids reliance on the experience level of experienced personnel, which could lead to low prediction accuracy, and thus improves the precision of the prediction results. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 A method for predicting the fatigue life of a wind turbine tower includes the following steps:

[0041] S1. Obtain the height of the wind turbine tower. ,diameter Wall thickness Material Data, and simultaneously, temperature data of the environment where the wind turbine generator is located. Wind speed data ;

[0042] S2, according to material Data calculation of the fatigue damage degree of the tower under ideal conditions. The specific algorithm expression is as follows:

[0043]

[0044] In the formula, This represents the ratio of the material's maximum stress level to its fatigue strength, i.e., the maximum stress the material experiences in actual operation. This represents the ratio between the fatigue life of the material and the total number of cycles, indicating the specific proportion of fatigue cycles the material has undergone. This involves assessing the fatigue life state of the material based on the contribution of each fatigue stress to the material's damage under maximum stress, which is essentially the degree of fatigue damage to the tower under ideal conditions. .

[0045] S3, Based on temperature data Wind speed data Calculate the load borne by the tower of the wind turbine in the environment. The algorithm expression is as follows:

[0046]

[0047] In the formula, Indicates the wind speed coefficient. Indicates the temperature coefficient. , The value is a constant, and its value is determined by the environment in which the wind turbine is located. This represents the nonlinear effect of wind on the tower structure; that is, as wind speed increases, the load on the tower increases quadratically. This indicates the specific degree of influence of temperature on the tower. The sum of these two factors represents the impact of wind speed and temperature on the tower load within the tower's environment, which is equivalent to the load borne by the tower. .

[0048] S4. Based on the height of the wind turbine tower ,diameter Wall thickness And the degree of fatigue damage under ideal conditions Establish a fatigue life prediction model for towers The algorithm expression is as follows:

[0049]

[0050] In the formula, This represents an empirical coefficient, a constant derived from fatigue tests of the tower material in a laboratory setting. This indicates a reference value for wind speed. This represents the average observed wind speed in the area where the wind turbine is installed. This represents the average temperature observed in the area where the wind turbine is installed. Indicates the temperature reference value. This indicates the reference height of the wind turbine tower, which is the average height of a conventional wind turbine tower. This indicates the reference diameter of the wind turbine tower, which is the average diameter of a conventional wind turbine tower. This indicates the reference wall thickness of the wind turbine tower, i.e., the average wall thickness of a conventional wind turbine tower. Indicates the wind speed influencing factor. Indicates the influence factor of temperature. Indicates the influencing factor of tower height. This indicates the influence factor of tower diameter. The factors influencing tower wall thickness should be noted. It should be observed that the factors influencing wind speed, temperature, tower height, tower diameter, and tower wall thickness can be directly obtained from actual simulation experiments and will not be elaborated further. Among these, empirical coefficients... The steps to obtain it are as follows:

[0051] 1. Establish a multiple regression model;

[0052] 2. Input multiple sets of experimental data into the multiple regression model;

[0053] 3. The empirical coefficients are derived from the fitting results.

[0054] S5. Tower fatigue life prediction model With the load borne by the tower The predicted lifespan of the final tower is obtained by combining these factors. The specific algorithm expression is as follows:

[0055]

[0056] In the formula, Indicates the first The fatigue damage level of the tower under ideal conditions is calculated, which represents the cumulative damage level of the tower. This indicates the total cumulative damage to the tower due to fatigue under specific natural conditions. This indicates that the predicted lifespan of the tower is derived by considering the effects of temperature and wind speed on the tower structure under natural environmental conditions. .

[0057] S6. Predict the final lifespan of the tower. With tower life threshold In comparison, when the predicted lifespan of the final tower... Less than the tower life threshold When the tower's lifespan is below the threshold, it is determined to be in a damaged state.

[0058] By calculating the fatigue damage degree of the tower under ideal conditions based on its height, diameter, and wall thickness, and by calculating the load on the tower based on the temperature and wind speed of the wind turbine's environment, a fatigue life prediction model for the tower is established based on the tower's height, diameter, wall thickness, and fatigue damage degree under ideal conditions. This prediction model is then combined with the load on the tower to calculate the tower's lifespan variation in the actual environment, i.e., the predicted lifespan. When the predicted lifespan is less than the tower's lifespan threshold, the tower is considered to be in a damaged state. By using the prediction model and the environment in which the tower is located to calculate the predicted lifespan of the tower, the reliance on the experience level of experienced personnel is avoided, which could lead to low prediction accuracy, thus improving the accuracy of the prediction results.

[0059] A wind turbine tower fatigue life prediction system, based on a wind turbine tower fatigue life prediction method, includes:

[0060] The data acquisition module is used to collect the height of the wind turbine tower. ,diameter Wall thickness Material Temperature data Wind speed data ;

[0061] The data processing module is used to receive the height of the wind turbine tower. ,diameter Wall thickness Material Temperature data Wind speed data And calculate the corresponding degree of fatigue damage. The load borne by the tower And based on the height of the wind turbine tower ,diameter Wall thickness And the degree of fatigue damage under ideal conditions Establish a fatigue life prediction model for towers And the tower fatigue life prediction model With the load borne by the tower The predicted lifespan of the final tower is obtained by combining these factors. ;

[0062] The data analysis module is used to receive the final predicted lifespan of the tower from the data processing module. and the received final tower's predicted lifespan With tower life threshold By comparison, it can be determined whether the tower is in a damaged state.

[0063] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a method for predicting the fatigue life of a wind turbine tower.

[0064] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for predicting the fatigue life of a wind turbine generator tower.

[0065] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the fatigue life of a wind turbine generator tower, characterized in that: Includes the following steps: S1. Obtain the height of the wind turbine tower. ,diameter Wall thickness Material Data, and simultaneously, temperature data of the environment where the wind turbine generator is located. Wind speed data The material of the wind turbine tower The data is derived from the maximum stress value of the material. Fatigue strength of materials Fatigue life of the material under maximum stress level and the total number of fatigue cycles of the material at the maximum stress level. composition; S2, according to material Data calculation of the fatigue damage degree of the tower under ideal conditions. The degree of fatigue damage to the tower under ideal conditions. The algorithm expression is as follows: In the formula, This represents the ratio of the material's maximum stress level to its fatigue strength, i.e., the maximum stress the material experiences in actual operation. This represents the ratio between the fatigue life of the material and the total number of cycles, indicating the specific proportion of fatigue cycles the material has undergone. This involves assessing the fatigue life state of the material based on the contribution of each fatigue stress to the material's damage under maximum stress, which is essentially the degree of fatigue damage to the tower under ideal conditions. ; S3, Based on temperature data Wind speed data Calculate the load borne by the tower of the wind turbine in the environment. The load borne by the tower The algorithm expression is as follows: In the formula, Indicates the wind speed coefficient. Indicates the temperature coefficient. , The value is a constant, and its value is determined by the environment in which the wind turbine is located. This represents the nonlinear effect of wind on the tower structure; that is, as wind speed increases, the load on the tower increases quadratically. This indicates the specific degree of influence of temperature on the tower. The sum of these two factors represents the impact of wind speed and temperature on the tower load within the tower's environment, which is equivalent to the load borne by the tower. ; S4. Based on the height of the wind turbine tower ,diameter Wall thickness And the degree of fatigue damage under ideal conditions Establish a fatigue life prediction model for towers The tower fatigue life prediction model The algorithm expression is as follows: In the formula, This represents an empirical coefficient, a constant derived from fatigue tests of the tower material in a laboratory setting. This indicates a reference value for wind speed. This represents the average observed wind speed in the area where the wind turbine is installed. This represents the average temperature observed in the area where the wind turbine is installed. Indicates the temperature reference value. This indicates the reference height of the wind turbine tower, which is the average height of a conventional wind turbine tower. This indicates the reference diameter of the wind turbine tower, which is the average diameter of a conventional wind turbine tower. This indicates the reference wall thickness of the wind turbine tower, i.e., the average wall thickness of a conventional wind turbine tower. Indicates the wind speed influencing factor. Indicates the influence factor of temperature. Indicates the influencing factor of tower height. This indicates the influence factor of tower diameter. Indicates the influence factor of tower wall thickness; The empirical coefficient The steps to obtain it are as follows: A1. Establish a multiple regression model; A2. Input multiple sets of experimental data into the multiple regression model; A3. Empirical coefficients are derived from the fitting results. ; S5. Tower fatigue life prediction model With the load borne by the tower The predicted lifespan of the final tower is obtained by combining these factors. The predicted lifetime The algorithm expression is as follows: In the formula, Indicates the first The fatigue damage level of the tower under ideal conditions is calculated, which represents the cumulative damage level of the tower. This indicates the total cumulative damage to the tower due to fatigue under specific natural conditions. This indicates that the predicted lifespan of the tower is derived by considering the influence of temperature and wind speed on the tower structure under natural conditions; S6, the final predicted lifespan of the tower is... With tower life threshold In comparison, when the predicted lifespan of the final tower... Less than the tower life threshold When the tower's lifespan is below the threshold, it is determined to be in a damaged state.

2. A fatigue life prediction system for wind turbine towers, based on the fatigue life prediction method for wind turbine towers as described in claim 1, characterized in that: include: The data acquisition module is used to collect the height of the wind turbine tower. ,diameter Wall thickness Material Temperature data Wind speed data ; The data processing module is used to receive the height of the wind turbine tower. ,diameter Wall thickness Material Temperature data Wind speed data And calculate the corresponding degree of fatigue damage. The load borne by the tower And based on the height of the wind turbine tower ,diameter Wall thickness And the degree of fatigue damage under ideal conditions Establish a fatigue life prediction model for towers And the tower fatigue life prediction model With the load borne by the tower The predicted lifespan of the final tower is obtained by combining these factors. ; The data analysis module is used to receive the final predicted lifespan of the tower from the data processing module. and the received final tower's predicted lifespan With tower life threshold By comparison, it can be determined whether the tower is in a damaged state.

3. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the wind turbine generator tower fatigue life prediction method according to claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for predicting the fatigue life of the wind turbine tower as described in claim 1.

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