Tower state monitoring method, device, controller and wind turbine generator set

Abstract

The present disclosure provides a tower state monitoring method, device, controller and wind turbine generator unit. The tower state monitoring method comprises: acquiring first nacelle acceleration data of the wind turbine generator unit in real time through a nacelle acceleration sensor; performing dimension algorithm processing on the first nacelle acceleration data and acquiring first equivalent acceleration based on the processing result; calculating first tower equivalent fatigue load based on the first equivalent acceleration and a predetermined calibration relationship; determining the state of the tower according to the first tower equivalent fatigue load, wherein the calibration relationship represents the change relationship between the first equivalent acceleration and the first tower equivalent fatigue load. The tower state monitoring method according to the embodiment of the present disclosure can monitor the state of the tower.

Description

Technical Field

[0001] This disclosure generally relates to the field of wind power, and more specifically, to a tower condition monitoring method, device, controller, and wind turbine generator set. Background Technology

[0002] As wind turbine generators operate for longer periods, their equipment inevitably ages and fails, posing safety hazards and even economic losses to wind power companies.

[0003] For wind turbine generators, the tower is a critical component, and the safety of the tower has a decisive impact on the safe operation of the entire unit.

[0004] At the same time, the demand for cost reduction of wind turbine generators is gradually increasing. Therefore, it is necessary to monitor the tower status under the premise of economic efficiency, accurately assess or predict the remaining life of the tower, and then adjust the control scheme and unit operation scheme based on the tower life (such as power-limited operation, speed-limited operation, shutdown, whole unit life extension and power increase, etc.) to maximize the overall benefits of the unit while ensuring the safe operation of the unit. Summary of the Invention

[0005] One of the objectives of the exemplary embodiments disclosed herein is to provide a condition monitoring method for monitoring the tower status of a wind turbine generator.

[0006] According to a first aspect of this disclosure, a method for monitoring the tower condition of a wind turbine generator set includes: acquiring first nacelle acceleration data of the wind turbine generator set in real time through a nacelle acceleration sensor; processing the first nacelle acceleration data using a measurement algorithm and obtaining a first equivalent acceleration based on the processing result; calculating the first equivalent tower equivalent fatigue load based on the first equivalent acceleration and a predetermined calibration relationship; and determining the tower condition based on the first tower equivalent fatigue load, wherein the calibration relationship characterizes the relationship between the first equivalent acceleration and the first tower equivalent fatigue load.

[0007] According to embodiments of this disclosure, calibration relationships can be determined based on nacelle acceleration data and tower load data obtained from simulation or wind turbines.

[0008] According to embodiments of this disclosure, the step of performing measurement algorithm processing on the first cabin acceleration data and obtaining the first equivalent acceleration based on the processing result may include: counting the first cabin acceleration data and obtaining the first equivalent acceleration based on the counting result.

[0009] According to embodiments of this disclosure, the step of determining the calibration relationship based on nacelle acceleration data and tower load data obtained from simulation or wind turbine may include: obtaining second nacelle acceleration data and second tower load data at a first tower height under different external environments through simulation or wind turbine; counting the second nacelle acceleration data to obtain a first counting result; counting the second tower load data to obtain a second counting result; calculating a second equivalent acceleration at the first tower height based on the first counting result; calculating the second equivalent fatigue load at the first tower height based on the second counting result; and performing trajectory fitting or machine learning on the second equivalent acceleration and the second equivalent fatigue load to obtain the calibration relationship at the first tower height.

[0010] According to embodiments of this disclosure, the calibration relationship can further characterize the relationship between the first equivalent acceleration and the first equivalent fatigue load of the first tower at different wind speeds or power levels. The tower condition monitoring method may also include obtaining the wind speed or the power of the wind turbine generator. The step of calculating the equivalent fatigue load of the first tower based on the first equivalent acceleration and the predetermined calibration relationship includes: determining the corresponding calibration relationship of the first tower height according to the wind speed or power; and calculating the equivalent fatigue load of the first tower based on the first equivalent acceleration of the first tower height and the corresponding calibration relationship.

[0011] According to an embodiment of this disclosure, the step of determining the state of the tower based on the equivalent fatigue load of the first tower may include: averaging the equivalent fatigue load of the first tower at the first tower height during time period M to obtain the average value of the equivalent fatigue load of the first tower at the first tower height; calculating the life of the tower based on the average value of the equivalent fatigue load of the first tower, the tower design load at the first tower height, and the fatigue material coefficient of the tower; and determining the state of the tower based on the life of the tower, wherein M is the time period during which the tower has been in operation.

[0012] According to embodiments of this disclosure, the step of calculating the life of a tower based on the average value of the equivalent fatigue load of the first tower, the tower design load at the height of the first tower, and the fatigue material coefficient of the tower may include: obtaining a power corresponding to the fatigue material coefficient based on the tower design load and the average value of the equivalent fatigue load of the first tower, and determining the parameter associated with the power as the life of the tower.

[0013] According to embodiments of this disclosure, a tower's abnormal condition can be determined in response to a predetermined condition being met between the tower's designed lifespan value and the difference between the tower's lifespan and a predetermined threshold.

[0014] According to embodiments of this disclosure, the tower condition monitoring method may further include: determining the tower condition based on the tower condition determined by the first tower equivalent fatigue load, generating an alarm signal to indicate an abnormal tower condition; controlling the wind turbine generator based on the tower condition determined by the first tower equivalent fatigue load; or both.

[0015] According to a second aspect of this disclosure, a computer-readable storage medium stores instructions or programs that, when executed by a processor, implement the above-described method for monitoring the tower status of a wind turbine generator.

[0016] According to a third aspect of this disclosure, a tower condition monitoring device for a wind turbine generator set includes: a data acquisition unit for acquiring real-time acceleration data of a first nacelle of the wind turbine generator set via a nacelle acceleration sensor; a processing unit for performing measurement algorithm processing on the first nacelle acceleration data and obtaining a first equivalent acceleration based on the processing result; a calculation unit for calculating the first equivalent fatigue load of the tower based on the first equivalent acceleration and a predetermined calibration relationship; and a determination unit for determining the state of the tower based on the first equivalent fatigue load, wherein the calibration relationship characterizes the relationship between the first equivalent acceleration and the first equivalent fatigue load of the tower.

[0017] According to embodiments of this disclosure, the tower condition monitoring device may further include a calibration unit configured to determine a calibration relationship based on nacelle acceleration data and tower load data obtained from simulation or wind turbine.

[0018] According to embodiments of this disclosure, the calibration unit may be further configured to: acquire second nacelle acceleration data and second tower load data at a first tower height under different external environments through simulation or wind turbine; count the second nacelle acceleration data to obtain a first counting result; count the second tower load data to obtain a second counting result; calculate a second equivalent acceleration at the first tower height based on the first counting result; calculate the second equivalent fatigue load at the first tower height based on the second counting result; and perform trajectory fitting or machine learning on the second equivalent acceleration and the second equivalent fatigue load to obtain the calibration relationship at the first tower height.

[0019] According to a fourth aspect of this disclosure, a controller for a wind turbine generator includes a processor and a computer-readable storage medium storing a program or instructions that, when executed by the processor, implement the aforementioned method for monitoring the tower status of the wind turbine generator.

[0020] According to a fifth aspect of this disclosure, a wind turbine generator set includes a tower condition monitoring device for the wind turbine generator set, or a controller for the wind turbine generator set.

[0021] The tower condition monitoring method and tower condition monitoring device according to embodiments of the present disclosure are capable of monitoring the condition of the tower.

[0022] The tower condition monitoring method and tower condition monitoring device according to the embodiments of this disclosure can determine whether the lifespan of the tower meets the requirements. Attached Figure Description

[0023] The above and other objects and features of exemplary embodiments of this disclosure will become clearer from the following description taken in conjunction with the accompanying drawings, which exemplarily illustrate the embodiments, wherein:

[0024] Figure 1 This is a flowchart illustrating a tower condition monitoring method according to a first embodiment of the present disclosure;

[0025] Figure 2 This is a flowchart illustrating the calibration process according to a first embodiment of the present disclosure;

[0026] Figure 3 This is a flowchart illustrating the determination of the tower's state according to an embodiment of the present disclosure;

[0027] Figure 4 This is a block diagram illustrating a tower condition monitoring device according to an embodiment of the present disclosure;

[0028] Figure 5 This is a timing diagram showing the acceleration of a simulated cabin according to an embodiment of the present disclosure;

[0029] Figure 6 This is a load timing diagram showing a cross-section of a simulated tower according to an embodiment of the present disclosure;

[0030] Figure 7 This illustrates the nacelle acceleration and tower load calibration relationship according to an embodiment of the present disclosure;

[0031] Figure 8 This is a timing diagram showing the measured cabin acceleration of a prototype according to an embodiment of the present disclosure;

[0032] Figure 9 This is a load timing diagram showing a cross-section of a prototype tower measured according to an embodiment of the present disclosure;

[0033] Figure 10 This illustrates the trajectory of unit operating time in relation to design life, theoretical life, life threshold, and remaining life according to embodiments of the present disclosure. Detailed Implementation

[0034] The following detailed description is provided to aid in obtaining a full understanding of the methods, apparatus, and / or systems described herein. However, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein; equivalent substitutions or changes may be made, except for operations that must occur or be performed in a specific order. Furthermore, for clarity and conciseness, descriptions of content well-known in the art will be omitted or simplified.

[0035] The features described herein may be implemented in different forms and should not be construed as being limited to the examples described herein.

[0036] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains upon understanding this disclosure. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this disclosure, and shall not be interpreted in an idealized or overly formalistic manner.

[0037] Unless otherwise specified, the same reference numerals generally refer to the same elements (e.g., components, steps, and methods). Reference numerals described in previous embodiments that reappear in later embodiments may be omitted. Furthermore, technical features described in different or the same embodiments can be combined in any way, as long as the combined embodiment or technical solution is complete and can solve the technical problem of this application or achieve the technical effects described or not described in this application but which can be determined based on the complete technical solution described above.

[0038] This disclosure obtains the relationship between the nacelle's equivalent acceleration and the tower's equivalent fatigue load through calibration. Once the calibration relationship is established, there is no longer a need for load sensors to monitor the tower's fatigue load, nor for simulations of the tower's fatigue load. Complex calculations are unnecessary, and tower load cannot be estimated from wind speed. Instead, the tower load can be extrapolated from operational data, and the tower's condition can be determined based on the tower load, its lifespan estimated. Furthermore, if the tower's lifespan does not meet requirements, the wind turbine generator can be controlled or an alarm signal can be issued. When the tower's lifespan exceeds design requirements, the wind turbine generator can be controlled or its lifespan extended to increase power generation. The following will combine... Figures 1 to 10 Specific embodiments of this disclosure are described below.

[0039] Figure 1 This is a flowchart illustrating a tower condition monitoring method according to a first embodiment of the present disclosure; Figure 2 This is a flowchart illustrating the calibration process according to a first embodiment of the present disclosure; Figure 3 This is a flowchart illustrating the determination of the tower's state according to embodiments of the present disclosure; and Figure 4 This is a block diagram illustrating a tower condition monitoring device according to an embodiment of the present disclosure; Figure 5 This is a timing diagram showing the acceleration of a simulated cabin according to an embodiment of the present disclosure; Figure 6 This is a load timing diagram showing a cross-section of a simulated tower according to an embodiment of the present disclosure; Figure 7 This illustrates the nacelle acceleration and tower load calibration relationship according to an embodiment of the present disclosure; Figure 8 This is a timing diagram showing the measured cabin acceleration of a prototype according to an embodiment of the present disclosure; Figure 9This is a load timing diagram showing a cross-section of a prototype tower measured according to an embodiment of the present disclosure; Figure 10 This illustrates the trajectory of unit operating time in relation to design life, theoretical life, life threshold, and remaining life according to embodiments of the present disclosure.

[0040] Reference Figure 1 The tower status monitoring method according to the first embodiment of the present disclosure may include steps S110, S120, S130 and S140.

[0041] In step S110, the acceleration data of the first nacelle of the wind turbine generator is acquired in real time through the nacelle acceleration sensor.

[0042] The tower condition is detected by acquiring the first nacelle acceleration data of the wind turbine generator set in real time. Specifically, the nacelle acceleration data of the wind turbine generator set during operation (e.g., during normal power generation) can be acquired in real time through the nacelle acceleration sensor.

[0043] In step S120, the first cabin acceleration data is processed by a measurement algorithm, and the first equivalent acceleration is obtained based on the processing result.

[0044] The steps of processing the first cabin acceleration data using a measurement algorithm and obtaining the first equivalent acceleration based on the processing result may include: counting the first cabin acceleration data (e.g., rainflow counting) and obtaining the first equivalent acceleration based on the counting result.

[0045] In other words, measurement algorithms can include various counting methods, such as amplitude counting and rainflow counting, that can record the number of times the load or acceleration occurs in a corresponding interval.

[0046] Rainflow counting is a widely used counting method in fatigue design and fatigue testing. The results of rainflow counting fall between those of peak counting and range counting, providing more realistic data. Rainflow counting is based on counting closed stress-strain hysteresis loops one by one. From a fatigue perspective, it can better reflect the entire process of random loading or acceleration.

[0047] The specific process of the rainflow counting method is as follows: the rainflow starts from the inner side of each peak in sequence; the raindrops fall on the next peak until a peak larger than the initial peak appears on the opposite side; the rainflow stops when it encounters rain left from the roof above; take out all full cycles according to the above process and record their respective ranges; finally, match the taken half cycles into full cycles according to the modified "range pair" counting method.

[0048] The counting results of the rainflow counting method can be a Markov matrix (i.e., a load matrix or acceleration matrix), which represents the number of cycles for each data point within the evaluation period at a certain load mean and a certain load amplitude, or the number of cycles for each acceleration mean and a certain acceleration amplitude. The equivalent fatigue load can be determined based on the number of cycles at a certain load mean and amplitude and the corresponding load; similarly, the equivalent acceleration can be determined based on the number of cycles at a certain acceleration mean and amplitude and the corresponding acceleration.

[0049] Compared with other counting methods, the rainflow counting method can accurately reproduce variable amplitude cyclic loading, identify events similar to constant amplitude fatigue data in complex sequences, improve the accuracy of fatigue analysis, facilitate accurate calculation of equivalent fatigue, and is simple to calculate and easy to program.

[0050] Specifically, the equivalent acceleration or equivalent fatigue load F can be determined by the following equation (1):

[0051]

[0052] Among them, f i : The load corresponding to the i-th load interval or the acceleration corresponding to the i-th acceleration interval; N i : The number of cycles of the load in the i-th load interval or the number of cycles of the acceleration corresponding to the i-th acceleration interval; N: the reference number; m: the fatigue material coefficient (e.g., m = 4). Both the equivalent acceleration and the tower equivalent fatigue load required for the subsequent calibration process can be calculated using the above formula (1).

[0053] Specifically, in step S130, the equivalent fatigue load of the first tower is calculated based on the first equivalent acceleration and the predetermined calibration relationship.

[0054] In other words, the equivalent fatigue load of the tower at different cross-sections (different tower heights) can be estimated or calculated based on the real-time acquired nacelle acceleration data, so there is no need to install load sensors at different locations on the tower.

[0055] As shown above, the first equivalent fatigue load of the tower can include equivalent fatigue loads with different tower cross sections or different tower heights. A predetermined calibration relationship can characterize the variation relationship between the first equivalent acceleration and the first equivalent fatigue load of the tower. This variation relationship can be a linear or nonlinear functional relationship, but this is just an example.

[0056] The predetermined calibration relationship can be the calibration relationship between the equivalent fatigue load and the equivalent acceleration of a certain tower section or a certain tower height, or it can be the calibration relationship between the equivalent fatigue load and the equivalent acceleration of towers at different heights. Therefore, the calibration relationship for different heights can be predetermined.

[0057] In other words, a first equivalent acceleration at a predetermined height can be obtained, and then, based on the first equivalent acceleration and the calibration relationship corresponding to the predetermined height, the first tower equivalent fatigue load at the predetermined height can be determined.

[0058] In step S140, the state of the tower is determined based on the equivalent fatigue load of the first tower.

[0059] For example, after determining the equivalent fatigue load of the first tower at a predetermined height, the tower's condition can be determined by combining it with the tower's design load at the corresponding height. Judging the tower's condition using the equivalent fatigue loads at different heights and the corresponding calibration relationships can improve the reliability of the judgment. During monitoring and judgment, the equivalent fatigue loads of towers at different heights can be obtained for monitoring and judgment.

[0060] The calibration relationship can be determined based on the nacelle acceleration data and tower load data obtained from simulation or wind turbine.

[0061] Reference Figure 2 The steps for determining the calibration relationship based on nacelle acceleration data and tower load data obtained from simulation or the wind turbine may include steps S1301, S1302, S1303, S1304, and S1305. Here, "wind turbine" refers to a single test unit, a test unit, or a representative unit (e.g., a prototype). The calibration relationship obtained through simulation or the test unit can be used for tower condition monitoring of wind turbines of the same model.

[0062] In step S1301, the acceleration data of the second nacelle and the load data of the second tower at the height of the first tower are obtained under different external environments through simulation or wind turbine.

[0063] On the one hand, the acceleration data of the second nacelle and the load data of the second tower at the height of the first tower under different external environments can be obtained through simulation.

[0064] Specifically, software such as Bladed and GTsim can be used to calculate acceleration and load data (e.g., unit load timing including tower load timing) under different external environments (i.e., different combinations of wind speed, turbulence, and air density).

[0065] The simulation nacelle acceleration timing sequence of a certain type of generator unit is as follows: Figure 5 As shown, the simulated tower load time sequence for a certain cross-section of a certain type of generator unit is as follows: Figure 6 As shown.

[0066] Reference Figure 5 The horizontal axis represents time in seconds, and the vertical axis represents cabin acceleration. The solid line trajectory G11 represents cabin acceleration in the y-direction, and the dashed line trajectory G12 represents cabin acceleration in the x-direction.

[0067] Reference Figure 6 The horizontal axis represents time in seconds, and the vertical axis represents the tower load. Trajectory G21 represents the tower load My in the y direction at a height of 9.12m, and trajectory G22 represents the tower load Mx in the x direction at a height of 9.12m.

[0068] In step S1302, the acceleration data of the second cabin is counted to obtain a first counting result.

[0069] In step S1303, the second tower load data is counted to obtain a second counting result.

[0070] In step S1304, the second equivalent acceleration of the first tower height is calculated based on the first counting result.

[0071] For example, calculated according to equation (1) above. Figure 5 The equivalent accelerations in the x and y directions are 0.6972 m / s² (m = 4) and 0.8374 m / s² (m = 4), respectively.

[0072] In step S1305, the second tower equivalent fatigue load at the first tower height is calculated based on the second counting result.

[0073] For example, calculated according to equation (1) above. Figure 6 The equivalent fatigue loads in the Mx and My directions are 7461 kNm (m=4) and 15100 kNm (m=4), respectively.

[0074] Specifically, the nacelle acceleration data can be counted to obtain a corresponding first counting result. Alternatively, the tower load data can be counted to obtain a corresponding second counting result. Based on the corresponding first counting result, the equivalent acceleration (equivalent acceleration Faccx in the x-direction and equivalent acceleration Faccy in the y-direction) can be calculated, and based on the corresponding second counting result, the equivalent fatigue load of the tower (equivalent fatigue load Fmx in the x-direction and equivalent fatigue load Fmy in the y-direction) can be calculated. The specific counting process can be as described above and will not be repeated here.

[0075] The counting results can be a Markov matrix or a table reflecting the frequency of load occurrences in different acceleration or load ranges. Using the Markov matrix obtained through rainflow counting for equivalent fatigue analysis and calculation during the calibration relationship acquisition process can more accurately reflect the relationship between equivalent acceleration and tower equivalent fatigue, facilitating the acquisition of a more precise calibration relationship, improving the accuracy of tower life prediction, and enhancing the reliability of tower monitoring.

[0076] In step S1306, trajectory fitting or machine learning is performed on the second equivalent acceleration and the second tower equivalent fatigue load to obtain the calibration relationship of the first tower height.

[0077] For example, the calibration relationship between Faccx and Fmx, and the calibration relationship between Faccy and Fmy can be obtained through linear fitting / machine learning.

[0078] For example, refer to Figure 7 Based on the scatter plot, the calibration relationship between Faccx and Fmx is obtained: Fmx = 20030Faccx + 450.15 (m = 4).

[0079] Alternatively, calibration relationships can be obtained through prototype testing. The specific steps are as follows: Load and acceleration data of the wind turbine generator set are collected using a prototype, and the unit's operating data and external environmental data can also be collected simultaneously; the nacelle acceleration data and tower load data are counted and statistically analyzed to obtain the corresponding counting results (e.g., Markov matrix); the equivalent acceleration and tower equivalent fatigue load are calculated; finally, the relationship between the two is obtained through linear fitting / machine learning.

[0080] Example of measured nacelle acceleration timing for a certain model prototype: Figure 8 As shown. An example of the measured tower load timing for a certain model prototype is shown below. Figure 9 As shown.

[0081] Reference Figure 8 The horizontal axis represents time in seconds, and the vertical axis represents acceleration. Trajectory G31 represents the cabin acceleration in the y-direction, and trajectory G32 represents the cabin acceleration in the x-direction.

[0082] Reference Figure 9 The horizontal axis represents time in seconds, and the vertical axis represents the tower load in Nm. Trajectory G41 represents the tower load My in the y-direction at a certain height, and trajectory G42 represents the tower load Mx in the x-direction at a certain height.

[0083] The calibration relationship can further characterize the variation relationship (e.g., functional relationship) between the first equivalent acceleration at the first tower height and the first equivalent fatigue load at different wind speeds or powers.

[0084] In other words, during the calibration process, the relationship between the first equivalent acceleration and the first equivalent fatigue load of a tower at a certain height under different wind speeds can be obtained. Although wind speed is used as an example to establish the relationship or calibration under this external environment, this is just an example; calibration relationships under more dimensions can also be established. In use, input variables of the corresponding dimensions (e.g., specific wind speed, specific power, and specific unit state) are obtained, and the equivalent fatigue load of the tower is calculated.

[0085] The tower condition monitoring method according to embodiments of this disclosure may further include the step of obtaining wind speed or the power of a wind turbine generator.

[0086] The step of calculating the equivalent fatigue load of the first tower based on the first equivalent acceleration and the predetermined calibration relationship may include determining the corresponding calibration relationship of the first tower height according to the wind speed or power; and calculating the equivalent fatigue load of the first tower based on the first equivalent acceleration of the first tower height and the corresponding calibration relationship.

[0087] As an example, the first equivalent acceleration at different heights and the corresponding calibration relationship can be obtained to calculate the corresponding first tower equivalent fatigue load. The state of the tower can be determined based on the first tower equivalent fatigue load at different tower heights, thus more accurately determining the tower life of the wind turbine generator.

[0088] Figure 3 This is a flowchart illustrating the determination of the tower's state according to an embodiment of the present disclosure.

[0089] The steps for determining the state of the tower based on the equivalent fatigue load of the first tower may include steps S1401, S1402, and S1403.

[0090] In step S1401, the average of the equivalent fatigue load of the first tower at the first tower height during the M time period (e.g., M years) is calculated to obtain the average value of the equivalent fatigue load of the first tower at the first tower height. Specifically, the average value of the equivalent fatigue load of the first tower at the first tower height Fmy_ave can be calculated using the following formula (2).

[0091]

[0092] Among them: Fmy i Here is the equivalent fatigue load data for the i-th tower; M is the operating period of the unit (e.g., years (positive integer)); m: fatigue material coefficient, generally taken as m = 4. Although in equation (2), the average equivalent fatigue load of the first tower is calculated based on the equivalent fatigue load of the tower in the y-direction, this is just an example, and it can also be calculated based on the equivalent fatigue load of the tower in the x-direction.

[0093] In step S1402, the life (i.e., expected life) of the tower is calculated based on the average value of the equivalent fatigue load of the first tower, the tower design load at the height of the first tower, and the fatigue material coefficient of the tower.

[0094] The steps for calculating the tower life based on the average value of the equivalent fatigue load of the first tower, the tower design load at the height of the first tower, and the fatigue material coefficient of the tower may include: obtaining the power corresponding to the fatigue material coefficient based on the tower design load and the average value of the equivalent fatigue load of the first tower, and determining the parameter associated with the power as the tower life.

[0095] As an example, the lifespan Y of the tower can be calculated using the following equation (3).

[0096]

[0097] Wherein: F design The tower design load is for the corresponding tower height; m: fatigue material coefficient, Fmy_ave is the average value of the equivalent fatigue load of the first tower.

[0098] In step S1403, the status of the tower is determined based on the tower's lifespan.

[0099] As an example, the remaining lifespan L of the tower can be calculated based on the tower's lifespan. The remaining lifespan L can be calculated using the following formula (4):

[0100]

[0101] Specifically, if the remaining service life L does not meet the design requirements, the tower's condition can be determined to be abnormal. Assuming M (the number of years the tower has been in operation) is 5 years, Y is 18 years, and the remaining service life L is 13 years, the remaining service life L is less than 15 years, indicating that the tower's condition is abnormal and the remaining service life does not meet the requirements.

[0102] Specifically, the tower's condition can be determined to be excellent if the remaining service life L exceeds the design requirements. Assuming M (the number of years the tower has been in operation) is 5 years, Y is 22 years, and the remaining service life L is 18 years, the remaining service life L is greater than 15, indicating that the tower's condition is excellent and the remaining service life meets the requirements.

[0103] As an example, the judgment can also be made directly based on the calculated tower life Y. For instance, if the tower life Y is less than 20, the wind turbine generator can be controlled or an alarm signal can be issued.

[0104] In other words, the abnormal state of the tower can be determined when the difference between the tower's designed lifespan and its lifespan meets a predetermined threshold.

[0105] As described above, if the difference between the tower's designed lifespan and its actual lifespan (i.e., remaining lifespan) is less than a predetermined threshold, the tower is determined to be in an abnormal condition; if the difference is greater than the predetermined threshold, the tower is determined to be in a good condition. The predetermined threshold can be the difference between the tower's theoretical design lifespan and the number of years the wind turbine generator has been in operation.

[0106] Figure 10 This is the trajectory showing the relationship between the operating life and design life of a certain type of wind turbine unit. Trajectory G51 represents the design life, trajectory G52 represents the expected life (Y), trajectory G53 represents the life threshold, and trajectory G54 represents the remaining life (L). According to this trajectory, the expected life (Y) is greater than the design life, and the remaining life (L) is greater than the life threshold, indicating that the wind turbine tower is in excellent condition.

[0107] If an abnormal condition is detected in the wind turbine generator set, an alarm signal will be generated, and the wind turbine generator set can also be controlled.

[0108] The tower condition monitoring method according to embodiments of this disclosure may further include: determining the tower condition based on the tower condition determined by the first tower equivalent fatigue load, and generating an alarm signal upon determining an abnormal tower condition; or including controlling the wind turbine generator based on the tower condition determined by the first tower equivalent fatigue load (e.g., the control method includes power-limited operation, speed-limited operation, operation beyond design power, operation beyond design speed, or shutdown, etc.); or including determining the tower condition based on the tower condition determined by the first tower equivalent fatigue load, generating an alarm signal upon determining an abnormal tower condition, and controlling the wind turbine generator. When the tower condition is good, after a detailed evaluation, the generator can be controlled to operate beyond design power (i.e., operation beyond design power, operation beyond design speed) to increase power generation or extend service life, thereby maximizing benefits.

[0109] Figure 4 This is a block diagram illustrating a tower condition monitoring device according to an embodiment of the present disclosure.

[0110] The tower condition monitoring device 400 according to an embodiment of the present disclosure may include a data acquisition unit 410, a processing unit 420, a calculation unit 430, and a determination unit 440.

[0111] The acquisition unit 410 can acquire the first nacelle acceleration data of the wind turbine generator in real time through the nacelle acceleration sensor.

[0112] The processing unit 420 can perform measurement algorithm processing on the first cabin acceleration data and obtain the first equivalent acceleration based on the processing result.

[0113] For example, the processing unit 420 can count the first cabin acceleration data using counting methods such as rainflow counting, peak counting, and range counting, and obtain the first equivalent acceleration based on the counting results.

[0114] The calculation unit 430 can calculate the equivalent fatigue load of the first tower based on the first equivalent acceleration and a predetermined calibration relationship, wherein the calibration relationship characterizes the relationship between the first equivalent acceleration and the equivalent fatigue load of the first tower.

[0115] The determination unit 440 can determine the state of the tower based on the equivalent fatigue load of the first tower.

[0116] The tower condition monitoring device 400 according to an embodiment of the present disclosure may further include a calibration unit 450, which may obtain the above-mentioned calibration relationship in advance. Specifically, the calibration unit 450 may be configured to determine the calibration relationship based on nacelle acceleration data and tower load data obtained from simulation or wind turbine.

[0117] For example, the calibration unit 450 can acquire second nacelle acceleration data and second tower load data at the first tower height under different external environments through simulation or wind turbines; count the second nacelle acceleration data to obtain a first counting result; count the second tower load data to obtain a second counting result; calculate the second equivalent acceleration at the first tower height based on the first counting result; calculate the second equivalent fatigue load at the first tower height based on the second counting result; and perform trajectory fitting or machine learning on the second equivalent acceleration and the second equivalent fatigue load to obtain the calibration relationship at the first tower height. The specific process is as described above and will not be repeated here.

[0118] The calibration unit 450 can also calibrate the relationship between the first equivalent acceleration and the equivalent fatigue load of the tower at different heights under different power levels. The calibration unit 450 can obtain multiple calibration relationships at different tower heights.

[0119] It should be understood that the various units or modules in the yaw control apparatus according to exemplary embodiments of this disclosure may be implemented as hardware components and / or software components. Those skilled in the art can implement the various units, for example, using field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), software algorithms, etc., depending on the processes performed by each defined unit.

[0120] Each of the above steps can be programmed as a software program or instruction. Therefore, the control method according to the exemplary embodiments of this disclosure can be implemented via software. The computer-readable storage medium of the exemplary embodiments of this disclosure can store a computer program that, when executed by a processor, implements the tower status monitoring method as described in the exemplary embodiments above.

[0121] According to various embodiments of this disclosure, apparatus (e.g., modules or their functions) or methods can be implemented by programs or instructions stored in a computer-readable storage medium. When such instructions are executed by a processor, the processor can perform a function corresponding to the instruction or perform a method corresponding to the instruction. At least a portion of a module can be implemented (e.g., executed) by a processor. At least a portion of a programmed module can include modules, programs, routines, instruction sets, and procedures for performing at least one function. In one example, the instructions or software include machine code (such as machine code generated by a compiler) that is directly executed by one or more processors or computers. In another example, the instructions or software include higher-level code that is executed by one or more processors or computers using an interpreter. Instructions or software can be written using any programming language based on the block diagrams and flowcharts shown in the accompanying drawings and the corresponding description in the specification.

[0122] Computer-readable storage media include non-transitory computer-readable storage media, such as magnetic media like floppy disks and magnetic tapes, optical media (including optical disc (CD) ROMs and DVD ROMs), magneto-optical media like flexible optical discs, hardware devices such as ROMs and RAMs designed for storing and executing program instructions, and flash memory. The program instructions include language code executable by a computer using an interpreter and machine language code generated by a compiler. The aforementioned hardware devices can be implemented by one or more software modules for performing the operations of the various embodiments of this disclosure.

[0123] The modules or programming modules disclosed herein may include at least one of the aforementioned components, with some components omitted or others added. The operations of the modules, programming modules, or other components may be executed sequentially, in parallel, cyclically, or probingly. Furthermore, some operations may be executed in a different order, may be omitted, or may be extended with other operations.

[0124] The computer-readable storage medium and / or tower condition monitoring device of exemplary embodiments of this disclosure may be part of the controller (e.g., main controller) of a wind turbine generator set.

[0125] The controller of a wind turbine generator set according to an exemplary embodiment of this disclosure may include a processor and a computer-readable storage medium storing a program or instructions that, when executed by the processor, implement the tower status monitoring method described above.

[0126] The wind turbine generator sets of embodiments of this disclosure may include the computer-readable storage medium, tower condition monitoring device, or controller described above.

[0127] The tower condition monitoring method and tower condition monitoring device according to embodiments of the present disclosure are capable of monitoring the condition of the tower.

[0128] The tower condition monitoring and tower condition monitoring device according to embodiments of the present disclosure can determine the remaining lifespan of the tower of a wind turbine generator set.

[0129] The tower condition monitoring method and tower condition monitoring device according to embodiments of the present disclosure can improve the safety of wind turbine generator sets.

[0130] The tower condition monitoring method and tower condition monitoring device according to embodiments of the present disclosure can improve the power generation of wind turbine generator sets.

[0131] While some exemplary embodiments of this disclosure have been shown and described, those skilled in the art will understand that modifications may be made to these embodiments without departing from the principles and spirit of this disclosure as defined by the claims and their equivalents. For example, technical features of different embodiments may be combined.

Claims

1. A method for monitoring the tower condition of a wind turbine generator set, characterized in that, include: Real-time acceleration data of the first nacelle of the wind turbine generator is obtained through the nacelle acceleration sensor; The first cabin acceleration data is processed by a measurement algorithm, and the first equivalent acceleration is obtained based on the processing result. The equivalent fatigue load of the first tower is calculated based on the first equivalent acceleration and the predetermined calibration relationship; The tower's condition is determined based on the equivalent fatigue load of the first tower. The calibration relationship describes the relationship between the first equivalent acceleration and the first tower's equivalent fatigue load. The step of processing the first cabin acceleration data using a measurement algorithm and obtaining the first equivalent acceleration based on the processing result includes: counting the first cabin acceleration data and obtaining the first equivalent acceleration based on the counting result. The first tower's equivalent fatigue load includes equivalent fatigue loads at different tower heights.

2. The method for monitoring the tower condition of a wind turbine generator according to claim 1, characterized in that, The calibration relationship is determined based on the nacelle acceleration data and tower load data obtained from simulation or wind turbine.

3. The method for monitoring the tower condition of a wind turbine generator according to claim 2, characterized in that, The steps for determining the calibration relationship based on nacelle acceleration data and tower load data obtained from simulation or wind turbines include: Acceleration data of the second nacelle and load data of the second tower at the height of the first tower were obtained under different external environments through simulation or wind turbines. The acceleration data of the second cabin are counted to obtain a first counting result; The second tower load data is counted to obtain a second counting result; Calculate the second equivalent acceleration based on the first counting result for the first tower height; The equivalent fatigue load of the second tower at the height of the first tower is calculated based on the second counting result; The calibration relationship of the first tower height is obtained by performing trajectory fitting or machine learning on the second equivalent acceleration and the second tower equivalent fatigue load.

4. The method for monitoring the tower condition of a wind turbine generator according to claim 3, characterized in that, The calibration relationship further characterizes the variation relationship between the first equivalent acceleration and the first equivalent fatigue load of the first tower at different wind speeds or power levels. The tower condition monitoring method also includes obtaining wind speed or the power of the wind turbine generator set. The steps for calculating the equivalent fatigue load of the first tower based on the first equivalent acceleration and the predetermined calibration relationship include: The corresponding calibration relationship for the height of the first tower is determined based on the wind speed or the power. The equivalent fatigue load of the first tower is calculated based on the first equivalent acceleration at the first tower height and the corresponding calibration relationship.

5. The method for monitoring the tower condition of a wind turbine generator as described in claim 4, characterized in that, The steps for determining the tower's condition based on the first tower's equivalent fatigue load include: The average equivalent fatigue load of the first tower at the first tower height during time period M is calculated to obtain the average equivalent fatigue load of the first tower at the first tower height. The life of the tower is calculated based on the average equivalent fatigue load of the first tower, the tower design load at the height of the first tower, and the fatigue material coefficient of the tower. The condition of the tower is determined based on its lifespan. Where M represents the time period during which the tower has been in operation.

6. The method for monitoring the tower condition of a wind turbine generator according to claim 5, characterized in that, The steps for calculating the tower life based on the average equivalent fatigue load of the first tower, the tower design load at the first tower height, and the tower's fatigue material coefficient include: The fatigue material coefficient is obtained by power of the average value of the tower design load and the first tower equivalent fatigue load, and the parameter associated with the power is determined as the tower life.

7. The method for monitoring the tower condition of a wind turbine generator according to claim 6, characterized in that, In response to a predetermined condition being met between the difference between the tower's designed lifespan and the tower's lifespan and a predetermined threshold, the tower's condition is determined to be abnormal.

8. The method for monitoring the tower condition of a wind turbine generator according to claim 7, characterized in that, The tower condition monitoring method also includes: Based on the tower's condition determined by the first tower's equivalent fatigue load, an alarm signal is generated when the tower's condition is deemed abnormal; or The wind turbine generator set is controlled based on the tower state determined by the equivalent fatigue load of the first tower. Or both.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or programs that, when executed by a processor, implement the tower condition monitoring method for wind turbine generators according to any one of claims 1 to 8.

10. A tower condition monitoring device for a wind turbine generator set, characterized in that, include: The data acquisition unit acquires real-time acceleration data of the first nacelle of the wind turbine generator through the nacelle acceleration sensor. The processing unit performs measurement algorithm processing on the first cabin acceleration data and obtains the first equivalent acceleration based on the processing result; The calculation unit calculates the equivalent fatigue load of the first tower based on the first equivalent acceleration and the predetermined calibration relationship; The determining unit determines the state of the tower based on the equivalent fatigue load of the first tower. The calibration relationship describes the relationship between the first equivalent acceleration and the first tower's equivalent fatigue load. The processing unit is further configured to: count the acceleration data of the first cabin and obtain a first equivalent acceleration based on the counting result. The first tower's equivalent fatigue load includes equivalent fatigue loads at different tower heights.

11. The tower condition monitoring device for wind turbine generators according to claim 10, characterized in that, The tower condition monitoring device also includes a calibration unit, which is configured to determine the calibration relationship based on nacelle acceleration data and tower load data obtained from simulation or wind turbine.

12. The tower condition monitoring device for wind turbine generators according to claim 11, characterized in that, The calibration unit is further configured as follows: Acceleration data of the second nacelle and load data of the second tower at the height of the first tower were obtained under different external environments through simulation or wind turbines. The acceleration data of the second cabin are counted to obtain a first counting result; The second tower load data is counted to obtain a second counting result; Calculate the second equivalent acceleration based on the first counting result for the first tower height; The equivalent fatigue load of the second tower at the height of the first tower is calculated based on the second counting result; The calibration relationship of the first tower height is obtained by performing trajectory fitting or machine learning on the second equivalent acceleration and the second tower equivalent fatigue load.

13. A controller for a wind turbine generator set, characterized in that, The method includes a processor and a computer-readable storage medium storing a program or instructions that, when executed by the processor, implement the tower condition monitoring method for a wind turbine generator according to any one of claims 1 to 8.

14. A wind turbine generator set, characterized in that, It includes a tower condition monitoring device for a wind turbine generator set according to any one of claims 10 to 12, or a controller for a wind turbine generator set according to claim 13.

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