Tower state monitoring method and device, controller and wind generating set

Abstract

The invention provides a tower state monitoring method and device, a controller and a wind generating set. The tower state monitoring method comprises the following steps: acquiring first cabin acceleration data of a wind generating set in real time through a cabin acceleration sensor; performing measurement algorithm processing on the first cabin acceleration data and acquiring a first equivalent acceleration based on a processing result; calculating a first tower equivalent fatigue load based on the first equivalent acceleration and a predetermined calibration relation; the state of the tower is determined according to the first tower equivalent fatigue load, and the calibration relation represents the change relation between the first equivalent acceleration and the first tower equivalent fatigue load. According to the tower state monitoring method disclosed by the embodiment of the invention, the state of the tower can be monitored.

Description

Technical Field

[0001] The present disclosure generally relates to the field of wind power, and more specifically, to a tower state monitoring method, device, controller and wind turbine generator set. Background Art

[0002] As the operating time of wind turbines increases, the equipment of wind turbines will inevitably age and fail, bringing safety hazards and even economic losses to wind power companies.

[0003] For wind turbines, the tower is a key 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 turbines is gradually increasing. Therefore, it is necessary to monitor the tower status under the premise of economy and accurately evaluate or predict the remaining life of the tower. Then, based on the life of the tower, the control plan and the unit operation plan can be adjusted (such as power limit operation, speed limit operation, shutdown, whole machine life extension and power increase and overgeneration, etc.), so as to maximize the whole machine profit while ensuring the safety of unit operation. Summary of the invention

[0005] One of the objectives of the exemplary embodiments of the present disclosure is to provide a state monitoring method for monitoring the tower state of a wind turbine generator set.

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

[0007] According to an embodiment of the present disclosure, the calibration relationship may be determined based on nacelle acceleration data and tower load data acquired through simulation or the wind turbine.

[0008] According to an embodiment of the present disclosure, performing a measurement algorithm processing on the first cabin acceleration data and acquiring the first equivalent acceleration based on the processing result may include: counting the first cabin acceleration data and acquiring the first equivalent acceleration based on the counting result.

[0009] According to an embodiment of the present disclosure, the step of determining a calibration relationship based on cabin acceleration data and tower load data obtained by simulation or a wind turbine may include: obtaining second cabin acceleration data under different external environments and second tower load data at a first tower height through simulation or a wind turbine; counting the second cabin 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 a second tower 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 tower equivalent fatigue load to obtain a calibration relationship at the first tower height.

[0010] According to an embodiment of the present disclosure, the calibration relationship can further characterize the changing relationship between the first equivalent acceleration of the first tower height under different wind speeds or powers and the first tower equivalent fatigue load. The tower state monitoring method may further include obtaining the wind speed or the power of the wind turbine generator set, and the step of calculating the first tower equivalent fatigue load 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; calculating the first tower equivalent fatigue load based on the first equivalent acceleration of the first tower height and the corresponding calibration relationship.

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

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

[0013] According to an embodiment of the present disclosure, in response to a difference between a tower life design value and the life of the tower satisfying a predetermined condition and a predetermined threshold, it may be determined that the state of the tower is abnormal.

[0014] According to an embodiment of the present disclosure, the tower status monitoring method may further include: determining that the state of the tower is abnormal and generating an alarm signal based on the state of the tower determined by the first tower equivalent fatigue load; controlling the wind turbine generator set based on the state of the tower determined by the first tower equivalent fatigue load; or both.

[0015] According to a second aspect of the present disclosure, a computer-readable storage medium stores instructions or programs, and when the instructions or programs are executed by a processor, the tower status monitoring method of the wind turbine generator set is implemented.

[0016] According to a third aspect of the present disclosure, a tower state monitoring device of a wind turbine generator set includes: a collection unit, which obtains first cabin acceleration data of the wind turbine generator set in real time through a cabin acceleration sensor; a processing unit, which performs measurement algorithm processing on the first cabin acceleration data and obtains a first equivalent acceleration based on the processing result; a calculation unit, which calculates a first tower equivalent fatigue load based on the first equivalent acceleration and a predetermined calibration relationship; and a determination unit, which determines the state of the tower according to the first tower equivalent fatigue load, wherein the calibration relationship characterizes the change relationship between the first equivalent acceleration and the first tower equivalent fatigue load.

[0017] According to an embodiment of the present disclosure, the tower state monitoring device may further include a calibration unit, which is configured to determine a calibration relationship based on nacelle acceleration data and tower load data obtained by simulation or wind turbine.

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

[0019] According to a fourth aspect of the present disclosure, a controller of a wind turbine generator set includes a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the tower status monitoring method of the wind turbine generator set is implemented.

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

[0021] The tower state monitoring method and the tower state monitoring device according to the embodiments of the present disclosure can monitor the state of a tower.

[0022] The tower state monitoring method and the tower state monitoring device according to the embodiments of the present disclosure can determine whether the service life of the tower meets the requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other objects and features of the exemplary embodiments of the present disclosure will become more apparent through the following description in conjunction with the accompanying drawings which exemplarily illustrate the embodiments, in which:

[0024] Figure 1 is a flow chart showing a tower state monitoring method according to a first embodiment of the present disclosure;

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

[0026] Figure 3 is a flow chart illustrating determining the status of a tower according to an embodiment of the present disclosure;

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

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

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

[0030] Figure 7 is a diagram showing a nacelle acceleration and tower load calibration relationship according to an embodiment of the present disclosure;

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

[0032] Fig. 9 is a load timing diagram showing a certain cross section of a tower measured by a prototype according to an embodiment of the present disclosure;

[0033] Fig.10 1 is a diagram showing the relationship between the unit operating time and the designed life, theoretical life, life threshold and remaining life according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] The following detailed description is provided to help gain a comprehensive understanding of the methods, devices and / or systems described herein. However, the order of operations described herein is only an example and is not limited to those orders set forth herein, but may be equivalently replaced or changed except for operations that must occur or be performed in a specific order. In addition, for greater clarity and simplicity, the description of content 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 limited to the examples described herein.

[0036] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by a person of ordinary skill in the art to which the present disclosure belongs 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 the present disclosure, and should not be interpreted in an idealized or overly formal 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 the previous embodiments may be omitted if they appear again in the subsequent embodiments. In addition, the technical features described in different or the same embodiments may be combined in any manner, as long as the combined embodiments or technical solutions are complete and can solve the technical problems of the present application or achieve the technical effects described or not described in the present application but can be determined based on the above complete technical solutions.

[0038] The present invention obtains the relationship between the equivalent acceleration of the cabin and the equivalent fatigue load of the tower by calibration. After the calibration relationship is determined, there is no need for a load sensor to monitor the fatigue load of the tower, nor is there a need to simulate the fatigue load of the tower. Complex calculations are not required, and there is no need to estimate the tower load by wind speed. Instead, the tower load can be inferred based on operating data, and then the state of the tower can be determined based on the tower load, and the life of the tower can be estimated. In the case that the tower life does not meet the requirements, the wind turbine can be controlled or an alarm signal can be issued. In the case that the tower life exceeds the design requirements, the wind turbine can be controlled or its life can be extended to increase power generation. The following will be combined with Figures 1 to 10 Specific embodiments of the present disclosure are described.

[0039] Figure 1 is a flow chart showing a tower state monitoring method according to a first embodiment of the present disclosure; Figure 2 is a flowchart showing a calibration process according to a first embodiment of the present disclosure; Figure 3 is a flow chart illustrating determining the status of a tower according to an embodiment of the present disclosure; and Figure 4 is a block diagram showing a tower state monitoring device according to an embodiment of the present disclosure; Figure 5 is a timing diagram showing simulated cabin acceleration according to an embodiment of the present disclosure; Figure 6 is a load timing diagram showing a certain cross section of a simulated tower according to an embodiment of the present disclosure; Figure 7 is a diagram showing a nacelle acceleration and tower load calibration relationship according to an embodiment of the present disclosure; Figure 8 is a timing diagram showing the measured cabin acceleration of a prototype according to an embodiment of the present disclosure; Fig. 9is a load timing diagram showing a certain cross section of a tower measured by a prototype according to an embodiment of the present disclosure; Fig.10 1 is a diagram showing the relationship between the unit operating time and the designed life, theoretical life, life threshold and remaining life according to an embodiment of the present disclosure.

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

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

[0042] The state of the tower is detected by acquiring the first nacelle acceleration data of the in-service wind turbine in real time. Specifically, the nacelle acceleration data of the wind turbine during operation (for example, during normal power generation) can be acquired in real time by a nacelle acceleration sensor.

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

[0044] The step of performing a 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 (eg, rain flow counting) and obtaining the first equivalent acceleration based on the counting result.

[0045] That is, the measurement algorithm may include various counting methods such as amplitude counting, rain flow counting, etc., which can record the number of times the load or acceleration occurs in the corresponding interval.

[0046] The rainflow counting method is a counting method widely used in fatigue design and fatigue testing. The counting results of the rainflow counting method are between the peak counting method and the variable range counting method, which can provide more realistic data. The rainflow counting method is based on counting the closed stress-strain hysteresis loop one by one. From the fatigue point of view, it can better reflect the whole process of random load or acceleration.

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

[0048] The counting result of the rainflow counting method can be a Markov matrix (i.e., a load matrix or an acceleration matrix), which indicates that within the assessment period, each data represents the number of cycles under a certain load mean and a certain load amplitude, or the number of cycles under each acceleration mean and a certain acceleration amplitude. The equivalent fatigue load can be determined based on the number of cycles under a certain load mean and amplitude and the corresponding load, and the equivalent acceleration can be determined based on the number of cycles under a certain acceleration mean and an acceleration amplitude and the corresponding acceleration.

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

[0050] Specifically, the equivalent acceleration or equivalent fatigue load F can be determined by the following formula (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 load cycles in the i-th load interval or the number of acceleration cycles corresponding to the i-th acceleration interval; N: Reference number; m: Fatigue material coefficient (for example, 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 first tower equivalent fatigue load is calculated based on the first equivalent acceleration and a predetermined calibration relationship.

[0054] That is to say, the equivalent fatigue load of the tower at different tower sections (different tower heights) can be estimated or calculated based on the cabin acceleration data acquired in real time, so there is no need to install load sensors at different positions of the tower.

[0055] As shown above, the first tower equivalent fatigue load here may include equivalent fatigue loads of different tower cross-sections or different tower heights. The predetermined calibration relationship may characterize the variation relationship between the first equivalent acceleration and the first tower equivalent fatigue load, and the variation relationship here may be a functional relationship such as a linear relationship or a nonlinear relationship, but this is only an example.

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

[0057] That is, a first equivalent acceleration at a predetermined height may be obtained, and then based on the first equivalent acceleration and a calibration relationship corresponding to the predetermined height, a first tower equivalent fatigue load at the predetermined height may be determined.

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

[0059] For example, after determining the first tower equivalent fatigue load at a predetermined height, the tower state can be determined in combination with the tower design load at the corresponding height. The tower state can be judged based on the tower equivalent fatigue loads at different heights and the calibration relationship at the corresponding height to improve the reliability of the judgment. During monitoring and judgment, the tower equivalent fatigue loads 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 the wind turbine.

[0061] Reference Figure 2 The step of determining the calibration relationship based on the cabin acceleration data and tower load data obtained by simulation or wind turbine may include step S1301, step S1302, step S1303, step S1304 and step S1305. The wind turbine here refers to a test unit, a test unit or a representative unit (e.g., a prototype). The calibration relationship obtained by simulation or test unit can be used for wind turbines of the same model to monitor the tower status.

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

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

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

[0065] The acceleration sequence of the simulated cabin of a certain model of unit is as follows Figure 5 As shown in the figure, the simulated tower load time series of a certain section of a certain model unit is as follows Figure 6 shown.

[0066] Reference Figure 5 , the horizontal axis represents time in seconds, the vertical axis represents cabin acceleration, the solid line trajectory G11 represents the cabin acceleration in the y direction, and the dotted line trajectory G12 represents the 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. The trajectory G21 represents the tower load My in the y direction at a height of 9.12 m, and the trajectory G22 represents the tower load Mx in the x direction at a height of 9.12 m.

[0068] In step S1302, the second cabin acceleration data 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, a second equivalent acceleration at the first tower height is calculated based on the first counting result.

[0071] For example, according to the above formula (1) Figure 5 The equivalent accelerations in the x-direction and y-direction are 0.6972m / s^2(m=4) and 0.8374m / s^2(m=4) respectively.

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

[0073] For example, according to the above formula (1) 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 cabin acceleration data may be counted to obtain a corresponding first counting result. The tower load data may also be counted to obtain a corresponding second counting result, and the equivalent acceleration (equivalent acceleration Faccx in the x direction and equivalent acceleration Faccy in the y direction) may be calculated according to the corresponding first counting result, and the tower equivalent fatigue load (equivalent tower fatigue load Fmx in the x direction and equivalent tower fatigue load Fmy in the y direction) may be calculated according to the corresponding second counting result. The specific counting process may be as described above, and will not be repeated here.

[0075] The result of the counting can be a Markov matrix or a table reflecting the number of load occurrences in different acceleration intervals or load intervals. In the process of obtaining the calibration relationship, the Markov matrix obtained by rain flow counting is used to analyze and calculate the equivalent fatigue, which can more accurately reflect the changing relationship between the equivalent acceleration and the equivalent fatigue of the tower, which is conducive to obtaining a more accurate calibration relationship, improving the accuracy of tower life prediction, and improving 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 a 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 by 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] In addition, the calibration relationship can be obtained by prototype testing. The specific steps are as follows: use the prototype to collect the load data and acceleration data of the wind turbine generator set, and also collect the operation data and external environment data of the unit at the same time; count the cabin acceleration data and tower load data respectively to obtain the corresponding counting results (for example, Markov matrix); calculate the equivalent acceleration and tower equivalent fatigue load; finally, obtain the relationship between the two by linear fitting / machine learning.

[0080] An example of the measured cabin acceleration timing of a certain model prototype is as follows: Figure 8 An example of the measured tower load sequence of a certain model prototype is shown in Fig. 9 shown.

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

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

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

[0084] That is to say, in the process of establishing the calibration relationship, the variation relationship between the first equivalent acceleration of a certain tower height and the first tower equivalent fatigue load under different wind speeds can be obtained. Although the wind speed is used as an example to establish the variation relationship or calibration relationship under the external environment, this is only an example, and calibration relationships under more dimensions can also be established. When in use, the input variables of the corresponding dimensions (for example, specific wind speed, specific power, and specific state of the unit, etc.) are obtained, and the equivalent fatigue load of the tower is calculated.

[0085] The tower status monitoring method according to an embodiment of the present disclosure may further include a step of obtaining a wind speed or a power of a wind turbine generator set.

[0086] The step of calculating the first tower equivalent fatigue load based on the first equivalent acceleration and a predetermined calibration relationship may include determining a corresponding calibration relationship of the first tower height according to the wind speed or power; and calculating the first tower equivalent fatigue load based on the first equivalent acceleration at 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, and the state of the tower can be determined according to the first tower equivalent fatigue load at different tower heights, so that the tower life of the wind turbine generator set can be determined more accurately.

[0088] Figure 3 is a flow chart illustrating determining a status of a tower according to an embodiment of the present disclosure.

[0089] The step of determining the state of the tower according to the first tower equivalent fatigue load may include step S1401 , step S1402 , and step S1403 .

[0090] In step S1401, the first tower equivalent fatigue load at the first tower height during M time periods (for example, M years) is averaged to obtain the first tower equivalent fatigue load average value at the first tower height. Specifically, the first tower equivalent fatigue load average value Fmy_ave at the first tower height can be calculated by the following formula (2).

[0091]

[0092] Among them: Fmy i is the equivalent fatigue load data of the ith tower; M is the period of operation of the unit (e.g., the number of years (positive integer)); m is the fatigue material coefficient, generally m = 4. Although in formula (2), the average value of the equivalent fatigue load of the first tower is calculated based on the equivalent fatigue load of the tower in the y direction, this is only an example, and the calculation can also be based on the equivalent fatigue load of the tower in the x direction.

[0093] In step S1402, the life of the tower (ie, expected life) is calculated according to the average value of the equivalent fatigue load of the first tower, the tower design load of the first tower height, and the fatigue material coefficient of the tower.

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

[0095] As an example, the life Y of the tower can be calculated by the following formula (3).

[0096]

[0097] Among them: F design is the tower design load of 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 state of the tower is determined according to the life of the tower.

[0099] As an example, the remaining life L of the tower can be calculated according to the life of the tower. The remaining life L can be calculated by the following formula (4):

[0100]

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

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

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

[0104] That is, in response to a difference between the tower life design value and the life of the tower satisfying a predetermined condition and a predetermined threshold, it may be determined that the state of the tower is abnormal.

[0105] As described above, if the difference between the tower life design value and the tower life (i.e., the remaining life) is less than a predetermined threshold, it is determined that the state of the tower is abnormal, and if the difference between the tower life design value and the tower life (i.e., the remaining life) is greater than a predetermined threshold, it is determined that the state of the tower is excellent. The predetermined threshold here can be the difference between the theoretical design life of the tower and the number of years the wind turbine generator has been in operation.

[0106] Fig.10 It is the trajectory of the relationship between the operating life and the design life of a certain type of 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 the trajectory, it can be seen that the expected life Y is greater than the design life, and the remaining life L is greater than the life threshold. The wind turbine tower is in excellent condition.

[0107] When it is determined that the state of the wind turbine generator set is abnormal, an alarm signal is generated and the wind turbine generator set can also be controlled.

[0108] The tower state monitoring method according to the embodiment of the present disclosure may also include: determining that the state of the tower is abnormal and generating an alarm signal according to the state of the tower determined by the first tower equivalent fatigue load; or including controlling the wind turbine generator set according to the state of the tower determined by the first tower equivalent fatigue load (for example, the control method includes limited power operation, limited speed operation, over-design power operation, over-design speed operation or shutdown, etc.); or including determining that the state of the tower is abnormal and generating an alarm signal and controlling the wind turbine generator set according to the state of the tower determined by the first tower equivalent fatigue load. When the tower state is good, after a detailed evaluation, the unit can be controlled to perform over-design operation (i.e., over-design power operation, over-design speed operation) to increase power generation or extend life operation to ensure maximum benefits.

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

[0110] The tower state monitoring device 400 according to an embodiment of the present disclosure may include a collecting unit 410 , a processing unit 420 , a calculating unit 430 , and a determining unit 440 .

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

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

[0113] For example, the processing unit 420 may count the first cabin acceleration data by using a counting method such as a rain flow counting method, a peak counting method, and a variable range counting method, and obtain the first equivalent acceleration based on the counting result.

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

[0115] The determining unit 440 may determine the state of the tower according to the first tower equivalent fatigue load.

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

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

[0118] The calibration unit 450 may also calibrate the variation relationship between the first equivalent acceleration and the tower equivalent fatigue load at different heights under different powers. The calibration unit 450 may obtain multiple calibration relationships at different tower heights.

[0119] It should be understood that each unit or module in the yaw control device according to the exemplary embodiment of the present disclosure may be implemented as a hardware component and / or a software component. Those skilled in the art may implement each unit, for example, using a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a software algorithm, etc., according to the processing performed by each defined unit.

[0120] Each operation of the above steps can be written as a software program or instruction. Therefore, the control method according to the exemplary embodiment of the present disclosure can be implemented via software. The computer-readable storage medium of the exemplary embodiment of the present disclosure can store a computer program. When the computer program is executed by a processor, the tower status monitoring method as described in the above exemplary embodiment is implemented.

[0121] According to various embodiments of the present disclosure, a device (e.g., a module or their functions) or a method may be implemented by a program or instruction stored in a computer-readable storage medium. When the instruction is executed by a processor, the processor may perform a function corresponding to the instruction or perform a method corresponding to the instruction. At least a portion of a module may be implemented (e.g., executed) by a processor. At least a portion of a programming module may include a module, a program, a routine, an instruction set, and a process for performing at least one function. In one example, an instruction or software includes a machine code (such as a machine code generated by a compiler) directly executed by one or more processors or computers. In another example, an instruction or software includes a higher level code executed by one or more processors or computers using an interpreter. Instructions or software may be written in any programming language based on the block diagrams and flow charts shown in the accompanying drawings and the corresponding descriptions in the specification.

[0122] Computer-readable storage media include non-transitory computer-readable storage media, for example, magnetic media such as floppy disks and tapes, optical media (including compact disks (CD) ROMs and DVD ROMs), magneto-optical media such as floppy disks, hardware devices such as ROMs, RAMs, and flash memories designed to store and execute program commands. The program commands include language codes executable by a computer using an interpreter and machine language codes generated by a compiler. The above-mentioned hardware devices can be implemented by one or more software modules for performing the operations of the various embodiments of the present disclosure.

[0123] The module or programming module of the present disclosure may include at least one of the aforementioned components when some components are omitted or other components are added. The operation of the module, programming module or other components may be performed sequentially, in parallel, in a loop or in a tentative manner. In addition, some operations may be performed in different orders, may be omitted or extended with other operations.

[0124] The computer-readable storage medium and / or the tower status monitoring device of the exemplary embodiments of the present disclosure may be part of a controller (eg, a main controller) of a wind turbine generator system.

[0125] The controller of the wind turbine generator set of the exemplary embodiment of the present disclosure may include a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the tower status monitoring method described above is implemented.

[0126] The wind turbine generator set according to the embodiment of the present disclosure may include the computer-readable storage medium, the tower status monitoring device, or the controller as described above.

[0127] The tower state monitoring method and the tower state monitoring device according to the embodiments of the present disclosure can monitor the state of a tower.

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

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

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

[0131] Although some exemplary embodiments of the present disclosure have been shown and described, it will be understood by those skilled in the art that modifications may be made to these embodiments without departing from the principles and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents; for example, the technical features of different embodiments may be combined.

Claims

1. A method for monitoring the tower status of a wind turbine generator set, characterized in that: include: Acquire the acceleration data of the first nacelle of the wind turbine generator set in real time through the nacelle acceleration sensor; performing a measurement algorithm processing on the first cabin acceleration data and obtaining a first equivalent acceleration based on the processing result; Calculating a first tower equivalent fatigue load based on the first equivalent acceleration and a predetermined calibration relationship; Determine the state of the tower according to the equivalent fatigue load of the first tower, The calibration relationship represents a variation relationship between the first equivalent acceleration and the first tower equivalent fatigue load.

2. The tower status monitoring method of a wind turbine generator set according to claim 1, characterized in that: The calibration relationship is determined according to the nacelle acceleration data and tower load data obtained by simulation or the wind turbine.

3. The tower status monitoring method of a wind turbine generator set according to claim 1, characterized in that: The step of performing a measurement algorithm processing on the first cabin acceleration data and obtaining a first equivalent acceleration based on a processing result includes: counting the first cabin acceleration data and obtaining a first equivalent acceleration based on a counting result.

4. The tower status monitoring method of a wind turbine generator set according to claim 2, characterized in that: The step of determining the calibration relationship according to the cabin acceleration data and tower load data obtained by simulation or wind turbine includes: Acquire the acceleration data of the second nacelle under different external environments and the load data of the second tower at the height of the first tower through simulation or a wind turbine; counting the second cabin acceleration data to obtain a first counting result; Counting the second tower load data to obtain a second counting result; Calculate a second equivalent acceleration at a first tower height based on the first counting result; Calculating a second tower equivalent fatigue load at the first tower height 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.

5. The tower status monitoring method of a wind turbine generator set according to claim 4, characterized in that: The calibration relationship further characterizes the variation relationship between the first equivalent acceleration and the first tower equivalent fatigue load at the first tower height under different wind speeds or powers, The tower state monitoring method further comprises obtaining the wind speed or the power of the wind turbine generator set, The step of calculating the first tower equivalent fatigue load based on the first equivalent acceleration and a predetermined calibration relationship includes: Determining a corresponding calibration relationship of a first tower height according to the wind speed or the power; A first tower equivalent fatigue load is calculated based on the first equivalent acceleration at the first tower height and the corresponding calibration relationship.

6. The tower status monitoring method of a wind turbine generator set according to claim 5, characterized in that: The step of determining the state of the tower according to the first tower equivalent fatigue load comprises: Averaging the first tower equivalent fatigue loads at the first tower height during the M period to obtain an average value of the first tower equivalent fatigue loads at the first tower height; Calculating the life of the tower according to the average value of the equivalent fatigue load of the first tower, the tower design load of the first tower height, and the fatigue material coefficient of the tower; determining the state of the tower according to the life of the tower, Wherein, M is the time period during which the tower has been in operation.

7. The tower status monitoring method of a wind turbine generator set according to claim 6, characterized in that: The step of calculating the life of the tower according to the average value of the equivalent fatigue load of the first tower, the tower design load of the first tower height, and the fatigue material coefficient of the tower comprises: A power corresponding to the fatigue material coefficient is obtained according to the tower design load and the average value of the first tower equivalent fatigue load, and a parameter associated with the power is determined as the life of the tower.

8. The tower status monitoring method of a wind turbine generator set according to claim 7, characterized in that: In response to a difference between a tower life design value and the life of the tower satisfying a predetermined condition and a predetermined threshold, it is determined that the state of the tower is abnormal.

9. The tower status monitoring method of a wind turbine generator set according to claim 8, characterized in that: The tower state monitoring method further comprises: Determining the state of the tower according to the state of the first tower equivalent fatigue load, determining that the state of the tower is abnormal and generating an alarm signal; or Controlling the wind turbine generator set according to a state of the tower determined by an equivalent fatigue load of the first tower; or both.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or programs, and when the instructions or programs are executed by a processor, the tower condition monitoring method of a wind turbine generator set according to any one of claims 1 to 9 is implemented.

11. A tower status monitoring device for a wind turbine generator set, characterized in that: include: The acquisition unit acquires the acceleration data of the first nacelle of the wind turbine generator set in real time through the nacelle acceleration sensor; a processing unit, performing a measurement algorithm processing on the first cabin acceleration data and acquiring a first equivalent acceleration based on a processing result; A calculation unit, calculating an equivalent fatigue load of a first tower based on the first equivalent acceleration and a predetermined calibration relationship; a determining unit, which determines a state of the tower according to an equivalent fatigue load of the first tower, The calibration relationship represents a variation relationship between the first equivalent acceleration and the first tower equivalent fatigue load.

12. The tower state monitoring device of a wind turbine generator set according to claim 11, characterized in that: The tower state monitoring device further comprises a calibration unit, which is configured to determine the calibration relationship according to nacelle acceleration data and tower load data obtained by simulation or wind turbine.

13. The tower state monitoring device of a wind turbine generator set according to claim 12, characterized in that: The calibration unit is further configured to: Acquire the acceleration data of the second nacelle under different external environments and the load data of the second tower at the height of the first tower through simulation or a wind turbine; counting the second cabin acceleration data to obtain a first counting result; Counting the second tower load data to obtain a second counting result; Calculate a second equivalent acceleration at a first tower height based on the first counting result; Calculating a second tower equivalent fatigue load at the first tower height 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.

14. A controller for a wind turbine generator set, characterized in that: The method comprises a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a program or an instruction, and when the program or the instruction is executed by the processor, the tower condition monitoring method of a wind turbine generator set according to any one of claims 1 to 9 is implemented.

15. A wind turbine generator set, characterized in that: The device comprises a tower condition monitoring device for a wind turbine generator set according to any one of claims 11 to 13, or a controller for a wind turbine generator set according to claim 14.

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