Abnormal vibration monitoring method and device for wind turbine generator set

Abstract

A method and device for monitoring abnormal vibration of a wind turbine are provided. The abnormal vibration monitoring method comprises: obtaining operating data of the wind turbine over a preset time period; determining, based on the operating data, a vibration energy trend and / or a low-frequency amplitude trend of the wind turbine; and determining, based on the vibration energy trend and / or the low-frequency amplitude trend, whether the vibration of the wind turbine is abnormal, wherein the low-frequency band is the frequency band within which the natural frequency of the wind turbine resides.

Description

Technical Field

[0001] The present disclosure generally relates to the field of electric power technology, and more specifically, to a method and device for monitoring abnormal vibration of a wind turbine generator set. Background Art

[0002] Wind turbines are continuously developing towards larger, higher-capacity, and offshore installations. For offshore wind turbines, which frequently encounter uneven winds and waves and tower foundation scour, monitoring abnormal vibrations is particularly important. This allows for prompt inspection and maintenance of the turbines when abnormal vibrations are detected.

[0003] In the prior art, the acceleration amplitude of the cabin of the unit is usually used directly to monitor abnormal vibration of the unit, and the accuracy of monitoring needs to be further improved. Summary of the Invention

[0004] An exemplary embodiment of the present disclosure provides a method and device for monitoring abnormal vibration of a wind turbine generator set, which can effectively and accurately monitor abnormal vibration of the turbine set.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for monitoring abnormal vibration of a wind turbine is provided, comprising: acquiring operating data of the wind turbine within a preset time period; determining a vibration energy trend and / or a low-frequency amplitude trend of the wind turbine based on the operating data; and determining whether the vibration of the wind turbine is abnormal based on the vibration energy trend and / or the low-frequency amplitude trend, wherein the low-frequency band is a frequency band in which the natural frequency of the wind turbine is located.

[0006] Optionally, based on the operating data, the step of determining the vibration energy trend of the wind turbine includes: determining the vibration energy of the wind turbine at each sampling time point based on the maximum vibration value or the effective vibration value at each sampling time point within the preset time length; based on the vibration energy at each sampling time point, calculating the trend index of the vibration energy within the preset time length, wherein the maximum vibration value is the maximum value between the x-direction vibration data and the y-direction vibration data, and the effective vibration value is the arithmetic square root of the sum of the squares of the x-direction vibration data and the y-direction vibration data.

[0007] Optionally, the step of determining the vibration energy of the wind turbine at each sampling time point based on the maximum vibration value or the effective vibration value at each sampling time point within the preset time length includes: calculating the vibration energy at each sampling time point based on the ambient wind speed value and the air density value, and the maximum vibration value or the effective vibration value at each sampling time point; filtering the calculated vibration energy based on the blade angle and / or the wind angle at each sampling time point to obtain the filtered vibration energy at each sampling time point.

[0008] Optionally, based on the vibration energy trend and / or the low-frequency band amplitude trend, the step of determining whether the vibration of the wind turbine is abnormal includes: when the trend indicator is within the corresponding preset range and the number of sampling time points at which the vibration energy exceeds the preset threshold exceeds a first preset number, determining that the vibration of the wind turbine is abnormal.

[0009] Optionally, the type of the trend indicator includes at least one of the following items: smoothed average convergence / divergence, exponential moving average, and moving average.

[0010] Optionally, based on the operating data, the step of determining the low-frequency amplitude trend of the wind turbine includes: performing fast Fourier transform on the vibration data in each time interval in the preset time length to obtain the amplitude data of each time interval in the low-frequency band; for each frequency interval in the low-frequency band, based on the amplitude data of each time interval in the frequency interval, determining whether the increase of the amplitude in the frequency interval exceeds the preset increase; and determining the number of frequency intervals whose increase exceeds the preset increase.

[0011] Optionally, based on the vibration energy trend and / or the low-frequency amplitude trend, the step of determining whether the vibration of the wind turbine is abnormal includes: when the number of frequency intervals whose increase exceeds the preset increase exceeds a second preset number, determining that the vibration of the wind turbine is abnormal.

[0012] Optionally, the abnormal vibration monitoring method further includes: displaying monitoring results, wherein the monitoring results are used to indicate whether the vibration of the wind turbine is abnormal, and / or the vibration energy trend and / or low-frequency amplitude trend of the wind turbine.

[0013] Optionally, the wind turbine generator set is an offshore wind turbine generator set, wherein the abnormal vibration monitoring method further comprises: when it is determined that the vibration of the offshore wind turbine generator set is abnormal, detecting the stability of the tower base of the offshore wind turbine generator set.

[0014] According to a second aspect of an embodiment of the present disclosure, there is provided an abnormal vibration monitoring device for a wind turbine, comprising: a data acquisition unit configured to acquire operating data of the wind turbine within a preset time period; a trend determination unit configured to determine the vibration energy trend and / or low-frequency band amplitude trend of the wind turbine based on the operating data; and an abnormality judgment unit configured to determine whether the vibration of the wind turbine is abnormal based on the vibration energy trend and / or the low-frequency band amplitude trend, wherein the low-frequency band is a frequency band in which the natural frequency of the wind turbine is located.

[0015] Optionally, the trend determination unit is configured to: determine the vibration energy of the wind turbine at each sampling time point based on the vibration maximum value or vibration effective value at each sampling time point within the preset time length; and calculate the trend indicator of the vibration energy within the preset time length based on the vibration energy at each sampling time point, wherein the vibration maximum value is the maximum value between the x-direction vibration data and the y-direction vibration data, and the vibration effective value is the arithmetic square root of the sum of the squares of the x-direction vibration data and the y-direction vibration data.

[0016] Optionally, the trend determination unit is configured to: calculate the vibration energy at each sampling time point based on the ambient wind speed value and air density value at each sampling time point, and the maximum vibration value or the effective vibration value; and filter the calculated vibration energy based on the blade angle and / or wind angle at each sampling time point to obtain the filtered vibration energy at each sampling time point.

[0017] Optionally, the abnormality judgment unit is configured to: determine that the vibration of the wind turbine is abnormal when the trend indicator is within a corresponding preset range and the number of sampling time points at which the vibration energy exceeds a preset threshold exceeds a first preset number.

[0018] Optionally, the type of the trend indicator includes at least one of the following items: smoothed average convergence / divergence, exponential moving average, and moving average.

[0019] Optionally, the trend determination unit is configured to: perform fast Fourier transform on the vibration data within each time interval in the preset time length to obtain amplitude data of each time interval in the low frequency band; for each frequency interval in the low frequency band, determine whether the increase of the amplitude in the frequency interval exceeds a preset increase based on the amplitude data of each time interval in the frequency interval; and determine the number of frequency intervals whose increase exceeds the preset increase.

[0020] Optionally, the abnormality judgment unit is configured to: determine that the vibration of the wind turbine is abnormal when the number of frequency intervals whose increase exceeds the preset increase exceeds a second preset number.

[0021] Optionally, the abnormal vibration monitoring device further includes: a result display unit configured to display monitoring results, wherein the monitoring results are used to indicate whether the vibration of the wind turbine is abnormal, and / or the vibration energy trend and / or low-frequency amplitude trend of the wind turbine.

[0022] Optionally, the wind turbine is an offshore wind turbine, wherein the abnormal vibration monitoring device further includes: a tower base detection unit configured to detect the stability of the tower base of the offshore wind turbine when it is determined that the vibration of the offshore wind turbine is abnormal.

[0023] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium storing a computer program is provided. When the computer program is executed by a processor, the processor is prompted to execute the abnormal vibration monitoring method for a wind turbine generator system as described above.

[0024] According to a fourth aspect of an embodiment of the present disclosure, a device for monitoring abnormal vibration of a wind turbine is provided, wherein the controller includes: a processor; and a memory storing a computer program. When the computer program is executed by the processor, the processor is prompted to execute the abnormal vibration monitoring method for the wind turbine as described above.

[0025] According to the abnormal vibration monitoring method and device of a wind turbine set of the exemplary embodiment of the present disclosure, whether the vibration of the wind turbine set is abnormal is determined by the vibration energy trend and / or low-frequency amplitude trend of the wind turbine set. There is no need to add new monitoring equipment outside the unit. It is possible to effectively and accurately monitor the abnormal vibration of the unit, play a role in safety warning, and avoid catastrophic accidents and safety hazards of the unit.

[0026] Additional aspects and / or advantages of the present general inventive concept will be set forth in part in the following description and in part will be apparent from the description, or may be learned through practice of the present general inventive concept. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and other objects and features of the exemplary embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings which exemplarily illustrate the embodiments, in which:

[0028] Figure 1 A flow chart showing a method for monitoring abnormal vibration of a wind turbine according to an exemplary embodiment of the present disclosure is provided;

[0029] Figure 2 A flow chart showing a method for determining a vibration energy trend of a wind turbine according to an exemplary embodiment of the present disclosure;

[0030] Figure 3 A flow chart showing a method for determining a low-frequency amplitude trend of a wind turbine according to another exemplary embodiment of the present disclosure;

[0031] Figures 4 to 7 shows an example of vibration energy trends according to an exemplary embodiment of the present disclosure;

[0032] Figure 8 An example of a MACD smoothing trend of vibration energy when vibration is abnormal according to an exemplary embodiment of the present disclosure is shown;

[0033] Figure 9A diagram showing a comparison of vibration energy MACD effects of normal vibration and abnormal vibration according to an exemplary embodiment of the present disclosure;

[0034] Figure 10 and Figure 11 A graph showing a comparison of amplitudes of normal vibration and abnormal vibration according to an exemplary embodiment of the present disclosure;

[0035] Figure 12 A structural block diagram showing an abnormal vibration monitoring device for a wind turbine generator system according to an exemplary embodiment of the present disclosure is shown;

[0036] Figure 13 A structural block diagram showing an abnormal vibration monitoring device for a wind turbine generator system according to another exemplary embodiment of the present disclosure is shown;

[0037] Figure 14 An example of a parameter setting interface according to an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0038] Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like parts throughout. The embodiments are described below with reference to the drawings in order to explain the present disclosure.

[0039] The present disclosure proposes to use the vibration energy trend and / or low-frequency amplitude trend of the wind turbine to realize abnormal vibration monitoring of the unit. As an example, the operating data of the wind turbine can be obtained from a data source, and then based on the obtained operating data, the vibration energy trend and / or low-frequency amplitude trend of the wind turbine can be determined to realize effective monitoring of abnormal vibration of the wind turbine. In addition, the vibration monitoring results can be provided to a display device so that the display device can display the vibration monitoring results, thereby providing support for on-site operation and maintenance personnel. For example, the controller of the wind turbine or the controller of the wind farm can read the operating data of the wind turbine from the data source, and use the read operating data to realize abnormal vibration monitoring of the unit, and can also provide the monitoring results to the display device in the control room for display.

[0040] It should be understood that the abnormal vibration monitoring method and apparatus for a wind turbine according to the exemplary embodiments of the present disclosure can be applied to monitor vibration conditions of various types of wind turbines, for example, offshore wind turbines or onshore wind turbines, without limitation in this disclosure.

[0041] Figure 1 A flow chart illustrating a method for monitoring abnormal vibration of a wind turbine according to an exemplary embodiment of the present disclosure is shown.

[0042] Reference Figure 1 In step S101, the operation data of the wind turbine generator system within a preset time period is obtained.

[0043] As an example, the operating data may be obtained from SCADA and / or the plant's own PLC.

[0044] As an example, the acquired operating data may be second-level or millisecond-level data.

[0045] For example, in addition to vibration data, the operational data may also include, but is not limited to, at least one of the following: ambient wind speed, ambient temperature, blade angle, angle to wind, and a time tag. For example, the time tag is used to indicate the chronological order in which the data was sampled. For example, the time tag may be the specific time of sampling.

[0046] As an example, the vibration data may include but is not limited to at least one of the following items: x-direction vibration data, y-direction vibration data, vibration maximum value, and vibration effective value. The vibration maximum value is the maximum value between the absolute value of the x-direction vibration data and the absolute value of the y-direction vibration data. The vibration effective value is the arithmetic square root of the sum of the squares of the x-direction vibration data and the y-direction vibration data, that is, abs((abs(accy) 2 +abs(accx) 2 ) 0.5 For example, the vibration data may be cabin vibration data. For example, the vibration data may be cabin acceleration data or cabin amplitude data. For example, the x-direction may refer to the lateral direction of the cabin, and the y-direction may refer to the longitudinal direction of the cabin.

[0047] As an example, step S10 may include: reading multiple pieces of real-time data from the wind turbine within a preset time period, and then performing data cleaning on the read real-time data to obtain the operating data. For example, the multiple pieces of real-time data read must at least meet a certain data volume n1, for example, n1>100,000. For example, the operating data obtained after cleaning must also meet a certain data volume n2.

[0048] As an example, real-time data that satisfies the following conditions: blade angle (0, 80], wind speed (0, 30], x-direction vibration data [-0.45, 0.45], y-direction vibration data [-0.45, 0.45], and humidity (0, 1) can be filtered out from the read real-time data to achieve data cleaning.

[0049] As an example, considering that the abnormal vibration problem manifests itself in a long-term process rather than occurring suddenly, a certain length of time needs to be ensured. For example, the preset time may be at least 1-2 months.

[0050] In step S102, based on the operating data, a vibration energy trend and / or a low-frequency amplitude trend of the wind turbine are determined. The vibration energy trend of the wind turbine is the change trend of the wind turbine's vibration energy over time, and the low-frequency amplitude trend of the wind turbine is the change trend of the wind turbine's low-frequency amplitude over time.

[0051] In one embodiment, step S102 may include: determining the vibration energy of the wind turbine at each sampling time point based on the vibration data at each sampling time point within the preset time length, and then determining the trend of the vibration energy within the preset time length over time based on the vibration energy at each sampling time point.

[0052] As an example, the vibration energy of the wind turbine at each sampling time point can be determined based on the maximum vibration value or the effective vibration value at each sampling time point within the preset time length. For example, when the operating data obtained from the data source includes the maximum vibration value or the effective vibration value, the vibration energy can be determined directly based on the obtained maximum vibration value or the effective vibration value. For example, when the operating data obtained from the data source does not include the maximum vibration value or the effective vibration value, the maximum vibration value or the effective vibration value can be calculated based on the x-direction vibration data and the y-direction vibration data in the operating data, and then the vibration energy can be determined based on the calculated maximum vibration value or the effective vibration value.

[0053] As an example, based on the maximum vibration value or the effective vibration value, the vibration energy of the wind turbine as the environment and the state of the turbine change can also be determined based on at least one of the ambient wind speed, ambient temperature, blade angle, and wind angle.

[0054] As an example, the vibration energy of the wind turbine may be determined using second-level operation data.

[0055] The following will be combined Figure 2 An exemplary embodiment of determining a vibration energy trend of a wind turbine is described in detail.

[0056] In another embodiment, step S102 may include determining a low-frequency amplitude trend of the wind turbine based on the x-direction vibration data and the y-direction vibration data within the preset time period. The low-frequency range is the frequency range within which the natural frequency of the wind turbine resides. For example, the natural frequency of the wind turbine may be the natural frequency of the tower. For example, the natural frequency of the tower may be within the frequency range of 0.1 to 0.4 Hz.

[0057] As an example, millisecond-level operating data may be used to determine the low-frequency amplitude trend of the wind turbine.

[0058] The following will be combined Figure 3An exemplary embodiment of determining the low-frequency amplitude trend of a wind turbine will be described in detail.

[0059] In step S103 , it is determined whether the vibration of the wind turbine is abnormal based on the vibration energy trend and / or the low-frequency amplitude trend.

[0060] As an example, when the vibration energy trend is an abnormally increasing trend of vibration energy and / or the low-frequency band amplitude trend is an abnormally increasing trend of amplitude, it can be determined that the vibration of the wind turbine is abnormal.

[0061] The following will be combined Figure 2 and Figure 3 An exemplary embodiment of step S103 is described in detail.

[0062] In addition, as an example, the abnormal vibration monitoring method of a wind turbine according to an exemplary embodiment of the present disclosure may also include: displaying monitoring results, wherein the monitoring results are used to indicate whether the vibration of the wind turbine is abnormal, and / or the vibration energy trend and / or low-frequency amplitude trend of the wind turbine.

[0063] Furthermore, as an example, the abnormal vibration monitoring method for a wind turbine according to an exemplary embodiment of the present disclosure may further include: issuing a vibration abnormality alarm when abnormal vibration of the wind turbine is determined. According to an exemplary embodiment of the present disclosure, a vibration abnormality alarm can be issued for abnormal vibrations caused by conditions such as an unstable tower base, tower base scouring, turbine tilt, and abnormalities in the entire turbine or its components (e.g., abnormalities in blades, towers, generators, etc.), thereby facilitating timely inspection and maintenance by operation and maintenance personnel.

[0064] Furthermore, as an example, when the wind turbine is an offshore wind turbine, the abnormal vibration monitoring method for a wind turbine according to an exemplary embodiment of the present disclosure may further include: upon determining that the offshore wind turbine vibration is abnormal, testing the stability of the offshore wind turbine's tower base. This helps identify the impact of changes in the geological structure or erosion of the offshore wind turbine's tower base, or changes in topography and geology, on the stability of the tower base.

[0065] Figure 2 A flow chart showing a method for determining a vibration energy trend of a wind turbine according to an exemplary embodiment of the present disclosure is shown. As an example, step S102 may include step S201 and step S202.

[0066] In step S201 , the vibration energy of the wind turbine generator system at each sampling time point is determined based on the maximum vibration value or the effective vibration value at each sampling time point within the preset time period.

[0067] As an example, the vibration energy at each sampling time point can be calculated based on the ambient wind speed value and air density value, as well as the maximum vibration value or the effective vibration value at each sampling time point; and based on the blade angle and / or wind angle at each sampling time point, the calculated vibration energy is filtered to obtain the filtered vibration energy at each sampling time point.

[0068] As an example, the calculated vibration energy may be filtered based on the normalized cosine of the blade angle and / or the angle to the wind to obtain filtered vibration energy at each sampling time point. For example, the calculated vibration energy may be divided by the normalized cosine of the blade angle and / or the angle to the wind to filter the calculated vibration energy.

[0069] As an example, the real-time air density value at each sampling time point can be calculated. For example, the air density can be calculated according to the altitude, ambient temperature, and ambient humidity using the following formula:

[0070] Atmospheric pressure formula: p = 101325 * e (-H / 8435) ;

[0071] pd=6.112*e ((17.62*Temperature) / (243.12+Temperature)) ;

[0072] Humidity formula: R = 287.058 / (1-(hum*(pd / p)*(1-(287.058 / 461.523))));

[0073] T=273.15+Temperature;

[0074] Air density: airdensity=p / (R*T);

[0075] Where H represents the altitude of the unit; Temperature represents the ambient temperature of the unit; p represents atmospheric pressure; hum represents the ambient humidity of the unit; 1 standard atmospheric pressure = 760 mm Hg = 76 cm Hg = 1.01325 × 10^5 Pa = 10.336 m water column; 1 standard atmospheric pressure = 101325 N / m2.

[0076] For example, the air density can be calculated based on the altitude and ambient temperature as follows:

[0077] airdensity=1.293*POWER(10,(-H / (18400*(1+0.003674*Temperature)))) / (1+0.003674*Temperature);

[0078] Where H represents the altitude of the unit, Temperature represents the ambient temperature of the unit, and Power represents a digital power function.

[0079] As an example, the normalized result of the blade angle may be: k*(blade1-min(blade1))+0.01,

[0080] Among them, blade1 represents the blade angle collected at the sampling time point without normalization; k = (maxr-minr) / (max(blade1)-min(blade1)); maxr = 20, minr = 0, the two parameters maxr and minr are the normalized data range boundary values, which can be adjusted according to actual conditions and needs. Minr can be set to 0, 0 is the angle of maximum wind energy absorption by the blade angle, and maxr is the common cut-out position of the control strategy; max(blade1) represents the maximum value of the blade angle in the operating data; min(blade1) represents the minimum value of the blade angle in the operating data.

[0081] In the first embodiment, the filtered vibration energy acc_nl at each sampling time point can be calculated by the following formula:

[0082] acc_nl=2*(accmax) / ((ws+0.001)*airdensity) / blade,

[0083] Where ws = wspeed 3 , wspeed represents the ambient wind speed value at the sampling time point, accmax represents the maximum vibration value at the sampling time point, airdensity represents the air density value at the sampling time point, and blade represents the normalized blade angle at the sampling time point.

[0084] For example, for the same unit and the same preset duration, when the accmax used to calculate the filtered vibration energy acc_nl at the sampling time point is the maximum vibration value directly obtained from the data source, the obtained vibration energy trend can be as follows: Figure 4 As shown; when the accmax used to calculate the filtered vibration energy acc_nl at the sampling time point is the maximum vibration value calculated based on the x-direction vibration data and the y-direction vibration data in the operating data, the vibration energy trend can be as follows Figure 7 shown.

[0085] In the second embodiment, the filtered vibration energy acc_nl at each sampling time point can be calculated by the following formula:

[0086] acc_nl=2*(accyx) / ((ws+0.001)*airdensity) / blade,

[0087] Where ws = wspeed 3 , wspeed represents the ambient wind speed value at the sampling time point, accyx represents the effective vibration value at the sampling time point, airdensity represents the air density value at the sampling time point, and blade represents the normalized blade angle at the sampling time point.

[0088] For example, for the same unit and the same preset duration, when the accyx used to calculate the filtered vibration energy acc_nl at the sampling time point is the vibration effective value directly obtained from the data source, the obtained vibration energy trend can be as follows: Figure 5 As shown; when the accyx used to calculate the filtered vibration energy acc_nl at the sampling time point is the effective vibration value calculated based on the x-direction vibration data and the y-direction vibration data in the operating data, the vibration energy trend can be as follows Figure 6 shown.

[0089] In step S202 , based on the vibration energy at each sampling time point, a trend index of the vibration energy within the preset time period is calculated.

[0090] As an example, a trend indicator may be an indicator used to evaluate the trend of a market. For example, the type of trend indicator may include, but is not limited to, at least one of the following: MACD, EMA, or MA.

[0091] Accordingly, as an example, step S103 may include: when the trend indicator is within a corresponding preset range, determining that the vibration of the wind turbine is abnormal. Further, as an example, step S103 may include: when the trend indicator is within a corresponding preset range and the number of sampling time points at which the vibration energy exceeds a preset threshold exceeds a first preset number, determining that the vibration of the wind turbine is abnormal.

[0092] It should be understood that different types of trend indicators may have different corresponding preset ranges. The preset threshold, first preset number, and preset range may be set based on actual circumstances and needs. For example, when the trend indicator is MACD, the preset range may be greater than 50, the first preset number may be 2, and the preset threshold may be 0.45.

[0093] As an example, if one of the multiple index values ​​of the trend index of the vibration energy within the preset time period calculated is within the corresponding preset range, it can be considered that the trend index is within the corresponding preset range.

[0094] MACD: It is the abbreviation of Moving Average Convergence Divergence, also known as exponential moving average. It is a momentum indicator derived from the moving average, and the essence of the moving average is to follow the trend. Therefore, it has the characteristic of tracking the trend. It is calculated based on the EMA of different periods. According to the MACD algorithm, let the sequence {xn} represent the closing price of a stock on the nth day as xn (in this disclosure, it is applied to the vibration energy of the wind turbine), then

[0095] DIF(x n )=EMA 12 (x n )–EMA 26 (x n )

[0096] DEA(x n )=EMA9[DIF(x n )]

[0097] MACD(x n )=[DIF(x n )–DEA(x n )]*2.

[0098] According to the above calculation process and the double EMA formula, the MACD result can be simplified as:

[0099]

[0100] The sum of the three coefficients in the above formula is zero. MACD can be viewed as comparing the upward and downward trends of data derived from EMAs of different periods, or as the "speed" of data change. In the market, when MACD increases from negative to zero, it indicates that the data has crossed a minimum value, suggesting a potential uptrend. When MACD decreases from positive to zero, it indicates that the data has crossed a maximum value, signaling an impending downtrend.

[0101] According to an exemplary embodiment of the present disclosure, the moving average period, short period, and long period parameters (9, 12, 26) among the parameters used in the calculation can be adjusted according to the period observed by the wind turbine generator system.

[0102] EMA: Exponential Moving Average (EXPMA or EMA) is a trend indicator. It works by taking a weighted arithmetic average of data and is used to determine future trends. It's a very effective analytical indicator.

[0103] EMA(n)=α*Cn+(1-α)*EMA(n-1), where EMA(n) is the EMA of the nth day; α is the smoothing coefficient, which is set to (2 / n+1); Cn is the nth calculated data; and EMA(n-1) is the EMA of the n-1th day.

[0104] When the trend indicator type is MACD, first set the MACD parameters: a_length = 10; nFast = round(nrow(cs_data) / a_length); nSlow = round(nFast * 2.146); nSig = round(nFast * 0.618). Here, nSig is the average moving period; nSlow is the long period; nFast is the short period; cs_data represents the sample data (i.e., vibration energy); nrow(cs_data) represents the sample data volume; and a_length represents the number of observation periods from which the sample data is truncated (for example, a setting between 6 and 10 provides better trend analysis). The period parameters are all integers, and the average moving period is an integer of the short period's golden section. For example, the long period is twice the short period plus the third golden section, which is (2 + 0.618 * 0.618 * 0.382). The parameter settings are based on and approximate the distance relationship between the default MACD parameters.

[0105] Next, the MACD parameters determined can be substituted into the MACD formula described above, and the vibration energy within the preset time period can be calculated, and the result is the percentage difference MACD value between the fast moving average and the slow moving average. For example, Figure 9 A comparison diagram of the vibration energy MACD effects of normal vibration and abnormal vibration according to an exemplary embodiment of the present disclosure is shown.

[0106] Here, by calculating MACD, on the one hand, we can see the oscillation trend of MACD. If the high point is setting a new high and the low point is not setting a new low, it means the trend is upward. If the high point is setting a new low and the low point is setting a new low, it means the trend is downward.

[0107] As an example, the step of displaying the monitoring results may include: drawing a line graph of the vibration energy (e.g., Figure 4-7 As shown), draw a line graph of the MACD value (for example, Figure 8 ), and output the drawn line graph.

[0108] Figure 3 A flow chart showing a method for determining a low-frequency amplitude trend of a wind turbine according to an exemplary embodiment of the present disclosure is shown. As an example, step S102 may include steps S301, S302, and S303.

[0109] Reference Figure 3 In step S301, a fast Fourier transform (FFT) is performed on the vibration data in each time interval of the preset time length to obtain the amplitude data of each time interval in the low frequency band.

[0110] Each time interval may be obtained by dividing the preset time length according to a preset time granularity. For example, the preset time granularity may be the time length corresponding to each data file obtained from the data source.

[0111] As an example, a fast Fourier transform may be performed on the x-direction vibration data within each time interval in the preset time length to obtain the x-direction amplitude data of each time interval in the low-frequency band; and a fast Fourier transform may be performed on the y-direction vibration data within each time interval in the preset time length to obtain the y-direction amplitude data of each time interval in the low-frequency band.

[0112] Step S302 : for each frequency interval in the low frequency band, based on the amplitude data of each time interval in the frequency interval, determining whether an increase in the amplitude in the frequency interval exceeds a preset increase.

[0113] As an example, each frequency interval may be obtained by dividing the low frequency band according to a preset frequency granularity. For example, the interval (0.1, 0.4) may be divided by a step size of 0.01 to obtain each frequency interval.

[0114] As an example, for each frequency interval, the average x-direction amplitude of each time interval in the frequency interval can be obtained, and based on the time sequence of each time interval, it is determined whether the increase in the x-direction amplitude average over time exceeds a first preset increase; for each frequency interval, the average y-direction amplitude of each time interval in the frequency interval can be obtained, and based on the time sequence of each time interval, it is determined whether the increase in the y-direction amplitude average over time exceeds a second preset increase.

[0115] Step S303: Determine the number of frequency intervals whose amplitude increases exceed a preset amplitude. In other words, count the number of frequency intervals whose amplitude increases exceed a preset amplitude over time.

[0116] As an example, the number of frequency intervals with an increase exceeding a preset increase may be: the sum of the number of frequency intervals with an x-direction amplitude increase exceeding a first preset increase and the number of frequency intervals with an y-direction amplitude increase exceeding a second preset increase.

[0117] Accordingly, as an example, step S103 may include: when the number of frequency intervals in which the increase exceeds the preset increase exceeds a second preset number, determining that the vibration of the wind turbine is abnormal. As an example, the second preset number, the first preset increase, and the second preset increase may be set according to actual conditions and needs. For example, the second preset number may be 2, the first preset increase may be 0.001, and the second preset increase may be 0.001. For example, Figure 10 and Figure 11 A graph comparing the amplitudes of normal vibration and abnormal vibration according to an exemplary embodiment of the present disclosure is shown.

[0118] As an example, the step of displaying the monitoring result may include: displaying a frequency spectrum of the amplitude.

[0119] In addition, as an example, when step S102 includes step S201, step S202, step S301, step S302, and step S303, as an example, step S103 may include: when the trend indicator is within the corresponding preset range, and the number of frequency intervals with an increase exceeding the preset increase exceeds a second preset number, determining that the vibration of the wind turbine is abnormal. Alternatively, as an example, step S103 may include: when the trend indicator is within the corresponding preset range, the number of sampling time points at which the vibration energy exceeds the preset threshold exceeds a first preset number, and the number of frequency intervals with an increase exceeding the preset increase exceeds a second preset number, determining that the vibration of the wind turbine is abnormal.

[0120] According to an exemplary embodiment of the present disclosure, the real-time operating data of the unit is used to monitor whether there is a trend of degradation in the vibration of the unit and an abnormal increase in the vibration energy. The abnormal increase in the vibration amplitude of the natural frequency of the unit tower can be used to determine whether it affects the natural frequency of the unit tower and whether there is a frequency transfer problem in the vibration of the tower. The monitoring of vibration energy based on real-time data can realize the discovery of large vibration energy under small amplitude in light wind, large vibration energy under small amplitude in shutdown or standby state, and large amplitude and large energy of the unit. It can be analyzed through the data itself to determine whether there is abnormal vibration energy, and the exponentially smoothed MACD indicator is used to determine whether an abnormal vibration alarm signal is issued.

[0121] The exemplary embodiments disclosed herein utilize vibration energy conversion filtering based on vibration data collected by the unit's own vibration sensors to achieve a more realistic unit measurement of the unit's vibration energy. Millisecond-level data can be used to monitor whether the unit's vibration frequency domain is fluctuating, and real-time data can be used to monitor long-term trends in vibration energy to determine whether the vibration energy is increasing. Transient SCADA data can be used to monitor the unit's vibration trends in real time for abnormalities, thereby identifying abnormal vibration energy signals and trends that would be difficult to detect in the data itself, thereby preventing the collapse, sinking, tilting, or deterioration of the unit's foundation, and thus avoiding catastrophic accidents and safety hazards.

[0122] This disclosure innovatively utilizes different types of data and calculation methods to determine if the unit's vibration is abnormal. Millisecond-level data uses frequency and amplitude spectral division to determine if there is a shift in the natural frequency point and an increase in amplitude. Real-time data monitors the unit's vibration energy abnormalities by determining whether there is an increasing trend in the vibration energy under the unit Betz law.

[0123] The vibration abnormality monitoring method disclosed in the present invention can be applied to the monitoring of abnormal vibration energy of the tower caused by tower base scouring of offshore wind turbine generator sets, and plays an auxiliary early warning role in equipment maintenance in harsh offshore maintenance environments, thereby avoiding safety problems caused by the tilted installation angle of the unit or unstable tower base. Similarly, it can also serve as a safety early warning for abnormal vibrations of land-based wind turbine generator sets.

[0124] Figure 12 A structural block diagram of an abnormal vibration monitoring device for a wind turbine generator system according to an exemplary embodiment of the present disclosure is shown.

[0125] like Figure 12 As shown, the abnormal vibration monitoring device for a wind turbine according to an exemplary embodiment of the present disclosure includes: a data acquisition unit 10 , a trend determination unit 20 , and an abnormality judgment unit 30 .

[0126] Specifically, the data acquisition unit 10 is configured to acquire the operation data of the wind turbine generator system within a preset time period.

[0127] The trend determination unit 20 is configured to determine the vibration energy trend and / or the low-frequency amplitude trend of the wind turbine generator system based on the operation data.

[0128] The abnormality determination unit 30 is configured to determine whether the vibration of the wind turbine generator set is abnormal based on the vibration energy trend and / or the low-frequency amplitude trend.

[0129] The low frequency band is the frequency band where the natural frequency of the wind turbine generator set is located.

[0130] As an example, the trend determination unit 20 can be configured to: determine the vibration energy of the wind turbine at each sampling time point based on the maximum vibration value or the effective vibration value at each sampling time point within the preset time length; and calculate the trend indicator of the vibration energy within the preset time length based on the vibration energy at each sampling time point, wherein the maximum vibration value is the maximum value between the x-direction vibration data and the y-direction vibration data, and the effective vibration value is the arithmetic square root of the sum of the squares of the x-direction vibration data and the y-direction vibration data.

[0131] As an example, the trend determination unit 20 can be configured to: calculate the vibration energy at each sampling time point based on the ambient wind speed value and air density value, and the maximum vibration value or effective vibration value at each sampling time point; and filter the calculated vibration energy based on the blade angle and / or wind angle at each sampling time point to obtain the filtered vibration energy at each sampling time point.

[0132] As an example, the abnormality judgment unit 30 may be configured to determine that the vibration of the wind turbine is abnormal when the trend indicator is within a corresponding preset range and the number of sampling time points at which the vibration energy exceeds a preset threshold exceeds a first preset number.

[0133] As an example, the type of the trend indicator may include at least one of the following items: a moving average convergence / divergence, an exponential moving average, and a moving average.

[0134] As an example, the trend determination unit 20 can be configured to: perform fast Fourier transform on the vibration data within each time interval in the preset time length to obtain the amplitude data of each time interval in the low frequency band; for each frequency interval in the low frequency band, determine whether the increase of the amplitude in the frequency interval exceeds the preset increase based on the amplitude data of each time interval in the frequency interval; and determine the number of frequency intervals whose increase exceeds the preset increase.

[0135] As an example, the abnormality judgment unit 30 may be configured to determine that the vibration of the wind turbine is abnormal when the number of frequency intervals whose increase exceeds a preset increase exceeds a second preset number.

[0136] As an example, when the wind turbine is an offshore wind turbine, the abnormal vibration monitoring device may further include: a tower base detection unit (not shown), which is configured to detect the stability of the tower base of the offshore wind turbine when it is determined that the vibration of the offshore wind turbine is abnormal.

[0137] Figure 13 A structural block diagram of an abnormal vibration monitoring device for a wind turbine generator system according to another exemplary embodiment of the present disclosure is shown.

[0138] like Figure 13 As shown, the abnormal vibration monitoring device for a wind turbine according to another exemplary embodiment of the present disclosure includes, in addition to a data acquisition unit 10 , a trend determination unit 20 , and an abnormality judgment unit 30 , a result display unit 40 .

[0139] The data acquisition unit 10 may include: a data reading unit 101 , a parameter reading unit 102 , and a data cleaning unit 103 .

[0140] Specifically, the data reading unit 101 is configured to read a plurality of real-time data of the wind turbine generator set within a preset time period from a data source (eg, a SCADA data source or a PLC data source).

[0141] The parameter reading unit 102 is configured to read the parameters used in the entire vibration anomaly monitoring process. For example, the parameters set by the user through the display screen can be obtained. Figure 14 Set parameters on the interactive interface shown.

[0142] The data cleaning unit 103 is configured to perform data cleaning on the read real-time data to obtain operation data.

[0143] The result display unit 40 is configured to display monitoring results, wherein the monitoring results are used to indicate whether the vibration of the wind turbine is abnormal and / or the vibration energy trend and / or low-frequency amplitude trend of the wind turbine.

[0144] As an example, the data acquisition unit 10 , the trend determination unit 20 , and the abnormality judgment unit 30 may be provided in a processor, and the result display unit 40 may be provided in a display.

[0145] As an example, the abnormal vibration monitoring device for a wind turbine according to an exemplary embodiment of the present disclosure may be provided in a controller of the wind turbine or a controller of a wind farm.

[0146] It should be understood that the specific processing performed by the abnormal vibration monitoring device for a wind turbine according to the exemplary embodiment of the present disclosure has been described with reference to Figures 1 to 11 The details are described in detail and will not be repeated here.

[0147] It should be understood that the various units in the abnormal vibration monitoring device for a wind turbine according to the exemplary embodiment of the present disclosure may be implemented as hardware components and / or software components. Those skilled in the art may implement the various units using, for example, a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC), depending on the processing performed by the defined units.

[0148] An exemplary embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor is prompted to execute the abnormal vibration monitoring method for a wind turbine as described in the exemplary embodiment above. The computer-readable storage medium is any data storage device that can store data read by a computer system. Examples of computer-readable storage media include: read-only memory, random access memory, read-only optical disc, magnetic tape, floppy disk, optical data storage device, and carrier wave (such as data transmission via the Internet via a wired or wireless transmission path).

[0149] According to an exemplary embodiment of the present disclosure, a device for monitoring abnormal vibration of a wind turbine generator system includes: a processor (not shown) and a memory (not shown), wherein the memory stores a computer program that, when executed by the processor, causes the processor to execute the method for monitoring abnormal vibration of a wind turbine generator system as described in the exemplary embodiment above. As an example, the device for monitoring abnormal vibration of a wind turbine generator system according to an exemplary embodiment of the present disclosure may be provided in a controller of the wind turbine generator system or a controller of a wind farm.

[0150] While some exemplary embodiments of the present disclosure have been shown and described, it will be appreciated by those skilled in the art that changes may be made to these embodiments without departing from the principles and spirit of the disclosure, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for monitoring abnormal vibration of a wind turbine generator system, characterized in that: include: Obtaining the operating data of the wind turbine within a preset time period; Determining a vibration energy trend and / or a low-frequency amplitude trend of the wind turbine generator system based on the operating data; Determining whether the vibration of the wind turbine is abnormal based on the vibration energy trend and / or the low-frequency amplitude trend; Wherein, the low frequency band is the frequency band where the natural frequency of the wind turbine generator set is located; The step of determining the vibration energy trend of the wind turbine generator system based on the operating data includes: Determining the vibration energy of the wind turbine generator set at each sampling time point based on the maximum vibration value or the effective vibration value at each sampling time point within the preset time period, wherein the maximum vibration value is the maximum value between the x-direction vibration data and the y-direction vibration data, and the effective vibration value is the arithmetic square root of the sum of the squares of the x-direction vibration data and the y-direction vibration data; Calculating a trend indicator of the vibration energy within the preset time period based on the vibration energy at each sampling time point; The step of determining the low-frequency amplitude trend of the wind turbine generator system based on the operating data includes: Performing a fast Fourier transform on the vibration data within each time interval of the preset duration to obtain amplitude data of each time interval in the low frequency band; For each frequency interval in the low frequency band, determining whether an increase in the amplitude in the frequency interval exceeds a preset increase based on the amplitude data of each time interval in the frequency interval; Determines the number of frequency bins whose increments exceed a preset increment.

2. The abnormal vibration monitoring method according to claim 1, characterized in that: The step of determining the vibration energy of the wind turbine generator set at each sampling time point based on the maximum vibration value or the effective vibration value at each sampling time point within the preset time period includes: Calculating the vibration energy at each sampling time point based on the ambient wind speed value and air density value at each sampling time point, and the maximum vibration value or the effective vibration value; Based on the blade angle and / or wind angle at each sampling time point, the calculated vibration energy is filtered to obtain filtered vibration energy at each sampling time point.

3. The abnormal vibration monitoring method according to claim 1 or 2, characterized in that: The step of determining whether the vibration of the wind turbine generator set is abnormal based on the vibration energy trend and / or the low-frequency amplitude trend includes: When the trend indicator is within a corresponding preset range and the number of sampling time points at which the vibration energy exceeds a preset threshold exceeds a first preset number, it is determined that the vibration of the wind turbine is abnormal.

4. The abnormal vibration monitoring method according to claim 1 or 2, characterized in that: The type of the trend indicator includes at least one of the following items: smoothed average convergence / divergence, exponential moving average, and moving average.

5. The abnormal vibration monitoring method according to claim 1, characterized in that: The step of determining whether the vibration of the wind turbine generator set is abnormal based on the vibration energy trend and / or the low-frequency amplitude trend includes: When the number of frequency intervals in which the increase exceeds the preset increase exceeds a second preset number, it is determined that the vibration of the wind turbine generator set is abnormal.

6. The abnormal vibration monitoring method according to claim 1, characterized in that: The abnormal vibration monitoring method further includes: The monitoring result is displayed, wherein the monitoring result is used to indicate whether the vibration of the wind turbine is abnormal, and / or the vibration energy trend and / or low-frequency amplitude trend of the wind turbine.

7. The abnormal vibration monitoring method according to claim 1, characterized in that: The wind turbine generator set is an offshore wind turbine generator set. The abnormal vibration monitoring method further includes: when it is determined that the vibration of the offshore wind turbine generator set is abnormal, detecting the stability of the tower base of the offshore wind turbine generator set.

8. An abnormal vibration monitoring device for a wind turbine generator set, characterized in that: include: A data acquisition unit is configured to acquire operating data of the wind turbine generator set within a preset time period; a trend determination unit configured to determine a vibration energy trend and / or a low-frequency amplitude trend of the wind turbine generator system based on the operating data; an abnormality judgment unit, configured to determine whether the vibration of the wind turbine is abnormal based on the vibration energy trend and / or the low-frequency amplitude trend, Wherein, the low frequency band is the frequency band where the natural frequency of the wind turbine generator set is located; The trend determination unit is specifically configured as follows: Determining the vibration energy of the wind turbine generator set at each sampling time point based on the maximum vibration value or the effective vibration value at each sampling time point within the preset time period, wherein the maximum vibration value is the maximum value between the x-direction vibration data and the y-direction vibration data, and the effective vibration value is the arithmetic square root of the sum of the squares of the x-direction vibration data and the y-direction vibration data; Calculating a trend indicator of the vibration energy within the preset time period based on the vibration energy at each sampling time point; The trend determination unit is specifically configured as follows: Performing a fast Fourier transform on the vibration data within each time interval of the preset duration to obtain amplitude data of each time interval in the low frequency band; For each frequency interval in the low frequency band, determining whether an increase in the amplitude in the frequency interval exceeds a preset increase based on the amplitude data of each time interval in the frequency interval; Determines the number of frequency bins whose increments exceed a preset increment.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it prompts the processor to execute the abnormal vibration monitoring method for a wind turbine generator set according to any one of claims 1 to 7.

10. An abnormal vibration monitoring device for a wind turbine generator system, characterized in that: The abnormal vibration monitoring device comprises: processor; The memory stores a computer program, which, when executed by a processor, prompts the processor to execute the abnormal vibration monitoring method for a wind turbine according to any one of claims 1 to 7.

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