A method and related device for early warning of abnormal acceleration of a wind turbine generator system

Abstract

The application discloses a method for early warning of abnormal acceleration of a wind turbine generator unit and a related device. The method comprises the following steps: when the wind turbine generator unit continuously generates power in a target time period, determining a target statistical data set from a plurality of statistical data sets by acceleration influence data of the wind turbine generator unit in the target time period, wherein different statistical data sets are set based on different acceleration influence data of the wind turbine generator unit in a simulation running condition, and correspond to different acceleration early warning data; determining acceleration characteristic data of the wind turbine generator unit belonging to the target statistical data set by the acceleration of the wind turbine generator unit in the target time period; and early warning of abnormal acceleration of the wind turbine generator unit when the acceleration characteristic data matches target acceleration early warning data corresponding to the target statistical data set. It can be seen that the method avoids the acceleration amplitude of the wind turbine generator unit maintaining a high level in an actual running condition, so as to guarantee the service life of the wind turbine generator unit.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, and in particular to a method and related device for early warning of abnormal acceleration of wind turbine generator sets. Background Technology

[0002] As data that influences the acceleration of wind turbine generators changes, the acceleration of wind turbine generators also changes. For example, as wind speed data changes, the acceleration amplitude of wind turbine generators also changes.

[0003] In related technologies, the acceleration of wind turbine generators is typically monitored. If the absolute value of the acceleration amplitude exceeds a warning threshold, an immediate warning is issued to the wind turbine generator to facilitate a shutdown due to a fault. This warning threshold is generally greater than the absolute value of the extreme acceleration amplitude of the wind turbine generator under simulated operating conditions, in order to avoid frequent warnings and shutdowns of the wind turbine generator under actual operating conditions.

[0004] However, the warning threshold for acceleration amplitude is fixed. There are cases where the absolute value of the acceleration amplitude of the wind turbine generator under actual operating conditions is greater than the extreme value of the acceleration amplitude of the wind turbine generator under simulated operating conditions, but less than the warning threshold for acceleration amplitude over a long period of time. That is, the acceleration amplitude of the wind turbine generator under actual operating conditions remains at a high level, resulting in a high actual tower fatigue load of the wind turbine generator and seriously affecting the life of the wind turbine generator. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method and related apparatus for early warning of abnormal acceleration of wind turbine generator sets, so as to avoid the acceleration amplitude of wind turbine generator sets remaining at a high level under actual operating conditions, reduce the actual tower fatigue load of wind turbine generator sets, and thus ensure the service life of wind turbine generator sets.

[0006] In a first aspect, embodiments of this application provide a method for early warning of abnormal acceleration in wind turbine generator sets, the method comprising:

[0007] If the wind turbine generator continues to generate electricity during the target time period, a target statistical data set is determined from multiple statistical data sets based on the acceleration impact data of the wind turbine generator during the target time period. Different statistical data sets are set based on different acceleration impact data of the wind turbine generator under simulated operating conditions, and different statistical data sets correspond to different acceleration warning data.

[0008] Based on the acceleration of the wind turbine generator set within the target time period, determine the acceleration characteristic data of the wind turbine generator set belonging to the target statistical data set;

[0009] If the acceleration feature data matches the target acceleration early warning data corresponding to the target statistical data set, an early warning will be issued for the acceleration anomaly of the wind turbine generator.

[0010] Optionally, the acceleration warning data is determined based on the acceleration characteristic data of the wind turbine generator under the simulated operating conditions corresponding to the statistical data set.

[0011] Optionally, determining the target statistical data set from multiple statistical data sets based on the acceleration impact data of the wind turbine generator within the target time period includes:

[0012] Based on the acceleration impact data of the wind turbine generator set within the target time period, the average value of the acceleration impact data corresponding to the acceleration impact data is obtained;

[0013] The target statistical data set is determined from multiple sets of statistical data based on the average value of the acceleration influence data.

[0014] Optionally, the method further includes:

[0015] The acceleration feature data is stored in the target statistical data set;

[0016] If the first cumulative number of acceleration feature data under the target statistical data set is greater than or equal to the target cumulative number corresponding to the target statistical data set, the proportion of acceleration feature data under the target statistical data set matching the target acceleration warning data is determined based on the first cumulative number and the second cumulative number of acceleration feature data under the target statistical data set matching the target acceleration warning data.

[0017] If the percentage is greater than or equal to the target warning percentage corresponding to the target statistical data set, an alarm is issued for the acceleration anomaly of the wind turbine generator.

[0018] Optionally, different sets of statistical data may correspond to different early warning percentages, or all of the different sets of statistical data may correspond to the target early warning percentage.

[0019] Optionally, the method further includes:

[0020] Based on whether the acceleration feature data matches the target acceleration warning data corresponding to the target statistical data set, the warning output value of the target statistical data set is determined;

[0021] Sum the warning output values ​​of multiple sets of statistical data;

[0022] If the sum of the warning output values ​​is greater than or equal to the preset sum, an alarm is issued indicating that the acceleration of the wind turbine generator set is abnormal.

[0023] Optionally, the acceleration influence data includes any one of wind speed data, power data, rotational speed data, and propeller pitch angle data; the acceleration characteristic data includes extreme values ​​of acceleration amplitude or dominant acceleration frequency.

[0024] Secondly, embodiments of this application provide a device for early warning of abnormal acceleration in wind turbine generator sets, the device comprising: a first determining unit, a second determining unit, and a first early warning unit;

[0025] The first determining unit is used to determine a target statistical data set from multiple statistical data sets based on the acceleration impact data of the wind turbine generator set during the target time period if the wind turbine generator set continues to generate electricity during the target time period. Different statistical data sets are set based on different acceleration impact data of the wind turbine generator set under simulated operating conditions, and different statistical data sets correspond to different acceleration warning data.

[0026] The second determining unit is used to determine the acceleration characteristic data of the wind turbine generator corresponding to the target statistical data set based on the acceleration of the wind turbine generator within the target time period.

[0027] The early warning unit is used to issue an early warning of an acceleration anomaly in the wind turbine generator set if the acceleration feature data matches the target acceleration early warning data corresponding to the target statistical data set.

[0028] Optionally, the acceleration warning data is determined based on the acceleration characteristic data of the wind turbine generator under the simulated operating conditions corresponding to the statistical data set.

[0029] Optionally, the first determining unit is used for:

[0030] Based on the acceleration impact data of wind turbine generators within the target time period, the average value of the acceleration impact data is obtained;

[0031] Based on the average value of the acceleration-affected data, the target statistical data set is determined from multiple statistical data sets.

[0032] Optionally, the device may also include: a storage unit, a third determination unit, and a second early warning unit;

[0033] Storage unit, used to store acceleration feature data into target statistical data set;

[0034] The third determining unit is used to determine the proportion of acceleration feature data matching target acceleration warning data under the target statistical data set if the first cumulative number of acceleration feature data under the target statistical data set is greater than or equal to the target cumulative number corresponding to the target statistical data set.

[0035] The second early warning unit is used to issue an early warning of abnormal acceleration of wind turbine generators if the proportion is greater than or equal to the target early warning proportion corresponding to the target statistical data set.

[0036] Optionally, different sets of statistical data may correspond to different warning percentages, or different sets of statistical data may all correspond to the target warning percentage.

[0037] Optionally, the device may also include: a fourth determining unit, a statistical unit, and a third early warning unit;

[0038] The fourth determining unit is used to determine the warning output value of the target statistical data set based on whether the acceleration feature data matches the target acceleration warning data corresponding to the target statistical data set;

[0039] The statistical unit is used to sum the warning output values ​​of multiple statistical data sets;

[0040] The third early warning unit is used to warn of abnormal acceleration of the wind turbine generator if the sum of the early warning output values ​​is greater than or equal to the preset sum.

[0041] Optionally, acceleration influence data may include any one of wind speed data, power data, rotational speed data, and pitch angle data; acceleration characteristic data may include extreme values ​​of acceleration amplitude or dominant acceleration frequency.

[0042] Thirdly, embodiments of this application provide a computer device, the computer device including a processor and a memory:

[0043] The memory is used to store program code and transmit the program code to the processor;

[0044] The processor is used to execute the method for early warning of acceleration anomalies in wind turbine generator sets as described in the first aspect, according to the instructions in the program code.

[0045] Fourthly, embodiments of this application provide a computer-readable storage medium for storing program code. When the program code is executed by a computer, the computer is used to perform the method for early warning of acceleration anomalies in wind turbine generator sets described in the first aspect.

[0046] Compared with the prior art, this application has at least the following advantages:

[0047] The technical solution of this application embodiment involves determining a target statistical data set from multiple statistical data sets based on acceleration impact data of the wind turbine generator within the target time period. Different statistical data sets are set based on different acceleration impact data of the wind turbine generator under simulated operating conditions, corresponding to different acceleration warning data. Acceleration characteristic data of the wind turbine generator belonging to the target statistical data set is determined based on the acceleration of the wind turbine generator within the target time period. When the acceleration characteristic data matches the target acceleration warning data corresponding to the target statistical data set, an acceleration anomaly warning is issued for the wind turbine generator. Therefore, it is necessary to first use the acceleration impact data of the wind turbine generator within the target time period to specifically determine the target acceleration warning data corresponding to the target statistical data set from different acceleration warning data corresponding to multiple statistical data sets. This allows for a more accurate judgment of whether the acceleration of the wind turbine generator within the target time period is abnormal, preventing the acceleration amplitude of the wind turbine generator from remaining at a consistently high level under actual operating conditions, reducing the actual tower fatigue load of the wind turbine generator, and thus ensuring the lifespan of the wind turbine generator. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic diagram of the system framework involved in one application scenario in the embodiments of this application;

[0050] Figure 2 A flowchart illustrating a method for early warning of acceleration anomalies in wind turbine generators, provided in an embodiment of this application;

[0051] Figure 3 Distribution diagrams of extreme values ​​of acceleration amplitude of wind turbine generator sets under different simulated operating conditions provided in the embodiments of this application;

[0052] Figure 4 A flowchart illustrating another method for early warning of acceleration anomalies in wind turbine generator sets provided in this application embodiment;

[0053] Figure 5 A flowchart illustrating another method for early warning of acceleration anomalies in wind turbine generator sets provided in this application embodiment;

[0054] Figure 6This is a schematic diagram of a device for early warning of abnormal acceleration of a wind turbine generator set, provided in an embodiment of this application. Detailed Implementation

[0055] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0056] Currently, the pre-set warning threshold for acceleration amplitude is higher than the extreme value of acceleration amplitude of the wind turbine generator under simulated operating conditions. By monitoring the acceleration of the wind turbine generator, if the absolute value of the acceleration amplitude exceeds the warning threshold, an immediate warning is issued to the wind turbine generator to facilitate a fault shutdown. However, the inventors have discovered that the warning threshold for acceleration amplitude is fixed. For extended periods, the absolute value of the acceleration amplitude of the wind turbine generator under actual operating conditions may exceed the extreme value of acceleration amplitude under simulated operating conditions, but fall below the warning threshold. In other words, the acceleration amplitude of the wind turbine generator remains consistently high under actual operating conditions, resulting in high fatigue loads on the actual tower and severely impacting the lifespan of the wind turbine generator.

[0057] To address this issue, in this embodiment, when the wind turbine generator continuously generates electricity within a target time period, a target statistical data set is determined from multiple statistical data sets based on the acceleration impact data of the wind turbine generator within the target time period. Different statistical data sets are set based on different acceleration impact data of the wind turbine generator under simulated operating conditions, corresponding to different acceleration warning data. Acceleration characteristic data of the wind turbine generator belonging to the target statistical data set is determined based on the acceleration of the wind turbine generator within the target time period. If the acceleration characteristic data matches the target acceleration warning data corresponding to the target statistical data set, an acceleration anomaly warning is issued for the wind turbine generator. Therefore, it is necessary to first use the acceleration impact data of the wind turbine generator within the target time period to specifically determine the target acceleration warning data corresponding to the target statistical data set from different acceleration warning data corresponding to multiple statistical data sets. This allows for a more accurate judgment of whether the acceleration of the wind turbine generator within the target time period is abnormal, preventing the acceleration amplitude of the wind turbine generator from remaining at a consistently high level under actual operating conditions, reducing the actual tower fatigue load of the wind turbine generator, and thus ensuring the lifespan of the wind turbine generator.

[0058] For example, one scenario in the embodiments of this application can be applied to, such as Figure 1 The scenario shown includes a wind turbine generator set 101 and a controller 102. The controller 102 adopts the implementation method provided in the embodiments of this application to realize the early warning of acceleration anomalies of the wind turbine generator set 101.

[0059] First, in the above application scenarios, although the action description of the implementation method provided in this application is executed by the controller 102, the implementation method of this application is not limited in terms of the execution subject, as long as the actions disclosed in the implementation method provided in this application are executed.

[0060] Secondly, the above scenario is only one example provided by the embodiments of this application, and the embodiments of this application are not limited to this scenario.

[0061] The following, in conjunction with the accompanying drawings, describes in detail the specific implementation of the method and device for early warning of abnormal acceleration of wind turbine generator sets in the embodiments of this application.

[0062] See Figure 2 This document illustrates a flowchart of a method for early warning of abnormal acceleration in a wind turbine generator according to an embodiment of this application. In this embodiment, the method may include, for example, the following steps:

[0063] Step 201: If the wind turbine generator continues to generate electricity within the target time period, based on the acceleration impact data of the wind turbine generator within the target time period, determine the target statistical data set from multiple statistical data sets. Different statistical data sets are set based on different acceleration impact data of the wind turbine generator under simulated operating conditions, and different statistical data sets correspond to different acceleration warning data.

[0064] In related technologies, the acceleration of wind turbine generators is monitored. If the absolute value of the acceleration amplitude exceeds a warning threshold, an immediate warning is issued to the wind turbine generator to facilitate a fault shutdown. While the warning threshold for acceleration amplitude remains constant under different operating conditions, there are instances where, for extended periods, the absolute value of the acceleration amplitude under actual operating conditions exceeds the extreme value under simulated operating conditions but falls below the warning threshold. In other words, the acceleration amplitude of the wind turbine generator remains consistently high under actual operating conditions, leading to high fatigue loads on the actual tower and severely impacting the lifespan of the wind turbine generator.

[0065] Therefore, considering that the acceleration of a wind turbine generator changes with its operating conditions; for example, see... Figure 3The figure shows the distribution of extreme values ​​of acceleration amplitude of wind turbine generators under different simulated operating conditions. The horizontal axis represents wind speed data for each simulated operating condition, and the vertical axis represents the extreme values ​​of acceleration amplitude for each simulated operating condition. The multiple scatter points corresponding to each wind speed data point represent the extreme values ​​of acceleration amplitude for multiple wind turbine generators under that specific wind speed condition. Based on this figure, it can be seen that the extreme values ​​of acceleration amplitude of wind turbine generators increase with the increase of wind speed data for the wind turbine generators.

[0066] In other words, since wind speed data affects the acceleration of wind turbine generators, and wind speed data corresponds to power data, speed data, and pitch angle data, any one of these data can be used as acceleration influence data. The extreme values ​​of acceleration amplitude are used to characterize the acceleration-related characteristics of wind turbine generators, and the dominant acceleration frequency can also be used to characterize these characteristics. Therefore, either the extreme values ​​of acceleration amplitude or the dominant acceleration frequency can be used as acceleration characteristic data.

[0067] In this embodiment of the application, different statistical data sets are set according to different acceleration impact data of wind turbine generators under simulated operating conditions; and acceleration warning data corresponding to each statistical data set is determined based on the acceleration characteristic data of wind turbine generators under simulated operating conditions corresponding to each statistical data set, so as to measure whether the acceleration of wind turbine generators is abnormal under actual operating conditions; wherein, different statistical data sets correspond to different acceleration warning data.

[0068] As an example, when the acceleration characteristic data is the extreme value of acceleration amplitude, for any of the above statistical data sets i, the product of the extreme value of acceleration amplitude of the wind turbine generator under the simulated operating conditions corresponding to the statistical data set and a preset multiple is used as the acceleration warning data corresponding to the statistical data set, that is, the warning threshold for the extreme value of acceleration amplitude. Specifically, the product of the maximum value of acceleration amplitude of the wind turbine generator under the simulated operating conditions corresponding to the statistical data set and a preset multiple, and the product of the minimum value of acceleration amplitude of the wind turbine generator under the simulated operating conditions corresponding to the statistical data set and a preset multiple are used as the acceleration warning data corresponding to the statistical data set, that is, the warning threshold a for the maximum value of acceleration amplitude. i1 The warning threshold a for the minimum acceleration amplitude i2 The preset multiplier is greater than 1; for example, the preset multiplier can be 1.15. Similarly, the acceleration characteristic data can also be the average acceleration amplitude or the equivalent acceleration amplitude.

[0069] As another example, when the acceleration characteristic data is the dominant acceleration frequency, for any of the above statistical data sets i, the dominant acceleration frequency h of the wind turbine generator under the simulated operating conditions corresponding to that statistical data set is... i Based on a preset deviation coefficient k, the upper and lower limits of the deviation for the acceleration dominance frequency are determined from the acceleration warning data corresponding to this statistical data set. Specifically, this is based on the acceleration dominance frequency h of the wind turbine generator under the simulated operating conditions corresponding to this statistical data set. i Combining the difference between 1 and the preset deviation coefficient k, and the sum of 1 and the preset deviation coefficient k, the acceleration warning data corresponding to this statistical data set is determined to be the lower limit of the deviation of the acceleration dominant frequency (1-k)h. i The upper limit of the deviation from the dominant frequency of acceleration is (1+k)h i Where k is less than 1, for example, the value of k ranges from 0.1 to 0.2.

[0070] Based on the above explanation, when the wind turbine generator continuously generates electricity within the target time period, the acceleration impact data of the wind turbine generator within the target time period and the acceleration of the wind turbine generator within the target time period are taken as a data segment. First, it is necessary to determine the corresponding statistical data set of the data segment from different statistical data sets through the acceleration impact data of the wind turbine generator within the target time period, and use it as the target statistical data set.

[0071] In the specific implementation of step 201, firstly, it is necessary to calculate the average value of the acceleration impact data for the wind turbine generator set using the acceleration impact data for the wind turbine generator set within the target time period; then, this average value of the acceleration impact data is matched with different acceleration impact data for the wind turbine generator set under different simulation operating conditions corresponding to different statistical data sets, and the target statistical data set is determined from multiple statistical data sets based on the matching results. Therefore, in an optional implementation of this application embodiment, step 201 may include, for example, the following steps A-B:

[0072] Step A: Based on the acceleration impact data of the wind turbine generator set within the target time period, obtain the average value of the acceleration impact data corresponding to the acceleration impact data.

[0073] Step B: Based on the average value of the acceleration influence data, determine the target statistical data set from multiple statistical data sets.

[0074] As an example, when the target time period is 1 minute and the acceleration influence data is wind speed data, the average wind speed data is calculated based on the wind speed data of the wind turbine generator within 1 minute; this average wind speed data is matched with different wind speed data of the wind turbine generator under different simulation operating conditions corresponding to different statistical data sets, and the target statistical data set is determined from multiple statistical data sets based on the matching results.

[0075] Step 202: Based on the acceleration of the wind turbine generators within the target time period, determine the acceleration characteristic data of the wind turbine generators belonging to the target statistical data set.

[0076] In this embodiment of the application, after determining the target statistical data set from multiple statistical data sets based on the acceleration impact data of the wind turbine generator set within the target time period in step 201, it is also necessary to determine the acceleration feature data that characterizes the acceleration-related features of the wind turbine generator set belonging to the target statistical data set through the acceleration of the wind turbine generator set within the target time period.

[0077] As an example, when the target time period is 1 minute and the acceleration characteristic data is the extreme value of acceleration amplitude, the extreme values ​​of acceleration amplitude of the wind turbine generators belonging to the target statistical data set are determined based on the acceleration of the wind turbine generators within 1 minute. That is, the maximum and minimum values ​​of acceleration amplitude of the wind turbine generators belonging to the target statistical data set are determined.

[0078] As an example, when the target time period is 1 minute and the acceleration characteristic data is the acceleration dominant frequency, the acceleration dominant frequency of the wind turbine generator set belonging to the target statistical data set is determined based on the acceleration of the wind turbine generator set within 1 minute. The acceleration dominant frequency refers to the frequency point corresponding to the maximum spectral energy in the spectral energy distribution obtained by performing a fast Fourier transform on the acceleration of the wind turbine generator set within 1 minute.

[0079] Step 203: If the acceleration feature data matches the target acceleration early warning data corresponding to the target statistical data set, issue an early warning of acceleration anomalies in the wind turbine generator set.

[0080] In this embodiment of the application, after determining the acceleration feature data of the wind turbine generator set belonging to the target statistical data set based on the acceleration of the wind turbine generator set within the target time period in step 202, it is necessary to determine whether the acceleration feature data of the wind turbine generator set belonging to the target statistical data set matches the target acceleration warning data corresponding to the target statistical data set. If so, it indicates that the acceleration of the wind turbine generator set is abnormal, and it is necessary to issue a warning for the acceleration abnormality of the wind turbine generator set.

[0081] As an example, if the target statistical data set is statistical data set 1, and the acceleration feature data is the extreme value of acceleration amplitude, then the warning threshold 'a' for wind turbine generators belonging to statistical data set 1 whose maximum acceleration amplitude is greater than or equal to the corresponding maximum acceleration amplitude value in statistical data set 1 is... i1 Alternatively, the warning threshold 'a' for wind turbine generators belonging to statistical data set 1 whose minimum acceleration amplitude is less than or equal to the corresponding minimum acceleration amplitude in statistical data set 1. i2 An abnormal acceleration of the wind turbine generator was detected.

[0082] As an example, when the target statistical data set is statistical data set 1 and the acceleration characteristic data is the acceleration dominant frequency, the acceleration dominant frequency of wind turbine generators belonging to statistical data set 1 is less than or equal to the lower limit of the deviation (1-k)h of the acceleration dominant frequency corresponding to statistical data set 1. i Alternatively, the dominant acceleration frequency of wind turbine generators belonging to statistical data set 1 is greater than or equal to the upper limit of the deviation (1+k)h of the dominant acceleration frequency corresponding to statistical data set 1. i An abnormal acceleration of the wind turbine generator was detected.

[0083] Furthermore, after determining the acceleration feature data of the wind turbine generators belonging to the target statistical data set based on the acceleration of the wind turbine generators within the target time period, the acceleration feature data can also be stored in the target statistical data set. Then, it is determined whether the first cumulative number of acceleration feature data under the target statistical data set is greater than or equal to the target cumulative number corresponding to the target statistical data set, where the target cumulative number represents the baseline sample size required for the warning acceleration anomaly corresponding to the target statistical data set. If so, a second cumulative number of acceleration feature data matching the target acceleration warning data under the target statistical data set is determined. Using the first and second cumulative numbers, the proportion of acceleration feature data matching the target acceleration warning data under the target statistical data set is calculated, i.e., the ratio of the second cumulative number to the first cumulative number. Based on this, it is determined whether the aforementioned proportion is greater than or equal to the target warning proportion corresponding to the target statistical data set. If so, it indicates an acceleration anomaly in the wind turbine generators, and a warning of the wind turbine generators' acceleration anomaly is required. Therefore, in an optional embodiment of this application, the method may further include steps C-E:

[0084] Step C: Store the acceleration feature data in the target statistical data set.

[0085] Step D: If the first cumulative number of acceleration feature data under the target statistical data set is greater than or equal to the target cumulative number corresponding to the target statistical data set, determine the proportion of acceleration feature data under the target statistical data set that matches the target acceleration warning data based on the first cumulative number and the second cumulative number of acceleration feature data under the target statistical data set that matches the target acceleration warning data.

[0086] Step E: If the proportion is greater than or equal to the target warning proportion corresponding to the target statistical data set, issue a warning about the acceleration anomaly of the wind turbine generator.

[0087] In this embodiment, different warning percentages can be set for different sets of statistical data, or the same warning percentage can be set, in which case all different sets of statistical data correspond to the target warning percentage. Therefore, in an optional implementation of this application, different sets of statistical data correspond to different warning percentages, or all different sets of statistical data correspond to the target warning percentage. For example, the target warning percentage can range from 5% to 10%.

[0088] Furthermore, in step 202, based on the acceleration of the wind turbine generator sets within the target time period, firstly, after determining the acceleration characteristic data of the wind turbine generator sets belonging to the target statistical data set, it is also possible to determine the warning output value of the target statistical data set by judging whether the acceleration characteristic data of the wind turbine generator sets belonging to the target statistical data set matches the target acceleration warning data corresponding to the target statistical data set; then, combining the warning output values ​​of multiple statistical data sets, the sum of the warning output values ​​of multiple statistical data sets is calculated; finally, it is determined whether the sum of the warning output values ​​is greater than or equal to a preset sum. If so, it indicates that the acceleration of the wind turbine generator sets is abnormal, and a warning of abnormal acceleration of the wind turbine generator sets is required. Therefore, in an optional embodiment of this application, the method may further include, for example, the following steps F-H:

[0089] Step F: Determine the warning output value of the target statistical data set based on whether the acceleration feature data matches the target acceleration warning data corresponding to the target statistical data set.

[0090] As an example, when the acceleration feature data matches the target acceleration warning data corresponding to the target statistical data set, the warning output value of the target statistical data set is determined to be 1; conversely, when the acceleration feature data does not match the target acceleration warning data corresponding to the target statistical data set, the warning output value of the target statistical data set is determined to be 0.

[0091] Step G: Sum the warning output values ​​of multiple statistical data sets.

[0092] In the specific implementation of step G, the warning output values ​​of multiple statistical data sets can be directly summed to obtain the sum of the warning output values ​​of multiple statistical data sets; alternatively, based on the weight of each statistical data set, the warning output values ​​of multiple statistical data sets can be weighted to obtain the sum of the warning output values ​​of multiple statistical data sets.

[0093] Step H: If the sum of the warning output values ​​is greater than or equal to the preset sum, an abnormal acceleration of the wind turbine generator set is detected.

[0094] Through the various implementation methods provided in this embodiment, when the wind turbine generator continuously generates electricity within a target time period, a target statistical data set is determined from multiple statistical data sets based on the acceleration impact data of the wind turbine generator within the target time period. Different statistical data sets are set based on different acceleration impact data of the wind turbine generator under simulated operating conditions, corresponding to different acceleration warning data. The acceleration characteristic data of the wind turbine generator belonging to the target statistical data set is determined based on the acceleration of the wind turbine generator within the target time period. When the acceleration characteristic data matches the target acceleration warning data corresponding to the target statistical data set, an acceleration anomaly warning is issued for the wind turbine generator. Therefore, it is necessary to first use the acceleration impact data of the wind turbine generator within the target time period to specifically determine the target acceleration warning data corresponding to the target statistical data set from different acceleration warning data corresponding to multiple statistical data sets. This allows for a more accurate judgment of whether the acceleration of the wind turbine generator within the target time period is abnormal, preventing the acceleration amplitude of the wind turbine generator from remaining at a consistently high level under actual operating conditions, reducing the actual tower fatigue load of the wind turbine generator, and thus ensuring the lifespan of the wind turbine generator.

[0095] Based on the above embodiments, taking wind speed data as the acceleration influence data and extreme values ​​of acceleration amplitude as the acceleration characteristic data as an example, see [link to example]. Figure 4 This document illustrates a flowchart of another method for early warning of abnormal acceleration in wind turbine generators, as described in an embodiment of this application. In this embodiment, the method may include, for example, the following steps:

[0096] Step 401: Determine whether the wind turbine generator is continuously generating electricity during the target time period. If so, proceed to step 402.

[0097] Step 402: Based on the wind speed data of the wind turbine generator set within the target time period, determine the target statistical data set from multiple statistical data sets. Different statistical data sets are set based on different wind speed data of the wind turbine generator set under simulated operating conditions. Different statistical data sets correspond to different warning thresholds for extreme values ​​of acceleration amplitude.

[0098] Step 403: Based on the acceleration of the wind turbine generator set within the target time period, determine the extreme values ​​of the acceleration amplitude of the wind turbine generator set belonging to the target statistical data set.

[0099] Step 404: Store the extreme values ​​of acceleration amplitude of wind turbine generators belonging to the target statistical data set into the target statistical data set.

[0100] Step 405: Determine whether the first cumulative number of extreme values ​​of acceleration amplitude under the target statistical data set is greater than or equal to the target cumulative number corresponding to the target statistical data set. If so, proceed to step 406.

[0101] Step 406: Based on the first cumulative quantity and the second cumulative quantity of the warning threshold for matching the extreme value of acceleration amplitude under the target statistical data set with the extreme value of target acceleration amplitude, determine the proportion of the warning threshold for matching the extreme value of acceleration amplitude under the target statistical data set with the extreme value of target acceleration amplitude. The warning threshold for the extreme value of target acceleration amplitude corresponds to the target statistical data set.

[0102] Step 407: Determine if the percentage is greater than or equal to the target warning percentage corresponding to the target statistical data set. If so, proceed to step 408.

[0103] Step 408: Warning of abnormal acceleration of wind turbine generator set.

[0104] Furthermore, taking wind speed data as the data influencing acceleration and acceleration characteristic data as the dominant acceleration frequency as an example, see [reference needed]. Figure 5 This illustration shows a flowchart of another method for early warning of abnormal acceleration in a wind turbine generator according to an embodiment of this application. In this embodiment, the method may include, for example, the following steps:

[0105] Step 501: Determine whether the wind turbine generator is continuously generating electricity within the target time period. If so, proceed to step 502.

[0106] Step 502: Based on the wind speed data of the wind turbine generator set within the target time period, determine the target statistical data set from multiple statistical data sets. Different statistical data sets are set based on different wind speed data of the wind turbine generator set under simulated operating conditions. Different statistical data sets correspond to different upper and lower limits of the deviation of the dominant acceleration frequency.

[0107] Step 503: Based on the acceleration of the wind turbine generators within the target time period, determine the dominant acceleration frequency of the wind turbine generators belonging to the target statistical data set.

[0108] Step 504: Store the dominant acceleration frequencies of wind turbine generators belonging to the target statistical data set into the target statistical data set.

[0109] Step 505: Determine whether the first cumulative number of acceleration dominant frequencies under the target statistical data set is greater than or equal to the target cumulative number corresponding to the target statistical data set. If so, proceed to step 506.

[0110] Step 506: Based on the first cumulative quantity and the second cumulative quantity of the early warning threshold of the acceleration dominant frequency matching the target acceleration dominant frequency under the target statistical data set, determine the proportion of the upper and lower limits of the deviation of the acceleration dominant frequency matching the target acceleration dominant frequency under the target statistical data set, where the upper and lower limits of the deviation of the target acceleration dominant frequency correspond to the target statistical data set.

[0111] Step 507: Determine if the percentage is greater than or equal to the target warning percentage corresponding to the target statistical data set. If so, proceed to step 508.

[0112] Step 508: Warning of abnormal acceleration of wind turbine generator set.

[0113] In response to the above-mentioned method for early warning of abnormal acceleration in wind turbine generator sets, this application also provides a device for early warning of abnormal acceleration in wind turbine generator sets.

[0114] See Figure 6 This illustration shows a schematic diagram of a device for early warning of abnormal acceleration in a wind turbine generator set according to an embodiment of this application. In this embodiment, the device may specifically include, for example, a first determining unit 601, a second determining unit 602, and an early warning unit 603;

[0115] The first determining unit 601 is used to determine a target statistical data set from multiple statistical data sets based on the acceleration impact data of the wind turbine generator set during the target time period if the wind turbine generator set continues to generate electricity during the target time period. The different statistical data sets are set based on different acceleration impact data of the wind turbine generator set under the simulated operating conditions, and different statistical data sets correspond to different acceleration warning data.

[0116] The second determining unit 602 is used to determine the acceleration characteristic data of the wind turbine generator set corresponding to the target statistical data set based on the acceleration of the wind turbine generator set within the target time period.

[0117] The first early warning unit 603 is used to issue an early warning of acceleration anomalies in wind turbine generators if the acceleration feature data matches the target acceleration early warning data corresponding to the target statistical data set.

[0118] In one optional embodiment of this application, the acceleration warning data is determined based on the acceleration characteristic data of the wind turbine generator under simulated operating conditions corresponding to the statistical data set.

[0119] In one optional embodiment of this application, the first determining unit 601 is configured to:

[0120] Based on the acceleration impact data of wind turbine generators within the target time period, the average value of the acceleration impact data is obtained;

[0121] Based on the average value of the acceleration-affected data, the target statistical data set is determined from multiple statistical data sets.

[0122] In one optional embodiment of this application, the device further includes: a storage unit, a third determining unit, and a second early warning unit;

[0123] Storage unit, used to store acceleration feature data into target statistical data set;

[0124] The third determining unit is used to determine the proportion of acceleration feature data matching target acceleration warning data under the target statistical data set if the first cumulative number of acceleration feature data under the target statistical data set is greater than or equal to the target cumulative number corresponding to the target statistical data set.

[0125] The second early warning unit is used to issue an early warning of abnormal acceleration of wind turbine generators if the proportion is greater than or equal to the target early warning proportion corresponding to the target statistical data set.

[0126] In one optional implementation of this application, different sets of statistical data correspond to different warning ratios, or different sets of statistical data all correspond to the target warning ratio.

[0127] In one optional embodiment of this application, the device further includes: a fourth determining unit, a statistics unit, and a third early warning unit;

[0128] The fourth determining unit is used to determine the warning output value of the target statistical data set based on whether the acceleration feature data matches the target acceleration warning data corresponding to the target statistical data set;

[0129] The statistical unit is used to sum the warning output values ​​of multiple statistical data sets;

[0130] The third early warning unit is used to warn of abnormal acceleration of the wind turbine generator if the sum of the early warning output values ​​is greater than or equal to the preset sum.

[0131] In one optional embodiment of this application, the acceleration influence data includes any one of wind speed data, power data, rotational speed data, and propeller pitch angle data; the acceleration characteristic data includes acceleration amplitude extreme values ​​or acceleration dominant frequencies.

[0132] Through the various implementation methods provided in this embodiment, when the wind turbine generator continuously generates electricity within a target time period, a target statistical data set is determined from multiple statistical data sets based on the acceleration impact data of the wind turbine generator within the target time period. Different statistical data sets are set based on different acceleration impact data of the wind turbine generator under simulated operating conditions, corresponding to different acceleration warning data. The acceleration characteristic data of the wind turbine generator belonging to the target statistical data set is determined based on the acceleration of the wind turbine generator within the target time period. When the acceleration characteristic data matches the target acceleration warning data corresponding to the target statistical data set, an acceleration anomaly warning is issued for the wind turbine generator. Therefore, it is necessary to first use the acceleration impact data of the wind turbine generator within the target time period to specifically determine the target acceleration warning data corresponding to the target statistical data set from different acceleration warning data corresponding to multiple statistical data sets. This allows for a more accurate judgment of whether the acceleration of the wind turbine generator within the target time period is abnormal, preventing the acceleration amplitude of the wind turbine generator from remaining at a consistently high level under actual operating conditions, reducing the actual tower fatigue load of the wind turbine generator, and thus ensuring the lifespan of the wind turbine generator.

[0133] Furthermore, embodiments of this application also provide a computer device, which includes a processor and a memory:

[0134] The memory is used to store program code and transfer the program code to the processor;

[0135] The processor is used to execute the method for early warning of acceleration anomalies in wind turbine generator sets as described above, according to instructions in the program code.

[0136] Furthermore, this application also provides a computer-readable storage medium for storing program code. When the program code is executed by a computer, the computer performs the method for early warning of acceleration anomalies in wind turbine generator sets as described above.

[0137] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0138] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0139] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0140] The above description is merely a preferred embodiment of this application and is not intended to limit the application in any way. Although this application has disclosed preferred embodiments above, it is not intended to limit the application. Any person skilled in the art can make many possible variations and modifications to the technical solutions of this application using the methods and techniques disclosed above, or modify them into equivalent embodiments with equivalent changes, without departing from the scope of the technical solutions of this application. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solutions of this application shall still fall within the protection scope of the technical solutions of this application.

Claims

1. A method for early warning of abnormal acceleration in wind turbine generator sets, characterized in that, include: If the wind turbine generator continues to generate electricity during the target time period, a target statistical data set is determined from multiple statistical data sets based on the acceleration impact data of the wind turbine generator during the target time period. Different statistical data sets are set based on different acceleration impact data of the wind turbine generator under simulated operating conditions, and different statistical data sets correspond to different acceleration warning data. Based on the acceleration of the wind turbine generator set within the target time period, determine the acceleration characteristic data of the wind turbine generator set belonging to the target statistical data set; If the acceleration feature data matches the target acceleration early warning data corresponding to the target statistical data set, an early warning will be issued for the acceleration anomaly of the wind turbine generator.

2. The method according to claim 1, characterized in that, The acceleration warning data is determined based on the acceleration characteristic data of the wind turbine generator under the simulated operating conditions corresponding to the statistical data set.

3. The method according to claim 1, characterized in that, The determination of the target statistical data set from multiple statistical data sets based on the acceleration impact data of the wind turbine generator within the target time period includes: Based on the acceleration impact data of the wind turbine generator set within the target time period, the average value of the acceleration impact data corresponding to the acceleration impact data is obtained; The target statistical data set is determined from multiple sets of statistical data based on the average value of the acceleration influence data.

4. The method according to claim 1, characterized in that, The method further includes: The acceleration feature data is stored in the target statistical data set; If the first cumulative number of acceleration feature data under the target statistical data set is greater than or equal to the target cumulative number corresponding to the target statistical data set, the proportion of acceleration feature data under the target statistical data set matching the target acceleration warning data is determined based on the first cumulative number and the second cumulative number of acceleration feature data under the target statistical data set matching the target acceleration warning data. If the percentage is greater than or equal to the target warning percentage corresponding to the target statistical data set, an alarm is issued for the acceleration anomaly of the wind turbine generator.

5. The method according to claim 4, characterized in that, Different sets of statistical data correspond to different warning proportions, or all of the different sets of statistical data correspond to the target warning proportion.

6. The method according to claim 1, characterized in that, The method further includes: Based on whether the acceleration feature data matches the target acceleration warning data corresponding to the target statistical data set, the warning output value of the target statistical data set is determined; Sum the warning output values ​​of multiple sets of statistical data; If the sum of the warning output values ​​is greater than or equal to the preset sum, an alarm is issued indicating that the acceleration of the wind turbine generator set is abnormal.

7. The method according to claim 1, characterized in that, The acceleration influence data includes any one of wind speed data, power data, rotational speed data, and propeller pitch angle data; the acceleration characteristic data includes extreme values ​​of acceleration amplitude or dominant acceleration frequency.

8. A device for early warning of abnormal acceleration in wind turbine generator sets, characterized in that, include: The first determining unit, the second determining unit, and the first early warning unit; The first determining unit is used to determine a target statistical data set from multiple statistical data sets based on the acceleration impact data of the wind turbine generator set during the target time period if the wind turbine generator set continues to generate electricity during the target time period. Different statistical data sets are set based on different acceleration impact data of the wind turbine generator set under simulated operating conditions, and different statistical data sets correspond to different acceleration warning data. The second determining unit is used to determine the acceleration characteristic data of the wind turbine generator corresponding to the target statistical data set based on the acceleration of the wind turbine generator within the target time period. The first early warning unit is used to issue an early warning of an acceleration anomaly in the wind turbine generator set if the acceleration feature data matches the target acceleration early warning data corresponding to the target statistical data set.

9. A computer device, characterized in that, The computer device includes a processor and memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the method for early warning of acceleration anomalies of wind turbine generator sets according to any one of the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code, which, when executed by a computer, is used by the computer to perform the method for early warning of acceleration anomalies in wind turbine generator sets as described in any one of claims 1-7.

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