Tower sweep risk warning method, device, system, equipment and medium

Abstract

The embodiment of the present application provides a tower sweep risk warning method, device, system, equipment and medium. The method includes: obtaining the operating parameters of the wind turbine generator set in a first time period; inputting the operating parameters into a pre-trained clearance distance estimation model to obtain the clearance distance of the wind turbine generator set in a second time period, and the second time period is later than the first time period; when it is determined that the clearance distance in the second time period meets the preset risk determination conditions, it is determined that the wind turbine generator set has a tower sweep risk in the second time period. According to the embodiment of the present application, timely warning of tower sweep risks can be provided.

Description

Technical Field

[0001] The present application belongs to the field of wind power generation technology, and in particular relates to tower sweep risk warning methods, devices, systems, equipment and media. Background Art

[0002] The clearance distance of a wind turbine generator set refers to the distance between the tip of the blade and the tower. During the operation of a wind turbine generator set, if the distance between the tip of the blade and the tower decreases sharply, causing the blade and the tower to collide, a "tower sweep" phenomenon occurs. In order to avoid the "tower sweep" phenomenon of a wind turbine, it is a very necessary safety measure to detect the clearance risk of the unit to determine whether the unit has a tower sweep risk.

[0003] However, in the prior art, it is often necessary to detect the tower clearance distance by installing a distance sensor or an image acquisition device on the blade, and then manually determine whether there is a tower sweep risk. This method cannot provide timely warning of tower sweep risks. Summary of the invention

[0004] The embodiments of the present application provide a tower sweep risk warning method, device, system, equipment and medium, which can provide timely warning of tower sweep risks.

[0005] In a first aspect, an embodiment of the present application provides a tower sweep risk warning method for a wind turbine generator set, the method comprising:

[0006] Obtaining operating parameters of the wind turbine generator set in a first time period;

[0007] Inputting the operating parameters into a pre-trained clearance distance estimation model to obtain a clearance distance of the wind turbine generator set in a second time period, where the second time period is later than the first time period;

[0008] When it is determined that the clearance distance in the second time period meets the preset risk determination condition, it is determined that the wind turbine generator set has a tower sweep risk in the second time period.

[0009] In a second aspect, an embodiment of the present application provides a tower sweep risk warning device for a wind turbine generator set, the device comprising:

[0010] A parameter acquisition module, used to acquire operating parameters of the wind turbine generator set within a first time period;

[0011] a clearance distance estimation module, for inputting the operating parameters into a pre-trained clearance distance estimation model to obtain a clearance distance of the wind turbine generator set in a second time period, the second time period being later than the first time period;

[0012] The risk determination module is used to determine that the wind turbine generator set has a tower sweep risk in the second time period when it is determined that the clearance distance in the second time period meets a preset risk determination condition.

[0013] In a third aspect, the embodiment of the present application provides a tower sweep risk warning system for a wind turbine generator set, the device system:

[0014] An operating parameter collection device is used to collect the operating parameters of the wind turbine generator set and send the operating parameters to the tower sweeping risk warning device;

[0015] A tower sweeping risk warning device is used to implement the tower sweeping risk warning method provided by the first aspect or any optional implementation manner of the first aspect.

[0016] In a fourth aspect, a tower sweep risk warning device for a wind turbine generator set is provided, comprising:

[0017] a processor and a memory storing computer program instructions;

[0018] The processor reads and executes computer program instructions to implement the tower sweep risk warning method for a wind turbine generator set provided by the first aspect or any optional implementation manner of the first aspect.

[0019] In a fifth aspect, a computer storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the tower scanning warning method for a wind turbine generator set provided by the first aspect or any optional implementation manner of the first aspect is implemented.

[0020] The tower sweep risk warning method, device, system, equipment and medium of the wind turbine generator set in the embodiment of the present application can use the clearance distance estimation model and the operating parameters of the wind turbine generator set in the first time period to estimate the clearance distance in the second time period. Since the second time period is later than the first time period, if the clearance distance in the second time period meets the preset risk judgment condition, it can be predicted in advance whether there is a tower sweep risk after the first time period, so that the tower sweep risk in the second time period can be warned in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 It is a schematic diagram of an application scenario of a wind turbine tower sweep risk warning solution provided in an embodiment of the present application;

[0023] Figure 2 It is a flow chart of a first wind turbine tower sweep risk warning method provided in an embodiment of the present application;

[0024] Figure 3 It is a flow chart of a second wind turbine tower sweep risk warning method provided in an embodiment of the present application;

[0025] Figure 4 It is a flow chart of a third wind turbine tower sweep risk warning method provided in an embodiment of the present application;

[0026] Figure 5 It is a flow chart of a fourth wind turbine tower sweep risk warning method provided in an embodiment of the present application;

[0027] Figure 6 It is a flow chart of a fifth wind turbine tower sweep risk warning method provided in an embodiment of the present application;

[0028] Figure 7 It is a logic diagram for determining that the clearance distance within the second time period meets the preset risk determination condition provided by the embodiment of the present application;

[0029] Figure 8 It is a flow chart of a sixth wind turbine tower sweep risk warning method provided in an embodiment of the present application;

[0030] Fig. 9 It is a flow chart of a seventh wind turbine tower sweep risk warning method provided in an embodiment of the present application;

[0031] Fig.10 It is a flow chart of an eighth wind turbine tower sweep risk warning method provided in an embodiment of the present application;

[0032] Fig.11 It is a schematic diagram of the working principle of the time domain simulation module provided in the embodiment of the present application;

[0033] Fig.12 It is a structural schematic diagram of a tower sweep risk warning device for a wind turbine generator set provided in an embodiment of the present application;

[0034] Fig.13 It is a structural schematic diagram of a tower sweep risk warning system for a wind turbine generator set provided in an embodiment of the present application;

[0035] Fig.14 A schematic diagram of the hardware structure of a tower sweep risk warning device for a wind turbine generator set provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0036] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0037] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0038] The clearance distance of a wind turbine generator set refers to the minimum gap between the blade tip and the tower surface. When the blades are not deformed, the clearance distance of the wind turbine generator set is equal to the preset distance. When the blades are deformed due to wind speed and other reasons, the clearance distance of the wind turbine generator set will decrease accordingly. If the clearance distance is less than the preset clearance threshold, it will cause the risk of tower sweeping, seriously affecting the operational safety and stability of the wind turbine generator set.

[0039] Therefore, considering the safety of wind turbines, a technical solution is needed to monitor whether a wind turbine has a tower sweep risk.

[0040] In a related technology, a camera or distance sensor may be installed on the blade to determine the position of the blade tip of the wind turbine and the position of the tower edge through the captured image or the signal sensed by the sensor, thereby obtaining the clearance distance of the wind turbine.

[0041] However, this technology has the following problems:

[0042] (1) This technology requires the installation of detection equipment such as cameras or distance sensors on the blades. The detection equipment may destroy the aerodynamic characteristics of the blades, resulting in a certain loss in the power generation of the unit.

[0043] (2) The detection accuracy of this technology depends on whether the installation position of the detection equipment such as cameras or sensors is accurate. However, the detection equipment often needs to be installed manually, which can easily lead to low detection accuracy due to inaccurate installation position.

[0044] (3) This technology can only detect the tower sweep risk in real time, but cannot provide early warning of the tower sweep risk of wind turbines.

[0045] Therefore, a tower sweeping risk detection solution is needed that can solve at least one of the above problems.

[0046] Based on this, the embodiments of the present application provide a tower sweep risk warning method, device, system, equipment and medium for wind turbines, which can be applied to the application scenario of tower sweep risk warning for wind turbines. Compared with the above-mentioned related technologies, the tower sweep risk of wind turbines can be warned in advance. Accordingly, the tower sweep risk can be responded to in advance, thereby improving the safety of wind turbines.

[0047] After introducing the above concepts, for ease of understanding, before specifically describing the fault warning scheme of the variable pitch system braking device provided in the embodiment of the present application, the following part of the embodiment of the present application will first specifically describe the application scenarios of the wind turbine tower sweeping risk warning scheme.

[0048] Figure 1 It is a schematic diagram of an application scenario of the wind turbine tower sweep risk warning solution provided in an embodiment of the present application.

[0049] like Figure 1 As shown, the wind turbine generator set includes blades 11 and a tower 12. The clearance distance of the wind turbine generator set can be the minimum gap between the tip of the blade 11 and the surface of the tower 12, that is, the clearance distance can be expressed as Figure 1 The L in.

[0050] If the blades are deformed, the clearance distance L may be reduced. When L=0, that is, when the blades of blade 11 touch the surface of tower 12, the wind turbine may sweep the tower, which may cause risks such as blade breakage and collapse, seriously affecting the safety of the wind turbine.

[0051] Therefore, it is necessary to determine in a timely manner whether there is a risk of tower sweeping in the wind turbine.

[0052] In order to better understand the present application, the tower sweep risk warning method, device, system, equipment and medium of the wind turbine generator set according to the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. It should be noted that these embodiments are not intended to limit the scope of the present application.

[0053] Figure 21 is a flow chart of the first wind turbine tower sweep risk warning method provided in the embodiment of the present application. The execution subject of each step in the embodiment of the present application may be a processor capable of controlling the wind turbine, such as a wind turbine master control. Figure 2 As shown, the tower sweep risk warning method for a wind turbine generator set includes S210 to S230.

[0054] S210: Obtain operating parameters of the wind turbine generator set in a first time period.

[0055] The operating parameters are used to characterize the operation of the wind turbine of the wind turbine generator set. In order to improve the accuracy of early warning, the operating parameters can be parameters that can affect the clearance distance. Exemplarily, the operating parameters include wind condition data. In another exemplary embodiment, in order to improve the accuracy of risk estimation, the operating parameters can also include parameters such as motor torque, pitch, yaw angle, power generation, and nacelle acceleration.

[0056] In some embodiments, the wind condition data in S210 may be measured by a laser wind measurement system, wherein the laser wind measurement system may be installed in a nacelle, a hub, or a blade of a wind turbine generator set. Alternatively, the wind condition data may be a nacelle anemometer or wind condition data of a flagship wind turbine generator set. The embodiment of the present application does not limit the source of the wind condition data.

[0057] It should be noted that in the embodiment of the present application, the existing detection module on the wind turbine generator set can be used to obtain the operating parameters of the wind turbine generator set, and there is no need to set up additional or fewer detection modules, so it will not affect the aerodynamic characteristics of the wind turbine generator set, and the power generation will not be damaged due to the aerodynamic characteristics being affected, thereby increasing the power generation. Moreover, it avoids the problem of low detection accuracy caused by inaccurate installation of hardware equipment, thereby improving the detection accuracy.

[0058] In one example, the wind condition data may be wind condition data obtained by a laser wind measurement system. The laser wind measurement system may be used to obtain wind condition data at the far end of the impeller.

[0059] In this example, compared with other sources of wind condition data, the laser wind measurement system can obtain extreme wind condition data, and the laser wind measurement system can simulate wind condition data in seconds. Among them, extreme wind condition data can be wind speed data that can reflect the extreme value of wind speed, such as the maximum wind speed in turbulent wind. Through this example, the wind condition data of the laser wind measurement system can improve the estimation accuracy and credibility of the clearance distance, thereby improving the estimation accuracy of the tower sweep risk.

[0060] In some embodiments, the operating parameters in S210 may be obtained from a main control controller of the wind turbine generator set. Accordingly, the main control controller may obtain the operating parameters of the wind turbine generator set from various sensors or an operating parameter calculation module of the wind turbine generator set.

[0061] S220, inputting the operating parameters into a pre-trained clearance distance estimation model to obtain the clearance distance of the wind turbine generator set in a second time period, wherein the second time period is later than the first time period.

[0062] First, the clearance distance estimation model may be a neural network model or a digital twin system or other model that can use the operating parameters of the wind turbine generator set in the first time period to estimate the clearance distance in the second time period.

[0063] In one example, a digital twin system is used as a clearance distance estimation model. Specifically, the clearance distance of the wind turbine in the second time period can be obtained by using the time domain simulation module in the digital twin model. Among them, the time domain simulation module in the digital twin system is a highly optimized ultra-real-time simulation system that can achieve several times more simulation time and CPU calculation time ratio. Optionally, the simulation software used by the time domain simulation module is not limited to software such as Bladed (i.e., a wind turbine design software), but includes all wind turbine time domain simulation calculation tools, software or codes.

[0064] In this example, compared with other models such as neural network models, the digital twin system can simulate extreme wind conditions. For example, the digital twin system can simulate wind conditions with high turbulence intensity or large wind shear. The credibility and accuracy of the digital twin system are higher than other models. Therefore, in this example, the selection of the digital twin system can improve the accuracy of the early warning of tower sweep risks.

[0065] Secondly, for the first time period and the second time period, the second time period is later than the first time period, and optionally, the start time of the second time period is later than the end time of the first time period. In other words, the clearance distance in the second time period can be estimated using the historical operation data of the first time period.

[0066] Secondly, regarding the specific implementation of S220. Specifically, the clearance distance estimation model can use a prediction algorithm to calculate the operating parameters of the wind turbine in the first time period, and calculate the clearance distance in the second time period. Exemplarily, the calculation result can be a curve representing the change of the clearance distance over time in the second time period, or it can be the clearance distance at multiple moments in the second time period, which is not limited. It should be noted that the prediction algorithm used by the clearance distance estimation model in the embodiment of the present application is not limited.

[0067] In some embodiments, the clearance distance estimation model may output clearance distances within a plurality of different time periods. Accordingly, the second time period may be a plurality of time periods of different lengths.

[0068] In one example, the clearance distance estimation model performs real-time warning in seconds, wherein the real-time warning in seconds means that the risk of tower sweeping can be warned a few seconds in advance. Accordingly, the clearance distance output by the clearance distance estimation model can be the clearance distance a few seconds after the first time period. For example, if the first time period is the time period between 10:50 and 11:50, the clearance distance estimation model can output the clearance distance between 11:50 and 12:00.

[0069] Through this example, taking an extreme wind mutation of 10 seconds as an example, the maximum wind speed can be captured 10 seconds before the maximum wind speed reaches the impeller plane. At this time, the clearance distance estimation model can determine the clearance distance corresponding to the maximum wind speed in real time. If the clearance distance is determined to have a tower sweep risk, the entire unit will have nearly 10 seconds of emergency pitch change or shutdown time.

[0070] It should be noted that in the embodiment of the present application, the real-time warning time is related to the device for collecting wind condition data. If the wind condition data is collected by a laser wind measurement system, the real-time warning time can reach tens of seconds. If the wind condition data is collected by other devices, the real-time warning time will be greatly reduced.

[0071] In another example, the clearance distance estimation model can be used for hourly short-term warning, wherein the hourly short-term warning means that the tower sweep risk can be warned several hours in advance. Accordingly, the clearance distance output by the clearance distance estimation model can be the clearance distance of several hours after the first time period. For example, if the first time period is the time period between 10:50 and 11:50, the clearance distance estimation model can output the clearance distance between 11:50 and 15:50.

[0072] It should be noted that the time of the short-term warning in the embodiment of the present application can also be other time levels, such as minute level, and the embodiment of the present application is not limited to this.

[0073] In another example, the clearance distance prediction model can be used for monthly long-term warning, wherein the monthly long-term warning means that the tower sweep risk can be warned several months in advance. Accordingly, the clearance distance output by the clearance distance prediction model can be the clearance distance of several months, half a year or a year after the first time period.

[0074] It should be noted that the long-term warning time in the embodiment of the present application can also be other time levels, such as day level, and the embodiment of the present application is not limited to this.

[0075] In another example, in order to balance the accuracy of warning and gain more time to deal with tower sweeping risks, the clearance distance estimation model can output the clearance distance for real-time warning, the clearance distance for short-term warning and the clearance distance for long-term warning respectively, and use different risk judgment conditions to judge the tower sweeping risks of clearance distances in different periods.

[0076] In this example, since the estimated accuracy of the clearance distance used for real-time warning is higher than the estimated accuracy of the clearance distance used for short-term warning and the clearance distance used for long-term warning, the warning accuracy of the clearance distance used for actual warning is higher than the warning accuracy of the remaining two clearance distances. However, short-term warning and long-term warning can buy more time to deal with the risk of tower sweeping. By combining multiple warning methods of different durations, it is possible to balance the accuracy of warning and buy more time to deal with the risk of tower sweeping.

[0077] In some embodiments, in order to improve the accuracy of the early warning, the operating parameters can be corrected before being input into the clearance distance estimation model, so as to eliminate abnormal data in the operating parameters, thereby avoiding the impact of abnormal data on the tower scanning risk judgment and improving the accuracy of the early warning.

[0078] In some embodiments, in order to improve the accuracy of the early warning, after the estimated clearance distance is obtained by using the clearance distance estimation model, the estimated clearance distance can be corrected, so that abnormal data in the clearance distance can be eliminated, thereby avoiding the influence of abnormal data on the tower sweep risk judgment, and improving the accuracy of the early warning. S230, when it is determined that the clearance distance in the second time period meets the preset risk judgment condition, it is determined that the wind turbine generator set has a tower sweep risk in the second time period.

[0079] The preset risk determination condition is used to determine whether there is a tower sweep risk according to the clearance distance in the second time period. The tower sweep risk in the embodiment of the present application indicates the risk that the clearance distance of the wind turbine generator set is less than the preset clearance distance threshold.

[0080] The tower sweep risk warning method of the wind turbine generator set in the embodiment of the present application can use the clearance distance estimation model and the operating parameters of the wind turbine generator set in the first time period to estimate the clearance distance in the second time period. Since the second time period is later than the first time period, if the clearance distance in the second time period meets the preset risk judgment condition, it can be predicted in advance whether there is a tower sweep risk after the first time period, so that the tower sweep risk in the second time period can be warned in time.

[0081] In some embodiments, the clearance distance in the second time period includes M clearance distances in the second time period, where M is a positive integer. The preset risk determination condition includes: the minimum value of the clearance distances at the M moments is less than the preset clearance distance threshold. In one example, the preset clearance distance threshold can be set according to the actual scenario and specific needs, and is not limited to this. For example, the preset clearance distance threshold is greater than or equal to 30% of the clearance distance in the non-deformed state.

[0082] Figure 3 It is a flow chart of a second wind turbine tower sweep risk warning method provided in an embodiment of the present application. Figure 3 and Figure 2 The difference is that before S230, it also includes a step of determining whether the clearance distance in the second time period meets the preset risk determination, that is, before S230, it can also include S241 and S242.

[0083] S241, determine the minimum value among the M clearance distances.

[0084] S242: When the minimum value is less than the preset clearance distance threshold, determine that the clearance distance in the second time period meets the preset risk determination condition. That is, when at least one clearance distance among the M clearance distances is less than the preset clearance distance threshold, determine that there is a tower sweep risk in the second time period.

[0085] In one example, the preset risk determination condition in this embodiment can be applied to the second-level real-time warning of tower sweeping risk. The minimum value can be determined first, and then the minimum value is compared with the preset clearance distance threshold to determine whether there is a tower sweeping risk. Since in this example, only one judgment can be made between the minimum value and the preset clearance distance threshold to determine whether there is a tower sweeping risk, it is possible to quickly determine whether the preset risk determination condition is met, thereby gaining more time to deal with the tower sweeping risk.

[0086] In some embodiments, the clearance distance within the second time period includes M clearance distances within the second time period, where M is a positive integer.

[0087] Accordingly, Figure 4 It is a flow chart of a third wind turbine tower sweep risk warning method provided in an embodiment of the present application. Figure 4 and Figure 2 The difference is that, before S230, it also includes a step of determining whether the clearance distance in the second time period meets the preset risk determination, that is, before S230, it can also include S251 and S252.

[0088] S251: Determine distribution characteristic parameters of the clearance distances in the second time period according to the M clearance distances, wherein the distribution characteristic parameters of the clearance distances in the second time period are used to reflect the distribution of the clearance distances in the second time period.

[0089] In one example, the distribution characteristic parameter may be the number of first clearance distances among the M clearance distances, wherein the first clearance distance is the number of clearance distances that are less than or equal to a preset clearance distance threshold.

[0090] In another example, the distribution characteristic parameter may be a first variation relationship obtained by fitting the M clearance distances and the corresponding moments of the M clearance distances.

[0091] In yet another example, the tower sweep risk probability within the second time period is obtained based on M clearance distances and the wind speeds corresponding to each of the M clearance distances.

[0092] S252: Determine, based on the distribution characteristic parameters and the preset distribution characteristic threshold, whether the clearance distance in the second time period satisfies the preset risk determination condition.

[0093] In one example, if the distribution characteristic parameter is the number of first clearance distances, the preset distribution characteristic threshold is a preset number threshold. Specifically, the preset number threshold is set according to actual scenarios and specific needs, and is not limited thereto.

[0094] Accordingly, the preset risk determination condition may include: the number of the first clearance distances is greater than a preset number threshold.

[0095] In another example, if the distribution characteristic parameter is a pre-fitted first change relationship, the preset distribution characteristic threshold is a preset time threshold. Specifically, the preset time threshold is set according to actual scenarios and specific needs, and is not limited thereto.

[0096] Accordingly, the preset risk determination condition may include: the preset clearance distance threshold is less than the preset time threshold at the first moment corresponding to the first change relationship.

[0097] In another example, if the distribution characteristic parameter is the tower sweep risk probability in the second time period, the preset distribution characteristic threshold is a preset probability threshold. Specifically, the preset probability threshold is set according to the actual scenario and specific needs, and is not limited to this.

[0098] Accordingly, the preset risk determination condition may include: the tower sweep risk probability in the second time period is greater than a preset probability threshold.

[0099] It should be noted that the preset risk judgment conditions in this embodiment can be applied to the hourly short-term warning or monthly long-term warning of tower sweeping risks. Since it is necessary to first estimate the clearance distance after a long time in the short-term warning or long-term warning process, although the short-term warning or monthly warning provides more time to avoid tower sweeping risks, the estimation accuracy of the clearance distance after a long time is low. This embodiment can judge whether the preset risk judgment conditions are met according to the distribution characteristics of the clearance distance in the second time period, and can accurately determine whether the preset risk judgment conditions are met according to the overall distribution of the clearance distance, thereby improving the estimation accuracy of the clearance distance for short-term warning or long-term warning.

[0100] In some embodiments, the second time period includes M sub-time periods, and the M clearance distances may be minimum values ​​within the M sub-time periods.

[0101] For example, a minimum value of the clearance distance may be determined every 10 minutes during the second time period.

[0102] In this embodiment, when the minimum value within M sub-time periods is selected for tower sweep risk warning, at least one of the following advantages can be achieved: (1) the efficiency of judging whether the preset risk judgment conditions are met can be improved; (2) overfitting can be prevented when fitting using the clearance distance; (3) extreme wind conditions can be reflected, thereby improving the accuracy of tower sweep risk estimation, especially in turbulent wind conditions.

[0103] For ease of understanding, the following part of the embodiment of the present application uses three examples to specifically explain the specific implementation methods of S252 in combination with the above-mentioned three distribution characteristic parameters.

[0104] In one example, if the distribution characteristic parameter is the number of first clearance distances, Figure 5 It is a flow chart of the fourth wind turbine tower sweep risk warning method provided in the embodiment of the present application. Figure 5 and Figure 4 The difference is that S252 specifically includes S2521 and S2522.

[0105] S2521, determine the number of first clearance distances.

[0106] S2522: When the number of the first clearance distances is greater than a preset number threshold, determine that the clearance distances within the second time period meet a preset risk determination condition.

[0107] In another example, if the distribution characteristic parameter is a pre-fitted first variation relationship, Figure 6 It is a flow chart of the fifth wind turbine tower sweep risk warning method provided in the embodiment of the present application. Figure 6and Figure 4 The difference is that S252 specifically includes S2523 and S2524.

[0108] S2523, determining a first moment corresponding to a preset clearance distance in the first change relationship;

[0109] S2524: When the first moment is less than the preset time threshold, determine that the clearance distance in the second time period meets the preset risk determination condition.

[0110] In a specific example, Figure 7 is a logic diagram for determining that the clearance distance within the second time period meets the preset risk determination condition provided by the embodiment of the present application. Figure 7 The horizontal axis is time, Figure 7 The vertical axis is the clearance distance. Figure 7 The dots in the middle are M clearance distances.

[0111] pass Figure 7 It can be seen that, according to the M clearance distances, the first variation relationship curve L1 can be fitted. And, the preset clearance distance is Figure 7 The star-shaped mark is the intersection of the first change relationship curve L1 and the horizontal line L2. The vertical coordinate of the star-shaped mark is the preset clearance distance, and the horizontal coordinate of the star-shaped mark is the first moment.

[0112] If the first moment is less than the preset time threshold, it is determined that the clearance distance within the second time period meets the preset risk determination condition.

[0113] In another example, if the distribution characteristic parameter is the tower sweep risk probability in the second time period, Figure 8 It is a flow chart of the sixth wind turbine tower sweep risk warning method provided in the embodiment of the present application. Figure 8 and Figure 4 The difference is that S252 specifically includes S2525.

[0114] S2525, when the tower sweep risk probability in the second time period is greater than a preset probability threshold, determine that the clearance distance in the second time period meets the preset risk determination condition.

[0115] In a specific example, the wind speed binning method can be used to determine the tower sweep risk probability in the second time period. Accordingly, before S2525, the method can also include steps A to C.

[0116] Step A: Based on the wind speeds corresponding to the M clearance distances, the M clearance distances are divided into N sub-bins, wherein the wind speed corresponding to the clearance distance in each sub-bin belongs to a wind speed interval, and N is a positive integer.

[0117] For example, if the wind speed varies from 0 to 20 m / s, the length of the wind bin can be 1 m / s, for example, 0 to 1 m / s as a wind bin, 1 to 2 m / s as a wind bin, ..., 19 to 20 m / s as a wind bin. It should be noted that the length of the wind bin can be set according to the specific scenario and actual needs, which will not be described in detail. For example, if the calculation accuracy needs to be improved, the length of the wind bin can be reduced, and the number of bins can be increased from the original 20 to 40 bins; if the calculation rate needs to be increased, the length of the wind bin can be increased, and the number of bins can be reduced from the original 20 to 10 bins.

[0118] As a specific example, if 5 clearance data among 20 clearance distances fall into the wind bin of 6-7 m / s, 7 clearance data fall into the wind bin of 7-8 m / s, 7 clearance data fall into the wind bin of 8-9 m / s, and 1 clearance data falls into the wind bin of 18-19 m / s.

[0119] Step B, determining the clearance risk probability of each of the N sub-compartments, the clearance risk probability of each sub-compartment being determined according to the product of the probability that the clearance distance in each sub-compartment is less than a preset clearance distance threshold and the probability that the wind speed falls into each sub-compartment. Exemplarily, the clearance risk probability of each sub-compartment is equal to the product of the probability that the clearance distance in each sub-compartment is less than the preset clearance distance threshold and the probability that the wind speed falls into each sub-compartment.

[0120] In an example, the probability that the clearance distance in each sub-bin is less than the preset clearance distance threshold is explained as follows: if 5 clearance data out of M clearance distances fall into the sub-bin, and 2 of them are less than the preset clearance distance threshold, then the probability that the clearance distance in the sub-bin is less than the preset clearance distance threshold can be 2 / 5.

[0121] In one example, the probability of the wind speed falling into each bin may be the frequency of the wind speed falling into the bin within a preset period. For example, the wind speed within a year may be counted, and if n wind speed values ​​are collected within a year, and i wind speed values ​​fall into the bin, then the probability of the wind speed falling into the bin is i / n.

[0122] Step C, determining the sum of the clearance risk probabilities of the N sub-warehouses as the tower sweep risk probability in the second time period.

[0123] Exemplarily, the tower sweep risk probability P in the second time period satisfies the following formula (1):

[0124]

[0125] Among them, P 1j is the probability that the clearance distance in the jth bin is less than the preset clearance distance threshold, P 2jis the probability that the wind speed falls into the jth bin.

[0126] In some embodiments, since the clearance distance of a wind turbine generator set is affected by the wind farm to which the wind turbine generator set belongs, the operation data of the wind turbine generator set includes the wind condition data of the wind turbine generator set and other operation data except the wind condition data of the wind turbine generator set.

[0127] The wind turbine tower sweep risk warning also includes step D1.

[0128] Step D1, input the wind condition data of the wind turbine generator set into a preset wind generating system to obtain the wind condition data of the wind farm to which the wind turbine generator set belongs. The wind generating system may be pre-trained based on the wind condition parameters of the wind turbine generator set and the wind condition parameters of the wind farm.

[0129] Accordingly, S220 may be specifically implemented as follows: inputting the wind farm wind condition data and other operation data into a pre-trained clearance distance estimation model to obtain the clearance distance of the wind turbine generator set in the second time period.

[0130] The wind condition data of the wind farm is obtained through the wind generation system, and the wind condition data of the wind farm is used to estimate the clearance distance in the second time period, which can better consider the impact of turbulence and improve the accuracy of early warning.

[0131] In yet another embodiment, the operating parameters of the wind turbine generator set may further include the wind speed distribution on the impeller surface, and the remote wind condition data may be input into a preset wind generating system to obtain the wind speed distribution on the impeller surface.

[0132] In some embodiments, since the embodiments of the present application need to use a pre-trained clearance distance prediction model for early warning, the embodiments of the present application also provide a step of training a clearance distance prediction module.

[0133] Step E1, obtaining training samples, wherein each training sample includes the wind turbine operating parameters within a third time period and the clearance distance within a preset time period after the third time period.

[0134] Step E2: training the initial clearance distance estimation model using multiple training samples to obtain a trained clearance distance estimation model.

[0135] In an example, step E2 specifically includes step E21 and step E22.

[0136] Step E21, inputting a plurality of training samples into a clearance distance estimation model to be trained to obtain an estimated clearance distance, where the estimated clearance distance is a clearance distance within a preset time length after a time period corresponding to each of the plurality of training samples.

[0137] Step E22, determine whether the preset training stop condition is met; if the preset training stop condition is not met, use the estimated clearance distance and the training labels of multiple training samples to adjust the model parameters of the clearance distance estimation model, and input multiple training samples into the adjusted clearance distance estimation model until the preset training stop condition is met to obtain the trained clearance distance estimation model.

[0138] In some embodiments, in order to avoid the risk of tower sweeping, Fig. 9 It is a flow chart of the seventh wind turbine tower sweep risk warning method provided in the embodiment of the present application. Fig. 9 and Figure 2 The difference is that, after S230, the method further includes S260.

[0139] S260: Execute a preset clearance distance control strategy.

[0140] Among them, the clearance distance control strategy is a control strategy that increases the clearance distance by adjusting the operating state of the fan.

[0141] Specifically, the preset clearance distance control strategy includes: controlling the wind turbine generator set to operate at reduced power, or controlling the wind turbine generator set to shut down.

[0142] In one example, a specific manner of controlling the wind turbine generator set to operate at reduced power may include at least one of the following: changing a pitch angle or reducing a load.

[0143] In one example, in order to increase the execution speed of the clearance distance control strategy, power reduction operation can be performed through a safety chain, or the wind turbine generator set can be controlled to shut down.

[0144] In this embodiment, after determining the tower sweep risk, an automatic response warning can be provided. For example, a preset clearance distance control strategy can be executed to avoid the tower sweep risk. This greatly reduces the work intensity and ability requirements of the on-duty personnel. In addition, since the clearance distance in the second time period output by the clearance distance estimation model can realize real-time warning, short-term warning and long-term warning, the real-time warning, short-term warning and long-term warning can be linked with the clearance distance control strategy, the operating stability and safety of the wind turbine can be improved.

[0145] In some embodiments, after determining the tower sweep risk, the corresponding fan operation data can also be sent to the remote monitoring system to facilitate the subsequent analysis of the causes of the tower sweep risk. In addition, the relevant operators can also use the remote monitoring system to adjust the clearance distance control strategy. Alternatively, the estimated clearance distance of the second time period can be sent to the remote monitoring system so that the relevant operators can formulate a clearance distance control strategy based on the estimated clearance distance of the second time period. Exemplarily, the clearance distance used for short-term warning or long-term warning can be sent to the relevant operators through the remote monitoring system, so as to gain more sufficient reaction time for the relevant operators to formulate a more appropriate control strategy and improve the overall operating stability and safety of the unit.

[0146] In order to fully understand the risk warning method of the embodiment of the present application, Fig.10 1 is a flow chart of the eighth wind turbine tower sweep risk warning method provided in the embodiment of the present application. Fig.10 As shown, the tower sweep risk warning method for a wind turbine generator set includes S1011 to S1018.

[0147] S1011, inputting the operating parameters of the wind turbine generator set in the first time period into the digital twin model, so that the digital twin model estimates the clearance distance in the second time period based on the operating parameters in the first time period. The operating parameters include wind condition data obtained from the main control of the unit, and other operating parameters other than the wind condition data. Alternatively, the operating parameters include wind condition data obtained from a laser wind measurement system.

[0148] S1012, sending the clearance distance of the wind turbine generator set within the second time period output by the digital twin model to the real-time warning model, so that the real-time warning model can determine whether the wind turbine generator set has a tower sweep risk within the second time period (for example, within a few seconds) according to preset risk judgment conditions.

[0149] S1013: If it is determined by the real-time warning model that the wind turbine generator set has a tower sweep risk within the second time period, power reduction operation or emergency shutdown is performed through a safety chain or a main control of the unit.

[0150] S1014, sending the clearance distance of the wind turbine generator set in the second time period output by the digital twin model to the short-term warning model, so that the short-term warning model can determine whether there is a tower sweep risk for the wind turbine generator set in the second time period (e.g., within a few hours) according to preset risk determination conditions.

[0151] S1015: If the short-term warning model is used to determine that the wind turbine generator set has a tower sweep risk within the second time period, the main control of the unit is used to reduce power or perform emergency shutdown.

[0152] In one example, the relevant information may also be sent to a remote monitoring system so that relevant operators can manually perform data analysis through the remote monitoring system and adjust the clearance distance control strategy on the unit master control. The relevant information may be the clearance distance of the wind turbine generator set in the second time period or the operating parameters of the wind turbine generator set in the first time period.

[0153] In one example, in addition to using the unit master control to perform power reduction operation or emergency shutdown, it can also be performed using a safety chain.

[0154] S1016, sending the clearance distance of the wind turbine generator set in the second time period output by the digital twin model to the long-term warning model, so that the long-term warning model can determine whether the wind turbine generator set has a tower sweep risk in the second time period (for example, within a few months) according to preset risk judgment conditions.

[0155] S1017: If it is determined by the long-term warning model that the wind turbine generator set has a tower sweep risk within the second time period, the main control of the unit is used to reduce power or perform emergency shutdown.

[0156] S1018, the relevant information may be sent to the remote monitoring system for the relevant operator to manually perform data analysis through the remote monitoring system. The relevant information may be the clearance distance of the wind turbine generator set in the second time period or the operating parameters of the wind turbine generator set in the first time period.

[0157] Exemplarily, the clearance distance within a second time period (such as within half a year or a year) and / or the operating parameters of the wind turbine within the first time period can be sent to the remote monitoring system, and the relevant operating personnel can determine whether there is a tower sweep risk; or if the tower sweep risk is triggered a large number of times within the second time period, the relevant personnel can determine the reason why the wind turbine has a tower sweep risk.

[0158] S1019, if the relevant operator adjusts the clearance distance control strategy on the unit master control through the remote monitoring system, the adjusted clearance distance control strategy is sent to the unit master control.

[0159] In order to fully understand the working principle of the time domain simulation module of the digital twin system of the embodiment of the present application, Fig.11 It is a schematic diagram of the working principle of the time domain simulation module provided in the embodiment of the present application.

[0160] like Fig.11 As shown, one or more operating parameters of the unit operating parameters, the wind condition data of the laser wind measurement system, and the wind condition data obtained by the cabin anemometer can be input into the pre-correction module for data correction.

[0161] Then, the corrected operating parameters are input into the wind generation system for data processing to obtain wind condition data. For example, the wind condition data may include: using the wind generation system to obtain airport wind condition data or generating the impeller surface wind speed distribution data at the predicted time based on the remote wind condition data.

[0162] Next, the wind condition data processed by the wind generating system is input into the time domain simulation module. The time domain simulation module combines the operating parameters of other units in the first time period to perform time domain analysis to obtain the clearance distance in the second time period.

[0163] Finally, in the early warning mode, the clearance distance in the second time period is input into the target early warning model to judge the tower sweep risk, where the target early warning module can be Fig.10 One or more of the real-time warning model, short-term warning module and long-term warning model shown.

[0164] Alternatively, in the training mode, the clearance distance in the second time period is stored in a training database, so that the training module reads the training data from the training database and trains the time domain simulation module.

[0165] Based on the same application concept, in addition to providing a tower sweep risk warning method for a wind turbine generator set, the embodiment of the present application also provides a corresponding tower sweep risk warning device for a wind turbine generator set.

[0166] The following is a detailed description of a tower sweep risk warning device for a wind turbine generator set according to an embodiment of the present application in conjunction with the accompanying drawings.

[0167] Fig.12 Schematic diagram of a wind turbine tower sweep risk warning device provided in an embodiment of the present application. Fig.12 As shown, the tower sweep risk warning device 1000 of a wind turbine generator set includes a parameter acquisition module 1010 , a clearance distance estimation module 1020 and a risk determination module 1030 .

[0168] The parameter acquisition module 1010 is used to acquire the operating parameters of the wind turbine generator set in the first time period;

[0169] A clearance distance estimation module 1020, for inputting the operating parameters into a pre-trained clearance distance estimation model to obtain a clearance distance of the wind turbine generator set in a second time period, the second time period being later than the first time period;

[0170] The risk determination module 1030 is used to determine that the wind turbine generator set has a tower sweep risk in the second time period when it is determined that the clearance distance in the second time period meets a preset risk determination condition.

[0171] In some embodiments of the present application, the clearance distance within the second time period includes M clearance distances within the second time period, where M is a positive integer.

[0172] The risk determination module 1030 specifically includes:

[0173] A first determining unit, configured to determine a minimum value among the M clearance distances;

[0174] The second determining unit is used to determine whether the clearance distance in the second time period meets the preset risk determination condition when the minimum value is less than the preset clearance distance threshold.

[0175] In some embodiments of the present application, the clearance distance in the second time period includes M clearance distances in the second time period, where M is a positive integer;

[0176] The risk determination module 1030 specifically includes:

[0177] A third determining unit is used to determine a distribution characteristic parameter of the clearance distance in a second time period according to the M clearance distances;

[0178] The fourth determining unit is used to determine whether the clearance distance in the second time period meets the preset risk determination condition according to the distribution characteristic parameter and the preset distribution characteristic threshold.

[0179] In some embodiments of the present application, the distribution characteristic parameter is the number of first clearance distances, the preset distribution characteristic threshold is a preset number threshold, and the first clearance distance is a clearance distance among the M clearance distances that is less than or equal to the preset clearance distance threshold;

[0180] The third determining unit includes:

[0181] A first determining subunit, configured to determine the amount of a first clearance distance;

[0182] The second determining subunit is used to determine that the clearance distances within the second time period meet the preset risk determination condition when the number of the first clearance distances is greater than a preset number threshold.

[0183] In some embodiments of the present application, the distribution characteristic parameter is a first change relationship obtained by fitting based on M clearance distances and the moments corresponding to the M clearance distances, and the preset distribution characteristic threshold is a preset time threshold, wherein the first change relationship is a change relationship of the clearance distance over time;

[0184] The third determining unit includes:

[0185] A third determining subunit is used to determine a first moment corresponding to a preset clearance distance in the first change relationship;

[0186] The fourth determining subunit is used to determine that the clearance distance in the second time period meets the preset risk determination condition when the first moment is less than the preset time threshold.

[0187] In some embodiments of the present application, the distribution characteristic parameter is a tower sweep risk probability in the second time period obtained according to the M clearance distances and the wind speeds corresponding to each of the M clearance distances, and the preset distribution characteristic threshold is a preset probability threshold;

[0188] The third determining unit includes:

[0189] The fifth determining subunit is used to determine that the clearance distance in the second time period meets the preset risk determination condition when the tower sweep risk probability in the second time period is greater than a preset probability threshold.

[0190] In some embodiments of the present application, the third determining unit includes:

[0191] The bin division subunit is used to perform bin division processing on the M clearance distances based on the wind speeds corresponding to the M clearance distances, so as to obtain N bins, wherein the wind speed corresponding to the clearance distance in each bin belongs to a wind speed interval, and N is a positive integer;

[0192] A sixth determination subunit is used to determine the clearance risk probability of each of the N sub-compartments, where the clearance risk probability of each sub-compartment is determined according to the probability that the clearance distance in each sub-compartment is less than a preset clearance distance threshold and the probability that the wind speed falls into each sub-compartment;

[0193] The seventh determination subunit is used to determine the sum of the clearance risk probabilities of the N sub-warehouses as the tower sweep risk probability in the second time period.

[0194] In some embodiments of the present application, the second time period includes M sub-time periods.

[0195] The M clearance distances are the extreme values ​​of the clearance distances within the M sub-time periods.

[0196] In some embodiments of the present application, the operating data of the wind turbine generator set includes the wind condition data of the wind turbine generator set and other operating data except the wind condition data of the wind turbine generator set.

[0197] The wind turbine tower sweep risk warning device 1000 also includes:

[0198] The parameter acquisition module 1010 is also used to input the wind condition data of the unit into the preset wind generation system to obtain the wind condition data of the wind farm to which the wind turbine generator set belongs.

[0199] The clearance distance estimation module 1020 is further used to input the wind farm wind condition data and other operation data into a pre-trained clearance distance estimation model to obtain the clearance distance of the wind turbine generator set in the second time period.

[0200] In some embodiments of the present application, the tower sweep risk warning device 1000 of a wind turbine generator set further includes:

[0201] A strategy execution module, used to execute a preset clearance distance control strategy;

[0202] The preset clearance distance control strategy includes: controlling the wind turbine generator set to operate at reduced power, or controlling the wind turbine generator set to shut down.

[0203] According to other details of the tower sweep risk warning device of the wind turbine generator set in the embodiment of the present application, combined with the above Figures 2 to 9 The tower sweep risk warning method for a wind turbine generator set described in the example shown is similar and can achieve its corresponding technical effect. For the sake of brevity, it will not be repeated here.

[0204] According to the tower sweep risk warning device of the wind turbine generator set of the embodiment of the present application, the clearance distance in the second time period can be estimated by using the clearance distance estimation model and the operating parameters of the wind turbine generator set in the first time period. Since the second time period is later than the first time period, if the clearance distance in the second time period meets the preset risk judgment condition, it can be predicted in advance whether there is a tower sweep risk after the first time period, so that the tower sweep risk in the second time period can be warned in time.

[0205] Based on the same application concept, in addition to providing a tower sweep risk warning device for a wind turbine generator set, the embodiment of the present application also provides a corresponding tower sweep risk warning system for a wind turbine generator set.

[0206] The tower sweep risk warning system for a wind turbine generator set according to an embodiment of the present application is described in detail below in conjunction with the accompanying drawings.

[0207] Fig.13 Schematic diagram of a wind turbine tower sweep risk warning system provided in an embodiment of the present application. Fig.13 As shown, the tower sweep risk warning system 1100 of the wind turbine generator set includes:

[0208] The operating parameter collection device 1110 is used to collect the operating parameters of the wind turbine generator set and send the operating parameters to the tower sweeping risk warning device.

[0209] The tower sweeping risk warning device 1000 is used to implement the tower sweeping risk warning method provided in the above-mentioned embodiment of the present application.

[0210] According to other details of the wind turbine tower sweep risk warning system of the embodiment of the present application, combined with the above Figures 2 to 9 The tower sweep risk warning method for a wind turbine generator set described in the example shown is similar and can achieve its corresponding technical effect. For the sake of brevity, it will not be repeated here.

[0211] According to the tower sweep risk warning system of the wind turbine generator set of the embodiment of the present application, the clearance distance in the second time period can be estimated by using the clearance distance estimation model and the operating parameters of the wind turbine generator set in the first time period. Since the second time period is later than the first time period, if the clearance distance in the second time period meets the preset risk determination conditions, it can be predicted in advance whether there is a tower sweep risk after the first time period, so that the tower sweep risk in the second time period can be timely warned.

[0212] Fig.14 A schematic diagram of the hardware structure of a tower sweep risk warning device for a wind turbine generator set provided by an embodiment of the present invention is shown.

[0213] The tower sweep risk warning device of a wind turbine generator set may include a processor 1201 and a memory 1202 storing computer program instructions.

[0214] Specifically, the processor 1201 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0215] Memory 1202 may include a large capacity memory for data or instructions. For example, but not limitation, memory 1202 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape or a universal serial bus (USB) drive or a combination of two or more of these. In some instances, memory 1202 may include a removable or non-removable (or fixed) medium, or memory 1202 is a non-volatile solid-state memory. In some embodiments, memory 1202 may be inside or outside the tower sweep risk warning device of a wind turbine.

[0216] In some examples, the memory 1202 may be a read-only memory (ROM). In one example, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0217] The memory 1202 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0218] The processor 1201 reads and executes the computer program instructions stored in the memory 1202 to implement Figure 2-Figure 9 The method in the embodiment shown in the figure is achieved Figure 2-Figure 9 The corresponding technical effects achieved by executing the method in the example shown are not repeated here for the sake of brevity.

[0219] In one example, the tower sweep risk warning device of a wind turbine generator set may further include a communication interface 1203 and a bus 1210. Fig.12 As shown, the processor 1201, the memory 1202, and the communication interface 1203 are connected via a bus 1210 and communicate with each other.

[0220] The communication interface 1203 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiment of the present invention.

[0221] Bus 1210 includes hardware, software or both, and the components of online data flow billing equipment are coupled to each other. For example, but not limitation, the bus may include Accelerated Graphics Port (AGP) or other graphics bus, Enhanced Industry Standard Architecture (EISA) bus, Front Side Bus (FSB), Hyper Transport (HT) interconnection, Industry Standard Architecture (ISA) bus, InfiniBand interconnection, Low Pin Count (LPC) bus, Memory bus, Micro Channel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 1210 may include one or more buses. Although the embodiment of the present invention describes and shows a specific bus, the present invention considers any suitable bus or interconnection.

[0222] The tower sweep risk warning device of the wind turbine generator set can execute the tower sweep risk warning method of the wind turbine generator set in the embodiment of the present invention, thereby realizing the combination of Figure 1 to Fig.12 A tower sweep risk early warning method and device for a wind turbine generator set are described.

[0223] In addition, in combination with the tower sweep risk warning method for wind turbine generator sets in the above embodiments, the embodiments of the present invention can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any of the tower sweep risk warning methods for wind turbine generator sets in the above embodiments is implemented.

[0224] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0225] The functional blocks shown in the above structured block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), appropriate firmware, plug-in, function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (Radio Frequency, RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0226] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in a different order from the embodiments, or several steps can be performed simultaneously.

[0227] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device, equipment and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0228] The above is only a specific implementation of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be covered within the protection scope of the present invention.

Claims

1. A wind turbine tower sweep risk warning method, characterized in that: include: Obtaining operating parameters of the wind turbine generator set within a first time period; Inputting the operating parameters into a pre-trained clearance distance estimation model to obtain a clearance distance of the wind turbine generator set in a second time period, where the second time period is later than the first time period; If it is determined that the clearance distance in the second time period meets the preset risk determination condition, determining that the wind turbine generator set has a tower sweep risk in the second time period; The clearance distance in the second time period includes M clearance distances in the second time period, where M is a positive integer; The determining that the clearance distance within the second time period meets the preset risk determination condition specifically includes: Determine a minimum value among the M clearance distances; When the minimum value is less than a preset clearance distance threshold, it is determined that the clearance distance in the second time period meets the preset risk determination condition.

2. The method according to claim 1, characterized in that The determining that the clearance distance within the second time period meets the preset risk determination condition specifically includes: Determining distribution characteristic parameters of the clearance distances in the second time period according to the M clearance distances; According to the distribution characteristic parameter and the preset distribution characteristic threshold, it is determined that the clearance distance in the second time period meets the preset risk determination condition.

3. The method according to claim 2, characterized in that The distribution characteristic parameter is the number of first clearance distances, the preset distribution characteristic threshold is a preset number threshold, and the first clearance distance is a clearance distance among the M clearance distances that is less than or equal to the preset clearance distance threshold; The determining, based on the distribution characteristic parameter and the preset distribution characteristic threshold, that the clearance distance in the second time period meets the preset risk determination condition specifically includes: determining the amount of the first clearance distance; When the number of the first clearance distances is greater than the preset number threshold, it is determined that the clearance distances within the second time period meet the preset risk determination condition.

4. The method according to claim 2, characterized in that: The distribution characteristic parameter is a first variation relationship obtained by fitting based on the M clearance distances and the moments corresponding to the M clearance distances, and the preset distribution characteristic threshold is a preset time threshold, wherein the first variation relationship is a variation relationship of the clearance distance over time; The determining, based on the distribution characteristic parameter and the preset distribution characteristic threshold, that the clearance distance in the second time period meets the preset risk determination condition specifically includes: In the first change relationship, determining a first moment corresponding to the preset clearance distance; When the first moment is less than the preset time threshold, it is determined that the clearance distance within the second time period meets the preset risk determination condition.

5. The method according to claim 2, characterized in that: The distribution characteristic parameter is a tower sweep risk probability in the second time period obtained according to the M clearance distances and the wind speeds corresponding to the M clearance distances, and the preset distribution characteristic threshold is a preset probability threshold; The determining, based on the distribution characteristic parameter and the preset distribution characteristic threshold, that the clearance distance in the second time period meets the preset risk determination condition specifically includes: When the tower sweep risk probability in the second time period is greater than the preset probability threshold, it is determined that the clearance distance in the second time period meets the preset risk determination condition.

6. The method according to claim 5, characterized in that Before determining, based on the distribution characteristic parameter and the preset distribution characteristic threshold, that the clearance distance within the second time period meets the preset risk determination condition, the method further includes: Based on the wind speeds corresponding to the M clearance distances, the M clearance distances are divided into N sub-bins, wherein the wind speed corresponding to the clearance distance in each sub-bin belongs to a wind speed interval, and N is a positive integer; Determine a clearance risk probability of each of the N sub-compartments, where the clearance risk probability of each sub-compartment is determined according to a probability that a clearance distance in each sub-compartment is less than a preset clearance distance threshold and a probability that a wind speed falls within each sub-compartment; The sum of the clearance risk probabilities of the N sub-warehouses is determined as the tower sweep risk probability in the second time period.

7. The method according to any one of claims 3 to 6, characterized in that: The second time period includes M sub-time periods, The M clearance distances are extreme values ​​of the clearance distances within the M sub-time periods.

8. The method according to any one of claims 1 to 6, characterized in that: The operation data of the wind turbine generator set includes the wind condition data of the wind turbine generator set, and other operation data except the wind condition data of the wind turbine generator set; Before inputting the operating parameters into a pre-trained clearance distance estimation model to obtain the clearance distance of the wind turbine generator set in the second time period, the method further includes: Inputting the wind condition data of the wind turbine generator set into a preset wind generating system to obtain the wind condition data of the wind farm to which the wind turbine generator set belongs; The step of inputting the operating parameters into a pre-trained clearance distance estimation model to obtain the clearance distance of the wind turbine generator set in the second time period specifically includes: The wind farm wind condition data and the other operating data are input into the pre-trained clearance distance estimation model to obtain the clearance distance of the wind turbine generator set in the second time period.

9. The method according to any one of claims 1 to 6, characterized in that: After determining that the wind turbine generator set has a tower clearance risk within the second time period, the method further includes: Execute preset clearance distance control strategy; Wherein, the preset clearance distance control strategy includes: The wind turbine generator set is controlled to operate at reduced power, or the wind turbine generator set is controlled to shut down.

10. A tower sweep risk warning device for a wind turbine generator set, characterized in that: The device comprises: A parameter acquisition module, used to acquire the operating parameters of the wind turbine generator set within a first time period; a clearance distance estimation module, configured to input the operating parameters into a pre-trained clearance distance estimation model to obtain a clearance distance of the wind turbine generator set in a second time period, the second time period being later than the first time period; A risk determination module, configured to determine that the wind turbine generator set has a tower sweep risk within the second time period if it is determined that the clearance distance within the second time period meets a preset risk determination condition; The clearance distance in the second time period includes M clearance distances in the second time period, where M is a positive integer; The risk determination module specifically includes: A first determining unit, configured to determine a minimum value among the M clearance distances; The second determining unit is configured to determine, when the minimum value is less than a preset clearance distance threshold, whether the clearance distance within the second time period satisfies the preset risk determination condition.

11. A wind turbine tower clearance risk warning system, characterized in that: The system comprises: An operating parameter collection device, used to collect the operating parameters of the wind turbine generator set and send the operating parameters to the tower sweep risk warning device; The tower sweeping risk warning device is used to implement the tower sweeping risk warning method described in any one of claims 1-9.

12. A risk warning device for tower clearance of a wind turbine generator set, characterized in that: The device comprises: a processor and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the risk warning method for tower clearance of a wind turbine generator set as described in any one of claims 1-9.

13. A computer storage medium, characterized in that: The computer storage medium stores computer program instructions, which, when executed by a processor, implement the risk warning method for tower clearance of a wind turbine generator set as described in any one of claims 1 to 9.

Images