Wind turbine generator clearance risk detection method and device based on wake flow influence

Abstract

The invention provides a wind turbine generator clearance risk detection method and device based on wake flow influence, and relates to the technical field of wind turbine generator wake flow simulation, and the method comprises the steps: obtaining the wind measurement data of a target wind power plant, and obtaining the topographic data of the target wind power plant and the position information of each wind turbine generator in the target wind power plant; determining the associated wind turbine generator of each wind turbine generator according to the wind measurement data and the position information of each wind turbine generator, and establishing a simulation model of each wind turbine generator; based on the wind measurement data, the predicted wind speed of each wind turbine generator is determined through a preset speed fitting model, and based on the predicted wind speed, the simulation model of each wind turbine generator is calculated through a preset wake flow model, so that the distribution of the wind speed of each wind turbine generator in the target wind power plant along with the height is obtained. The clearance risk of the wake flow area unit can be effectively monitored, the potential safety hazard of the wind turbine unit is reduced, and the unit performance is improved.

Description

Technical Field

[0001] The present application relates to the technical field of wind turbine wake simulation, and in particular to a wind turbine clearance risk detection method based on wake influence, a wind turbine clearance risk detection device based on wake influence, and a computer-readable storage medium. Background Art

[0002] In recent years, with the large-scale development of the wind power industry, large-scale wind turbines with high towers and long blades have been used more and more frequently. However, the current wind resource assessment methods make it difficult to accurately assess the wind resources of large-scale wind turbines. For wind turbines, accurate assessment of wind resources is conducive to improving unit safety, reducing unit safety hazards, and improving unit performance. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a wind turbine clearance risk detection method based on wake influence, a wind turbine clearance risk detection device based on wake influence, and a computer-readable storage medium to solve the above problems.

[0004] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a wind turbine clearance risk detection method based on wake influence, comprising: Acquiring wind measurement data of a target wind farm, as well as terrain data of the target wind farm and location information of each wind turbine in the target wind farm; Determine the associated wind turbine groups of each wind turbine group according to the wind measurement data and the position information of each wind turbine group, and establish a simulation model of each wind turbine group based on each wind turbine group and its associated wind turbine groups; Based on the wind measurement data, the predicted wind speed of each wind turbine is determined by a preset speed fitting model, and based on the predicted wind speed, a simulation model of each wind turbine is calculated by a preset wake model to obtain the distribution of wind speed with height at each wind turbine in the target wind farm; The wind shear value of the section from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine is determined based on the terrain data of the target wind farm and the distribution of wind speed with height at each wind turbine group. When it is determined that the wind shear value of the section from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine group is lower than a preset threshold, it is determined that the current wind turbine group has a clearance risk and an alarm message is generated.

[0005] Optionally, the wind measurement data includes: The main wind direction at each wind turbine in the target wind farm, the average wind speed in each wind direction within a preset period of time, and the target shear value under different wind directions; Obtain wind measurement data of the target wind farm, including: Obtaining wind speed and wind direction at different heights of the target wind farm within the preset time period through a wind measuring tower; Determine the abnormal wind speed and the abnormal wind direction, and delete the abnormal wind speed and the abnormal wind direction from the acquired wind speed and wind direction; Determining the main wind direction of the target wind farm according to the wind directions at different heights of the target wind farm, and determining the average wind speeds at different heights of the target wind farm within the preset time period according to the wind speeds at different heights of the target wind farm; and The wind shear values ​​of each wind turbine at different heights under different wind directions are determined according to the wind speeds at different heights of the target wind farm, and the obtained wind shear values ​​are sorted from low to high. Each wind shear value is counted according to the sorting order of the wind shear values. If the ratio of the count of the current wind shear value to the total number of wind shear values ​​reaches a preset threshold, the current wind shear value is determined to be the target wind shear value of the current wind turbine at the current wind speed.

[0006] Optionally, determining the abnormal wind speed and abnormal wind direction includes: Determine that a corresponding wind speed whose average wind speed is less than a first wind speed value or greater than a second wind speed value in a first sampling period is an abnormal wind speed, and the first wind speed value is less than the second wind speed value; Determine that in the second sampling period, the corresponding wind direction whose average wind direction is less than the first wind direction angle or greater than the second wind direction angle is an abnormal wind direction; and It is determined that at every two adjacent sampling heights, the corresponding wind speed whose wind speed change value is greater than a preset wind speed change threshold is an abnormal wind speed.

[0007] Optionally, the speed fitting model includes:

[0008] Where f represents frequency, S represents power spectrum density, represents the velocity standard deviation, L represents the turbulence scale parameter, V hub represents the wind speed at the fan hub height, k is the velocity component subscript; Among them, when k=1, = , L K =8.1 ; When k=2, = , L K =2.7 ; When k=3, = , L K =0.66 ; represents the standard deviation of the average wind speed V1 at the hub height of the wind turbine, It is 42m.

[0009] Optionally, the predicted wind speed of each wind turbine is determined by a preset speed fitting model, including: Performing an inverse Fourier transform on the speed fitting model to obtain an initial predicted wind speed at the hub height of the wind turbine that varies with time; The target shear value is used to superimpose the initial predicted wind speed at the height of the wind turbine hub that changes with time, so as to obtain the predicted wind speed at the height of the wind turbine hub that changes with time of the corresponding wind turbine group.

[0010] Optionally, determining the associated wind turbine groups of each wind turbine group according to the wind measurement data and the position information of each wind turbine group includes: For each wind turbine: If there are other wind turbines in the main wind direction of the current wind turbine, the wind turbine with the shortest distance from the current wind turbine among all wind turbines in the main wind direction of the current wind turbine is determined as the associated wind turbine of the current wind turbine.

[0011] Optionally, the wake model includes:

[0012]

[0013] Among them, u pre (x, z) represents the initial wind speed, u(x, z) represents the wind speed at height z at position x, u0(z) represents the wind speed of the upstream wind turbine with height variation, r represents the distance from any point on the impeller surface to the center point of the impeller surface, x represents the distance between the impeller and the fan, s=x / D, D represents the impeller diameter of the fan, and a represents the axial induction factor of the fan, where a=1 / 2-1 / 2*(1-C T )^0.5,C T is the thrust coefficient of the fan, r x =D / 2((1-a)(1-2a))^0.5, k xz is the new wake expansion coefficient; Among them, k xz =k0I wake，x，z / I0(z),I wake，x，z =(I0(z)^2+I add,x ^2)^0.5,I add,x =0.29*s^(-1 / 3)*(1-(1-C T )^0.5)^0.5, k0 is the wake expansion coefficient of the wake model, k0=0.5 / (ln(z hub / z0)), z0 is the surface roughness height, z hub is the fan hub height, I wake，x，zis the wake turbulence intensity, I add,x is the additional turbulence intensity, and I0 is the turbulence intensity.

[0014] Optionally, the simulation model of each wind turbine is calculated by a preset wake model to obtain the distribution of wind speed with height at each wind turbine in the target wind farm, including: If there are no other wind turbines in the main wind direction of the current wind turbine, the predicted wind speed is used as the initial wind speed of the wake model, and the distribution of wind speed with height at the current wind turbine is calculated based on the wake model; If the current wind turbine is in the main wind direction of any wind turbine, the distribution of wind speed with height at the current wind turbine is used as the initial wind speed of the wake model, and the distribution of wind speed with height at the associated wind turbine of the current wind turbine is calculated based on the wake model.

[0015] In a second aspect of the present application, a wind turbine clearance risk detection device based on wake influence is provided, comprising: A data acquisition module is configured to obtain wind measurement data of a target wind farm, as well as terrain data of the target wind farm and location information of each wind turbine in the target wind farm; A model building module is configured to determine the associated wind turbines of each wind turbine according to the wind measurement data and the location information of each wind turbine, and to establish a simulation model of each wind turbine based on each wind turbine and its associated wind turbines; A wind speed calculation module is configured to determine the predicted wind speed of each wind turbine generator set through a preset speed fitting model based on the wind measurement data, and calculate the simulation model of each wind turbine generator set through a preset wake model based on the predicted wind speed to obtain the distribution of wind speed with height at each wind turbine generator set in the target wind farm; The risk warning module is configured to determine the wind shear value from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine group based on the terrain data of the target wind farm and the distribution of wind speed with height at each wind turbine group, and when it is determined that the wind shear value from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine group is lower than a preset threshold, it is determined that the current wind turbine group has a clearance risk and an alarm message is generated.

[0016] In a third aspect of the present application, a computer-readable storage medium is provided, which stores a computer program which, when executed by a processor, causes the processor to execute the wind turbine clearance risk detection method based on wake influence as described above.

[0017] In a fourth aspect of the present application, a terminal device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned wind turbine clearance risk detection method based on wake influence when executing the computer program.

[0018] The embodiments provided in this application have the following beneficial effects: Based on the wind measurement data of the wind farm, the present application can perform modeling and simulation calculations on all machine sites in the wind farm through a pre-constructed wake model, so as to systematically evaluate the clearance risk of each machine site, especially the machine sites in the wake area, thereby reducing the safety hazards of the wind turbines and improving the performance of the units.

[0019] Other features and advantages of the embodiments or implementations of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings: Figure 1 A method flow chart of a wind turbine clearance risk detection method based on wake influence according to an embodiment of the present application is schematically shown; Figure 2 A schematic diagram of a wind turbine simulation model according to an embodiment of the present application is schematically shown; Figure 3 A schematic diagram schematically shows a wind resource background field calculated by Fluent according to an embodiment of the present application; Figure 4 A schematic diagram of the wind turbine wake effect according to an embodiment of the present application is schematically shown; Figure 5 A schematic diagram showing the distribution of wind speed with height after the influence of the wakes of multiple wind turbines in an embodiment of the present application is shown; Figure 6 A schematic block diagram of a wind turbine clearance risk detection device based on wake influence according to an embodiment of the present application is schematically shown; Figure 7 A schematic diagram of the structure of a terminal device according to an embodiment of the present application is shown schematically.

[0021] Description of Reference Numerals 10-terminal device, 100-processor, 101-memory, 102-computer program. DETAILED DESCRIPTION

[0022] The specific implementation of the embodiment of the present application is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present application, and is not used to limit the embodiment of the present application.

[0023] In order to solve the above problems, Figure 1 As shown, the first aspect of the present application provides a wind turbine clearance risk detection method based on wake influence, comprising: S100, obtaining wind measurement data of a target wind farm, as well as terrain data of the target wind farm and location information of each wind turbine in the target wind farm; S200, determining the associated wind turbines of each wind turbine according to the wind measurement data and the location information of each wind turbine, and establishing a simulation model of each wind turbine based on each wind turbine and its associated wind turbine; S300, based on the wind measurement data, determine the predicted wind speed of each wind turbine using a preset speed fitting model, and based on the predicted wind speed, calculate the simulation model of each wind turbine using a preset wake model to obtain the distribution of wind speed with height at each wind turbine in the target wind farm; S400, determining the wind shear value from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine based on the terrain data of the target wind farm and the distribution of wind speed with height at each wind turbine, and when it is determined that the wind shear value from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine is lower than a preset threshold, determining that the current wind turbine has a clearance risk, and generating an alarm message.

[0024] In this way, the present application can perform modeling and simulation calculations on all machine sites in the wind farm based on the wind measurement data of the wind farm through a pre-constructed wake model, so as to systematically evaluate the clearance risk of each machine site, especially the machine sites in the wake area, thereby reducing the safety hazards of the wind turbines and improving the performance of the units.

[0025] In step S100, the present application first acquires the wind measurement data of the target wind farm, including: acquiring a preset time period through a preset wind tower, for example, acquiring the wind speed and wind direction at different heights of the wind tower of the target wind farm in the past year; performing integrity and validity analysis on the wind tower data for a full year to delete invalid data, specifically, determining the abnormal wind speed and abnormal wind direction therein, and deleting the abnormal wind speed and abnormal wind direction from the acquired wind speed and wind direction; determining the main wind direction of the target wind farm according to the wind direction at different heights of the wind tower, and determining the average wind speed at different heights within a preset time period according to the wind speed at different heights of the target wind farm, wherein the determination of the main wind direction is a prior art and is not limited here, for example, the wind direction that appears most frequently at different heights in the past year is taken as the main wind direction, or the wind direction that appears most frequently at the height of the wind turbine hub in the past year is taken as the main wind direction of the wind turbine; and solving The shear values ​​at all times are obtained, and the wind speed, wind direction and speed segment are counted to obtain the characteristic negative shear values, average wind speed and turbulence under different wind directions, for example, the turbulence values ​​at different heights of the wind tower at a wind speed of 15m / s, which are used as the loading data for subsequent simulations. Specifically, the wind shear values ​​at different heights under different wind directions are determined according to the wind speeds at different heights of the target wind farm, and the obtained wind shear values ​​are sorted from low to high, and each wind shear value is counted according to the sorting order of the wind shear values. If the ratio of the count of the current wind shear value to the total number of wind shear values ​​reaches a preset threshold, the current wind shear value is determined to be the target wind shear value under the current wind speed. For example, the wind directions are divided into zones, and the wind shear of each height layer wind speed every 10 minutes in each zone is calculated and sorted, and the counting starts from the lowest negative shear. When the count divided by the total number is 10%, the shear value corresponding to the count is the target wind shear value. Through the above steps, the wind measurement data of the target wind farm is obtained, including: the main wind direction of the target wind farm, the average wind speed of each wind direction within a preset time period, and the target shear values ​​under different wind directions.

[0026] In the present application, determining abnormal wind speed and abnormal wind direction includes: determining that within a first sampling period, the corresponding wind speed whose average wind speed is less than a first wind speed value or greater than a second wind speed value is an abnormal wind speed, and the first wind speed value is less than the second wind speed value. For example, the first sampling period is 10 minutes, the first wind speed value is 3m / s, and the second wind speed value is 30m / s, that is, the reasonable data is 3≤10min average wind speed≤30m / s, and the wind speed outside this range is determined as abnormal wind speed; determining that within a second sampling period, the average wind direction is less than the first wind direction angle or The corresponding wind direction that is greater than the second wind direction angle is an abnormal wind direction. The second sampling period is 10 minutes, that is, the reasonable data is 0°≤10min average wind direction angle≤360°. The wind direction outside this range is determined to be an abnormal wind direction; it is determined that at every two adjacent sampling heights, the corresponding wind speed whose wind speed change value is greater than the preset wind speed change threshold is an abnormal wind speed. For example, the preset wind speed change threshold is 2m / s. For every 10m difference in the height difference of the wind measuring towers, the wind speed change of ≤2m / s is a reasonable data, and the wind speed outside this range is an abnormal wind speed.

[0027] In the present application, the associated wind turbines of each wind turbine are determined based on the wind measurement data and the location information of each wind turbine, including: for each wind turbine: taking the main wind direction of the target wind farm as the main wind direction of each wind turbine, if there are other wind turbines in the main wind direction of the current wind turbine, determine that among all wind turbines in the main wind direction of the current wind turbine, the wind turbine with the smallest distance from the current wind turbine is the associated wind turbine of the current wind turbine. For example, there are wind turbine 1, wind turbine 2, wind turbine 3 and wind turbine 4, wind turbine 2, wind turbine 3 and wind turbine 4 are all in the main wind direction of wind turbine 1, and wind turbine 2 is closest to wind turbine 1, then, wind turbine 2 is determined to be the associated wind turbine of wind turbine 1, and when modeling, a simulation model is constructed with wind turbine 1 and wind turbine 2 as a group, if wind turbine 3 is closest to wind turbine 2, then a simulation model is constructed with wind turbine 2 and wind turbine 3 as a group, and so on. Specifically, this application uses the fluent simulation software to establish a two-dimensional simulation model of a wind turbine according to the terrain map, the main wind direction data and the machine position arrangement. The modeling principle of this application is as follows: When there is a wind turbine in the main wind direction of a unit, such as Figure 2 As shown, the established model needs to include the units in the main wind direction, and the horizontal boundary of the model needs to be at least 500m away from the units, and the model height must be at least 10 times the height difference of the bottom surface of the model; when there are multiple wind turbines in the main wind direction of a unit, the model only considers the wind turbine closest to the current wind turbine for modeling, and the model requirements are the same as above; when there is special terrain around a wind turbine, including terrain higher than the location of the unit or terrain with a slope greater than 25°, and large terrain undulations, such as the ruggedness index RIX>20, in addition to considering the main wind energy direction, it is also necessary to establish a two-dimensional simulation model under the special terrain direction, and the model requirements are the same as above.

[0028] This application uses the fluent simulation software and the ke turbulence model to simulate the wind turbines in the target wind farm. Specifically, the parameters of fluent are first set, and the ke turbulence model is selected for steady-state simulation. The boundary conditions, velocity inlet, pressure outlet, and wall boundary parameters are set according to the requirements. For example, V1 can be set to 6m / s, z hub =100m, α1=-0.1, model velocity inlet boundary terrain height z0=1160m; and set monitoring points for the machine site location. In this application, the speed change curve of each wind turbine unit with height is mainly monitored; at the same time, initialize the calculation domain, set the time step and calculation step, and set the grid quality standard to two-dimensional grid quality>0.7, grid positive slope ratio<200, and Cront number<5. In this application, the velocity inlet of the model uses udf for velocity fitting, and the fitting parameters include the average wind speed V1 of the wind tower and the 10% percentile α1 of the negative shear of the wind tower. The parameters of the fitting model need to be adjusted according to the numerical model to ensure the wind turbine hub height z at the velocity inlet hub The wind speed at the location is the average wind speed V1 of the wind tower, and the speed variation index is the 10% quantile α1 of the negative shear of the wind tower.

[0029] Specifically, the speed fitting model of the present application includes:

[0030] Where f represents frequency, S represents power spectrum density, represents the velocity standard deviation, L represents the turbulence scale parameter, V hub represents the wind speed at the fan hub height, k is the speed component subscript; when k=1, = , L K =8.1 ; When k=2, = , L K =2.7 ; When k=3, = , L K =0.66 ; represents the standard deviation of the average wind speed V1 at the hub height of the wind turbine, It is 42m.

[0031] Specifically, the predicted wind speed of the target wind farm is determined by a preset speed fitting model, including: performing an inverse Fourier transform on the speed fitting model to obtain an initial predicted wind speed that varies with time at the height of the wind turbine hub. It can be understood that the speed fitting model describes how the wind speed changes with frequency. The speed fitting model is inversely transformed to convert the speed fitting model from the frequency domain to the time domain, thereby obtaining a time domain representation of the wind speed. The inverse Fourier transform process is a prior art and is not limited here. After obtaining the initial predicted wind speed that varies with time, the initial predicted wind speed that varies with time at the height of the wind turbine hub is further superimposed with the target shear value to obtain the target predicted wind speed that varies with time at the height of the wind turbine hub of the corresponding wind turbine. For example, the superposition process can be expressed as u(z, t)=u ref (t)+α1(zz ref ), where u(z, t) represents the wind speed at any height z, u ref (t) represents the target predicted wind speed that changes with time, z ref It represents the reference height, i.e. the height of the fan hub at the velocity inlet, which is not limited here.

[0032] The present application uses a wake model to calculate the simulation model of each wind turbine using the model inlet wind speed obtained by the velocity fitting model, thereby calculating and analyzing the two-dimensional model of the wind turbine in the main wind direction. The wake model of the present application includes:

[0033]

[0034] Among them, u pre (x, z) represents the initial wind speed, u(x, z) represents the wind speed at height z at position x, u0(z) represents the wind speed of the upstream wind turbine with height variation, r represents the distance from any point on the impeller surface to the center point of the impeller surface, x represents the distance between the impeller and the fan, s=x / D, D represents the impeller diameter of the fan, and a represents the axial induction factor of the fan, where a=1 / 2-1 / 2*(1-C T )^0.5,C T is the thrust coefficient of the fan, r x =D / 2((1-a)(1-2a))^0.5, k xz is the new wake expansion coefficient; Among them, k xz =k0I wake，x，z / I0(z),I wake，x，z =(I0(z)^2+I add,x ^2)^0.5,I add,x =0.29*s^(-1 / 3)*(1-(1-C T)^0.5)^0.5, k0 is the wake expansion coefficient of the wake model, k0=0.5 / (ln(z hub / z0)), z0 is the surface roughness height, z hub is the fan hub height, I wake，x，z is the wake turbulence intensity, I add,x is the additional turbulence intensity, I0 is the turbulence intensity. For example, the turbulence value at a wind speed of 15 m / s at different heights of the wind tower is used, and I0(z) represents the turbulence value at a wind speed of 15 m / s at height Z.

[0035] The above superposition method is used to form a UDF code, and the UDF code is used as the inflow condition, such as Figure 3 As shown, the fluent numerical simulation calculation is performed by the fluent simulation software to obtain the wind resource background field of the wind turbine, and the wind speed in front of the wind turbine impeller in the wind resource background field calculated by fluent is used as the loading speed of the wake model to perform the wake model superposition to obtain the wake distribution of the wind turbine. Then, in this application, the simulation model of each wind turbine is calculated by the preset wake model to obtain the distribution of wind speed with height at each wind turbine in the target wind farm, including: if there are no other wind turbines in the main wind direction of the current wind turbine, the predicted wind speed is used as the initial wind speed of the wake model, and the distribution of wind speed with height at the current wind turbine is calculated based on the wake model; if the current wind turbine is in the main wind direction of any wind turbine, the distribution of wind speed with height at the current wind turbine is used as the initial wind speed of the wake model, and the distribution of wind speed with height at the associated wind turbines of the current wind turbine is calculated based on the wake model.

[0036] For example, taking wind turbines 1, 2, 3 and 4 in the target wind farm as an example, if there are three wind turbines in the main wind direction of wind turbine 4, namely wind turbine 1, wind turbine 2 and wind turbine 3, and wind turbine 3 is closest to wind turbine 4, and wind turbine 1 is farthest from wind turbine 4, that is, in order of distance from wind turbine 4 from far to near, the upstream of wind turbine 4 are wind turbine 3, wind turbine 2 and wind turbine 1 respectively. When constructing the simulation model of the wind turbine group, wind turbine 4 and wind turbine 3 are in a two-dimensional model, wind turbine 3 and wind turbine 2 are in a two-dimensional model, and wind turbine 2 and wind turbine 1 are in a two-dimensional model. At this time, when calculating the wind speed distribution under the influence of the wake of wind turbine 4, it is necessary to simultaneously consider the wake influence of wind turbines 1, 2 and 3 on wind turbine 4.

[0037] For example, Figure 4 and Figure 5As shown, the wind speed distribution of wind turbine 2 after being affected by the wake of wind turbine 1 is first calculated. First, the predicted wind speed is used as the initial wind speed of the wake model, and the distribution of wind speed of wind turbine 1 with height is brought into the wake model to obtain the wake influence at wind turbine 2, and the obtained wake influence at wind turbine 2 is used as the initial wind speed of the wake model to obtain the distribution of wind speed with height after being affected by the wake of wind turbine 1; then the wind speed of wind turbine 2 after being affected by the wake of wind turbine 1 is brought into the wake model as the initial wind speed to calculate the influence of the wake of wind turbine 2 on wind turbine 3, and at the same time, the wind speed of wind turbine 1 is brought into the wake model to calculate the influence of the wake of wind turbine 3 on wind turbine 1. According to the simulated wind speed of wind turbine 3, that is, the influence of the wake of wind turbine 3 on wind turbine 1 The wake influence of wind turbine 2 and the wake influence of wind turbine 1 on wind turbine 3 are taken into consideration, and the distribution of wind speed with height after the influence of the wake is obtained through the wake model; the wind speed of wind turbine 3 after the influence of the wake is taken as the initial wind speed and brought into the wake model to calculate the influence of the wake of wind turbine 3 on wind turbine 4, the wind speed of wind turbine 2 after the influence of the wake is brought into the wake model to calculate the influence of the wake of wind turbine 2 on wind turbine 4, and the wind speed of wind turbine 1 is brought into the wake model to calculate the influence of the wake of wind turbine 4 on wind turbine 1. According to the simulated wind speed of wind turbine 4, that is, wind turbine 4 is affected by the wake of wind turbine 3, the wake of wind turbine 2 and the wake of wind turbine 1, the distribution of wind speed with height after the influence of the wake is obtained through the wake model.

[0038] It is understandable that if there is only one unit in the main wind direction, it is only necessary to calculate the wake effect of one unit on the fan. If there are multiple units in the main wind direction, it is necessary to repeatedly calculate the wake effect of multiple units on the fan. Through the above steps, after calculating the distribution of wind speed with height after each fan is affected by the wake, the calculated wind speed with height distribution after each position is affected by the wake is brought into step S400. Specifically, step S400 can be implemented by Bladed aerodynamic simulation software. Through the Bladed aerodynamic simulation software, the wind shear situation in the range from the lower blade tip to the hub height can be calculated according to the ground height, the wind speed distribution with height at each machine position, the hub height and blade diameter of the unit, and other information, so as to obtain the shear situation of all machine positions under different terrain conditions. Among them, the range from the lower blade tip to the hub height refers to the height range from the blade tip to the hub of the wind turbine when the wind turbine blade is vertically facing downward. By inputting specific information such as the blade model and hub height, the negative shear threshold when the blade reaches the minimum clearance can be obtained. The unit shear under different terrain conditions is judged based on this threshold. When the unit shear is less than the threshold, it is determined that the unit has a clearance risk and an alarm signal is generated. Otherwise, it is considered that the unit theoretically has no clearance risk.

[0039] like Figure 6 As shown, in a second aspect of the present application, a wind turbine clearance risk detection device based on wake influence is provided, comprising: A data acquisition module is configured to obtain wind measurement data of a target wind farm, as well as terrain data of the target wind farm and location information of each wind turbine in the target wind farm; A model building module is configured to determine the associated wind turbines of each wind turbine according to the wind measurement data and the location information of each wind turbine, and to establish a simulation model of each wind turbine based on each wind turbine and its associated wind turbines; The wind speed calculation module is configured to determine the predicted wind speed of each wind turbine generator set through a preset speed fitting model based on the wind measurement data, and calculate the simulation model of each wind turbine generator set through a preset wake model based on the predicted wind speed to obtain the distribution of wind speed with height at each wind turbine generator set in the target wind farm; The risk warning module is configured to determine the wind shear value from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine group based on the terrain data of the target wind farm and the distribution of wind speed with height at each wind turbine group, and when it is determined that the wind shear value from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine group is lower than a preset threshold, it is determined that the current wind turbine group has a clearance risk and an alarm message is generated.

[0040] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0041] In a third aspect of the present application, a computer-readable storage medium is provided, which stores a computer program which, when executed by a processor, causes the processor to execute the wind turbine clearance risk detection method based on wake influence as described above.

[0042] In a fourth aspect of the present application, a terminal device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned wind turbine clearance risk detection method based on wake influence when executing the computer program.

[0043] like Figure 7FIG. 1 is a schematic diagram of a terminal device provided in an embodiment of the present application. Figure 7 As shown, the terminal device 10 of this embodiment includes: a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, the steps in the above method embodiment are implemented. Alternatively, when the processor 100 executes the computer program 102, the functions of each module / unit in the above device embodiments are implemented.

[0044] Exemplarily, the computer program 102 may be divided into one or more modules / units, one or more modules / units are stored in the memory 101, and are executed by the processor 100 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 102 in the terminal device 10.

[0045] The terminal device 10 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will appreciate that Figure 7 It is only an example of the terminal device 10 and does not constitute a limitation of the terminal device 10. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0046] The processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0047] The memory 101 may be an internal storage unit of the terminal device 10, such as a hard disk or memory of the terminal device 10. The memory 101 may also be an external storage device of the terminal device 10, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (FlashCard), etc. equipped on the terminal device 10. Further, the memory 101 may also include both an internal storage unit of the terminal device 10 and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the terminal device 10. The memory 101 may also be used to temporarily store data that has been output or is to be output.

[0048] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0049] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0050] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A wind turbine clearance risk detection method based on wake influence, characterized in that: include: Acquiring wind measurement data of a target wind farm, as well as terrain data of the target wind farm and location information of each wind turbine in the target wind farm; Determine the associated wind turbine groups of each wind turbine group according to the wind measurement data and the position information of each wind turbine group, and establish a simulation model of each wind turbine group based on each wind turbine group and its associated wind turbine groups; Based on the wind measurement data, the predicted wind speed of each wind turbine is determined by a preset speed fitting model, and based on the predicted wind speed, a simulation model of each wind turbine is calculated by a preset wake model to obtain the distribution of wind speed with height at each wind turbine in the target wind farm; The wind shear value of the section from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine is determined based on the terrain data of the target wind farm and the distribution of wind speed with height at each wind turbine group. When it is determined that the wind shear value of the section from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine group is lower than a preset threshold, it is determined that the current wind turbine group has a clearance risk and an alarm message is generated.

2. The wind turbine clearance risk detection method based on wake influence according to claim 1 is characterized in that: The wind measurement data includes: The main wind direction of the target wind farm, the average wind speed of each wind direction within a preset period of time, and the target shear value under different wind directions; Obtain wind measurement data of the target wind farm, including: Obtaining wind speed and wind direction at different heights of the target wind farm within the preset time period through a wind measuring tower; Determine the abnormal wind speed and the abnormal wind direction, and delete the abnormal wind speed and the abnormal wind direction from the acquired wind speed and wind direction; Determining the main wind direction of the target wind farm according to the wind directions at different heights of the target wind farm, and determining the average wind speeds at different heights of the target wind farm within the preset time period according to the wind speeds at different heights of the target wind farm; and The wind shear values ​​at different heights under different wind directions are determined according to the wind speeds at different heights of the target wind farm, and the obtained wind shear values ​​are sorted from low to high. Each wind shear value is counted according to the sorting order of the wind shear values. If the ratio of the count of the current wind shear value to the total number of wind shear values ​​reaches a preset threshold, the current wind shear value is determined to be the target wind shear value under the current wind speed.

3. The wind turbine clearance risk detection method based on wake influence according to claim 2 is characterized in that: Determine abnormal wind speed and direction, including: Determine that a corresponding wind speed whose average wind speed is less than a first wind speed value or greater than a second wind speed value in a first sampling period is an abnormal wind speed, and the first wind speed value is less than the second wind speed value; Determine that in the second sampling period, the corresponding wind direction whose average wind direction is less than the first wind direction angle or greater than the second wind direction angle is an abnormal wind direction; and It is determined that at every two adjacent sampling heights, the corresponding wind speed whose wind speed change value is greater than a preset wind speed change threshold is an abnormal wind speed.

4. The wind turbine clearance risk detection method based on wake influence according to claim 2 is characterized in that: The speed fitting model comprises: Where f represents frequency, S represents power spectrum density, represents the velocity standard deviation, L represents the turbulence scale parameter, V hub represents the wind speed at the fan hub height, k is the velocity component subscript; Among them, when k=1, = , L K =8.1 ; When k=2, = , L K =2.7 ; When k=3, = , L K =0.66 ; represents the standard deviation of the average wind speed V1 at the hub height of the wind turbine, It is 42m.

5. The wind turbine clearance risk detection method based on wake influence according to claim 4 is characterized in that: The predicted wind speed of each wind turbine is determined by a preset speed fitting model, including: Performing an inverse Fourier transform on the speed fitting model to obtain an initial predicted wind speed at the wind turbine hub height that varies with time; The target shear value is used to superimpose the initial predicted wind speed at the height of the wind turbine hub that changes with time, so as to obtain the predicted wind speed at the height of the wind turbine hub that changes with time of the corresponding wind turbine generator set.

6. The wind turbine clearance risk detection method based on wake influence according to claim 5 is characterized in that: Determining the associated wind turbine groups of each wind turbine group according to the wind measurement data and the position information of each wind turbine group includes: For each wind turbine: If there are other wind turbines in the main wind direction of the current wind turbine, the wind turbine with the shortest distance from the current wind turbine among all wind turbines in the main wind direction of the current wind turbine is determined as the associated wind turbine of the current wind turbine.

7. The wind turbine clearance risk detection method based on wake influence according to claim 6 is characterized in that: The wake model comprises: Among them, u pre (x, z) represents the initial wind speed, u(x, z) represents the wind speed at height z at position x, u0(z) represents the wind speed of the upstream wind turbine with height variation, r represents the distance from any point on the impeller surface to the center point of the impeller surface, x represents the distance between the impeller and the fan, s=x / D, D represents the impeller diameter of the fan, and a represents the axial induction factor of the fan, where a=1 / 2-1 / 2*(1-C T )^0.5,C T is the thrust coefficient of the fan, r x =D / 2((1-a)(1-2a))^0.5, k xz is the new wake expansion coefficient; Among them, k xz =k0I wake，x，z / I0(z),I wake，x，z =(I0(z)^2+I add,x ^2)^0.5,I add,x =0.29*s^(-1 / 3)*(1-(1-C T )^0.5)^0.5, k0 is the wake expansion coefficient of the wake model, k0=0.5 / (ln(z hub / z0)), z0 is the surface roughness height, z hub is the fan hub height, I wake，x，z is the wake turbulence intensity, I add,x is the additional turbulence intensity, and I0 is the turbulence intensity.

8. The wind turbine clearance risk detection method based on wake influence according to claim 7 is characterized in that: The simulation model of each wind turbine is calculated by a preset wake model to obtain the distribution of wind speed with height at each wind turbine in the target wind farm, including: If there are no other wind turbines in the main wind direction of the current wind turbine, the predicted wind speed is used as the initial wind speed of the wake model, and the distribution of wind speed with height at the current wind turbine is calculated based on the wake model; If the current wind turbine is in the main wind direction of any wind turbine, the distribution of wind speed with height at the current wind turbine is used as the initial wind speed of the wake model, and the distribution of wind speed with height at the associated wind turbine of the current wind turbine is calculated based on the wake model.

9. A wind turbine clearance risk detection device based on wake influence, characterized in that: include: A data acquisition module is configured to obtain wind measurement data of a target wind farm, and obtain terrain data of the target wind farm and location information of each wind turbine in the target wind farm; A model building module is configured to determine the associated wind turbines of each wind turbine according to the wind measurement data and the location information of each wind turbine, and to establish a simulation model of each wind turbine based on each wind turbine and its associated wind turbine; A wind speed calculation module is configured to determine the predicted wind speed of each wind turbine generator set through a preset speed fitting model based on the wind measurement data, and calculate the simulation model of each wind turbine generator set through a preset wake model based on the predicted wind speed to obtain the distribution of wind speed with height at each wind turbine generator set in the target wind farm; The risk warning module is configured to determine the wind shear value from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine group based on the terrain data of the target wind farm and the distribution of wind speed with height at each wind turbine group, and when it is determined that the wind shear value from the lower tip of the wind turbine blade to the wind turbine hub of each wind turbine group is lower than a preset threshold, it is determined that the current wind turbine group has a clearance risk and an alarm message is generated.

10. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the wind turbine clearance risk detection method based on wake influence as claimed in any one of claims 1 to 8.

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