Wind power icing thickness prediction method and device, equipment and storage medium

Abstract

The invention relates to a wind power icing thickness prediction method and device, equipment and a storage medium. According to the method, the predicted meteorological data of the geographic position of the wind driven generator is determined through inverse distance weight interpolation calculation, and then the temperature and the wind speed of the position of the fan of the wind driven generator are determined through vertical height correction based on the predicted meteorological data, so that the accuracy of the meteorological data of the position of the fan of the wind driven generator can be improved; and determining the total icing thickness of the fan within the preset duration based on the temperature and the wind speed of the position of the fan and the predicted meteorological data. The accuracy of predicting the icing thickness of the fan can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wind power, and particularly to a method, device, equipment and storage medium for predicting the icing thickness of wind power. Background Art

[0002] Icing on the fan of a wind turbine is one of the high-impact meteorological disasters in wind power generation. It is a physical phenomenon caused by meteorological conditions, which is manifested as the situation where ice layers adhere to the blades of the fan under specific weather conditions. After icing occurs on the fan, it will cause an increase in the weight of the blade, an increase in the blade load, and due to the uneven ice layer thickness, it is very likely to cause a change in the shape of the blade, thereby affecting the service life of the fan. Extremely cold weather will also cause the fans of large-scale wind farms in the area to stop due to icing, posing challenges to the safe and stable operation of the power grid.

[0003] Currently, the resolution of existing numerical prediction models is relatively coarse, the accuracy of the meteorological data obtained at the location where the fan is located is poor, and the accuracy of the predicted icing thickness of the fan is low. Summary of the Invention

[0004] In order to solve the above technical problems, the present disclosure provides a method, device, equipment and storage medium for predicting the icing thickness of wind power.

[0005] The first aspect of the present disclosure provides a method for predicting the icing thickness of wind power, including:

[0006] Obtaining the meteorological gridded data around the wind turbine within each time period included in the preset time period;

[0007] For each time period in the preset time period, in the meteorological gridded data corresponding to the time period, select a preset number of prediction grid points around the target geographical location where the wind turbine is located as the target grid points corresponding to the time period;

[0008] Based on the distances between the geographical locations of the preset number of target grid points corresponding to the time period and the target geographical location respectively, perform inverse distance weighted interpolation calculation on the meteorological data of the target geographical location through the gridded data in the target grid points to obtain the predicted meteorological data of the target geographical location in the time period;

[0009] Perform vertical height correction on the target temperature and target wind speed in the predicted meteorological data within the time period to obtain the corrected temperature and corrected wind speed at the location where the fan of the wind turbine is located within the time period;

[0010] Based on the corrected temperature and corrected wind speed at the location where the fan is located within the time period and the predicted meteorological data of the target geographical location in the time period, calculate the icing thickness of the fan within the time period;

[0011] Sum up the ice accretion thickness of the wind turbine in each time period within the preset duration to obtain the total ice accretion thickness of the wind turbine within the preset duration.

[0012] The second aspect of the present disclosure provides a wind power ice accretion thickness prediction device, including:

[0013] An acquisition module for acquiring the meteorological gridded data around the wind turbine in each time period included in the preset duration;

[0014] A selection module for, for each time period within the preset duration, selecting a preset number of prediction grid points around the target geographical location where the wind turbine is located from the meteorological gridded data corresponding to the time period as the target grid points corresponding to the time period;

[0015] An interpolation module for, based on the distances between the geographical locations of the preset number of target grid points corresponding to the time period and the target geographical location respectively, performing inverse distance weighted interpolation calculation on the meteorological data of the target geographical location through the gridded data in the target grid points to obtain the predicted meteorological data of the target geographical location in the time period;

[0016] A correction module for respectively performing vertical height correction on the target temperature and target wind speed in the predicted meteorological data in the time period to obtain the corrected temperature and corrected wind speed at the location where the wind turbine of the wind turbine is located in the time period;

[0017] A first calculation module for calculating the ice accretion thickness of the wind turbine in the time period based on the corrected temperature and corrected wind speed at the location where the wind turbine is located in the time period and the predicted meteorological data of the target geographical location in the time period;

[0018] A summation module for summing up the ice accretion thickness of the wind turbine in each time period within the preset duration to obtain the total ice accretion thickness of the wind turbine within the preset duration.

[0019] The third aspect of the present disclosure provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the wind power ice accretion thickness prediction method in the first aspect can be implemented.

[0020] The fourth aspect of the present disclosure provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by the processor, the wind power ice accretion thickness prediction method in the first aspect can be implemented.

[0021] The technical solution provided by the present disclosure has the following advantages compared with the prior art:

[0022] The present disclosure obtains the meteorological grid data around a wind turbine within each time period included in a preset duration; for each time period in the preset duration, in the meteorological grid data corresponding to the time period, a preset number of prediction grid points around the target geographical location where the wind turbine is located are selected as the target grid points corresponding to the time period; based on the distances between the geographical locations of the preset number of target grid points corresponding to the time period and the target geographical location respectively, the inverse distance weighted interpolation calculation is performed on the meteorological data of the target geographical location through the grid data in the target grid points, so as to obtain the predicted meteorological data of the target geographical location in the time period; the target temperature and the target wind speed in the predicted meteorological data within the time period are respectively corrected for the vertical height, so as to obtain the corrected temperature and the corrected wind speed at the position where the fan of the wind turbine is located within the time period; based on the corrected temperature and the corrected wind speed at the position where the fan is located within the time period and the predicted meteorological data of the target geographical location, the ice accretion thickness of the fan within the time period is calculated; the ice accretion thicknesses of the fan in each time period in the preset duration are summed up to obtain the total ice accretion thickness of the fan within the preset duration. The present disclosure determines the predicted meteorological data of the geographical location where the wind turbine is located through the inverse distance weighted interpolation calculation, and then based on the predicted meteorological data, the temperature and the wind speed at the position where the fan of the wind turbine is located are determined through the vertical height correction, which can improve the accuracy of the meteorological data at the position where the fan of the wind turbine is located. Based on the temperature and the wind speed at the position where the fan is located and the predicted meteorological data, the total ice accretion thickness of the fan within the preset duration is determined, which can improve the accuracy of the prediction of the ice accretion thickness of the fan. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 is a flowchart of a method for predicting the ice accretion thickness of a wind power generation;

[0026] Figure 2 is a schematic structural diagram of a device for predicting the ice accretion thickness of a wind power generation provided by an embodiment of the present disclosure;

[0027] Figure 3 is a schematic structural diagram of a computer device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In order to more clearly understand the above-mentioned objects, features, and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other.

[0029] In the following description, many specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present disclosure, rather than all of the embodiments.

[0030] It should be understood that the various steps recorded in the method embodiments of the present disclosure may be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0031] It should be noted that, in this article, relational terms such as "first" and "second" 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 "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0032] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly specified in the context, it should be understood as "one or more".

[0033] The wind power icing thickness prediction method provided by the embodiments of the present disclosure can be executed by a computer device, which can be understood as any device with processing and computing capabilities. This device may include, but is not limited to, mobile terminals such as smart phones, laptop computers, tablet computers (PADs), etc., and fixed electronic devices such as digital TVs, desktop computers, etc.

[0034] In order to better understand the inventive concept of the embodiments of the present disclosure, the technical solutions of the embodiments of the present disclosure will be described below in conjunction with exemplary embodiments.

[0035] Figure 1It is a flowchart of a method for predicting the ice thickness on wind turbines provided by an embodiment of the present disclosure. This method can be executed by a computer device, such as Figure 1 As shown, the method for predicting the ice thickness on wind turbines provided in this embodiment includes the following steps:

[0036] Step 110: Obtain the meteorological grid data around the wind turbine in each time period included in the preset time period.

[0037] In the embodiment of the present disclosure, the computer device can obtain the meteorological grid data around the wind turbine in each time period included in the preset time period. For example, the meteorological grid data can be obtained from weather forecast data.

[0038] The preset time period can be set as needed and is not limited here.

[0039] The meteorological grid data can be understood as merging the meteorological data that is unevenly distributed in space according to a certain geometric grid, calculating the average value of the meteorological data in each grid, and placing it at the center of the grid. The meteorological data formed by this processing method has a high spatio-temporal resolution and can effectively reflect the spatial information of climate elements.

[0040] The meteorological grid data can include meteorological observation data such as temperature, relative humidity, horizontal wind speed (three-dimensional), vertical wind speed (three-dimensional), precipitation, and cloud water content.

[0041] Specifically, when the time period is a time period before the current moment, the meteorological grid data is the meteorological reanalysis grid data in that time period;

[0042] When the time period is a time period after the current moment, the meteorological grid data is the predicted meteorological grid data in that time period.

[0043] Step 120: For each time period in the preset time period, in the meteorological grid data corresponding to that time period, select a preset number of prediction grid points around the target geographical location where the wind turbine is located as the target grid points corresponding to the time period.

[0044] In the embodiment of the present disclosure, for each time period in the preset time period, the computer device can select a preset number of prediction grid points around the target geographical location where the wind turbine is located as the target grid points corresponding to the time period in the meteorological grid data corresponding to that time period.

[0045] The preset number can be set as needed, for example, 4, and is not limited here.

[0046] The target geographical location where the wind turbine is located is the longitude and latitude position of the wind turbine.

[0047] In some embodiments, the grid corresponding to the target grid point may include the wind turbines of the above-mentioned wind power generators. Thereby, the accuracy of the subsequent inverse distance weighted interpolation calculation can be improved, and the accuracy of the predicted meteorological data of the target geographical location where the wind power generator is located can be improved.

[0048] Step 130: Based on the distances between the geographical locations of the preset number of target grid points corresponding to this time period and the target geographical location respectively, perform inverse distance weighted interpolation calculation on the meteorological data of the target geographical location through the gridded data in the target grid points, to obtain the predicted meteorological data of the target geographical location in this time period.

[0049] In the embodiments of the present disclosure, for each time period in the preset duration, the computer device may perform inverse distance weighted interpolation calculation on the meteorological data of the target geographical location through the gridded data in the target grid points based on the distances between the geographical locations of the preset number of target grid points corresponding to this time period and the target geographical location respectively, to obtain the predicted meteorological data of the target geographical location in this time period.

[0050] The predicted meteorological data of the target geographical location where the wind power generator is located in this time period may include meteorological observation data such as the target temperature, target relative humidity, target horizontal wind speed (three-dimensional), target vertical wind speed (three-dimensional), target precipitation, target cloud water content, etc. of the target geographical location where the wind power generator is located.

[0051] The basic principle of inverse distance weighted (IDW) interpolation is to calculate the distances between the point to be interpolated and each known point, then assign weights to each known point according to the reciprocal of the distance, and finally perform weighted averaging on the attribute values of these known points according to the assigned weights, so as to obtain the estimated value of the point to be interpolated. Specifically, the closer a known point is to the point to be interpolated, the greater its weight and the greater its influence on the interpolation result; conversely, the farther a known point is from the point to be interpolated, the smaller its weight and the smaller its influence on the interpolation result.

[0052] For example, perform inverse distance weighted interpolation calculation on the meteorological data of the target geographical location to obtain the predicted meteorological data Z of the target geographical location in this time period 0 It can be obtained through formula (1):

[0053]

[0054] where Z 0 represents the predicted meteorological data of the target geographical location where the wind power generator is located in this time period; Z i represents the gridded data in the i-th target grid point;

[0055] D i represents the distance between the geographical location of the i-th target grid point and the target geographical location where the wind power generator is located; X0 Represents the longitude coordinate of the target geographical location; Y 0 Represents the latitude coordinate of the target geographical location; X i Represents the longitude coordinate of the geographical location of the target grid point; Y i Represents the latitude coordinate of the geographical location of the target grid point; n represents the preset quantity.

[0056] Step 140: Vertically correct the target temperature and target wind speed in the predicted meteorological data for this time period to obtain the corrected temperature and corrected wind speed at the location where the fan of the wind turbine is located during this time period.

[0057] In the embodiments of the present disclosure, for each time period in the preset duration, the computer device can vertically correct the target temperature and target wind speed in the predicted meteorological data for this time period to obtain the corrected temperature and corrected wind speed at the location where the fan of the wind turbine is located during this time period.

[0058] The location where the fan of the wind turbine is located is the location of the fan hub.

[0059] Specifically, vertically correcting the target temperature and target wind speed in the predicted meteorological data for this time period to obtain the corrected temperature and corrected wind speed at the location where the fan of the wind turbine is located during this time period may include S11 - S18:

[0060] S11: Calculate the distance between the fan of the wind turbine and the target geographical location to obtain the terrain height of the fan.

[0061] S12: Sum the altitude corresponding to the target geographical location where the wind turbine is located and the terrain height of the fan to obtain the actual height of the fan.

[0062] S13: Obtain the altitude of each target grid point from the gridded data of the preset number of target grid points corresponding to this time period.

[0063] S14: Calculate the average value of the altitudes of the preset number of target grid points to obtain the average altitude of the preset number of target grid points.

[0064] S15: Obtain the target temperature at the location where the fan is located during this time period from the predicted meteorological data of the target geographical location for this time period.

[0065] S16: Calculate the corrected temperature at the location where the fan is located during this time period based on the actual height, average altitude, and target temperature at the location where the fan is located corresponding to this time period.

[0066] The principle of temperature correction is to consider altitude correction. Within the atmospheric boundary layer, the air temperature decreases with altitude at a rate of 0.6 °C per 100 meters. For example, the corrected temperature for this period can be calculated using Equation (2):

[0067]

[0068] Where, T new represents the corrected temperature for this period;

[0069] T 0 represents the target temperature at the target geographical location where the wind turbine is located during this period;

[0070] H represents the actual height of the wind turbine;

[0071] h represents the average altitude of a preset number of target grid points corresponding to this period.

[0072] S17. Obtain the altitude of the grid points of the first grid containing the location of the wind turbine as the first vertical height and the wind speed of the grid points of the first grid as the first wind speed from the meteorological gridded data corresponding to this period. Obtain the altitude of the grid points of the second grid containing the location of the wind turbine as the second vertical height and the wind speed of the grid points of the second grid as the second wind speed.

[0073] S18. Calculate the corrected wind speed at the location of the wind turbine during this period based on the first vertical height, the second vertical height, the first wind speed, the second wind speed, and the actual height.

[0074] For example, the corrected wind speed for this period can be calculated using Equation (3):

[0075]

[0076] Where, U new represents the corrected wind speed for this period;

[0077] U 0 represents the wind speed of the grid points of the first grid containing the location of the wind turbine during this period, that is, the first wind speed;

[0078] U 1 represents the wind speed of the grid points of the second grid containing the location of the wind turbine during this period, that is, the second wind speed;

[0079] H 0 represents the altitude of the grid points of the first grid containing the location of the wind turbine during this period, that is, the first vertical height;

[0080] H 1 represents the altitude of the grid points of the second grid containing the location of the wind turbine during this period, that is, the second vertical height;

[0081] H represents the actual height of the wind turbine.

[0082] Step 150: Calculate the ice accretion thickness of the wind turbine during this period based on the corrected temperature and corrected wind speed at the location of the wind turbine during this period and the predicted meteorological data of the target geographical location during this period.

[0083] Specifically, calculating the ice accretion thickness of the wind turbine during this period based on the corrected temperature and corrected wind speed at the location of the wind turbine during this period and the predicted meteorological data of the target geographical location during this period may include S21 - 24:

[0084] S21: Obtain the target precipitation, target cloud water content, and target relative humidity of the target geographical location during this period from the predicted meteorological data of the target geographical location during this period.

[0085] S22: Calculate the glaze ice accretion thickness of the wind turbine during this period based on the corrected temperature at the location of the wind turbine during this period and the target precipitation of the target geographical location during this period.

[0086] Specifically, when the corrected temperature at the location of the wind turbine during this period is within the first temperature range, calculate the product of the target precipitation of the target geographical location during this period and the preset conversion coefficient to obtain the glaze ice accretion thickness of the wind turbine during this period;

[0087] When the corrected temperature at the location of the wind turbine during this period is outside the first temperature range and within the second temperature range, determine that the glaze ice accretion thickness of the wind turbine during this period is 0.

[0088] The preset conversion coefficient can be set as needed, for example, 0.35, and is not limited here.

[0089] For example, the glaze ice accretion thickness of the wind turbine during this period can be calculated by Equation (4):

[0090]

[0091] Where, ΔIce r represents the glaze ice accretion thickness of the wind turbine during this period;

[0092] R represents the target precipitation of the target geographical location where the wind turbine is located during this period;

[0093] r represents the preset conversion coefficient;

[0094] T new represents the corrected temperature at the location of the wind turbine during this period, in degrees Celsius;

[0095] - 8 < T new < 0 represents the first temperature range;

[0096] T new > 0 and T new < -8 represents the second temperature range.

[0097] S23. Calculate the freezing fog icing thickness of the wind turbine during this period based on the corrected wind speed at the position of the wind turbine during this period, the target cloud water content and the target relative humidity at the target geographical location during this period.

[0098] Specifically, when the target relative humidity at the target geographical location during this period is greater than or equal to the preset humidity threshold, calculate the product of the corrected wind speed and the target cloud water content at the target geographical location during this period to obtain the freezing fog icing thickness of the wind turbine during this period;

[0099] When the target relative humidity at the target geographical location during this period is less than the preset humidity threshold, determine that the freezing fog icing thickness of the wind turbine during this period is 0.

[0100] The preset humidity threshold can be set as needed, for example, 80%, which is not limited here.

[0101] For example, the freezing fog icing thickness of the wind turbine during this period can be calculated by Equation (5):

[0102]

[0103] Among them, ΔIce f represents the freezing fog icing thickness of the wind turbine during this period;

[0104] U represents the corrected wind speed at the position of the wind turbine during this period;

[0105] Qc represents the target cloud water content at the target geographical location where the wind turbine is located during this period;

[0106] Rh represents the target relative humidity at the target geographical location where the wind turbine is located during this period;

[0107] 80% represents the preset humidity threshold.

[0108] S24. Sum up the freezing rain icing thickness and the freezing fog icing thickness during this period to obtain the icing thickness of the wind turbine during this period.

[0109] Step 160. Sum up the icing thickness of the wind turbine during each period within the preset duration to obtain the total icing thickness of the wind turbine within the preset duration.

[0110] For example, the total icing thickness of the wind turbine within the preset duration can be calculated by Equation (6):

[0111]

[0112] Among them, Ice represents the total ice accretion thickness of the wind turbine within a preset duration;

[0113] t0 represents the starting moment of the preset duration; t1 represents the ending moment of the preset duration;

[0114] ΔIce r represents the glaze ice accretion thickness of the wind turbine within a time period;

[0115] ΔIce f represents the rime ice accretion thickness of the wind turbine within a time period.

[0116] Thus, through the inverse distance weighted interpolation calculation, the predicted meteorological data of the geographical location where the wind turbine is located is determined. Then, based on the predicted meteorological data, the temperature and wind speed at the location of the wind turbine are determined through vertical height correction, which can improve the accuracy of the meteorological data at the location of the wind turbine. Based on the temperature and wind speed at the location of the wind turbine and the predicted meteorological data, the total ice accretion thickness of the wind turbine within a preset duration is determined, which can improve the accuracy of the ice accretion thickness prediction of the wind turbine.

[0117] In some embodiments of the present disclosure, after summing up the ice accretion thickness of the wind turbine within each time period in the preset duration to obtain the total ice accretion thickness of the wind turbine within the preset duration, the computer device may determine the relationship between the total ice accretion thickness of the wind turbine within the preset duration and a preset ice accretion thickness threshold:

[0118] When the total ice accretion thickness of the wind turbine within the preset duration is less than the preset ice accretion thickness threshold, based on the planned output power, total ice accretion thickness, and preset ice accretion thickness threshold of the wind turbine within the preset duration, calculate the power attenuation amount of the wind turbine within the preset duration;

[0119] When the total ice accretion thickness of the wind turbine within the preset duration is greater than or equal to the preset ice accretion thickness threshold, determine that the power attenuation amount of the wind turbine within the preset duration is 0, that is, control the wind turbine to stop running.

[0120] The preset ice accretion thickness threshold can be set as needed and is not limited here.

[0121] For example, the power attenuation amount of the wind turbine within the preset duration can be calculated by formula (7):

[0122]

[0123] Among them, ΔW represents the power attenuation amount of the wind turbine within the preset duration;

[0124] Ice represents the total ice accretion thickness of the wind turbine within the preset duration;

[0125] Ice’ represents the preset ice accretion thickness threshold.

[0126] Thus, when the total ice accretion thickness of the wind turbine is less than the preset ice accretion thickness threshold, the power attenuation amount of the wind turbine can be adjusted according to the total ice accretion thickness of the wind turbine; when the total ice accretion thickness of the wind turbine is greater than or equal to the preset ice accretion thickness threshold, the operation of the wind turbine is controlled to stop, and the output power of the wind turbine can be adjusted in time according to the total ice accretion thickness of the wind turbine, improving the safety of the operation of the wind turbine.

[0127] Figure 2 FIG. 4 is a schematic structural diagram of a wind power ice accretion thickness prediction device provided by an embodiment of the present disclosure. This device can be understood as the above computer device or some functional modules in the above computer device. As Figure 2 shown, the wind power ice accretion thickness prediction device 200 includes:

[0128] An acquisition module 210, configured to acquire meteorological grid data around the wind turbine in each time period included in the preset time period;

[0129] A selection module 220, configured to, for each time period in the preset time period, select a preset number of prediction grid points around the target geographical location where the wind turbine is located as the target grid points corresponding to the time period from the meteorological grid data corresponding to the time period;

[0130] An interpolation module 230, configured to perform inverse distance weighted interpolation calculation on the meteorological data of the target geographical location through the grid data in the target grid points based on the distances between the geographical locations of the preset number of target grid points corresponding to the time period and the target geographical location respectively, so as to obtain the predicted meteorological data of the target geographical location in the time period;

[0131] A correction module 240, configured to respectively perform vertical height correction on the target temperature and the target wind speed in the predicted meteorological data in the time period to obtain the corrected temperature and the corrected wind speed at the position where the wind turbine of the wind turbine is located in the time period;

[0132] A first calculation module 250, configured to calculate the ice accretion thickness of the wind turbine in the time period based on the corrected temperature and the corrected wind speed at the position where the wind turbine is located in the time period and the predicted meteorological data of the target geographical location in the time period;

[0133] A summation module 260, configured to sum up the ice accretion thicknesses of the wind turbine in each time period in the preset time period to obtain the total ice accretion thickness of the wind turbine in the preset time period.

[0134] Optionally, when the time period is a time period before the current moment, the meteorological grid data is the meteorological reanalysis grid data in the time period;

[0135] When the time period is a time period after the current moment, the meteorological grid data is the predicted meteorological grid data in the time period.

[0136] Optionally, the above correction module includes:

[0137] The first calculation sub-module is used to calculate the distance between the wind turbine of the wind power generator and the target geographical location to obtain the terrain height of the wind turbine.

[0138] The first summation sub-module is used to sum the altitude corresponding to the target geographical location where the wind power generator is located and the terrain height of the wind turbine to obtain the actual height of the wind turbine.

[0139] The first acquisition sub-module is used to acquire the altitude of each target grid point from the gridded data of a preset number of target grid points corresponding to the time period.

[0140] The second calculation sub-module is used to calculate the average value of the altitudes of a preset number of target grid points to obtain the average altitude of the preset number of target grid points.

[0141] The second acquisition sub-module is used to acquire the target temperature of the target geographical location within the time period from the predicted meteorological data of the target geographical location in the time period.

[0142] The third calculation sub-module is used to calculate the corrected temperature at the location of the wind turbine within the time period based on the actual height, average altitude, and target temperature at the location of the wind turbine corresponding to the time period.

[0143] The third acquisition sub-module is used to acquire the altitude of the grid points of the first grid containing the location of the wind turbine as the first vertical height and the wind speed of the grid points of the first grid as the first wind speed, and acquire the altitude of the grid points of the second grid containing the location of the wind turbine as the second vertical height and the wind speed of the grid points of the second grid as the second wind speed from the meteorological gridded data corresponding to the time period.

[0144] The fourth calculation sub-module is used to calculate the corrected wind speed at the location of the wind turbine within the time period based on the first vertical height, second vertical height, first wind speed, second wind speed, and actual height.

[0145] Optionally, the above first calculation module includes:

[0146] The fourth acquisition sub-module is used to acquire the target precipitation, target cloud water content, and target relative humidity of the target geographical location within the time period from the predicted meteorological data of the target geographical location in the time period.

[0147] The fifth calculation sub-module is used to calculate the freezing rain icing thickness of the wind turbine within the time period based on the corrected temperature at the location of the wind turbine within the time period and the target precipitation of the target geographical location within the time period.

[0148] The sixth calculation sub-module is used to calculate the freezing fog icing thickness of the wind turbine within the time period based on the corrected wind speed at the location of the wind turbine within the time period, the target cloud water content, and the target relative humidity of the target geographical location within the time period.

[0149] A second summing sub-module, configured to sum the glaze ice thickness and the freezing fog ice thickness within a time period to obtain the ice thickness of the wind turbine within the time period.

[0150] Optionally, the above-mentioned fifth calculation sub-module includes:

[0151] A first calculation unit, configured to calculate the product of the target precipitation and a preset conversion coefficient when the corrected temperature is within a first temperature range, to obtain the glaze ice thickness of the wind turbine within the time period;

[0152] A first determination unit, configured to determine that the glaze ice thickness of the wind turbine within the time period is 0 when the corrected temperature is within a second temperature range outside the first temperature range;

[0153] Optionally, the above-mentioned sixth calculation sub-module includes:

[0154] A second calculation unit, configured to calculate the product of the corrected wind speed and the target cloud water content when the target relative humidity at the target geographical location within the time period is greater than or equal to a preset humidity threshold, to obtain the freezing fog ice thickness of the wind turbine within the time period;

[0155] A second determination unit, configured to determine that the freezing fog ice thickness of the wind turbine within the time period is 0 when the target relative humidity at the target geographical location within the time period is less than the preset humidity threshold.

[0156] Optionally, the above-mentioned wind power ice thickness prediction device includes:

[0157] A second calculation module, configured to calculate the power attenuation amount of the wind turbine within a preset time period based on the planned output power, the total ice thickness, and the preset ice thickness threshold of the wind turbine within the preset time period when the total ice thickness of the wind turbine within the preset time period is less than the preset ice thickness threshold;

[0158] A determination module, configured to determine that the power attenuation amount of the wind turbine within the preset time period is 0 when the total ice thickness of the wind turbine within the preset time period is greater than or equal to the preset ice thickness threshold.

[0159] Optionally, the grid corresponding to the above-mentioned target grid point includes a wind turbine of a wind power generator.

[0160] The wind power ice thickness prediction device provided by the embodiments of the present disclosure can implement the method of any of the above embodiments, and its implementation manner and beneficial effects are similar, which will not be elaborated here.

[0161] The embodiments of the present disclosure further provide a computer device, which includes a processor and a memory. Among them, a computer program is stored in the memory, and when the computer program is executed by the processor, the method of any of the above embodiments can be implemented, and its implementation manner and beneficial effects are similar, which will not be elaborated here.

[0162] Figure 3 It is a schematic structural diagram of a computer device provided by an embodiment of the present disclosure. As Figure 3 shown, the computer device 300 may include a processor 310 and a memory 320. Among them, a computer program 321 is stored in the memory 320. When the computer program 321 is executed by the processor 310, the methods provided in any of the above embodiments can be implemented. Their execution manners and beneficial effects are similar and will not be elaborated here.

[0163] Of course, for simplicity, Figure 3 only some of the components related to the present invention in the computer device 300 are shown in [the figure], and components such as a bus, an input / output interface, an input device, and an output device are omitted. In addition, according to specific application scenarios, the computer device 300 may further include any other appropriate components.

[0164] An embodiment of the present disclosure provides a computer-readable storage medium. A computer program is stored in the storage medium. When the computer program is executed by a processor, the methods provided in any of the above embodiments can be implemented. Their execution manners and beneficial effects are similar and will not be elaborated here.

[0165] The above computer-readable storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0166] The above computer program may be written in any combination of one or more programming languages to write program codes for performing the operations of the embodiments of the present disclosure. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program codes may be executed entirely on the user's computer device, partially on the user device, executed as an independent software package, partially on the user's computer device and partially on a remote computer device, or entirely on a remote computer device or server.

[0167] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present disclosure.

[0168] In addition, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment may also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0169] The above are only specific implementation manners of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments described herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting wind power ice thickness, characterized in that: include: Obtaining meteorological grid data around the wind turbine in each time period included in the preset time length; For each period of the preset duration, in the meteorological grid data corresponding to the period, a preset number of prediction grid points around the target geographical location where the wind turbine is located are selected as target grid points corresponding to the period; Based on the distances between the geographical locations of the preset number of target grid points corresponding to the time period and the target geographical location, an inverse distance weighted interpolation calculation is performed on the meteorological data of the target geographical location through the gridded data in the target grid points to obtain the predicted meteorological data of the target geographical location in the time period; Performing vertical height corrections on the target temperature and target wind speed in the forecasted meteorological data within the time period, respectively, to obtain a corrected temperature and a corrected wind speed at the location of the wind turbine of the wind turbine within the time period; Calculate the ice thickness of the wind turbine during the period based on the corrected temperature and corrected wind speed at the location of the wind turbine during the period and the predicted meteorological data of the target geographical location during the period; The ice coating thickness of the wind turbine in each time period of the preset time length is summed to obtain the total ice coating thickness of the wind turbine in the preset time length.

2. The method according to claim 1, characterized in that: When the time period is a time period before the current time, the meteorological gridded data is the meteorological reanalysis grid data within the time period; When the time period is a time period after the current moment, the meteorological gridded data is the predicted meteorological gridded data within the time period.

3. The method according to claim 1, characterized in that The target temperature and target wind speed in the forecasted meteorological data within the time period are respectively corrected vertically to obtain the corrected temperature and corrected wind speed at the location of the wind turbine of the wind turbine within the time period, including: Calculating the distance between a wind turbine of the wind turbine and the target geographical location to obtain the terrain height of the wind turbine; Summing the altitude corresponding to the target geographical location where the wind turbine is located and the terrain height of the wind turbine to obtain the actual height of the wind turbine; Obtaining the altitude of each target grid point from the gridded data of a preset number of target grid points corresponding to the time period; Calculating the average altitude of the preset number of target grid points to obtain the average altitude of the preset number of target grid points; acquiring a target temperature of the target geographical location within the time period from the forecasted meteorological data of the target geographical location during the time period; Calculating a corrected temperature at the location of the fan during the time period based on the actual height corresponding to the time period, the average altitude, and the target temperature at the location of the fan; From the meteorological grid data corresponding to the time period, obtain the altitude of the grid points of the first grid including the location of the wind turbine as the first vertical height and the wind speed of the grid points of the first grid as the first wind speed, obtain the altitude of the grid points of the second grid including the location of the wind turbine as the second vertical height and the wind speed of the grid points of the second grid as the second wind speed; A corrected wind speed at the location of the wind turbine during the time period is calculated based on the first vertical height, the second vertical height, the first wind speed, the second wind speed, and the actual height.

4. The method according to claim 1, characterized in that: The calculating the ice thickness of the wind turbine in the time period based on the corrected temperature and corrected wind speed at the location of the wind turbine in the time period and the predicted meteorological data of the target geographical location in the time period includes: Obtaining, from the predicted meteorological data of the target geographical location in the time period, a target precipitation, a target cloud water content, and a target relative humidity at the target geographical location in the time period; Calculate the thickness of freezing rain ice covering the wind turbine during the period based on the corrected temperature at the location of the wind turbine during the period and the target precipitation at the target geographical location during the period; Calculate the thickness of the freezing fog ice covering the wind turbine during the period based on the corrected wind speed at the location of the wind turbine during the period, the target cloud water content and the target relative humidity at the target geographical location during the period; The thickness of ice covering the wind turbine during the period is obtained by summing the thickness of ice covering the freezing rain and the thickness of ice covering the freezing fog during the period.

5. The method according to claim 4, characterized in that The calculating the freezing rain ice thickness of the wind turbine in the time period based on the corrected temperature at the location of the wind turbine in the time period and the target precipitation at the target geographical location in the time period includes: When the corrected temperature is in the first temperature interval, the product of the target precipitation and the preset conversion coefficient is calculated to obtain the freezing rain ice thickness of the wind turbine in the time period; When the corrected temperature is in a second temperature interval outside the first temperature interval, determining that the freezing rain ice thickness of the wind turbine in the time period is 0; The calculating the freezing fog ice thickness of the wind turbine during the period based on the corrected wind speed at the location of the wind turbine during the period, the target cloud water content and the target relative humidity at the target geographical location during the period, comprises: When the target relative humidity of the target geographical location within the time period is greater than or equal to a preset humidity threshold, calculating the product of the corrected wind speed and the target cloud water content to obtain the freezing fog ice thickness of the wind turbine within the time period; When the target relative humidity of the target geographical location within the time period is less than a preset humidity threshold, it is determined that the freezing fog ice thickness of the wind turbine within the time period is 0.

6. The method according to claim 1, characterized in that After summing the ice coating thickness of the wind turbine in each time period of the preset time length to obtain the total ice coating thickness of the wind turbine in the preset time length, the method further includes: When the total ice thickness of the wind turbine within the preset time period is less than the preset ice thickness threshold, the power attenuation of the wind turbine within the preset time period is calculated based on the planned output power of the wind turbine within the preset time period, the total ice thickness and the preset ice thickness threshold; When the total ice coating thickness of the wind turbine within the preset time period is greater than or equal to a preset ice coating thickness threshold, it is determined that the power attenuation of the wind turbine within the preset time period is 0.

7. The method according to claim 1, characterized in that The grid corresponding to the target grid point includes a wind turbine of the wind turbine.

8. A wind power ice thickness prediction device, characterized in that: include: An acquisition module, used to acquire the meteorological grid data around the wind turbine in each time period included in the preset time length; A selection module is used for selecting, for each period of the preset duration, a preset number of prediction grid points around the target geographical location where the wind turbine is located from the meteorological grid data corresponding to the period as the target grid points corresponding to the period; An interpolation module is used to perform inverse distance weighted interpolation calculation on the meteorological data of the target geographical location through the gridded data in the target grid points based on the distances between the geographical locations of the preset number of target grid points corresponding to the time period and the target geographical location, so as to obtain the predicted meteorological data of the target geographical location in the time period; A correction module, used to perform vertical height correction on the target temperature and target wind speed in the forecasted meteorological data within the time period, and obtain a corrected temperature and a corrected wind speed at the location of the wind turbine of the wind turbine within the time period; A first calculation module is used to calculate the ice thickness of the wind turbine in the time period based on the corrected temperature and corrected wind speed at the location of the wind turbine in the time period and the predicted meteorological data of the target geographical location in the time period; The summing module is used to sum the ice coating thickness of the wind turbine in each time period of the preset time length to obtain the total ice coating thickness of the wind turbine in the preset time length.

9. A computer device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the wind power ice thickness prediction method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting wind power ice thickness according to any one of claims 1 to 7 is implemented.

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