Wind power plant wind speed prediction method and system for complex terrain

Abstract

The invention provides a wind power plant wind speed prediction method and system for complex terrains, and belongs to the field of wind field prediction. The wind power plant wind speed prediction method for the complex terrain comprises the steps that historical monitoring data, climate temperature data and wind power plant geographic data in a wind power plant are acquired; analyzing wind speed prediction influence data according to the climate temperature data and the wind power plant geographic data; determining basic predicted wind speed data according to the historical monitoring data; calculating comprehensive wind speed prediction data according to the wind speed prediction influence data and the basic prediction wind speed data; by adopting the scheme, the fan operation parameters can be adjusted in advance by predicting the wind speed of the wind power plant, the possibility of cooperative work faults between the fan and the wind speed is reduced, and the power generation efficiency of the fan is improved.

Description

Technical Field

[0001] The present application relates to the field of wind farm prediction, and in particular to a method and system for predicting wind speed in wind farms on complex terrain. Background Art

[0002] A wind farm is a large-scale power generation site that converts wind energy into electricity by centrally arranging multiple wind turbines, or wind turbines. It usually includes core equipment such as wind turbines and auxiliary systems such as monitoring equipment and wind towers. Generally speaking, there are two types of wind farms, namely offshore wind farms and onshore wind farms. Among them, onshore wind farms are affected by complex terrain. Therefore, for onshore wind farms, in order to improve power generation efficiency, it is necessary to predict wind speed in advance. Summary of the Invention

[0003] The present application provides a wind speed prediction method and system for wind farms in complex terrain, which can predict wind speed and adjust wind turbine operating parameters in advance so that the wind turbine operates within the optimal power generation efficiency range at different wind speeds, thereby reducing losses.

[0004] In a first aspect, the present application provides a method for predicting wind speed in a wind farm in complex terrain. The method comprises:

[0005] Acquire historical monitoring data, climate temperature data, and wind farm geographic data within the wind farm; the historical monitoring data includes historical wind speed data; the historical wind speed data includes multiple historical average wind speed data with historical time tags; the historical time tags include year, month, day, and time period data; the wind farm geographic data includes wind tower location data and hilly terrain data;

[0006] Analyzing wind speed prediction impact data based on the climate temperature data and the wind farm geographical data; the wind speed prediction impact data is associated with the climate temperature data, the wind tower location data, and the hilly terrain data;

[0007] Determine basic predicted wind speed data based on the historical monitoring data; the basic predicted wind speed data is associated with the historical average wind speed data;

[0008] Comprehensive wind speed prediction data is calculated based on the wind speed prediction influence data and the basic prediction wind speed data; the comprehensive wind speed prediction data is positively correlated with the wind speed prediction influence data and the basic prediction wind speed data.

[0009] Furthermore, the wind speed prediction impact data is analyzed based on the climate temperature data and the wind farm geographical data; the wind speed prediction impact data is associated with the climate temperature data, the wind tower location data and the hilly terrain data, including:

[0010] Analyzing temperature impact data according to the climate temperature data; the climate temperature data includes temperature data, season data and climate data; the temperature impact data is associated with the temperature data, season data and climate data;

[0011] Analyzing terrain relief data, roughness data, obstacle distribution data and special terrain data based on the hilly terrain data and wind tower location data;

[0012] Analyzing terrain impact data based on the terrain relief data, roughness data, obstacle distribution data, and special terrain data; wherein the terrain impact data is associated with the terrain relief data, roughness data, and obstacle distribution data;

[0013] The wind speed prediction impact data is analyzed according to the temperature impact data and the terrain impact data; the wind speed prediction impact data is associated with the temperature impact data and the terrain impact data.

[0014] Furthermore, the determining of basic predicted wind speed data based on the historical monitoring data; the associating of the basic predicted wind speed data with the historical average wind speed data includes:

[0015] Obtain a task time tag for a wind speed prediction task; the task time tag includes year, month, day, and time period data;

[0016] A first reference group is selected based on the predicted task time data; the first reference group includes a plurality of first reference wind speed data; the first reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are month, day and time period data;

[0017] A second reference group is selected based on the predicted task time data; the second reference group includes a plurality of second reference wind speed data; the second reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are both monthly and time period data;

[0018] First wind speed basic quantity data is calculated based on a first reference group; second wind speed basic quantity data is calculated based on a second reference group; basic predicted wind speed data is determined based on the first wind speed basic quantity data and the second wind speed basic quantity data; the basic predicted wind speed data is associated with the first wind speed basic quantity data and the second wind speed basic quantity data.

[0019] Furthermore, the calculation method of the basic predicted wind speed data includes:

[0020] Assume that the first reference group includes n first reference wind speed data, then the i-th first reference wind speed data is

[0021] Assume that the second reference group includes m second reference wind speed data, then the jth second reference wind speed data is

[0022] FP ws =B ws ×K1+D ws ×(1-K1)

[0023]

[0024] Where FP ws Based on the predicted wind speed data, B ws is the first wind speed basic data, D ws is the second wind speed basic data; K1 is the preset calculation weight.

[0025] Furthermore, the calculation method of the preset calculation weight includes:

[0026] Calculating a first variance of the first reference group based on all first reference wind speed data included in the first reference group;

[0027] calculating a second variance of the second reference group based on all second reference wind speed data included in the second reference group;

[0028]

[0029] In the formula, K1 is the preset calculation weight, S1 is the first variance, and S2 is the second variance; the first variance is negatively correlated with the preset calculation weight, and the second variance is positively correlated with the preset calculation weight.

[0030] In a second aspect, the present application provides a wind speed prediction system for wind farms in complex terrain. The system comprises:

[0031] An acquisition module is configured to acquire historical monitoring data, climate temperature data, and wind farm geographic data within the wind farm; the historical monitoring data includes historical wind speed data; the historical wind speed data includes a plurality of historical average wind speed data with historical time tags; the historical time tags include year, month, day, and time period data; the wind farm geographic data includes wind tower location data and hilly terrain data;

[0032] an analysis module, configured to analyze wind speed prediction impact data based on the climate temperature data and the wind farm geographic data; the wind speed prediction impact data being associated with the climate temperature data, the wind tower location data, and the hilly terrain data;

[0033] A determination module, configured to determine basic predicted wind speed data based on the historical monitoring data; the basic predicted wind speed data is associated with the historical average wind speed data;

[0034] A calculation module is used to calculate comprehensive wind speed prediction data based on the wind speed prediction influence data and the basic prediction wind speed data; the comprehensive wind speed prediction data is positively correlated with the wind speed prediction influence data and the basic prediction wind speed data.

[0035] Furthermore, the analysis module is further configured to analyze the wind speed prediction impact data based on the climate temperature data and the wind farm geographical data; the wind speed prediction impact data associated with the climate temperature data, the wind tower location data, and the hilly terrain data includes:

[0036] Analyzing temperature impact data according to the climate temperature data; the climate temperature data includes temperature data, season data and climate data; the temperature impact data is associated with the temperature data, season data and climate data;

[0037] Analyzing terrain relief data, roughness data, obstacle distribution data and special terrain data based on the hilly terrain data and wind tower location data;

[0038] Analyzing terrain impact data based on the terrain relief data, roughness data, obstacle distribution data, and special terrain data; wherein the terrain impact data is associated with the terrain relief data, roughness data, and obstacle distribution data;

[0039] The wind speed prediction impact data is analyzed according to the temperature impact data and the terrain impact data; the wind speed prediction impact data is associated with the temperature impact data and the terrain impact data.

[0040] Furthermore, the determination module is further configured to determine basic predicted wind speed data based on the historical monitoring data; the basic predicted wind speed data is associated with the historical average wind speed data and includes:

[0041] Obtain a task time tag for a wind speed prediction task; the task time tag includes year, month, day, and time period data;

[0042] A first reference group is selected based on the predicted task time data; the first reference group includes a plurality of first reference wind speed data; the first reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are month, day and time period data;

[0043] A second reference group is selected based on the predicted task time data; the second reference group includes a plurality of second reference wind speed data; the second reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are both monthly and time period data;

[0044] First wind speed basic quantity data is calculated based on a first reference group; second wind speed basic quantity data is calculated based on a second reference group; basic predicted wind speed data is determined based on the first wind speed basic quantity data and the second wind speed basic quantity data; the basic predicted wind speed data is associated with the first wind speed basic quantity data and the second wind speed basic quantity data.

[0045] Furthermore, the determination module is further configured such that the calculation method of the basic predicted wind speed data includes:

[0046] Assume that the first reference group includes n first reference wind speed data, then the i-th first reference wind speed data is

[0047] Assume that the second reference group includes m second reference wind speed data, then the jth second reference wind speed data is

[0048] FP ws =B ws ×K1+D ws ×(1-K1)

[0049]

[0050] Where FP ws Based on the predicted wind speed data, B ws is the first wind speed basic data, D ws is the second wind speed basic data; K1 is the preset calculation weight.

[0051] Furthermore, the determination module is further configured such that the calculation method of the preset calculation weight includes:

[0052] Calculating a first variance of the first reference group based on all first reference wind speed data included in the first reference group;

[0053] calculating a second variance of the second reference group based on all second reference wind speed data included in the second reference group;

[0054]

[0055] In the formula, K1 is the preset calculation weight, S1 is the first variance, and S2 is the second variance; the first variance is negatively correlated with the preset calculation weight, and the second variance is positively correlated with the preset calculation weight.

[0056] By adopting the above technical solution, the wind speed of the wind farm is predicted. The basic predicted wind speed data is determined based on the historical monitoring data, and the wind speed prediction impact data is determined based on the climate temperature data and the wind farm geographical data. The basic predicted wind speed data and the wind speed prediction impact data are then combined to calculate the final comprehensive wind speed prediction data. Based on the comprehensive wind speed prediction data, a wind turbine pre-adjustment plan is formulated within the task time corresponding to the corresponding wind speed prediction task, and the operating parameters of the wind turbine are pre-adjusted so that the wind turbine can generate electricity more in line with the wind speed, thereby improving the power generation efficiency of the wind farm and reducing losses.

[0057] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0059] Figure 1 A flow chart of a method for predicting wind speed in a wind farm on complex terrain according to an embodiment of the present application is shown;

[0060] Figure 2 A block diagram of a wind speed prediction system for a wind farm in complex terrain according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0061] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0062] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0063] The present application provides a method and system for predicting wind speed in wind farms with complex terrain, which can:

[0064] In a first aspect, the present application provides a method for predicting wind speed in a wind farm in complex terrain. Figure 1 The specific steps included in the method are as follows.

[0065] Step S110: Acquire historical monitoring data, climate temperature data, and wind farm geographic data within the wind farm; the historical monitoring data includes historical wind speed data; the historical wind speed data includes multiple historical average wind speed data with historical time tags; the historical time tags include year, month, day, and time period data; the wind farm geographic data includes wind tower location data and hilly terrain data.

[0066] In this plan, the specific complex terrain refers to hilly terrain; in hilly terrain, there are differences in altitude, such as regional height differences and terrain undulations; special terrain includes ridges and valleys; and such terrain features will affect the wind speed measurement of the wind farm by the wind tower. In order to ensure the accuracy of the wind speed prediction in the hilly wind farm, it is necessary to consider data such as terrain characteristics, meteorological seasons, historical wind speeds, etc., so as to make the adjustment of wind turbine operating parameters more accurate and improve the power generation efficiency of the wind farm.

[0067] It can be understood that the wind speed prediction in this plan refers to the average wind speed of the wind farm, not the wind speed of a single wind turbine; the conventional wind speed measurement method can be obtained by measuring through a wind tower, and the average wind speed data detected by the wind speed measuring device on the wind tower (including the 10-minute average wind speed, measured once per second; the hourly average wind speed, the maximum wind speed, measured every 3 seconds) is then subjected to regression analysis to obtain the average wind speed of the wind farm; the average wind speed of the wind farm reflects the overall level of wind energy resources in the area; it is an important indicator for evaluating the power generation potential of a wind farm. The higher the average wind speed, the richer the wind energy resources available to the wind farm, and theoretically the more electricity it can generate.

[0068] In this solution, the historical average wind speed data mentioned above with historical time tags generally contains time period data with one hour as one time period. The historical time tag is the year, month, date plus the specific time period, such as the historical average wind speed data from 8:00 to 9:00 on April 22, 2012, and each time period is a wind speed measurement cycle. The historical average wind speed data here is the processed average wind speed, not the wind speed data directly collected by the wind tower.

[0069] It can be understood that the side tower location data and hilly terrain data here can be obtained through high-resolution remote sensing images obtained through satellite remote sensing, and then specific analysis based on the remote sensing images can be performed to obtain the corresponding terrain and location data; the historical monitoring data here can be obtained through the data upload cloud platform corresponding to the wind farm, and the wind farm will upload the collected wind farm-related data to the corresponding cloud platform for storage.

[0070] Step S120: analyzing wind speed prediction impact data according to the climate temperature data and the wind farm geographical data; the wind speed prediction impact data is associated with the climate temperature data, the wind tower location data, and the hilly terrain data.

[0071] Specifically, temperature impact data is analyzed based on the climate temperature data; the climate temperature data includes temperature data, season data and climate data; the temperature impact data is associated with the temperature data, season data and climate data; terrain undulation data, roughness data, obstacle distribution data and special terrain data are analyzed based on the hilly terrain data and wind tower location data; terrain impact data is analyzed based on the terrain undulation data, roughness data, obstacle distribution data and special terrain data; the terrain impact data is associated with terrain undulation data, roughness data and obstacle distribution data; wind speed prediction impact data is analyzed based on the temperature impact data and the terrain impact data; the wind speed prediction impact data is associated with the temperature impact data and the terrain impact data.

[0072] Among them, analyzing the temperature impact data according to the climate temperature data specifically includes: determining the season label and climate label of the wind farm at the current moment based on the season data and climate data; obtaining the task time label of the wind speed prediction task, and the task time label includes year, month, day and time period data; obtaining the first preset grid temperature data of the wind farm in the previous wind speed measurement cycle at the current moment; obtaining the historical preset grid temperature data of the wind farm in the historical wind speed measurement cycle with the same season label, climate label and task time label as the previous wind speed measurement cycle; determining the cell temperature data of a preset number of cells in a preset range around each wind tower for multiple different wind speed measurement cycles based on the first preset grid temperature data and the historical preset grid temperature data. ; Use the cell temperature data of the same cell in the previous wind speed measurement cycle as the reference temperature data, and use the cell temperature data of the same cell in the remaining wind speed measurement cycles as the sample temperature data to obtain a reference temperature data and multiple sample temperature data; calculate the sample average temperature data, which is the average value of the multiple sample temperature data; calculate the temperature difference data between the reference temperature data and the sample average temperature data; use the same processing method for the cell temperature data of each cell to obtain multiple temperature difference data; calculate the variance value of the temperature difference data, which is the sample variance value; calculate the average value of the temperature difference data, which is the sample average value; calculate the temperature influence data based on the sample variance value and the sample average value.

[0073] Specifically, the calculation method of temperature impact data includes:

[0074]

[0075] Where, T im is the temperature effect data, S a is the sample mean, is the preset average value, S v is the sample variance value; the smaller the difference between the sample average value and the preset average value, the closer the sample average value is to the preset average value, which in turn means that the temperature of the previous wind speed measurement period is close to the historical average temperature; the smaller the sample variance value is, the smaller the fluctuation of the temperature difference data is, and the more stable it is, which means that the temperature of the previous wind speed measurement period of each cell is close to the historical average temperature; and the smaller the above two influencing factors are, the greater the temperature impact data is, and the temperature impact data here represents the contribution of temperature to wind speed prediction.

[0076] It can be understood that the preset grid temperature data here refers to dividing the wind farm into multiple grids of the same specifications, and performing temperature tests on each grid to obtain the corresponding grid temperature data; the purpose of this is to capture the spatial heterogeneity of temperature within the wind farm, and use equidistant regular grids when dividing the grids, preferably square grids; the geographic coordinates of the grid can be determined using UTM projection (such as WGS84 coordinate system, and the projection band is determined according to the longitude of the wind farm); the grid of the entire wind farm contains multiple cells, and by determining the latitude and longitude center coordinates of each cell, and then importing it into a GIS platform (such as QGIS) to overlay the wind farm topographic map, wind The geographic information of each cell can be determined by the location of the wind turbine, the location of the wind tower, and the distribution of meteorological stations. The temperature test of each cell can be obtained by combining sensors, drone temperature measurement, satellite remote sensing data, etc. The temperature data of the cell can be obtained by placing temperature sensors in the center or four corners of the cell, or through remote sensing images. Specifically, when dividing the wind farm grid, 1km resolution can be used. "1km resolution" usually refers to the smallest spatial unit used in spatial analysis or data collection, that is, the side length of the cell is 1km, that is, the actual ground area corresponding to each cell is 1km×1km.

[0077] The hilly terrain data here can be obtained through satellite remote sensing images. The terrain relief data is the difference in altitude between the highest and lowest points in a specific area. Satellite remote sensing images can be used to determine the altitude data of the highest and lowest points in the wind farm, and then the difference between the two can be calculated to obtain the terrain relief data. Similarly, satellite remote sensing images can also be used to obtain the altitude data of each wind tower location.

[0078] The roughness data and obstacle distribution data of the entire wind farm can be determined through satellite image classification, field surveys, LiDAR point clouds or high-precision maps. The roughness data reflects the resistance of the surface to airflow and is determined by the surface type. The roughness data in this solution is the roughness length. The roughness length here represents the characteristic parameter of the surface roughness, and the unit is m. The smaller z0 is, the smoother the surface is, and vice versa, the rougher the surface is. The specific acquisition method can be obtained through a standard roughness length table or satellite remote sensing images. According to the surface type (such as grassland, farmland, forest, city), check the standard roughness length table (for example: grassland z0≈0.0 1-0.1m, forest z0≈1-5m), combined with satellite images (such as vegetation coverage, land use type) or high-precision terrain data (such as LiDAR), z0 is estimated through the model; the height here refers to the height of the wind speed measurement point; the friction velocity here reflects the shear resistance of the surface to the wind, and is a measure of the momentum exchange between the surface and the atmospheric boundary layer. The high-frequency pulsating wind speed can be measured by an ultrasonic anemometer, the Reynolds stress is calculated, and then the friction velocity is calculated based on the Reynolds stress; it can be understood that the specific implementation process and data acquisition method given above are all conventional methods, which can be obtained through conventional technology and will not be described here one by one.

[0079] The special terrain data here includes the location of ridges and valleys in hilly terrain. A ridge refers to a long, narrow stretch of elevated terrain in hilly terrain, with a convex profile and steep slopes on both sides. When air flows over a ridge, wind speed increases due to the uplift of the terrain and the contraction of streamlines. This is especially true at the top of the ridge, where wind speeds may be 20% to 40% higher than those in the surrounding plains, affecting the stability of wind speed predictions. Valleys refer to the concave areas between adjacent ridges, often covered by rivers or vegetation. If a valley is long and narrow and aligns with the prevailing wind direction, a narrow channel effect may form, significantly increasing wind speed (such as when a north-south valley corresponds to a northerly or southerly wind). If the valley is perpendicular to the wind direction, the wind speed may weaken (blocked by the mountains on both sides), which also affects the stability of wind speed predictions.

[0080] Obtain historical wind direction data, including the wind direction data of the wind tower during each wind speed measurement cycle. Based on the wind direction data and obstacle distribution data, determine the altitude difference between each wind tower and the special terrain in the same wind direction, the number of obstacles, and the volume of each obstacle. When calculating the altitude difference data, calculate the difference by subtracting the altitude data of the wind tower location from the altitude data of the special terrain. The altitude of the wind tower location is based on the surface altitude data of the wind tower location. If the special terrain is a ridge, the altitude data of the highest point of the ridge is used as the basis; if the special terrain is a valley, the altitude data of the lowest point of the valley is used as the basis. The number and volume of obstacles here can be obtained and processed using satellite remote sensing imagery. The same wind direction here means that the wind blows from the special terrain toward the wind tower, and the straight-line distance between the wind tower and the special terrain should not be greater than the preset straight-line distance.

[0081] Specifically, the calculation method of terrain influence data includes: there are n wind towers, and the roughness data of the a-th wind tower is The altitude of the ath wind tower is h a , there are m special terrains with the same wind direction corresponding to the a-th wind tower, and the altitude of the b-th special terrain is h ab There are q obstacles between the a-th wind tower and the corresponding b-th special terrain with the same wind direction, and the obstacle volume of the p-th obstacle is V abp ;

[0082]

[0083] F1=f1×k1+f2×k2+f3×k3

[0084]

[0085]

[0086] In the formula, TID is the terrain influence data, F1, f1, f2, and f3 are the first intermediate function, the second intermediate function, the third intermediate function, and the fourth intermediate function, respectively; k1, k2, and k3 are the preset first sub-weight coefficient, the preset second sub-weight coefficient, and the preset third sub-weight coefficient, respectively; and k1+k2+k3=1; specifically, k1, k2, and k3 can be 0.3, 0.3, and 0.4, respectively.

[0087] After obtaining the temperature impact data and terrain impact data, the wind speed prediction impact data is calculated by weighted summing the temperature impact data and the terrain impact data; the weights of the two are both 0.5, and the sum of the corresponding weights is 1.

[0088] Step S130: determining basic predicted wind speed data based on the historical monitoring data; the basic predicted wind speed data is associated with the historical average wind speed data.

[0089] Specifically, a task time tag of a wind speed prediction task is obtained; the task time tag includes year, month, day and time period data; a first reference group is filtered according to the prediction task time data; the first reference group includes multiple first reference wind speed data; the first reference wind speed data is historical average wind speed data with some historical time tags being the same as some task time tags; the some historical time tags and some task time tags are both month, day and time period data; a second reference group is filtered according to the prediction task time data; the second reference group includes multiple second reference wind speed data; the second reference wind speed data is historical average wind speed data with some historical time tags being the same as some task time tags; the some historical time tags and some task time tags are both month and time period data; first wind speed basic quantity data is calculated based on the first reference group; second wind speed basic quantity data is calculated based on the second reference group; basic prediction wind speed data is determined based on the first wind speed basic quantity data and the second wind speed basic quantity data; the basic prediction wind speed data is associated with the first wind speed basic quantity data and the second wind speed basic quantity data.

[0090] The calculation method of the basic forecast wind speed data specifically includes: assuming that the first reference group includes n first reference wind speed data, then the i-th first reference wind speed data is Assume that the second reference group includes m second reference wind speed data, then the jth second reference wind speed data is

[0091] FP ws =B ws ×K1+D ws ×(1-K1)

[0092]

[0093] Where FP ws Based on the predicted wind speed data, B ws is the first wind speed basic data, D ws is the second wind speed basic data; K1 is the preset calculation weight.

[0094] Furthermore, the calculation method of the preset calculation weight specifically includes calculating a first variance of the first reference group based on all first reference wind speed data included in the first reference group; calculating a second variance of the second reference group based on all second reference wind speed data included in the second reference group;

[0095]

[0096] In the formula, K1 is the preset calculation weight, S1 is the first variance, and S2 is the second variance; the first variance is negatively correlated with the preset calculation weight, and the second variance is positively correlated with the preset calculation weight.

[0097] It can be understood that the task time label contained in the wind speed prediction task here refers to the time period for which the wind farm needs to predict the average wind speed. For example, if the current time is 10 o'clock in the morning, the average wind speed of the wind farm in the future 11-12 o'clock time period needs to be predicted; the first reference group here refers to the historical average wind speed data whose month, date and time period are the same as the corresponding month, date and time period of the task time label, that is, the historical average wind speed data of the same month, day and time period of each year; the second reference group here refers to the historical average wind speed data whose month and time period are the same as the corresponding month calculation time period of the task time label, that is, the historical average wind speed data of the same time period of each day in the same month of each year; the basic predicted wind speed data can be obtained by calculating the average value of all the historical average wind speed data corresponding to the above two reference groups, and then performing weighted summation on the two average values; the first wind speed basic quantity data and the second wind speed basic quantity data here are both positively correlated with the basic predicted wind speed data.

[0098] Here, the calculation method for the first variance and the second variance both adopts the conventional variance calculation method. The variance calculation is performed on all the first reference wind speed data in the first reference group to obtain the first variance, and the variance calculation is performed on all the second reference wind speed data in the second reference group to obtain the second variance. The variance is data that reflects the stability of the data. The smaller the variance, the more stable the data. So, reflected in this scheme for the basic predicted wind speed data, the more stable the data, the greater the contribution of the average value of all the first reference wind speed data corresponding to the first wind speed basic data to the basic predicted wind speed data. Reflected in the formula, the higher the weight, conversely, the lower the weight. Therefore, the larger the first variance, the lower the data stability in the first reference group, the lower the contribution of the first wind speed basic data corresponding to the first reference group, the lower the weight, the smaller K1, and conversely, the larger K1.

[0099] Step S140: calculating comprehensive wind speed prediction data according to the wind speed prediction influencing data and the basic predicted wind speed data; the comprehensive wind speed prediction data is positively correlated with the wind speed prediction influencing data and the basic predicted wind speed data.

[0100] Specifically, the comprehensive wind speed prediction data = wind speed prediction impact data * basic prediction wind speed data; after the comprehensive wind speed prediction data is calculated, the operating parameters of the wind turbine can be adjusted in advance according to the predicted wind speed data, thereby improving the power generation efficiency of the wind farm.

[0101] In this solution, basic predicted wind speed data is obtained by using historical average wind speed data to perform calculations such as average value, variance, and summation. The wind speed prediction impact data is then analyzed in combination with influencing data such as hilly terrain, wind tower location, geographical factors, and meteorological season factors. Finally, the basic predicted wind speed data and wind speed prediction impact data are combined to obtain more practical comprehensive wind speed prediction data. Based on the comprehensive wind speed prediction data, the wind speed data for a certain time period in the future can be obtained in advance. Combined with the wind speed data, the operating parameters of each wind turbine are adjusted accordingly, so that the wind turbine can better cooperate with the corresponding wind speed and generate electricity better, thereby reducing losses and improving the power generation efficiency of the overall wind farm.

[0102] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to the embodiments of this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for this application.

[0103] In the second aspect, the present application provides a wind speed prediction system for wind farms in complex terrain; Figure 2 As shown, the system includes an acquisition module 210 for acquiring historical monitoring data, climate temperature data, and wind farm geographic data within the wind farm; the historical monitoring data includes historical wind speed data; the historical wind speed data includes a plurality of historical average wind speed data with historical time tags; the historical time tags include year, month, day, and time period data; the wind farm geographic data includes wind tower location data and hilly terrain data;

[0104] An analysis module 220 is configured to analyze wind speed prediction impact data based on the climate temperature data and the wind farm geographic data; the wind speed prediction impact data is associated with the climate temperature data, the wind tower location data, and the hilly terrain data;

[0105] A determination module 230 is configured to determine basic predicted wind speed data based on the historical monitoring data; the basic predicted wind speed data is associated with the historical average wind speed data;

[0106] The calculation module 240 is configured to calculate comprehensive wind speed prediction data based on the wind speed prediction influencing data and the basic predicted wind speed data; the comprehensive wind speed prediction data is positively correlated with the wind speed prediction influencing data and the basic predicted wind speed data.

[0107] Furthermore, the analysis module 220 is further configured to analyze the wind speed prediction impact data based on the climate temperature data and the wind farm geographical data; the wind speed prediction impact data associated with the climate temperature data, the wind tower location data, and the hilly terrain data includes:

[0108] Analyzing temperature impact data according to the climate temperature data; the climate temperature data includes temperature data, season data and climate data; the temperature impact data is associated with the temperature data, season data and climate data;

[0109] Analyzing terrain relief data, roughness data, obstacle distribution data and special terrain data based on the hilly terrain data and wind tower location data;

[0110] Analyzing terrain impact data based on the terrain relief data, roughness data, obstacle distribution data, and special terrain data; wherein the terrain impact data is associated with the terrain relief data, roughness data, and obstacle distribution data;

[0111] The wind speed prediction impact data is analyzed according to the temperature impact data and the terrain impact data; the wind speed prediction impact data is associated with the temperature impact data and the terrain impact data.

[0112] Furthermore, the determination module 230 is further configured to determine basic predicted wind speed data based on the historical monitoring data; the basic predicted wind speed data is associated with the historical average wind speed data and includes:

[0113] Obtain a task time tag for a wind speed prediction task; the task time tag includes year, month, day, and time period data;

[0114] A first reference group is selected based on the predicted task time data; the first reference group includes a plurality of first reference wind speed data; the first reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are month, day and time period data;

[0115] A second reference group is selected based on the predicted task time data; the second reference group includes a plurality of second reference wind speed data; the second reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are both monthly and time period data;

[0116] First wind speed basic quantity data is calculated based on a first reference group; second wind speed basic quantity data is calculated based on a second reference group; basic predicted wind speed data is determined based on the first wind speed basic quantity data and the second wind speed basic quantity data; the basic predicted wind speed data is associated with the first wind speed basic quantity data and the second wind speed basic quantity data.

[0117] Furthermore, the determination module 230 is further configured to calculate the basic predicted wind speed data in the following manner:

[0118] Assume that the first reference group includes n first reference wind speed data, then the i-th first reference wind speed data is

[0119] Assume that the second reference group includes m second reference wind speed data, then the jth second reference wind speed data is

[0120] FP ws =B ws ×K1+D ws ×(1-K1)

[0121]

[0122] Where FP ws Based on the predicted wind speed data, B ws is the first wind speed basic data, D ws is the second wind speed basic data; K1 is the preset calculation weight.

[0123] Furthermore, the determination module 230 is further configured to calculate the preset calculation weight in the following manner:

[0124] Calculating a first variance of the first reference group based on all first reference wind speed data included in the first reference group;

[0125] calculating a second variance of the second reference group based on all second reference wind speed data included in the second reference group;

[0126]

[0127] In the formula, K1 is the preset calculation weight, S1 is the first variance, and S2 is the second variance; the first variance is negatively correlated with the preset calculation weight, and the second variance is positively correlated with the preset calculation weight.

[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0129] Based on the disclosures of the first and second aspects of this application, the core creativity of this application is summarized as follows:

[0130] 1. The solution disclosed in this application features a multi-source heterogeneous data fusion mechanism. It creatively combines three key data sources: historical monitoring data (including time-tagged historical average wind speeds), climate temperature data (temperature, season, climate type), and wind farm geographic data (wind tower location, hilly terrain, roughness, and obstacle distribution). Through correlation analysis, it constructs a comprehensive prediction model to address the issue of low wind speed prediction accuracy in complex terrain.

[0131] 2. The solution disclosed in this application creatively designs a specific method for quantifying wind speed prediction impact data, specifically using a method including temperature impact factor, terrain impact factor and dynamic weighted summation. Among them, the temperature impact factor is based on the temperature difference, sample variance and average value calculated from the climate temperature data, and the temperature impact coefficient (T_impact = (|ΔT_avg| + σ 2 ) -1 ), the terrain impact factor takes into account the extraction of hilly terrain undulation, roughness, obstacle distribution and ridge / valley location, and considers the relationship between the wind tower location and wind direction to calculate parameters such as altitude difference and obstacle volume. In addition, terrain impact data is generated through a weighted function (including roughness, altitude difference, and obstacle contribution). Dynamic weighted fusion combines temperature and terrain impact factors according to weights (such as 0.5:0.5) to synthesize wind speed prediction impact data;

[0132] 3. The solution proposed in this application creatively designs an adaptive dual-reference group strategy for basic forecast wind speed, which includes a first reference group and a second reference group. The first reference group includes historical wind speed data that are screened and completely match the forecast task "month + day + time period" (high time accuracy), and the second reference group includes historical wind speed data that are screened and match the forecast task "month + time period" (relaxed date constraints). Dynamic weights are assigned to the first and second reference groups, and the variance of the two groups of data (σ1 2 、 σ2 2 ); weights are assigned inversely proportional to variance: ω = σ2 2 / (σ1 2 +σ2 2 ), ensuring that the data group with high stability has a higher weight, the basic wind speed formula is: V_base=ω·(ΣV 1i / n)+(1-ω)·(ΣV 2j / m)

[0133] 4. The solution proposed in this application creatively designs a coupled calculation model for comprehensive prediction. It directly multiplies the base predicted wind speed (V_base) with the wind speed prediction impact data (Impact): V_final = V_base × Impact. This achieves dynamic coupling of historical statistical laws and real-time environmental factors, improving the robustness of predictions in complex terrain.

[0134] 5. The solution provided in this application creatively carries out specific and refined processing for the hilly terrain of specific wind power scenarios, such as introducing parameters such as terrain undulation, roughness length, obstacle volume, quantifying the disturbance of terrain on airflow, and distinguishing between ridges (acceleration effect) and valleys (narrow pipe / blocking effect), and dynamically adjusting the terrain influence coefficient in combination with wind direction.

[0135] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A wind speed prediction method for a wind farm in complex terrain, characterized in that: include: Acquire historical monitoring data, climate temperature data, and wind farm geographic data within the wind farm; the historical monitoring data includes historical wind speed data; the historical wind speed data includes multiple historical average wind speed data with historical time tags; the historical time tags include year, month, day, and time period data; the wind farm geographic data includes wind tower location data and hilly terrain data; Analyzing wind speed prediction impact data based on the climate temperature data and the wind farm geographical data; the wind speed prediction impact data is associated with the climate temperature data, the wind tower location data, and the hilly terrain data; Determine basic predicted wind speed data based on the historical monitoring data; the basic predicted wind speed data is associated with the historical average wind speed data; Comprehensive wind speed prediction data is calculated based on the wind speed prediction influence data and the basic prediction wind speed data; the comprehensive wind speed prediction data is positively correlated with the wind speed prediction influence data and the basic prediction wind speed data.

2. The method according to claim 1, characterized in that The analyzing of wind speed prediction impact data based on the climate temperature data and the wind farm geographical data; wherein the wind speed prediction impact data is associated with the climate temperature data, the wind tower location data, and the hilly terrain data, includes: Analyzing temperature impact data according to the climate temperature data; the climate temperature data includes temperature data, season data and climate data; the temperature impact data is associated with the temperature data, season data and climate data; Analyzing terrain relief data, roughness data, obstacle distribution data and special terrain data based on the hilly terrain data and wind tower location data; Analyzing terrain impact data based on the terrain relief data, roughness data, obstacle distribution data, and special terrain data; wherein the terrain impact data is associated with the terrain relief data, roughness data, and obstacle distribution data; The wind speed prediction impact data is analyzed according to the temperature impact data and the terrain impact data; the wind speed prediction impact data is associated with the temperature impact data and the terrain impact data.

3. The method according to claim 2, characterized in that The determining of basic predicted wind speed data based on the historical monitoring data; and associating the basic predicted wind speed data with the historical average wind speed data include: Obtain a task time tag for a wind speed prediction task; the task time tag includes year, month, day, and time period data; A first reference group is selected based on the predicted task time data; the first reference group includes a plurality of first reference wind speed data; the first reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are month, day and time period data; A second reference group is selected based on the predicted task time data; the second reference group includes a plurality of second reference wind speed data; the second reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are both monthly and time period data; First wind speed basic quantity data is calculated based on a first reference group; second wind speed basic quantity data is calculated based on a second reference group; basic predicted wind speed data is determined based on the first wind speed basic quantity data and the second wind speed basic quantity data; the basic predicted wind speed data is associated with the first wind speed basic quantity data and the second wind speed basic quantity data.

4. The method according to claim 3, characterized in that The calculation method of the basic predicted wind speed data includes: Assume that the first reference group includes n first reference wind speed data, then the i-th first reference wind speed data is Assume that the second reference group includes m second reference wind speed data, then the jth second reference wind speed data is FP ws =B ws ×K1+D ws ×(1-K1) Where FP ws Based on the predicted wind speed data, B ws is the first wind speed basic data, D ws is the second wind speed basic data; K1 is the preset calculation weight.

5. The method according to claim 4, characterized in that The calculation method of the preset calculation weight includes: Calculating a first variance of the first reference group based on all first reference wind speed data included in the first reference group; calculating a second variance of the second reference group based on all second reference wind speed data included in the second reference group; In the formula, K1 is the preset calculation weight, S1 is the first variance, and S2 is the second variance; the first variance is negatively correlated with the preset calculation weight, and the second variance is positively correlated with the preset calculation weight.

6. A wind speed prediction system for wind farms in complex terrain, characterized in that: include: An acquisition module (210) is used to acquire historical monitoring data, climate temperature data and wind farm geographical data within the wind farm; The historical monitoring data includes historical wind speed data; the historical wind speed data includes a plurality of historical average wind speed data with historical time tags; the historical time tags include year, month, day and time period data; the wind farm geographical data includes wind tower location data and hilly terrain data; An analysis module (220) is used to analyze wind speed prediction impact data based on the climate temperature data and the wind farm geographical data; the wind speed prediction impact data is associated with the climate temperature data, the wind tower location data, and the hilly terrain data; A determination module (230) is configured to determine basic predicted wind speed data based on the historical monitoring data; the basic predicted wind speed data is associated with the historical average wind speed data; A calculation module (240) is used to calculate comprehensive wind speed prediction data based on the wind speed prediction influence data and the basic prediction wind speed data; the comprehensive wind speed prediction data is positively correlated with the wind speed prediction influence data and the basic prediction wind speed data.

7. The system according to claim 6, characterized in that The analysis module (220) is further configured to analyze the wind speed prediction impact data based on the climate temperature data and the wind farm geographical data; The wind speed prediction impact data is associated with the climate temperature data, the wind tower location data and the hilly terrain data and includes: Analyzing temperature impact data according to the climate temperature data; the climate temperature data includes temperature data, season data and climate data; the temperature impact data is associated with the temperature data, season data and climate data; Analyzing terrain relief data, roughness data, obstacle distribution data and special terrain data based on the hilly terrain data and wind tower location data; Analyzing terrain impact data based on the terrain relief data, roughness data, obstacle distribution data, and special terrain data; wherein the terrain impact data is associated with the terrain relief data, roughness data, and obstacle distribution data; The wind speed prediction impact data is analyzed according to the temperature impact data and the terrain impact data; the wind speed prediction impact data is associated with the temperature impact data and the terrain impact data.

8. The system according to claim 7, characterized in that The determination module (230) is further configured to determine basic predicted wind speed data based on the historical monitoring data; the basic predicted wind speed data associated with the historical average wind speed data includes: Obtain a task time tag for a wind speed prediction task; the task time tag includes year, month, day, and time period data; A first reference group is selected based on the predicted task time data; the first reference group includes a plurality of first reference wind speed data; the first reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are month, day and time period data; A second reference group is selected based on the predicted task time data; the second reference group includes a plurality of second reference wind speed data; the second reference wind speed data is historical average wind speed data with some historical time tags and some task time tags having the same historical time tags; and the some historical time tags and some task time tags are both monthly and time period data; First wind speed basic quantity data is calculated based on a first reference group; second wind speed basic quantity data is calculated based on a second reference group; basic predicted wind speed data is determined based on the first wind speed basic quantity data and the second wind speed basic quantity data; the basic predicted wind speed data is associated with the first wind speed basic quantity data and the second wind speed basic quantity data.

9. The system according to claim 8, characterized in that The determination module (230) is further configured such that the calculation method of the basic predicted wind speed data includes: Assume that the first reference group includes n first reference wind speed data, then the i-th first reference wind speed data is Assume that the second reference group includes m second reference wind speed data, then the jth second reference wind speed data is FP ws =B ws ×K1+D ws ×(1-K1) Where FP ws Based on the predicted wind speed data, B ws is the first wind speed basic data, D ws is the second wind speed basic data; K1 is the preset calculation weight.

10. The system according to claim 9, characterized in that The determination module (230) is further configured such that the calculation method of the preset calculation weight includes: Calculating a first variance of the first reference group based on all first reference wind speed data included in the first reference group; calculating a second variance of the second reference group based on all second reference wind speed data included in the second reference group; In the formula, K1 is the preset calculation weight, S1 is the first variance, and S2 is the second variance; the first variance is negatively correlated with the preset calculation weight, and the second variance is positively correlated with the preset calculation weight.

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