Wind belt transmission rule evaluation method and device for wind power prediction

Abstract

The invention discloses a wind belt transmission rule evaluation method and device for wind power prediction. The method comprises the steps of constructing a backward trajectory model; counting the proportion of the high value of the wind speed passed by the historical backward trajectory of each grid, so as to determine the spatial distribution of the wind zone according to the obtained potential peak contribution factor value; calculating the ratio of the number of trajectories when peak transmission occurs to the total number of trajectories when strong wind occurs, and recording the ratio as a peak transmission probability; determining propagation paths among the wind power plants; calculating to obtain peak transmission time of the target wind power plant and the upstream wind power plant in the same wind zone; and according to the wind zone space distribution and the peak value transmission time, evaluating a wind zone transmission rule. Quantification of the wind belt transmission process is achieved, detailed information of the wind belt source, the wind belt rule and the airflow characteristics of the wind power plant in the target area is obtained, and powerful reference can be provided for wind energy utilization.

Description

[Technical field]

[0001] The present invention relates to the technical field of wind speed forecasting, and in particular to a method and device for evaluating wind belt transmission law in wind power forecasting and related products. [Background technology]

[0002] At present, wind power forecasting plays an important role in wind farm management, but it also faces many challenges. First, wind speed changes are affected by multiple factors such as terrain, meteorological conditions and seasonal changes, making wind power forecasting complex and challenging. Secondly, sudden strong winds may cause wind turbines to be cut off, affecting the stable operation of the power grid. Therefore, predicting strong winds and taking corresponding adjustments has become a difficult problem. Current research has achieved some results. By applying advanced meteorological models and big data analysis technology, the understanding of wind speed changes in wind farms and the accuracy of prediction are improved.

[0003] However, current research still has some limitations in considering the transmission law of wind belts, and has not fully explored the potential in this area. Therefore, it is necessary to further study the impact of wind belt transmission law on wind power forecasting to fill the gaps in existing research. In order to achieve this goal, how to analyze the wind speed, wind direction and meteorological data in the wind power area to understand its distribution in time and space, reveal the wind speed transmission characteristics and wind belt laws, and evaluate the transmission relationship between wind zones has become an urgent problem to be solved.

[0004] In view of this, it is necessary to provide a method and device for evaluating wind belt transmission law in wind power forecasting to overcome the above-mentioned defects. [Summary of the invention]

[0005] The purpose of the present invention is to provide a method and device for evaluating the wind belt transfer law in wind power forecasting, aiming to solve the problem of how to analyze the wind speed, wind direction and meteorological data of the wind power area to understand its distribution in time and space, so as to reveal the wind speed transmission characteristics and wind belt laws, and evaluate the transmission relationship between wind zones, so as to obtain detailed information on the wind belt source, wind belt laws and airflow characteristics of the wind farm, and provide a strong reference for wind energy utilization.

[0006] In order to achieve the above object, the present invention provides a first aspect of a wind belt transmission law evaluation method for wind power forecasting, comprising:

[0007] Step S10: acquiring historical wind farm data of each wind farm or wind zone, and constructing a backward trajectory model;

[0008] Step S20: Divide each wind farm and wind zone into a number of equal grids, and use the backward trajectory model to count the proportion of high wind speed values ​​passed by the historical backward trajectory of each grid, so as to determine the spatial distribution of the wind belt according to the obtained potential peak contribution factor value, and visualize it in a chart or map;

[0009] Step S30: Calculate the ratio of the number of trajectories when peak transmission occurs between the target wind farm and the upstream wind farm and the total number of trajectories when strong wind occurs in the target wind farm in combination with the spatial distribution of the wind belt, and record it as the peak transmission probability;

[0010] Step S40: Determine the propagation path between the wind farms in combination with the peak transmission probability of each wind farm;

[0011] Step S50: Based on the acquired wind speed data of the meteorological forecast and the propagation paths between the wind farms, the peak transmission time between the target wind farm and the upstream wind farm in the same wind belt is calculated using a backward trajectory model and a preset delay correlation algorithm;

[0012] Step S60: Evaluate the wind belt transmission law according to the wind belt spatial distribution and the peak transmission time.

[0013] In a preferred embodiment, the step S20 includes:

[0014] A grid ij is defined in a chart or a map, and a preset wind speed threshold is obtained, and a historical backward trajectory with a wind speed exceeding the wind speed threshold is recorded as a high wind trajectory;

[0015] Calculate the potential peak contribution factor wPPCF of each grid ij according to the preset formula ij , the formula is:

[0016]

[0017] Among them, W(n ij ) is the weight factor corresponding to the grid ij, m ij is the number of endpoints of the strong wind trajectory in the grid ij, n ij is the total number of trajectory endpoints in the grid ij, n avg n ij average value.

[0018] In a preferred embodiment, the step S50 includes:

[0019] Get the wind speed data simulated by WRF;

[0020] The number of endpoints on the backward trajectory of the target wind farm whose wind speed is greater than the preset wind speed threshold and falls within the preset range of the upstream wind farm is taken as the transfer time, and then all transfer times are sorted from small to large and the median is taken as the peak transfer time between the upstream wind farm and the target wind farm.

[0021] In a preferred embodiment, the step S50 further includes:

[0022] The delayed correlation of wind speed time series between different wind zones is obtained, and the delay time with the largest correlation coefficient is recorded as the peak transmission time between the two wind zones.

[0023] In a preferred embodiment, the steps are also included:

[0024] According to the position distribution relationship of the wind zone in the wind belt, the wind speed forecast of the wind zone is corrected using the upstream and downstream relationship; the stronger the wind belt, the greater the correction.

[0025] A second aspect of the present invention provides a wind belt transmission law evaluation device for wind power prediction, comprising:

[0026] A model building module is used to obtain historical wind farm data of each wind farm or wind zone and build a backward trajectory model;

[0027] A factor calculation module is used to divide each wind farm and wind zone into a number of equal grids, and use the backward trajectory model to count the proportion of high wind speed values ​​passed by the historical backward trajectory of each grid, so as to determine the spatial distribution of the wind belt according to the obtained potential peak contribution factor value, and visualize it in a chart or map;

[0028] A probability calculation module, used to calculate the ratio of the number of trajectories when peak transmission occurs between the target wind farm and the upstream wind farm and the total number of trajectories when strong wind occurs in the target wind farm, based on the spatial distribution of the wind belt, and record it as the peak transmission probability;

[0029] A path determination module is used to determine the propagation path between wind farms in combination with the peak transmission probability of each wind farm;

[0030] The time calculation module is used to calculate the peak transmission time between the target wind farm and the upstream wind farm in the same wind belt based on the wind speed data obtained from the meteorological forecast and the propagation path between each wind farm, using the backward trajectory model and the preset delay correlation algorithm;

[0031] The transmission evaluation module is used to evaluate the wind belt transmission law according to the spatial distribution of the wind belt and the peak transmission time.

[0032] A third aspect of the present invention provides a terminal, which includes a memory, a processor, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the various steps of the method for evaluating wind belt transfer laws in wind power forecasting as described in any one of the above-mentioned embodiments.

[0033] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the various steps of the method for evaluating wind belt transfer laws in wind power forecasting as described in any one of the above-mentioned embodiments.

[0034] A fifth aspect of the present invention provides a computer program product, including a computer program or instructions, which, when processed and executed, implement the various steps of the method for evaluating wind belt transfer laws in wind power forecasting as described in any of the above-mentioned embodiments.

[0035] The method and device for evaluating the wind belt transmission law in wind power prediction provided by the present invention utilize the potential peak contribution factor method to count the proportion of high wind speed values ​​in the grids passed by the historical backward trajectory, and obtain the grid area with a large proportion of high wind speed values, that is, the potential large wind source area for the wind farm or wind zone, and this large wind source area better indicates the direction of the wind belt; adopt the backward trajectory model to explore the wind speed peak transmission process between the wind zones from a spatial perspective, and calculate the probability of peak transmission between the wind zones; by calculating the time difference of the wind speed peak in the wind zone based on the backward trajectory method and the delayed correlation method, the peak transmission time is evaluated, and the wind belt transmission process is quantified, and the detailed information on the wind belt source, wind belt law and airflow characteristics of the wind farm in the target area is obtained, which can provide a strong reference for wind energy utilization.

Brief Description of the Drawings

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A flow chart of a method for evaluating wind belt transmission rules in wind power forecasting provided by the present invention;

[0038] Figure 2 It is the spatial distribution map of potential peak contribution factor of a wind farm in a strong wind belt area of ​​a certain region;

[0039] Figure 3 for Figure 2The peak transfer probability diagram of a wind farm is shown;

[0040] Figure 4 for Figure 2 The peak wind transfer time diagram of a certain area calculated based on WRF data is shown;

[0041] Figure 5 A framework diagram of a wind belt transfer law assessment device for wind power prediction provided by the present invention. [Specific implementation method]

[0042] In order to make the purpose, technical solution and beneficial technical effect of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be understood that the specific implementation methods described in this specification are only for explaining the present invention, not for limiting the present invention.

[0043] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0044] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0045] Embodiment 1

[0046] In an embodiment of the present invention, a method for evaluating the transmission law of wind belts in wind power forecasting is provided, which is used to combine historical wind speed data to perform potential peak contribution factor method and backward trajectory analysis on wind farm scale data, so as to confirm the existence of wind belts and the approximate transmission relationship between wind zones. On the basis of past project research results, the wind speed data of each wind zone during the windy day period can be used to confirm the propagation path of the wind belt in a specific case. This helps to gain a deeper understanding of the dynamic relationship between different wind zones within a wind farm and provide a more comprehensive perspective for the operation and management of the wind farm. Among them, in the embodiments of the present invention, the wind farm in Xinjiang will be used as an example for the convenience of readers' understanding.

[0047] like Figure 1 As shown, the method of the present invention includes steps S10-S60.

[0048] Step S10: Acquire historical wind farm data of each wind farm or wind zone, and construct a backward trajectory model.

[0049] Among them, by combining the backward trajectory model with meteorological Li's observation data, the propagation and distribution of air in a specific time and space range can be accurately simulated and predicted. Among them, the basic principles and specific implementation methods of constructing the backward trajectory model can refer to the existing technology and will not be repeated here.

[0050] Step S20: Divide each wind farm and wind zone into a number of equal grids, and use the backward trajectory model to count the proportion of high wind speed values ​​passed by the historical backward trajectory of each grid, so as to determine the spatial distribution of the wind belt according to the obtained potential peak contribution factor value, and visualize it in a chart or map.

[0051] In this step, the weighted Potential Peak Contribution Function (wPPCF) method is used to divide the study area into several equal small grids, and a threshold is set, which is the ratio of the number of endpoints of the strong wind backward trajectory of a certain grid ij (that is, the trajectory when the wind speed exceeds the set wind speed threshold, 10m / s is taken as the threshold in this case) to the total number of endpoints of all backward trajectories passing through the grid ij. At the same time, the weight factor W (n ij ) to reduce the uncertainty of the calculation results caused by the small number of backward trajectory endpoints in some grids.

[0052] Specifically, step S20 includes: defining a grid ij in a chart or map, obtaining a preset wind speed threshold, and recording the historical backward trajectory with wind speed exceeding the wind speed threshold as a high wind trajectory; calculating the potential peak contribution factor value wPPCF of each grid ij according to a preset formula ij , the formula is:

[0053]

[0054] Among them, W(n ij ) is the weight factor corresponding to the grid ij, m ij is the number of endpoints of the strong wind trajectory in the grid ij, n ij is the total number of trajectory endpoints in the grid ij, n avg n ij average value.

[0055] It should be noted that the potential peak contribution factor method is a method used to analyze the contribution of a certain factor to a specific result. First, a foundation is established by collecting relevant data such as meteorological and geographical information. Then the factors that affect the results are determined, such as selecting wind speed as a contribution factor in wind energy research. Mathematical models or statistical methods are used to calculate the potential contribution of each factor to the result, revealing the relationship between the factor and the result. Through spatial distribution visualization, the potential peak contribution factor is displayed in a chart or map to intuitively understand the contribution of different regions. Finally, the calculation results are analyzed and interpreted to understand the spatial distribution law of each factor and its impact on the results. In wind energy research, this method can be used to determine the distribution and utilization potential of wind energy resources, and provide strong support for wind farm site selection and wind power generation benefit evaluation. Of course, the specific calculation method and model selection will vary depending on the research field and problem.

[0056] Combination Figure 2 As shown in the figure, in order to further explore the wind belt sources and wind belt rules of each wind farm in Xinjiang, this project uses the potential peak contribution factor method to analyze the spatial distribution of the potential peak contribution factors of each wind farm in Xinjiang. The color is the area where the airflow passes. The more the grid color tends to red, the greater the wPPCF value. The grid with a high wPPCF value can be regarded as a potential large wind source area (that is, the wind farm in this area and the target wind farm have a high probability of having a transmission relationship), and the wind speed is relatively large. At the same time, the potential peak contribution factor method can also be used to further divide the wind farms in Xinjiang into strong wind belt areas, weak wind belt areas and areas without obvious wind belts. Among them, the magnitude of the potential peak contribution factor in the strong wind belt area is basically above 0.3, and the magnitude of the large-scale area is above 0.05, and it can be seen that the typical wind belt distribution is obvious; the magnitude of the potential peak contribution factor in the weak wind belt area is between 0.05 and 0.2, and the area where the typical wind belt range appears is not as large and significant as the strong wind belt area; and the magnitude of the potential peak contribution factor in the area without obvious wind belt is basically below 0.05, and only the magnitude of individual grid points is greater than 0.05. Generally speaking, the main direction of airflow in wind farms in Xinjiang is east-west, and the dominant wind direction throughout the year is easterly and westerly.

[0057] Hami 1270, Turpan 450, Urumqi 447 and Hami 354 wind farms have strong wind belts. Their potential strong wind source areas are the most significant, and the strong wind airflow mainly comes from the northwest, belonging to the strong wind belt area. The wPPCF value of the grid area in a large range exceeds 0.3, and the spatial distribution of their potential peak contribution factors is consistent with the peak backward trajectory clustering results mentioned above. When the Siberian cold air enters Xinjiang, the flow rate will become faster due to the "narrow tube effect" of the terrain, and the density will increase due to the accumulation of gas. Therefore, its peak airflow trajectory basically comes from the northwest, and when the air passes through the mountain pass, the streamlines are denser and the wind force increases. Hami 1270, Turpan 450 and Hami 354 wind farms are located in the first wind belt, and the extreme wind is mainly transmitted from the Irtysh River Valley wind area, while the Urumqi 447 wind farm is located in the second wind belt, and the extreme wind is mainly transmitted from the Maitas wind area.

[0058] Therefore, the present invention uses the HYSPLIT (Hybrid Single Particle Lagrangian IntegratedTrajectory, a popular meteorological trajectory model that can simulate the movement of gas particles in the three-dimensional atmosphere and take into account a variety of complex factors, such as wind fields, etc.) model and potential peak contribution factors to analyze the airflow trajectories of various wind farms in Xinjiang and their potential strong wind source areas, and summarizes the two main wind belts in Xinjiang from a macro perspective.

[0059] Step S30: Calculate the ratio of the number of trajectories when peak transfer occurs between the target wind farm and the upstream wind farm to the total number of trajectories with strong winds in the target wind farm in combination with the spatial distribution of the wind belt, and record it as the peak transfer probability (pp).

[0060] Here, it is assumed in advance that there is wind speed peak transmission between wind farms, and the peak transmission process between wind farms is discussed. That is, the peak transmission probability method will be used to consider only the wind speed peak backward trajectory distribution corresponding to each wind farm when there is wind speed transmission between wind farms, so as to trace the relationship between the wind speed peak backward trajectory of the target wind farm and the upstream wind farm in a more in-depth manner.

[0061] Step S40: Determine the transmission paths between the wind farms in combination with the peak transmission probabilities of the wind farms.

[0062] Among them, the magnitude of the peak transmission probability can reflect the possibility that the wind speed peak of the upstream wind farm will reach the target wind farm after the transmission process. Figure 3As shown in the figure, the peak transmission probability of wind farms in the strong wind belt area of ​​Xinjiang is larger in the northwest direction, which is consistent with the spatial distribution results of the potential peak contribution factors mentioned above. For example, the peak wind of Hami 1270 wind farm located in the first wind belt is mainly transmitted from Turpan 450 wind farm, and the wind speed peak transmission trajectory accounts for about 7.2% of the total wind speed peak trajectory of Hami 1270 wind farm, followed by Altay 208, accounting for about 3.2%. In contrast, the peak wind of Urumqi 447 wind farm located in the second wind belt is mainly transmitted from Urumqi 253 wind farm, and the wind speed peak transmission trajectory accounts for about 58.7% of the total wind speed peak trajectory of Urumqi 225 wind farm, followed by Urumqi 351, accounting for about 57.3%. The peak transmission probability between the remaining wind farms is about less than 5%.

[0063] Step S50: Based on the acquired wind speed data of the weather forecast and the propagation paths between the wind farms, the peak transmission time between the target wind farm and the upstream wind farm in the same wind belt is calculated using a backward trajectory model and a preset delay correlation algorithm.

[0064] From the above analysis, it can be concluded that there is a certain time delay in the transmission of wind speed peaks between wind farms in the same wind belt. In order to further explore the wind belt transmission process between wind farms, this section will use WRF (Weather Research and Forecasting Model, an open source model suitable for complex terrain and advanced numerical weather forecasting) wind speed data to explore the peak transmission time (unit: hour) between different wind farms and different wind zones.

[0065] Specifically, step S50 includes: obtaining the wind speed data simulated by WRF, taking the number of endpoints on the backward trajectory of the target wind farm that is greater than a preset wind speed threshold (for example, 10 m / s) and falls within a preset range (for example, a 20 km radius range) of the upstream wind farm as the transfer time, and then sorting all the transfer times from small to large and taking the median as the peak transfer time (Pass time, pt) between the upstream wind farm and the target wind farm.

[0066] As shown in Table 1 below, the column name of the table is the target wind farm, and the row name is the upstream wind farm. Since the peak wind speed is transmitted from the upstream wind farm to the downstream wind farm, the wind farms appear in a certain order. Therefore, when checking the transmission time between two wind farms, the target wind farm (selected from the column name) should be locked first, and then the corresponding upstream wind farm (selected from the row name) should be obtained. In addition, due to the limited sample size of cases, if the number of cases is too small, it will lead to a large deviation in the transmission time. Therefore, the following table only summarizes the peak transmission time when the number of cases is greater than 10.

[0067] It can be seen from Table 1 that the peak transfer time obtained from the WRF wind speed data intuitively reflects the transfer process between wind farms. The number of endpoints of the trajectory between two wind farms is the transfer time, and one endpoint is recorded as one hour. In theory, the longer the distance between wind farms, the longer the transfer time required. For example, in the first wind belt, the transfer from Hami 1270 wind farm to Hami 264 wind farm takes the longest time, which is about 21 hours; the transfer from Hami 207 or Hami 381 wind farm to Hami 246 wind farm takes the shortest time, which is only 1 hour.

[0068] Table 1: Peak transfer time between wind farms based on WRF data

[0069]

[0070]

[0071] Furthermore, step S50 also includes: obtaining the delayed correlation relationship of the wind speed time series between different wind zones, and recording the delay time with the largest correlation coefficient as the peak transmission time between the two wind zones.

[0072] Combination Figure 4 As shown in the figure, in real wind speed transmission cases, wind energy will be affected by factors such as air resistance, obstruction by obstacles such as mountains, and friction, causing the wind speed to change during the transmission process. Therefore, capturing the time delay law between wind zones can show the wind speed peak transmission process in a more macroscopic way.

[0073] Figure 4 The peak transmission time of the wind zone is calculated from WRF data. The Irtysh River Valley wind zone is relatively large, including the Altay 208 wind farm located on the southeast edge of the wind zone. The transmission time from the Irtysh River Valley wind zone to the Changji wind zone is about 11 hours, and the transmission time from the Changji wind zone to the southeastern Hami wind zone is about 16 hours. Although the straight-line distance from Changji to the southeast of Hami is shorter than the straight-line distance from the Irtysh River Valley to Changji, the Bogda Mountain in the eastern section of the Tianshan Mountains blocks the transmission of the wind belt, and the airflow bypasses, so that the wind belt must be transmitted along one side of the mountain range to Hami, which increases the transmission time. At the same time, a small wind zone is formed in the southeast of Hami. Compared with the second wind belt, it takes about 20 hours to transmit from the Maitas wind zone to the Dabancheng wind zone, and it takes about 3 hours to transmit from the Dabancheng wind zone to the Xiaocao Lake wind zone.

[0074] Furthermore, the method also includes the steps of: according to the position distribution relationship of the wind zone in the wind belt, using the upstream and downstream relationship to correct the wind speed forecast of the wind zone; wherein, the stronger the wind belt, the greater the correction range.

[0075] That is, the potential peak contribution factor method is used to divide each wind farm into a strong wind belt area, a weak wind belt area and an area with no obvious wind belt, so as to distinguish which wind farms or wind areas are affected by the wind belt, and use the upstream and downstream relationship to correct the wind speed forecast. When the wind belt is strong, the correction can be stronger.

[0076] Step S60: Evaluate the wind belt transmission pattern according to the wind belt spatial distribution and peak transmission time.

[0077] In summary, combined with the peak transfer time between wind fields calculated by WRF data, the actual distance between wind zones, the geographical situation of wind zones and previous research experience of scholars on Xinjiang wind zones, the peak transfer time of the two main wind belts in Xinjiang is summarized. The first wind belt starts from the Irtysh River Valley wind zone and takes about 11 hours to transfer to the Changji wind zone, and it takes 16 hours from Changji to the next wind zone, the eastern Hami wind zone; the second wind belt starts from the Maitas wind zone and takes about 20 hours to transfer to the Dabashan wind zone, and it takes 3 hours from the Dabashan wind zone to the Xiaocao Lake wind zone.

[0078] Therefore, the present invention uses the potential peak contribution factor method to count the proportion of high wind speed values ​​in the grids passed by the historical backward trajectory, and obtains the grid area with a large proportion of high wind speed values, that is, the potential large wind source area for the wind farm or wind zone. This large wind source area better indicates the direction of the wind belt; using the backward trajectory model and its method, the wind speed peak transmission process between wind zones taking Xinjiang as an example is discussed from a spatial perspective, and the probability of peak transmission between wind zones is calculated; based on the backward trajectory method and the delayed correlation method, the time difference of the wind speed peak in the wind zone is calculated, the peak transmission time is evaluated, and the wind belt transmission process is quantified, which provides a powerful reference for wind energy utilization.

[0079] Embodiment 2

[0080] The present invention provides a wind belt transfer law assessment device 100 for use in wind power forecasting, which is used to gain an in-depth understanding of the dynamic relationship between different wind zones within a wind farm, and to provide a more comprehensive perspective for the operation and management of the wind farm. It should be noted that the implementation principle and specific implementation method of the wind belt transfer law assessment device 100 for use in wind power forecasting are consistent with the above-mentioned wind belt transfer law assessment method for wind power forecasting, and therefore will not be described in detail below.

[0081] like Figure 5 As shown, the wind belt transmission law evaluation device 100 for wind power prediction includes:

[0082] A model building module 10 is used to obtain historical wind farm data of each wind farm or wind zone and build a backward trajectory model;

[0083] The factor calculation module 20 is used to divide each wind farm and wind zone into a number of equal grids, and use the backward trajectory model to count the proportion of high wind speed values ​​passed by the historical backward trajectory of each grid, so as to determine the spatial distribution of the wind belt according to the obtained potential peak contribution factor value, and visualize it in a chart or map;

[0084] The probability calculation module 30 is used to calculate the ratio of the number of trajectories when the target wind farm and the upstream wind farm have peak transmission and the total number of trajectories when the target wind farm has strong winds, based on the spatial distribution of the wind belt, and record it as the peak transmission probability;

[0085] A path determination module 40, used to determine the propagation path between the wind farms in combination with the peak transmission probability of each wind farm;

[0086] The time calculation module 50 is used to calculate the peak transmission time between the target wind farm and the upstream wind farm in the same wind belt by using the backward trajectory model and the preset delay correlation algorithm based on the wind speed data of the meteorological forecast and the propagation path between each wind farm;

[0087] The transmission evaluation module 60 is used to evaluate the wind belt transmission law according to the wind belt spatial distribution and the peak transmission time.

[0088] Embodiment 3

[0089] The present invention provides a terminal, which includes a memory, a processor and a computer program stored in the memory. When the computer program is executed by the processor, the computer program implements the various steps of the wind belt transfer law evaluation method for wind power forecasting as described in any one of the above-mentioned implementation modes.

[0090] Embodiment 4

[0091] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the various steps of the method for evaluating wind belt transfer laws in wind power forecasting as described in any one of the above-mentioned implementation modes.

[0092] Embodiment 5

[0093] The present invention provides a computer program product, including a computer program or instructions, which, when processed and executed, implements the various steps of the method for evaluating wind belt transfer laws in wind power forecasting as described in any one of the above-mentioned implementation modes.

[0094] In summary, the method and device for evaluating the wind belt transmission law in wind power prediction provided by the present invention use the potential peak contribution factor method to count the proportion of high wind speed values ​​in the grids passed by the historical backward trajectory, and obtain the grid area with a large proportion of high wind speed values, that is, the potential large wind source area for the wind farm or wind zone, and this large wind source area better indicates the direction of the wind belt; using the backward trajectory model, the wind speed peak transmission process between the wind zones is discussed from a spatial perspective, and the probability of peak transmission between wind zones is calculated; based on the backward trajectory method and the delayed correlation method, the time difference of the occurrence of wind speed peak in the wind zone is calculated, the peak transmission time is evaluated, and the wind belt transmission process is quantified, and the detailed information on the wind belt source, wind belt law and airflow characteristics of the wind farm in the target area is obtained, which can provide a strong reference for wind energy utilization.

[0095] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0096] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0098] In the embodiments provided by the present invention, it should be understood that the disclosed systems or devices / terminal equipment and methods can be implemented in other ways. For example, the system or device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the system or unit can be electrical, mechanical or other forms.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0101] The present invention is not limited to what is described in the specification and implementation modes, and therefore additional advantages and modifications can be easily realized by those skilled in the art. Therefore, without departing from the spirit and scope of the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details, representative devices, and illustrative examples shown and described herein.

Claims

1. A method for evaluating wind belt transmission law in wind power forecasting, characterized in that: include: Step S10: acquiring historical wind farm data of each wind farm or wind zone, and constructing a backward trajectory model; Step S20: Divide each wind farm and wind zone into a number of equal grids, and use the backward trajectory model to count the proportion of high wind speed values ​​passed by the historical backward trajectory of each grid, so as to determine the spatial distribution of the wind belt according to the obtained potential peak contribution factor value, and visualize it in a chart or map; Step S30: Calculate the ratio of the number of trajectories when peak transmission occurs between the target wind farm and the upstream wind farm and the total number of trajectories when strong wind occurs in the target wind farm in combination with the spatial distribution of the wind belt, and record it as the peak transmission probability; Step S40: Determine the propagation path between the wind farms in combination with the peak transmission probability of each wind farm; Step S50: Based on the acquired wind speed data of the meteorological forecast and the propagation paths between the wind farms, the peak transmission time between the target wind farm and the upstream wind farm in the same wind belt is calculated using a backward trajectory model and a preset delay correlation algorithm; Step S60: Evaluate the wind belt transmission law according to the wind belt spatial distribution and the peak transmission time.

2. The wind belt transmission law evaluation method for wind power prediction according to claim 1, characterized in that: The step S20 comprises: A grid ij is defined in a chart or a map, and a preset wind speed threshold is obtained, and a historical backward trajectory with a wind speed exceeding the wind speed threshold is recorded as a high wind trajectory; Calculate the potential peak contribution factor wPPCF of each grid ij according to the preset formula ij , the formula is: Among them, W(n ij ) is the weight factor corresponding to the grid ij, m ij is the number of endpoints of the strong wind trajectory in the grid ij, n ij is the total number of trajectory endpoints in the grid ij, n avg n ij average value.

3. The wind belt transmission law evaluation method for wind power prediction according to claim 1, characterized in that: The step S50 comprises: Get the wind speed data simulated by WRF; The number of endpoints on the backward trajectory of the target wind farm whose wind speed is greater than the preset wind speed threshold and falls within the preset range of the upstream wind farm is taken as the transfer time, and then all transfer times are sorted from small to large and the median is taken as the peak transfer time between the upstream wind farm and the target wind farm.

4. The wind belt transmission law evaluation method for wind power prediction according to claim 3, characterized in that: The step S50 further includes: The delayed correlation of wind speed time series between different wind zones is obtained, and the delay time with the largest correlation coefficient is recorded as the peak transmission time between the two wind zones.

5. The wind belt transmission law evaluation method for wind power prediction according to claim 1, characterized in that: Also includes the steps: According to the position distribution relationship of the wind zone in the wind belt, the wind speed forecast of the wind zone is corrected using the upstream and downstream relationship; the stronger the wind belt, the greater the correction.

6. A wind belt transmission law evaluation device for wind power prediction, characterized in that: include: A model building module is used to obtain historical wind farm data of each wind farm or wind zone and build a backward trajectory model; A factor calculation module is used to divide each wind farm and wind zone into a number of equal grids, and use the backward trajectory model to count the proportion of high wind speed values ​​passed by the historical backward trajectory of each grid, so as to determine the spatial distribution of the wind belt according to the obtained potential peak contribution factor value, and visualize it in a chart or map; A probability calculation module, used to calculate the ratio of the number of trajectories when peak transmission occurs between the target wind farm and the upstream wind farm and the total number of trajectories when strong wind occurs in the target wind farm, based on the spatial distribution of the wind belt, and record it as the peak transmission probability; A path determination module is used to determine the propagation path between wind farms in combination with the peak transmission probability of each wind farm; The time calculation module is used to calculate the peak transmission time between the target wind farm and the upstream wind farm in the same wind belt based on the wind speed data obtained from the meteorological forecast and the propagation path between each wind farm, using the backward trajectory model and the preset delay correlation algorithm; The transmission evaluation module is used to evaluate the wind belt transmission law according to the spatial distribution of the wind belt and the peak transmission time.

7. A terminal, characterized in that: The terminal includes a memory, a processor, and a computer program stored in the memory. When the computer program is executed by the processor, each step of the method for evaluating wind belt transfer laws in wind power forecasting as described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, each step of the method for evaluating wind belt transfer laws in wind power forecasting according to any one of claims 1 to 5 is implemented.

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