Power grid facility-oriented extreme wind speed prediction method and system

Abstract

The invention belongs to the technical field of electric power meteorology, and provides a power grid facility-oriented extreme wind speed prediction method and system, and the method comprises the steps: constructing a power grid facility-oriented extreme wind speed prediction conceptual model; the method comprises the following steps: acquiring power grid facility position, height data and numerical weather forecast data, establishing a reference wind speed prediction model at the height of the power grid facility, establishing a prediction model for local topography and landform and turbulence activity contribution, and establishing a prediction model for convection activity contribution of middle and low atmosphere. And substituting the three prediction models into the extreme wind speed prediction conceptual model, and calculating the extreme wind speed. According to the method, a wind speed diagnosis model at a power grid facility height under different atmospheric stability conditions is constructed by adopting a Monin-Obukhov similarity theory. The model considers the influence of atmospheric stability on the vertical wind profile, so that the predicted wind speed is closer to the actual situation, and the prediction precision of the extreme wind speed is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power meteorology, and in particular relates to an extreme wind speed prediction method and system for power grid facilities. Background Art

[0002] Against the backdrop of intensified global climate change, the frequent, multiple and recurring extreme weather events have brought severe challenges to the safe and stable operation of power grid facilities. Among them, extreme wind speed events are one of the typical scenarios of power meteorological disasters with the most significant impact and the most challenging forecast. Extreme wind speed has an important impact on the power settings of all links in the production and operation of the power grid. It is not only easy to cause the wind turbines to shut down due to strong winds, affect the power generation efficiency, and lead to the instability of the power grid, but also cause damage to the blades, towers and towers of the wind turbines, leading to electrical system failures and control system failures; it is also very easy to cause the collapse or damage of transmission lines, causing wind deviation short circuits and damage to equipment such as tower insulators, affecting the reliable supply of electricity and the safe operation of the power grid, and may even cause serious economic losses and social effects. The occurrence of extreme wind speeds increases the risk of safe operation of power grid facilities, and the maintenance and repair costs increase significantly. Therefore, how to accurately carry out forecasts and warnings of extreme wind speeds for power grid facilities is an important issue that needs to be solved to ensure the safe operation of power grid facilities.

[0003] However, the accurate extreme wind speed forecast and warning for power grid facilities have the following shortcomings. First, the current wind speed diagnosis technology at the height of power grid facilities is usually based on the assumption of neutral atmospheric conditions, and uses logarithmic wind profiles or power exponential wind profiles to interpolate to obtain the wind speed at a specific height, failing to fully consider the impact of the atmosphere on the vertical wind profile under different stability conditions. Second, existing technologies often ignore the complex influence of local topography and the contribution of turbulent vertical transport to extreme wind speed events, resulting in low accuracy in extreme wind speed forecasts. Third, current forecasting technology cannot well forecast deep convective systems such as gusts and severe convection, and therefore does not consider the contribution of convective activities to extreme wind speeds. These deficiencies have led to low accuracy in extreme wind speed forecasts, which cannot fully meet the needs of safe power grid operation. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for predicting extreme wind speeds for power grid facilities, so as to solve the problem of low forecasting accuracy in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides an extreme wind speed prediction method for power grid facilities, comprising: Construct a conceptual model for extreme wind speed prediction for power grid facilities; Obtain the location and height data of power grid facilities and numerical weather forecast data, establish a benchmark wind speed prediction model at the height of power grid facilities, establish a prediction model for the contribution of local topography and turbulent activity, and establish a prediction model for the contribution of convective activity in the middle and lower atmosphere.

[0006] The three prediction models are substituted into the extreme wind speed prediction conceptual model to calculate the extreme wind speed.

[0007] Optionally, the constructing of a conceptual model for extreme wind speed prediction for power grid facilities includes: Based on the factors affecting the safe operation of power grid facilities, including the baseline wind speed at the height of the power grid facilities, local topography and turbulent activities, and mid- and low-level atmospheric convection activities, a conceptual model for extreme wind speed prediction for power grid facilities is constructed: (1.) in, To prevent extreme wind speeds that may affect the safe operation of power grid facilities, Height of power grid facilities The reference wind speed at The wind speed increment represents the contribution of local topography and turbulent activity at the power grid facility, The wind speed increment represents the contribution of convective activity in the middle and lower atmosphere.

[0008] Optionally, the obtaining of power grid facility location, height data and numerical weather forecast data includes: The basic information of the power grid facilities under study includes: the longitude and latitude of the transmission lines and new energy station facilities, the height of the facilities, and the topographic information of the area where they are located. The longitude and latitude of the power grid facilities are recorded as , , the height is recorded as ; Extract the conventional numerical weather forecast data of the grid corresponding to the power grid facilities: Based on the gridded numerical weather forecast data covering the power grid facilities, according to the longitude and latitude information of the facilities, establish the matching relationship between the power grid facilities and the numerical weather forecast grids, and extract the set height wind speed, friction speed, surface roughness and MO length data of the corresponding grid points from the numerical weather forecast results, which are recorded as , , and .

[0009] Optionally, establishing a reference wind speed prediction model at the height of a power grid facility includes: By applying the Monin-Obukhov similarity theory of characteristic scale of turbulent exchange in the atmospheric boundary layer, a wind speed diagnostic model at the height of power grid facilities is proposed, as shown in formula (2): (2.) in, Indicates the height of the power grid facility The wind speed at a certain integral time step at , is the wind speed at the set height extracted from the numerical weather forecast, is the extracted surface roughness, is the length of the extracted MO; is the stability function of the atmosphere; Atmospheric stability function Calculation: (3.) in, is the Karman constant, which is 0.4. is an empirical coefficient used to adjust the strength of the stability function, with a value range of [3,5]; different atmospheric conditions are based on The value division of When When , it is a neutral atmosphere, when When the temperature is low, the atmosphere is stable.

[0010] Optionally, the establishment of a prediction model for the contribution of local topography and turbulent activity includes: Friction speed and MO length The turbulence contribution term characterizing extreme wind speed is shown in formula (4): (4.) in, The wind speed increment contributed by the local topography and turbulence activity at the grid facility, represents the turbulent mixing parameter, and its value range is [7.2, 7.71], is the friction velocity extracted from the numerical weather forecast, Defined as the mixing layer height.

[0011] Optionally, the establishment of a prediction model for the contribution of mid- and low-level atmospheric convective activities includes: The function of vertical wind shear is used to characterize the convective contribution, as shown in formula (5): (5.) in, represents the wind speed increment contributed by the convective activities in the middle and lower atmosphere, represents the convective mixing parameter, with a value range of [0.3,0.6], and They respectively represent the wind speeds at the 850hPa and 950hPa isobaric surfaces at the corresponding grid points in the numerical weather forecast. The wind speed at 850hPa represents the wind field in the middle atmosphere, and the wind speed at 950hPa represents the wind field in the lower atmosphere. The vertical shear between the two layers of wind fields characterizes the intensity of convective activity between the middle and lower atmospheres.

[0012] Optionally, substituting the three prediction models into the extreme wind speed prediction conceptual model to calculate the extreme wind speed includes: For power grid facilities, the data of each integral time step of the corresponding numerical weather forecast are substituted into the three prediction models into the extreme wind speed prediction conceptual model, and a calculation is performed respectively. The maximum value of the calculation result within the forecast period is taken as the extreme wind speed of the power grid facilities predicted this time.

[0013] In a second aspect, the present invention provides an extreme wind speed prediction system for power grid facilities, comprising: A model building module for building a conceptual model for extreme wind speed prediction for power grid facilities; The data acquisition module is used to obtain the location and height data of power grid facilities and numerical weather forecast data, establish a benchmark wind speed prediction model at the height of power grid facilities, establish a prediction model for the contribution of local topography and turbulent activity, and establish a prediction model for the contribution of mid- and low-level atmospheric convective activity.

[0014] The prediction output module is used to substitute the three prediction models into the extreme wind speed prediction conceptual model to calculate the extreme wind speed.

[0015] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the extreme wind speed prediction method for power grid facilities when executing the computer program.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for extreme wind speed prediction for power grid facilities.

[0017] Compared with the prior art, the present invention has the following technical effects: The present invention adopts the Monin-Obukhov similarity theory to construct a wind speed diagnosis model at the height of power grid facilities under different atmospheric stability conditions. This model takes into account the impact of atmospheric stability on the vertical wind profile, making the predicted wind speed closer to the actual situation and significantly improving the prediction accuracy of extreme wind speed.

[0018] The present invention further improves the extreme wind speed prediction model by introducing surface roughness and atmospheric stability to characterize turbulence contribution terms, and using vertical wind shear to characterize convection contribution terms. These considerations enable the model to more accurately reflect the impact of complex topography, vertical turbulence transport, and mid- and low-level atmospheric convection activities on extreme wind speed, thereby further improving the prediction accuracy.

[0019] The extreme wind speed prediction model proposed by the present invention combines the basic information of power grid facilities with the conventional data of numerical weather forecast, making the model more applicable. Regardless of the terrain conditions where the power grid facilities are located, as long as the relevant basic information and numerical weather forecast data can be obtained, the model can be used to predict extreme wind speeds.

[0020] By adopting the Monin-Obukhov similarity theory, vertical turbulent transport, deep convective system, vertical wind shear and other related theories in meteorology, the present invention constructs an extreme wind speed prediction model with a solid theoretical basis. This enables the model to more accurately reflect the actual situation of atmospheric physical processes during the prediction process, and improves the reliability and accuracy of the prediction.

[0021] The present invention helps reduce the structural damage and failure of power grid facilities caused by strong winds by improving the prediction accuracy of extreme wind speeds, thereby reducing power generation losses and maintenance costs. At the same time, accurate extreme wind speed prediction can also provide important decision support for power grid dispatching and operation. Power grid operators can take necessary preventive measures in advance based on the prediction results, such as adjusting power generation plans, strengthening equipment inspections, etc., to ensure the stable operation of the power grid under extreme weather conditions.

[0022] By combining the basic information of power grid facilities and numerical weather forecast data, and adopting advanced meteorological theories and methods, the present invention successfully constructs an extreme wind speed prediction model with high accuracy and strong applicability. This achievement not only provides a strong guarantee for the safe operation of power grid facilities, but also provides a reference and reference for extreme weather prediction and disaster prevention and mitigation in other fields. In the future, with the continuous development of meteorology and numerical weather forecasting technology, it is believed that the extreme wind speed prediction method and system proposed in the present invention will be more widely used and promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a logic block diagram of the present invention.

[0024] Figure 2 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0025] The present invention is further described below in conjunction with the accompanying drawings: Example 1, please refer to Figure 2The present invention provides an extreme wind speed prediction method for power grid facilities, comprising: Construct a conceptual model for extreme wind speed prediction for power grid facilities; Obtain the location and height data of power grid facilities and numerical weather forecast data, establish a benchmark wind speed prediction model at the height of power grid facilities, establish a prediction model for the contribution of local topography and turbulent activity, and establish a prediction model for the contribution of convective activity in the middle and lower atmosphere.

[0026] The three prediction models are substituted into the extreme wind speed prediction conceptual model to calculate the extreme wind speed.

[0027] Obtaining the location and height data of power grid facilities and numerical weather forecast data provides the necessary basic data for establishing specific prediction models. The location and height data of power grid facilities are key factors in determining the benchmark wind speed, while numerical weather forecast data provides an important basis for predicting future wind speed changes.

[0028] These data are the basis for subsequent model training and verification, ensuring the accuracy and reliability of the predictive model.

[0029] Establishing a benchmark wind speed prediction model at the height of power grid facilities can accurately predict the benchmark wind speed at a specific height (i.e. the height of the power grid facilities). This is the basis for extreme wind speed prediction, because extreme wind speed is often generated on the basis of the benchmark wind speed due to the influence of other factors (such as topography, turbulent activity, atmospheric convection, etc.).

[0030] The prediction model of local topography and turbulent activity contribution was established, which took into account the impact of topography and turbulent activity on wind speed and improved the accuracy of the prediction. Topography (such as mountains, canyons, urban buildings, etc.) and turbulent activity (such as the turbulent movement of airflow near obstacles) can have a significant impact on wind speed, so the establishment of this model is crucial for accurately predicting extreme wind speeds.

[0031] The establishment of a prediction model for the contribution of convective activity in the middle and lower atmosphere further considers the impact of atmospheric convective activity on wind speed, especially convective activity under extreme weather conditions. Atmospheric convection is an important driving force for weather changes and has a significant impact on wind speed and wind direction. Therefore, the establishment of this model helps to more comprehensively predict extreme wind speeds.

[0032] By integrating three specific prediction models, a complete extreme wind speed prediction system is formed. This system can comprehensively consider the impact of multiple factors on wind speed, thereby providing more accurate and reliable extreme wind speed prediction results.

[0033] Embodiment 2, the present invention provides an extreme wind speed prediction method for power grid facilities, specifically comprising: The present invention intends to solve the technical problem that it is difficult to accurately predict extreme wind speeds for power grid facilities. Based on the basic information of power grid facilities and high-precision gridded numerical weather forecast data, the Monin-Obukhov similarity theory is adopted, and the influence of different atmospheric stabilities on vertical wind profiles is considered. A wind speed diagnostic model at the height of power grid facilities under different atmospheric stability conditions is constructed, and by adding turbulent contribution terms that describe local topography and turbulent activities, as well as considering the convective contribution terms of the convective activities of the middle and lower atmosphere, the maximum value of the forecast period is obtained by iterative calculation at each integral time step as the extreme wind speed for power grid facilities. The present invention significantly improves the prediction accuracy of extreme wind speeds, which not only helps to reduce structural damage and failures of power grid facilities, but also effectively reduces power generation losses and improves the overall operational efficiency and safety of the power grid. The invention aims to achieve accurate prediction of extreme wind speed events and provide solid technical support and guarantee for the safe operation of power grid facilities and equipment protection.

[0034] Step 1: Construct a conceptual model for extreme wind speed prediction for power grid facilities. Based on the case of strong wind disasters in power grid facilities and meteorological analysis, it is proposed that the extreme wind speed that affects the safe operation of power grid facilities is composed of three parts: the first is the baseline wind speed at the height of the power grid facilities, the second is the contribution of local terrain and turbulent activities, and the third is the contribution of mid- and low-level atmospheric convection activities. A conceptual model for extreme wind speed prediction is also given.

[0035] Step 2: Obtain basic information on power grid facilities and conventional numerical weather forecast data. Power grid facilities include transmission lines, new energy stations and other equipment in all aspects of the power grid that are susceptible to strong winds. Obtain basic information on power grid facilities, including longitude and latitude, altitude, topography, etc. Establish a matching relationship with high-precision gridded numerical weather forecast data, and extract conventional data of numerical weather forecasts at grid points corresponding to power grid facilities, including wind speed at 10 meters, friction speed, roughness, MO length, etc.

[0036] Step 3: Establish a benchmark wind speed prediction model at the height of power grid facilities. Since there are many types of power grid facilities and their heights above the ground are different, it is necessary to establish an accurate benchmark wind speed prediction model for the specific height of power grid facilities. In order to be closer to the actual situation, considering the impact of different atmospheric stability conditions on the vertical wind profile, it is proposed to apply the Monin-Obukhov similarity theory to construct a benchmark wind speed prediction model at the height of power grid facilities.

[0037] Step 4: Establish a prediction model that considers the contribution of local topography and turbulent activity. Since power grid facilities are usually built in mountainous areas far away from towns, complex topographic conditions and vertical turbulent activities contribute to and enhance the occurrence of strong winds. It is proposed to use surface roughness and atmospheric stability to characterize turbulent contribution terms and improve the extreme wind speed prediction model for power grid facilities.

[0038] Step 5: Establish a prediction model that considers the contribution of mid- and low-level atmospheric convective activities. The occurrence of extreme wind speed events that affect the safe operation of power grid facilities is mostly related to gusty and severe convective events, that is, the contribution of extreme wind speeds in deep convective systems needs to be considered. It is proposed to use vertical wind shear to characterize the convective contribution term to further improve the extreme wind speed prediction model for power grid facilities.

[0039] Step 6: Iteratively calculate the extreme wind speed for each integral time step during the forecast period. Substitute the prediction models of steps 3, 4, and 5 into step 1. At each integral time step of the numerical weather forecast, the extreme wind speed is calculated, and finally the maximum value calculated during the forecast period is used as the predicted extreme wind speed.

[0040] Step 1 specifically includes: Step 1-1: Construct a conceptual model for extreme wind speed prediction for power grid facilities. It is proposed that extreme wind speed consists of three parts, which are specifically expressed as shown in formula (1): (6.) in, To prevent extreme wind speeds that may affect the safe operation of power grid facilities, Height of power grid facilities The reference wind speed at The wind speed increment represents the contribution of local topography and turbulent activity at the power grid facility, The wind speed increment represents the contribution of convective activity in the middle and lower atmosphere.

[0041] Step 2 specifically includes: Step 2-1: Obtain basic information on the power grid facilities under study, including the longitude and latitude of power transmission lines, new energy stations, and other facilities, facility heights, and underlying surface information such as the topography of the area where they are located. The longitude and latitude of the power grid facilities are recorded as , , the height is recorded as .

[0042] Step 2-2: Extract the conventional numerical weather forecast data of the grid corresponding to the power grid facilities. Based on the grid numerical weather forecast data covering the power grid facilities, according to the facility longitude and latitude information in step 2-1, establish the matching relationship between the power grid facilities and the numerical weather forecast grid, and extract the 10-meter height wind speed, friction speed, surface roughness and MO length of the corresponding grid points from the numerical weather forecast results, respectively, and record them as , , and .

[0043] Special note: In fact, the conventional data of the above numerical weather forecast is a time series, that is, the sequence data at multiple time points within the validity period of a forecast. The size of the time series depends on the forecast timeliness and time resolution of the numerical weather forecast. The method and model proposed in the present invention need to be calculated for each integral time step of the numerical weather forecast. Therefore, in order to simplify the expression form, it is recorded in the above form, but it should be noted that all the methods and models hereinafter need to be iteratively calculated at each integral time step of the numerical weather forecast.

[0044] Step 3 specifically includes: Step 3-1: Construct a wind speed diagnostic model at the height of the power grid facility based on similarity theory. Meteorological analysis of extreme wind speed events at power grid facilities shows that they usually occur under unstable atmospheric conditions. Therefore, it is impossible to use the logarithmic wind profile or power exponential wind profile function that assumes the atmosphere is neutral to interpolate and calculate the wind speed at the height of the power grid facility. The present invention applies the Monin-Obukhov similarity theory and proposes a wind speed diagnostic model at the height of the power grid facility on the basis of fully considering the influence of atmospheric stability conditions on the vertical wind profile, as shown in formula (2): (7.) in, Indicates the height of the power grid facility The wind speed at a certain integral time step at , is the 10-meter wind speed from the numerical weather forecast extracted in step 2-2, is the surface roughness extracted in step 2-2, is the MO length extracted in step 2-2. The newly introduced benchmark wind speed diagnostic model , is the stability function of the atmosphere.

[0045] Step 3-2: Calculate the stability function under different atmospheric stability conditions. The topography of the power grid facilities is relatively complex. Although most extreme wind speeds occur under unstable atmospheric conditions, they may also occur under stable and neutral atmospheric conditions. Therefore, the present invention provides the atmospheric stability function under different atmospheric conditions. The calculation method is used to make the atmospheric stability function only related to the MO length and grid facility height Equation (3) gives the atmospheric stability function under different atmospheric conditions: Calculation method: (8.) in, is the Karman constant, which is approximately 0.4. is an empirical coefficient used to adjust the strength of the stability function, with a value range of [3,5]. The value division of When When , it is a neutral atmosphere, when When , it is a stable atmosphere. The stability function of formula (3) can dynamically adjust the calculation result of the wind speed diagnosis model formula (2) according to the different stability states of the atmosphere. Through the calculation of the atmospheric stability function in this step, a more accurate diagnosis of the wind speed at the height of the power grid facility can be achieved.

[0046] Step 4 specifically includes: Step 4-1: Establish an extreme wind speed prediction model that takes into account the contribution of local terrain and turbulence. Extreme wind speed events that can affect the safe operation of power grid facilities are often affected by complex terrain and the growth rate of turbulent activities. The vertical turbulent transport will further enhance the wind speed. Therefore, it is proposed to use the friction velocity and MO length The turbulence contribution term characterizing extreme wind speed is shown in formula (4): (9.) in, The wind speed increment contributed by the local topography and turbulence activity at the grid facility, represents the turbulent mixing parameter, and its value range is [7.2, 7.71], is the friction velocity of the numerical weather forecast extracted in step 2-2, which reflects the influence of local topography, Defined as the mixing layer height, the present invention is set to 1000 meters, representing the typical height of the atmospheric boundary layer, where turbulent activities mainly occur.

[0047] Step 5 specifically includes: Step 5-1: Establish an extreme wind speed prediction model that considers the contribution of mid- and low-level atmospheric convection activities. Further analysis found that extreme wind speed events that have an adverse impact on power grid facilities often occur simultaneously with local gusty winds or severe convection events, that is, mid- and low-level atmospheric convection activities have a further accelerating effect on extreme wind speeds. Therefore, it is proposed to use a function of vertical wind shear to characterize the convection contribution term, as shown in formula (5): (10.) in, represents the wind speed increment contributed by the convective activities in the middle and lower atmosphere, represents the convective mixing parameter, with a value range of [0.3,0.6], and They respectively represent the wind speeds at the 850hPa and 950hPa isobaric surfaces at the corresponding grid points in the numerical weather forecast. The wind speed at 850hPa represents the wind field in the middle atmosphere, and the wind speed at 950hPa represents the wind field in the lower atmosphere. The vertical shear between the two layers of wind fields characterizes the intensity of convective activity between the middle and lower atmospheres.

[0048] Step 6 specifically includes: Step 6-1: Take the maximum value of the calculation result of each integral time step of the numerical forecast as the extreme wind speed. For the power grid facility of interest, substitute the data of each integral time step of the corresponding numerical weather forecast, and substitute the prediction model of step 3, step 4, and step 5 into step 1, perform a calculation respectively, and take the maximum value of the calculation result within the forecast time as the extreme wind speed of the power grid facility in this forecast.

[0049] The present invention is based on the basic information of power grid facilities and the numerical weather forecast data matched therewith. Firstly, the Monin-Obukhov similarity theory is adopted to propose a wind speed diagnosis model at the height of power grid facilities under different atmospheric stability conditions. Then, the contribution of local topography and vertical turbulent transport of power grid facilities is considered, and the turbulent contribution term is characterized by surface roughness and atmospheric stability, and the extreme wind speed prediction model method is improved. Then, the contribution of deep convective system activities such as gusts and severe convection is considered, and the convection contribution term is characterized by the middle and low-level vertical wind shear function, and an extreme wind speed prediction model is established. Finally, by iterative calculation of each integral time step within the numerical forecast time, the maximum value within the forecast period is obtained as the predicted extreme wind speed. The present invention combines the basic information of power grid facilities and the conventional data of numerical weather forecast, adopts the Monin-Obukhov similarity theory, vertical turbulent transport, deep convective system, vertical wind shear and other related theories in meteorology, and constructs an extreme wind speed prediction model for power grid facilities, which perfectly solves the current technical problems, is more in line with the actual situation, and has a stronger theoretical model and higher forecast accuracy.

[0050] In yet another embodiment of the present invention, a system for predicting extreme wind speeds for power grid facilities is provided, which can be used to implement the above-mentioned method for predicting extreme wind speeds for power grid facilities. Specifically, the system includes: A model building module for building a conceptual model for extreme wind speed prediction for power grid facilities; The data acquisition module is used to obtain the location and height data of power grid facilities and numerical weather forecast data, establish a benchmark wind speed prediction model at the height of power grid facilities, establish a prediction model for the contribution of local topography and turbulent activity, and establish a prediction model for the contribution of mid- and low-level atmospheric convective activity.

[0051] The prediction output module is used to substitute the three prediction models into the extreme wind speed prediction conceptual model to calculate the extreme wind speed.

[0052] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0053] In another embodiment of the present invention, a computer device is provided, the computer device including a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of an extreme wind speed prediction method for power grid facilities.

[0054] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the above-mentioned embodiment regarding an extreme wind speed prediction method for power grid facilities.

[0055] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting extreme wind speeds for power grid facilities, characterized in that: include: Construct a conceptual model for extreme wind speed prediction for power grid facilities; Obtain the location and height data of power grid facilities and numerical weather forecast data, establish a benchmark wind speed prediction model at the height of power grid facilities, establish a prediction model for the contribution of local topography and turbulent activity, and establish a prediction model for the contribution of mid- and low-level atmospheric convective activity; The three prediction models are substituted into the extreme wind speed prediction conceptual model to calculate the extreme wind speed.

2. The method for predicting extreme wind speed for power grid facilities according to claim 1, characterized in that: The construction of a conceptual model for extreme wind speed prediction for power grid facilities includes: Based on the factors affecting the safe operation of power grid facilities, including the baseline wind speed at the height of the power grid facilities, local topography and turbulent activities, and mid- and low-level atmospheric convection activities, a conceptual model for extreme wind speed prediction for power grid facilities is constructed: （1） in, To prevent extreme wind speeds that may affect the safe operation of power grid facilities, Height of power grid facilities The reference wind speed at The wind speed increment represents the contribution of local topography and turbulent activity at the power grid facility, The wind speed increment represents the contribution of convective activity in the middle and lower atmosphere.

3. The extreme wind speed prediction method for power grid facilities according to claim 1, characterized in that: The obtaining of the location, height data and numerical weather forecast data of power grid facilities includes: The basic information of the power grid facilities under study includes: the longitude and latitude of the transmission lines and new energy station facilities, the height of the facilities, and the topographic information of the area where they are located. The longitude and latitude of the power grid facilities are recorded as , , the height is recorded as ; Extract the conventional numerical weather forecast data of the grid corresponding to the power grid facilities: Based on the gridded numerical weather forecast data covering the power grid facilities, according to the longitude and latitude information of the facilities, establish the matching relationship between the power grid facilities and the numerical weather forecast grids, and extract the set height wind speed, friction speed, surface roughness and MO length data of the corresponding grid points from the numerical weather forecast results, which are recorded as , , and .

4. The method for predicting extreme wind speed for power grid facilities according to claim 3, characterized in that: The step of establishing a reference wind speed prediction model at the height of a power grid facility comprises: By applying the Monin-Obukhov similarity theory of the characteristic scale of turbulent exchange in the atmospheric boundary layer, a wind speed diagnostic model at the height of power grid facilities is proposed, as shown in formula (2): （2） in, Indicates the height of the power grid facility The wind speed at a certain integral time step at , is the wind speed at the set height extracted from the numerical weather forecast, is the extracted surface roughness, is the length of the extracted MO; is the stability function of the atmosphere; Atmospheric stability function Calculation: （3） in, is the Karman constant, which is 0.

4. is an empirical coefficient used to adjust the strength of the stability function, with a value range of [3,5]; different atmospheric conditions are based on The value division of When When , it is a neutral atmosphere, when When the temperature is low, the atmosphere is stable.

5. The method for predicting extreme wind speed for power grid facilities according to claim 3, characterized in that: The establishment of a prediction model for the contribution of local topography and turbulent activity includes: Friction speed and MO length The turbulence contribution term characterizing extreme wind speed is shown in formula (4): （4） in, The wind speed increment contributed by the local topography and turbulence activity at the grid facility, represents the turbulent mixing parameter, and its value range is [7.2, 7.71], is the friction velocity extracted from the numerical weather forecast, Defined as the mixing layer height.

6. The method for predicting extreme wind speed for power grid facilities according to claim 1, characterized in that: The prediction model for the contribution of mid- and low-level atmospheric convective activities is established, including: The function of vertical wind shear is used to characterize the convective contribution, as shown in formula (5): （5） in, represents the wind speed increment contributed by the convective activities in the middle and lower atmosphere, represents the convective mixing parameter, with a value range of [0.3,0.6], and They respectively represent the wind speeds at the 850hPa and 950hPa isobaric surfaces at the corresponding grid points in the numerical weather forecast. The wind speed at 850hPa represents the wind field in the middle atmosphere, and the wind speed at 950hPa represents the wind field in the lower atmosphere. The vertical shear between the two layers of wind fields characterizes the intensity of convective activity between the middle and lower atmospheres.

7. The method for predicting extreme wind speed for power grid facilities according to claim 1, characterized in that: Substituting the three prediction models into the extreme wind speed prediction conceptual model to calculate the extreme wind speed includes: For power grid facilities, the data of each integral time step of the corresponding numerical weather forecast are substituted into the three prediction models into the extreme wind speed prediction conceptual model, and a calculation is performed respectively. The maximum value of the calculation result within the forecast period is taken as the extreme wind speed of the power grid facilities predicted this time.

8. An extreme wind speed prediction system for power grid facilities, characterized in that: include: A model building module for building a conceptual model for extreme wind speed prediction for power grid facilities; The data acquisition module is used to obtain the location and height data of power grid facilities and numerical weather forecast data, establish a benchmark wind speed prediction model at the height of power grid facilities, establish a prediction model for the contribution of local topography and turbulent activities, and establish a prediction model for the contribution of mid- and low-level atmospheric convective activities; The prediction output module is used to substitute the three prediction models into the extreme wind speed prediction conceptual model to calculate the extreme wind speed.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the processor implements the steps of the extreme wind speed prediction method for power grid facilities as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the extreme wind speed prediction method for power grid facilities as claimed in any one of claims 1 to 7 are implemented.

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