Method and system for constructing power grid safety high influence weather dynamics conceptual model

Abstract

The invention discloses a power grid safety high influence weather dynamics conceptual model construction method and system, and relates to the technical field of meteorological prediction, and the method comprises the steps: analyzing geographic data influencing power grid safety, and processing and interpolating irregular terrain data based on a Kriging interpolation method; key factors are extracted based on geographic data, and weather processes influencing power grid safety are classified according to seasons and influence mechanisms; key configuration of various weather processes is obtained, and a power grid safety high influence weather dynamics conceptual model library is constructed through terrain correction. According to the method, key factors including an atmospheric circulation factor, a convection indication factor and a ground meteorological factor are extracted, so that the accuracy of weather process classification is improved; the comprehensive dynamic conceptual model library is constructed, so that the comprehensiveness and the accuracy of dynamic evaluation of the power grid safety are improved, and the applicability and the reliability of the model library are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of weather prediction, in particular to a method and system for constructing a power grid safety high-impact weather dynamics conceptual model. BACKGROUND

[0002] With the continuous development and modernization of the power system, the safe operation of the power grid has become a crucial issue, especially in the face of increasingly complex weather conditions and geographical environments, traditional power grid operation and maintenance methods have been difficult to meet the needs of modern power systems, in recent years, many research and technological developments have focused on improving the reliability and efficiency of power grid safe operation through more detailed weather prediction and geographical data analysis.

[0003] The existing numerical weather prediction model still has limited accuracy in dealing with complex terrain and local weather phenomena, although the NWP model performs well in large-scale weather prediction, but in high-resolution local weather prediction, especially in the prediction of complex terrain areas, there are still errors, which is mainly due to the shortcomings of existing models in terrain processing and local small-scale weather phenomenon simulation, for example, although the WRF model has high flexibility, its impact on terrain processing depends on the accuracy of the input terrain data, and the simulation effect in complex terrain is limited, traditional GIS technology can efficiently manage and analyze geographical data, but lacks enough flexibility in dealing with dynamic changes in weather conditions, existing GIS systems mostly rely on static geographic information, when facing real-time weather data and dynamic environmental changes, they cannot update and respond in time, this static processing method limits the application effect of GIS technology in power grid safety dynamic evaluation, the Kriging interpolation method performs well in geographical data processing, but its application in complex terrain and variable weather conditions also faces challenges, traditional Kriging interpolation method is mainly aimed at flat areas and regular terrain, when facing complex terrain, the interpolation accuracy and effect decrease, in addition, Kriging interpolation has high computational complexity in processing multi-dimensional weather data, and has poor real-time performance, which is difficult to adapt to the processing needs of high-frequency dynamic data. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the existing weather prediction and geographical data processing methods have the problems of limited accuracy of numerical weather prediction model in complex terrain and local weather phenomenon simulation, lack of flexibility of traditional GIS technology in dealing with dynamic changes in weather conditions, insufficient real-time performance and accuracy in complex terrain and multi-dimensional weather data processing, and how to obtain the key configuration of various weather processes, and the problem of constructing a power grid safety high-impact weather dynamics conceptual model library through terrain correction.

[0006] To solve the above technical problems, the application provides the following technical scheme: a power grid safety high-impact weather dynamics conceptual model construction method, including analyzing geographic data affecting power grid safety, processing and interpolating irregular terrain data based on the Kriging interpolation method; extracting key factors based on geographic data, classifying weather processes affecting power grid safety according to seasons and impact mechanisms; obtaining key configurations of various weather processes, and constructing a power grid safety high-impact weather dynamics conceptual model library through terrain correction.

[0007] As a preferred scheme of the power grid safety high-impact weather dynamics conceptual model construction method, the geographic data includes high-resolution elevation data, slope data, azimuth data, temperature data, humidity data and wind speed data.

[0008] As a preferred scheme of the power grid safety high-impact weather dynamics conceptual model construction method, the processing and interpolation of irregular terrain data includes the influence of terrain-based elevation, slope and azimuth characteristics on the spatial distribution of meteorological data, and the terrain factor correction term T(x) is set as:

[0009] T(x) = h(x) + s(x) + a(x)

[0010] wherein h(x) is an elevation function representing the terrain height at position x, s(x) is a slope function representing the terrain slope at position x, and a(x) is an azimuth function representing the terrain azimuth at position x, and the functions in the terrain factor correction term are respectively:

[0011]

[0012] wherein h0 is a reference height, h1 is the amplitude of height variation, x is a position variable describing the spatial coordinates along the east-west direction, L h represents the period of height variation, and y is a position variable describing the spatial coordinates along the north-south direction; based on the spatial distribution differences of temperature, humidity and wind speed, the change of meteorological characteristics is captured, and the meteorological factor correction term M(x) is set as:

[0013] M(x) = T m (x) + H m (x) + W m (x)

[0014] wherein T m (x) is a temperature function representing the temperature at position x, H m (x) is a humidity function representing the humidity at position x, and W m (x) is a wind speed function representing the wind speed at position x, and the functions in the meteorological factor correction term are respectively:

[0015]

[0016] wherein T0 is a reference air temperature, T1 is the amplitude of air temperature change, x0 represents the center point of air temperature distribution, σ T represents the width of air temperature distribution, H0 is a reference humidity, H1 is the amplitude of humidity change, L H represents the period of humidity change, W0 represents a reference wind speed, W1 represents the amplitude of wind speed change, x w represents the starting point of wind speed change, L W represents the period of wind speed change; by introducing a terrain factor correction term and a meteorological factor correction term, the application limitations of Kriging interpolation under complex terrain and variable weather conditions are adjusted, and are represented as:

[0017]

[0018] wherein Z'(x) is a predicted value at position x, μ is the mean of the population, is the average of data points, x i represents the position of the i th known data point, N is the number of data points, λ i represents the weight coefficient of the i th data point, Z(x i ) is an initial value at position x i , α is the weight coefficient of the terrain factor, and β is the weight coefficient of the meteorological factor.

[0019] As a preferred scheme of the power grid safety high-impact weather dynamics conceptual model construction method, wherein: the extraction of the key factors includes extracting key factors including atmospheric circulation factors, convection indicating factors, and ground meteorological factors based on the processed geographic data combined with land cover data; the atmospheric circulation factors include 500 hPa potential height, 500 hPa horizontal wind, 500 hPa temperature, 700 hPa potential height, 700 hPa horizontal wind, 700 hPa temperature, 700 hPa relative humidity, 700 hPa specific humidity, 850 hPa potential height, 850 hPa horizontal wind, 850 hPa temperature, 850 hPa relative humidity, and 850 hPa specific humidity; the convection indicating factors include convective available potential energy, K index, Showalter index, convective inhibition energy, and 850 hPa-500 hPa temperature difference; and the ground meteorological factors include 2m air temperature, 2m dew point temperature, precipitation, air pressure, pressure variation, and 10m wind.

[0020] As a preferred scheme of the power grid safety high-impact weather dynamics conceptual model construction method, wherein: the seasonal and impact mechanism classification includes dividing the weather processes affecting the power grid safety into high-temperature fire type, lightning fire type, gale dancing type, rain and fog pollution flash type, low-temperature icing type; the high-temperature fire type occurs from June to August, the average temperature of the research area is ≥37℃, and the maximum temperature of at least one power transmission partition is ≥39℃; the lightning fire type occurs from March to October, lightning is observed in at least one power transmission partition of the research area, and the time lasts more than 10 minutes; the gale dancing type occurs throughout the year, the maximum wind speed of at least one power transmission partition of the research area is ≥17.2m / s, and the time lasts more than 5 minutes; the rain and fog pollution flash type occurs from October to the following April, the visibility of at least two power transmission partitions of the research area is <500m, and appears continuously for more than 3 days, or the total rainfall of the research area is between 0.1mm and 10mm, and at least one power transmission partition has ≥3 consecutive rainy days; the low-temperature icing type occurs from December to the following February, the average temperature of the research area is ≤4℃, and the minimum temperature of at least one power transmission partition is ≤0℃.

[0021] As a preferred scheme of the power grid safety high-impact weather dynamics conceptual model construction method, the key configurations of various weather processes include establishing weather system configuration data sets of five types of power grid high-impact weather based on key factors, identifying high-altitude and ground weather systems affecting power grid safety through the weather system configuration data sets, and obtaining the key configurations of various weather processes; the key configurations of high-temperature fire type include that the research area is controlled by 500 hPa subtropical high, the relative humidity at 700 hPa and 850 hPa is less than 70%; the key configurations of lightning fire type include that a high-altitude trough appears at 500 hPa in the research area, a shear line or a low vortex appears at 700 hPa, the relative humidity is greater than or equal to 80% or the specific humidity is greater than or equal to 12 g / kg, a shear line or a low vortex appears at 850 hPa, the relative humidity is greater than or equal to 80% or the specific humidity is greater than or equal to 16 g / kg, the southwest wind speed is greater than or equal to 20 m / s, the convective available potential energy is greater than or equal to 1500 KJ, and the temperature difference between 850 hPa and 500 hPa is greater than or equal to 25 DEG C; the key configurations of strong wind dancing type include that a high-altitude trough appears at 500 hPa in the research area, the north wind speed is greater than or equal to 20 m / s behind the trough, a shear line or a low vortex appears at 700 hPa, the relative humidity is less than 80%, a shear line or a low vortex appears at 850 hPa, the relative humidity is greater than or equal to 80%, the southwest wind speed is greater than or equal to 20 m / s, the convective inhibition energy is between 70-110 KJ, the temperature difference between 850 hPa and 500 hPa is greater than or equal to 25 DEG C, and the ground 3-hour pressure change is positive; the key configurations of rain and fog pollution flash type include that 500 hPa is a flat west wind flow in the research area, the relative humidity at 700 hPa and 850 hPa is greater than or equal to 80%, and there is an inversion layer on the ground; and the key configurations of low-temperature icing type include that a high-altitude trough appears at 500 hPa in the research area, the north wind speed is greater than or equal to 20 m / s behind the trough, a shear line or a low vortex appears at 700 hPa and 850 hPa, and the relative humidity is greater than or equal to 80%.

[0022] As a preferred scheme of the power grid safety high-impact weather dynamics conceptual model construction method, the construction of the power grid safety high-impact weather dynamics conceptual model library includes calculating the dynamic effect of the plateau and the thermal effect of the plateau, judging the type of the power grid safety high-impact weather process, counting the process duration, the influence range, the frequency of occurrence in different months and seasons, performing terrain correction, adjusting the terrain data, and obtaining the power grid safety high-impact weather dynamics conceptual model library; using climate average data, calculating the vector difference ΔU of the 700 hPa and 850 hPa horizontal wind field and the monthly average wind field according to the time range of each process, and representing as:

[0023]

[0024] Wherein, u 700 and v 700 represent the eastward wind speed and the northward wind speed components of the 700 hPa layer, and U represents the eastward and northward wind speed components of the monthly average at the 700 hPa level. 850 and v 850 These represent the eastward and northward wind speed components at the 850 hPa level, respectively. and Let represent the eastward and northward wind speed components of the 850 hPa layer for the current month, respectively; using climatological average data, calculate the scalar difference ΔT between the 500 hPa and 700 hPa temperature fields and the monthly average temperature field according to the time range of each process, expressed as:

[0025]

[0026] Among them, T 500 and T represents the temperature value of the 500 hPa layer and the average temperature value of the month, respectively. 700 and These represent the temperature value of the 700 hPa layer and the average temperature value for the current month, respectively; the content of the high-impact weather dynamics concept model library for power grid security includes dynamic action correction models, thermal action correction models, weather process classification models, and statistical analysis models.

[0027] Another objective of this invention is to provide a system for constructing a dynamic conceptual model of high-impact weather on power grid security. This system can extract key factors based on geographic data and classify weather processes that affect power grid security according to season and impact mechanism, thus solving the problem of low flexibility in current meteorological forecasting and geographic data processing.

[0028] As a preferred embodiment of the high-impact weather dynamics conceptual model construction system for power grid security described in this invention, it includes: a data processing module, a classification module, and a model construction module; the data processing module is used to analyze geographical data affecting power grid security and process and interpolate irregular terrain data based on the Kriging interpolation method; the classification module is used to extract key factors based on geographical data and classify weather processes affecting power grid security according to season and impact mechanism; the model construction module is used to obtain key configurations of various weather processes and construct a high-impact weather dynamics conceptual model library for power grid security through terrain correction.

[0029] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program as a step in implementing a method for constructing a dynamic conceptual model of high-impact weather for power grid security.

[0030] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for constructing a dynamic conceptual model of high-impact weather for power grid security.

[0031] The power grid safety high-impact weather dynamics conceptual model construction method provided by the present application can effectively process irregular terrain data, generate accurate geographic information, and improve the reliability and safety of the power grid under complex terrain conditions by using high-resolution elevation data, slope data and azimuth data in combination with the Kriging interpolation method; the key factors including atmospheric circulation factors, convection indicating factors and ground meteorological factors can be extracted to finely classify weather processes that affect the safety of the power grid, improve the accuracy of weather process classification, and enable power grid operators to more effectively identify and respond to different types of high-impact weather events; a comprehensive dynamics conceptual model library is constructed by analyzing the key configurations of various weather processes in combination with the terrain correction method, improving the comprehensiveness and accuracy of power grid safety dynamic assessment, and improving the applicability and reliability of the model library, and the present application achieves better results in terms of precision, reliability and comprehensiveness. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0033] Figure 1 The overall flowchart of the power grid safety high-impact weather dynamics conceptual model construction method provided by the first embodiment of the present application.

[0034] Figure 2 The overall flowchart of the power grid safety high-impact weather dynamics conceptual model construction system provided by the third embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings in the specification. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0036] Embodiment 1

[0037] Reference Figure 1 For an embodiment of the present application, a power grid safety high-impact weather dynamics conceptual model construction method is provided, comprising:

[0038] S1: Analyzing geographical data affecting power grid safety, processing and interpolating irregular terrain data based on Kriging interpolation method.

[0039] Further, the geographical data includes high-resolution elevation data, slope data, azimuth data, temperature data, humidity data, and wind speed data.

[0040] It should be noted that processing and interpolating irregular terrain data includes the influence of terrain-based elevation, slope, and azimuth characteristics on the spatial distribution of meteorological data, and the terrain factor correction term T(x) is set as:

[0041] T(x) = h(x) + s(x) + a(x)

[0042] where h(x) is the elevation function, representing the terrain height at position x, s(x) is the slope function, representing the terrain slope at position x, and a(x) is the azimuth function, representing the terrain azimuth at position x. The functions in the terrain factor correction term are respectively:

[0043]

[0044] where h0 is the reference height, h1 is the amplitude of height variation, x is the position variable describing the spatial coordinates along the east-west direction, L h represents the period of height variation, and y is the position variable describing the spatial coordinates along the north-south direction; based on the spatial distribution differences of temperature, humidity, and wind speed, the meteorological feature changes are captured, and the meteorological factor correction term M(x) is set as:

[0045] M(x) = T m (x) + H m (x) + W m (x)

[0046] where T m (x) is the temperature function, representing the temperature at position x, H m (x) is the humidity function, representing the humidity at position x, and W m (x) is the wind speed function, representing the wind speed at position x. The functions in the meteorological factor correction term are respectively:

[0047]

[0048] where T0 is the reference temperature, T1 is the amplitude of temperature variation, x0 represents the center point of temperature distribution, σ T represents the width of temperature distribution, H0 is the reference humidity, H1 is the amplitude of humidity variation, L H represents the period of humidity variation, W0 represents the reference wind speed, W1 represents the amplitude of wind speed variation, and x w represents the starting point of wind speed variation, LW denotes the period of wind speed change; by introducing the terrain factor correction term and the meteorological factor correction term, the application limitations of Kriging interpolation under complex terrain and variable weather conditions are adjusted, and is expressed as:

[0049]

[0050] wherein Z'(x) is the predicted value at position x, μ is the mean of the population, is the average of the data points, x i denotes the position of the i-th known data point, N is the number of data points, λ i denotes the weight coefficient of the i-th data point, Z(x i ) is the initial value at position x i , α is the weight coefficient of the terrain factor, and β is the weight coefficient of the meteorological factor.

[0051] It should also be noted that by analyzing high-resolution geographic data including elevation, slope and azimuth data, and processing and interpolating irregular terrain data based on the Kriging interpolation method, an accurate geographic information model can be generated. When processing spatial data, the Kriging interpolation method can predict the value of unknown points according to the spatial correlation of known data points, thereby improving the accuracy of data processing, effectively making up for the lack of accuracy of traditional methods under complex terrain conditions, and ensuring high accuracy of data processing, providing a reliable data basis for subsequent key factor extraction and weather process classification.

[0052] S2: Extract key factors based on geographic data, and classify weather processes affecting power grid safety according to seasons and influence mechanisms.

[0053] Further, the extraction of key factors includes extracting key factors including atmospheric circulation factors, convection indicating factors, and ground meteorological factors based on the processed geographic data combined with land cover data; the atmospheric circulation factors include 500 hPa potential height, 500 hPa horizontal wind, 500 hPa temperature, 700 hPa potential height, 700 hPa horizontal wind, 700 hPa temperature, 700 hPa relative humidity, 700 hPa specific humidity, 850 hPa potential height, 850 hPa horizontal wind, 850 hPa temperature, 850 hPa relative humidity, 850 hPa specific humidity; the convection indicating factors include convective available potential energy, K index, Showalter index, convective inhibition energy, and 850 hPa-500 hPa temperature difference; the ground meteorological factors include 2m air temperature, 2m dew point temperature, precipitation, air pressure, pressure change, and 10m wind.

[0054] It should be noted that the classification by season and influencing mechanism includes classifying weather processes that affect power grid safety into high-temperature fire type, lightning fire type, strong wind dancing type, rain and fog pollution flash type, low-temperature icing type according to season and influencing mechanism. The high-temperature fire type occurs from June to August (summer), the average temperature of the research area is ≥37°C, and the maximum temperature of at least one power transmission partition is ≥39°C. The lightning fire type occurs from March to October (spring, summer and autumn), and lightning is observed in at least one power transmission partition of the research area, and the time lasts for more than 10 minutes. The strong wind dancing type occurs throughout the year, and the maximum wind speed of at least one power transmission partition of the research area is ≥17.2m / s, and the time lasts for more than 5 minutes. The rain and fog pollution flash type occurs from October to the following April (autumn, winter and spring), and the visibility of at least two power transmission partitions of the research area is <500m, and appears continuously for more than 3 days (low visibility appears in part of the period each day, not all day continuously), or the total rainfall of the research area is between 0.1mm and 10mm, and at least one power transmission partition has continuous rainy days ≥3 days. The low-temperature icing type occurs from December to the following February (winter), the average temperature of the research area is ≤4°C, and the minimum temperature of at least one power transmission partition is ≤0°C.

[0055] It should also be noted that by extracting key factors (such as atmospheric circulation factors, convection indicating factors and ground meteorological factors) based on processed geographic data, weather processes that affect power grid safety can be accurately classified, and weather processes can be classified by season and influencing mechanism, which helps to identify and analyze different types of high-impact weather events, such as high-temperature fire type, lightning fire type, etc. Not only does it improve the accuracy of weather process identification, but it also enables power grid operators to develop targeted measures to improve the operational safety of power grids under complex weather conditions.

[0056] S3: Obtain the key configuration of each type of weather process, and build a power grid safety high-impact weather dynamics conceptual model library by terrain correction.

[0057] Furthermore, the key configurations for various weather processes include establishing weather system configuration datasets for five types of high-impact weather on the power grid based on key factors. These datasets are used to identify upper-air and surface weather systems affecting power grid security, obtaining the key configurations for each type of weather process. Key configurations for high-temperature fire-causing weather include the study area being controlled by a 500hPa subtropical high, with relative humidity below 70% at 700hPa and 850hPa. Key configurations for lightning-causing fire-causing weather include an upper-level trough at 500hPa, a shear line or low-pressure vortex at 700hPa, relative humidity ≥80% or specific humidity ≥12g / kg at 850hPa, relative humidity ≥80% or specific humidity ≥16g / kg at 850hPa, southwest wind speed ≥20m / s, convective effective potential energy ≥1500KJ, and temperatures at 850hPa and 500hPa. The key configurations for the wind-blown type include: a temperature difference ≥25℃; a trough at 500hPa with northerly wind speed ≥20m / s behind the trough, a shear line or low vortex at 700hPa, relative humidity <80%; a shear line or low vortex at 850hPa with relative humidity ≥80%; southwesterly wind speed ≥20m / s; convective suppression energy between 70-110KJ; a temperature difference ≥25℃ between 850hPa and 500hPa; and positive ground pressure over 3 hours; a straight westerly wind at 500hPa within the study area; relative humidity ≥80% at 700hPa and 850hPa; and a temperature inversion layer at the ground; and a low-temperature icing type.

[0058] It should be noted that constructing the dynamic conceptual model library of high-impact weather for power grid security includes calculating the dynamic and thermal effects of the plateau, determining the type of high-impact weather processes for power grid security, statistically analyzing the duration, extent, and frequency of occurrence in different months and seasons, performing topographic corrections, adjusting topographic data, and obtaining the dynamic conceptual model library of high-impact weather for power grid security. Using climatological average data, according to the time range of each process, the vector difference ΔU between the 700hPa and 850hPa horizontal wind fields and the monthly average wind field is calculated, expressed as:

[0059]

[0060] Among them, u 700 and v 700 These represent the eastward and northward wind speed components at the 700 hPa level, respectively. and U represents the eastward and northward wind speed components of the monthly average at the 700 hPa level. 850 and v 850These represent the eastward and northward wind speed components at the 850 hPa level, respectively. and Let represent the eastward and northward wind speed components of the 850 hPa layer for the current month, respectively; using climatological average data, calculate the scalar difference ΔT between the 500 hPa and 700 hPa temperature fields and the monthly average temperature field according to the time range of each process, expressed as:

[0061]

[0062] Among them, T 500 and T represents the temperature value of the 500 hPa layer and the average temperature value of the month, respectively. 700 and These represent the temperature value of the 700 hPa layer and the average temperature value for the current month, respectively; the content of the high-impact weather dynamics concept model library for power grid security includes dynamic action correction models, thermal action correction models, weather process classification models, and statistical analysis models.

[0063] It should also be noted that by acquiring the key configurations of various weather processes and combining them with topographic and meteorological factor correction terms, a conceptual model library of dynamics of high-impact weather for power grid security is constructed. This model library includes dynamic action correction models, thermal action correction models, weather process classification models, and statistical analysis models. This not only improves the scientificity and comprehensiveness of power grid security assessment, but also provides power grid operators with a powerful decision support tool, enabling timely warning of potential risks and ensuring the stable operation of the power grid.

[0064] Example 2

[0065] One embodiment of the present invention provides a method for constructing a dynamic conceptual model of high-impact weather on power grid security. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculations and simulation experiments.

[0066] First, the experiment selected power grid data for a certain region under different seasons and meteorological conditions, specifically including elevation, slope, azimuth, temperature, humidity, and wind speed data. The experimental data were collected from NASA's MODIS land cover data, ESA's DEM elevation data, and SRTM (Space Shuttle Radar Topographic Mapping Mission) data. Meteorological data came from the ECMWF ERA5 dataset. High-resolution geographic data for the region, including elevation, slope, and azimuth data, were collected through remote sensing and field measurements. This data was then cleaned and formatted. Temperature, humidity, and wind speed data at altitudes of 500 hPa, 700 hPa, and 850 hPa were obtained from meteorological databases, and the data were cleaned and completed, outliers were removed, and Kriging interpolation was used to process and interpolate irregular terrain data. By setting terrain factor correction terms, the effects of terrain height, slope, and azimuth were calculated. Based on the processed geographic data and combined with land cover data, key factors including atmospheric circulation factors (such as 500hPa geopotential height and 700hPa horizontal wind), convection indicator factors (such as convective effective potential energy and K index), and surface meteorological factors (such as 2m air temperature and precipitation) were extracted. According to the season and the impact mechanism, the weather processes affecting power grid security were divided into high-temperature fire type, lightning fire type, strong wind galloping type, rain, fog, pollution flashover type, and low-temperature icing type. The key configurations of each type of weather process were obtained. Through terrain correction, a dynamic conceptual model library of high-impact weather for power grid security was constructed. This model library includes dynamic action correction models, thermal action correction models, weather process classification models, and statistical analysis models. Refer to Table 1 for the recording and analysis of the experimental process.

[0067] Table 1 Experimental Data Recording Table

[0068]

[0069] Analysis of the above data tables reveals the advantages of our invention in predicting and analyzing high-impact weather for power grid safety. Firstly, by combining high-resolution geographic data (elevation, slope, azimuth) and meteorological data (temperature, humidity, wind speed), it accurately reflects the impact of different weather types on power grid safety. For example, in high-temperature fire types, high temperature and low humidity are significant characteristics; in lightning-induced fire types, high humidity and strong wind speed are key factors. By using the Kriging interpolation method and terrain factor correction terms, high-precision processing of irregular terrain data is achieved, effectively compensating for the shortcomings of traditional interpolation methods in handling complex terrain, ensuring the accuracy of geographic data. Experimental data shows that the data processing results under different terrain conditions are accurate. This data reflects actual terrain changes and provides reliable data support for power grid safety assessment. By extracting key factors based on geographical data and classifying weather processes affecting power grid safety according to season and influencing mechanism, it achieves accurate classification of different weather types. The data in the table clearly shows the key factor characteristics of different weather types, such as high temperature and low humidity for high temperature fire type and high humidity and high wind speed for lightning fire type. By obtaining the key configurations of various weather processes, terrain correction is performed, and the constructed model library includes dynamic action correction models and thermal action correction models. This model library can dynamically assess power grid safety and provide timely warnings of potential risks. The data table shows that the model library's prediction results for power grid safety have high accuracy and reliability under different geographical and meteorological conditions.

[0070] Example 3

[0071] Reference Figure 2 As an embodiment of the present invention, a system for constructing a dynamic conceptual model of high-impact weather for power grid security is provided, including a data processing module, a classification module, and a model construction module.

[0072] The data processing module is used to analyze geographical data that affects power grid security, and to process and interpolate irregular terrain data based on the Kriging interpolation method; the classification module is used to extract key factors based on geographical data and classify weather processes that affect power grid security according to season and impact mechanism; the model building module is used to obtain the key configurations of various weather processes and to build a dynamic conceptual model library of high-impact weather for power grid security by performing terrain correction.

[0073] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0075] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0076] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for constructing a dynamic conceptual model of high-impact weather for power grid security, characterized in that, include: Analyze geographical data that affects power grid security, and process and interpolate irregular terrain data based on the Kriging interpolation method; Based on the extraction of key factors from geographic data, weather processes that affect power grid security are classified according to season and impact mechanism; Obtain key configurations for various weather processes and construct a conceptual model library of high-impact weather dynamics for power grid security by performing terrain correction.

2. The method for constructing a dynamic conceptual model of high-impact weather for power grid security as described in claim 1, characterized in that: The geographic data includes high-resolution elevation data, slope data, azimuth data, temperature data, humidity data, and wind speed data.

3. The method for constructing a dynamic conceptual model of high-impact weather for power grid security as described in claim 2, characterized in that: The processing and interpolation of irregular terrain data includes the influence of terrain elevation, slope, and azimuth characteristics on the spatial distribution of meteorological data. The terrain factor correction term T(x) is set as follows: T(x) = h(x) + s(x) + a(x) Where h(x) is the elevation function, representing the terrain height at location x; s(x) is the slope function, representing the terrain slope at location x; a(x) is the azimuth function, representing the terrain azimuth at location x; and the functions in the terrain factor correction term are expressed as follows: Where h0 is the reference height, h1 is the magnitude of the height change, x is the position variable describing the spatial coordinates along the east-west direction, and L... h The period of altitude change is represented by y, which is a positional variable describing the spatial coordinates along the north-south direction. Based on the spatial distribution differences of temperature, humidity, and wind speed, changes in meteorological characteristics are captured, and the meteorological factor correction term M(x) is set as follows: M(x)=T m (x)+H m (x)+W m (x) Among them, T m (x) is a temperature function, representing the air temperature at location x, H. m (x) is a humidity function, representing the humidity at location x, W m (x) is the wind speed function, representing the wind speed at location x. The functions in the meteorological factor correction term are expressed as follows: Where T0 is the reference temperature, T1 is the magnitude of temperature change, x0 represents the center point of the temperature distribution, and σ T H0 represents the width of the temperature distribution, H1 represents the baseline humidity, and L represents the range of humidity variation. H The period representing humidity change, W0 represents the reference wind speed, W1 represents the magnitude of wind speed change, and x represents the range of humidity change. w L represents the starting point of the wind speed change. W Indicates the period of wind speed change; By introducing topographic and meteorological correction terms, the limitations of Kriging interpolation in complex terrain and variable weather conditions are adjusted, as follows: Where Z'(x) is the predicted value at position x, μ is the population mean, and is the average value of the data points. i This represents the position of the i-th known data point, where N is the number of data points, and λ is the position of the i-th known data point. i Z(x) represents the weight coefficient of the i-th data point. i ) is at position x i The initial values ​​are given at the location, where α is the weighting coefficient of the topographic factor and β is the weighting coefficient of the meteorological factor.

4. The method for constructing a dynamic conceptual model of high-impact weather for power grid security as described in claim 3, characterized in that: The extraction of key factors includes extracting atmospheric circulation factors, convection indicator factors, and surface meteorological factors based on processed geographic data and combined with land cover data. Atmospheric circulation factors include 500 hPa geopotential height, 500 hPa horizontal wind, 500 hPa temperature, 700 hPa geopotential height, 700 hPa horizontal wind, 700 hPa temperature, 700 hPa relative humidity, 700 hPa specific humidity, 850 hPa geopotential height, 850 hPa horizontal wind, 850 hPa temperature, 850 hPa relative humidity, and 850 hPa specific humidity. Convection indicator factors include convective available potential energy, K index, Sablius index, convection suppression energy, and temperature difference between 850 hPa and 500 hPa; Surface meteorological factors include 2m air temperature, 2m dew point temperature, precipitation, air pressure, pressure change, and 10m wind.

5. The method for constructing a dynamic conceptual model of high-impact weather for power grid security as described in claim 4, characterized in that: The classification based on season and impact mechanism includes classifying weather processes affecting power grid safety into high-temperature fire type, lightning fire type, strong wind gallop type, rain, fog, pollution flashover type, and low-temperature icing type. High-temperature fires occurred between June and August, with an average temperature ≥37℃ in the study area and at least one transmission zone having a maximum temperature ≥39℃. Lightning-induced fires occurred between March and October, with lightning observed in at least one transmission zone in the study area for more than 10 minutes. The gale-driven winds occur throughout the year, with at least one power transmission zone in the study area experiencing a maximum wind speed ≥17.2 m / s for more than 5 minutes. The rain-fog-pollution flashover type occurs from October to April of the following year. At least two transmission zones in the study area have visibility <500m and this occurs for more than 3 consecutive days, or the total rainfall in the study area is between 0.1mm and 10mm and at least one transmission zone has ≥3 consecutive rainy days. Low-temperature icing occurs from December to February of the following year, with an average temperature of ≤4℃ in the study area and at least one power transmission zone having a minimum temperature of ≤0℃.

6. The method for constructing a dynamic conceptual model of high-impact weather for power grid security as described in claim 5, characterized in that: The key configurations for various weather processes include establishing weather system configuration datasets for five types of high-impact weather on the power grid based on key factors. The weather system configuration datasets are used to identify upper-air and surface weather systems that affect power grid security and to obtain the key configurations for various weather processes. The key configuration for high-temperature ignition type includes the study area being controlled by the 500hPa subtropical high pressure, and the relative humidity being below 70% at 700hPa and 850hPa. Key configurations for lightning-induced fires include: an upper-level trough at 500 hPa, a shear line or low vortex at 700 hPa, relative humidity ≥ 80% or specific humidity ≥ 12 g / kg, a shear line or low vortex at 850 hPa, relative humidity ≥ 80% or specific humidity ≥ 16 g / kg, southwest wind speed ≥ 20 m / s, convective effective potential energy ≥ 1500 kJ, and a temperature difference ≥ 25℃ between 850 hPa and 500 hPa. Key configurations for a gale-driven wind pattern include: an upper-level trough at 500 hPa with northerly wind speeds ≥20 m / s behind the trough; a shear line or low vortex at 700 hPa with relative humidity <80%; a shear line or low vortex at 850 hPa with relative humidity ≥80%; southwesterly wind speeds ≥20 m / s; convective suppression energy between 70-110 kJ; a temperature difference ≥25°C between 850 hPa and 500 hPa; and positive ground pressure over 3 hours. The key configurations for rain, fog, pollution, and flashover include a straight westerly wind at 500 hPa in the study area, and a ground inversion layer with relative humidity ≥80% at 700 hPa and 850 hPa. Key configurations for low-temperature icing include an upper-level trough at 500 hPa in the study area, northerly wind speeds ≥20 m / s behind the trough, shear lines or low vortices at 700 hPa and 850 hPa, and relative humidity ≥80%.

7. The method for constructing a dynamic conceptual model of high-impact weather for power grid security as described in claim 6, characterized in that: The construction of the dynamic conceptual model library of high-impact weather for power grid security includes calculating the dynamic and thermal effects of the plateau, determining the type of high-impact weather processes for power grid security, statistically analyzing the duration, scope, and frequency of occurrence in different months and seasons, performing topographic corrections, adjusting topographic data, and obtaining the dynamic conceptual model library of high-impact weather for power grid security. Using climatological average data, the vector difference ΔU between the 700hPa and 850hPa horizontal wind fields and the monthly average wind field is calculated according to the time range of each process, and expressed as: Among them, u 700 and v 700 These represent the eastward and northward wind speed components at the 700 hPa level, respectively. and U represents the eastward and northward wind speed components of the monthly average at the 700 hPa level. 850 and v 850 These represent the eastward and northward wind speed components at the 850 hPa level, respectively. and These represent the eastward and northward wind speed components of the 850 hPa layer for the current month, respectively. Using climatological average data, the scalar difference ΔT between the 500 hPa and 700 hPa temperature fields and the monthly average temperature field is calculated according to the time range of each process, and expressed as: Among them, T 500 and T represents the temperature value of the 500 hPa layer and the average temperature value of the month, respectively. 700 and These represent the temperature value of the 700 hPa layer and the average temperature value for the current month, respectively. The database of dynamic conceptual models for high-impact weather on power grid security includes dynamic action correction models, thermal action correction models, weather process classification models, and statistical analysis models.

8. A system employing the method for constructing a dynamic conceptual model of high-impact weather for power grid security as described in any one of claims 1 to 7, characterized in that: It includes a data processing module, a classification module, and a model building module; The data processing module is used to analyze geographical data that affects power grid security, and to process and interpolate irregular terrain data based on the Kriging interpolation method. The classification module is used to extract key factors based on geographic data and classify weather processes that affect power grid security according to season and impact mechanism. The model building module is used to obtain key configurations for various weather processes and to build a conceptual model library of high-impact weather dynamics for power grid security by performing terrain correction.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for constructing a dynamic conceptual model of high-impact weather for power grid security as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for constructing a dynamic conceptual model of high-impact weather for power grid security as described in any one of claims 1 to 7.

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