Method and system for predicting and defending natural disasters of power grid and medium

Abstract

The invention provides a method and a system for predicting and defending natural disasters of a power grid, and a medium. The method comprises the steps of collecting historical monitoring information of a target regional power grid, extracting power grid facility monitoring record data, obtaining facility design characteristic data of the target regional power grid, and performing various processing according to the power grid facility monitoring record data and the facility design characteristic data, correspondingly obtaining a tower anti-wind capability deviation index, a tower anti-icing overload index, a transformer substation flood prevention capability deviation index, a transformer substation lightning resistance capability deviation index and a transformer substation shock resistance capability deviation index; and processing by combining the transformer substation flood prevention capability deviation index, the transformer substation lightning resistance capability deviation index and the transformer substation shock resistance capability deviation index to obtain a power grid disaster defense capability deviation index, and performing corresponding adjustment so as to realize prediction and defense of natural disasters of the power grid.

Description

Technical Field

[0001] The present application relates to the technical field of power grids, and specifically, to methods, systems, and media for predicting and preventing natural disasters in power grids. Background Art

[0002] Different natural disasters will cause different hazards and damages to power grids. For example, strong winds may cause transmission line towers to collapse or break, freezing rain will form ice layers on the surface of transmission lines, increasing the weight of the lines and the vertical load on the towers, and even causing the towers to collapse. Floods will submerge power facilities such as substations, damaging electrical equipment. Lightning strikes on transmission lines or substation equipment may cause insulator flashovers and equipment damage. Strong earthquakes will cause the ground to shake violently, damaging substation buildings and equipment.

[0003] Traditional methods for predicting and preventing natural disasters in power grids are relatively single and cannot comprehensively evaluate the comprehensive natural disaster prevention ability of power grids.

[0004] In view of the above problems, there is an urgent need for effective technical solutions. Summary of the Invention

[0005] The purpose of the present application is to provide a method, system, and medium for predicting and preventing natural disasters in power grids. By collecting historical monitoring information of the power grid in the target area, extracting monitoring record data of power grid facilities, obtaining facility design characteristic data of the power grid in the target area, and performing various processes respectively according to the monitoring record data of power grid facilities and in combination with the facility design characteristic data, the tower wind resistance capacity deviation index, tower anti-icing overload index, substation flood prevention capacity deviation index, substation lightning resistance capacity deviation index, and substation earthquake resistance capacity deviation index can be obtained. Through further processing, the power grid disaster prevention capacity deviation index is obtained, and corresponding adjustments are made to realize the technology of predicting and preventing natural disasters in power grids.

[0006] The present application also provides a method for predicting and preventing natural disasters in power grids, including the following steps:

[0007] Collect historical monitoring information of the power grid in the target area, and extract monitoring record data of power grid facilities, including meteorological record data, geographical feature data, topographical feature data, and geological monitoring record data;

[0008] Obtain the facility design characteristic data of the power grid in the target area, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data;

[0009] Process according to the tower design characteristic data, in combination with the meteorological record data and topographical feature data, to obtain the tower wind resistance capacity deviation index;

[0010] Based on the design characteristic data of the pole and tower, combined with the meteorological record data and the line parameter characteristic data for processing, the anti-icing overload index of the pole and tower is obtained;

[0011] Based on the design characteristic data of the substation, combined with the meteorological record data and the geographical characteristic data for processing, the flood control capacity deviation index of the substation is obtained;

[0012] Based on the design characteristic data of the substation, combined with the meteorological record data for processing, the lightning resistance capacity deviation index of the substation is obtained;

[0013] Based on the design characteristic data of the substation, combined with the geological monitoring record data for processing, the seismic resistance capacity deviation index of the substation is obtained;

[0014] Based on the wind resistance capacity deviation index and the anti-icing overload index of the pole and tower, combined with the flood control capacity deviation index, the lightning resistance capacity deviation index and the seismic resistance capacity deviation index of the substation for processing, the grid disaster prevention and defense capacity deviation index is obtained and corresponding adjustments are made.

[0015] Optionally, in the method for predicting and preventing natural disasters of the power grid described in this application, the historical monitoring information of the power grid in the target area is collected, and the monitoring record data of the power grid facilities is extracted, including meteorological record data, geographical characteristic data, terrain characteristic data, and geological monitoring record data, including:

[0016] Collect the historical monitoring information of the power grid in the target area, and extract the monitoring record data of the power grid facilities, including meteorological record data, geographical characteristic data, terrain characteristic data, and geological monitoring record data;

[0017] The meteorological record data includes wind speed record data, icing record data, flood record data, and lightning record data;

[0018] Extract the average wind speed data, maximum wind speed data, and extreme wind speed data according to the wind speed record data;

[0019] Extract the historical extreme value data of the icing thickness, the maximum value data of the icing growth rate, and the maximum value data of the icing density according to the icing record data;

[0020] Extract the water level exceeding warning rate data and the maximum water level value data according to the flood record data;

[0021] Extract the lightning strike return number data, the lightning day proportion data, and the maximum value data of the lightning current amplitude according to the lightning record data;

[0022] The geographical characteristic data includes the substation altitude data and the substation slope data;

[0023] The terrain feature data includes terrain type data and terrain roughness data;

[0024] The geological monitoring record data includes peak ground acceleration data, peak ground velocity data, and seismic activity frequency data.

[0025] Optionally, in the method for predicting and preventing power grid natural disasters described in this application, the obtaining of the facility design characteristic data of the power grid in the target area, including pole tower design characteristic data, substation design characteristic data, and line parameter characteristic data, includes:

[0026] Obtain the facility design characteristic data of the power grid in the target area, including pole tower design characteristic data, substation design characteristic data, and line parameter characteristic data;

[0027] The pole tower design characteristic data includes pole tower wind resistance design wind speed data and pole tower ice - load - bearing data;

[0028] The substation design characteristic data includes substation flood - control design capacity data, substation lightning - resistance level data, and substation seismic bearing capacity data;

[0029] The line parameter characteristic data includes insulator string length data and span size data.

[0030] Optionally, in the method for predicting and preventing power grid natural disasters described in this application, the processing of the pole tower design characteristic data in combination with the meteorological record data, meteorological statistical characteristic data, and terrain feature data to obtain the pole tower wind - resistance capacity deviation index includes:

[0031] Process the terrain type data and terrain roughness data through a preset wind field numerical model to obtain a wind speed acceleration effect coefficient;

[0032] Process the average wind speed data, maximum wind speed data, and extreme wind speed data in combination with the wind speed acceleration effect coefficient to obtain wind speed destructive force data;

[0033] Process the pole tower wind - resistance design wind speed data in combination with the wind speed destructive force data to obtain the pole tower wind - resistance capacity deviation index.

[0034] Optionally, in the method for predicting and preventing power grid natural disasters described in this application, the processing of the pole tower design characteristic data in combination with the meteorological record data and line parameter characteristic data to obtain the pole tower ice - overload index includes:

[0035] Process the historical extreme ice thickness data, the maximum ice growth rate data, and the maximum ice density data, and combine the insulator string length data and the span size data to obtain ice loading data;

[0036] Process the tower anti-ice load-bearing data in combination with the ice loading data to obtain the tower anti-ice overload index.

[0037] Optionally, in the method for predicting and preventing power grid natural disasters described in this application, the processing based on the tower wind resistance deviation index and the tower anti-ice overload index, in combination with the substation flood prevention ability deviation index, the substation lightning resistance ability deviation index, and the substation earthquake resistance ability deviation index to obtain the power grid disaster prevention ability deviation index and perform corresponding adjustments includes:

[0038] Process the tower wind resistance deviation index and the tower anti-ice overload index, in combination with the substation flood prevention ability deviation index, the substation lightning resistance ability deviation index, and the substation earthquake resistance ability deviation index to obtain the power grid disaster prevention ability deviation index;

[0039] Compare the power grid disaster prevention ability deviation index with a preset power grid disaster prevention ability deviation threshold to obtain a threshold comparison result;

[0040] Judge whether the natural disaster prevention ability of the power grid in the target area meets the standard according to the threshold comparison result;

[0041] If the threshold comparison result is less than the preset threshold, the natural disaster prevention ability of the power grid in the target area meets the standard;

[0042] Otherwise, it is determined that it does not meet the standard, and corresponding adjustments are made to the power grid in the target area.

[0043] In a second aspect, this application provides a power grid natural disaster prediction and prevention system, which includes: a memory and a processor. The memory includes a program for the power grid natural disaster prediction and prevention method. When the program for the power grid natural disaster prediction and prevention method is executed by the processor, the following steps are implemented:

[0044] Collect the historical monitoring information of the power grid in the target area, and extract the power grid facility monitoring record data, including meteorological record data, geographical feature data, topographical feature data, and geological monitoring record data;

[0045] Obtain the facility design characteristic data of the power grid in the target area, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data;

[0046] Based on the tower design characteristic data, combined with the meteorological record data and terrain feature data for processing, a tower wind resistance deviation index is obtained;

[0047] Based on the tower design characteristic data, combined with the meteorological record data and line parameter feature data for processing, a tower ice-overload resistance index is obtained;

[0048] Based on the substation design characteristic data, combined with the meteorological record data and geographical feature data for processing, a substation flood control capacity deviation index is obtained;

[0049] Based on the substation design characteristic data, combined with the meteorological record data for processing, a substation lightning resistance capacity deviation index is obtained;

[0050] Based on the substation design characteristic data, combined with the geological monitoring record data for processing, a substation seismic resistance capacity deviation index is obtained;

[0051] Based on the tower wind resistance deviation index and tower ice-overload resistance index, combined with the substation flood control capacity deviation index, substation lightning resistance capacity deviation index and substation seismic resistance capacity deviation index for processing, a power grid disaster prevention capacity deviation index is obtained, and corresponding adjustments are made.

[0052] Optionally, in the power grid natural disaster prediction and prevention system described in this application, the historical monitoring information of the target area power grid is collected, and the monitoring record data of power grid facilities is extracted, including meteorological record data, geographical feature data, terrain feature data and geological monitoring record data, including:

[0053] Collect the historical monitoring information of the target area power grid, and extract the monitoring record data of power grid facilities, including meteorological record data, geographical feature data, terrain feature data and geological monitoring record data;

[0054] The meteorological record data includes wind speed record data, icing record data, flood record data and lightning record data;

[0055] Based on the wind speed record data, the average wind speed data, maximum wind speed data and extreme wind speed data are extracted;

[0056] Based on the icing record data, the historical extreme value data of icing thickness, the maximum value data of icing growth rate and the maximum value data of icing density are extracted;

[0057] Based on the flood record data, the water level over-warning rate data and the highest water level value data are extracted;

[0058] Based on the lightning record data, the lightning strike return number data, the lightning day proportion data and the maximum value data of lightning current amplitude are extracted;

[0059] The geographical feature data includes substation altitude data and substation slope data;

[0060] The terrain feature data includes terrain type data and terrain roughness data;

[0061] The geological monitoring record data includes peak ground acceleration data, peak ground velocity data, and seismic activity frequency data.

[0062] Optionally, in the power grid natural disaster prediction and defense system described in this application, obtaining the facility design characteristic data of the power grid in the target area includes tower design characteristic data, substation design characteristic data, and line parameter characteristic data, including:

[0063] Obtaining the facility design characteristic data of the power grid in the target area includes tower design characteristic data, substation design characteristic data, and line parameter characteristic data;

[0064] The tower design characteristic data includes tower wind-resistant design wind speed data and tower ice-loading bearing capacity data;

[0065] The substation design characteristic data includes substation flood control design capacity data, substation lightning withstand level data, and substation seismic bearing capacity data;

[0066] The line parameter characteristic data includes insulator string length data and span size data.

[0067] In a third aspect, this application also provides a computer-readable storage medium storing a program for the power grid natural disaster prediction and defense method. When the program for the power grid natural disaster prediction and defense method is executed by a processor, the steps of the power grid natural disaster prediction and defense method described in any one of the above are implemented.

[0068] As described above, the method, system and medium for predicting and preventing power grid natural disasters disclosed in the present invention collect the historical monitoring information of the power grid in the target area, extract the monitoring record data of power grid facilities, including meteorological record data, geographical feature data, terrain feature data and geological monitoring record data, obtain the facility design characteristic data of the power grid in the target area, including tower design characteristic data, substation design characteristic data and line parameter characteristic data, process according to the tower design characteristic data, combined with the meteorological record data and the terrain feature data, to obtain the wind resistance deviation index of the tower, process according to the tower design characteristic data, combined with the meteorological record data and the line parameter characteristic data, to obtain the ice overload resistance index of the tower, process according to the substation design characteristic data, combined with the meteorological record data and the geographical feature data, to obtain the flood prevention capacity deviation index of the substation, process according to the substation design characteristic data, combined with the meteorological record data, to obtain the lightning resistance capacity deviation index of the substation, process according to the substation design characteristic data, combined with the geological monitoring record data, to obtain the seismic resistance capacity deviation index of the substation, process according to the wind resistance deviation index of the tower and the ice overload resistance index of the tower, combined with the flood prevention capacity deviation index of the substation, the lightning resistance capacity deviation index of the substation and the seismic resistance capacity deviation index of the substation, to obtain the power grid disaster prevention capacity deviation index, and make corresponding adjustments, so as to realize the technology of predicting and preventing power grid natural disasters.

[0069] Other features and advantages of the present application will be described in the subsequent specification, and, in part, will become apparent from the specification or be understood by implementing the embodiments of the present application. The objectives and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written specification and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0071] Figure 1 It is a flowchart of the method for predicting and preventing power grid natural disasters provided by the embodiment of the present application;

[0072] Figure 2 It is a flowchart of extracting the monitoring record data of power grid facilities in the method for predicting and preventing power grid natural disasters provided by the embodiment of the present application;

[0073] Figure 3 It is a flowchart of obtaining the wind resistance deviation index of the tower in the method for predicting and preventing power grid natural disasters provided by the embodiment of the present application;

[0074] Figure 4 It is a flowchart for obtaining the tower anti-icing overload index of the power grid natural disaster prediction and defense method provided by the embodiments of the present application. Specific embodiments

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0076] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0077] Please refer to Figure 1 , Figure 1 It is a flowchart of the power grid natural disaster prediction and defense method in some embodiments of the present application. This power grid natural disaster prediction and defense method is used in terminal devices, such as computers, mobile phone terminals, etc. This power grid natural disaster prediction and defense method includes the following steps:

[0078] S11. Collect the historical monitoring information of the power grid in the target area, and extract the monitoring record data of power grid facilities, including meteorological record data, geographical feature data, terrain feature data, and geological monitoring record data;

[0079] S12. Obtain the facility design characteristic data of the power grid in the target area, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data;

[0080] S13. Process according to the tower design characteristic data, combined with the meteorological record data and terrain feature data, to obtain the tower wind resistance capacity deviation index;

[0081] S14. Process according to the tower design characteristic data, combined with the meteorological record data and line parameter characteristic data, to obtain the tower anti-icing overload index;

[0082] S15. Process according to the substation design characteristic data in combination with the meteorological record data and geographical feature data to obtain the deviation index of the substation's flood prevention ability;

[0083] S16. Process according to the substation design characteristic data in combination with the meteorological record data to obtain the deviation index of the substation's lightning withstand ability;

[0084] S17. Process according to the substation design characteristic data in combination with the geological monitoring record data to obtain the deviation index of the substation's seismic resistance ability;

[0085] S18. Process according to the deviation index of the tower's wind resistance ability and the tower's ice overload resistance index in combination with the deviation index of the substation's flood prevention ability, the deviation index of the substation's lightning withstand ability, and the deviation index of the substation's seismic resistance ability to obtain the deviation index of the power grid disaster prevention ability and make corresponding adjustments.

[0086] It should be noted that different natural disasters will cause different hazards and damages to the power grid. For example, strong winds may cause transmission line towers to collapse and wires to break. Freezing rain will also form ice layers on the surface of transmission lines, increasing the weight of the lines and the vertical load on the towers, and even causing the towers to collapse. Floods will submerge power facilities such as substations, damaging electrical equipment. Lightning strikes on transmission lines or substation equipment may cause insulator flashovers and equipment damage. Strong earthquakes will cause the ground to shake violently, resulting in damage to substation buildings and equipment. Traditional methods for predicting and preventing natural disasters in the power grid are relatively single and cannot comprehensively evaluate the comprehensive natural disaster prevention ability of the power grid. Therefore, a method for predicting and preventing natural disasters in the power grid that can be comprehensive is needed, which can comprehensively consider whether there are deviations in the power grid's defense capabilities against various natural disasters, so as to adjust and improve in a timely manner. In this embodiment, first, historical monitoring information of the power grid in the target area is collected, and monitoring record data of power grid facilities is extracted, including meteorological record data, geographical feature data, terrain feature data, and geological monitoring record data. Facility design characteristic data of the power grid in the target area is obtained, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data. According to the tower design characteristic data, combined with meteorological record data and terrain feature data, a tower wind resistance deviation index is obtained. According to the tower design characteristic data, combined with meteorological record data and line parameter characteristic data, a tower ice overload resistance index is obtained. According to the substation design characteristic data, combined with meteorological record data and geographical feature data, a substation flood prevention ability deviation index is obtained. According to the substation design characteristic data, combined with meteorological record data, a substation lightning resistance ability deviation index is obtained. According to the substation design characteristic data, combined with geological monitoring record data, a substation earthquake resistance ability deviation index is obtained. According to the tower wind resistance deviation index and the tower ice overload resistance index, combined with the substation flood prevention ability deviation index, the substation lightning resistance ability deviation index, and the substation earthquake resistance ability deviation index, a power grid disaster prevention ability deviation index is obtained, and corresponding adjustments are made, so as to realize the technology of predicting and preventing natural disasters in the power grid.

[0087] Please refer to Figure 2 , Figure 2 is a flowchart of extracting monitoring record data of power grid facilities in the method for predicting and preventing natural disasters in the power grid according to some embodiments of the present application. According to an embodiment of the present invention, the collecting historical monitoring information of the power grid in the target area and extracting monitoring record data of power grid facilities, including meteorological record data, geographical feature data, terrain feature data, and geological monitoring record data, includes:

[0088] S21. Collect the historical monitoring information of the power grid in the target area, and extract the monitoring record data of power grid facilities, including meteorological record data, geographical feature data, topographical feature data, and geological monitoring record data;

[0089] S22. The meteorological record data includes wind speed record data, icing record data, flood record data, and lightning record data;

[0090] S23. Extract the average wind speed data, maximum wind speed data, and extreme wind speed data according to the wind speed record data;

[0091] S24. Extract the historical extreme value data of icing thickness, maximum value data of icing growth rate, and maximum value data of icing density according to the icing record data;

[0092] S25. Extract the water level over-warning rate data and maximum water level value data according to the flood record data;

[0093] S26. Extract the lightning strike return number data, lightning day proportion data, and maximum value data of lightning current amplitude according to the lightning record data;

[0094] S27. The geographical feature data includes substation altitude data and substation slope data;

[0095] S28. The topographical feature data includes topographical type data and topographical roughness data;

[0096] S29. The geological monitoring record data includes peak ground acceleration data, peak ground velocity data, and seismic activity frequency data.

[0097] It should be noted that for collecting historical monitoring information of the power grid in the target area, the historical duration can be set as needed. For example, the historical monitoring information from the commissioning of the regional power grid to the present or the past 20 years can be used. Then, based on the historical monitoring information, monitoring record data of power grid facilities are extracted, including meteorological record data, geographical feature data, topographic feature data, and geological monitoring record data. Among them, the meteorological record data includes wind speed record data, icing record data, flood record data, and lightning record data. Average wind speed, maximum wind speed, and extreme wind speed data are extracted from the wind speed record data. Among them, through extreme value statistical methods such as Gumbel distribution and Pearson-TypeⅢ distribution, the historical maximum wind speed data is fitted to calculate the extreme wind speed under a certain recurrence period (such as once in 30 years, 50 years, or 100 years). Ice thickness historical extreme value, maximum ice growth rate, and maximum ice density data are extracted from the icing record data. Among them, the ice thickness historical extreme value refers to the maximum ice thickness data collected over the years in the area where the power grid is located, which can be obtained from power grid operation and maintenance records, meteorological departments, or on-site monitoring equipment. The ice density varies depending on the type of icing (such as rime, glaze, etc.) and meteorological conditions. Water level over-warning rate and maximum water level value data are extracted from the flood record data. Lightning strike return number, lightning day proportion, and maximum lightning current amplitude data are extracted from the lightning record data. Among them, the lightning day refers to the number of days with lightning activities in the area, which is a simple indicator to measure the frequency of lightning activities. The number of lightning days can be obtained from the local meteorological department. Generally, the more lightning days there are, the greater the probability of the substation being struck by lightning. The geographical feature data includes the altitude of the substation and the slope data of the substation. The topographic feature data includes the terrain type and terrain roughness data. The geological monitoring record data includes peak ground acceleration, peak ground velocity, and seismic activity frequency data. Among them, the peak ground acceleration is the maximum ground acceleration during the seismic motion process, and the peak ground velocity is the maximum ground velocity.

[0098] According to an embodiment of the present invention, obtaining the facility design characteristic data of the target area power grid, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data, includes:

[0099] Obtaining the facility design characteristic data of the target area power grid, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data;

[0100] The tower design characteristic data includes tower wind-resistant design wind speed data and tower ice-loading bearing capacity data;

[0101] The substation design characteristic data includes substation flood control design capacity data, substation lightning resistance level data, and substation seismic bearing capacity data;

[0102] The line parameter characteristic data includes insulator string length data and span size data.

[0103] It should be noted that obtaining the facility design characteristic data of the power grid in the target area includes tower design characteristic data, substation design characteristic data, and line parameter characteristic data. Among them, the tower design characteristic data includes the tower wind resistance design wind speed and the tower ice load-bearing data, the substation design characteristic data includes the substation flood control design ability, the substation lightning resistance level, and the substation seismic bearing capacity data, and the line parameter characteristic data includes insulator string length data and span size data. The above data can be obtained by querying materials such as the design drawings of the regional power grid.

[0104] Please refer to Figure 3 , Figure 3 is a flowchart for obtaining the tower wind resistance ability deviation index of the power grid natural disaster prediction and defense method in some embodiments of the present application. According to the embodiments of the present invention, the tower design characteristic data is processed in combination with the meteorological record data, meteorological statistical characteristic data, and terrain characteristic data to obtain the tower wind resistance ability deviation index, including:

[0105] S31. Process the terrain type data and terrain roughness data through a preset wind field numerical model to obtain a wind speed acceleration effect coefficient;

[0106] S32. Process the average wind speed data, maximum wind speed data, and extreme wind speed data in combination with the wind speed acceleration effect coefficient to obtain wind speed destructive force data;

[0107] S33. Process the tower wind resistance design wind speed data in combination with the wind speed destructive force data to obtain the tower wind resistance ability deviation index.

[0108] It should be noted that strong winds may cause transmission line towers to tilt or break. For example, when the typhoon wind speed exceeds the designed wind speed that the tower can withstand, the lateral force on the tower foundation is too large, and it is prone to tilt or collapse, resulting in large-scale power outages. Therefore, it is necessary to evaluate whether there is a deviation in the ability of the tower to resist strong wind damage. First, since different terrains have a significant impact on wind speed, and terrain roughness is a parameter that describes the influence of the ground surface on wind speed, which is related to factors such as ground vegetation and buildings. Different roughnesses result in different near-surface wind speed profiles. In areas with large roughness (such as the city center with a large number of high-rise buildings), the change rate of wind speed with height is relatively large, while in areas with small roughness (such as open grasslands), the change of wind speed with height is relatively slow. Therefore, according to the terrain type data and terrain roughness data, they are input into a preset wind field numerical model for processing to obtain the wind speed acceleration effect coefficient. Among them, the wind field numerical model belongs to the computational fluid dynamics (CFD) model, such as the Reynolds-averaged Navier-Stokes (RANS) equation solver, which can simulate the wind field under complex terrain conditions. Input the terrain type data and terrain roughness data into the model, set appropriate boundary conditions (such as wind direction, reference wind speed, etc.), obtain the wind speeds at different positions through numerical solution, and then calculate the wind speed acceleration effect coefficient based on the simulated wind speed and the reference wind speed. Next, according to the average wind speed, maximum wind speed, and extreme wind speed data, combined with the wind speed acceleration effect coefficient for processing, obtain the wind speed destructive force data, and then combined with the tower's wind resistance design wind speed data for processing to obtain the tower wind resistance ability deviation index;

[0109] Among them, the calculation formula for the wind speed destructive force data is:

[0110]

[0111] Among them, f s is the wind speed destructive force data, z x , a s , q w are the average wind speed data, maximum wind speed data, and extreme wind speed data respectively, p o is the wind speed acceleration effect coefficient, β 1 , β 2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0112] Among them, the calculation formula for the tower wind resistance ability deviation index is:

[0113]

[0114] Among them, K f is the tower wind resistance ability deviation index, F s , f sThey are the wind-resistant design wind speed data and wind speed destructive force data of the pole tower respectively, and α is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset power grid natural disaster prevention and monitoring platform).

[0115] Please refer to Figure 4 , Figure 4 FIG. Figure 4 is a flowchart of obtaining the ice-overloading resistance index of the power grid natural disaster in some embodiments of the present application. According to the embodiments of the present invention, processing the pole tower design characteristic data in combination with the meteorological record data and the line parameter characteristic data to obtain the ice-overloading resistance index of the pole tower includes:

[0116] S41. Processing the historical extreme value data of ice thickness, the maximum value data of ice growth rate, and the maximum value data of ice density in combination with the insulator string length data and the span size data to obtain ice loading data;

[0117] S42. Processing the ice-overloading resistance data of the pole tower in combination with the ice loading data to obtain the ice-overloading resistance index of the pole tower.

[0118] It should be noted that since freezing rain will form ice layers on the surface of the transmission line, increasing the weight of the line and the vertical load borne by the pole tower, and even causing the pole tower to collapse, it is necessary to evaluate whether there is a deviation in the ice-overloading resistance ability of the pole tower. First, process the historical extreme value data of ice thickness, the maximum value data of ice growth rate, and the maximum value data of ice density in combination with the insulator string length data and the span size data to obtain ice loading data, and then process it in combination with the ice-overloading resistance data of the pole tower to obtain the ice-overloading resistance index of the pole tower. Among them, the length of the insulator string will affect the insulation distance and mechanical strength of the line. A longer insulator string can provide a larger insulation distance under icing conditions, but it will also increase the vertical load of the conductor. The span is the horizontal distance between adjacent pole towers. The span size directly affects the tension of the conductor and the distribution of the ice weight on the pole tower. A larger span will cause the conductor to generate greater tension and sag when icing, increasing the load on the pole tower;

[0119] Among them, the calculation formula of the ice loading data is:

[0120]

[0121] Among them, b z is the ice loading data, x c , s d , w e are the historical extreme value data of ice thickness, the maximum value data of ice growth rate, and the maximum value data of ice density respectively, i u , k j are the insulator string length data and the span size data respectively, δ1 、 δ 2 、 δ 3 、 ε 1 、 ε 2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0122] Among them, the calculation formula for the tower anti-icing overload index is:

[0123]

[0124] Among them, G b is the tower anti-icing overload index, B z and b z are respectively the tower anti-icing load-bearing data and ice-loading data, and φ is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform).

[0125] According to an embodiment of the present invention, processing is performed by combining the tower wind resistance ability deviation index and the tower anti-icing overload index with the substation flood prevention ability deviation index, substation lightning protection ability deviation index, and substation seismic resistance ability deviation index to obtain a power grid disaster prevention ability deviation index, and corresponding adjustments are made, including:

[0126] Processing is performed by combining the tower wind resistance ability deviation index and the tower anti-icing overload index with the substation flood prevention ability deviation index, substation lightning protection ability deviation index, and substation seismic resistance ability deviation index to obtain a power grid disaster prevention ability deviation index;

[0127] Comparing the power grid disaster prevention ability deviation index with a preset power grid disaster prevention ability deviation threshold to obtain a threshold comparison result;

[0128] Judging whether the natural disaster prevention ability of the power grid in the target area meets the standard according to the threshold comparison result;

[0129] If the threshold comparison result is less than the preset threshold, the natural disaster prevention ability of the power grid in the target area meets the standard;

[0130] Otherwise, it is determined as not meeting the standard, and corresponding adjustments are made to the power grid in the target area.

[0131] It should be noted that, based on the tower wind resistance deviation index and the tower ice - overload resistance index, combined with the substation flood - control capacity deviation index, the substation lightning - resistance capacity deviation index, and the substation earthquake - resistance capacity deviation index for processing, the power grid disaster prevention capacity deviation index is obtained. Then, it is compared with the preset power grid disaster prevention capacity deviation threshold to obtain the threshold comparison result. According to the threshold comparison result, it is judged whether the natural disaster prevention capacity of the power grid in the target area meets the standard. If the threshold comparison result is less than the preset threshold, it indicates that the natural disaster prevention capacity of the power grid in the target area meets the standard and no adjustment is required. Otherwise, it indicates that the natural disaster prevention capacity of the power grid in the target area does not meet the standard and corresponding adjustments need to be made to the power grid in the target area;

[0132] Among them, the calculation formula for the power grid disaster prevention capacity deviation index is:

[0133]

[0134] Among them, Y w is the power grid disaster prevention capacity deviation index, K f , G b , S f , N l , Z k are respectively the tower wind resistance deviation index, the tower ice - overload resistance index, the substation flood - control capacity deviation index, the substation lightning - resistance capacity deviation index, and the substation earthquake - resistance capacity deviation index, and γ 1 , γ 2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through the preset power grid natural disaster prevention monitoring platform).

[0135] According to the embodiments of the present invention, it further includes:

[0136] Processing according to the substation design characteristic data, combined with the meteorological record data and the geographical feature data to obtain the substation flood - control capacity deviation index, specifically including:

[0137] Processing according to the water level over - warning rate data and the highest water level value data, combined with the substation altitude data and the substation slope data to obtain the flood destructive force data;

[0138] Processing according to the substation flood - control design capacity data combined with the flood destructive force data to obtain the substation flood - control capacity deviation index;

[0139] Among them, the calculation formula for the flood destructive force data is:

[0140]

[0141] Among them, h s is the flood destructive force data, cv 、d f are the data of the water level exceeding the warning rate and the data of the highest water level value respectively, and e r 、t y are the data of the altitude of the substation and the data of the slope of the substation respectively, and η 1 、η 2 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0142] Among them, the calculation formula of the flood prevention ability deviation index of the substation is:

[0143]

[0144] Among them, S f is the flood prevention ability deviation index of the substation, H s 、h s are the data of the flood prevention design ability of the substation and the data of the flood destructive force respectively, and μ 1 、μ 2 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform).

[0145] It should be noted that since floods will submerge power facilities such as substations and damage electrical equipment, it is necessary to evaluate whether there is a deviation in the flood prevention ability of the substation. First, according to the data of the water level exceeding the warning rate and the data of the highest water level value, combined with the data of the altitude of the substation and the data of the slope of the substation, the data of the flood destructive force is obtained, and then combined with the data of the flood prevention design ability of the substation, the flood prevention ability deviation index of the substation is obtained.

[0146] According to the embodiment of the present invention, it further includes:

[0147] Processing the substation design characteristic data in combination with the meteorological record data to obtain a lightning resistance ability deviation index of the substation, specifically including:

[0148] Processing the data of the lightning strike return stroke times, the proportion of lightning days, and the maximum value of the lightning current amplitude to obtain the data of the lightning destructive force;

[0149] Processing the lightning resistance level data of the substation in combination with the data of the lightning destructive force to obtain the lightning resistance ability deviation index of the substation;

[0150] Among them, the calculation formula of the data of the lightning destructive force is:

[0151]

[0152] Among them, l d is the data of the lightning destructive force, b n 、gh , r t are respectively the data of lightning strike return stroke times, the data of the proportion of lightning days, and the data of the maximum value of lightning current amplitude. ν 1 , ν 2 , ν 3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0153] Among them, the calculation formula of the lightning withstand capacity deviation index of the substation is:

[0154]

[0155] Among them, N l is the lightning withstand capacity deviation index of the substation, L d , l d are respectively the lightning withstand level data and the lightning damage force data of the substation. π 1 , π 2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset power grid natural disaster prevention and monitoring platform).

[0156] It should be noted that since lightning strike is one of the main reasons for power grid failures, when lightning strikes the substation equipment, it may cause equipment damage. Therefore, it is necessary to evaluate whether there is a deviation in the lightning withstand capacity of the substation. First, process the data of lightning strike return stroke times, the data of the proportion of lightning days, and the data of the maximum value of lightning current amplitude to obtain the lightning damage force data, and then combine it with the lightning withstand level data of the substation for processing to obtain the lightning withstand capacity deviation index of the substation.

[0157] According to the embodiments of the present invention, it further includes:

[0158] Processing the substation design characteristic data in combination with the geological monitoring record data to obtain the seismic capacity deviation index of the substation, specifically including:

[0159] Processing the peak ground acceleration data, the peak ground velocity data, and the seismic activity frequency data to obtain the seismic damage force data;

[0160] Processing the seismic bearing capacity data of the substation in combination with the seismic damage force data to obtain the seismic capacity deviation index of the substation;

[0161] Among them, the calculation formula of the seismic damage force data is:

[0162]

[0163] Among them, d p is the seismic damage force data, n m , j h , yu They are respectively the peak ground acceleration data, peak ground velocity data, and seismic activity frequency data, and σ 1 , σ 2 , σ 3 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0164] Among them, the calculation formula for the seismic capacity deviation index of the substation is:

[0165]

[0166] Among them, Z k is the seismic capacity deviation index of the substation, D p , d p are respectively the seismic bearing capacity data and seismic destructive force data of the substation, and λ 1 , λ 2 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform).

[0167] It should be noted that since a strong earthquake will cause the ground to shake violently, resulting in damage to substation buildings and equipment, it is necessary to evaluate whether there is a deviation in the seismic capacity of the substation. First, the seismic destructive force data is obtained by processing the peak ground acceleration data, peak ground velocity data, and seismic activity frequency data, and then the seismic capacity deviation index of the substation is obtained by combining with the seismic bearing capacity data of the substation.

[0168] In the second aspect, the present invention also discloses a power grid natural disaster prediction and defense system, including a memory and a processor. The memory includes a power grid natural disaster prediction and defense method program. When the power grid natural disaster prediction and defense method program is executed by the processor, the following steps are implemented:

[0169] Collect the historical monitoring information of the power grid in the target area, and extract the power grid facility monitoring record data, including meteorological record data, geographical feature data, terrain feature data, and geological monitoring record data;

[0170] Obtain the facility design characteristic data of the power grid in the target area, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data;

[0171] According to the tower design characteristic data, combine with the meteorological record data and terrain feature data for processing to obtain the tower wind resistance capacity deviation index;

[0172] According to the tower design characteristic data, combine with the meteorological record data and line parameter characteristic data for processing to obtain the tower anti-icing overload index;

[0173] Based on the design characteristic data of the substation, combined with the meteorological record data and geographical feature data for processing, a flood control capacity deviation index of the substation is obtained;

[0174] Based on the design characteristic data of the substation, combined with the meteorological record data for processing, a lightning withstand capacity deviation index of the substation is obtained;

[0175] Based on the design characteristic data of the substation, combined with the geological monitoring record data for processing, a seismic resistance capacity deviation index of the substation is obtained;

[0176] Based on the wind resistance capacity deviation index and ice overload resistance index of the pole tower, combined with the flood control capacity deviation index, lightning withstand capacity deviation index and seismic resistance capacity deviation index of the substation for processing, a power grid disaster prevention and control capacity deviation index is obtained and corresponding adjustments are made.

[0177] It should be noted that different natural disasters can cause different hazards and damages to the power grid. For example, strong winds may cause transmission line towers to collapse and wires to break. Freezing rain will form ice layers on the surface of transmission lines, increasing the weight of the lines and the vertical load on the towers, and even causing the towers to collapse. Floods can submerge power facilities such as substations and damage electrical equipment. Lightning strikes on transmission lines or substation equipment may cause insulator flashovers and equipment damage. Strong earthquakes will cause the ground to shake violently, resulting in damage to substation buildings and equipment. Traditional methods for predicting and preventing natural disasters in the power grid are relatively single and cannot comprehensively evaluate the comprehensive natural disaster prevention ability of the power grid. Therefore, a method for predicting and preventing natural disasters in the power grid that can be comprehensive is needed, which can comprehensively consider whether there are deviations in the power grid's defense capabilities against various natural disasters, so as to adjust and improve in a timely manner. In this embodiment, first, historical monitoring information of the power grid in the target area is collected, and monitoring record data of power grid facilities is extracted, including meteorological record data, geographical feature data, terrain feature data, and geological monitoring record data. Facility design characteristic data of the power grid in the target area is obtained, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data. According to the tower design characteristic data, combined with meteorological record data and terrain feature data, a tower wind resistance deviation index is obtained. According to the tower design characteristic data, combined with meteorological record data and line parameter characteristic data, a tower ice overload resistance index is obtained. According to the substation design characteristic data, combined with meteorological record data and geographical feature data, a substation flood prevention ability deviation index is obtained. According to the substation design characteristic data, combined with meteorological record data, a substation lightning resistance ability deviation index is obtained. According to the substation design characteristic data, combined with geological monitoring record data, a substation earthquake resistance ability deviation index is obtained. According to the tower wind resistance deviation index and the tower ice overload resistance index, combined with the substation flood prevention ability deviation index, the substation lightning resistance ability deviation index, and the substation earthquake resistance ability deviation index, a power grid disaster prevention ability deviation index is obtained and corresponding adjustments are made, thereby realizing the technology of predicting and preventing natural disasters in the power grid.

[0178] According to an embodiment of the present invention, the collecting of historical monitoring information of the power grid in the target area and the extraction of monitoring record data of power grid facilities, including meteorological record data, geographical feature data, terrain feature data, and geological monitoring record data, include:

[0179] Collecting historical monitoring information of the power grid in the target area and extracting monitoring record data of power grid facilities, including meteorological record data, geographical feature data, terrain feature data, and geological monitoring record data;

[0180] The meteorological record data includes wind speed record data, icing record data, flood record data, and lightning record data;

[0181] Extract the average wind speed data, maximum wind speed data, and extreme wind speed data based on the recorded wind speed data;

[0182] Extract the historical extreme value data of ice coating thickness, maximum value data of ice coating growth rate, and maximum value data of ice coating density based on the recorded ice coating data;

[0183] Extract the water level over-warning rate data and maximum water level value data based on the recorded flood data;

[0184] Extract the lightning strike return number data, lightning day proportion data, and maximum value data of lightning current amplitude based on the recorded lightning data;

[0185] The geographical feature data includes the substation altitude data and the substation slope data;

[0186] The terrain feature data includes the terrain type data and the terrain roughness data;

[0187] The geological monitoring record data includes the peak ground acceleration data, peak ground velocity data, and seismic activity frequency data.

[0188] It should be noted that for collecting historical monitoring information of the power grid in the target area, the historical duration can be set as needed. For example, the historical monitoring information from the commissioning of the regional power grid to the present or the past 20 years can be used. Then, based on the historical monitoring information, the monitoring record data of power grid facilities are extracted, including meteorological record data, geographical feature data, topographical feature data, and geological monitoring record data. Among them, the meteorological record data includes wind speed record data, icing record data, flood record data, and lightning record data. The average wind speed, maximum wind speed, and extreme wind speed data are extracted from the wind speed record data. Among them, through extreme value statistical methods, such as Gumbel distribution, Pearson-TypeⅢ distribution, etc., the historical maximum wind speed data is fitted to calculate the extreme wind speed under a certain recurrence period (such as once in 30 years, 50 years, or 100 years). The historical extreme value of icing thickness, the maximum value of icing growth rate, and the maximum value of icing density data are extracted from the icing record data. Among them, the historical extreme value of icing thickness refers to the maximum icing thickness data collected over the years in the area where the power grid is located, which can be obtained from power grid operation and maintenance records, meteorological departments, or on-site monitoring equipment. The icing density varies depending on the icing type (such as rime, glaze, etc.) and meteorological conditions. The water level over-warning rate and the maximum water level value data are extracted from the flood record data. The lightning strike return number, the proportion of lightning days, and the maximum value of lightning current amplitude data are extracted from the lightning record data. Among them, the lightning day refers to the number of days with lightning activities in the area, which is a simple indicator to measure the frequency of lightning activities. The number of lightning days can be obtained from the local meteorological department. Generally, the more the number of lightning days, the greater the probability of the substation being struck by lightning. The geographical feature data includes the altitude of the substation and the slope data of the substation. The topographical feature data includes the terrain type and the terrain roughness data. The geological monitoring record data includes the peak value of ground motion acceleration, the peak value of ground motion velocity, and the earthquake activity frequency data. Among them, the peak value of ground motion acceleration is the maximum value of the ground acceleration during the ground motion process, and the peak value of ground motion velocity is the maximum value of the ground velocity.

[0189] According to an embodiment of the present invention, obtaining the facility design characteristic data of the power grid in the target area includes tower design characteristic data, substation design characteristic data, and line parameter characteristic data, including:

[0190] Obtaining the facility design characteristic data of the power grid in the target area includes tower design characteristic data, substation design characteristic data, and line parameter characteristic data;

[0191] The tower design characteristic data includes tower wind-resistant design wind speed data and tower ice-loading bearing data;

[0192] The substation design characteristic data includes substation flood control design capacity data, substation lightning protection level data, and substation seismic bearing capacity data;

[0193] The line parameter characteristic data includes insulator string length data and span size data.

[0194] It should be noted that obtaining the facility design characteristic data of the target area power grid includes tower design characteristic data, substation design characteristic data, and line parameter characteristic data. Among them, the tower design characteristic data includes the tower wind resistance design wind speed and the tower ice load-bearing data, the substation design characteristic data includes the substation flood control design capacity, the substation lightning resistance level, and the substation seismic bearing capacity data, and the line parameter characteristic data includes insulator string length data and span size data. The above data can be obtained by querying materials such as the design drawings of the regional power grid.

[0195] According to an embodiment of the present invention, processing the tower design characteristic data in combination with the meteorological record data, meteorological statistical characteristic data, and terrain characteristic data to obtain a tower wind resistance ability deviation index includes:

[0196] Processing the terrain type data and terrain roughness data through a preset wind field numerical model to obtain a wind speed acceleration effect coefficient;

[0197] Processing the average wind speed data, maximum wind speed data, and extreme wind speed data in combination with the wind speed acceleration effect coefficient to obtain wind speed destructive force data;

[0198] Processing the tower wind resistance design wind speed data in combination with the wind speed destructive force data to obtain a tower wind resistance ability deviation index.

[0199] It should be noted that strong winds may cause transmission line towers to tilt or break. For example, when the typhoon wind speed exceeds the designed wind speed that the tower can withstand, the lateral force on the tower foundation is too large, and it is prone to tilt or collapse, resulting in large-scale power outages. Therefore, it is necessary to evaluate whether there is a deviation in the ability of the tower to resist strong wind damage. First, since different terrains have a significant impact on wind speed, and terrain roughness is a parameter that describes the influence of the ground surface on wind speed, which is related to factors such as ground vegetation and buildings. Different roughnesses result in different near-surface wind speed profiles. In areas with large roughness (such as the city center with a large number of high-rise buildings), the change rate of wind speed with height is relatively large, while in areas with small roughness (such as open grasslands), the change of wind speed with height is relatively slow. Therefore, according to the terrain type data and terrain roughness data, they are input into a preset wind field numerical model for processing to obtain the wind speed acceleration effect coefficient. Among them, the wind field numerical model belongs to the computational fluid dynamics (CFD) model, such as the Reynolds-averaged Navier-Stokes (RANS) equation solver, which can simulate the wind field under complex terrain conditions. Input the terrain type data and terrain roughness data into the model, set appropriate boundary conditions (such as wind direction, reference wind speed, etc.), obtain the wind speed at different positions through numerical solution, and then calculate the wind speed acceleration effect coefficient based on the simulated wind speed and the reference wind speed. Then, according to the average wind speed, maximum wind speed, and extreme wind speed data, combined with the wind speed acceleration effect coefficient for processing, obtain the wind speed destructive force data, and then combined with the tower's wind resistance design wind speed data for processing to obtain the tower wind resistance ability deviation index;

[0200] Among them, the calculation formula for the wind speed destructive force data is:

[0201]

[0202] Among them, f s is the wind speed destructive force data, z x , a s , q w are the average wind speed data, maximum wind speed data, and extreme wind speed data respectively, p o is the wind speed acceleration effect coefficient, β 1 , β 2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0203] Among them, the calculation formula for the tower wind resistance ability deviation index is:

[0204]

[0205] Among them, K f is the tower wind resistance ability deviation index, F s , f sThey are the wind - resistant design wind speed data and wind speed destructive force data of the pole tower respectively, and α is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform).

[0206] According to an embodiment of the present invention, processing the pole - tower design characteristic data in combination with the meteorological record data and the line - parameter characteristic data to obtain a pole - tower anti - icing overload index includes:

[0207] Processing the historical extreme value data of ice - coating thickness, the maximum value data of ice - coating growth rate, and the maximum value data of ice - coating density in combination with the insulator string length data and the span size data to obtain ice - coating load data;

[0208] Processing the pole - tower anti - icing load - bearing data in combination with the ice - coating load data to obtain a pole - tower anti - icing overload index.

[0209] It should be noted that since freezing rain will form ice layers on the surface of the transmission line, increasing the line weight and the vertical load borne by the pole tower, and even causing the pole tower to collapse. Therefore, it is necessary to evaluate whether there is a deviation in the pole - tower's ability to resist ice - coating load. First, process the historical extreme value data of ice - coating thickness, the maximum value data of ice - coating growth rate, and the maximum value data of ice - coating density in combination with the insulator string length data and the span size data to obtain ice - coating load data, and then process it in combination with the pole - tower anti - icing load - bearing data to obtain a pole - tower anti - icing overload index. Among them, the length of the insulator string will affect the insulation distance and mechanical strength of the line. A longer insulator string can provide a larger insulation distance under ice - coating conditions, but it will also increase the vertical load of the conductor. The span is the horizontal distance between adjacent pole towers, and the span size directly affects the tension of the conductor and the distribution of ice - coating weight on the pole tower. A larger span will cause the conductor to generate greater tension and sag when ice - coated, increasing the load on the pole tower;

[0210] Among them, the calculation formula of the ice - coating load data is:

[0211]

[0212] Among them, b z is the ice - coating load data, x c , s d , w e are the historical extreme value data of ice - coating thickness, the maximum value data of ice - coating growth rate, and the maximum value data of ice - coating density respectively, i u , k j are the insulator string length data and the span size data respectively, δ 1 , δ 2 , δ 3 , ε 1 , ε 2is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0213] Among them, the calculation formula for the tower anti-icing overload index is:

[0214]

[0215] Among them, G b is the tower anti-icing overload index, B z , b z are respectively the tower anti-icing load-bearing data and the ice-loading data, and φ is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform).

[0216] According to an embodiment of the present invention, processing the tower wind resistance ability deviation index and the tower anti-icing overload index, in combination with the substation flood prevention ability deviation index, the substation lightning resistance ability deviation index, and the substation earthquake resistance ability deviation index to obtain a power grid disaster prevention ability deviation index, and performing corresponding adjustments, including:

[0217] Processing the tower wind resistance ability deviation index and the tower anti-icing overload index, in combination with the substation flood prevention ability deviation index, the substation lightning resistance ability deviation index, and the substation earthquake resistance ability deviation index to obtain a power grid disaster prevention ability deviation index;

[0218] Comparing the power grid disaster prevention ability deviation index with a preset power grid disaster prevention ability deviation threshold to obtain a threshold comparison result;

[0219] Judging whether the natural disaster prevention ability of the power grid in the target area meets the standard according to the threshold comparison result;

[0220] If the threshold comparison result is less than the preset threshold, the natural disaster prevention ability of the power grid in the target area meets the standard;

[0221] Otherwise, it is determined as not meeting the standard, and corresponding adjustments are made to the power grid in the target area.

[0222] It should be noted that, according to the deviation index of the wind resistance ability of the pole tower and the deviation index of the ice-overloading resistance ability of the pole tower, combined with the deviation index of the flood prevention ability of the substation, the deviation index of the lightning resistance ability of the substation, and the deviation index of the earthquake resistance ability of the substation for processing, the deviation index of the power grid disaster prevention ability is obtained, and then compared with the preset deviation threshold of the power grid disaster prevention ability to obtain the result of the threshold comparison. According to the result of the threshold comparison, it is judged whether the natural disaster prevention ability of the power grid in the target area meets the standard. If the result of the threshold comparison is less than the preset threshold, it indicates that the natural disaster prevention ability of the power grid in the target area meets the standard and no adjustment is required. Otherwise, it indicates that the natural disaster prevention ability of the power grid in the target area does not meet the standard and corresponding adjustments need to be made to the power grid in the target area;

[0223] Among them, the calculation formula of the deviation index of the power grid disaster prevention ability is:

[0224]

[0225] Among them, Y w is the deviation index of the power grid disaster prevention ability, K f , G b , S f , N l , Z k are respectively the deviation index of the wind resistance ability of the pole tower, the deviation index of the ice-overloading resistance ability of the pole tower, the deviation index of the flood prevention ability of the substation, the deviation index of the lightning resistance ability of the substation, and the deviation index of the earthquake resistance ability of the substation, γ 1 , γ 2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through the preset power grid natural disaster prevention monitoring platform).

[0226] According to the embodiment of the present invention, it further includes:

[0227] Processing according to the substation design characteristic data, combined with the meteorological record data and the geographical feature data to obtain the deviation index of the flood prevention ability of the substation, specifically including:

[0228] Processing according to the water level over-warning rate data and the highest water level value data, combined with the substation altitude data and the substation slope data to obtain the flood destructive force data;

[0229] Processing according to the substation flood prevention design ability data combined with the flood destructive force data to obtain the deviation index of the flood prevention ability of the substation;

[0230] Among them, the calculation formula of the flood destructive force data is:

[0231]

[0232] Among them, h s is the flood destructive force data, cv and d f are respectively the data of the water level exceeding the warning rate and the data of the highest water level value, and e r and t y are respectively the data of the altitude of the substation and the data of the slope of the substation, and η 1 and η 2 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0233] Among them, the calculation formula of the flood prevention ability deviation index of the substation is:

[0234]

[0235] Among them, S f is the flood prevention ability deviation index of the substation, and H s and h s are respectively the flood prevention design ability data of the substation and the flood damage force data, and μ 1 and μ 2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset power grid natural disaster prevention and monitoring platform).

[0236] It should be noted that since floods will submerge power facilities such as substations and damage electrical equipment, it is necessary to evaluate whether there is a deviation in the flood prevention ability of the substation. First, based on the water level exceeding the warning rate data and the highest water level value data, combined with the substation altitude data and the substation slope data for processing, the flood damage force data is obtained, and then combined with the substation flood prevention design ability data for processing to obtain the substation flood prevention ability deviation index.

[0237] According to the embodiment of the present invention, it further includes:

[0238] Processing according to the substation design characteristic data and combining the meteorological record data to obtain the lightning resistance ability deviation index of the substation, specifically including:

[0239] Processing according to the lightning strike return stroke number data, lightning day proportion data and maximum lightning current amplitude data to obtain the lightning damage force data;

[0240] Processing according to the substation lightning resistance level data combined with the lightning damage force data to obtain the lightning resistance ability deviation index of the substation;

[0241] Among them, the calculation formula of the lightning damage force data is:

[0242]

[0243] Among them, l d is the lightning damage force data, and b n and gh , r t are the data of lightning strike return stroke times, the proportion of lightning days, and the maximum value of lightning current amplitude respectively. ν 1 , ν 2 , ν 3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0244] Among them, the calculation formula of the lightning resistance ability deviation index of the substation is:

[0245]

[0246] Among them, N l is the lightning resistance ability deviation index of the substation, L d , l d are the lightning resistance level data and lightning damage force data of the substation respectively, π 1 , π 2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying through a preset power grid natural disaster prevention and monitoring platform).

[0247] It should be noted that since lightning strike is one of the main reasons for power grid failures, when lightning strikes the substation equipment, it may cause equipment damage. Therefore, it is necessary to evaluate whether there is a deviation in the lightning resistance ability of the substation. First, process the lightning strike return stroke times data, the proportion of lightning days data, and the maximum value of lightning current amplitude data to obtain the lightning damage force data, and then combine the lightning resistance level data of the substation for processing to obtain the lightning resistance ability deviation index of the substation.

[0248] According to the embodiment of the present invention, it further includes:

[0249] Processing the substation design characteristic data in combination with the geological monitoring record data to obtain the seismic resistance ability deviation index of the substation, specifically including:

[0250] Processing the peak ground acceleration data, peak ground velocity data, and seismic activity frequency data to obtain the seismic damage force data;

[0251] Processing the seismic bearing capacity data of the substation in combination with the seismic damage force data to obtain the seismic resistance ability deviation index of the substation;

[0252] Among them, the calculation formula of the seismic damage force data is:

[0253]

[0254] Among them, d p is the seismic damage force data, n m , j h , yu They are respectively the peak ground acceleration data, peak ground velocity data, and seismic activity frequency data, and σ 1 , σ 2 , σ 3 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform);

[0255] Among them, the calculation formula for the seismic capacity deviation index of the substation is:

[0256]

[0257] Among them, Z k is the seismic capacity deviation index of the substation, D p , d p are respectively the seismic bearing capacity data and seismic destructive force data of the substation, and λ 1 , λ 2 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying through a preset power grid natural disaster prevention and monitoring platform).

[0258] It should be noted that due to strong earthquakes causing the ground to shake violently, resulting in damage to substation buildings and equipment, it is necessary to evaluate whether there is a deviation in the seismic capacity of the substation. First, the seismic destructive force data is obtained by processing the peak ground acceleration data, peak ground velocity data, and seismic activity frequency data, and then the seismic capacity deviation index of the substation is obtained by combining with the seismic bearing capacity data of the substation.

[0259] The third aspect of the present invention provides a readable storage medium, in which a program for predicting and preventing power grid natural disasters is stored. When the program for predicting and preventing power grid natural disasters is executed by a processor, the steps of the method for predicting and preventing power grid natural disasters as described in any one of the above are realized.

[0260] The method, system and medium for predicting and preventing power grid natural disasters disclosed by the present invention collect the historical monitoring information of the power grid in the target area, extract the monitoring record data of power grid facilities, including meteorological record data, geographical feature data, topographical feature data and geological monitoring record data, obtain the facility design characteristic data of the power grid in the target area, including tower design characteristic data, substation design characteristic data and line parameter characteristic data, process according to the tower design characteristic data, in combination with the meteorological record data and the topographical feature data, to obtain the tower wind resistance deviation index, process according to the tower design characteristic data, in combination with the meteorological record data and the line parameter characteristic data, to obtain the tower anti-icing overload index, process according to the substation design characteristic data, in combination with the meteorological record data and the geographical feature data, to obtain the substation flood control capacity deviation index, process according to the substation design characteristic data, in combination with the meteorological record data, to obtain the substation lightning resistance capacity deviation index, process according to the substation design characteristic data, in combination with the geological monitoring record data, to obtain the substation earthquake resistance capacity deviation index, process according to the tower wind resistance deviation index and the tower anti-icing overload index, in combination with the substation flood control capacity deviation index, the substation lightning resistance capacity deviation index and the substation earthquake resistance capacity deviation index, to obtain the power grid disaster prevention capacity deviation index, and make corresponding adjustments, so as to realize the technology of predicting and preventing power grid natural disasters.

[0261] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling or communication connection between the components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0262] The units described as separate components above may or may not be physically separated. The components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0263] In addition, in each embodiment of the present invention, the functional units can all be integrated in one processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in one unit; the above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0264] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks and other various media that can store program codes.

[0265] Alternatively, if the above integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the aforementioned storage medium includes: mobile storage devices, ROM, RAM, magnetic disks, or optical disks and other various media that can store program codes.

Claims

1. A method for predicting and preventing natural disasters in a power grid, characterized in that: The following steps are involved: Collect historical monitoring information of the target area power grid and extract monitoring record data of power grid facilities, including meteorological record data, geographical feature data, topographic feature data and geological monitoring record data; Acquiring facility design characteristic data of the target area power grid, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data; According to the tower design characteristic data, combined with the meteorological record data and the terrain feature data, processing is performed to obtain a tower wind resistance deviation index; According to the tower design characteristic data, combined with the meteorological record data and line parameter characteristic data, processing is performed to obtain the tower anti-icing overload index; According to the substation design characteristic data, combined with the meteorological record data and the geographical feature data, processing is performed to obtain a substation flood control capacity deviation index; According to the substation design characteristic data, combined with the meteorological record data, processing is performed to obtain a substation lightning resistance deviation index; According to the substation design characteristic data, combined with the geological monitoring record data, processing is performed to obtain the substation seismic capacity deviation index; According to the tower wind resistance deviation index and the tower ice overload resistance index, combined with the substation flood control capability deviation index, the substation lightning resistance capability deviation index and the substation earthquake resistance capability deviation index, the power grid disaster defense capability deviation index is obtained and adjusted accordingly.

2. The method for predicting and preventing natural disasters in power grid according to claim 1, characterized in that: The historical monitoring information of the target area power grid is collected, and the monitoring record data of the power grid facilities is extracted, including meteorological record data, geographical feature data, terrain feature data and geological monitoring record data, including: Collect historical monitoring information of the target area power grid and extract monitoring record data of power grid facilities, including meteorological record data, geographical feature data, topographic feature data and geological monitoring record data; The meteorological record data includes wind speed record data, ice cover record data, flood record data and lightning record data; Extracting average wind speed data, maximum wind speed data and extreme wind speed data according to the wind speed record data; Extracting historical extreme value data of ice thickness, maximum value data of ice growth rate and maximum value data of ice density according to the ice record data; Extracting water level exceeding warning rate data and maximum water level value data according to the flood record data; Extracting lightning return stroke data, lightning daily percentage data and lightning current amplitude maximum value data according to the lightning record data; The geographical feature data includes substation altitude data and substation slope data; The terrain feature data includes terrain type data and terrain roughness data; The geological monitoring record data includes seismic acceleration peak data, seismic velocity peak data and seismic activity frequency data.

3. The method for predicting and preventing natural disasters in power grid according to claim 2, characterized in that: The obtaining of the facility design characteristic data of the target area power grid, including tower design characteristic data, substation design characteristic data and line parameter characteristic data, comprises: Acquiring facility design characteristic data of the target area power grid, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data; The tower design characteristic data includes tower wind resistance design wind speed data and tower ice resistance bearing data; The substation design characteristic data includes substation flood control design capacity data, substation lightning resistance level data and substation seismic bearing capacity data; The line parameter characteristic data includes insulator string length data and span size data.

4. The method for predicting and preventing natural disasters in power grid according to claim 3, characterized in that: The tower wind resistance deviation index is obtained by processing the tower design characteristic data in combination with the meteorological record data, meteorological statistical characteristic data and terrain characteristic data, including: Processing the terrain type data and terrain roughness data through a preset wind field numerical model to obtain a wind speed acceleration effect coefficient; The wind speed destructive force data is obtained by processing the average wind speed data, the maximum wind speed data and the extreme wind speed data in combination with the wind speed acceleration effect coefficient; The tower wind resistance design wind speed data is processed in combination with the wind speed destructive force data to obtain a tower wind resistance capacity deviation index.

5. The method for predicting and preventing natural disasters in power grid according to claim 4, characterized in that: The tower design characteristic data is processed in combination with the meteorological record data and the line parameter characteristic data to obtain the tower anti-icing overload index, including: According to the historical extreme value data of ice thickness, the maximum value data of ice growth rate and the maximum value data of ice density, combined with the insulator string length data and the span size data, the ice load data is obtained by processing; The tower anti-icing overload index is obtained by processing the tower anti-icing bearing data in combination with the icing loading data.

6. The method for predicting and preventing natural disasters in power grid according to claim 5, characterized in that: The method of processing the tower wind resistance capacity deviation index and the tower ice resistance overload index in combination with the substation flood control capacity deviation index, the substation lightning resistance capacity deviation index and the substation earthquake resistance capacity deviation index to obtain the power grid disaster defense capacity deviation index and make corresponding adjustments includes: According to the tower wind resistance capacity deviation index and the tower ice resistance overload index, combined with the substation flood control capacity deviation index, the substation lightning resistance capacity deviation index and the substation earthquake resistance capacity deviation index, a grid disaster defense capacity deviation index is obtained; Comparing the power grid disaster defense capability deviation index with a preset power grid disaster defense capability deviation threshold to obtain a threshold comparison result; Determining whether the natural disaster defense capability of the target area power grid meets the standard according to the threshold comparison result; If the threshold comparison result is less than the preset threshold, the natural disaster defense capability of the target area power grid meets the standard; Otherwise, it is determined to be not up to standard, and the target area power grid is adjusted accordingly.

7. A natural disaster prediction and defense system for power grids, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of a method for predicting and defending against natural disasters in a power grid, and when the program of the method for predicting and defending against natural disasters in a power grid is executed by the processor, the following steps are implemented: Collect historical monitoring information of the target area power grid and extract monitoring record data of power grid facilities, including meteorological record data, geographical feature data, topographic feature data and geological monitoring record data; Acquiring facility design characteristic data of the target area power grid, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data; According to the tower design characteristic data, combined with the meteorological record data and the terrain feature data, processing is performed to obtain a tower wind resistance deviation index; According to the tower design characteristic data, combined with the meteorological record data and line parameter characteristic data, processing is performed to obtain the tower anti-icing overload index; According to the substation design characteristic data, combined with the meteorological record data and the geographical feature data, processing is performed to obtain a substation flood control capacity deviation index; According to the substation design characteristic data, combined with the meteorological record data, processing is performed to obtain a substation lightning resistance deviation index; According to the substation design characteristic data, combined with the geological monitoring record data, processing is performed to obtain the substation seismic capacity deviation index; According to the tower wind resistance deviation index and the tower ice overload resistance index, combined with the substation flood control capability deviation index, the substation lightning resistance capability deviation index and the substation earthquake resistance capability deviation index, the power grid disaster defense capability deviation index is obtained and adjusted accordingly.

8. The system for predicting and preventing natural disasters in power grid according to claim 7, characterized in that: The historical monitoring information of the target area power grid is collected, and the monitoring record data of the power grid facilities is extracted, including meteorological record data, geographical feature data, terrain feature data and geological monitoring record data, including: Collect historical monitoring information of the target area power grid and extract monitoring record data of power grid facilities, including meteorological record data, geographical feature data, topographic feature data and geological monitoring record data; The meteorological record data includes wind speed record data, ice cover record data, flood record data and lightning record data; Extracting average wind speed data, maximum wind speed data and extreme wind speed data according to the wind speed record data; Extracting historical extreme value data of ice thickness, maximum value data of ice growth rate and maximum value data of ice density according to the ice record data; Extracting water level exceeding warning rate data and maximum water level value data according to the flood record data; Extracting lightning return stroke data, lightning daily percentage data and lightning current amplitude maximum value data according to the lightning record data; The geographical feature data includes substation altitude data and substation slope data; The terrain feature data includes terrain type data and terrain roughness data; The geological monitoring record data includes seismic acceleration peak data, seismic velocity peak data and seismic activity frequency data.

9. The system for predicting and preventing natural disasters in power grid according to claim 8, characterized in that: The obtaining of the facility design characteristic data of the target area power grid, including tower design characteristic data, substation design characteristic data and line parameter characteristic data, comprises: Acquiring facility design characteristic data of the target area power grid, including tower design characteristic data, substation design characteristic data, and line parameter characteristic data; The tower design characteristic data includes tower wind resistance design wind speed data and tower ice resistance bearing data; The substation design characteristic data includes substation flood control design capacity data, substation lightning resistance level data and substation seismic bearing capacity data; The line parameter characteristic data includes insulator string length data and span size data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a method program for predicting and preventing natural disasters in a power grid. When the method program for predicting and preventing natural disasters in a power grid is executed by a processor, the steps of the method for predicting and preventing natural disasters in a power grid as described in any one of claims 1 to 6 are implemented.

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