Overhead transmission line lightning stroke risk early warning method and device and medium

Abstract

The invention discloses an overhead transmission line lightning stroke risk early warning method and device and a medium. The method comprises the following steps: training a pre-constructed neural network based on historical data of a lightning stroke event of the overhead transmission line, and generating a lightning current amplitude prediction model; based on the lightning current amplitude in the historical data, constructing a lightning current amplitude probability distribution function of each region; obtaining input data of lightning current amplitude prediction through meteorological thunderstorm data and numerical weather forecast data; inputting the input data into a lightning current amplitude prediction model, obtaining a predicted lightning current amplitude, inputting the predicted lightning current amplitude into a corresponding lightning current amplitude probability distribution function, and determining a lightning current amplitude probability; and determining a lightning trip-out rate of the overhead transmission line according to the predicted lightning current amplitude and the lightning current amplitude probability in combination with the transmission line data, and determining a lightning flashover risk early warning level of the transmission line according to the lightning trip-out rate.

Description

Technical Field

[0001] The present invention relates to the field of high voltage technology, and more specifically, to a method, device and medium for warning of lightning strike risk of overhead power transmission lines. Background Art

[0002] The power system is an important pillar of national economic and social development. Its overhead transmission network for transmitting electric energy has been subjected to long-term lightning damage due to its wide distribution and long lines. Lightning has become one of the most important factors affecting the safe and stable operation of transmission lines. For a long time, in order to solve the problem of lightning protection in power grids, many lightning protection technical measures have been proposed and widely applied to transmission lines, and have achieved remarkable results. However, these technical measures basically adopt the route of "passive lightning protection", and cannot solve all protection problems in real time for lightning activities with great differences in time distribution, spatial distribution, frequency, intensity and other characteristics. How to achieve active lightning defense capabilities for transmission lines has always been the direction of efforts of many authors and scholars. One of the key issues in solving active lightning protection is to study and implement lightning strike warning technology for transmission lines. This technology is of great and practical significance for dynamically scheduling line transmission loads, making preparations for line repair and operation in advance, and thus avoiding and reducing losses caused by lightning in power grids.

[0003] At present, many scholars at home and abroad have proposed methods, devices and systems for lightning (disaster) warning, mainly using radar, meteorological satellite, lightning monitoring network, atmospheric electric field instrument and other methods for large-scale, medium-scale and small-scale warning. For example, in foreign countries, Hondl et al. found that Doppler weather radar echo data can reflect the initial characteristics of thunderstorms by analyzing the thunderstorm process in central Florida. Brandom et al. used WSR-88D radar and some other meteorological data to study the prediction of cloud-to-ground flashes. Smith compared and analyzed the synchronous satellite infrared cloud images and the ground-to-ground flash positioning results of the US National Lightning Monitoring Network and found that lightning warning can be carried out by monitoring the cooling rate of cloud top temperature. In China, scholars from the China Meteorological Science Academy, based on Doppler radar data, combined with corresponding lightning and temperature sounding data, summarized a set of preliminary forecasting methods that can determine whether it is a thunderstorm cell and predict the period of the first lightning. Scholars from the PLA University of Science and Technology integrated radar echoes with atmospheric electric field data to warn of lightning. Scholars from the Chinese Academy of Meteorological Sciences used the observation data of the MLDARS lightning location system and combined it with sounding data to make potential forecasts of lightning activities, and used the detection data of ground electric field meters to provide lightning monitoring and forecasting. Scholars from the Xiamen Lightning Protection Center achieved more accurate short-term lightning warnings by using two warning methods: the atmospheric electric field meter, which combines the electric field strength and the lightning distance (the distance between the lightning location and the electric field meter). Scholars from the Shanghai Meteorological Disaster Prevention Engineering Technology Center proposed a lightning warning method based on the electric field time difference threshold based on the ground electric field meter by analyzing the basic characteristics of the atmospheric electric field time difference between sunny days and thunderstorms. Scholars from Nanjing University of Information Science and Technology proposed a method for lightning warning using the atmospheric electric field strength value and its difference value as warning parameters, combined with electric field, lightning location and radar echo data, as well as a lightning warning method based on thunderstorm data and using multivariate regression technology.

[0004] These lightning warning methods are generally aimed at warning or forecasting whether lightning occurs in a certain area, and they are still unable to warn of the hazards caused by lightning. Overhead transmission lines have a certain level of lightning resistance. Whether lightning can hit the line and cause flashover is closely related to the structure and insulation characteristics of the line, the intensity characteristics of lightning, and the terrain and geomorphic characteristics around the line. It is not very meaningful and valuable to only warn whether lightning activities occur in a certain area nearby. How to achieve lightning flashover warning for overhead transmission lines still needs detailed research. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method, device and medium for warning of lightning strike risk of overhead power transmission lines.

[0006] According to one aspect of the present invention, there is provided a method for early warning of lightning strike risk of overhead transmission lines, comprising:

[0007] A pre-built neural network is trained based on historical data of lightning strikes on overhead transmission lines to generate a lightning current amplitude prediction model;

[0008] Based on the lightning current amplitude in historical data, the probability distribution function of lightning current amplitude in each region is constructed;

[0009] The input data for lightning current amplitude prediction is obtained through meteorological thunderstorm data and numerical weather forecast data;

[0010] Inputting the input data into the lightning current amplitude prediction model to obtain the predicted lightning current amplitude, and inputting the predicted lightning current amplitude into the corresponding lightning current amplitude probability distribution function to determine the lightning current amplitude probability;

[0011] Based on the predicted lightning current amplitude and lightning current amplitude probability, combined with the transmission line data, the lightning tripping rate of the overhead transmission line is determined, and the lightning flashover risk warning level of the transmission line is determined based on the lightning tripping rate.

[0012] Optionally, the historical data includes echo intensity, echo top height, vertical accumulated liquid water content, combined reflectivity factor and lightning current amplitude.

[0013] Optionally, the input data includes echo intensity, echo top height, vertically accumulated liquid water content, and a combined reflectivity factor.

[0014] Optionally, the expression of the lightning current amplitude probability distribution function is:

[0015]

[0016] Where P is the probability of occurrence of lightning current with an amplitude greater than I; I is the lightning current amplitude; a is the median current; and b is the concentration parameter of the lightning current amplitude distribution.

[0017] Optionally, the transmission line data includes line structural characteristics and topographical features of the line corridor.

[0018] Optionally, a pre-built neural network is trained based on historical data of lightning strike events on overhead transmission lines to generate a lightning current amplitude prediction model, including:

[0019] Apply the neural network function to establish the BP network and initialize the weights and thresholds of each connection chain of the BP network;

[0020] Input sample data from historical data into the BP network, use the BP algorithm to forward calculate the output values ​​of the hidden layer and output layer of the BP network, reversely calculate the equivalent error values ​​of each layer of neurons, adjust the connection weights and thresholds of each layer until the error meets the requirements, save the calculation results, and generate a lightning current amplitude prediction model.

[0021] According to another aspect of the present invention, there is provided an overhead transmission line lightning risk warning device, comprising:

[0022] A generation module for training a pre-built neural network based on historical data of lightning strikes on overhead transmission lines to generate a lightning current amplitude prediction model;

[0023] A construction module is used to construct a probability distribution function of lightning current amplitude in each region based on lightning current amplitude in historical data;

[0024] The module is used to obtain input data for lightning current amplitude prediction through meteorological thunderstorm data and numerical weather forecast data;

[0025] An acquisition module is used to input the input data into the lightning current amplitude prediction model to obtain the predicted lightning current amplitude, and input the predicted lightning current amplitude into the corresponding lightning current amplitude probability distribution function to determine the lightning current amplitude probability;

[0026] The determination module is used to determine the lightning trip rate of the overhead transmission line based on the predicted lightning current amplitude and the lightning current amplitude probability in combination with the transmission line data, and to determine the lightning flashover risk warning level of the transmission line based on the lightning trip rate.

[0027] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0028] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0029] Thus, the present invention obtains the correlation model between the lightning current amplitude and the echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor through meteorological thunderstorm data and numerical weather forecast data, and calculates the probability corresponding to the lightning current amplitude. Then, the lightning trip rate of the overhead transmission line is given in combination with the structure and insulation characteristics of the line, the lightning intensity characteristics, the terrain and geomorphic characteristics around the line, and the lightning trip rate index is used to give the lightning flashover risk warning level of the overhead transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0031] Figure 1 It is a flow chart of a method for early warning of lightning strike risk of overhead transmission lines provided by an exemplary embodiment of the present invention;

[0032] Figure 2 It is a flow chart of a method for early warning of lightning strike risk of overhead transmission lines provided by an exemplary embodiment of the present invention;

[0033] Figure 3 It is a structural schematic diagram of an overhead transmission line lightning strike risk warning device provided by an exemplary embodiment of the present invention;

[0034] Figure 4 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0035] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0036] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0037] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0038] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0039] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0040] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0041] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0042] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0043] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0044] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0045] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0046] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0047] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0048] Exemplary Methods

[0049] Figure 1 FIG. 1 is a flow chart of a method for early warning of lightning strike risk of overhead transmission lines provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the overhead transmission line lightning strike risk warning method 100 includes the following steps:

[0050] Step 101, training a pre-built neural network based on historical data of lightning strike events on overhead transmission lines to generate a lightning current amplitude prediction model;

[0051] Step 102, constructing a lightning current amplitude probability distribution function for each region based on the lightning current amplitude in the historical data;

[0052] Step 103, obtaining input data for lightning current amplitude prediction through meteorological thunderstorm data and numerical weather forecast data;

[0053] Step 104, inputting the input data into a lightning current amplitude prediction model to obtain a predicted lightning current amplitude, and inputting the predicted lightning current amplitude into a corresponding lightning current amplitude probability distribution function to determine a lightning current amplitude probability;

[0054] Step 105, determining the lightning trip rate of the overhead transmission line according to the predicted lightning current amplitude and the lightning current amplitude probability in combination with the transmission line data, and determining the lightning flashover risk warning level of the transmission line according to the lightning trip rate.

[0055] Specifically, the present invention obtains the correlation model between the lightning current amplitude and the echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor through meteorological thunderstorm data and numerical weather forecast data, and calculates the probability corresponding to the lightning current amplitude. Then, the lightning trip rate of the overhead transmission line is given in combination with the structure and insulation characteristics of the line, the lightning intensity characteristics, the terrain and geomorphic characteristics around the line, etc., and the lightning flashover risk warning level of the overhead transmission line is given according to the lightning trip rate index.

[0056] (1) Lightning current amplitude and probability prediction method

[0057] Lightning current is a non-periodic shock wave, which is related to meteorological conditions, geographical environment and other factors. It is a random variable. Radar and other lightning detection equipment cannot obtain the predicted amplitude of lightning current, but a large amount of statistical data shows that the intensity of lightning activity is related to parameter values ​​such as echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor, while parameter values ​​such as echo intensity can be predicted.

[0058] Therefore, the correlation model between the lightning current amplitude and the echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor is obtained through meteorological thunderstorm data and numerical weather forecast data, as shown in formula (1). Then, the predicted value of the lightning current amplitude is obtained based on the real-time parameters obtained from the numerical weather forecast platform.

[0059] I=f(X 1 , X 2 , X 3 , X 4 ) (1)

[0060] Where: X 1 is the echo intensity; X 2 is the echo top height; X 3 is the vertical accumulation of liquid water content; X4 is the combined reflectivity factor.

[0061] In order to obtain the specific expression of formula (1), M historical transmission line lightning strike events are selected as samples, and the lightning current amplitude, lightning current waveform and lightning-related characteristic parameters of the accident lightning location system are analyzed and studied. The input variables are four parameter values ​​such as the echo intensity recorded by the numerical weather forecast platform when the transmission line trips, and the output is the lightning current amplitude when the tripping occurs. The present invention uses the back propagation (BP) neural network algorithm to identify the relationship between the lightning current amplitude and the echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor. The specific steps are as follows:

[0062] a) Obtain parameter information when M transmission line lightning strikes occur as calculation sample data.

[0063] b) Apply the neural network function to establish the BP network and initialize the weights and thresholds of each connection chain in the network.

[0064] c) The BP algorithm calculates the network, inputs the calculation samples, forward calculates the output values ​​of the hidden layer and output layer of the network, reversely calculates the equivalent error values ​​of each layer of neurons, and then adjusts the connection weights and thresholds of each layer until the error meets the requirements, and finally saves the calculation results.

[0065] d) Compare the calculation results with historical data to verify the correctness of the calculated network.

[0066] There are regional differences in the probability distribution of lightning current amplitude, which is generally expressed by the cumulative distribution function, which can be obtained by two methods:

[0067] a) The lightning current amplitude is directly measured by sensors such as magnetic steel rods installed on the transmission line towers, and the empirical formula for the cumulative probability distribution of the lightning current amplitude is fitted based on a large amount of measurement data. Formula (2) can be used to represent the general areas of my country, and formula (3) can be used to represent the northwest region outside southern Shaanxi and parts of the Inner Mongolia Autonomous Region:

[0068]

[0069] Where: P is the probability of occurrence of lightning current with an amplitude greater than I; I is the lightning current amplitude, the unit is kiloampere (kA).

[0070] b) Based on the lightning ground-to-ground lightning signal information measured by my country's wide-area lightning-to-ground lightning monitoring system, the lightning current amplitude is obtained through inversion calculation. A large amount of lightning current amplitude data can be statistically fitted to obtain the lightning current amplitude cumulative probability distribution function expressed by formula (4):

[0071]

[0072] Where: P is the probability of occurrence of lightning current with an amplitude greater than I; I is the lightning current amplitude, in kiloamperes (kA); a is the median current (meaning that the probability of occurrence of lightning current exceeding this amplitude is 50%), in kiloamperes (kA); b is the concentration parameter of the lightning current amplitude distribution.

[0073] The fitting accuracy of lightning current amplitude probability distribution depends on long-term data accumulation. For areas with incomplete lightning monitoring data, a=31 and b=2.6 can be taken for the first lightning discharge.

[0074] (2) Warning method for lightning flashover risk of overhead transmission lines

[0075] Through meteorological thunderstorm data and numerical weather forecast data, the correlation model between lightning current amplitude and echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor is obtained, and the probability corresponding to the lightning current amplitude is calculated. Then, combined with the structure and insulation characteristics of the line, lightning intensity characteristics, and terrain characteristics around the line, the lightning trip rate of the overhead transmission line is given. Based on the lightning trip rate index, the lightning flashover risk warning level of the overhead transmission line is given. Lightning risk warning methods such as Figure 2 shown.

[0076] The principle of lightning risk warning for transmission lines is mainly divided into three parts:

[0077] (1) Parameter statistics, including lightning parameter statistics and line data statistics. The former is obtained through meteorological thunderstorm data and numerical weather thunderstorm forecast data; the latter includes line structure characteristics and topographical features of the line corridor;

[0078] (2) Based on parameter statistics, the lightning risk assessment model for overhead transmission lines is used to calculate the lightning tripping rate of each tower on the entire line to obtain the lightning tripping rate of each tower;

[0079] The calculation of line lightning trip rate is divided into two steps:

[0080] a) Calculate the lightning trip rate of a tower according to formula (9):

[0081] N i =N Li η(g i P 1i +P sfi ) (9)

[0082] Where: N i is the lightning tripping rate of the ith tower, in units of times per 100 kilometers per year [times / (100km·a)]. When calculating the lightning tripping rate of a tower, the line length is the horizontal span of the tower; N Lig is the number of lightning strikes on the ith tower per year, in units of times per 100 km per year [times / (100 km·a)], and the calculation method is shown in formula (10); η is the arcing rate, and the calculation method for η of AC lines is shown in formula (11), and η is 1 for DC lines; g i is the pole striking rate of the ith pole tower, 1 / 6 in plains and 1 / 4 in mountainous areas; 1i The lightning strike resistance level I of the i-th tower when it exceeds the top of the tower and the ground line 1 The lightning current probability, P(I 0 ≥I 1 ). 1 The calculation method is in accordance with Appendix D.1.6 of GB / T 50064-2014 and Appendix B.1.6 of GB / T 24842-2018. 1 The system working voltage varies with the instantaneous value of the lightning strike, and is evenly distributed according to a power frequency cycle, and then the weighted average value is calculated using the interval combination statistical method; P sfi is the shielding failure probability of the ith tower, which should take into account the instantaneous value of the operating voltage at the time of lightning strike, evenly distributed according to one power frequency cycle, and calculated using the interval combination statistical method. The shielding failure calculation method is shown in Appendix E.

[0083] N Li =0.1N g (28h t 0.6 +b) (10)

[0084] Where: h t is the height of the tower, in meters (m); b is the distance between the two ground wires, in meters (m).

[0085] η=(4.5E 0.75 -14)×10 -2 (11)

[0086] Where, E is the average operating voltage (effective value) gradient of the insulator string, expressed in kilovolts per meter (kV / m). For a neutral point effective grounding system, U n is the system average operating voltage (effective value), l i is the discharge distance of the insulator string.

[0087] b) Calculate the lightning trip rate of the entire line using the weighted statistical method according to formula (12):

[0088]

[0089] Where: N is the weighted statistical value of the lightning trip rate of the entire line, with the unit of times per 100 kilometers per year [times / (100km·a)];

[0090] L i is the horizontal span of the ith tower, in kilometers (km).

[0091] For double-circuit lines on the same tower, the back-fighting tripping rate of a single-circuit line is equal to half of the total back-fighting tripping rate, and the back-fighting tripping rate is calculated separately.

[0092] (3) Warning of lightning flashover risk on overhead transmission lines: Based on the set standards and the calculation results of the tower lightning tripping rate, the lightning resistance performance of each tower is evaluated and the risk warning level is issued.

[0093] In one embodiment of the present invention, taking the Shaanxi Power Grid 330kV Xihan 1 Line as an example, since no lightning strike occurs in the grid where the line is located in the future time period, the effectiveness of the lightning warning model is verified by comparing and analyzing the traditional tripping rate of the line with the actual operation data.

[0094] Line parameters of 330kV Xiahan 1 line:

[0095] (1) Line length, ground wire type and annual number of thunderstorm days

[0096] The 330kV Xiahan 1 line is a single-circuit line with a total length of about 176.074km and a total of 391 towers. The conductor model is 2×LGJ-300 / 40, split in two and arranged horizontally; the ground wire model is GJ-50. The number of thunderstorm days per year is 40 days. The terrain and altitude along the line are shown in Table 1.

[0097] Table 1 Topography and altitude along the Xi-Han Line 1

[0098]

[0099] Note: Equivalent tower height: plain hd = 26-11*2 / 3 = 19m; hilly hd = 26m; mountainous hd = 2*26 = 52m.

[0100] (2) Information such as tower number, longitude and latitude, tower nature, span, height, model, grounding resistance, insulator type and string length has been obtained.

[0101] The comparison table of the warning results and actual operation results of the lightning trip rate of the 330kV Xiahan 1 line is shown in Table 2. It can be seen that the deviation is less than 15%, which is basically consistent. The evaluation algorithm and model have high accuracy.

[0102] Table 2 Comparison of the warning results and actual operation results of the lightning trip rate

[0103]

[0104] Thus, the present invention obtains the correlation model between the lightning current amplitude and the echo intensity, echo top height, vertical accumulated liquid water content, and combined reflectivity factor through meteorological thunderstorm data and numerical weather forecast data, and calculates the probability corresponding to the lightning current amplitude. Then, the lightning trip rate of the overhead transmission line is given in combination with the structure and insulation characteristics of the line, the lightning intensity characteristics, the terrain and geomorphic characteristics around the line, and the lightning trip rate index is used to give the lightning flashover risk warning level of the overhead transmission line.

[0105] Exemplary Devices

[0106] Figure 3 FIG. 1 is a schematic diagram of a lightning strike risk warning device for overhead transmission lines provided by an exemplary embodiment of the present invention. Figure 3 As shown, the device 300 includes:

[0107] A generation module 310 is used to train a pre-built neural network based on historical data of lightning strike events on overhead power transmission lines to generate a lightning current amplitude prediction model;

[0108] A construction module 320 is used to construct a probability distribution function of lightning current amplitude in each region based on the lightning current amplitude in the historical data;

[0109] An obtaining module 330 is used to obtain input data for lightning current amplitude prediction through meteorological thunderstorm data and numerical weather forecast data;

[0110] An acquisition module 340 is used to input the input data into the lightning current amplitude prediction model, obtain the predicted lightning current amplitude, and input the predicted lightning current amplitude into the corresponding lightning current amplitude probability distribution function to determine the lightning current amplitude probability;

[0111] The determination module 350 is used to determine the lightning trip rate of the overhead transmission line according to the predicted lightning current amplitude and the lightning current amplitude probability in combination with the transmission line data, and determine the lightning flashover risk warning level of the transmission line according to the lightning trip rate.

[0112] Optionally, the historical data includes echo intensity, echo top height, vertical accumulated liquid water content, combined reflectivity factor and lightning current amplitude.

[0113] Optionally, the input data includes echo intensity, echo top height, vertically accumulated liquid water content, and a combined reflectivity factor.

[0114] Optionally, the expression of the lightning current amplitude probability distribution function is:

[0115]

[0116] Where P is the probability of occurrence of lightning current with an amplitude greater than I; I is the lightning current amplitude; a is the median current; and b is the concentration parameter of the lightning current amplitude distribution.

[0117] Optionally, the transmission line data includes line structural characteristics and topographical features of the line corridor.

[0118] Optionally, the generating module 310 includes:

[0119] The initialization submodule is used to establish a BP network by applying a neural network function and to initialize the weights and thresholds of each connection chain of the BP network;

[0120] The generation submodule is used to input sample data from historical data into the BP network, use the BP algorithm to forward calculate the output values ​​of the hidden layer and output layer of the BP network, reversely calculate the equivalent error values ​​of each layer of neurons, adjust the connection weights and thresholds of each layer until the error meets the requirements, save the calculation results, and generate a lightning current amplitude prediction model.

[0121] Exemplary Electronic Devices

[0122] Figure 4 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 4 As shown, the electronic device 40 includes one or more processors 41 and a memory 42 .

[0123] The processor 41 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0124] The memory 42 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 41 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 43 and an output device 44, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0125] In addition, the input device 43 may also include, for example, a keyboard, a mouse, etc.

[0126] The output device 44 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0127] Of course, to simplify, Figure 4 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0128] Exemplary computer program products and computer-readable storage media

[0129] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0130] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0131] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0132] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0133] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0134] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0135] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0136] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0137] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0138] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A lightning strike risk warning method for overhead transmission lines, characterized in that: include: A pre-built neural network is trained based on historical data of lightning strikes on overhead transmission lines to generate a lightning current amplitude prediction model; Based on the lightning current amplitude in the historical data, constructing a lightning current amplitude probability distribution function for each region; The input data for lightning current amplitude prediction is obtained through meteorological thunderstorm data and numerical weather forecast data; Inputting the input data into the lightning current amplitude prediction model to obtain a predicted lightning current amplitude, and inputting the predicted lightning current amplitude into a corresponding lightning current amplitude probability distribution function to determine a lightning current amplitude probability; According to the predicted lightning current amplitude and the lightning current amplitude probability, combined with the transmission line data, the lightning trip rate of the overhead transmission line is determined, and the lightning flashover risk warning level of the transmission line is determined according to the lightning trip rate.

2. The method according to claim 1, characterized in that The historical data include echo intensity, echo top height, vertical accumulated liquid water content, combined reflectivity factor and lightning current amplitude.

3. The method according to claim 1, characterized in that The input data include echo intensity, echo top height, vertically accumulated liquid water content and combined reflectivity factor.

4. The method according to claim 1, characterized in that The expression of the lightning current amplitude probability distribution function is: Where P is the probability of occurrence of lightning current with an amplitude greater than I; I is the lightning current amplitude; a is the median current; and b is the concentration parameter of the lightning current amplitude distribution.

5. The method according to claim 1, characterized in that The transmission line data includes line structure characteristics and topographical features of the line corridor.

6. The method according to claim 1, characterized in that A pre-built neural network is trained based on historical data of lightning strikes on overhead transmission lines to generate a lightning current amplitude prediction model, including: Applying a neural network function to establish a BP network, and initializing the weights and thresholds of each connection chain of the BP network; Input sample data in the historical data is input into the BP network, the BP algorithm is used to forward calculate the output values ​​of the hidden layer and the output layer of the BP network, the equivalent error values ​​of the neurons in each layer are reversely calculated, the connection weights and thresholds of each layer are adjusted until the error meets the requirements, the calculation results are saved, and the lightning current amplitude prediction model is generated.

7. An overhead power transmission line lightning strike risk warning device, characterized in that: include: A generation module for training a pre-built neural network based on historical data of lightning strikes on overhead transmission lines to generate a lightning current amplitude prediction model; A construction module, used to construct a probability distribution function of lightning current amplitude in each region based on the lightning current amplitude in the historical data; The module is used to obtain input data for lightning current amplitude prediction through meteorological thunderstorm data and numerical weather forecast data; An acquisition module, used for inputting the input data into the lightning current amplitude prediction model to obtain the predicted lightning current amplitude, and inputting the predicted lightning current amplitude into the corresponding lightning current amplitude probability distribution function to determine the lightning current amplitude probability; A determination module is used to determine the lightning trip rate of the overhead transmission line according to the predicted lightning current amplitude and the lightning current amplitude probability in combination with transmission line data, and determine the lightning flashover risk warning level of the transmission line according to the lightning trip rate.

8. The device according to claim 7, characterized in that The historical data include echo intensity, echo top height, vertical accumulated liquid water content, combined reflectivity factor and lightning current amplitude.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

10. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 6.

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