Indirect lightning stroke performance evaluation optimization method and system for overhead line
The method improves the accuracy and reliability of indirect lightning strike performance evaluation on overhead lines by comprehensive parameter collection and iterative optimization, ensuring alignment with actual conditions and optimizing protection measures.
Patent Information
- Application Number
- CN202411099633.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-12
- Publication Date
- 2024-12-27
AI Technical Summary
Existing methods for evaluating indirect lightning strike performance on overhead lines lack comprehensive lightning parameter collection, result in inaccurate model construction, and lack a systematic optimization strategy for lateral distance and lightning event selection, hindering automation and intelligence in power system protection.
A method involving the collection of detailed lightning parameters, construction of a virtual OHL model based on actual configurations, generation of probability distribution functions for lightning currents, and iterative adjustment of lateral distance and lightning event numbers to optimize evaluation accuracy.
Enhances the accuracy and reliability of lightning performance evaluation by aligning model simulations with actual conditions, reducing evaluation errors, and optimizing lightning protection measures through systematic and predictive assessment.
Smart Images
Figure CN119199407A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power system protection, and in particular to an indirect lightning strike performance evaluation and optimization method and system for overhead lines. Background Art
[0002] In recent years, with the continuous improvement of my country's power infrastructure, the safe operation of overhead lines (OHL) has received increasing attention. In the natural environment, lightning activity is one of the main causes of overhead line failures. Indirect lightning strikes, that is, when lightning strikes the ground or other objects near the line, the overvoltage generated on the overhead line through electromagnetic induction or inductive coupling often causes line insulator flashover, which in turn causes power system failures. Therefore, accurate evaluation of the indirect lightning performance of overhead lines is of great significance for improving the line's lightning resistance and ensuring the safety of the power system.
[0003] However, the existing technology has limitations in the collection of lightning parameters. The parameters of lightning current have a direct impact on the evaluation results, but the current methods often fail to fully consider the multi-dimensional characteristics of lightning current, such as the maximum amplitude, front time, duration, peak time, rise rate and fall rate of lightning current. The lack of these parameters will lead to a large deviation between the evaluation results and the actual situation. The existing virtual OHL model construction method is not accurate enough. Most models fail to fully consider the specific configuration and parameters of the actual overhead line, such as line height, soil conductivity and other factors. Therefore, the simplified processing of the model often leads to low accuracy of the evaluation results. The existing technology lacks an effective optimization strategy for the selection of lateral distance and total number of lightning events during the evaluation process. The selection of lateral distance has a significant impact on the evaluation results, and the existing methods are often selected based on experience or trial and error, lacking scientificity and systematicity. This decentralized evaluation method is not conducive to the automation and intelligence of the evaluation process, and limits the widespread application of overhead line indirect lightning performance evaluation technology in the field of power system protection. Summary of the invention
[0004] In view of the above-mentioned problems, the present invention is proposed.
[0005] Therefore, the technical problem solved by the present invention is: the existing power system protection technical methods have the problems of incomplete lightning parameter collection, low model accuracy, unclear evaluation parameter optimization strategy, and how to accurately evaluate and optimize the indirect lightning strike performance of overhead lines.
[0006] To solve the above technical problems, the present invention provides the following technical solutions: an optimization method for indirect lightning performance evaluation of overhead lines, comprising collecting lightning parameters, constructing a virtual OHL model based on actual OHL configuration and parameters; generating a probability distribution function of lightning current parameters in an evaluation area, and selecting a longitudinal distance of the evaluation area; selecting an initial lateral distance and a total number of lightning events, iteratively adjusting the lateral distance and the total number of lightning events, and outputting an optimal lateral distance.
[0007] As a preferred solution of the indirect lightning strike performance evaluation optimization method for overhead lines described in the present invention, the lightning parameters include the maximum amplitude of the lightning current, the leading edge time of the lightning current, the duration of the lightning current, the peak time of the lightning current, the rise rate of the lightning current, the fall rate of the lightning current, the frequency and location of the lightning strike, and the lightning current waveform parameters.
[0008] As a preferred solution of the indirect lightning performance evaluation optimization method for overhead lines described in the present invention, wherein: the construction of the virtual OHL model includes constructing a 1 km long virtual OHL model based on the actual OHL configuration and parameters, calculating the lateral distance y of the evaluation area l It is expressed as:
[0009]
[0010] Where CFO is the critical flashover voltage, h is the line height, σ g represents soil conductivity, and I0 represents the amplitude of lightning current.
[0011] As a preferred solution of the indirect lightning performance evaluation optimization method for overhead lines described in the present invention, the probability distribution function includes a probability distribution function for generating lightning current parameters in the evaluation area, and a mixed Gaussian probability distribution function f(I0) of the maximum amplitude of the lightning current is generated by combining lognormal distribution components and fitting the distribution form, which is expressed as:
[0012]
[0013] Where n represents the number of Gaussian distributions that fit the probability distribution of the maximum amplitude of lightning current, α i represents the weight of the i-th Gaussian component, μ i represents the logarithmic standard deviation of the i-th Gaussian component, μ i represents the logarithmic mean of the i-th Gaussian component; based on the extreme impulse characteristics in lightning events, the generalized extreme value distribution probability density function f(T1) of the lightning current front time is generated, which is expressed as:
[0014]
[0015] Where T1 represents the leading edge time of lightning current, σ' represents the scale parameter, μ' represents the location parameter, and ξ represents the shape parameter. Based on the probability distribution function of lightning current parameters, random lightning events in the assessment area are simulated.
[0016] As a preferred solution of the indirect lightning performance evaluation optimization method for overhead lines described in the present invention, the longitudinal distance includes the lateral distance y of the selected evaluation area. l The longitudinal distance d and the transverse distance are adjusted in real time from the initial value. The longitudinal distance is selected based on the line equipped with lightning arresters. When the line is equipped with lightning arresters, the longitudinal distance selects the distance between two adjacent lightning arresters. When the line is not equipped with lightning arresters or the lightning arresters are unevenly distributed, the longitudinal distance selects a pole distance.
[0017] As a preferred solution of the indirect lightning performance evaluation optimization method for overhead lines described in the present invention, wherein: the selection of the initial lateral distance and the total number of lightning events includes calculating the flashover rate FOR by simulating lightning events in the evaluation area; the specific operation includes randomly generating N tot lightning events, simulating the lightning induced voltage on the line; recording the number of flashover events n FO And calculate the flashover rate FOR, and calculate the estimated value of FOR caused by indirect impact:
[0018]
[0019] FOR=100·FO p ·N g ·(2y l )
[0020] Among them, N g is the lightning density per square kilometer per year, FO p represents the probability of overhead line flashover caused by randomly generated lightning events in the assessment area, iteratively increasing the lateral distance y l And repeat the calculation until the flashover rate stabilizes.
[0021] As a preferred solution of the indirect lightning strike performance evaluation optimization method for overhead lines described in the present invention, the output of the optimal lateral distance includes adjusting the lateral distance and the number of random events accordingly, and repeating the evaluation process until the flashover rate has no obvious change in two consecutive iterations.
[0022] Another object of the present invention is to provide an indirect lightning performance evaluation optimization system for overhead lines, which can generate a probability distribution function of lightning current parameters in the evaluation area and select the longitudinal distance of the evaluation area, thereby solving the problem of large data deviation in current lightning parameter collection technology.
[0023] As a preferred solution of the indirect lightning performance evaluation and optimization system for overhead lines described in the present invention, it includes: a data acquisition module, a longitudinal distance evaluation module, and a lateral distance evaluation module; the data acquisition module is used to collect lightning parameters and build a virtual OHL model based on the actual OHL configuration and parameters; the longitudinal distance evaluation module is used to generate a probability distribution function of lightning current parameters in an evaluation area and select the longitudinal distance of the evaluation area; the lateral distance evaluation module is used to select an initial lateral distance and a total number of lightning events, iteratively adjust the lateral distance and the total number of lightning events, and output an optimal lateral distance.
[0024] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of an indirect lightning strike performance evaluation optimization method for overhead lines.
[0025] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an optimization method for evaluating the indirect lightning performance of overhead lines.
[0026] Beneficial effects of the present invention: The indirect lightning performance evaluation optimization method for overhead lines provided by the present invention collects lightning parameters and constructs a virtual OHL model based on actual OHL configuration and parameters, thereby realizing accurate simulation of overhead lines in specific lightning environments, ensuring that the characteristics of overhead lines reflected by the model are highly consistent with actual operating conditions, and improving the accuracy and pertinence of the evaluation model; by generating a probability distribution function of lightning current parameters in an evaluation area and selecting the longitudinal distance of the evaluation area, a quantitative description of lightning activity characteristics and an accurate definition of the evaluation area are realized, providing data support for lightning risk assessment and ensuring the scientificity and rationality of the evaluation results. , which enhances the systematicness and predictability of the evaluation process, reduces the evaluation error caused by the uncertainty of lightning parameters, and thus improves the reliability of the indirect lightning performance evaluation of overhead lines; by simulating lightning events at different lateral distances, the optimal lateral distance that can stabilize the flashover rate is found, and the lightning protection design of overhead lines is optimized, ensuring the effectiveness and economy of lightning protection measures. At the same time, it reduces the problem of over-protection or under-protection caused by improper selection of lateral distances, and improves the safe operation level of overhead lines in lightning environments. The present invention achieves better results in the accurate assessment of lightning strike risks of overhead lines, lightning protection design of overhead lines, and operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS
[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0028] Figure 1 An overall flow chart of an indirect lightning strike performance evaluation and optimization method for overhead lines provided in the first embodiment of the present invention.
[0029] Figure 2 An evaluation region diagram in a Monte Carlo evaluation of an optimization method for evaluating indirect lightning strike performance of overhead lines provided in a first embodiment of the present invention.
[0030] Figure 3 A flashover probability-lateral distance diagram for an optimization method for evaluating indirect lightning strike performance of overhead lines is provided in the first embodiment of the present invention.
[0031] Figure 4 A flashover rate-lateral distance diagram of an indirect lightning strike performance evaluation optimization method for overhead lines provided in the first embodiment of the present invention.
[0032] Figure 5 An overall flow chart of an indirect lightning strike performance evaluation and optimization system for overhead lines provided in a third embodiment of the present invention. DETAILED DESCRIPTION
[0033] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.
[0034] Example 1, reference Figure 1-Figure 4 , as an embodiment of the present invention, provides an optimization method for evaluating indirect lightning performance of overhead lines, comprising:
[0035] S1: Collect lightning parameters and build a virtual OHL model based on the actual OHL configuration and parameters.
[0036] Furthermore, lightning parameters include the maximum amplitude of lightning current, the leading edge time of lightning current, the duration of lightning current, the peak time of lightning current, the rising rate of lightning current, the falling rate of lightning current, the frequency and location of lightning strikes, and lightning current waveform parameters.
[0037] It should be noted that the construction of the virtual OHL model includes building a 1 km long virtual OHL model based on the actual OHL configuration and parameters, and calculating the lateral distance y of the assessment area. l It is expressed as:
[0038]
[0039] Where CFO is the critical flashover voltage, h is the line height, σ g represents soil conductivity, and I0 represents the amplitude of lightning current.
[0040] It should also be noted that by collecting lightning parameters and building a virtual OHL model based on the actual OHL configuration and parameters, an accurate simulation of the overhead line in a specific lightning environment is achieved, which can ensure that the overhead line characteristics reflected by the model are highly consistent with the actual operating conditions, improve the accuracy and pertinence of the evaluation model, and make the subsequent lightning performance evaluation closer to the actual working conditions, thereby providing a more accurate reference basis for the design of overhead lines and lightning protection measures.
[0041] S2: Generate the probability distribution function of the lightning current parameters in the assessment area and select the longitudinal distance of the assessment area.
[0042] Furthermore, the probability distribution function includes generating a probability distribution function of lightning current parameters within the evaluation area, by combining log-normal distribution components and fitting the distribution morphology to generate a mixed Gaussian probability distribution function f(I0) of the maximum amplitude of the lightning current, expressed as:
[0043]
[0044] Where n represents the number of Gaussian distributions that fit the probability distribution of the maximum amplitude of lightning current, α i represents the weight of the i-th Gaussian component, μ i represents the logarithmic standard deviation of the i-th Gaussian component, μ i represents the logarithmic mean of the i-th Gaussian component; based on the extreme impulse characteristics in lightning events, the generalized extreme value distribution probability density function f(T1) of the lightning current front time is generated, which is expressed as:
[0045]
[0046] Where T1 represents the leading edge time of lightning current, σ' represents the scale parameter, μ' represents the location parameter, and ξ represents the shape parameter. Based on the probability distribution function of lightning current parameters, random lightning events in the assessment area are simulated.
[0047] It should be noted that the longitudinal distance includes the lateral distance y of the selected evaluation area. land longitudinal distance d, the transverse distance is adjusted in real time from the initial value, and the longitudinal distance is selected based on the line equipped with lightning arresters; when the line is equipped with lightning arresters, the longitudinal distance selects the distance between two adjacent lightning arresters; when the line is not equipped with lightning arresters or the lightning arresters are unevenly distributed, the longitudinal distance selects a pole distance, and the pattern formed is as follows Figure 2 shown.
[0048] It should also be noted that by generating the probability distribution function of lightning current parameters in the assessment area and selecting the longitudinal distance of the assessment area, a quantitative description of the characteristics of lightning activity and an accurate definition of the assessment area are achieved, providing data support for lightning risk assessment, ensuring the scientificity and rationality of the assessment results, enhancing the systematic and predictive nature of the assessment process, reducing the assessment error caused by the uncertainty of lightning parameters, and thereby improving the reliability of the indirect lightning performance assessment of overhead lines.
[0049] S3: Select the initial lateral distance and the total number of lightning events, iteratively adjust the lateral distance and the total number of lightning events, and output the optimal lateral distance.
[0050] Furthermore, the selection of the initial lateral distance and the total number of lightning events involves calculating the flashover rate FOR by simulating lightning events in the assessment area; the resulting pattern is as follows Figure 3-Figure 4 As shown, the specific operation includes randomly generating N tot lightning events, simulating the lightning induced voltage on the line; recording the number of flashover events n FO And calculate the flashover rate FOR, and calculate the estimated value of FOR caused by indirect impact:
[0051]
[0052] FOR=100·FOpNg(2y)
[0053] Where Ng is the lightning density per square kilometer per year, FOp represents the probability of a randomly generated lightning event in the evaluation area causing a flashover of the overhead line, and the lateral distance y is iteratively increased. l And repeat the calculation until the flashover rate stabilizes.
[0054] It should be noted that outputting the optimal lateral distance includes adjusting the lateral distance and the number of random events accordingly, and repeating the evaluation process until the flashover rate has no significant change in two consecutive iterations.
[0055] It should also be noted that by selecting the initial lateral distance and the total number of lightning events, and iteratively adjusting the lateral distance and the total number of lightning events, and outputting the optimal lateral distance, the optimization of the overhead line lightning risk assessment parameters is achieved, the effectiveness and economy of lightning protection measures are ensured, and at the same time, the problem of over-protection or under-protection caused by improper lateral distance selection is reduced, and the safe operation level of overhead lines in lightning environments is improved. By iterative calculation and reasonable selection of the lateral distance of the assessment area, the optimal lateral distance is determined, thereby reducing the calculation burden and assessment error. By introducing the selection method of the optimal lateral distance, the lateral distance of the assessment area is reasonably determined, and the number of lightning events that need to be simulated is reduced, thereby reducing the calculation burden. The present invention ensures the accuracy of the assessment by iterative calculation and optimization of model parameters. Reasonable selection of the lateral distance of the assessment area avoids the assessment error caused by too small or too large distance, and improves the reliability of the assessment result. The present invention provides a systematic method to determine the optimal lateral distance of the assessment area, fills the gap in the prior art in this regard, and makes the assessment process more scientific and standardized.
[0056] Example 2, an embodiment of the present invention, provides an optimization method for evaluating the indirect lightning performance of overhead lines. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
[0057] Firstly, lightning data from a certain area over the past 10 years were collected, including the maximum amplitude, front time, duration, peak time, rise rate, fall rate, frequency and location of lightning strikes, and lightning current waveform parameters. Based on these data and the actual configuration of overhead lines, a 1-km-long virtual OHL model was constructed. Assuming that the critical flashover voltage is 1500kV, the line height is 20 meters, the soil conductivity is 0.01S / m, and the maximum amplitude of lightning current ranges from 10kA to 100kA, the probability distribution function of lightning current parameters was generated using the collected lightning data. By combining the log-normal distribution components, the mixed high-frequency distribution of the maximum amplitude of lightning current was fitted. The probability distribution function of the generalized extreme value distribution is used to generate the probability density function of the generalized extreme value distribution to describe the extreme impact characteristics of the lightning current front time. The longitudinal distance of the evaluation area is selected as the distance between two adjacent lightning arresters, which is about 300 meters. The initial lateral distance is set to 200 meters, and the total number of lightning events is set to 1000. The flashover rate is calculated by simulating lightning events in the evaluation area. The specific operations include randomly generating 1000 lightning events, simulating the lightning induced voltage on the line, recording the number of flashover events and calculating the flashover rate, iteratively increasing the lateral distance and the number of random events, and repeatedly calculating the flashover rate until the flashover rate has no obvious change in two consecutive iterations. Refer to Table 1 to record and analyze the experimental data.
[0058] Table 1 Overhead line lightning performance evaluation data table
[0059]
[0060] It can be seen from the data that with the increase of the maximum amplitude of the lightning current, the leading edge time also increases accordingly, which is in line with the actual situation, because a larger lightning current is usually accompanied by a longer rise time. The data show that there is a positive correlation between the duration and peak time of the lightning current. A longer duration is usually accompanied by a longer peak time. With the increase of the lightning current rise rate, the decline rate also increases accordingly, which reflects the characteristic of the lightning current rapidly decaying after reaching the peak value. The data show that the lightning strike frequency is positively correlated with other parameters of the lightning current. A higher lightning strike frequency is usually accompanied by a larger lightning current and a longer duration. It can be seen from the above data analysis that the present invention can comprehensively consider various parameters of the lightning current when simulating and evaluating the indirect lightning strike performance of overhead lines, and improve the accuracy and reliability of the evaluation by iteratively optimizing the lateral distance and the number of lightning events.
[0061] Example 3, reference Figure 5 , as an embodiment of the present invention, provides an indirect lightning performance evaluation and optimization system for overhead lines, including a data acquisition module, a longitudinal distance evaluation module, and a lateral distance evaluation module.
[0062] The data acquisition module is used to collect lightning parameters and build a virtual OHL model based on the actual OHL configuration and parameters; the longitudinal distance evaluation module is used to generate the probability distribution function of lightning current parameters in the evaluation area and select the longitudinal distance of the evaluation area; the lateral distance evaluation module is used to select the initial lateral distance and the total number of lightning events, iteratively adjust the lateral distance and the total number of lightning events, and output the optimal lateral distance.
[0063] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.
[0064] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.
[0065] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.
[0066] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.
[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
Claims
1. A method for optimizing the indirect lightning performance evaluation of overhead lines, characterized in that: include: Collect lightning parameters and build a virtual OHL model based on actual OHL configuration and parameters; Generate the probability distribution function of the lightning current parameters in the assessment area and select the longitudinal distance of the assessment area; Select the initial lateral distance and the total number of lightning events, iteratively adjust the lateral distance and the total number of lightning events, and output the optimal lateral distance.
2. The indirect lightning performance evaluation and optimization method for overhead lines according to claim 1, characterized in that: The lightning parameters include the maximum amplitude of the lightning current, the leading edge time of the lightning current, the duration of the lightning current, the peak time of the lightning current, the rising rate of the lightning current, the falling rate of the lightning current, the frequency and location of the lightning strike, and the lightning current waveform parameters.
3. The indirect lightning performance evaluation and optimization method for overhead lines according to claim 2, characterized in that: The construction of the virtual OHL model includes constructing a 1 km long virtual OHL model based on the actual OHL configuration and parameters, calculating the lateral distance y of the assessment area l It is expressed as: Where CFO is the critical flashover voltage, h is the line height, σ g represents soil conductivity, and I0 represents the amplitude of lightning current.
4. The indirect lightning performance evaluation and optimization method for overhead lines according to claim 3, characterized in that: The probability distribution function includes generating a probability distribution function of lightning current parameters in the evaluation area, and generating a mixed Gaussian probability distribution function f(I0) of the maximum amplitude of the lightning current by combining lognormal distribution components and fitting the distribution form, which is expressed as: Where n represents the number of Gaussian distributions that fit the probability distribution of the maximum amplitude of lightning current, α i represents the weight of the i-th Gaussian component, μ i represents the logarithmic standard deviation of the i-th Gaussian component, μ i represents the logarithmic mean of the i-th Gaussian component; Based on the extreme impulse characteristics of lightning events, the generalized extreme value distribution probability density function f(T1) of the lightning current front time is generated, which is expressed as: Where T1 represents the leading edge time of lightning current, σ' represents the scale parameter, μ' represents the location parameter, and ξ represents the shape parameter. Based on the probability distribution function of lightning current parameters, random lightning events in the assessment area are simulated.
5. The indirect lightning performance evaluation and optimization method for overhead lines according to claim 4, characterized in that: The longitudinal distance includes the lateral distance y of the selected evaluation area. l and longitudinal distance d, the transverse distance is adjusted in real time from the initial value, and the longitudinal distance is selected based on the lightning arrester configuration of the line; When the line is equipped with lightning arresters, the longitudinal distance is selected as the distance between two adjacent lightning arresters; When the line is not equipped with lightning arresters or the lightning arresters are unevenly distributed, a pole distance is selected as the longitudinal distance.
6. The indirect lightning performance evaluation and optimization method for overhead lines according to claim 5, characterized in that: The selecting of the initial lateral distance and the total number of lightning events includes calculating the flashover rate FOR by simulating lightning events in the assessment area; The specific operation includes randomly generating N tot A lightning event to simulate the lightning induced voltage on the line; Number of recorded flashover events n FO And calculate the flashover rate FOR, and calculate the estimated value of FOR caused by indirect impact: FOR=100·FO p ·N g ·(2y l ) Among them, N g is the lightning density per square kilometer per year, FO p represents the probability of flashover of overhead lines caused by randomly generated lightning events in the assessment area, iteratively increasing the lateral distance y l And repeat the calculation until the flashover rate stabilizes.
7. The indirect lightning performance evaluation and optimization method for overhead lines according to claim 6, characterized in that: Outputting the optimal lateral distance includes adjusting the lateral distance and correspondingly adjusting the number of random events, and repeating the evaluation process until the flashover rate has no significant change in two consecutive iterations.
8. A system using the indirect lightning performance evaluation and optimization method for overhead lines according to any one of claims 1 to 7, characterized in that: It includes data acquisition module, longitudinal distance evaluation module and transverse distance evaluation module; The data acquisition module is used to collect lightning parameters and build a virtual OHL model based on the actual OHL configuration and parameters; The longitudinal distance assessment module is used to generate a probability distribution function of lightning current parameters within the assessment area and select the longitudinal distance of the assessment area; The lateral distance evaluation module is used to select an initial lateral distance and a total number of lightning events, iteratively adjust the lateral distance and the total number of lightning events, and output an optimal lateral distance.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the indirect lightning strike performance evaluation and optimization method for overhead lines according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the indirect lightning strike performance evaluation and optimization method for overhead lines according to any one of claims 1 to 7 are implemented.