Lightning hazard cause diagnosis and lightning protection measure optimization method, system, medium and device

Abstract

This invention discloses a method, system, medium, and equipment for diagnosing the causes of lightning damage and optimizing lightning protection measures. The method includes: determining multiple parameters affecting the lightning trip rate and defining reference ranges for each parameter; normalizing each parameter based on its reference range; calculating the gradient value of each parameter based on the normalized parameters, wherein a larger gradient value indicates that the corresponding parameter is diagnosed as a major factor in the cause of lightning damage; taking corresponding counter-attack measures or bypass measures for each parameter according to its gradient value, and calculating the lightning trip rate after taking optimized measures; determining whether the lightning trip rate after taking optimized measures reaches a preset standard, and if it reaches the preset standard, determining the optimized measures taken for each parameter as the optimal comprehensive measures. This invention can efficiently and accurately diagnose the causes of lightning damage and efficiently and accurately propose optimal lightning protection measures.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lightning protection measures optimization of power transmission lines, and in particular to a lightning hazard cause diagnosis and lightning protection measure optimization method, system, medium and equipment. BACKGROUND

[0002] The power transmission line is a basic component of the power grid, and its safe operation is of great significance to the safety of the power grid. The terrain of the power transmission corridor is quite complex, and the topography and meteorological conditions are highly diversified, and its safe operation is greatly threatened by lightning hazards. Related research has shown that the terrain, line structure, and lightning activity intensity difference of the power transmission line may lead to different risks of lightning hazard accidents, in addition, the parameters of the tower itself are the decisive factor of lightning hazard risk. Therefore, according to the voltage grade and single / double circuit conditions of the power transmission line, based on the differences in the lightning activity intensity of the line corridor, the topographic and geomorphic characteristics, and the line structure parameters, the intelligent diagnosis of the causes and measures of the line lightning hazard is further developed, and the scientific and standardized intelligent recommendation method of the optimization measures is of great significance to the safe and stable operation of the power grid.

[0003] At present, the diagnosis of lightning hazard causes and the proposal of lightning protection measures in the research depend on manual work, which needs manual work to combine environmental parameters, tower parameters and lightning trip-out rate to analyze the line or tower in detail. This will result in low efficiency and may be affected by subjective judgment. SUMMARY

[0004] The present application aims to at least partially solve the technical problems in the related art. To this end, the first object of the present application is to provide a lightning hazard cause diagnosis and lightning protection measure optimization method, which can efficiently and accurately diagnose the causes of lightning hazards and propose optimal lightning protection measures.

[0005] The second object of the present application is to provide a lightning hazard cause diagnosis and lightning protection measure optimization system.

[0006] The third object of the present application is to provide a computer readable storage medium.

[0007] The fourth object of the present application is to provide an electronic device.

[0008] To achieve the above-mentioned objects, the present application realizes the following technical solutions:

[0009] A lightning hazard cause diagnosis and lightning protection measure optimization method, comprising:

[0010] determining a plurality of parameters affecting the lightning trip-out rate, and determining the reference interval of each parameter;

[0011] normalizing each parameter based on the reference interval of each parameter;

[0012] The gradient value of each parameter is calculated based on the normalized parameters. The larger the gradient value, the more likely the corresponding parameter is to be diagnosed as a major factor in the cause of lightning damage.

[0013] Based on the gradient values ​​of each parameter, corresponding countermeasures or bypass measures are taken for each parameter, and the lightning tripping rate after taking the optimization measures is calculated;

[0014] To determine whether the lightning trip rate after optimization measures have reached the preset standard, if the preset standard has been reached, the optimization measures taken for each parameter will be determined as the optimal comprehensive measures.

[0015] Preferably, several parameters affecting the lightning tripping rate include grounding resistance, insulator string length, tower height, lightning conductor protection angle, ground tilt angle, and ground flash density.

[0016] Preferably, for the grounding resistance parameter, after performing a logarithmic operation on the grounding resistance parameter, a normalization process is then performed.

[0017] Preferably, the gradient value of each parameter is calculated based on the normalized parameters, including:

[0018] Using ground tilt angle and ground flash density as constants, determine the lightning trip rate as a function of grounding resistance, insulator string length, tower height, and lightning conductor protection angle;

[0019] For the four parameters of grounding resistance, insulator string length, tower height and lightning protection angle, calculate the change in lightning trip rate after adding a preset optimization step size to each parameter;

[0020] Calculate the sum of squares of the changes in lightning tripping rate after adding a preset optimization step size to each parameter;

[0021] The gradient value of each parameter is determined by the change in lightning tripping rate after increasing the preset optimization step size for each parameter and the sum of squares.

[0022] Preferably, the gradient values ​​of each parameter are adjusted accordingly to take corresponding countermeasures or bypass measures for each parameter.

[0023] Preferably, if the lightning tripping rate after optimization measures does not reach the preset standard and the number of optimizations reaches the upper limit, then measures such as installing lightning arresters on the towers should be taken.

[0024] Preferably, if the lightning trip rate after optimization measures does not reach the preset standard and the number of optimizations has not reached the upper limit of the number of optimizations, the process returns to the step of calculating the change in lightning trip rate after increasing the preset optimization step size for each parameter, so as to recalculate the gradient value of each parameter.

[0025] To achieve the above objectives, a second aspect of the present invention provides a system for diagnosing the causes of lightning damage and optimizing lightning protection measures, comprising:

[0026] The determination module is used to determine multiple parameters that affect the lightning trip rate and to determine the reference range for each parameter;

[0027] The first solution module is used to normalize each parameter based on the reference range of each parameter;

[0028] The second solution module is used to calculate the gradient value of each parameter based on the normalized parameters. The larger the gradient value, the more likely the corresponding parameter is to be diagnosed as a major factor in the cause of lightning damage.

[0029] The processing module is used to take corresponding countermeasures or bypass measures for each parameter based on the gradient value of each parameter;

[0030] The third calculation module is used to calculate the lightning trip rate after optimization measures are taken;

[0031] The judgment module is used to determine whether the lightning trip rate after optimization measures have reached the preset standard.

[0032] The evaluation module is used to determine the optimal comprehensive measures for each parameter when the lightning tripping rate after optimization measures are implemented reaches the preset standard.

[0033] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the above-described method for diagnosing the causes of lightning damage and optimizing lightning protection measures.

[0034] To achieve the above objectives, a fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for diagnosing the causes of lightning damage and optimizing lightning protection measures.

[0035] This invention has at least the following technical effects:

[0036] (1) The lightning damage cause diagnosis and lightning protection measure optimization method provided by the present invention considers a large number of parameters and a wide range of factors, so the accuracy and reliability of lightning damage cause diagnosis and lightning protection measure optimization are higher.

[0037] (2) The present invention uses a normalization method to process each parameter to eliminate the differences in the dimensions and amplitudes of each parameter. Then, based on the normalized parameters, a lightning trip rate function is established, and the gradient value of each parameter is calculated based on the lightning trip rate function and the gradient idea, so as to facilitate the optimization of lightning protection measures based on the gradient value of each parameter.

[0038] (3) Based on the gradient concept, the gradient values ​​of each parameter can be calculated. Then, based on the gradient values ​​of each parameter, the cause of lightning damage can be automatically analyzed without relying on manual labor. Therefore, this method is less affected by subjective judgment.

[0039] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0040] Figure 1 This is a flowchart of the method for diagnosing the causes of lightning damage and optimizing lightning protection measures according to an embodiment of the present invention.

[0041] Figure 2 This is a flowchart illustrating the overall implementation of the lightning damage cause diagnosis and lightning protection measure optimization method according to an embodiment of the present invention. Detailed Implementation

[0042] The following describes this embodiment in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention.

[0043] The following description, with reference to the accompanying drawings, outlines the method, system, medium, and equipment for diagnosing lightning damage causes and optimizing lightning protection measures in this embodiment.

[0044] Figure 1 This is a flowchart illustrating the method for diagnosing the causes of lightning damage and optimizing lightning protection measures according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0045] Step S101: Determine the multiple parameters that affect the lightning trip rate and determine the reference range for each parameter.

[0046] Among the parameters that affect the lightning tripping rate are grounding resistance, insulator string length, tower height, lightning protection angle, ground tilt angle, and ground flash density.

[0047] In this embodiment, reference intervals are selected for six parameters—grounding resistance, insulator string length, tower height, lightning protection angle, ground tilt angle, and ground flash density—based on relevant standards and line statistical data, and these intervals serve as normalized benchmark values. The reference intervals can generally cover the reasonable range of the parameters and eliminate individual extreme data.

[0048] Specifically, reference ranges for grounding resistance, insulator string length, tower height, lightning protection angle, ground tilt angle, and ground flash density can be determined based on the standard "Design Code for 110kV~750kV Overhead Transmission Lines (GB50545-2010)" and actual tower statistics.

[0049] For example, based on the statistical data of 97 towers in the 110kV Songxunyuan line, 139 towers in the 220kV Songyu line, and 187 towers in the 500kV Yiquanyi line, the tower height distribution under each voltage level is obtained and Gaussian fitted to it. The reference range of tower height is determined based on the three-σ principle (Laida criterion) of normal distribution.

[0050] Subsequently, based on statistics, the left and right ground tilt angles of 2000 towers were determined using Gaussian curve fitting and the three σ principle of normal distribution. This included 1228 towers in 433 110kV lines such as Puzheng Line, 523 towers in 201 220kV lines such as Baizhu Line, and 258 towers in 80 500kV lines such as Ajinjia Line.

[0051] Finally, based on DL / T 1533-2016 "Method for Drawing Lightning Distribution Maps of Power Systems" and combined with the statistical data of lightning density, 11.0 times / (km2·a) was selected as the worst value of lightning density, and the optimal value of lightning density was 0 times / (km2·a). Thus, the reference range of lightning density can be determined.

[0052] Step S102: Normalize each parameter based on its reference range.

[0053] In this embodiment, based on the reference range of each parameter in step S101, the parameters can be further normalized to eliminate the differences in the dimensions and amplitudes of each parameter, which serves as the basis for analyzing the causes of lightning damage and optimizing measures.

[0054] Specifically, if we take the insulator string length, tower height, lightning protection angle, ground inclination angle, and flashover density as the first parameter, and the grounding resistance as the second parameter, then for the first parameter:

[0055]

[0056] In the formula, This represents the per-unit value of the first type of parameter, x. 1 This indicates the first parameter before using the per-unit value, x 1 best and x 1 worstThese refer to the optimal and worst values ​​of the first parameter within the reference range under the current voltage level and single / dual circuit conditions, respectively. For the ground tilt angle, the maximum ground tilt angle is selected to calculate the per-unit value.

[0057] For grounding resistance, the second parameter, it is more accurate to perform logarithmic calculation followed by normalization to reflect the actual situation. For grounding resistance, we have:

[0058]

[0059] In the formula, This represents the per-unit value of the grounding resistance parameter, x. 2 This indicates the grounding resistance parameter before adopting the per-unit value, x 2 best and x 2 worst These refer to the optimal and worst values ​​of the reference range for the grounding resistance parameter, respectively.

[0060] The normalization method based on parameter reference intervals makes it possible to compare the quality of parameters under different dimensions. A per-unit value of 0 corresponds to the worst value in the parameter reference interval; a per-unit value of 1 corresponds to the best value in the parameter reference interval. For example, the reference interval for grounding resistance is 5–30Ω, where 5Ω is the best value and 30Ω is the worst value. Therefore, 5Ω corresponds to a normalized per-unit value of 1, and 30Ω corresponds to a normalized per-unit value of 0. In this embodiment, when the per-unit value is less than 1, the difference between the current parameter per-unit value and 1 can represent the distance between the current parameter state and a good state, and can characterize the degree to which the poor state of the parameter affects the lightning damage risk level. For parameters with a per-unit value greater than or equal to 1, it can be considered that a good state has been reached and is not considered a cause of high lightning damage risk.

[0061] Step S103: Calculate the gradient value of each parameter based on the normalized parameters. The larger the gradient value, the more likely the corresponding parameter is to be diagnosed as a major factor in the cause of lightning damage.

[0062] In this embodiment, the gradient values ​​of each parameter are calculated based on the normalized parameters, including: determining the lightning trip rate as a function of grounding resistance, insulator string length, tower height, and lightning conductor protection angle, with ground tilt angle and ground flash density as constants; calculating the change in lightning trip rate after adding a preset optimization step size to each of the four parameters: grounding resistance, insulator string length, tower height, and lightning conductor protection angle; summing the squares of the changes in lightning trip rate after adding a preset optimization step size to each parameter; and determining the gradient value of each parameter based on the changes in lightning trip rate after adding a preset optimization step size and the sum of squares.

[0063] Specifically, taking the ground tilt angle and lightning density as constants, the lightning tripping rate of the tower is considered as a function of multiple independent variables, including grounding resistance, insulator string length, tower height, and lightning protection angle. If there exists a function z = f(x,y) with continuous first-order partial derivatives in the planar region D, and e = {cosα,cosβ} is a unit vector in direction l, its rate of change in direction l can be expressed by the following formula:

[0064]

[0065] Where f represents the function z, x and y are the independent variables of the function z, and α and β are two direction angles used to determine the direction of the unit vector.

[0066] Therefore, when the chosen unit vector e = {cosα, cosβ} and When in the same direction, It has a maximum value. And a vector... The gradient of the function z = f(x,y) at point P(x,y) is called gradf(x,y) or gradf(x,y). Therefore, the function has the largest rate of change along the gradient direction at a point.

[0067] Furthermore, by locally linearizing the lightning trip rate, and changing the grounding resistance, insulator string length, tower height, and lightning conductor protection angle by one unit length within the linear neighborhood according to different optimization directions, and characterizing the partial derivative by calculating the rate of change of the lightning trip rate, the gradient direction of the lightning trip rate under the current state can be obtained. The lightning trip rate changes most rapidly along this gradient direction, thus reducing the lightning trip rate most efficiently. In other words, to reduce the lightning trip rate by a certain amount, moving along the gradient direction results in the target point being closest to the original state point, with the least change to the tower, and therefore can be considered the most efficient approach.

[0068] Based on the idea of ​​the gradient method above, we first make a linear first-order approximation in a certain neighborhood, and use the rate of change of the lightning tripping rate to characterize the partial derivative, as shown in the following equation:

[0069]

[0070] in, Let be the gradient value of the lightning trip rate function with respect to each parameter. These are the normalized per-unit values ​​of grounding resistance, insulator string length, tower height, and lightning protection angle, respectively. w0, x0, y0, and z0 represent the grounding resistance, insulator string length, tower height, and lightning protection angle, respectively. This is the lightning trip rate function. Optimization of per-unit grounding resistance The lightning trip rate function value after the change. This represents the change in the per-unit value of grounding resistance. Optimization of per-unit value for insulator string length The lightning trip rate function value after the change. This represents the change in the per-unit value of the insulator string length. Optimization of tower height per unit value The lightning trip rate function value after the change. This represents the change in the per-unit value of the tower height. Optimization of the per-unit value for lightning protection line protection The lightning trip rate function value after the change. The change in the unit value of the lightning protection line. The lightning trip rate function value when each variable takes the current per-unit value.

[0071] Since the above analysis is based on the premise of approximating the lightning trip rate function in the neighborhood of the state point in a first-order manner, a multi-round optimization and iterative solution scheme is adopted.

[0072] Based on multiple tests, it was found that using a per-unit value of 0.1 as the optimization step size (i.e., the amount of change) has a good effect. Specifically, after taking the per-unit value, each parameter advances by 0.1 in the coordinate axis direction to calculate the rate of change of the lightning tripping rate to determine the gradient. The comprehensive measures then advance by 0.1 in the gradient direction as the optimization magnitude for each round, as shown in the following formula:

[0073]

[0074] in, These represent the per-unit changes in grounding resistance, insulator string length, tower height, and lightning protection angle, respectively, in the first individual optimization measure. These represent the per-unit changes in grounding resistance, insulator string length, tower height, and lightning protection angle, respectively, in the second separate optimization measure. These represent the per-unit changes in grounding resistance, insulator string length, tower height, and lightning protection angle, respectively, in the third separate optimization measure. These represent the per-unit changes in grounding resistance, insulator string length, tower height, and lightning protection angle, respectively, in the fourth individual optimization measure. These represent the per-unit changes in grounding resistance, insulator string length, tower height, and lightning protection angle, respectively, within the comprehensive measures. These represent the unit vectors for grounding resistance, insulator string length, tower height, and lightning protection angle in the integrated measures, respectively.

[0075] Understandably, the first type of individual optimization measure optimizes only the grounding resistance, similarly, the second type optimizes only the insulator string length, while the fifth comprehensive measure optimizes all parameters. When optimizing all parameters, it is necessary to determine the gradient value of each parameter, i.e., the per-unit value change, which is also the determination of...

[0076] It should be noted that in this embodiment, the optimal value of each parameter reference interval can be selected as the optimization threshold. If a parameter has reached the threshold, it is considered a constant, that is, the lightning tripping rate function is reduced by one dimension, and only the gradient formed by other parameters is studied. Measures for that parameter will no longer appear in the comprehensive measures.

[0077] Although formula (4) characterizes the gradient of the lightning trip rate function with respect to each parameter, the gradient values ​​of each parameter in the integrated measures can be obtained by the following method.

[0078] Specifically, in the gradient expression given by equation (5), since The values ​​are respectively Their values ​​are all 0.1, and the relationship between the gradients of each parameter in the comprehensive measures can be obtained, as shown in equation (6):

[0079]

[0080] in, The per-unit value of grounding resistance is optimized by 0.1, which is the lightning trip rate function value after the preset optimization step size. The lightning trip rate function value is optimized by 0.1 for the per-unit value of the insulator string length. The lightning trip rate function value is optimized by 0.1 for the tower height per unit value. The lightning trip rate function value is optimized to 0.1 for the per-unit value of the lightning protection angle.

[0081] Equation (6) provides the expressions for each gradient component in the integrated measures, using the grounding resistance gradient component in the integrated measures. For example:

[0082]

[0083] Therefore, for the four parameters—grounding resistance, insulator string length, tower height, and lightning protection angle—the change in lightning trip rate after increasing the preset optimization step size by 0.1 is calculated for each parameter. The sum of squares of the changes in lightning trip rate after increasing the preset optimization step size for each parameter is then calculated. Based on the changes in lightning trip rate after increasing the preset optimization step size for each parameter and the sum of squares, the gradient value of each parameter, i.e., the gradient component of each parameter, can be determined. Therefore, the gradient values ​​of each parameter can be directly determined by equation (7).

[0084] Step S104: Take corresponding countermeasures or bypass measures for each parameter based on the gradient value of each parameter, and calculate the lightning trip rate after taking the optimization measures.

[0085] Specifically, the gradient values ​​of each parameter can be adjusted to implement corresponding countermeasures or bypass measures. For example, if Δw5 is calculated to be 0.05 and Δx5 to be 0.01, then the optimization step size for the grounding resistance and insulator string length will be increased by 0.05 and 0.01 respectively. Of course, corresponding optimization measures will also be taken for the gradient values ​​of the tower height and the lightning protection angle. Figure 2 As shown, corresponding backflashover measures are taken for grounding resistance, insulator string length, and tower height, and bypass measures are taken for the lightning protection angle. Then, the lightning tripping rate after taking these comprehensive measures is calculated and judged.

[0086] It should be noted that the larger the gradient value, the more likely the corresponding parameter is to be diagnosed as a major factor in the cause of lightning damage. For example, if the calculated result of Δw5 is the largest, then the surface grounding resistance is the major factor in the cause of lightning damage, and therefore the optimization effort for it is also the greatest. Thus, the diagnosis of the cause of lightning damage can be achieved based on the gradient concept.

[0087] Step S105: Determine whether the lightning trip rate after taking optimization measures has reached the preset standard. If it is determined that the preset standard has been reached, the optimization measures taken for each parameter will be determined as the optimal comprehensive measures.

[0088] If the lightning trip rate after optimization measures does not reach the preset standard, and the number of optimizations reaches the upper limit, then measures such as installing surge arresters on the towers will be taken. If the lightning trip rate after optimization measures does not reach the preset standard, and the number of optimizations does not reach the upper limit, then the process returns to calculating the change in lightning trip rate after increasing the preset optimization step size for each parameter, in order to recalculate the gradient value of each parameter.

[0089] In this embodiment, the upper limit of the lightning risk level II specified in "Q GDW 11452-2015 Lightning Protection Guidelines for Overhead Transmission Lines" can be used as the expected level, which is adapted to the preset optimization step size of 0.1 per unit value. It is assumed that the optimization upper limit is reached when the optimization is generally performed 3 times. If the expected level is reached, i.e., the preset standard or the optimization upper limit, the iteration is terminated.

[0090] The entire optimization process of this method is completed iteratively to ensure that each optimization measure falls within a linearly approximate range. First, the lightning trip rate is calculated for the initial state to determine if it meets a preset standard. If yes, the iteration exits; otherwise, optimization is required. Next, it is determined if the upper limit for the number of optimization iterations has been reached. If yes, a surge arrester should be installed on the tower; otherwise, optimization measures continue. Then, measures with a per-unit value of 0.1 are selected based on the voltage level and single / double-circuit configuration. Simultaneously, it is determined whether each parameter has reached the optimization threshold. If yes, optimization of that parameter is not considered; otherwise, the lightning trip rate after parameter optimization is calculated. Subsequently, the change in the lightning trip rate after optimization of each parameter is obtained by subtracting it from the initial lightning trip rate. Then, the gradient value of each parameter is obtained based on the gradient method. Then, comprehensive measures are taken accordingly, and the tower parameters after the comprehensive measures are used as the initial state for the new round. The lightning trip rate of the new initial state after the comprehensive measures are taken is then calculated. A preset standard is then applied until the lightning trip rate of the new initial state reaches the preset standard or the upper limit of the number of optimization attempts is reached, at which point the optimization process ends. Specifically, as follows... Figure 2 As shown.

[0091] Therefore, this method is based on the gradient concept, and combines the ground tilt angle and ground flash density as constants. It treats the tower lightning trip rate as a function of multiple independent variables such as grounding resistance, insulator string length, tower height, and lightning protection angle. Then, through an iterative approach, it continuously determines the current comprehensive optimization direction with the gradient concept, striving to minimize the change in parameter state, thereby realizing intelligent optimization of lightning protection measures based on the gradient concept.

[0092] Furthermore, the present invention also provides a lightning damage cause diagnosis and lightning protection measure optimization system, comprising a determination module, a first calculation module, a second calculation module, a processing module, a third calculation module, a judgment module, and an evaluation module connected in sequence.

[0093] The system comprises the following modules: a determination module to identify multiple parameters affecting the lightning trip rate and a reference range for each parameter; a first calculation module to normalize each parameter based on its reference range; a second calculation module to calculate the gradient value of each parameter based on the normalized parameters, where a larger gradient value indicates that the parameter is a major contributing factor to lightning damage; a processing module to implement corresponding counter-attack or bypass measures for each parameter based on its gradient value; a third calculation module to calculate the lightning trip rate after implementing optimization measures; a judgment module to determine whether the lightning trip rate after implementing optimization measures meets a preset standard; and an evaluation module to determine the optimization measures implemented for each parameter as the optimal comprehensive measures when the lightning trip rate after implementing optimization measures meets the preset standard.

[0094] It should be noted that the specific implementation of the lightning damage cause diagnosis and lightning protection measure optimization system in this embodiment can be found in the specific implementation of the lightning damage cause diagnosis and lightning protection measure optimization method described above. To avoid redundancy, it will not be repeated here.

[0095] In summary, the lightning damage cause diagnosis and lightning protection measure optimization method provided by this invention considers a large number of parameters and a wide range of factors, thus resulting in higher accuracy and reliability. This invention employs a normalization method to process each parameter, eliminating differences in dimensions and amplitudes. Then, based on the normalized parameters, a lightning trip rate function is established, and the gradient values ​​of each parameter are calculated based on this function and the gradient concept. This facilitates the optimization of lightning protection measures based on the gradient values ​​of each parameter. Based on the gradient concept, this invention can calculate the gradient values ​​of each parameter, and then automatically analyze the causes of lightning damage based on these gradient values, eliminating the need for manual intervention. Therefore, this method is less affected by subjective judgment.

[0096] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-mentioned method for diagnosing the causes of lightning damage and optimizing lightning protection measures.

[0097] Furthermore, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned method for diagnosing the causes of lightning damage and optimizing lightning protection measures.

[0098] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0099] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A lightning hazard cause diagnosis and lightning protection measure optimization method, characterized in that, The method comprises the following steps: determining a plurality of parameters affecting the lightning trip-out rate and determining the reference interval of each parameter; normalizing each parameter based on the reference interval of each parameter; calculating the gradient value of each parameter based on the normalized parameters, wherein the greater the gradient value, the more likely the corresponding parameter is the main factor causing lightning damage; taking corresponding counterattack measures or shielding measures for each parameter according to the gradient value of each parameter, and calculating the lightning trip-out rate after taking optimization measures; determining whether the lightning trip-out rate after taking optimization measures reaches a preset standard, and determining the optimization measures taken for each parameter as the optimal comprehensive measures when it is determined that the lightning trip-out rate reaches the preset standard; the plurality of parameters affecting the lightning trip-out rate include ground resistance, insulator string length, tower height, lightning protection angle, ground inclination and ground flash density; calculating the gradient value of each parameter based on the normalized parameters, comprising: determining the function of the lightning trip-out rate with respect to the ground resistance, insulator string length, tower height and lightning protection angle, with the ground inclination and ground flash density as constants; for the four parameters of ground resistance, insulator string length, tower height and lightning protection angle, calculating the change in lightning trip-out rate after each parameter increases by a preset optimization step; squaring the change in lightning trip-out rate after each parameter increases by a preset optimization step; determining the gradient value of each parameter based on the change in lightning trip-out rate after each parameter increases by a preset optimization step and the square sum.

2. The method according to claim 1, wherein the lightning hazard diagnosis and lightning protection measure optimization method is characterized by, For the ground resistance parameter, logarithmic operation is performed before normalization.

3. The method according to claim 1, wherein the lightning hazard diagnosis and lightning protection measure optimization method is characterized by, Adjusting the corresponding gradient value of each parameter to take corresponding counterattack measures or shielding measures for each parameter.

4. The method according to claim 1, wherein the lightning hazard diagnosis and lightning protection measure optimization method is characterized by, When the lightning trip-out rate after taking optimization measures does not reach the preset standard and the optimization times reach the upper limit of the optimization times, taking the measure of installing a lightning arrester on the tower.

5. The method according to claim 4, wherein the lightning hazard diagnosis and lightning protection measure optimization method is characterized by, When the lightning trip-out rate after taking optimization measures does not reach the preset standard and the optimization times do not reach the upper limit of the optimization times, returning to the step of calculating the change in lightning trip-out rate after each parameter increases by a preset optimization step to calculate the gradient value of each parameter again.

6. A lightning hazard cause diagnosis and lightning protection measure optimization system, characterized in that, The method comprises the following steps: a determining module for determining a plurality of parameters affecting the lightning trip-out rate and determining the reference interval of each parameter; a first calculation module for normalizing each parameter based on the reference interval of each parameter; a second calculation module for calculating the gradient value of each parameter based on the normalized parameters, wherein the greater the gradient value, the more likely the corresponding parameter is the main factor causing lightning damage; a processing module for taking corresponding counterattack measures or shielding measures for each parameter according to the gradient value of each parameter; a third calculation module for calculating the lightning trip-out rate after taking optimization measures; a judgment module for determining whether the lightning trip-out rate after taking optimization measures reaches a preset standard; an evaluation module for determining the optimization measures taken for each parameter as the optimal comprehensive measures when it is determined that the lightning trip-out rate reaches the preset standard; the plurality of parameters affecting the lightning trip-out rate include ground resistance, insulator string length, tower height, lightning protection angle, ground inclination and ground flash density; calculating the gradient value of each parameter based on the normalized parameters, comprising: determining the function of the lightning trip-out rate with respect to the ground resistance, insulator string length, tower height and lightning protection angle, with the ground inclination and ground flash density as constants; Determine the function of lightning trip-out rate about grounding resistance, insulator string length, tower height and lightning protection angle with ground inclination and lightning density as constants; For the four parameters of grounding resistance, insulator string length, tower height and lightning protection angle, calculate the change of lightning trip-out rate after each parameter increases a preset optimization step; Square the sum of the change of lightning trip-out rate after each parameter increases a preset optimization step; Determine the gradient value of each parameter based on the change of lightning trip-out rate after each parameter increases a preset optimization step and the squared sum.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the lightning hazard cause diagnosis and lightning protection measure optimization method of any one of claims 1-5.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor, when executing the computer program, implements the lightning hazard cause diagnosis and lightning protection measure optimization method of any one of claims 1-5.

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