Power transmission line tower line system weak point positioning method suitable for micrometeorological area

Abstract

The invention provides a power transmission line tower line system weak point positioning method suitable for a micro-meteorological area, and belongs to the technical field of power transmission line tower operation and maintenance. According to meteorological data obtained by a meteorological detection device and a meteorological station around a power transmission line, whether the meteorological data meet data characteristics of micro-meteorological is judged; then determining the span position of the power transmission line with the microclimate; all meteorological data are converted into load data, and then a finite element analysis method is adopted to apply loads to all components of the power transmission tower; calculating the safe operation performance of the power transmission tower corresponding to each evaluation index in the micro-meteorological area; and multi-element power transmission tower safety evaluation indexes are formed. According to the method, the weak point of the power transmission tower is reversely searched by adopting the multivariate positioning logic model, the main material position when the tower falling probability of the power transmission tower is maximum is positioned, the weak point of the power transmission tower under various evaluation indexes is finally determined, and the problem that the weak point of the power transmission tower in a micrometeorological area is difficult to position is solved.

Description

Technical Field

[0001] The invention relates to the technical field of transmission line iron tower operation and maintenance, and in particular to a method for locating weak points of a transmission line tower system suitable for micro-meteorological areas. Background Art

[0002] The economic development of a country is inseparable from the transmission of electric energy. Electric energy is transmitted over long distances through overhead transmission lines. The terrain of the areas through which it passes is complex. The micro-meteorological areas generated by these complex micro-topography are too different from the general climate of the area, which makes the transmission lines more prone to failure than other areas. The prior art usually conducts a safety assessment of the transmission line based on the meteorological environment around the transmission line. For example, the patent document with the publication number of CN 113537846A discloses a "risk analysis method for transmission and distribution line towers based on meteorological disasters". In this invention, the meteorological area is gridded and the disaster prevention performance of the transmission tower is analyzed. However, such an analysis method cannot determine the micro-meteorological area and cannot guide the operation and maintenance staff to conduct a safety inspection of the transmission tower. Since the transmission line usually passes through multiple micro-meteorological areas, and there are uncertain differences between each micro-meteorological area due to terrain reasons, it is difficult to find the weak points of the transmission tower, and it is difficult for the staff to determine the location where the failure is prone to occur, so that they cannot make decisions in time. Therefore, there is an urgent need for a method for locating weak points of transmission line tower systems in micro-meteorological areas to solve the problem that weak points of transmission towers in micro-meteorological areas are difficult to locate. Summary of the invention

[0003] In view of this, the present invention aims to solve the problem that it is difficult to locate the weak points of transmission towers in micro-meteorological areas by providing a method for locating weak points of transmission line tower systems suitable for micro-meteorological areas.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for locating weak points of a transmission line tower system suitable for a micro-meteorological area, comprising the following steps:

[0005] Step 1: Based on the meteorological data obtained by the meteorological detection devices and meteorological stations around the transmission line, the ECMWF model is established in combination with the terrain through which the transmission line passes. The meteorological data processed by the One class SVM algorithm is used to determine whether the meteorological data meets the data characteristics of micrometeorology;

[0006] Step 2: Then, GIS technology is combined with the ECMWF model to determine the transmission line span locations where micro-meteorological conditions exist, and the transmission towers in the micro-meteorological areas are numbered;

[0007] Step 3: Make Gauss distribution of wind speed according to the meteorological data of micro-meteorology, extract the data by Monte Carlo algorithm, convert all meteorological data into load data, and then apply load to each component of the transmission tower by finite element analysis method according to the obtained load data;

[0008] Step 4: Select a variety of transmission tower safety operation indicators according to national standards, and use the CZA algorithm to rank the evaluation indicators by weight;

[0009] Step 5: Number and sort each main material of the transmission tower, and remove the numbered main materials in sequence when performing finite element analysis. Under the condition of removing the main materials, calculate the safe operation performance of the transmission tower corresponding to each evaluation index in the micro-meteorological area;

[0010] Step 6: Use the data essence algorithm to construct a multi-evaluation index matrix of the transmission tower, arrange and combine the various indicators of the transmission tower, and combine them with the finite element analysis results to form a multivariate transmission tower safety evaluation index;

[0011] Step 7: Use the multivariate positioning logic model to reversely search for the weak points of the transmission tower, locate the main material position when the probability of the transmission tower collapse is the highest, and finally determine the weak points of the transmission tower under multiple evaluation indicators.

[0012] Furthermore, in step one, the meteorological monitoring device includes a wind speed detector and an ice thickness detector, and the meteorological data includes wind speed and ice thickness; the meteorological data is obtained by acquiring the meteorological data obtained by the meteorological monitoring device and the meteorological station, and the most unfavorable data for the transmission tower is selected from the data, that is, the maximum wind speed and the maximum ice thickness are taken in the micro-meteorological area respectively; the ECMWF model combines the extracted meteorological data with the local topography to form a meteorological-topographic data cloud map; the Oneclass SVM algorithm selects micro-meteorological data based on the extracted meteorological data and the existing historical data of other surrounding areas, and defines the area within the micro-topography range as micro-meteorological data in combination with the ECMWF model and displays it on the meteorological-topographic data cloud map of the ECMWF model.

[0013] Furthermore, in step 2, the relevant data of the transmission line, including the tower number and the shape of the transmission tower, are bid and extracted through GIS technology, and then combined with the ECMWF model to determine the tower number and tower shape of the transmission line in the micro-meteorological area.

[0014] Furthermore, in step 3, the Gaussian distribution of meteorological data is as follows:

[0015]

[0016]

[0017] Where z is the wind load sample data; μ z is the mathematical expectation of wind load; σ z is the standard deviation of wind load; σ z 2 is the variance of wind load; b is the sample data of ice thickness; μ b is the mathematical expectation of ice thickness; σ b is the standard deviation of ice thickness; σ b 2 is the variance of ice thickness.

[0018] Furthermore, in step 4, the CZA algorithm steps are as follows:

[0019] Assuming there are m safety assessment indicators, the transmission tower simulation runs n times:

[0020] X=(x ij ) n×m (i=1,2...n;j=1,2,...m)

[0021] In the formula, x ij is the indicator element in the matrix; before constructing the model, it is necessary to eliminate the dimension of each score value, so for x ij To standardize:

[0022]

[0023] In the formula, y ij Each element in the normalized matrix is ​​normalized; since the normalized data must be positive, it is necessary to perform positive transformation to convert the minimum evaluation index into the maximum evaluation index:

[0024]

[0025] Finally, the evaluation matrix Y is obtained * , the forward formula is the decision matrix:

[0026]

[0027] In the formula, y ij * is the evaluation data of the jth evaluation index under the i-th simulation condition; the information essential value e j The calculation formula is:

[0028]

[0029] Where: –1 / lnm is the information coefficient; the role of the information coefficient is to make e j The value of is always in the interval [0,1]; then calculate the weight w of the jth evaluation indexj :

[0030]

[0031] The matrix W is the weight vector and its specific calculation formula is:

[0032] W=[w 1 ,w 2 ,w 3 ,…,w j ].

[0033] Furthermore, in step 5, since the tower shape, height, structure, etc. of each transmission tower in the transmission line are different, the main materials of each transmission tower need to be sorted before the main material destruction method is performed for the subsequent operation calculation; the selected transmission tower evaluation index is based on the standards clearly stated in the national standard, including: tower inclination, main material stress, and main material bending degree; according to the above standards, it is determined whether the transmission tower collapses:

[0034] S=[S 1 , S 2 , S 3 ]

[0035]

[0036]

[0037]

[0038]

[0039] Among them, S represents the failure probability matrix of multiple evaluation indicators, S 1 represents the failure probability of the transmission tower’s tower inclination, S 2 represents the failure probability of the main material stress of the transmission tower, S 3 represents the failure probability of the bending degree of the main material of the transmission tower, {(ME-N)≤0} represents the number of elements in the matrix that are less than or equal to 0, M represents the standard value of the tower inclination of the transmission tower, N represents the n-order matrix of the actual value of the inclination of the transmission tower, R represents the standard value of the main material stress of the transmission tower, T represents the n-order matrix of the actual value of the main material stress of the transmission tower, X represents the standard value of the bending degree of the main material of the transmission tower, Z represents the n-order matrix of the actual value of the bending degree of the main material of the transmission tower, and n represents the number of data samples extracted using the Monte Carlo algorithm. represents the one-to-one mapping correlation between the evaluation criteria and the operating conditions of the transmission line, MC[f(z), f(b)] represents the data extracted from the wind speed and ice thickness of the transmission line through the Monte Carlo algorithm, GS represents the cross-sectional area of ​​the main material components of the transmission line, GR represents the material of the main material components of the transmission line, GT represents the cross-sectional type of the main material components of the transmission line, and GX represents the type of transmission tower.

[0040] Furthermore, in step six, a multi-evaluation index matrix of the transmission tower is constructed using the data essence algorithm:

[0041]

[0042] Among them, W p The weight coefficient matrix representing the Pth dimension is used to arrange and combine the various indicators of the transmission tower and combine them with the finite element analysis results to form a multivariate transmission tower safety assessment index:

[0043] GB ij =W 1 S 1 T §W 2 S 2 T §W 3 S 3 T ...W p S p T

[0044]

[0045]

[0046] Among them, S p represents the failure probability matrix of the evaluation index with the same dimension as the weight coefficient, § represents the parallel effectiveness of integrating evaluation indicators of different dimensions, GB ij represents the probability of the multi-dimensional tower collapse after the jth main material of the i-th transmission tower is removed, B 1 represents the probability set of the No. 1 transmission tower falling in the micro-meteorological area, B 2 represents the probability set of the No. 2 transmission tower falling in the micro-meteorological area, B n represents the collapse probability set of n transmission towers in the micro-meteorological area.

[0047] Furthermore, in step 7, the multivariate positioning logic model is specifically described as:

[0048]

[0049] Fp [max] = {GB ij ,i,j}

[0050] in, Indicates the search for weak points of each transmission tower in the pth dimension, F p [max] means that the failure probability of the transmission tower corresponding to the weak point, the transmission tower number and the corresponding main material number are extracted here; then the probability of tower collapse of the transmission towers in the micro-meteorological area is sorted, and the transmission tower most likely to cause a tower collapse accident is searched, and the main material components in the transmission tower are sorted, and finally the weakest main material component is located, and reinforcement, replacement or maintenance measures are taken for it.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention is applicable to a method for locating weak points of a transmission line tower system in a micro-meteorological area. A staff member can establish an ECMWF model by using meteorological data obtained by a meteorological detection device around the transmission line and a meteorological station, combined with the terrain through which the transmission line passes, and use the meteorological data processed by a One class SVM algorithm to determine whether the meteorological data meets the data characteristics of the micro-meteorology. Subsequently, the transmission line span position where micro-meteorology exists is determined by combining GIS technology with the ECMWF model, and the transmission towers in the micro-meteorological area are numbered. A variety of transmission tower safety operation indicators are selected according to national standards, and the evaluation indicators are weighted and sorted using the CZA algorithm. According to the meteorological data of the micro-meteorology, the wind speed is Gauss distributed, the data is extracted using the Monte Carlo algorithm, all the meteorological data are converted into load data, and then the finite element analysis method is used to apply loads to each component of the transmission tower according to the obtained load data. Each main material of the transmission tower is numbered and sorted, and the numbered main materials are removed in sequence when performing finite element analysis, and the safe operation performance of the transmission tower corresponding to each evaluation index in the micro-meteorological area is calculated under the condition of removing the main materials. The multi-evaluation index matrix of the transmission tower is constructed by using the data essence algorithm, and the various indicators of the transmission tower are arranged and combined, and combined with the results of finite element analysis to form a multivariate transmission tower safety evaluation index. The multivariate positioning logic model is used to reversely search for the weak points of the transmission tower, locate the main material position when the probability of the transmission tower collapse is the highest, and finally determine the weak points of the transmission tower under multiple evaluation indicators, solving the problem of difficult positioning of the weak points of the transmission tower in the micro-meteorological area. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a method flow chart of an embodiment of the present invention;

[0054] Figure 2is a schematic diagram of an iron tower in which a power transmission line is located in a micro-meteorological area in an embodiment of the present invention;

[0055] Figure 3 is a failure probability diagram of weak points of a transmission tower in a micro-meteorological area according to an embodiment of the present invention;

[0056] Figure 4 It is a logic model diagram of multi-dimensional positioning of a transmission line in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0058] Example: Figures 1 to 4 As shown, an embodiment of the present invention provides a method for locating weak points of a transmission line tower system applicable to a micro-meteorological area, comprising the following steps:

[0059] Step 1: Based on the meteorological data obtained by the meteorological detection devices and meteorological stations around the transmission line, the ECMWF model is established in combination with the terrain through which the transmission line passes. The meteorological data processed by the One class SVM algorithm is used to determine whether the meteorological data meets the data characteristics of micrometeorology;

[0060] Step 2: Then, GIS technology is combined with the ECMWF model to determine the transmission line span locations where micro-meteorological conditions exist, and the transmission towers in the micro-meteorological areas are numbered;

[0061] Step 3: Make Gauss distribution of wind speed according to the meteorological data of micro-meteorology, extract the data by Monte Carlo algorithm, convert all meteorological data into load data, and then apply load to each component of the transmission tower by finite element analysis method according to the obtained load data;

[0062] Step 4: Select a variety of transmission tower safety operation indicators according to national standards, and use the CZA algorithm to rank the evaluation indicators by weight;

[0063] Step 5: Number and sort each main material of the transmission tower, and remove the numbered main materials in sequence when performing finite element analysis. Under the condition of removing the main materials, calculate the safe operation performance of the transmission tower corresponding to each evaluation index in the micro-meteorological area;

[0064] Step 6: Use the data essence algorithm to construct a multi-evaluation index matrix of the transmission tower, arrange and combine the various indicators of the transmission tower, and combine them with the finite element analysis results to form a multivariate transmission tower safety evaluation index;

[0065] Step 7: Use the multivariate positioning logic model to reversely search for the weak points of the transmission tower, locate the main material position when the probability of the transmission tower collapse is the highest, and finally determine the weak points of the transmission tower under multiple evaluation indicators.

[0066] Specifically, in step one, the meteorological monitoring device includes a wind speed detector and an ice thickness detector, and the meteorological data includes wind speed and ice thickness; the meteorological data is obtained by acquiring the meteorological data obtained by the meteorological monitoring device and the meteorological station, and the most unfavorable data for the transmission tower is selected from the data, that is, the maximum wind speed and the maximum ice thickness in the micro-meteorological area are respectively taken; the ECMWF model combines the extracted meteorological data with the local topography to form a meteorological-topographic data cloud map; the Oneclass SVM algorithm selects micro-meteorological data based on the extracted meteorological data and the existing historical data of other surrounding areas, and defines the area within the micro-topography range as micro-meteorological data in combination with the ECMWF model and displays it on the meteorological-topographic data cloud map of the ECMWF model.

[0067] In step 2, the relevant data of the transmission line, including the tower number and the shape of the transmission tower, are bid and extracted through GIS technology, and then combined with the ECMWF model to determine the tower number and tower shape in the micro-meteorological area of ​​the transmission line.

[0068] In step 3, the Gaussian distribution of meteorological data is as follows:

[0069]

[0070]

[0071] Where z is the wind load sample data; μ z is the mathematical expectation of wind load; σ z is the standard deviation of wind load; σ z 2 is the variance of wind load; b is the sample data of ice thickness; μ b is the mathematical expectation of ice thickness; σ b is the standard deviation of ice thickness; σ b 2 is the variance of ice thickness.

[0072] In step 4, the CZA algorithm steps are as follows:

[0073] Assuming there are m safety assessment indicators, the transmission tower simulation runs n times:

[0074] X=(x ij ) n×m (i=1,2...n;j=1,2,...m)

[0075] In the formula, x ij is the indicator element in the matrix; before constructing the model, it is necessary to eliminate the dimension of each score value, so for x ij To standardize:

[0076]

[0077] In the formula, y ij Each element in the normalized matrix is ​​normalized; since the normalized data must be positive, it is necessary to perform positive transformation to convert the minimum evaluation index into the maximum evaluation index:

[0078]

[0079] Finally, the evaluation matrix Y is obtained * , the forward formula is the decision matrix:

[0080]

[0081] In the formula, y ij * is the evaluation data of the jth evaluation index under the i-th simulation condition; the information essential value e j The calculation formula is:

[0082]

[0083] Where: –1 / lnm is the information coefficient; the role of the information coefficient is to make e j The value of is always in the interval [0,1]; then calculate the weight w of the jth evaluation index j :

[0084]

[0085] The matrix W is the weight vector and its specific calculation formula is:

[0086] W=[w 1 ,w 2 ,w 3 ,…,w j ].

[0087] In step 5, since the tower shape, height, structure, etc. of each transmission tower in the transmission line are different, the main materials of each transmission tower need to be sorted before the main material destruction method is used for the subsequent operation calculation; the selected transmission tower evaluation index is based on the standards clearly stated in the national standard, including: tower inclination, main material stress, and main material bending degree; according to the above standards, it is determined whether the transmission tower collapses:

[0088] S=[S 1 , S 2 , S 3 ]

[0089]

[0090]

[0091]

[0092]

[0093] Among them, S represents the failure probability matrix of multiple evaluation indicators, S 1 represents the failure probability of the transmission tower’s tower inclination, S 2 represents the failure probability of the main material stress of the transmission tower, S 3 represents the failure probability of the bending degree of the main material of the transmission tower, {(ME-N)≤0} represents the number of elements in the matrix that are less than or equal to 0, M represents the standard value of the tower inclination of the transmission tower, N represents the n-order matrix of the actual value of the inclination of the transmission tower, R represents the standard value of the main material stress of the transmission tower, T represents the n-order matrix of the actual value of the main material stress of the transmission tower, X represents the standard value of the bending degree of the main material of the transmission tower, Z represents the n-order matrix of the actual value of the bending degree of the main material of the transmission tower, and n represents the number of data samples extracted using the Monte Carlo algorithm. represents the one-to-one mapping correlation between the evaluation criteria and the operating conditions of the transmission line, MC[f(z), f(b)] represents the data extracted from the wind speed and ice thickness of the transmission line through the Monte Carlo algorithm, GS represents the cross-sectional area of ​​the main material components of the transmission line, GR represents the material of the main material components of the transmission line, GT represents the cross-sectional type of the main material components of the transmission line, and GX represents the type of transmission tower.

[0094] In step six, the multi-evaluation index matrix of the transmission tower is constructed using the data essence algorithm:

[0095]

[0096] Among them, W pThe weight coefficient matrix representing the Pth dimension is used to arrange and combine the various indicators of the transmission tower and combine them with the finite element analysis results to form a multivariate transmission tower safety assessment index:

[0097] GB ij =W 1 S 1 T §W 2 S 2 T §W 3 S 3 T ...W p S p T

[0098]

[0099] Among them, S p represents the failure probability matrix of the evaluation index with the same dimension as the weight coefficient, § represents the parallel effectiveness of integrating evaluation indicators of different dimensions, GB ij represents the probability of the multi-dimensional tower collapse after the jth main material of the i-th transmission tower is removed, B 1 represents the probability set of the No. 1 transmission tower falling in the micro-meteorological area, B 2 represents the probability set of the No. 2 transmission tower falling in the micro-meteorological area, B n represents the collapse probability set of n transmission towers in the micro-meteorological area.

[0100] In step 7, the multivariate positioning logic model is specifically described as:

[0101]

[0102] F p [max] = {GB ij ,i,j}

[0103] in, Indicates the search for weak points of each transmission tower in the pth dimension, F p [max] means that the failure probability of the transmission tower corresponding to the weak point, the transmission tower number and the corresponding main material number are extracted here; then the probability of tower collapse of the transmission towers in the micro-meteorological area is sorted, and the transmission tower most likely to cause a tower collapse accident is searched, and the main material components in the transmission tower are sorted, and finally the weakest main material component is located, and reinforcement, replacement or maintenance measures are taken for it.

[0104] Application examples:

[0105] There are 4 transmission towers in a certain transmission line in the micro-meteorological area, and the most unfavorable conditions for the safe operation of the transmission towers in the micro-meteorological area are a wind speed of 27m / s and an ice thickness of 15cm. The wind speed and ice thickness are converted into load data, and then Gaussian distribution is performed on them. The data in the Gaussian distribution are randomly sampled using the Monte Carlo algorithm. The weights of the selected transmission tower evaluation indicators are determined according to the CZA algorithm, including: tower inclination, main material stress, main material bending degree, and the coefficient matrix W = [0.29, 0.35, 0.33]. Subsequently, the data essence algorithm is used to construct the multi-evaluation indicator matrix of the transmission tower. Since there are three evaluation indicators, there are 6 dimensions in the weight coefficient matrix. Finally, the weak point position F of each transmission tower in different evaluation dimensions is searched through the multivariate positioning logic model. 1 [max] = {0.75, 1, 1487}, F 2 [max] = {0.87, 1, 1448}, F 3 [max] = {0.72, 2, 1259}, F 4 [max] = {0.58, 4, 245}, F 5 [max] = {1, 3, 2087}, F 6 [max] = {0.95, 4, 487}.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art can still modify or make equivalent substitutions to the specific implementations of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention are within the scope of protection of the claims of the present invention to be approved.

Claims

1. A method for locating weak points of transmission line tower systems in micro-meteorological areas. It is characterized in that The following steps are involved: Step 1: Based on the meteorological data obtained by the meteorological detection devices and meteorological stations around the transmission line, the ECMWF model is established in combination with the terrain through which the transmission line passes. The meteorological data processed by the One class SVM algorithm is used to determine whether the meteorological data meets the data characteristics of micrometeorology; Step 2: Then, GIS technology is combined with the ECMWF model to determine the transmission line span locations where micro-meteorological conditions exist, and the transmission towers in the micro-meteorological areas are numbered; Step 3: Make Gauss distribution of wind speed according to the meteorological data of micro-meteorology, extract the data by Monte Carlo algorithm, convert all meteorological data into load data, and then apply load to each component of the transmission tower by finite element analysis method according to the obtained load data; Step 4: Select a variety of transmission tower safety operation indicators according to national standards, and use the CZA algorithm to rank the evaluation indicators by weight; Step 5: Number and sort each main material of the transmission tower, and remove the numbered main materials in sequence when performing finite element analysis. Under the condition of removing the main materials, calculate the safe operation performance of the transmission tower corresponding to each evaluation index in the micro-meteorological area; Step 6: Use the data essence algorithm to construct a multi-evaluation index matrix of the transmission tower, arrange and combine the various indicators of the transmission tower, and combine them with the finite element analysis results to form a multivariate transmission tower safety evaluation index; Step 7: Use the multivariate positioning logic model to reversely search for the weak points of the transmission tower, locate the main material position when the probability of the transmission tower collapse is the highest, and finally determine the weak points of the transmission tower under multiple evaluation indicators.

2. The method for locating weak points of a power transmission line tower system applicable to a micro-meteorological area according to claim 1, Features: In step one, the meteorological monitoring device includes a wind speed detector and an ice thickness detector, and the meteorological data includes wind speed and ice thickness; the meteorological data obtained by the meteorological monitoring device and the meteorological station are obtained, and the most unfavorable data for the transmission tower is selected, that is, the maximum wind speed and the maximum ice thickness in the micro-meteorological area are respectively taken; the ECMWF model combines the extracted meteorological data with the local topography to form a meteorological-topographic data cloud map; the One class SVM algorithm selects micro-meteorological data based on the extracted meteorological data and the existing historical data of other surrounding areas, and defines the area within the micro-topography range as micro-meteorological data in combination with the ECMWF model and displays it on the meteorological-topographic data cloud map of the ECMWF model.

3. According to the method for locating weak points of a transmission line tower system applicable to a micro-meteorological area as claimed in claim 1, Features: In step 2, the relevant data of the transmission line, including the tower number and the shape of the transmission tower, are bid and extracted through GIS technology, and then combined with the ECMWF model to determine the tower number and tower shape in the micro-meteorological area of ​​the transmission line.

4. The method for locating weak points of a power transmission line tower system applicable to a micro-meteorological area according to claim 1, Features: In step 3, the Gaussian distribution of meteorological data is as follows: Where z is the wind load sample data; μ z is the mathematical expectation of wind load; σ z is the standard deviation of wind load; σ z 2 is the variance of wind load; b is the sample data of ice thickness; μ b is the mathematical expectation of ice thickness; σ b is the standard deviation of ice thickness; σ b 2 is the variance of ice thickness.

5. According to claim 1, the method for locating weak points of a transmission line tower system applicable to a micro-meteorological area, Features: In step 4, the CZA algorithm steps are as follows: Assuming there are m safety assessment indicators, the transmission tower simulation runs n times: X=(x ij ) n×m (i=1,2...n;j=1,2,...m) In the formula, x ij is the indicator element in the matrix; before constructing the model, it is necessary to eliminate the dimension of each score value, so for x ij To standardize: In the formula, y ij Each element in the normalized matrix is ​​normalized; since the normalized data must be positive, it is necessary to perform positive transformation to convert the minimum evaluation index into the maximum evaluation index: Finally, the evaluation matrix Y is obtained * , the forward formula is the decision matrix: In the formula, y ij * is the evaluation data of the jth evaluation index under the i-th simulation condition; the information essential value e j The calculation formula is: Where: –1 / lnm is the information coefficient; the role of the information coefficient is to make e j The value of is always in the interval [0,1]; then calculate the weight w of the jth evaluation index j : The matrix W is the weight vector and its specific calculation formula is: In=[in 1 ,In 2 ,In 3 ,…,In j ]。 6. The method for locating weak points of a power transmission line tower system applicable to a micro-meteorological area according to claim 1, Features: In step 5, since the tower shape, height, structure, etc. of each transmission tower in the transmission line are different, the main materials of each transmission tower need to be sorted before the main material destruction method is performed to facilitate the subsequent operation calculation; The selected transmission tower evaluation indicators are based on the standards clearly stated in the national standards, including: tower inclination, main material stress, and main material bending degree; according to the above standards, whether the transmission tower collapses is determined: S=[S 1 、S 2 、S 3 ] Among them, S represents the failure probability matrix of multiple evaluation indicators, S 1 The failure probability of the transmission tower is represented by the inclination of the tower, S 2 represents the failure probability of the main material stress of the transmission tower, S 3 represents the failure probability of the bending degree of the main material of the transmission tower, {(ME-N)≤0} represents the number of elements in the matrix that are less than or equal to 0, M represents the standard value of the tower inclination of the transmission tower, N represents the n-order matrix of the actual value of the inclination of the transmission tower, R represents the standard value of the main material stress of the transmission tower, T represents the n-order matrix of the actual value of the main material stress of the transmission tower, X represents the standard value of the bending degree of the main material of the transmission tower, Z represents the n-order matrix of the actual value of the bending degree of the main material of the transmission tower, and n represents the number of data samples extracted using the Monte Carlo algorithm. represents the one-to-one mapping correlation between the evaluation criteria and the operating conditions of the transmission line, MC[f(z), f(b)] represents the data extracted from the wind speed and ice thickness of the transmission line through the Monte Carlo algorithm, GS represents the cross-sectional area of ​​the main material components of the transmission line, GR represents the material of the main material components of the transmission line, GT represents the cross-sectional type of the main material components of the transmission line, and GX represents the type of transmission tower.

7. The method for locating weak points of a power transmission line tower system applicable to a micro-meteorological area according to claim 1, Features: In step six, the multi-evaluation index matrix of the transmission tower is constructed using the data essence algorithm: Among them, W p The weight coefficient matrix representing the Pth dimension is used to arrange and combine the various indicators of the transmission tower and combine them with the finite element analysis results to form a multivariate transmission tower safety assessment index: GB ij =In 1 S 1 T §IN 2 S 2 T §IN 3 S 3 T ……IN p S p T B 1 ={GB 11 、GB 12 、……GB 1m } B 2 ={GB 21 、GB 22 、……GB 2m } B n ={GB n1 、GB n2 、……GB nm } Among them, S p represents the failure probability matrix of the evaluation index with the same dimension as the weight coefficient, § represents the parallel effectiveness of integrating evaluation indicators of different dimensions, GB ij represents the probability of the multi-dimensional tower collapse after the jth main material of the i-th transmission tower is removed, B 1 represents the probability set of the No. 1 transmission tower falling in the micro-meteorological area, B 2 represents the probability set of the No. 2 transmission tower falling in the micro-meteorological area, B n represents the collapse probability set of n transmission towers in the micro-meteorological area.

8. The method for locating weak points of a power transmission line tower system applicable to a micro-meteorological area according to claim 1, Features: In step 7, the multivariate positioning logic model is specifically described as: F p [max]={GB ij ,i,j} in, Indicates the search for weak points of each transmission tower in the pth dimension, F p [max] means that the failure probability of the transmission tower corresponding to the weak point, the transmission tower number and the corresponding main material number are extracted here; then the probability of tower collapse of the transmission towers in the micro-meteorological area is sorted, and the transmission tower most likely to cause a tower collapse accident is searched, and the main material components in the transmission tower are sorted, and finally the weakest main material component is located, and reinforcement, replacement or maintenance measures are taken for it.

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