Icing risk assessment method for overhead transmission line

Abstract

The invention relates to an overhead power transmission line icing risk assessment method, which analyzes icing influence factors of different sources of an overhead power transmission line, ensures that selected indexes can accurately reflect the icing risk of the power transmission line, analyzes risk assessment indexes from the three aspects of the historical icing condition, the current operation condition and the meteorological condition of the power transmission line, and determines the icing risk of the power transmission line. The method comprises the following specific steps: step 1, collecting related information of an overhead transmission line to be evaluated; step 2, calculating the weight of each index by using an improved analytic hierarchy process for subsequent risk assessment; and step 3, carrying out data fusion, determining the grade of the corresponding risk, and completing evaluation. The method has the advantages that the icing risk of the overhead transmission line can be evaluated, a guidance basis is provided for line anti-icing work, and safe and stable operation of a power grid is guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of overhead transmission line risk assessment, and particularly relates to an overhead transmission line icing risk assessment method. BACKGROUND

[0002] Transmission line icing can bring many hazards to the power system. First, line mechanical damage. Icing can greatly increase the weight of the transmission line. When the icing thickness exceeds the limit that the conductor can bear, the conductor will break due to the heavy burden, resulting in interruption of power transmission. Second, tower tilting or collapse. Transmission line towers have a certain bearing capacity when designed. Excessive icing can cause the tower to bear too much pressure, which may cause the tower to tilt. If the icing is serious, the unbalanced tension on the tower exceeds its bearing range, and a collapse accident will occur, which will cause serious damage to the safe operation of the power system. Therefore, reasonably and accurately assessing the possibility of line icing and the impact of icing on power grid operation after failure can strengthen the operation and maintenance management and protection measures of the transmission line and improve the reliability of the transmission line in winter operation. The patent document with the application publication number CN110866693A discloses an overhead transmission line icing risk assessment method based on a GIS model, which comprises the following steps: (1) obtaining the geographic profile and device cloud data of the tower line in the transmission line corridor, generating a digital line corridor three-dimensional visualization model and determining the line type; (2) obtaining the line icing radius; (3) obtaining the average height and minimum sag height of each conductor; (4) determining the unit cross-sectional area icing amount of the transmission line using the results of (2) and (3); (5) using the above results combined with the line path information to output a transmission line icing stress calculation report using the ansys mechanical analysis module; (6) determining the icing risk level of the transmission line in different regions according to the icing stress calculation report and the design stress value of the corresponding equipment. SUMMARY

[0003] The purpose of the present application is to overcome the shortcomings of the above-mentioned technology, and to provide an overhead transmission line icing risk assessment method, which can scientifically identify the icing factors affecting the safe operation of the overhead transmission line, improve the accuracy of the overhead transmission line icing risk assessment, guide the operation and maintenance unit to carry out line anti-icing work in order of priority, prevent dancing, tower collapse and icing flashover events, and ensure the safe and reliable operation of the line.

[0004] To achieve the above-mentioned purpose, the following technical solution is adopted: An overhead transmission line icing risk assessment method, which analyzes the icing influence factors of different sources of the overhead transmission line, ensures that the selected indicators can accurately reflect the icing risk of the transmission line, analyzes the risk assessment indicators from the three aspects of the historical icing condition of the transmission line, the current operation condition and the meteorological condition, and constructs the corresponding risk assessment system structure, the specific steps are as follows:

[0005] Step 1, collect the relevant information of the overhead transmission line to be evaluated, including the body characteristics of the transmission line and the environmental characteristics of the surrounding environment, the body characteristics are the length, height, protection angle and fault hidden danger index of the transmission line, and the environmental characteristics are the altitude, humidity, temperature, topography and wind speed index of the transmission line;

[0006] Step 2, calculate the weight of each index by using the improved analytic hierarchy process, calculate the subjective weight of different risk indexes of each level by using the analytic hierarchy process, and improve the analytic hierarchy process by using the minimum entropy relative principle for subsequent risk assessment;

[0007] Step 3, data fusion, use data mining to extract the corresponding risk characteristics, use the risk characteristics as the basis for icing risk assessment of the transmission line, and determine the corresponding risk level, complete the evaluation.

[0008] Preferably, the collection of the relevant information of the overhead transmission line to be evaluated in step 1 analyzes and processes the historical icing data, and the specific steps are:

[0009] 1) Data classification includes normalization processing of quantitative data and assignment of qualitative processing;

[0010] 2) Mine data attributes;

[0011] 3) Extract data features;

[0012] 4) Dimension reduction is performed on the data, and the historical icing data arrangement is completed.

[0013] Preferably, the establishment principle of the risk assessment system structure includes historical icing, transmission line operation, and transmission line surrounding environment and meteorological conditions,

[0014] The above risk assessment system structure is used as the basis to form a judgment matrix, and the formula is:

[0015]

[0016] In the formula, T x is the judgment matrix; a ij is the relative importance of risk index i and risk index j; n is the number of risk indexes. a ij The value of a is determined according to the relative importance of different indexes: when the importance of two risk indexes is the same, take 0.5; if the importance of risk index i is much lower than that of risk index j, take 0.1, if much greater than that of risk index j, take 0.9.

[0017] Preferably, the calculation of the subjective weight value of each level of different risk indexes in step 2) is calculated by using the analytic hierarchy process, and the formula is:

[0018] wherein w ij is the subjective weight value of the i th risk index X i relative to the j th risk index X j ; w ix , w jx are the overall weight values of the risk index X i , X j at the x level; w y is the subjective weight value at the y level; w xy is the subjective weight value of X i at the y level; and M is the analytic hierarchy function.

[0019] Preferably, the risk level in step 3 is divided into:

[0020] I, qualitative value < 0.2, quantitative data is 1;

[0021] II, qualitative value 0.2≤f<0.4, quantitative data is 2;

[0022] III, qualitative value 0.4≤f<0.6, quantitative data is 3;

[0023] IV, qualitative value 0.6≤f<0.8, quantitative data is 4;

[0024] V, qualitative value > 0.8, quantitative data is 5.

[0025] Beneficial effects: Compared with the prior art, the present application can realize the evaluation of icing risk of overhead transmission line, provide guidance basis for line anti-icing work, and guarantee the safe and stable operation of power grid. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a flowchart of icing risk evaluation;

[0027] Figure 2 is a flowchart of analysis and processing of historical icing data. DETAILED DESCRIPTION

[0028] In order to enable the above-mentioned objects, features and advantages of the present application to be clearer, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments and features in the embodiments can be combined with each other without conflict. In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application. The described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the scope of protection of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application.

[0029] As shown in the drawings, the embodiment provides an overhead transmission line icing risk assessment method, analyzes different sources of icing influencing factors of the overhead transmission line, ensures that the selected indicators can accurately reflect the icing risk of the transmission line, analyzes the risk assessment indicators from three aspects of the historical icing condition of the transmission line, the current operation condition and the meteorological condition, constructs a corresponding risk assessment system structure, and the specific steps are as follows:

[0030] Step 1, collecting related information of the overhead transmission line to be evaluated, including the body characteristics and the environmental characteristics of the overhead transmission line, the body characteristics are the length, height, protection angle and fault hidden danger indicators of the overhead transmission line, and the environmental characteristics are the altitude, humidity, temperature, topography and wind force and wind speed indicators of the overhead transmission line;

[0031] Step 2, calculating the weight of each indicator by using the improved analytic hierarchy process, calculating the subjective weight of different risk indicators of each level by using the analytic hierarchy process, and improving the analytic hierarchy process by using the minimum entropy relative principle for subsequent risk assessment;

[0032] Step 3, data fusion is performed, the corresponding risk characteristics are extracted by using data mining, the risk characteristics are taken as a basis for icing risk assessment of the transmission line, the corresponding risk grade is determined, and the evaluation is completed.

[0033] The preferred scheme of the embodiment is shown in the drawings Figure 2 The related information of the overhead transmission line to be evaluated in step 1 is collected, the historical icing data is analyzed and processed, and the specific steps are as follows:

[0034] 1) Data classification includes normalization processing of quantitative data and assignment of qualitative processing;

[0035] 2) Mining data attributes;

[0036] 3) extracting data features;

[0037] 4) reducing dimensionality of data, ending history icing data arrangement.

[0038] The preferred scheme of the embodiment is that the establishment principle of the risk assessment system architecture includes history icing, operation condition of the power transmission line, and environment and meteorological condition around the power transmission line,

[0039] The judgment matrix formed based on the above risk assessment system architecture is as follows:

[0040]

[0041] In the formula, T x is the judgment matrix; a ij is the relative importance of risk index i and risk index j; and n is the number of risk indexes. The value of a ij is determined according to the relative importance of different indexes: when the importance of two risk indexes is the same, 0.5 is taken; if the importance of risk index i is much lower than that of risk index j, 0.1 is taken, and if the importance of risk index i is much higher than that of risk index j, 0.9 is taken.

[0042] The preferred scheme of the embodiment is that step 2) uses the analytic hierarchy process to calculate the subjective weight value of each level of different risk indexes, and the formula is as follows:

[0043] In the formula, w ij is the subjective weight value of the i th risk index X i relative to the j th risk index X j ; w ix , w jx are the overall weight values of risk indexes X i , X j in x level; w y is the subjective weight value in y level; w xy is the subjective weight value of X i in y level; and M is the analytic hierarchy function.

[0044] The preferred scheme of the embodiment is that the risk grade in step 3 is divided into:

[0045] I. qualitative value < 0.2, and quantitative data is 1;

[0046] II. qualitative value 0.2≤f<0.4, and quantitative data is 2;

[0047] III. qualitative value 0.4≤f<0.6, and quantitative data is 3;

[0048] IV. qualitative value 0.6≤f<0.8, and quantitative data is 4;

[0049] V, qualitative data > 0.8, quantitative data is 5.

[0050] The present application considers the icing influencing factors of different sources of overhead transmission lines, ensures that the selected indicators can accurately reflect the icing risk of the transmission line, and therefore analyzes the risk assessment indicators from three aspects of the historical icing condition of the transmission line, the current operation condition and the meteorological condition, and constructs the corresponding risk assessment system structure. For details, see Table 1 risk assessment system structure.

[0051] Table 1 risk assessment system structure

[0052]

[0053]

[0054] The influence of historical icing events on overhead transmission lines is considered, the historical icing data is analyzed and processed, and the risk characteristics of the historical data are extracted, as shown in the process of Figure 1

[0055] Firstly, the collected historical icing disaster data is classified into quantitative data and qualitative data, the quantitative data is normalized, and the qualitative data is valued according to the severity; then, the data characteristics are extracted according to the attributes of the data and are processed by dimension reduction; finally, according to the extracted data characteristics f, the corresponding risk level is determined according to the risk division level, which is the basis of risk assessment. For details, see Table 2 risk level.

[0056] Table 2 risk level

[0057] Risk level Qualitative value Quantitative value I ＜0.2 1 II 0.2≤f＜0.4 2 III 0.4≤f＜0.6 3 IV 0.6≤f＜0.8 4 V ＞0.8 5

[0058] The icing risk assessment model of overhead transmission lines is composed of three parts, namely basic information, calculation weight and determination of risk level. The basic information includes line body information and environmental information, then the corresponding weight is calculated according to the basic information, the risk characteristics of the historical data are extracted, and finally the icing risk level is determined to complete the evaluation.

[0059] Embodiment

[0060] A 220kV line is selected as the to-be-evaluated line, which is located in the mountainous area, and the line body characteristics, environmental characteristics and historical icing information are collected.

[0061] (I) Assessment of promotion level division

[0062] According to the "target layer - first level - second index" division, the specific is as follows:

[0063] Table 3 level division

[0064] ​

[0065] (ii) Core parameter definition (matching new document formula)

[0066] 1. Analytic hierarchy function (M): In combination with the characteristics of the line located in the plain, avoid excessive subjective adjustment, ensure that the weight calculation fits the actual scene, set M = 1.0;

[0067] 2. Data standardization rules: quantitative indicators are normalized by extreme value Qualitative indicators are assigned values according to risk levels (high risk = 0.7-1.0, medium risk = 0.4-0.6, low risk = 0.1-0.3).

[0068] (iii) Calculate w according to formula (2) ix

[0069] w ix is the overall weight value of the x level where the indicator X i is located, because w y = 1.0 (target layer weight), M = 1.0, the formula is simplified as w ix = w iy (i.e. the weight of the first-level layer to which the indicator belongs), the calculation results of the w ix of the 14 indicators are as follows:

[0070] Table 4 w ix calculation results

[0071]

[0072] (iv) Calculate w ij

[0073] 1. w ij is the subjective weight value of the i-th indicator X i relative to the j-th indicator X j , which needs to be calculated two by two for the 14 indicators (a total of 196 groups of data). Here, the calculation process of the core indicators is first shown (the calculation logic of non-core indicators is consistent), and then the "average subjective weight" of all indicators is given, which is the average value of w ij of the indicator relative to the other 13 indicators, as the basis for subsequent minimum entropy correction.

[0074] Table 5 w ix calculation results

[0075]

[0076]

[0077] 2. Average subjective weight of 14 indicators

[0078] By calculating the w of each indicator relative to the other 13 indicators ij , take the arithmetic mean as the "average subjective weight", the results are as follows (make sure there is no negative weight, if the calculation result <0, take 0, >1, take 1, all results here are in the interval of 0-1)

[0079] Table 5 Average subjective weight of 14 indicators

[0080]

[0081] (Five) Correction of weight based on the principle of minimum entropy

[0082] The core of the principle of minimum entropy is to measure the dispersion of weight distribution through "entropy value", the smaller the entropy value, the more concentrated the weight distribution (that is, the greater the difference in risk contribution between indicators), which needs to be corrected to make the weight more in line with the actual risk impact.

[0083] 1. Entropy value calculation

[0084] The formula is

[0085] (1) Calculate the proportion of weight (p i ), The sum of the average subjective weight of the 14 indicators is 7.15.

[0086] (2) Calculate p i and lnp i for each indicator, for example:

[0087] Indicator 8: p8 = 0.62 / 7.15 = 0.0867, p8lnp8 = 0.0867x(-2.44) = -0.211;

[0088] (3) Calculate the total entropy value

[0089]

[0090] 2. Correction of weight calculation

[0091] The formula is Since ∑p i =1, the formula is simplified as w' i =p i .

[0092] Since the total entropy value E = 0.674 (in the reasonable interval 0.5-0.8), the weight proportion p i can reflect the risk contribution, so the corrected weight directly uses p i (If E>0.8, further adjustment is needed), the final corrected weight is as follows (make sure the sum of the weights =1)

[0093] Table 6 Weight values of 14 indicators after correction

[0094]

[0095]

[0096] (6) Data standardization and risk characteristic value calculation 1. Standardization of 14 indicators

[0097] Table 7 Standardization of 14 indicators

[0098]

[0099]

[0100] 2. Calculation of risk characteristic value

[0101] The formula is The calculation gives f = 0.583, and according to the risk grade determination table, it is determined to be medium risk.

[0102] The above detailed description of the reference examples is illustrative rather than limiting, and a number of examples can be listed within the defined range, and therefore changes and modifications without departing from the overall concept of the present application shall be within the scope of protection of the present application.

Claims

1. A method for assessing icing risk of an overhead power transmission line, characterized in that: The analysis of the icing influencing factors of different sources of overhead transmission lines ensures that the selected indexes can accurately reflect the icing risk of the transmission lines. The risk assessment indexes are analyzed from three aspects of the historical icing conditions, the current operation conditions and the meteorological conditions of the transmission lines, and the corresponding risk assessment system structure is constructed, and the specific steps are as follows: Step 1, collecting the related information of the overhead transmission lines to be evaluated, including the intrinsic characteristics and the environmental characteristics of the overhead transmission lines, the intrinsic characteristics are the length, height, protection angle and fault hidden danger indexes of the overhead transmission lines, and the environmental characteristics are the altitude, humidity, temperature, topography and wind force and wind speed indexes of the overhead transmission lines; Step 2, calculating the weight of each index by using the improved analytic hierarchy process, calculating the subjective weight of each index of different risks by using the analytic hierarchy process, and improving the analytic hierarchy process by using the minimum entropy relative principle for subsequent risk assessment; Step 3, data fusion, using data mining to extract the corresponding risk characteristics, taking the risk characteristics as the basis for icing risk assessment of the transmission lines, and determining the corresponding risk level to complete the evaluation.

2. The overhead power line ice accretion risk assessment method of claim 1, wherein: The related information of the overhead transmission lines to be evaluated in step 1 is collected, and the historical icing data is analyzed and processed, and the specific steps are as follows: 1) data classification includes normalization processing of quantitative data and assignment of qualitative processing; 2) data attribute mining; 3) data feature extraction; 4) dimensionality reduction of data, and end of historical icing data arrangement.

3. The method of claim 1, wherein: The establishment principles of the risk assessment system structure include historical icing, operation conditions of the transmission lines and environmental and meteorological conditions around the transmission lines, The judgment matrix is formed based on the above risk assessment system structure, and the formula is: where T x is the judgment matrix; a ij is the relative importance of risk indicator i and risk indicator j; n is the number of risk indicators.a ij The value of a is determined according to the relative importance of different indicators: 0.5 when the importance of two risk indicators is the same; 0.1 if the importance of risk indicator i is much lower than that of risk indicator j, and 0.9 if the importance of risk indicator i is much higher than that of risk indicator j.

4. The method of claim 1, wherein: The step 2) uses analytic hierarchy process to calculate subjective weight values of different risk indexes in each level, and the formula is: wherein w ij is the subjective weight value of the i-th risk indicator X i relative to the j-th risk indicator X j ; w ix , w jx are the overall weight values of the risk indicators X i , X j at the x-level; w y is the subjective weight value at the y-level; w xy is the subjective weight value of X i at the y-level; and M is the analytic hierarchy function.

5. The method of claim 1, wherein: The risk level in step 3 is divided into: I, qualitative value <0.2, quantitative data is 1; II, qualitative value 0.2≤f<0.4, quantitative data is 2; III, qualitative value 0.4≤f<0.6, quantitative data is 3; IV, qualitative value 0.6≤f<0.8, quantitative data is 4; V, qualitative value >0.8, quantitative data is 5.

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