CN114757077A - Construction method of wind deflection angle prediction model of double-split line suspension insulator string - Google Patents
Construction method of wind deflection angle prediction model of double-split line suspension insulator string Download PDFInfo
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Abstract
The invention discloses a construction method of a wind deflection angle prediction model of a double-split line suspension insulator string, which comprises the following steps of: s1, establishing a simulation object, S2, S3, establishing a wind drift angle prediction model, and training by utilizing a training data set to obtain the prediction model based on a random forest algorithm. The advantages are that: compared with the traditional wind deflection angle calculation method, the wind deflection angle prediction model of the suspension insulator string based on the random forest algorithm considers the random wind load effect, fully learns the complex nonlinear relation under different parameter combinations, can accurately predict the wind deflection angle of the suspension insulator string, has high prediction precision and strong generalization capability, is convenient and efficient to use, meets the engineering design requirement in precision, and is effectively used for designing the insulation gap of the tower.
Description
The technical field is as follows:
the invention relates to the technical field of transmission line tower design, in particular to a construction method of a wind drift angle prediction model of a double-split line suspension insulator string.
Background art:
the ground wire of the power transmission line can deflect under the action of wind load, so that the suspension insulator string swings, and the deflection angle of the suspension insulator string is called as a wind deflection angle; if the wind deflection angle is too large, the electric insulation gap between the conducting wire and the tower is too small, flashover and even tripping accidents occur, and the safe operation of the line is seriously threatened.
The traditional method for designing the insulation gap between the conducting wire and the tower head of the tower is as follows: simplifying the suspension insulator string into a rigid straight rod, calculating a wind deflection angle by utilizing the static balance condition of the insulator string according to the wind load and the dead load borne by the lead and the suspension insulator string under the action of the wind load, and calculating a gap between the lead and a tower according to a geometric relationship; the design of the positions of the cross arm and the hanging point during the design of the tower can be further determined according to the voltage grade, the wind deflection angle of the suspension insulator string and the requirements of the electrical insulation gap; however, this conventional design method has the following disadvantages: on one hand, the dynamic effect of pulsating wind is not considered, and the wind deflection angle obtained through calculation is small, so that wind deflection flashover accidents occur frequently; on the other hand, the traditional method has a complex process of calculating the wind deflection angle and is inconvenient to use.
The method for calculating the wind deflection angle generally uses a finite element numerical simulation method, in the simulation calculation process, the pulsatility influence of random wind is considered, and the calculation result is more practical, but the finite element numerical simulation method is adopted, finite element modeling and solving calculation are required to be carried out each time, the finite element modeling is complex and time-consuming, in the wind deflection angle calculation process, the time for random wind time-course simulation and the time for finite element implicit dynamics solving calculation are long, and the efficiency is low.
The invention content is as follows:
the invention aims to provide a construction method of a wind deflection angle prediction model of a double-split line suspension insulator string, and solves the problems that the process of calculating the wind deflection angle is complicated and the wind deflection flashover accident frequently occurs due to the fact that the dynamic effect of pulsating wind is not considered in the traditional design method and errors exist.
The invention is implemented by the following technical scheme: the construction method of the wind deflection angle prediction model of the double-split line suspension insulator string comprises the following steps:
s1: building a simulated object
S11 sets parameters: setting structural parameters and basic design wind speed of a plurality of groups of transmission lines in finite element software, wherein the structural parameters comprise span, height difference, wire model and initial tension;
s12, establishing a geometric model of the power transmission line: in the finite element software, establishing a geometric model of the power transmission line with the continuous gear number more than or equal to 4 gears by using the parameters set in the step S11, wherein the geometric model comprises a lead, a suspension insulator string and a strain insulator string;
s2: building a data set
S21 calculates the random wind load: generating a random wind speed time course corresponding to the basic design wind speed by using a harmonic synthesis method and adopting a Kaimal wind speed spectrum and a Davenport coherent function, and obtaining a random wind load according to the random wind speed time course and a wire wind load calculation formula;
S22 random wind load is applied: simulating wind speed points every 10 meters along the direction of the wire of the geometric model established in the step S12, wherein each wind speed point corresponds to a section of wire segment, and random wind load is applied to each wind speed point;
s23: simulating the wind deviation time course response of the power transmission line under random wind load by using finite element software numerical values, extracting the mean value and root mean square value of the wind deviation angle of the line suspension insulator string from the wind deviation time course response, and calculating the statistical value of the wind deviation angle according to the mean value and the root mean square value; constructing a data set, and dividing the data set into a training data set and a testing data set by adopting a random sampling method;
s3: building a wind drift angle prediction model
And based on a random forest algorithm, training by using the training data set divided in the step S23 to obtain a prediction model.
Further, the wind deflection angle prediction model is tested, that is, the generalization ability of the wind deflection angle prediction model trained in step S3 is tested by using the test data set in step S23.
Further, in step S23, the statistical value of the wind deflection angle is calculated according to the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the angle of the wind deflection,
the mean value of wind deflection angles under the action of random wind loads, delta is a root mean square value, mu is a guarantee coefficient, and the value of the guarantee coefficient mu specified by the national standard is 2.2.
Further, in step S3, the constructed random forest includes 200 decision trees, the maximum splitting feature number of the decision trees is 8, i.e. the input vector feature dimension, and the minimum splitting sample number is set to 2; adopting a Bootstrap replaced sampling method to obtain 200 training subsets, and respectively using each training subset for the growth of a decision tree; during prediction, each decision tree gives a prediction result, and the prediction value output by the last random forest is the average value of all decision tree results.
Furthermore, the test method for the generalization ability of the wind deflection angle prediction model comprises the following steps that the abscissa is a wind deflection angle statistical value of a test data set sample point determined by using a numerical simulation result, namely a simulated wind deflection angle; the ordinate is a predicted value of the prediction model on a sample point corresponding to the test data set, namely a predicted wind drift angle; by determining the coefficient R2Evaluating the prediction performance of the prediction model; wherein the coefficient R2Has a value range interval of [0,1 ]]。
The invention has the advantages that: simulating the wind deflection time course response of the power transmission line under random wind load by using ABAQUS finite element software numerical values, extracting the mean value and the root mean square value of the wind deflection angle of the line suspension insulator string from the wind deflection time course response, and calculating the statistic value of the wind deflection angle according to the mean value and the root mean square value; constructing a data set, and dividing the data set into a training data set and a testing data set by adopting a random sampling method; secondly, a random forest algorithm is adopted, a suspension insulator string wind deflection angle prediction model is constructed by utilizing data training of a training data set, and then the prediction model is tested by utilizing data of a testing data set; by the aid of the prediction model, the span, the height difference, the initial tension, the wire model, the basic wind speed, the guarantee rate and the like of a line are used as input parameters, the wind deflection angle of the suspension insulator string can be rapidly obtained, and finite element numerical simulation calculation is not needed.
Compared with the traditional wind deflection angle calculation method, the wind deflection angle prediction model of the suspension insulator string based on the random forest algorithm considers the random wind load effect, fully learns the complex nonlinear relation under different parameter combinations, can accurately predict the wind deflection angle of the suspension insulator string, is high in prediction precision and generalization capability, is convenient and efficient to use, meets the engineering design requirement in precision, and is effectively used for designing the insulation gap of the tower.
Description of the drawings:
in order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a geometric model of a power transmission line;
FIG. 2 is a block diagram of a process for constructing a wind drift angle prediction model based on a random forest algorithm;
FIG. 3 is a graph of the results of testing a wind deflection angle prediction model;
fig. 4 is a model software interface for predicting wind deflection angles of the double-split line suspension insulator string.
Fig. 5 is a schematic structural diagram of a tower.
The components in the drawings are numbered as follows: the device comprises a suspension insulator string 1, a strain insulator string 2, a lead 3, a pole tower 4 and a tower head 5.
The specific implementation mode is as follows:
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a construction method of a wind deflection angle prediction model of a double-split line suspension insulator string, which comprises the following steps of:
s1: creating a simulated object
S11 sets parameters: in ABAQUS finite element software, setting structure parameters and basic design wind speed of 250 groups of transmission lines, wherein the structure parameters comprise span, height difference, wire model and initial tension; in the national standard, the basic design wind speed refers to the average wind speed of 10m in height and 10 minutes, and is determined by combining the meteorological record data of a line passing through regions;
S12, establishing a geometric model of the power transmission line: in finite element software, a geometric model of the power transmission line with the continuous gear number of 4 gears is established by using parameters set in the step S11, and according to the existing research results, when the continuous gear number is more than or equal to 4 gears, the wind deflection angle of the suspension insulator string 1 in the middle of the line hardly changes along with the increase of the gear number, so that the 4-gear continuous gear line is taken as a simulation object in the embodiment; the geometric model of the power transmission line comprises a lead 3, a suspension insulator string 1 and a strain insulator string 2;
s2 construction of a data set
S21 calculates the random wind load: generating a random wind speed time range corresponding to the basic design wind speed set in the step S11 by using a harmonic synthesis method and adopting a Kaimal wind speed spectrum and a Davenport coherent function, and obtaining a random wind load according to the random wind speed time range and a wire wind load calculation formula; the calculation formula of the wind load of the conducting wire is an existing formula in the standard 'overhead transmission line load standard' of the power industry, and the basic wind speed in the formula corresponds to the random wind speed time course of the embodiment;
s22 random wind load is applied: simulating a wind speed point every 10 meters along the direction of the wire 3 of the geometric model established in the step S12, wherein each wind speed point corresponds to a wire section with the length of 10 meters, and random wind load is applied to each wind speed point, namely the corresponding wire section;
S23: simulating wind deflection time course responses under random wind loads respectively corresponding to 250 sets of parameters set in the step S11 of the power transmission line by using finite element software numerical values, and extracting the mean value and root mean square value of wind deflection angles of the line suspension insulator string from the wind deflection time course responses; calculating a wind drift angle statistic value according to the mean value and the root mean square value;
the statistical value of the wind deflection angle is calculated according to the following formula:
wherein
Is the wind deflection angle of the wind turbine,
is the mean value of the wind deflection angle under the action of random wind, delta is the root mean square value, and mu is a guarantee coefficient; the value of the guarantee coefficient specified by the national standard is generally 2.2, and the corresponding guarantee rate is 98.61%.
And constructing 250 groups of data sets, wherein each group of data set sample points comprises structural parameters of the power transmission line and statistical values of basic design wind speed and wind drift angle, and dividing the data sets into a training data set and a testing data set by adopting a random sampling method.
S3: construction of a wind deflection angle prediction model
Based on a random forest algorithm, training by using the training data set divided in the step S23 according to the process shown in FIG. 2 to obtain a prediction model;
the random forest algorithm combines a plurality of CART decision trees based on the Kini exponent splitting rule into a strong model, and in order to reduce the overfitting risk and obtain higher prediction accuracy, the algorithm adopts a Bootstrap sampling method and a splitting characteristic random selection two effective random processes, namely sample random and characteristic random; compared with a single CART decision tree algorithm, the random forest algorithm has the advantages of high prediction precision, capability of processing high-dimensional nonlinear data, noise resistance, overfitting resistance, convenience in implementation, high training efficiency and the like.
The random forest constructed by the invention comprises 200 decision trees, the maximum splitting characteristic number of the decision trees is 8, namely the input vector characteristic dimension, and the minimum splitting sample number is set to be 2; obtaining 200 training subsets by adopting a Bootstrap sampling method with put-back, and respectively using each training subset for the growth of a decision tree;
during prediction, each decision tree gives a prediction result, and the prediction value output by the random forest finally takes the average value of the results of all decision trees; the prediction model constructed based on random forest algorithm training only needs dozens of seconds.
Testing the wind deflection angle prediction model, namely testing the generalization ability of the wind deflection angle prediction model trained in the step S3 by using the test data set of the step S23; the specific test method is as follows: the abscissa is a statistical value of the wind deflection angle of the test data set sample point determined by using the numerical simulation result, namely a simulated wind deflection angle; the ordinate is a predicted value of the prediction model on a sample point corresponding to the test data set, namely a predicted windage yaw angle; the invention adopts the determination coefficient R2Evaluating the prediction performance of the prediction model, coefficient R2Value range ofThe enclosure interval is [0,1](ii) a The more accurate the model prediction result is, the closer the coefficient is to 1; statistically finding out the R of the prediction model on the test data set according to the prediction result 2The value is 0.995, the prediction precision is high, and the generalization capability is outstanding; the smaller the error between the predicted value and the simulated value under the same condition, the closer the point is to the diagonal line, and all the discrete points are distributed near the diagonal line as shown in fig. 3.
In order to facilitate the use of users, the established prediction model of the wind deflection angle of the double-split line suspension insulator string is further packaged into software, as shown in fig. 4; parameters such as the span, the height difference, the initial tension, the wire model, the basic design wind speed and the guarantee rate of a line are input into a software interface of the wind deflection angle prediction model, and the model can rapidly output the wind deflection angle of the suspension insulator string 1. The basic wind speed depends on the micro-terrain meteorological conditions of the line, and the guarantee rate depends on the requirements on the safety of the line, the consideration of the construction cost of the line and the like.
In the embodiment, the wind deflection angle of the suspension insulator string under 6 typical working conditions is predicted by utilizing a developed double-split line suspension insulator string wind deflection angle prediction model and a software interface thereof, and the predicted value is compared with a finite element numerical simulation result, as shown in table 1;
TABLE 1 typical conditions for comparative analysis of wind deflection angle
As can be seen from Table 1, the wind deflection angle prediction model prediction value is very consistent with the finite element numerical simulation result, and the error is small; meanwhile, compared with the complex and time-consuming modeling and calculating process of finite element numerical simulation, the method has the advantages that the wind deflection angle of the suspension insulator string is predicted by the prediction model only within a few seconds; therefore, the wind deflection angle prediction model of the suspension insulator string directly takes parameters such as the span, the height difference, the initial tension, the wire model, the basic wind speed, the guarantee rate and the like of a line as input, the prediction is fast and accurate, the use and the design of engineering personnel are greatly facilitated, and meanwhile, the wind deflection angle prediction model can further provide technical support for the wind deflection early warning and prediction.
According to the invention, the wind deflection angle can be rapidly calculated through the suspension insulator string wind deflection angle prediction model, and then the insulation gap R between the line and the tower head 5 of the tower 4 can be calculated by utilizing the existing formula according to the geometrical relationship among the length of the suspension insulator string, the wind deflection angle and the suspension point shown in figure 5.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. The construction method of the wind deflection angle prediction model of the double-split line suspension insulator string is characterized by comprising the following steps of:
s1: creating a simulated object
S11 sets parameters: setting structural parameters and basic design wind speed of a plurality of groups of transmission lines in finite element software, wherein the structural parameters comprise span, height difference, wire model and initial tension;
s12, establishing a geometric model of the power transmission line: in the finite element software, establishing a geometric model of the power transmission line with the continuous gear number more than or equal to 4 gears by using the parameters set in the step S11, wherein the geometric model comprises a lead, a suspension insulator string and a strain insulator string;
S2: building a data set
S21 calculates the random wind load: generating a random wind speed time course corresponding to the basic design wind speed by using a harmonic synthesis method and adopting a Kaimal wind speed spectrum and a Davenport coherent function, and obtaining a random wind load according to the random wind speed time course and a wire wind load calculation formula;
s22 random wind load is applied: simulating wind speed points every 10 meters along the wire direction of the geometric model established in the step S12, wherein each wind speed point corresponds to a wire section, and random wind loads are applied to each wind speed point;
s23: simulating the wind deflection time course response of the power transmission line under random wind load by using finite element software numerical values, extracting the mean value and root mean square value of the wind deflection angle of the line suspension insulator string from the wind deflection time course response, and calculating the statistic value of the wind deflection angle according to the mean value and the root mean square value; constructing a data set, and dividing the data set into a training data set and a testing data set by adopting a random sampling method;
s3: construction of a wind deflection angle prediction model
And based on a random forest algorithm, training by using the training data set divided in the step S23 to obtain a prediction model.
2. The method for constructing the wind deflection angle prediction model of the double-split line suspension insulator string as claimed in claim 1, wherein the wind deflection angle prediction model is tested, that is, the generalization ability of the wind deflection angle prediction model trained in step S3 is tested by using the test data set of step S23.
3. The method for constructing the wind drift angle prediction model of the double-split line suspension insulator string according to claim 1, wherein in step S23, the statistical value of the wind drift angle is calculated according to the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the angle of the wind deflection,
the mean value of the wind deflection angle under the action of random wind load, delta is a root mean square value, mu is a guarantee coefficient, and the value of the guarantee coefficient mu specified by the national standard is 2.2.
4. The method for constructing the wind deflection angle prediction model of the double-split line suspension insulator string as claimed in claim 1, wherein in step S3, the constructed random forest comprises 200 decision trees, the maximum splitting feature number of the decision trees is 8, namely the input vector feature dimension, and the minimum splitting sample number is set to be 2; adopting a Bootstrap replaced sampling method to obtain 200 training subsets, and respectively using each training subset for the growth of a decision tree;
during prediction, each decision tree gives a prediction result, and the prediction value output by the final random forest is the average value of all decision tree results.
5. The method for constructing the wind deflection angle prediction model of the double-split line suspension insulator string according to claim 2, wherein the method for testing the generalization ability of the wind deflection angle prediction model comprises the following steps: the abscissa is a wind deflection angle statistic value of a test data set sample point determined by using a numerical simulation result, namely a simulated wind deflection angle; the ordinate is a predicted value of the prediction model on a sample point corresponding to the test data set, namely a predicted windage yaw angle; by determining the coefficient R 2Evaluating the prediction performance of the prediction model; wherein the coefficient R2Has a value range interval of [0,1 ]]。
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106096105A (en) * | 2016-06-02 | 2016-11-09 | 浙江大学 | Power transmission circuit caused by windage transient response computational methods |
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| WO2021022970A1 (en) * | 2019-08-05 | 2021-02-11 | 青岛理工大学 | Multi-layer random forest-based part recognition method and system |
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|---|---|---|---|---|
| CN106096105A (en) * | 2016-06-02 | 2016-11-09 | 浙江大学 | Power transmission circuit caused by windage transient response computational methods |
| CN107977492A (en) * | 2017-11-14 | 2018-05-01 | 国网新疆电力有限公司电力科学研究院 | Based on the non-linear windage yaw reliability degree calculation method of Monte Carlo insulator chain |
| WO2021022970A1 (en) * | 2019-08-05 | 2021-02-11 | 青岛理工大学 | Multi-layer random forest-based part recognition method and system |
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| 孔德怡;李黎;龙晓鸿;梁政平;: "悬垂绝缘子串动态风偏角有限元分析", 电力建设, no. 09, 30 September 2008 (2008-09-30) * |
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