CN114757077B - 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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- 239000012212 insulator Substances 0.000 title claims abstract description 44
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- H02G—INSTALLATION OF ELECTRIC CABLES OR LINES, OR OF COMBINED OPTICAL AND ELECTRIC CABLES OR LINES
- H02G7/00—Overhead installations of electric lines or cables
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Abstract
The invention discloses a method for constructing a wind deflection angle prediction model of a double-split-line suspension insulator string, which comprises the following steps: s1, establishing a simulation object, S2, establishing a data set, S3, establishing a wind deflection angle prediction model, and training by using a training data set based on a random forest algorithm to obtain the prediction model. 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 provided by the invention 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 engineering design requirements in precision, and is effectively used for pole tower insulation gap design.
Description
Technical field:
The invention relates to the technical field of power transmission line tower design, in particular to a method for constructing a wind deflection angle prediction model of a double-split-line suspension insulator string.
The background technology is as follows:
The ground wire of the power transmission line can deflect under the action of wind load, so that the swinging of the suspension insulator string is caused, and the deflection angle of the insulator string is called as wind deflection angle; if the windage yaw angle is too large, the electric insulation gap between the wire and the pole tower is too small, so that flashover or even tripping accidents occur, and the safe operation of the line is seriously threatened.
Traditionally, the method for designing the insulation gap between the wire and the tower head of the tower is as follows: simplifying the suspension insulator string into a rigid straight rod, and calculating a wind deflection angle by using a static balance condition of the insulator string according to wind load and dead weight load borne by the lead and the suspension insulator string under the action of wind load, so that a gap between the lead and a pole tower is calculated according to a geometric relation; the design of the cross arm and the hanging point position 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 electric insulation gap; however, this conventional design method has the following disadvantages: on one hand, the dynamic effect of the pulsating wind is not considered, and the calculated wind deflection angle is smaller, so that wind deflection flashover accidents frequently occur; on the other hand, the traditional method has complicated process of calculating the windage yaw angle and inconvenient use.
The current method for calculating the wind deflection angle generally also uses a finite element numerical simulation method, in the process of simulating calculation, the influence of the pulsatility of random wind is considered, the calculation result is more pertinent and practical, but the finite element numerical simulation method is adopted, finite element modeling and solving calculation are needed each time, the finite element modeling is complex and time-consuming, in the process of calculating the wind deflection angle, the time of random wind time course simulation and finite element implicit dynamics solving calculation time is long, and the efficiency is low.
The invention comprises the following steps:
The invention aims to provide a construction method of a wind deflection angle prediction model of a double-split-line suspension insulator string, which solves the problems that the wind deflection angle calculation process is complicated and the wind deflection flashover accident frequently occurs because the dynamic effect of pulsating wind is not considered and errors exist in the traditional design method.
The invention is implemented by the following technical scheme: the method for constructing the wind deflection angle prediction model of the double split line suspension insulator string comprises the following steps:
S1: establishing a simulated object
S11, setting parameters: setting structural parameters and basic design wind speed of a plurality of groups of power transmission lines in finite element software, wherein the structural parameters comprise a span, a height difference, a wire model and initial tension;
s12, establishing a geometric model of the transmission line: in finite element software, establishing a geometric model of the power transmission line with the continuous gear number being greater than or equal to 4 gears by utilizing the parameters set in the step S11, wherein the geometric model comprises a wire, a suspension insulator string and a tension insulator string;
s2: constructing a dataset
S21, calculating random wind load: generating a random wind speed time course corresponding to the basic design wind speed by adopting Kaimal wind speed spectrums and a Davenport coherent function by utilizing a harmonic synthesis method, and obtaining a random wind load according to a wire wind load calculation formula according to the random wind speed time course;
S22, applying a random wind load: simulating a wind speed point every 10 meters along the wire direction of the geometric model established in the step S12, wherein each wind speed point corresponds to a section of wire segment, and applying random wind load on each wind speed point;
S23: simulating wind deflection time-course response of the power transmission line under random wind load by utilizing 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 statistical 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 test data set by adopting a random sampling method;
S3: constructing wind deflection angle prediction model
Based on a random forest algorithm, training is carried out by utilizing 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 capability of the wind deflection angle prediction model trained in the step S3 is tested by using the test data set in the step S23.
Further, in step S23, the statistical value of the windage angle is calculated as follows: Wherein,/> is the wind deflection angle,/> is the mean value of the wind deflection angle under the random wind load, delta is the root mean square value, mu is the guarantee coefficient, and the guarantee coefficient mu specified by the national specification takes on the value of 2.2.
Further, in step S3, the constructed random forest contains 200 decision trees, the maximum splitting feature number of which is 8, i.e. the feature dimension of the input vector, and the minimum splitting sample number is set to 2; 200 training subsets are obtained by adopting a sampling method of Bootstrap with replacement, and each training subset is respectively used for the growth of a decision tree; in the prediction process, each decision tree gives a prediction result, and the prediction value output by the random forest finally takes the average value of all decision tree results.
Further, the method for testing the generalization capability of the wind deflection angle prediction model comprises the steps that the abscissa is a wind deflection angle statistical value of a test data set sample point determined by utilizing a numerical simulation result, namely a simulated wind deflection angle; the ordinate is the predicted value of the prediction model on the corresponding sample point of the test data set, namely the predicted windage yaw angle; evaluating the prediction performance of the prediction model by adopting a determination coefficient R 2; wherein the range of the value of the coefficient R 2 is [0,1].
The invention has the advantages that: according to the method, the wind deflection time-course response of the power transmission line under random wind load is numerically simulated by using ABAQUS finite element software, the mean value and the root mean square value of the wind deflection angle of the line suspension insulator string are extracted from the wind deflection time-course response, and the statistical value of the wind deflection angle is calculated 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 test data set by adopting a random sampling method; then, a random forest algorithm is adopted, the data training of a training data set is utilized to construct a wind deflection angle prediction model of the suspension insulator string, and the data testing prediction model of a testing data set is utilized; according to the prediction model, the wind deflection angle of the suspension insulator string can be obtained rapidly by taking the span, the height difference, the initial tension, the wire type, the basic wind speed, the guarantee rate and the like of the line as input parameters, 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 provided by the invention 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 engineering design requirements in precision, and is effectively used for pole tower insulation gap design.
Description of the drawings:
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a geometric model of a transmission line;
FIG. 2 is a flow chart of constructing a wind deflection angle prediction model based on a random forest algorithm;
FIG. 3 is a graph of results of testing a windage prediction model;
FIG. 4 is a software interface of a wind deflection angle prediction model of a double split line suspension insulator string.
Fig. 5 is a schematic structural view of a tower.
The components in the drawings are marked as follows: the suspension insulator string 1, the strain insulator string 2, the lead 3, the pole tower 4 and the tower head 5.
The specific embodiment is as follows:
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a method for constructing a wind deflection angle prediction model of a double-split-line suspension insulator string, which comprises the following steps:
S1: establishing a simulated object
S11, setting parameters: setting 250 groups of structural parameters and basic design wind speed of a power transmission line in ABAQUS finite element software, wherein the structural parameters comprise a span, a height difference, a wire model and initial tension; in the national standard, the basic design wind speed is the average wind speed of 10m high for 10 minutes, and is determined by combining the meteorological record data of the region where the line passes;
S12, establishing a geometric model of the transmission line: in finite element software, the parameters set in the step S11 are utilized to establish a geometric model of the power transmission line with 4 continuous gear numbers, and according to the existing research results, when the continuous gear numbers are greater than or equal to 4 gears, the wind deflection angle of the suspension insulator string 1 in the middle of the line is hardly changed along with the increase of the gear numbers, so that in the embodiment, the 4-gear continuous gear line is taken as a simulation object; 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 dataset
S21, calculating random wind load: generating a random wind speed time course at the basic design wind speed set in the corresponding step S11 by adopting Kaimal wind speed spectrums and Davenport coherent functions by utilizing a harmonic synthesis method, and obtaining a random wind load according to a wire wind load calculation formula according to the random wind speed time course; the wire wind load calculation formula is an existing formula in the electric power industry standard overhead transmission line load specification, and the basic wind speed in the formula corresponds to the random wind speed time course of the embodiment;
s22, applying a random wind load: simulating a wind speed point every 10 meters along the direction of the lead 3 of the geometric model established in the step S12, wherein each wind speed point corresponds to a 10-meter lead section, and applying random wind load on each wind speed point, namely the corresponding lead section;
s23: simulating wind deflection time course responses of the power transmission line under random wind loads respectively corresponding to 250 groups of parameters set in the step S11 by utilizing finite element software numerical values, and 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 responses; calculating a statistical value of the windage yaw angle according to the mean value and the root mean square value;
The statistical value of the windage angle is calculated as follows:
Wherein is the windage angle,/> is the mean value of the windage angle under the action of random wind, delta is the root mean square value, and mu is the assurance coefficient; the value of the assurance coefficient regulated by the national regulations is generally 2.2, and the corresponding assurance rate is 98.61%.
And constructing 250 groups of data sets, wherein each group of data set sample points comprise structural parameters of the power transmission line and statistical values of basic design wind speed and wind deflection angle, and dividing the data sets into a training data set and a test data set by adopting a random sampling method.
S3: constructing wind deflection angle prediction model
Training to obtain a prediction model according to the process shown in fig. 2 by using the training data set divided in the step S23 based on a random forest algorithm;
The random forest algorithm combines a plurality of CART decision trees based on a radix index splitting rule into a strong model, and in order to reduce the risk of overfitting and obtain higher prediction precision, the algorithm adopts a Bootstrap sampling method and splitting characteristics to randomly select two effective random processes, namely sample randomness and characteristic randomness; 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, wherein the maximum splitting characteristic number of the decision tree is 8, namely the characteristic dimension of an input vector, and the minimum splitting sample number is set to be 2; 200 training subsets are obtained by adopting a sampling method of Bootstrap with put back, and each training subset is respectively used for the growth of a decision tree;
In the prediction process, each decision tree gives a prediction result, and finally the prediction value output by the random forest takes the average value of all decision tree results; the prediction model constructed based on random forest algorithm training only needs tens of seconds.
Testing the wind deflection angle prediction model, namely testing the generalization capability of the wind deflection angle prediction model trained in the step S3 by utilizing the test data set in the step S23; the specific test method is as follows: the abscissa is the statistical value of the wind deflection angle of the test data set sample points determined by using the numerical simulation result, namely the simulated wind deflection angle; the ordinate is the predicted value of the prediction model on the corresponding sample point of the test data set, namely the predicted windage yaw angle; the invention adopts a determination coefficient R 2 to evaluate the prediction performance of the prediction model, and the value range interval of the coefficient R 2 is [0,1]; the more accurate the model prediction result, the closer the coefficient is to 1; statistics of the prediction result shows that the R 2 value of the prediction model on the test data set is 0.995, the prediction accuracy is high, and the generalization capability is outstanding; the smaller the predicted and simulated errors for the same operating condition, the closer the point is to the diagonal, as shown in FIG. 3, with all discrete points distributed around the diagonal.
In order to facilitate the use of users, the invention further packages the prediction model of the wind deflection angle of the established double-split-line suspension insulator string into software, as shown in figure 4; parameters such as the span, the height difference, the initial tension, the wire type, the basic design wind speed, the guarantee rate and the like 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-topography condition of the line, the guarantee rate depends on the requirement of the line safety, the consideration of the line construction cost, and the like.
In the embodiment, the wind deflection angles of the suspension insulator strings under 6 typical working conditions are predicted by using a developed wind deflection angle prediction model of the double-split-line suspension insulator string and a software interface thereof, and the predicted values and the finite element numerical simulation results are compared, as shown in table 1;
TABLE 1 typical operating conditions for windage comparative analysis
As can be seen from table 1, the wind deflection angle prediction model has very consistent prediction value and finite element numerical simulation result, and small error; meanwhile, compared with the complex and time-consuming modeling and calculating process of finite element numerical simulation, the wind deflection angle of the suspension insulator string is predicted by using the prediction model only in 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 type, the basic wind speed, the guarantee rate and the like of a line as input, is rapid and accurate in prediction, greatly facilitates the use and design of engineering personnel, and can further provide technical support for wind deflection early warning and prediction.
According to the invention, the wind deflection angle can be rapidly calculated through the wind deflection angle prediction model of the suspension insulator string, 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 geometric relationship among the length of the suspension insulator string, the wind deflection angle and the suspension point shown in fig. 5.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (5)
1. The method for constructing the wind deflection angle prediction model of the double split line suspension insulator string is characterized by comprising the following steps of:
S1: establishing a simulated object
S11, setting parameters: setting structural parameters and basic design wind speed of a plurality of groups of power transmission lines in finite element software, wherein the structural parameters comprise a span, a height difference, a wire model and initial tension;
s12, establishing a geometric model of the transmission line: in finite element software, establishing a geometric model of the power transmission line with the continuous gear number being greater than or equal to 4 gears by utilizing the parameters set in the step S11, wherein the geometric model comprises a wire, a suspension insulator string and a tension insulator string;
s2: constructing a dataset
S21, calculating random wind load: generating a random wind speed time course corresponding to the basic design wind speed by adopting Kaimal wind speed spectrums and a Davenport coherent function by utilizing a harmonic synthesis method, and obtaining a random wind load according to a wire wind load calculation formula according to the random wind speed time course;
S22, applying a random wind load: simulating a wind speed point every 10 meters along the wire direction of the geometric model established in the step S12, wherein each wind speed point corresponds to a section of wire segment, and applying random wind load on each wind speed point;
S23: simulating wind deflection time-course response of the power transmission line under random wind load by utilizing 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 statistical 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 test data set by adopting a random sampling method;
S3: constructing wind deflection angle prediction model
Based on a random forest algorithm, training is carried out by utilizing 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 according to claim 1, wherein the wind deflection angle prediction model is tested, namely the generalization capability of the wind deflection angle prediction model trained in the step S3 is tested by using the test data set in the step S23.
3. The method for constructing a wind deflection angle prediction model of a double split line suspension insulator string according to claim 1, wherein in step S23, the statistical value of the wind deflection angle is calculated according to the following formula:
Wherein is the wind deflection angle,/> is the mean value of the wind deflection angle under the action of random wind load, delta is the root mean square value, mu is the guarantee coefficient, and the guarantee coefficient mu specified by national specifications takes on a value of 2.2.
4. The method for constructing a wind deflection angle prediction model of a double split line suspension insulator string according to claim 1, wherein in step S3, the constructed random forest comprises 200 decision trees, the maximum split feature number of the decision tree is 8, namely the input vector feature dimension, and the minimum split sample number is set to 2; 200 training subsets are obtained by adopting a sampling method of Bootstrap with replacement, and each training subset is respectively used for the growth of a decision tree;
in the prediction process, each decision tree gives a prediction result, and the prediction value output by the random forest finally takes 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 capability of the wind deflection angle prediction model is as follows: the abscissa is the wind deflection angle statistical value of the test data set sample points determined by using the numerical simulation result, namely the simulated wind deflection angle; the ordinate is the predicted value of the prediction model on the corresponding sample point of the test data set, namely the predicted windage yaw angle; evaluating the prediction performance of the prediction model by adopting a determination coefficient R 2; wherein the range of the value of the coefficient R 2 is [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 |
| 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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Patent 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 |
| 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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| Title |
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| 500kV架空输电线路风偏数值模拟研究;贾玉琢;肖茂祥;王永杰;;广东电力;20110228(第02期);全文 * |
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