CN-122021110-A - Transmission tower stress distribution diagnosis method based on information entropy

CN122021110ACN 122021110 ACN122021110 ACN 122021110ACN-122021110-A

Abstract

The invention relates to a transmission tower stress distribution diagnosis method based on information entropy, which comprises the following steps of 1, establishing a transmission tower finite element model and extracting an effective stress data set, 2, carrying out probability conversion on stress data to convert a stress field into a probability distribution form, 3, quantifying the dispersion degree of stress distribution by adopting Shannon entropy, calculating an information entropy, a normalized entropy value and an entropy deviation degree, 4, constructing an accumulated stress proportion curve and a stress concentration curve, calculating the area under the curve, identifying a key stress unit, 5, establishing a stress distribution mode library, determining the distribution mode of a current structure through mode matching, and outputting a diagnosis conclusion and an optimization suggestion. According to the invention, the information entropy theory is introduced into the field of stress analysis of the power transmission tower, a complete analysis model from data acquisition to diagnosis output is constructed, and the idea conversion from the attention stress peak value to the attention distribution characteristic is realized. According to the method, global stress distribution characteristics are comprehensively considered, accurate positioning of a stress concentration area is achieved through multi-dimensional characteristic extraction and pattern recognition, calculation is simple and efficient, and the method is suitable for power transmission tower design optimization and in-service structure health assessment.

Inventors

GONG LEI
YOU WEIGUANG
WANG SONGBO
YAN XINGANG
LIU YUNHONG
CHEN ZHE
GAO PENG
DAI YONG
LI DUODUO
WANG HONGYU

Assignees

国网天津市电力公司
国家电网有限公司
国网天津市电力公司城东供电分公司

Dates

Publication Date: 20260512
Application Date: 20251222

Claims (6)

1. The transmission tower stress distribution diagnosis model construction method based on the information entropy is characterized by comprising the following steps of: step 1, establishing a finite element model of a power transmission tower, acquiring stress field data, and extracting an effective stress data set; Step 2, carrying out probability conversion on the effective stress data set obtained in the step 1, converting a stress field into a probability distribution form, and obtaining stress duty ratio probability distribution; Step 3, calculating information entropy, normalized entropy and entropy deviation degree by adopting a shannon entropy formula based on the stress duty ratio probability distribution obtained in the step 2; Step 4, calculating the unit information contribution degree based on the stress ratio obtained in the step 2 and the information entropy, the normalized entropy and the entropy deviation degree obtained in the step 3, constructing an accumulated stress ratio curve and a stress concentration curve, calculating the area under the curve, and identifying a key stress unit; And 5, establishing a stress distribution pattern library, determining the stress distribution pattern to which the feature vector of the current structure obtained in the step 4 belongs through pattern matching, and outputting a diagnosis conclusion and an optimization suggestion.
2. The transmission tower stress distribution diagnosis model construction method based on the information entropy of claim 1, wherein the specific steps of the step 1 comprise: (1) Establishing a finite element model of the transmission tower, applying load boundary conditions according to actual working conditions, and calculating stress response of the structure through a finite element solver; (2) Extracting equivalent stress sigma eq,i of each unit i (i=1, 2,..n, N is the total number of units) in the transmission tower finite element model based on the stress response data of the structure obtained by the calculation in the step (1); (3) Based on the equivalent stress of each cell extracted in step (2), an equivalent stress dataset { σ eq,1 , σ eq,2 , ..., σ eq,N }, (4) And (3) carrying out validity check on the equivalent stress data set established in the step (3), only reserving effective units actually participating in bearing, and recording the number of the effective units as N '(N'. Ltoreq.N), thereby obtaining an effective stress data set.
3. The transmission tower stress distribution diagnosis model construction method based on the information entropy of claim 1, wherein the specific steps of the step 2 comprise: (1) Calculating an equivalent stress sum Σtotal of all the effective units based on the effective stress data set obtained in the step 1, wherein the sum represents the total stress level born by the structure; Σtotal = Σσ eq,i (i=1,2,...,N') (2) Calculating the stress ratio p i of each effective unit i based on the stress sum Σtotal calculated in the step (1), wherein the stress ratio p i represents the relative proportion of the stress born by the unit i in the whole stress distribution; p i = σ eq,i / Σtotal, i=1,2,...,N'; (3) And (3) carrying out probability distribution property verification on the stress proportion data obtained in the step (2), and checking whether non-negativity (p i is more than or equal to 0) and normalization (Σp i =1) are met or not, so that mathematical effectiveness of subsequent information entropy calculation is ensured.
4. The transmission tower stress distribution diagnosis model construction method based on the information entropy of claim 1, wherein the specific steps of the step 3 comprise: (1) Calculating information entropy H by adopting a shannon entropy formula based on the stress duty ratio probability distribution obtained in the step 2, wherein the larger the information entropy H value is, the stress is distributed in a scattered manner among the units, and the smaller the H value is, the stress is concentrated in a few units; Calculating the information entropy H using shannon entropy formula is defined as h= - Σ [ p i ·log 2 (p i ) ] (i=1, 2,., N'), wherein when p i =0, p i ·log 2 (p i ) =0 is defined; (2) Calculating a theoretical maximum entropy value H max in a completely uniform distribution state based on the effective unit number N' obtained in the step 1, wherein the theoretical maximum entropy value H max corresponds to an ideal state that all unit stresses are completely equal and is used as a reference standard for evaluating the current stress distribution; H max = log 2 (N') (3) Based on the actual information entropy H calculated in the step (1) and the theoretical maximum entropy H max calculated in the step (2), calculating a normalized entropy value H norm , wherein the value range is 0 to 1, eliminating the influence of the number of units and facilitating the transverse comparison between models of different scales; the value range of H norm = H / H max ,H norm is [0,1]; (4) Calculating the entropy deviation delta H based on the theoretical maximum entropy H max of the step (2) and the actual information entropy H of the step (1), wherein the larger the value is, the higher the degree of deviation of the current stress distribution from the ideal uniform state is, and the greater the possibility of stress concentration is; ΔH = H max - H。
5. The transmission tower stress distribution diagnosis model construction method based on the information entropy of claim 1, wherein the specific steps of the step 4 include: (1) Calculating an information contribution degree C i of each unit i based on the stress ratio p i obtained in the step 2, wherein the information contribution degree C i reflects the contribution of the units in the overall stress distribution uncertainty; C i = p i ·log 2 (1/p i ) = -p i · log 2 (p i ) (2) Based on the stress ratio P i of each unit in the step (1), arranging all units in descending order from large to small according to the stress ratio, and accumulating and calculating the total stress ratio P cum (k) born by the first k high-stress units one by one, wherein the function describes the accumulated distribution characteristics of stress among the units; P cum (k) = Σp j (j=1,2,...,k) (3) Calculating a normalized unit number ratio function R (k) based on the accumulated stress ratio P cum (k) and the effective unit number N' obtained in the step (2), and drawing a relation curve of P cum (k) and R (k) to form a stress concentration curve, wherein the curve form reflects the stress concentration degree; R(k) = k/N' (4) Calculating the area A under the curve for the stress concentration curve obtained in the step (3), wherein the smaller the A value is, the more uneven the stress distribution is, and the closer the A value is to 0.5, the more even the stress distribution is; A = [Σ P cum (k)] / N'(k=1,2,...,N') (5) Calculating the stress ratio beta i of each unit based on the unit stress ratio p i and the effective unit number N' after sequencing in the step (2), and identifying the unit stress ratio as a key stress unit when beta i is obviously higher than 1 and the unit stress ratio is obviously higher than the average level; β i = p i / (1/N') = p i ·N' (6) And (3) outputting detailed diagnosis information for the key stress unit identified in the step (5), wherein the detailed diagnosis information comprises a unit number, a space position coordinate, a stress value sigma eq,i , a stress duty ratio P i , a stress duty ratio multiple beta i , an accumulated stress duty ratio P cum (k) calculated in the step (2), the type of the member, the geometric parameter and the unit information contribution degree C i calculated in the step (1).
6. The transmission tower stress distribution diagnosis model construction method based on the information entropy of claim 1, wherein the specific steps of the step 5 comprise: (1) Establishing a stress distribution mode library, dividing stress distribution into four typical modes of uniform distribution type (mode I), light concentration type (mode II), high concentration type (mode III) and extreme concentration type (mode IV), wherein each mode defines a characteristic parameter range of the stress distribution mode library, and comprises a normalized entropy value interval, an entropy deviation interval, a cumulative stress ratio curve morphological characteristic and a maximum stress ratio characteristic; (2) Defining physical significance and corresponding engineering suggestions of each mode established in the step (1), wherein the uniform distribution type indicates that the stress of the structure is reasonable, other performance check can be continued, the light concentration type unit needing to be identified is used for evaluating the safety, the high concentration type unit needs to be reinforced and optimized, and the extreme concentration type unit needs to be immediately adjusted to prevent the single-point failure risk; (3) Extracting a characteristic vector F of the current structure based on the normalized entropy value H norm and the entropy deviation delta H obtained by the calculation in the step 3, the form of the accumulated stress proportion curve P cum (k), the area A under the curve and the maximum stress proportion max { pi }; (4) Matching and matching the feature vector F extracted in the step (3) with the mode library established in the step (1), and selecting the most conforming mode as a diagnosis result according to the proximity degree of the feature parameter and the feature range of each mode; (5) Based on the mode type determined in the step (4), and combining the key stress unit identified in the step 4 and the engineering suggestion defined in the step (2), outputting a complete diagnosis conclusion, wherein the complete diagnosis conclusion comprises the mode type, the key unit list, the optimization target for the mode, and specific values of normalized entropy value H norm , entropy deviation delta H, maximum stress ratio max { p i } and area under curve A equalization index calculated in the step 3 and the step 4.

Description

Transmission tower stress distribution diagnosis method based on information entropy Technical Field The invention belongs to the technical field of power facility structure analysis and health assessment, relates to a transmission tower stress distribution diagnosis method, and particularly relates to a transmission tower stress distribution diagnosis method based on information entropy. Background The transmission tower is used as a key supporting structure of the power transmission system and bears important functions of supporting wires, maintaining a line corridor, resisting external loads and the like. In the actual service process, the tower structure bears the comprehensive effects of various complex loads such as wind load, icing load, wire tension, temperature stress, earthquake excitation and the like for a long time. These loads have time-varying, random and coupled characteristics, resulting in complex stress distributions for the tower structure. Unreasonable stress distribution can cause stress concentration phenomenon, so that progressive damage modes such as structural fatigue accumulated damage, loosening of connecting bolts, weld microcrack expansion, local buckling of a member and the like are caused, and serious power safety accidents such as large-area power failure and the like can be caused by unstable collapse of the whole structure. With the rapid development of computer simulation technology, a transmission tower structure design and evaluation method based on finite element analysis gradually becomes a mainstream technical scheme. However, the existing stress analysis method has the following significant problems in terms of theoretical system and technical implementation: first, the evaluation method lacks global. The conventional stress evaluation method is mainly based on the maximum stress criterion, namely judging the structural safety by checking whether the maximum stress value in the structure exceeds the allowable stress of the material. This approach focuses only on local stress peaks and does not reflect the distribution characteristics of the stress throughout the structure. In actual engineering, the situation that the maximum stress meets the requirement but the stress distribution is extremely uneven may occur, and the conventional method cannot effectively evaluate the rationality of the overall stress distribution. Second, stress concentration identification relies on subjective experience. The stress concentration coefficient method is another commonly used evaluation method, but the method needs to manually select a reference point and an evaluation point, and the evaluation result is greatly influenced by subjective experience of engineering technicians and lacks objectivity and systemicity. For complex truss structures such as transmission towers, which contain hundreds or even thousands of units, it is time consuming and easy to manually determine the stress concentration areas one by one to miss critical locations. Third, there is a lack of objective quantitative indicators of stress distribution characteristics. While the engineering community generally recognizes the importance of stress distribution uniformity to structural performance, there is currently no scientific, objective quantitative analysis. The existing methods mostly adopt qualitative description or simple statistical indexes (such as stress standard deviation, variation coefficient and the like), the indexes cannot comprehensively reflect the real characteristics of stress distribution, and the complex stress field distribution is difficult to be converted into operable diagnosis information. Fourth, a systematic diagnostic analysis framework is lacking. The traditional method can only give out the judgment of whether the stress exceeds the standard, and can not further answer the key questions in engineering practice such as whether the stress distribution is reasonable, where the questions are specific, how the questions should be optimized, and the like. The lack of a complete analysis chain from stress data acquisition to diagnostic conclusion output results in difficulty in efficient conversion of finite element analysis results into engineering decision basis. Therefore, in order to solve the above problems, it is highly desirable to develop a transmission tower stress distribution diagnosis method based on information entropy. No prior art publication is found, which is the same or similar to the present invention, upon searching. Disclosure of Invention Aiming at the defects of the prior art, the invention provides the transmission tower stress distribution diagnosis method based on the information entropy, which has strong scientificity, good objectivity and high operability, objective quantitative indexes of stress distribution characteristics are established, a complete analysis model from data to diagnosis is constructed, accurate identification and positioning of a stress conce