Method, system, storage medium and equipment for dynamic response analysis of power transmission tower based on wind wave fluctuation process

Abstract

The present application relates to the transmission tower dynamic response analysis method, system, storage medium and equipment based on wind fluctuation process, its method includes steps: S1, obtains low frequency reconstruction component and high frequency reconstruction component, and selects component data representing data characteristics;S2, the nonlinear correlation coefficient is calculated using the processed data;S3, the time series of wind speed and tower response is divided into several fluctuation sections;The extreme value of wind speed trend term is used to divide wind speed stage;After wind speed data is normalized, wind speed fluctuation section is segmented according to time sequence;According to the division of wind speed fluctuation section, the fluctuation process of the wavelet reconstruction time sequence of the vibration acceleration and the inclination angle of each node of the tower is divided.The present application carries out wavelet decomposition to the response of the tower, uses neural network to learn and analyze the result, divides the data into segmented data related to wind field data, can obtain the fluctuation characteristics of the tower, and further provides reference basis for risk assessment and mechanical analysis of the tower.

Description

Technical Field

[0001] This invention relates to the field of dynamic response analysis technology for power transmission towers, and in particular to a method, system, storage medium, and equipment for dynamic response analysis of power transmission towers based on wind fluctuation processes. Background Technology

[0002] Coastal areas of my country experience severe weather events such as strong winds and typhoons every year. Tower collapses caused by strong winds are frequent, potentially leading to localized power outages, causing significant inconvenience to people's lives and livelihoods, and seriously affecting the safe operation of the power grid. As the "backbone" of urban power grids, overhead transmission lines need to maintain high safety and reliability in the face of various extreme weather impacts. Conducting risk assessments of towers under severe weather conditions is of great significance for disaster prevention and mitigation efforts.

[0003] Traditional wind load analysis typically only considers the linear relationship between wind speed and the stress or deformation of the transmission tower, easily neglecting the turbulence effects of the wind field and the fluctuating characteristics of the tower's dynamic response. In continuous and complex wind fields, the dynamic mechanical response of transmission towers also exhibits certain patterns in the frequency domain, which can be reflected in its time-series fluctuation characteristics. Therefore, in addition to analyzing the time series of the tower's mechanical response, it is also necessary to study the temporal fluctuation process of each response, and then use the mechanical modal response fluctuation characteristic parameters of each node of the tower to calculate the node deformation constraints, serving as the basis for the condition assessment of the transmission tower. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a method, system, storage medium, and device for dynamic response analysis of transmission towers based on wind wave processes. By performing wavelet decomposition on the tower's response and using a neural network to learn and analyze the results, the data is divided into segmented data related to the wind field data, thereby obtaining the tower's wave characteristics and providing a reference for tower risk assessment and mechanical analysis.

[0005] The method of this invention is implemented using the following technical solution: a dynamic response analysis method for transmission towers based on wind fluctuation processes, comprising the following steps:

[0006] S1. Obtain wind field and response data, preprocess the data, remove error data, and standardize the data; use the Wavelet Toolbox in MATLAB to perform wavelet decomposition on each excitation and response, merge and reconstruct each low-frequency component and each high-frequency component, obtain low-frequency reconstructed components and high-frequency reconstructed components, and select component data that represent the data characteristics.

[0007] S2. Calculate the nonlinear correlation coefficient using the processed data; select the correlation coefficients between each response parameter of the measuring point and the wind field characteristic parameters, and use the type of wind field characteristic parameter at the point where the correlation coefficient is the largest as the standard for dividing the fluctuation segment;

[0008] S3. Using the adjacent minimum values ​​of the low-frequency reconstruction component of wind speed as boundaries, the time series of wind speed and tower response are divided into several fluctuation segments for study; the extreme values ​​of the wind speed trend term are used to divide the wind speed stages, including high wind segment, low wind segment, rising wind segment, and low wind segment; after normalizing the wind speed data, the wind speed fluctuation segments are further divided according to the time series; based on the division of the wind speed fluctuation segments, the wavelet reconstruction time series of vibration acceleration and tilt angle of each node of the tower is divided into fluctuation processes, and then the fluctuation characteristic parameters in each response fluctuation process, including amplitude and wavelength, are used to perform discrete calculation of the displacement deformation of the tower nodes.

[0009] The present invention employs the following technical solution: a dynamic response analysis system for transmission towers based on wind-induced wave processes, comprising:

[0010] Reconstruction component acquisition module: By acquiring wind field and response data, the data is preprocessed, error data is removed, and the data is standardized; Wavelet decomposition of various excitations and responses is performed using the Wavelet Toolbox in MATLAB, and each low-frequency component and each high-frequency component is merged and reconstructed to obtain low-frequency reconstructed components and high-frequency reconstructed components, and component data representing the data characteristics are selected.

[0011] The correlation coefficient calculation module between wind field and response parameters calculates the nonlinear correlation coefficient using the processed data; it selects the correlation coefficients between each response parameter and the wind field characteristic parameters at the measurement points, and uses the type of wind field characteristic parameter at the point where the correlation coefficient is the largest as the standard for dividing the fluctuation segment.

[0012] The fluctuation segmentation module divides the time series of wind speed and tower response into several fluctuation segments by using the adjacent minimum values ​​of the low-frequency reconstruction component of wind speed as boundaries. It further divides wind speed stages into high-wind, low-wind, rising-wind, and low-wind segments using the extreme values ​​of the wind speed trend term. After normalizing the wind speed data, the wind speed fluctuation segments are then segmented according to time sequence. Based on the division of wind speed fluctuation segments, the wavelet reconstruction time series of vibration acceleration and tilt angle of each tower node is divided into fluctuation processes. Then, using the fluctuation characteristic parameters of each response fluctuation process, including amplitude and wavelength, the discrete calculation of tower node displacement deformation is performed.

[0013] The storage medium of the present invention stores computer instructions, which, when executed by a processor, implement the steps of the above-described dynamic response analysis method for power transmission towers.

[0014] The device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, it implements the above-described dynamic response analysis method for power transmission towers.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0016] 1. This invention performs wavelet decomposition on the response of the tower and uses a neural network to learn and analyze the results, dividing the data into segmented data related to the wind field data, thereby obtaining the tower's wave characteristics and providing a reference for tower risk assessment and mechanical analysis. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention;

[0018] Figure 2 This is a schematic diagram of the wavelet decomposition results of this invention;

[0019] Figure 3 This is a schematic diagram showing the results of dividing the time series of irregular wind speed and tower response over a long period of time into several fluctuation segments to study the characteristics of the amplitude, wavelength, and number of peaks of the fluctuation segments. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0021] Example

[0022] like Figure 1 As shown, this embodiment of the dynamic response analysis method for transmission towers based on wind fluctuation processes includes the following steps:

[0023] S1. Obtain wind field and response data, preprocess the data, remove error data, and standardize the data; use the Wavelet Toolbox in MATLAB to perform wavelet decomposition on each excitation and response, merge and reconstruct each low-frequency component and each high-frequency component, obtain low-frequency reconstructed components and high-frequency reconstructed components, and select component data that represent the data characteristics.

[0024] S2. Calculate the nonlinear correlation coefficient using the processed data; select the correlation coefficients between each response parameter of the measuring point and the wind field characteristic parameters, and use the type of wind field characteristic parameter at the point where the correlation coefficient is the largest as the standard for dividing the fluctuation segment;

[0025] S3. Using the adjacent minimum values ​​of the low-frequency reconstruction component of wind speed as boundaries, the time series of wind speed and tower response are divided into several fluctuation segments for study; the extreme values ​​of the wind speed trend term are used to divide the wind speed stages, including high wind segment, low wind segment, rising wind segment, and low wind segment; after normalizing the wind speed data, the wind speed fluctuation segments are further divided according to the time series; based on the division of the wind speed fluctuation segments, the wavelet reconstruction time series of vibration acceleration and tilt angle of each node of the tower is divided into fluctuation processes, and then the fluctuation characteristic parameters of each response fluctuation process, including amplitude and wavelength, are used to perform discrete calculation of the displacement deformation of the tower nodes.

[0026] Specifically, in this embodiment, the specific process of obtaining wind field and response data in step S1 is as follows:

[0027] S111. By statistically analyzing meteorological station data, high-precision gridded data is obtained using the inverse distance weighted grid interpolation algorithm.

[0028] S112. Correct the interpolated data by combining GIS and terrain data;

[0029] S113. Write an algorithm to calculate high-precision gridded wind field data and image information.

[0030] Specifically, in this embodiment, the meteorological station data in step S111 includes wind speed over a certain period of time; turbulence intensity, such as 1-minute average wind speed, 1-minute turbulence intensity, and 10-minute turbulence integral scale; and tower response data, including nodal acceleration, angular acceleration, and tilt angle.

[0031] Specifically, in this embodiment, the wavelet decomposition in step S1 uses the Wavelet Toolbox in MATLAB to perform a 6-level decomposition on the normalized data. The results are as follows: Figure 2 As shown in the figure, s is the normalized original data, d1-d6 are the vibration components with frequencies from high to low, and a6 is the approximate component of the original signal.

[0032] Specifically, components with high frequency and large fluctuations are defined as high-frequency components, such as d1-d3; components with low frequency and small fluctuations are defined as low-frequency components, such as d4-d6 and a6. These two types of components respectively reflect the random variation characteristics and overall trend of non-stationary signals. High-frequency components (d1+d2+d3) mainly reflect the random variation characteristics of excitation and response, and are not suitable for classifying and matching wind field parameters and tower dynamic responses. Therefore, the low-frequency reconstruction component (a6+d6+d5+d4) is used as the research object for the fluctuation process. Using the adjacent minimum values ​​of the low-frequency reconstruction component of wind speed (d4+d5+d6) as boundaries, the time series of long-term irregular wind speed and tower response is divided into several fluctuation segments to study the patterns of characteristic parameters such as amplitude, wavelength, and number of crests in these fluctuation segments. Figure 3 As shown. Trend analysis is conducted using the approximate component a6.

[0033] Specifically, in this embodiment, there is a complex intrinsic relationship between wind field characteristics and the dynamic response of transmission towers. Orthogonal correlation analysis is performed on various wind field parameters, such as 1-minute average wind speed, 1-minute turbulence intensity, and 10-minute turbulence integral scale; and on tower response parameters, including nodal acceleration, angular acceleration, and tilt angle, to study the correlation between various excitations and responses. This allows for the identification of the excitation and response parameters with the highest correlation coefficients, which are then used as the objects for classification and matching. Since the time series of both wind field and tower response parameters are non-stationary signals, a nonlinear correlation coefficient ρ is introduced in step S2. xy This formula is used to comprehensively reflect the phase and amplitude information of each frequency component in the time series signals of wind field and tower response parameters, and to describe the similarity between the two time series signals of wind field and tower response parameters within the same time period, as shown in the following formula:

[0034]

[0035] Where x(n) and y(n) are the low-frequency reconstruction components of the wind field parameters and the tower response parameters, respectively.

[0036] Specifically, the correlation coefficients between the response parameters of each measuring point and the wind field characteristic parameters were selected, as shown in Table 1.

[0037] Table 1

[0038]

[0039]

[0040] Specifically, Table 1 shows only the correlation coefficients between the acceleration and tilt angle of the measuring point and the 1-minute average wind speed, with relatively large absolute values. The correlation coefficients between the angular velocity of the measuring point and various wind field characteristic parameters are relatively small. Furthermore, the correlation coefficient between the vertical response (Z-axis) and wind field parameters is relatively small because the sensor is easily affected by electromagnetic interference in the vertical direction, causing measurement deviations. It can be seen that the 1-minute average wind speed has a significant impact on the tower, while the correlation coefficients between turbulence intensity and turbulence integral scale and the tower response are relatively small, failing to reflect the correspondence between localized strong winds and the tower response. Therefore, this embodiment selects the time series of average wind speed as the standard for dividing wind fluctuation segments.

[0041] Specifically, in this embodiment, the specific process of dividing the wind speed fluctuation segment in step S3 is as follows:

[0042] S31. Define the feature vector of the fluctuation segment. After normalizing the wind speed data, use a self-organizing map (SOM) neural network to identify the 39 fluctuation segments of its periodic term. Input the feature vector:

[0043] C[S max ,S min ,R,T]

[0044] S32. Using SOM neural network clustering, the periodic fluctuation segment is divided into four types: long-term large fluctuation, short-term large fluctuation, long-term small fluctuation, and short-term small fluctuation; combined with trend segmentation, the wind speed state distribution map is obtained, and the wind speed fluctuation segment is divided into 25 segments according to time sequence.

[0045] Specifically, in this embodiment, the feature vectors of the fluctuation segment are defined as shown in Table 2.

[0046] Table 2

[0047]

[0048] Accordingly, the dynamic response analysis system for transmission towers based on wind fluctuation processes of the present invention includes:

[0049] Reconstruction component acquisition module: By acquiring wind field and response data, the data is preprocessed, error data is removed, and the data is standardized; Wavelet decomposition of various excitations and responses is performed using the Wavelet Toolbox in MATLAB, and each low-frequency component and each high-frequency component is merged and reconstructed to obtain low-frequency reconstructed components and high-frequency reconstructed components, and component data representing the data characteristics are selected.

[0050] The correlation coefficient calculation module between wind field and response parameters calculates the nonlinear correlation coefficient using the processed data; it selects the correlation coefficients between each response parameter and the wind field characteristic parameters at the measurement points, and uses the type of wind field characteristic parameter at the point where the correlation coefficient is the largest as the standard for dividing the fluctuation segment.

[0051] The fluctuation segmentation module divides the time series of wind speed and tower response into several fluctuation segments by using the adjacent minimum values ​​of the low-frequency reconstruction component of wind speed as boundaries. It further divides wind speed stages into high-wind, low-wind, rising-wind, and low-wind segments using the extreme values ​​of the wind speed trend term. After normalizing the wind speed data, the wind speed fluctuation segments are then segmented according to time sequence. Based on the division of wind speed fluctuation segments, the wavelet reconstruction time series of vibration acceleration and tilt angle of each tower node is divided into fluctuation processes. Then, using the fluctuation characteristic parameters in each response fluctuation process, including amplitude and wavelength, the discrete calculation of tower node displacement deformation is performed.

[0052] Specifically, the wind field and response data acquired in the reconstructed component acquisition module include wind speed over a certain period of time; turbulence intensity; and tower response data, including nodal acceleration, angular acceleration, and tilt angle.

[0053] Specifically, the correlation coefficient calculation module for wind field and response parameters introduces a nonlinear correlation coefficient to comprehensively reflect the phase and amplitude information of each frequency component in the time series signals of wind field and tower response parameters, and to describe the similarity between the two time series signals of wind field and tower response parameters in the same period.

[0054] Furthermore, this invention also proposes a storage medium and a device. The storage medium stores a computer program, which, when executed by a processor, implements steps S1-S3 of the power transmission tower dynamic response analysis method of this invention. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the power transmission tower dynamic response analysis method of this invention, which includes the aforementioned steps S1-S1.

[0055] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for analyzing the dynamic response of transmission towers based on wind fluctuation processes, characterized in that, Includes the following steps: S1. Obtain wind field and response data, preprocess the data, remove error data, and standardize the data; use the Wavelet Toolbox in MATLAB to perform wavelet decomposition on each excitation and response, merge and reconstruct each low-frequency component and each high-frequency component, obtain low-frequency reconstructed components and high-frequency reconstructed components, and select component data that represent the data characteristics. S2. Calculate the nonlinear correlation coefficient using the processed data; The correlation coefficients between the response parameters of each measuring point and the wind field characteristic parameters are selected, and the type of wind field characteristic parameter at the point where the correlation coefficient is the largest is used as the standard for dividing the fluctuation segment. S3. Using the adjacent minimum values ​​of the low-frequency reconstructed component of wind speed as boundaries, the time series of wind speed and tower response are divided into several fluctuation segments for study; the extreme values ​​of the wind speed trend term are used to divide the wind speed stages, including high wind segment, low wind segment, rising wind segment, and low wind segment. After normalizing the wind speed data, the wind speed fluctuation segment is then segmented according to the time sequence. Based on the segmentation of the wind speed fluctuation segment, the wavelet reconstruction time sequence of the vibration acceleration and tilt angle of each node of the tower is divided into fluctuation processes. Then, the fluctuation characteristic parameters of each response fluctuation process, including amplitude and wavelength, are used to perform discrete calculation of the displacement deformation of the tower node. In step S2, a nonlinear correlation coefficient is introduced. This formula is used to comprehensively reflect the phase and amplitude information of each frequency component in the time series signals of wind field and tower response parameters, and to describe the similarity between the two time series signals of wind field and tower response parameters within the same time period, as shown in the following formula: Where x(n) and y(n) are the low-frequency reconstruction components of the wind field parameters and the tower response parameters, respectively.

2. The method for dynamic response analysis of transmission towers based on wind fluctuation process according to claim 1, characterized in that, The specific process for obtaining wind field and response data in step S1 is as follows: S111. By statistically analyzing meteorological station data, high-precision gridded data is obtained using the inverse distance weighted grid interpolation algorithm. S112. Correct the interpolated data by combining GIS and terrain data; S113. Write an algorithm to calculate high-precision gridded wind field data and image information.

3. The method for dynamic response analysis of transmission towers based on wind fluctuation process according to claim 2, characterized in that, The meteorological station data in step S111 includes wind speed over a certain period of time; turbulence intensity; and tower response data, including nodal acceleration, angular acceleration, and tilt angle.

4. The method for dynamic response analysis of transmission towers based on wind fluctuation process according to claim 1, characterized in that, The specific process of dividing the wind speed fluctuation segment in step S3 is as follows: S31. Define the feature vector of the fluctuation segment. After normalizing the wind speed data, use a self-organizing map (SOM) neural network to identify the fluctuation segment of its periodic term. Input the feature vector: S32. Using SOM neural network clustering, the periodic term fluctuation segment is divided into four types: long-term large fluctuation, short-term large fluctuation, long-term small fluctuation, and short-term small fluctuation. By combining trend terms to obtain the wind speed state distribution map, the wind speed fluctuation segment is segmented according to time sequence.

5. A dynamic response analysis system for transmission towers based on the dynamic response analysis method for transmission towers based on wind fluctuation processes as described in claim 1, characterized in that, include: Reconstruction component acquisition module: By acquiring wind field and response data, the data is preprocessed, error data is removed, and the data is standardized; Wavelet decomposition of various excitations and responses is performed using the Wavelet Toolbox in MATLAB, and each low-frequency component and each high-frequency component is merged and reconstructed to obtain low-frequency reconstructed components and high-frequency reconstructed components, and component data representing the data characteristics are selected. Correlation coefficient calculation module between wind field and response parameters: calculates nonlinear correlation coefficients using processed data; The correlation coefficients between the response parameters of each measuring point and the wind field characteristic parameters are selected, and the type of wind field characteristic parameter at the point where the correlation coefficient is the largest is used as the standard for dividing the fluctuation segment. Fluctuation segmentation module: By using the adjacent minimum values ​​of the low-frequency reconstructed component of wind speed as the boundary, the time series of wind speed and tower response are divided into several fluctuation segments for study; the extreme values ​​of the wind speed trend term are used to divide the wind speed stages, including high wind segment, low wind segment, rising wind segment, and low wind segment. After normalizing the wind speed data, the wind speed fluctuation segments are then segmented according to time sequence. Based on the segmentation of the wind speed fluctuation segments, the wavelet reconstruction time sequence of the vibration acceleration and tilt angle of each node of the tower is divided into fluctuation processes. Then, the fluctuation characteristic parameters of each response fluctuation process, including amplitude and wavelength, are used to perform discrete calculation of the displacement deformation of the tower nodes.

6. The dynamic response analysis system for transmission towers based on wind fluctuation process according to claim 5, characterized in that, The wind field and response data acquired in the reconstructed component acquisition module include wind speed over a certain period of time; turbulence intensity; and tower response data, including nodal acceleration, angular acceleration, and tilt angle.

7. A storage medium having computer instructions stored thereon, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the dynamic response analysis method for power transmission towers as described in any one of claims 1-4.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor runs the computer program, it implements the dynamic response analysis method for power transmission towers as described in any one of claims 1-4.

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