A method and system for monitoring conductor vibration by wind

Abstract

This invention provides a method and system for monitoring conductor vibration under light wind. The method involves deploying sparse vibration sensors at high-risk locations within a target transmission area to collect raw data on conductor vibration under light wind. This data is then preprocessed to obtain sparse measurement point preprocessed data. The natural modal frequencies of the conductor are calculated, and a physical constraint modal dictionary matrix is ​​constructed step-by-step by combining the conductor vibration boundary conditions and mode shape functions. Modal weights are dynamically adjusted based on a three-factor coupling formula involving wind speed, terrain undulation, and span. An improved compressed sensing reconstruction model is constructed based on the sparse measurement point preprocessed data, the physical constraint modal dictionary matrix, and the modal weights. Measured data collected by sparse vibration sensors at verification points are acquired, and the measured data is compared with the corresponding reconstructed data to calculate the comprehensive reconstruction error. Based on the comprehensive reconstruction error, the modal weights are iteratively updated, and a secondary reconstruction calibration is performed to obtain high-precision global conductor vibration data.

Description

Technical Field

[0001] This invention belongs to the field of conductor wind vibration monitoring technology, and specifically relates to a method and system for conductor wind vibration monitoring. Background Technology

[0002] Transmission lines are exposed to the outdoor environment for a long time, and under the excitation of a light breeze, they will generate continuous micro-vibration. Long-term high-frequency micro-vibration can cause faults such as conductor strand breakage, hardware wear, and tower fatigue damage, which is one of the core hidden dangers affecting the safe and stable operation of transmission lines.

[0003] Existing wind vibration monitoring technologies have two major drawbacks: First, full-area monitoring relies on the deployment of sensors at all points, requiring the installation of monitoring equipment in every span of the conductor. This results in high deployment costs, difficult maintenance, and high power consumption, making it unsuitable for large-scale monitoring in large power transmission areas. Second, existing sparse monitoring point schemes only use traditional linear interpolation, spline interpolation, and other purely mathematical fitting methods to expand the full-area data. They do not take into account the inherent physical modal characteristics of conductor wind vibration, nor do they consider the differences in working conditions such as micro-meteorological conditions, span length, and terrain undulations. This leads to extremely large fitting errors, making it impossible to accurately reproduce the core parameters such as the amplitude, frequency, and mode shape of conductor vibration across the entire area. Furthermore, there is no adaptive correction mechanism, and the monitoring accuracy drops sharply under complex working conditions, making it difficult to effectively identify the risk of conductor vibration across the entire area. Summary of the Invention

[0004] Based on this, the present invention provides a method and system for monitoring conductor aerodynamic vibration, which aims to achieve accurate monitoring of the aerodynamic vibration status of conductors across a wide area in scenarios with large spans, multiple distances, and complex terrain of overhead transmission lines, by relying on a small number of conductor monitoring points.

[0005] A first aspect of the present invention provides a method for monitoring conductor vibration in light wind, the method comprising: By deploying sparse vibration sensors at high-risk locations within the target power transmission area, raw data of conductor vibration under light wind is collected and preprocessed to obtain sparse measurement point preprocessed data. Calculate the natural modal frequencies of the conductor, and based on the calculated natural modal frequencies, combine the conductor vibration boundary conditions and mode shape functions to construct the physical constraint mode dictionary matrix step by step; The modal weights are dynamically adjusted based on a coupling formula of three factors: wind speed, terrain undulation, and span. Based on the sparse measurement point preprocessing data, the physical constraint mode dictionary matrix, and the mode weights, an improved compressed sensing reconstruction model is constructed to realize the reconstruction of the whole domain conductor vibration data. Acquire measured data from sparse vibration sensors at the verification points, compare the measured data with the corresponding reconstructed data, and calculate the overall reconstruction error; Based on the comprehensive reconstruction error, the modal weights are iteratively updated, and high-precision global conductor vibration data are obtained through secondary reconstruction and calibration.

[0006] Furthermore, in the step of calculating the natural modal frequencies of the conductor and, based on the calculated natural modal frequencies, constructing the physical constraint modal dictionary matrix step by step in conjunction with the conductor vibration boundary conditions and mode shape functions, the formula for calculating the natural modal frequencies of the conductor is as follows: ; in, Let n be the nth natural vibration frequency of the conductor, n be the modal order, L be the conductor span, T be the conductor running tension, and m be the mass per unit length of the conductor.

[0007] Furthermore, the step of calculating the natural modal frequencies of the conductor and, based on the calculated natural modal frequencies, combining the conductor vibration boundary conditions and mode shape functions, constructing the physical constraint mode dictionary matrix in steps includes: Determine the basic dimension parameters of the modality dictionary matrix, including the dimension of single-period sampled data and the effective modal order; Construct the time-domain mode shape basis functions for each order mode, and perform L2 norm normalization on the time-domain mode shape basis functions for each order mode; The normalized modal time-domain vibration mode basis functions of each order are concatenated as dictionary column vectors in order of order. Each column corresponds to the complete vibration characteristics of the first-order natural mode, and finally the initial physical constraint mode dictionary matrix is ​​generated. The initial physical constraint mode dictionary matrix is ​​subjected to Gram-Schmidt orthogonalization to obtain the final physical constraint mode dictionary matrix.

[0008] Furthermore, in the step of dynamically correcting the modal weights based on the coupling formula of wind speed, terrain undulation, and span, the formula for calculating the modal weights is as follows: ; in, The weights are adjusted in real time for the nth mode. The initial intrinsic weights of the nth mode are... denoted as , where is the deviation between the real-time wind speed and the baseline light wind speed; h is the topographic relief of the monitoring point; L is the conductor span; and α, β, γ, and δ are normalization correction coefficients.

[0009] Furthermore, in the step of constructing an improved compressed sensing reconstruction model based on sparse measurement point preprocessing data, physical constraint mode dictionary matrix, and mode weights to reconstruct the global conductor vibration data, the observation equation is first constructed, expressed as: ; Wherein, Y is the measured vibration matrix of sparse measuring points, which is composed of preprocessed data from sparse measuring points. For sparse observation matrices, This is the physical constraint mode dictionary matrix. This is the diagonal matrix of modal weights constructed from the modal weights. This is the sparse coefficient matrix of the global conductor vibration. This is the comprehensive error term; Then, based on the observation equation, residual constraints are constructed, and combined with the vibration sparse prior, an improved compressed sensing reconstruction model is established. The improved compressed sensing reconstruction model is expressed as: ; in, It is an L1 norm. It is the L2 norm. The preset reconstruction error threshold is used; Finally, the optimized compressed sensing reconstruction model is solved to obtain the optimal sparse coefficients. The optimal sparse coefficients are then substituted back into the observation equation to calculate the vibration amplitude, frequency, and displacement time series data of all traverse points in the entire domain.

[0010] Furthermore, in the step of acquiring the measured data collected by the sparse vibration sensors at the verification points, comparing the measured data with the corresponding reconstructed data, and calculating the comprehensive reconstruction error, the formula for calculating the comprehensive reconstruction error is as follows: ; Where E is the overall reconstruction error, A recon A test These are the reconstructed amplitude and the measured amplitude at the verification point, respectively; f recon f test These are the reconstruction frequency and the measured frequency at the verification point, respectively; S recon S test These are the reconstructed displacement time series matrix and the measured displacement time series matrix, respectively; η1, η2, and η3 are normalized weight coefficients.

[0011] Furthermore, in the step of iteratively updating the modal weights based on the comprehensive reconstruction error and obtaining high-precision global conductor vibration data through secondary reconstruction calibration, the iterative correction formula is as follows: ; in, The correction coefficient is for the k-th iteration. For the updated coefficients, For the iterative learning rate, Let be the gradient of the error function.

[0012] A second aspect of this invention provides a conductor wind vibration monitoring system for implementing the conductor wind vibration monitoring method of the first aspect. The system includes: The acquisition module is used to collect raw data of conductor vibration by deploying sparse vibration sensors at high-risk locations within the target power transmission area, and to preprocess the data to obtain sparse measurement point preprocessed data. The calculation module is used to calculate the natural modal frequencies of the conductor, and based on the calculated natural modal frequencies of the conductor, combined with the conductor vibration boundary conditions and mode shape functions, to construct the physical constraint mode dictionary matrix step by step; The correction module is used to dynamically correct the modal weights based on a coupling formula of three factors: wind speed, terrain undulation, and span. The module is used to build an improved compressed sensing reconstruction model based on sparse measurement point preprocessed data, physical constraint mode dictionary matrix and mode weights, so as to realize the reconstruction of global conductor vibration data; The comparison module is used to acquire the measured data collected by the sparse vibration sensors at the verification points, compare the measured data with the corresponding reconstructed data, and calculate the comprehensive reconstruction error. The update module is used to iteratively update the modal weights based on the comprehensive reconstruction error, and obtain high-precision global conductor vibration data through secondary reconstruction calibration.

[0013] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the wire aerodynamic vibration monitoring method provided in the first aspect.

[0014] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the wire vibration monitoring method provided in the first aspect.

[0015] This invention provides a method and system for monitoring conductor aerobatic vibration. The method involves deploying sparse vibration sensors at high-risk locations within a target transmission area to collect raw data on conductor aerobatic vibration. This data is then preprocessed to obtain sparse measurement point preprocessed data. The method calculates the conductor's natural modal frequencies and, based on these frequencies and in conjunction with conductor vibration boundary conditions and mode shape functions, constructs a physical constraint modal dictionary matrix step-by-step. Modal weights are dynamically adjusted according to a three-factor coupling formula involving wind speed, terrain undulation, and span length. Finally, the method uses the sparse measurement point preprocessed data and the physical constraint modal dictionary matrix to... An improved compressed sensing reconstruction model is constructed using canonical matrices and modal weights to reconstruct the vibration data of the entire conductor. Measured data collected by sparse vibration sensors at verification points are acquired, and the measured data is compared with the corresponding reconstructed data to calculate the comprehensive reconstruction error. Based on the comprehensive reconstruction error, the modal weights are iteratively updated, and a secondary reconstruction calibration is performed to obtain high-precision vibration data of the entire conductor. Specifically, based on the aforementioned comprehensive micro-wind vibration monitoring scheme that utilizes a small number of sparse measuring points, combines the physical mechanism of conductor vibration, and possesses environmental adaptive correction capabilities, full coverage monitoring of the regional conductor is achieved with low cost and high precision. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of a method for monitoring conductor vibration in a breeze, as provided in Embodiment 1 of the present invention. Figure 2 This is a structural block diagram of a conductor wind vibration monitoring system provided in Embodiment 2 of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0017] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0018] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 According to an embodiment of the present invention, a method for monitoring conductor vibration by a breeze is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0021] This first embodiment provides a method for monitoring conductor vibration in light wind, which can be used in electronic devices, such as computers. Please refer to... Figure 1 , Figure 1 The flowchart of a method for monitoring wind vibration of a conductor provided in Embodiment 1 of the present invention is shown, specifically including steps S01 to S06.

[0022] Step S01: By deploying sparse vibration sensors at high-risk locations within the target power transmission area, raw data of conductor vibration under light wind is collected and preprocessed to obtain sparse measurement point preprocessed data.

[0023] In this embodiment of the invention, 5%-8% of the total number of conductor spans in the area are selected as monitoring points, covering only key and sensitive locations. Priority is given to four types of high vibration risk locations: large spans (span ≥ 300m), windward slopes, valley entrances, and old conductor sections, while avoiding low-risk sections with terrain obstruction and stable airflow. Both monitoring and verification points use triaxial fiber optic vibration sensors to collect bidirectional micro-wind vibration data of the conductors in both vertical and horizontal directions. In addition, based on the sparse monitoring points, an extra 1%-2% of the total number of conductor spans in the area are selected as independent verification points.

[0024] Furthermore, to address the environmental noise and impulse interference issues present in the field-collected data, a wavelet threshold denoising algorithm was employed to purify the data, retaining effective micro-wind vibration signals, thus obtaining sparse measurement point preprocessed data, providing a clean data source for subsequent full-domain reconstruction.

[0025] Step S02: Calculate the natural modal frequencies of the conductor, and based on the calculated natural modal frequencies of the conductor, combine the conductor vibration boundary conditions and mode shape functions to construct the physical constraint mode dictionary matrix step by step.

[0026] Specifically, transmission lines under light wind vibration belong to a typical sparse modal vibration system. The vibration mode can be characterized by the linear superposition of a finite number of natural modes. The formula for calculating the natural modal frequencies of the conductor is as follows: ; in, Let n be the nth natural vibration frequency of the conductor, n be the modal order, L be the conductor span, T be the conductor running tension, and m be the mass per unit length of the conductor. In this embodiment of the invention, the modal order is taken as 1-6, covering the dominant modes of aerodynamic vibration.

[0027] It should be noted that the basic dimension parameters for determining the modal dictionary matrix include the single-cycle sampling data dimension and the effective modal order. In this embodiment of the invention, the single-cycle sampling data dimension M is matched with the sensor's 200Hz sampling frequency and single-frame sampling duration, and the number of sampling points per frame is fixed at 1024, i.e., M=1024. The effective modal order K is fixed at K=6 based on the energy distribution characteristics of the transmission line's micro-wind vibration. Finally, a modal dictionary matrix with a dimension of 1024×6 is constructed. A single-mode time-domain vibration mode basis function is constructed, and L2 norm normalization is applied to the basis functions of each mode. The transmission line is suspended and fixed at both ends, conforming to the fixed-end boundary constraint. Its single-mode vibration displacement time-domain vibration mode basis function adopts the inherent sinusoidal vibration model, closely reflecting the actual vibration mode of the conductor. The calculation formula for the single-mode time-domain vibration mode basis function is as follows: ; Let n be the time-domain mode shape basis functions. The normalized amplitude coefficient of the nth mode. Let be the nth natural vibration frequency of the conductor, t be the sampling time series, x be the axial position coordinate of the conductor, and n be the modal order; The normalization formula is: ; These are the normalized time-domain mode shape basis functions of the nth mode. It is an L2 norm; The normalized modal time-domain vibration mode basis functions of each order are concatenated as dictionary column vectors in order of order. Each column corresponds to the complete vibration characteristics of the first-order natural mode, and finally the initial physical constraint mode dictionary matrix is ​​generated. ; The initial physical constraint mode dictionary matrix is ​​subjected to Gram-Schmidt orthogonalization to obtain the final physical constraint mode dictionary matrix. Gram-Schmidt orthogonalization is a classic vector orthogonal transformation algorithm in linear algebra. This method can transform a set of linearly correlated vectors into a pairwise mutually orthogonal vector set while preserving the inherent physical characteristics of the original vectors. This method is then used to optimize the mode dictionary, removing the correlation between the basis vectors of different orders of modes. Without changing the inherent characteristics of conductor vibration, this reduces computational redundancy and errors, making the mode dictionary more suitable for the computational requirements of vibration signal decomposition and data reconstruction.

[0028] Step S03: Dynamically adjust the modal weights based on the coupling formula of three factors: wind speed, terrain undulation, and span.

[0029] Specifically, the formula for calculating modal weights is: ; in, The weights are adjusted in real time for the nth mode. The initial intrinsic weights of the nth mode are... The value represents the deviation between the real-time wind speed and the benchmark light wind speed, h represents the topographic relief of the monitoring point, L represents the conductor span, and α, β, γ, and δ represent normalization correction coefficients. It can be understood that the modal weights are adaptively adjusted in real time according to the field conditions.

[0030] Step S04: Based on the sparse measurement point preprocessing data, the physical constraint mode dictionary matrix, and the mode weights, construct an improved compressed sensing reconstruction model to realize the reconstruction of the whole-domain conductor vibration data.

[0031] It should be noted that the observation equation is first constructed, expressed as: ; Where Y is the measured vibration matrix of sparse measuring points, which is composed of preprocessed data from sparse measuring points, i.e., known quantities, the actual data collected by the sensors on site. It is a sparse observation matrix, determined by the location of the measurement points. Its function is to filter out the measurement point data of the deployed sensors from the global signal. This is the physical constraint mode dictionary matrix. This is the modal weight diagonal matrix constructed from modal weights (results of wind speed, terrain, and span coupling correction). This represents the sparse coefficient matrix of the global conductor vibration, with the core unknowns being the modal decomposition coefficients corresponding to all conductor locations across the entire monitoring area. It is large in dimension and remains to be solved. This is a comprehensive error term (including sensor measurement noise, environmental interference, and model approximation error). Then, based on the observation equation, residual constraints are constructed, and combined with the vibration sparse prior, an improved compressed sensing reconstruction model is established. The improved compressed sensing reconstruction model is expressed as: ; in, It is an L1 norm. It is the L2 norm. To preset a reconstruction error threshold, it can be understood that, under the condition that... Under the premise of, find The minimum value; Finally, the optimized compressed sensing reconstruction model is solved to obtain the optimal sparse coefficients. The optimal sparse coefficients are then substituted back into the observation equation to calculate the vibration amplitude, frequency, and displacement time series data of all traverse points in the entire domain.

[0032] Understandably, the observation equation is a forward mapping model characterizing the conductor vibration signal from the entire domain to sparse measuring points, clarifying the mathematical relationships between measured data, modal characteristics, dynamic weights, observation locations, and measurement noise. Since the sparse measuring points result in an underdetermined system of equations that cannot be directly solved, residual constraints are derived based on this observation equation. Combining this with the physical characteristics of the sparse modes of conductor vibration under light winds, an improved compressed sensing reconstruction model minimizing the L1 norm is constructed. The reconstruction model uses the observation equation as the error constraint boundary and the inherent sparsity of the vibration signal as the optimization objective. Under the premise of conforming to the field observation patterns, a unique optimal global vibration coefficient is obtained, ultimately achieving the reconstruction monitoring of the entire conductor vibration state from a small number of measuring points.

[0033] Step S05: Obtain the measured data collected by the sparse vibration sensor at the verification point, compare the measured data with the corresponding reconstructed data, and calculate the comprehensive reconstruction error.

[0034] Specifically, the measured vibration data from all verification points are purified to remove environmental noise, a high-precision verification ground truth dataset is obtained, the reconstructed data at the corresponding locations of the verification points is extracted, and the comprehensive reconstruction error is calculated, expressed as: ; Where E is the overall reconstruction error, A recon A test These are the reconstructed amplitude and the measured amplitude at the verification point, respectively; f recon f test These are the reconstruction frequency and the measured frequency at the verification point, respectively; S recon S test These are the reconstructed displacement time series matrix and the measured displacement time series matrix, respectively; η1, η2, and η3 are normalized weight coefficients.

[0035] Step S06: Based on the comprehensive reconstruction error, the modal weights are iteratively updated, and the secondary reconstruction calibration yields high-precision global conductor vibration data.

[0036] It should be noted that the system has a preset maximum allowed reconstruction error threshold of E0 = 0.08, and adaptive optimization is performed based on the error determination results. ① When E≤E0: the algorithm reconstruction accuracy is deemed acceptable, the current modal weights and algorithm parameters remain unchanged, and the global reconstructed vibration data is directly output; ② When E > E0: The reconstruction error is determined to be excessive, and parameter iterative optimization is initiated. The dynamic mode weight correction coefficient is iteratively updated. The iterative correction formula is as follows: ; in, These are the correction coefficients for the k-th iteration (corresponding to α, β, γ, δ). For the updated coefficients, For the iterative learning rate, The gradient of the error function is used to achieve the optimal solution of parameters through gradient descent.

[0037] After completing the parameter iteration correction, update the modal weight matrix and reconstruction constraints, re-execute the global vibration data reconstruction until the comprehensive error meets the threshold requirement, and output the final high-precision global conductor vibration data.

[0038] In summary, the conductor aerodynamic vibration monitoring method in the above embodiments of the present invention involves deploying sparse vibration sensors at high-risk locations within the target transmission area to collect raw data on conductor aerodynamic vibration, and preprocessing this data to obtain sparse measurement point preprocessed data. The method calculates the conductor's natural modal frequencies and, based on these frequencies, constructs a physical constraint modal dictionary matrix in steps, combining the conductor vibration boundary conditions and mode shape functions. It dynamically adjusts the modal weights according to a three-factor coupling formula involving wind speed, terrain undulation, and span. Finally, it uses the sparse measurement point preprocessed data and the physical constraint modal dictionary matrix to... An improved compressed sensing reconstruction model is constructed using canonical matrices and modal weights to reconstruct the vibration data of the entire conductor. Measured data collected by sparse vibration sensors at verification points are acquired, and the measured data is compared with the corresponding reconstructed data to calculate the comprehensive reconstruction error. Based on the comprehensive reconstruction error, the modal weights are iteratively updated, and a secondary reconstruction calibration is performed to obtain high-precision vibration data of the entire conductor. Specifically, based on the aforementioned comprehensive micro-wind vibration monitoring scheme that utilizes a small number of sparse measuring points, combines the physical mechanism of conductor vibration, and possesses environmental adaptive correction capabilities, full coverage monitoring of the regional conductor is achieved with low cost and high precision.

[0039] Example 2 Please see Figure 2 , Figure 2This is a structural block diagram of a conductor wind vibration monitoring system according to Embodiment 2 of the present invention. This conductor wind vibration monitoring system 200 is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0040] Specifically, the conductor aerodynamic vibration monitoring system 200 includes: a data acquisition module 21, a calculation module 22, a correction module 23, a construction module 24, a comparison module 25, and an update module 26, wherein: The acquisition module 21 is used to collect raw data of conductor vibration by deploying sparse vibration sensors at high-risk locations within the target power transmission area, and to preprocess the data to obtain sparse measurement point preprocessed data. Calculation module 22 is used to calculate the natural modal frequencies of the conductor. Based on the calculated natural modal frequencies, and combined with the conductor vibration boundary conditions and mode shape functions, it constructs the physical constraint mode dictionary matrix step by step. The formula for calculating the natural modal frequencies of the conductor is: ; in, Let n be the nth natural vibration frequency of the conductor, n be the modal order, L be the conductor span, T be the conductor running tension, and m be the mass per unit length of the conductor. Correction module 23 is used to dynamically correct the modal weights based on the coupling formula of three factors: wind speed, terrain undulation, and span. The formula for calculating the modal weights is as follows: ; in, The weights are adjusted in real time for the nth mode. The initial intrinsic weights of the nth mode are... denoted as the deviation between the real-time wind speed and the benchmark light wind speed, h as the topographic relief of the monitoring point, L as the span of the conductor, and α, β, γ, and δ as normalization correction coefficients. Module 24 is used to construct an improved compressed sensing reconstruction model based on sparse measurement point preprocessing data, physical constraint mode dictionary matrix and mode weights, so as to realize the reconstruction of global conductor vibration data; First, the observation equation is constructed, expressed as: ; Wherein, Y is the measured vibration matrix of sparse measuring points, which is composed of preprocessed data from sparse measuring points. For sparse observation matrices, This is the physical constraint mode dictionary matrix. This is the diagonal matrix of modal weights constructed from the modal weights. This is the sparse coefficient matrix of the global conductor vibration. This is the comprehensive error term; Then, based on the observation equation, residual constraints are constructed, and combined with the vibration sparse prior, an improved compressed sensing reconstruction model is established. The improved compressed sensing reconstruction model is expressed as: ; in, It is an L1 norm. It is the L2 norm. The preset reconstruction error threshold is used; Finally, the optimized compressed sensing reconstruction model is solved to obtain the optimal sparse coefficients. These optimal sparse coefficients are then substituted back into the observation equations to calculate the vibration amplitude, frequency, and displacement time series data for all traverse points across the entire domain. Comparison module 25 is used to acquire the measured data collected by the sparse vibration sensors at the verification points, compare the measured data with the corresponding reconstructed data, and calculate the comprehensive reconstruction error. The formula for calculating the comprehensive reconstruction error is as follows: ; Where E is the overall reconstruction error, A recon A test These are the reconstructed amplitude and the measured amplitude at the verification point, respectively; f recon f test These are the reconstruction frequency and the measured frequency at the verification point, respectively; S recon S test These are the reconstructed displacement time series matrix and the measured displacement time series matrix, respectively; η1, η2, and η3 are normalized weighting coefficients. Update module 26 is used to iteratively update the modal weights based on the comprehensive reconstruction error. Secondary reconstruction calibration yields high-precision global conductor vibration data. Iterative correction formula: ; in, The correction coefficient is for the k-th iteration. For the updated coefficients, For the iterative learning rate, Let be the gradient of the error function.

[0041] Furthermore, in some optional embodiments of the present invention, the computing module 22 includes: A determining unit is used to determine the basic dimension parameters of the modality dictionary matrix, wherein the basic dimension parameters include the dimension of single-cycle sampling data and the effective modality order; The building blocks are used to construct the time-domain mode shape basis functions of a single-order mode and to perform L2 norm normalization on the time-domain mode shape basis functions of each order. The splicing unit is used to splice the normalized modal time-domain vibration mode basis functions of each order as dictionary column vectors in order of order. Each column corresponds to the complete vibration characteristics of the first-order natural mode, and finally generates the initial physical constraint mode dictionary matrix. The processing unit is used to perform Gram-Schmidt orthogonalization on the initial physical constraint mode dictionary matrix to obtain the final physical constraint mode dictionary matrix.

[0042] Example 3 In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The electronic device shown is an embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and run on the processor. When the processor 10 executes the computer program 30, it implements the above-described method for monitoring the vibration of a conductor in a light breeze.

[0043] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0044] The memory 20 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0045] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0046] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for monitoring conductor vibration in a light breeze.

[0047] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0048] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0049] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0050] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0051] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for monitoring conductor vibration in a light breeze, characterized in that, The methods include: By deploying sparse vibration sensors at high-risk locations within the target power transmission area, raw data of conductor vibration under light wind is collected and preprocessed to obtain sparse measurement point preprocessed data. Calculate the natural modal frequencies of the conductor, and based on the calculated natural modal frequencies, combine the conductor vibration boundary conditions and mode shape functions to construct the physical constraint mode dictionary matrix step by step; The modal weights are dynamically adjusted based on a coupling formula of three factors: wind speed, terrain undulation, and span. Based on the sparse measurement point preprocessing data, the physical constraint mode dictionary matrix, and the mode weights, an improved compressed sensing reconstruction model is constructed to realize the reconstruction of the whole domain conductor vibration data. Acquire measured data from sparse vibration sensors at the verification points, compare the measured data with the corresponding reconstructed data, and calculate the overall reconstruction error; Based on the comprehensive reconstruction error, the modal weights are iteratively updated, and high-precision global conductor vibration data are obtained through secondary reconstruction and calibration.

2. The method for monitoring conductor vibration in a breeze according to claim 1, characterized in that, In the step of calculating the natural modal frequencies of the conductor and, based on the calculated natural modal frequencies, constructing the physical constraint modal dictionary matrix in stages using the conductor vibration boundary conditions and mode shape functions, the formula for calculating the natural modal frequencies of the conductor is as follows: ； in, Let n be the nth natural vibration frequency of the conductor, n be the modal order, L be the conductor span, T be the conductor running tension, and m be the mass per unit length of the conductor.

3. The method for monitoring conductor vibration by a breeze according to claim 2, characterized in that, The steps of calculating the natural modal frequencies of the conductor and, based on the calculated natural modal frequencies, constructing the physical constraint modal dictionary matrix in steps, combined with the conductor vibration boundary conditions and mode shape functions, include: Determine the basic dimension parameters of the modality dictionary matrix, including the dimension of single-period sampled data and the effective modal order; Construct the time-domain mode shape basis functions for each order mode, and perform L2 norm normalization on the time-domain mode shape basis functions for each order mode; The normalized modal time-domain vibration mode basis functions of each order are concatenated as dictionary column vectors in order of order. Each column corresponds to the complete vibration characteristics of the first-order natural mode, and finally the initial physical constraint mode dictionary matrix is ​​generated. The initial physical constraint mode dictionary matrix is ​​subjected to Gram-Schmidt orthogonalization to obtain the final physical constraint mode dictionary matrix.

4. The method for monitoring conductor vibration in a breeze according to claim 3, characterized in that, In the step of dynamically correcting the modal weights based on the coupling formula of wind speed, terrain undulation, and span, the formula for calculating the modal weights is as follows: ； in, The weights are adjusted in real time for the nth mode. The initial intrinsic weights of the nth mode are... denoted as , where is the deviation between the real-time wind speed and the baseline light wind speed; h is the topographic relief of the monitoring point; L is the conductor span; and α, β, γ, and δ are normalization correction coefficients.

5. The method for monitoring conductor vibration in a breeze according to claim 4, characterized in that, In the step of constructing an improved compressed sensing reconstruction model based on sparse measurement point preprocessing data, physical constraint mode dictionary matrix, and mode weights to reconstruct the whole-domain conductor vibration data, the observation equation is first constructed, expressed as: ； Wherein, Y is the measured vibration matrix of sparse measuring points, which is composed of preprocessed data from sparse measuring points. For sparse observation matrices, This is the physical constraint mode dictionary matrix. This is the diagonal matrix of modal weights constructed from the modal weights. This is the sparse coefficient matrix of the global conductor vibration. This is the comprehensive error term; Then, based on the observation equation, residual constraints are constructed, and combined with the vibration sparse prior, an improved compressed sensing reconstruction model is established. The improved compressed sensing reconstruction model is expressed as: ； in, It is an L1 norm. It is the L2 norm. The preset reconstruction error threshold is used; Finally, the optimized compressed sensing reconstruction model is solved to obtain the optimal sparse coefficients. The optimal sparse coefficients are then substituted back into the observation equation to calculate the vibration amplitude, frequency, and displacement time series data of all traverse points in the entire domain.

6. The method for monitoring conductor vibration by a breeze according to claim 5, characterized in that, In the step of acquiring the measured data collected by the sparse vibration sensor at the verification point, comparing the measured data with the corresponding reconstructed data, and calculating the comprehensive reconstruction error, the formula for calculating the comprehensive reconstruction error is as follows: ； Where E is the overall reconstruction error, A recon A test These are the reconstructed amplitude and the measured amplitude at the verification point, respectively; f recon f test These are the reconstruction frequency and the measured frequency at the verification point, respectively; S recon S test These are the reconstructed displacement time series matrix and the measured displacement time series matrix, respectively; η1, η2, and η3 are normalized weight coefficients.

7. The method for monitoring conductor vibration in a breeze according to claim 6, characterized in that, In the step of iteratively updating the modal weights based on the comprehensive reconstruction error and obtaining high-precision global conductor vibration data through secondary reconstruction calibration, the iterative correction formula is as follows: ； in, The correction coefficient is for the k-th iteration. For the updated coefficients, For the iterative learning rate, Let be the gradient of the error function.

8. A conductor vibration monitoring system, characterized in that, For implementing the conductor aerodynamic vibration monitoring method as described in any one of claims 1-7, the system comprises: The acquisition module is used to collect raw data of conductor vibration by deploying sparse vibration sensors at high-risk locations within the target power transmission area, and to preprocess the data to obtain sparse measurement point preprocessed data. The calculation module is used to calculate the natural modal frequencies of the conductor, and based on the calculated natural modal frequencies of the conductor, combined with the conductor vibration boundary conditions and mode shape functions, to construct the physical constraint mode dictionary matrix step by step; The correction module is used to dynamically correct the modal weights based on a coupling formula of three factors: wind speed, terrain undulation, and span. The module is used to build an improved compressed sensing reconstruction model based on sparse measurement point preprocessed data, physical constraint mode dictionary matrix and mode weights, so as to realize the reconstruction of global conductor vibration data; The comparison module is used to acquire the measured data collected by the sparse vibration sensors at the verification points, compare the measured data with the corresponding reconstructed data, and calculate the comprehensive reconstruction error. The update module is used to iteratively update the modal weights based on the comprehensive reconstruction error, and obtain high-precision global conductor vibration data through secondary reconstruction calibration.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for monitoring conductor vibration in a breeze as described in any one of claims 1-7.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the conductor aerodynamic vibration monitoring method as described in any one of claims 1-7.

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