Method and device for identifying galloping mode of power transmission line based on magnetic field induction

Abstract

The application relates to the field of non-contact monitoring of overhead transmission lines, and particularly provides a method and device for identifying the galloping mode of a transmission line based on magnetic field induction. The method comprises the following steps: obtaining the initial magnetic induction intensity of a magnetic dipole in each direction in a space coordinate system when a target overhead conductor gallops; wherein the initial magnetic induction intensity is measured by two magnetic induction sensors above the target overhead conductor; inversely calculating the conductor movement by using a nonlinear optimization method according to the magnetic induction intensity in each direction; wherein the conductor movement comprises the vertical displacement and the torsion angle of the target overhead conductor; and identifying the target mode of the target overhead conductor galloping by using a random subspace method based on the vertical displacement and the torsion angle. The technical scheme provided by the application can improve the accuracy of the galloping mode identification of the transmission line to a certain extent.

Description

Technical Field

[0001] This invention relates to the field of non-contact monitoring technology for overhead transmission lines, and in particular to a method and apparatus for identifying transmission line galloping modes based on magnetic field induction. Background Technology

[0002] Galloping of transmission lines is a low-frequency, large-amplitude self-excited vibration generated by external excitations such as wind and ice, seriously threatening power grid safety. In related technologies, radar monitoring can be used to monitor and analyze transmission line galloping. Radar monitoring obtains the point cloud coordinates of the conductor surface by transmitting a beam and receiving the echo, and then analyzes its motion. However, radar mainly provides target position information and cannot directly and accurately measure the conductor's torsion angle around its axis. This leads to a lack of crucial motion information. When targeting long-distance, slender conductors, the radar point cloud suffers from sparseness and high noise. Performance also degrades in rainy or snowy weather, making it difficult to stably and accurately reconstruct the continuous spatial morphology of the conductor, thus affecting the reliability of modal parameter identification. Summary of the Invention

[0003] The present invention provides a method, apparatus, electronic device, storage medium, and computer program product for identifying transmission line galloping modes based on magnetic field induction, which improves the accuracy of transmission line galloping mode identification to a certain extent.

[0004] In a first aspect, the present invention provides a method for identifying transmission line galloping modes based on magnetic field induction, the method comprising:

[0005] The initial magnetic induction intensity of the magnetic dipole in each direction in the spatial coordinate system is obtained when the target overhead conductor is swaying; wherein, the initial magnetic induction intensity is measured by two magnetic induction sensors directly above the target overhead conductor;

[0006] Based on the magnetic induction intensity in each direction, a nonlinear optimization method is used to invert the conductor motion; wherein, the conductor motion includes the vertical displacement and torsional angle of the target overhead conductor;

[0007] Based on the vertical displacement and the torsion angle, the target mode of the target overhead conductor galloping is identified using the random subspace method.

[0008] In one embodiment of the present invention, the motion of the conductor is inverted using a nonlinear optimization method based on the magnetic induction intensity in each direction, including:

[0009] Based on the initial magnetic flux density, the target magnetic flux density of the magnetic dipole is determined;

[0010] Construct an expression relating the target magnetic induction intensity to the magnetic moment of the target overhead conductor, the target unit vector from the target overhead conductor to the magnetic induction sensor, the unit vector components of the target unit vector in each direction of the spatial coordinate system, and the target distance between the target overhead conductor and the magnetic induction sensor;

[0011] Based on the aforementioned relational expression, the vertical displacement and torsion angle of the target overhead conductor are determined.

[0012] In one embodiment of the present invention, let the spatial coordinates of the magnetic induction sensor in the spatial coordinate system be... The spatial coordinates of the measurement point when the target overhead conductor is oscillating are: The method further includes:

[0013] Determine the target spatial vector (X, Y, Z) between the spatial coordinates of the measurement point when the target overhead conductor is galloping and the spatial coordinates of the magnetic induction sensor; where... , Indicates the vertical displacement of the target overhead conductor;

[0014] Based on the spatial vector, calculate the target distance between the spatial coordinates of the measurement point when the target overhead conductor is swaying and the target spatial coordinates of the magnetic induction sensor.

[0015] Divide the target space vector by the target distance to obtain the target unit vector.

[0016] In one embodiment of the present invention, determining the vertical displacement and torsion angle of the target overhead conductor according to the relational expression includes:

[0017] Constructing a joint objective function ;in, This indicates the vertical displacement of the target overhead conductor. This indicates the deflection angle of the target overhead conductor; where, The theoretical magnetic field value of the conductor in the k-th iteration on the i-th magnetic induction sensor is expressed as follows: ; The expression is ;

[0018] Based on the Gauss-Newton method, iterative measurements of the vertical displacement and the deflection angle are performed until the number of iterations reaches a preset maximum number of iterations or the residual is less than a preset residual; wherein, In the formula, Represents the residual. Represents the Jacobian matrix, Indicates the step size.

[0019] In one embodiment of the present invention, based on the vertical displacement and the torsional angle, the target mode of the galloping of the target overhead conductor is identified using a random subspace method, including:

[0020] A position vector is constructed based on the vertical displacement and torsional angle of each target measurement point on the target overhead conductor; and an output matrix is ​​solved based on the position vector and the state vector; wherein, the state vector is used to characterize the galloping information of the target overhead conductor;

[0021] A Topletz matrix is ​​constructed based on the output matrix; and singular value decomposition is performed on the Topletz matrix; wherein, the singular value decomposition includes decomposing the Topletz matrix into a left singular vector matrix, a singular value matrix, and a right singular vector matrix;

[0022] Construct a state matrix based on the left singular vector matrix and the singular value matrix;

[0023] The state matrix is ​​decomposed into eigenvalues ​​to obtain an eigenvector matrix and an eigenvalue matrix; wherein the diagonal elements of the eigenvalue matrix are the eigenvalues ​​of the state matrix.

[0024] Calculate the damping ratio corresponding to each of the aforementioned feature values, and when the damping ratio is negative, identify the mode corresponding to the feature value as the target mode of the target overhead conductor galloping.

[0025] In one embodiment of the present invention, constructing a state matrix based on the left singular vector matrix and the singular value matrix includes:

[0026] Extracting the first n rows of the left singular vector matrix yields the first block matrix. ; and, extract the first n rows of the singular value matrix to obtain the second block matrix; where n is the number of states in the state vector;

[0027] Multiply the square roots of the first block matrix and the second block matrix to obtain the observability matrix;

[0028] The state matrix is ​​obtained by multiplying the pseudo-inverse of the observability matrix with the third block matrix; wherein the third block matrix is ​​obtained by removing 2N from the observability matrix, and N is the number of target measurement points on the target overhead conductor.

[0029] In one embodiment of the present invention, the method further includes:

[0030] Construct the true mode matrix; wherein the true mode matrix is ​​the product of the output matrix and the eigenvector matrix;

[0031] Multiply the pseudo-inverse of the real mode matrix with the position vector to obtain the modal coordinates;

[0032] Based on the modal coordinates, determine the complex modal coordinates of each mode;

[0033] Based on the complex modal coordinates, the kinetic energy density and potential energy density of the target mode are calculated, and the sum of the kinetic energy density and the potential energy density is integrated over the length of the target overhead conductor to obtain the target energy corresponding to the target mode.

[0034] In one embodiment of the present invention, the method further includes:

[0035] Based on the relationship between mass data, stiffness data, and generalized damping, the generalized damping is determined; and based on the generalized damping and the current damping, the equivalent contribution damping of the target mode is determined.

[0036] The equivalent contribution damping is added to the current damping so that the damping ratio of the target mode is greater than or equal to 0.

[0037] Secondly, the present invention provides a device for identifying transmission line galloping modes based on magnetic field induction, the device comprising:

[0038] The data acquisition module is used to acquire the initial magnetic induction intensity of the magnetic dipole in each direction in the spatial coordinate system when the target overhead conductor is swaying; wherein, the initial magnetic induction intensity is measured by two magnetic induction sensors directly above the target overhead conductor;

[0039] The motion inversion module is used to invert the conductor motion using a nonlinear optimization method based on the magnetic induction intensity in each direction; wherein, the conductor motion includes the vertical displacement and torsional angle of the target overhead conductor;

[0040] The galloping mode recognition module is used to identify the target mode of the galloping of the target overhead conductor based on the vertical displacement and the torsion angle using the random subspace method.

[0041] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for identifying transmission line galloping modes based on magnetic field induction as described in any of the preceding claims.

[0042] Fourthly, the present invention provides a computer program product, including a computer program, which, when executed by a processor, causes the computer to perform the method for identifying transmission line galloping modes based on magnetic field induction as described in any of the preceding claims.

[0043] This invention obtains the initial magnetic flux density of the magnetic dipole in each direction in the spatial coordinate system when a target overhead conductor gallops. Then, based on the magnetic flux density in each direction, a nonlinear optimization method is used to invert the conductor motion; wherein, the conductor motion includes the vertical displacement and torsion angle of the target overhead conductor; finally, based on the vertical displacement and the torsion angle, a random subspace method is used to identify the target mode of the galloping of the target overhead conductor, thereby improving the accuracy of transmission line galloping mode identification to a certain extent. Attached Figure Description

[0044] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating a method for identifying transmission line galloping modes based on magnetic field induction, according to an embodiment of the present invention.

[0046] Figure 2 This is a schematic diagram of a tower model and the placement of a magnetic induction sensor provided in an embodiment of the present invention.

[0047] Figure 3 This is a flowchart illustrating a modal energy identification and damping control method provided in an embodiment of the present invention.

[0048] Figure 4 This is a schematic diagram of the structure of a transmission line galloping mode identification device based on magnetic field induction provided in an embodiment of the present invention.

[0049] Figure 5 This is a schematic diagram of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0050] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0051] Line galloping is a low-frequency, large-amplitude self-excited vibration generated by wind, which seriously threatens the safety of the power grid. Among related technologies, conductor galloping monitoring technology mainly includes accelerometer sensing, video imaging, and radar monitoring. Accelerometer sensing is based on contact installation on the surface of the conductor or accessories. By installing a triaxial accelerometer on the conductor, the displacement is obtained through double integration, and then modal analysis is performed.

[0052] However, this method has practical limitations, such as requiring power outages for installation, high installation and maintenance costs, and the sensor encapsulation adding mass to the conductor, significantly altering the conductor's natural frequency and mode shape. Video imaging methods use cameras mounted on poles to track the movement of marker points on the conductor through image recognition, thereby performing modal analysis. However, this method suffers from image blurring due to environmental factors such as fog, haze, rain, and snow; requires supplemental lighting at night; introduces additional equipment and complexity; and is prone to motion blur during high-speed swaying, leading to a surge in the cost of high-speed cameras. Radar monitoring obtains the point cloud coordinates of the conductor surface by emitting beams and receiving echoes, then analyzes its motion. However, radar primarily provides target position information and struggles to directly and accurately measure the conductor's torsion angle around its axis, resulting in a lack of crucial motion information. When targeting long, slender conductors at long distances, radar point clouds exhibit sparseness and high noise levels, and performance degrades in rain and snow, making it difficult to stably and accurately reconstruct the continuous spatial morphology of the conductor, thus affecting the reliability of modal parameter identification.

[0053] In summary, existing methods are insufficient to meet the need for accurate monitoring of dancing modes under extreme environmental conditions.

[0054] Based on this, the present invention obtains the initial magnetic induction intensity of the magnetic dipole in each direction in the spatial coordinate system when the target overhead conductor gallops. Then, based on the magnetic induction intensity in each direction, a nonlinear optimization method is used to invert the conductor motion; wherein, the conductor motion includes the vertical displacement and torsion angle of the target overhead conductor; finally, based on the vertical displacement and the torsion angle, a random subspace method is used to identify the target mode of the galloping of the target overhead conductor, thereby improving the accuracy of transmission line galloping mode identification to a certain extent.

[0055] Please see Figure 1 The present invention provides a method for identifying transmission line galloping modes based on magnetic field induction. This method may include the following steps.

[0056] Step S110: Obtain the initial magnetic induction intensity of the magnetic dipole in each direction in the spatial coordinate system when the target overhead conductor is dancing; wherein, the initial magnetic induction intensity is measured by two magnetic induction sensors directly above the target overhead conductor.

[0057] In this embodiment, the magnetic dipole is a magnetic unit equivalent to the target overhead conductor. When the conductor moves, the position and orientation of the magnetic dipole change with the movement of the conductor, thereby changing the spatial magnetic field distribution. It is the core object of magnetic field induction measurement.

[0058] In this embodiment, a magnetic induction sensor is used to detect the magnitude and direction of magnetic induction intensity at a point in space. Placing it directly above the conductor reduces interference from other magnetic fields. Simultaneous measurement by two sensors improves inversion accuracy and avoids the uncertainty of single-point measurements. The magnetic induction sensor, after calibration, is placed on top of the tower connecting the target overhead conductor. Each magnetic induction sensor consists of a TRM2103 sensor and a data processor. The TRM2103 sensor is a triaxial magnetoresistive sensor. The data processing unit acquires and processes the signal from the magnetic induction sensor directly above the target overhead conductor. Data processing includes an instrumentation amplifier, a bandpass filter, a programmable amplifier, and a microprocessor. The instrumentation amplifier filters out common-mode interference, the bandpass filter improves the signal-to-noise ratio, the programmable amplifier amplifies the signal, and the microprocessor performs analog-to-digital conversion.

[0059] In this embodiment, the spatial coordinate system is a three-dimensional coordinate system defined to quantify the direction of magnetic induction intensity and the position of the conductor. This three-dimensional coordinate system is typically set with x, y, and z axes, with the y-axis being the vertical direction, corresponding to the vertical direction of the conductor's galloping, and serves as the basis for subsequent coordinate calculations and vector analysis. A triaxial magnetoresistive sensor can acquire the magnetic induction intensity in the x, y, and z directions. For details, please refer to [link to relevant documentation]. Figure 2 It includes a total of 3 target overhead conductors, with 2 magnetic induction sensors directly above each target overhead conductor.

[0060] Step S120: Based on the magnetic induction intensity in each direction, the conductor motion is inverted using a nonlinear optimization method; wherein, the conductor motion includes the vertical displacement and torsion angle of the target overhead conductor.

[0061] In this embodiment, the nonlinear optimization method addresses the nonlinear relationship between conductor motion and magnetic field distribution. It uses an objective function and iterative solution to inversely deduce the unknown conductor motion parameters, making it more closely aligned with the actual physical model compared to linear methods. The conductor motion parameters include the conductor's vertical displacement and torsional angle.

[0062] Step S130: Based on the vertical displacement and the torsion angle, the target mode of the target overhead conductor galloping is identified using the random subspace method.

[0063] In this embodiment, the random subspace method, a modal identification method based on system output data, does not require a preset system model and can directly identify modal parameters through measured vertical displacement and torsional angle, making it suitable for modal analysis of complex vibration systems.

[0064] The above embodiment utilizes two magnetic induction sensors placed on an overhead tower above the conductor to obtain the magnetic induction intensity, and uses magnetic field inversion to retrieve the conductor motion, Gauss-Newton method to obtain the conductor's vertical displacement and torsion angle, and then uses the random subspace method to identify the conductor modes from the obtained conductor motion data and obtain the relationship between the mode energy and the damping ratio, thus realizing the identification of the conductor galloping modes.

[0065] Furthermore, the transmission line galloping identification method provided by this invention has advantages such as non-contact installation, strong environmental adaptability, low cost, and high reliability, overcoming the drawbacks of traditional measurement methods.

[0066] In some embodiments, step S120, inverting the conductor motion using a nonlinear optimization method based on the magnetic induction intensity in each direction, may include the following steps.

[0067] Step S121: Determine the target magnetic induction intensity of the magnetic dipole based on the initial magnetic induction intensity.

[0068] Step S122: Construct the relationship expression between the target magnetic induction intensity and the magnetic moment of the target overhead conductor, the target unit vector from the target overhead conductor to the magnetic induction sensor, the unit vector components of the target unit vector in each direction in the spatial coordinate system, and the target distance between the target overhead conductor and the magnetic induction sensor.

[0069] Step S123: Determine the vertical displacement and torsion angle of the target overhead conductor according to the relationship expression.

[0070] In this embodiment, the target magnetic flux density is the magnetic flux density after removing interference from the Earth's magnetic field. The expression for the target magnetic flux density can be given as:

[0071]

[0072]

[0073]

[0074] In the formula, , , These are the initial magnetic induction intensities measured by the magnetic induction sensor. , , This represents the components of the Earth's magnetic field on the x, y, and z axes, and its effect on the sensors on each phase conductor can be considered consistent. , , This represents the target magnetic flux density of the magnetic dipole along the x, y, and z axes. Specifically, in... Figure 2 In the illustrated embodiment, i = 1, 2, 3, 4, 5, 6.

[0075] In this embodiment, the relationship between the target magnetic induction intensity of the target overhead conductor in various directions can be expressed as:

[0076]

[0077]

[0078]

[0079] In the formula, The magnetic moment of the target overhead conductor is expressed as follows: , This represents the initial magnetic moment of the conductor; These represent the three components of the unit vector from the magnetic sensor to the wire; This represents the target unit vector from the wire to the magnetic sensor. This indicates the distance from the wire to the target of the magnetic sensor; It represents the magnetic permeability of vacuum.

[0080] In this embodiment, the vertical displacement and torsion angle of the target overhead conductor can be determined by solving the relational expressions for each direction using a nonlinear optimization method.

[0081] The above embodiments clarify the core logic of nonlinear optimization inversion. By constructing a quantitative relationship between magnetic field parameters and conductor motion parameters, the measured values ​​of magnetic induction intensity are transformed into vertical displacement and torsion angle that can directly characterize the galloping state of the conductor, providing accurate basic data for subsequent modal identification.

[0082] In some embodiments, the spatial coordinates of the magnetic sensor in the spatial coordinate system are defined as follows: The spatial coordinates of the measurement point when the target overhead conductor is oscillating are: The method for identifying transmission line galloping modes based on magnetic field induction may also include the following steps.

[0083] Step S1211: Determine the target spatial vector (X, Y, Z) between the spatial coordinates of the measurement point when the target overhead conductor is galloping and the spatial coordinates of the magnetic induction sensor; wherein, , This indicates the vertical displacement of the target overhead conductor.

[0084] Step S1212: Based on the spatial vector, calculate the target distance between the spatial coordinates of the measurement point when the target overhead conductor is swaying and the target spatial coordinates of the magnetic induction sensor.

[0085] Step S1213: Divide the target space vector by the target distance to obtain the target unit vector.

[0086] In this embodiment, the spatial coordinates of the magnetic induction sensor ( The initial coordinates of the target overhead conductor measurement point are fixed values. Spatial calibration can be performed after installation to obtain its spatial coordinates. The vertical displacement Δy(t) during the dance changes with time, therefore the real-time coordinates are... In this invention, it is assumed that there is no significant motion in the x and z directions. The invention focuses on the vertical displacement and torsional angle of the dancing core.

[0087] In this embodiment, it is assumed that the target overhead conductor has no movement in the x and z directions, but moves vertically in the y direction. The change in the target space vector represents the three-dimensional vector of the relative position between the traverse measurement point and the sensor, with its components X and Z being fixed values. d is the difference between the initial vertical position of the sensor and the initial vertical position of the traverse, which is also a fixed value.

[0088] In this embodiment, the target distance is the magnitude of the spatial vector, that is, the straight-line distance between the traverse measurement point and the sensor, calculated using the following formula: It changes with the vertical displacement of the conductor. Therefore, the target unit vector It can be represented as:

[0089]

[0090] In the formula, Represents the target space vector.

[0091] Accordingly, in the above embodiments, The expression is In other words, ; ; .

[0092] In some embodiments, determining the vertical displacement and torsion angle of the target overhead conductor in step S123 according to the relational expression may include the following steps.

[0093] Step S1231: Construct the joint objective function ;in, This indicates the vertical displacement of the target overhead conductor. This indicates the deflection angle of the target overhead conductor; where, The theoretical magnetic field value of the conductor in the k-th iteration on the i-th magnetic induction sensor is expressed as follows: ; The expression is .

[0094] Step S1232: Based on the Gauss-Newton method, iteratively measure the vertical displacement and the deflection angle until the number of iterations reaches a preset number of iterations or the residual is less than a preset parameter; wherein, In the formula, Represents the residual. Represents the Jacobian matrix, Indicates the step size.

[0095] In this embodiment, the joint objective function is used to measure the deviation between the measured magnetic flux density and the theoretical calculated value of the two sensors. The goal is to minimize the value of this function by adjusting Δy and θ. In other words, by adjusting Δy and θ, the deviation between the measured magnetic flux density and the theoretical magnetic flux density is minimized.

[0096] In this embodiment, the Gauss-Newton method is a commonly used nonlinear least squares optimization method. By linearizing the objective function and using the Jacobian matrix to approximate the gradient information, the parameters to be solved (Δy, θ) are updated iteratively step by step. It has a fast convergence speed and high accuracy, and is suitable for the parameter inversion scenario of this invention.

[0097] In this embodiment, the Gauss-Newton iteration method can be expressed as:

[0098]

[0099] In the formula, Indicates the step size, usually 0 < ≤1 is used to control the parameter update magnitude in each iteration to avoid iteration divergence. Represents the residual. Let represent the Jacobian matrix. The iteration objective is to stop the sum of the residuals in the residual matrix from being less than or equal to a preset residual, typically set to 10. -6 The iteration continues until the preset maximum number of iterations is reached.

[0100] In this embodiment, the Jacobian matrix can be represented as:

[0101]

[0102] In the formula, the Jacobian matrix represents the partial derivatives of the theoretical magnetic field values ​​of the two magnetic sensors in the x, y, and z directions with respect to Δy and θ, reflecting the degree of influence of parameter changes on the magnetic field values.

[0103] In this embodiment, the residual matrix can be represented as:

[0104]

[0105] In the formula, the residual matrix represents the measured values ​​of the magnetic induction sensors, that is, the deviation between the initial magnetic induction intensity of the two magnetic induction sensors in the x, y, and z directions and their corresponding theoretical magnetic field values. The sum of all elements is the sum of the residuals. The theoretical magnetic field magnitude of the conductor at the i-th sensor in the k-th iteration is expressed as: ; The expression is ;at last .

[0106] In the above embodiments, specific iterative inversion algorithms and mathematical models are provided. By constructing a joint objective function to quantify measurement deviations, and using the Gauss-Newton method to accurately solve vertical displacement and torsion angle, the convergence and accuracy problems of nonlinear system parameter inversion are solved, ensuring that the iterative results are stable and reliable, and providing high-quality motion parameter data for subsequent modal identification.

[0107] In some embodiments, step S130, based on the vertical displacement and the torsion angle, identifies the target mode of the target overhead conductor galloping using the random subspace method, which may include the following steps.

[0108] Step S131: Construct a position vector based on the vertical displacement and torsion angle of each target measurement point on the target overhead conductor; and solve for the output matrix based on the position vector and the state vector; wherein, the state vector is used to characterize the galloping information of the target overhead conductor.

[0109] Step S132: Construct the Topletz matrix based on the output matrix; and perform singular value decomposition on the Topletz matrix; wherein the singular value decomposition includes decomposing the Topletz matrix into a left singular vector matrix, a singular value matrix, and a right singular vector matrix.

[0110] Step S133: Construct a state matrix based on the left singular vector matrix and the singular value matrix.

[0111] Step S134: Perform eigenvalue decomposition on the state matrix to obtain an eigenvector matrix and an eigenvalue matrix; wherein the diagonal elements of the eigenvalue matrix are the eigenvalues ​​of the state matrix.

[0112] Step S135: Calculate the damping ratio corresponding to each of the aforementioned feature values, and when the damping ratio is negative, identify the mode corresponding to the feature value as the target mode of the target overhead conductor galloping.

[0113] In this embodiment, the position vector is a matrix composed of data on the vertical displacement and torsion angle of multiple measurement points as a function of time. The number of elements in the position vector is 2 × the number of sampling points on the target overhead conductor, used to characterize the spatial distribution characteristics of conductor galloping. In other words, each sampling point has two elements in the position vector, one element representing the vertical displacement of the sampling point and the other element representing the torsion angle of the sampling point.

[0114] In this embodiment, the state vector is used to describe the motion state of the system at a certain moment. It usually contains the displacement and velocity components of each measurement point and can be obtained through the difference operation of the position vector, providing a basis for the construction of the output matrix.

[0115] In this embodiment, in the stochastic subspace method, the system is modeled as a discrete-time state-space model:

[0116]

[0117] In the formula, This is the state vector at the current moment, which includes n-dimensional data; This is the state vector at the next measurement moment. As a position vector, it can be represented as C is the output matrix.

[0118] In this embodiment, the Toeplitz matrix is ​​a special type of symmetric matrix whose elements satisfy T(i,j)=T(i+1,j+1), constructed from the autocorrelation function of the output matrix, and can effectively extract modal information from the dancing data. The Toeplitz matrix can be represented as:

[0119]

[0120] In the formula, R i The covariance matrix of the output data is expressed as follows: M represents the total number of sampling points.

[0121] In this embodiment, singular value decomposition (SVD) decomposes the Toplitz matrix into three mutually orthogonal matrices. The diagonal elements (singular values) of the singular value matrix represent the energy proportion of each modal component, while the left and right singular vector matrices represent the spatial distribution characteristics of the modes. Specifically, SVD of the Toplitz matrix can be performed as follows:

[0122]

[0123] In the formula, S matrix is ​​the singular value matrix. The U matrix is ​​a left singular vector matrix; the V matrix is ​​a right singular vector matrix.

[0124] In this embodiment, the state matrix A represents the evolution of the system state over time. Constructed using singular value decomposition (SVD), it reflects the dynamic characteristics of the galloping system and serves as a key carrier for modal parameter extraction. eigenvalue decomposition of the state matrix A can be expressed as:

[0125]

[0126] In the formula, The eigenvector matrix, It is the eigenvalue matrix.

[0127] In this embodiment, the damping ratio is a parameter characterizing the energy dissipation capability of a vibration system; a negative damping ratio indicates that the system exhibits self-excited vibration. Transmission line galloping is essentially a self-excited vibration. Therefore, the true galloping modes can be selected and interfering modes eliminated by using the sign of the damping ratio.

[0128] In this embodiment, before determining the damping ratio, the continuous-time characteristic value can be determined first. The continuous-time characteristic value can be expressed as:

[0129]

[0130] In the formula, Let be the eigenvalues ​​in the eigenvalue matrix, and be a complex number. The sampling time interval, , The sampling frequency.

[0131] Then, the damping ratio is determined based on the continuous-time eigenvalues. :

[0132]

[0133] In the formula, This indicates taking the real part of the continuous-time eigenvalues. If , indicating negative damping, this mode is unstable and produces a galloping motion.

[0134] In the above embodiments, the random subspace method is combined with conductor motion parameters. Through matrix operations and damping ratio screening, the accurate identification of the galloping target mode is achieved, which solves the problems of mode aliasing and misjudgment of interference modes in complex vibration scenarios. Moreover, no system model needs to be preset, which is suitable for the nonlinear and random characteristics of transmission line galloping.

[0135] In some embodiments, the process of constructing a state matrix based on the left singular vector matrix and the singular value matrix in step S133 may include the following steps.

[0136] Step S1331: Extract the first n rows of the left singular vector matrix to obtain the first block matrix. ; and, extract the first n rows of the singular value matrix to obtain the second block matrix; where n is the number of states in the state vector.

[0137] Step S1332: Multiply the square roots of the first block matrix and the second block matrix to obtain the observability matrix.

[0138] Step S1333: Multiply the pseudo-inverse matrix of the observability matrix with the third block matrix to obtain the state matrix; wherein, the third block matrix is ​​obtained by removing 2N from the observability matrix, and N is the number of target measurement points on the target overhead conductor.

[0139] In this embodiment, the block matrix is ​​truncated because the left singular vector matrix and singular value matrix have high dimensions. By truncating the first n rows based on the number of state vectors n, redundant information can be eliminated, focusing on the core components related to the system state and ensuring the relevance of the block matrix. The first block matrix is ​​obtained by truncating the left singular matrix, and the second block matrix is ​​obtained by truncating the singular value matrix.

[0140] In this embodiment, the observability matrix is ​​used to characterize the degree to which the system state can be observed through the output quantities. The larger its rank, the better the system observability. It is constructed by multiplying block matrices, providing a foundation for the derivation of the state matrix. The output quantities are vertical displacement and torsional angle. Observability matrix It can be represented as:

[0141]

[0142] In the formula, This is the first block matrix. ; , .

[0143] In this embodiment, the state matrix A can be represented as:

[0144]

[0145] In the formula, This represents the pseudo-inverse of a matrix. for Remove the last 2N rows of the matrix.

[0146] The above embodiments clarify the specific construction process of the state matrix. Through steps such as block extraction and matrix operation, core information related to the system state is extracted, redundant data interference is avoided, the accuracy of the state matrix is ​​improved, and the foundation is laid for subsequent feature value decomposition and modality recognition, further ensuring the accuracy of modality recognition.

[0147] In some embodiments, the method for identifying transmission line galloping modes based on magnetic field induction may further include the following steps.

[0148] Step S1341: Construct the actual mode matrix; wherein the actual mode matrix is ​​the product of the output matrix and the eigenvector matrix.

[0149] Step S1342: Multiply the pseudo-inverse matrix of the actual mode matrix with the position vector to obtain the modal coordinates.

[0150] Step S1343: Based on the modal coordinates, determine the complex modal coordinates of each mode.

[0151] Step S1344: Calculate the kinetic energy density and potential energy density of the target mode based on the complex mode coordinates, and integrate the sum of the kinetic energy density and the potential energy density over the length of the target overhead conductor to obtain the target energy corresponding to the target mode.

[0152] In this embodiment, the mode shape of the i-th order mode It can be represented as:

[0153]

[0154] In the formula, Represents the eigenvector matrix The i-th column.

[0155] In this embodiment, the real mode shape matrix represents the amplitude distribution of each mode at different measurement points. It is obtained by multiplying the output matrix and the eigenvector matrix, reflecting the spatial mode shape characteristics of the mode. Since the mode shape is a complex number, the decomposition yields the real mode shape matrix as follows: .in, Indicates taking The real part in; Im indicates taking The imaginary part.

[0156] In this embodiment, modal coordinates are coordinates transformed from physical coordinates to modal space, with each mode corresponding to one modal coordinate. Physical coordinates represent vertical displacement and torsional angle. Modal coordinates can characterize the vibration behavior of that mode, such as the amplitude and phase varying with time. It can be represented as: = .

[0157] In this embodiment, the complex modal coordinates are modal coordinates that consider modal damping and phase difference. The complex modal coordinates are in complex form, with the real part representing the amplitude and the imaginary part representing the phase, which better reflects the actual vibration characteristics of transmission line galloping. The complex modal coordinates can be expressed as: .

[0158] In this embodiment, kinetic energy density and potential energy density are the kinetic and potential energy per unit length of the conductor, calculated from the velocity and displacement components of the complex modal coordinates, reflecting the distribution of modal energy along the conductor. Kinetic energy density It can be represented as:

[0159]

[0160] In the formula, For linear density, The moment of inertia per unit length. This represents the time derivative of the coordinates of the i-th modal.

[0161] Potential energy density It can be represented as:

[0162]

[0163] In the formula, , T represents tension.

[0164] Then, the sum of the kinetic energy density and the potential energy density is integrated over the length of the target overhead conductor to obtain the target energy corresponding to the target mode. :

[0165]

[0166] In the formula, L is the length of the target overhead power line.

[0167] The above embodiments supplement the calculation process of modal energy, realizing the quantitative assessment of galloping modes. That is, it can not only identify the modal type, but also quantify the magnitude and distribution of modal energy, providing a key basis for judging the contribution of each mode to galloping and predicting the risk of conductor fatigue damage, thus enriching the dimensions of galloping monitoring.

[0168] In some embodiments, the identification of transmission line galloping modes based on magnetic field induction may further include the following steps.

[0169] Step S1351: Determine the generalized damping based on the relationship between mass data, stiffness data and generalized damping; and determine the equivalent contribution damping of the target mode based on the generalized damping and the current damping.

[0170] Step S1352: Add the equivalent contribution damping to the current damping so that the damping ratio of the target mode is greater than or equal to 0.

[0171] In this embodiment, mass data and stiffness data are matrices characterizing the structural characteristics of the transmission line. Mass data reflects the mass distribution of different parts of the conductor, and stiffness data reflects the elastic characteristics of the conductor. Both can be pre-calculated using parameters such as conductor material, cross-sectional dimensions, and span. Generalized damping is a damping parameter derived from mass data and stiffness data based on structural dynamics theory. It characterizes the overall energy dissipation characteristics of the system and is distinct from the damping ratio of a single mode. The relationship between mass data, stiffness data, and generalized damping can be expressed as:

[0172]

[0173] In the formula, This represents the quality data of the i-th mode, where, . Denotes the generalized damping of the i-th mode. . Let be the angular frequency of the i-th mode. It can be converted from a fixed frequency of the i-th mode. Wherein, . , where represents the natural frequency of the i-th mode. This represents the stiffness data of the i-th mode, where, , For bending stiffness.

[0174] In this embodiment, the energy decay rate of the i-th mode can be expressed as:

[0175]

[0176]

[0177]

[0178]

[0179] In the formula, Characterizes the rate of energy change of the i-th mode; This represents a change in energy decay. It is the equivalent contribution damping to the i-th mode; x m c represents the position of the m-th spacer. m is the damping coefficient.

[0180] For details, please refer to Figure 3 First, the magnetic field is measured in real time using a sensor array. Then, the vertical displacement of the conductor is calculated from the magnetic field data using nonlinear inversion and the Gauss-Newton method. and twist angle The process involves iteration; after obtaining multi-point motion data, modal parameters are determined through multi-point data fusion and random subspace identification. , and The identification of the modal coordinates is performed; the modal energy is calculated by decomposing the modal coordinates, thereby obtaining the relationship between the modal energy of the conductor galloping and the damping; finally, the generation of conductor galloping is suppressed by controlling the target damping.

[0181] The above embodiments utilize magnetic induction sensors placed at specific locations to acquire magnetic induction intensity and thereby analyze conductor galloping modes, conductor galloping mode energy, and the relationship between galloping mode energy and damping ratio. This achieves non-contact mode recognition and damping control of conductor icing galloping, and has the advantages of convenient installation and high accuracy.

[0182] Please see Figure 4 One embodiment of the present invention provides a device for identifying the galloping modes of transmission lines based on magnetic field induction. The device may include: a data acquisition module, a motion inversion module, and a galloping mode identification module.

[0183] The data acquisition module is used to acquire the initial magnetic induction intensity of the magnetic dipole in each direction in the spatial coordinate system when the target overhead conductor is dancing; wherein, the initial magnetic induction intensity is measured by two magnetic induction sensors directly above the target overhead conductor.

[0184] The motion inversion module is used to invert the conductor motion using a nonlinear optimization method based on the magnetic induction intensity in each direction; wherein, the conductor motion includes the vertical displacement and torsion angle of the target overhead conductor.

[0185] The galloping mode recognition module is used to identify the target mode of the galloping of the target overhead conductor based on the vertical displacement and the torsion angle using the random subspace method.

[0186] The specific functions and effects of the transmission line galloping mode identification device based on magnetic field induction can be explained by referring to other embodiments in this specification, and will not be repeated here. Each module in the transmission line galloping mode identification device based on magnetic field induction can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0187] Please see Figure 5 One embodiment of the present invention can provide an electronic device, the electronic device comprising:

[0188] A memory, and one or more processors communicatively connected to the memory;

[0189] The memory stores instructions that can be executed by the one or more processors. These instructions are executed by the one or more processors to enable the one or more processors to implement the method for constructing a power consumption prediction model based on meteorological factors as described in any of the above embodiments, or the method for identifying transmission line galloping modes based on magnetic field induction as described in any of the above embodiments.

[0190] An embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for constructing a power consumption prediction model based on meteorological factors as described in any of the above embodiments, or the method for identifying transmission line galloping modes based on magnetic field induction as described in any of the above embodiments.

[0191] This specification also provides a computer program product containing instructions that, when executed by a computer, cause the computer to perform the method for constructing a power consumption prediction model based on meteorological factors as described in any of the above embodiments, or the method for identifying transmission line galloping modes based on magnetic field induction as described in any of the above embodiments.

[0192] It is understood that the specific examples in this document are only intended to help those skilled in the art better understand the embodiments described herein, and are not intended to limit the scope of the invention.

[0193] It is understood that in the various embodiments described in this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments described in this specification.

[0194] It is understood that the various implementation methods described in this specification can be implemented individually or in combination, and the implementation methods in this specification are not limited in this respect.

[0195] Unless otherwise stated, all technical and scientific terms used in the embodiments of this specification have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this specification. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0196] It is understood that the processor in this invention can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method implementation can be completed by the integrated logic circuitry in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this specification. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this specification can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0197] It is understood that the memory in this invention can be volatile memory or non-volatile memory, or may include both. Specifically, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0198] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0199] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0200] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.

[0201] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0202] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0203] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0204] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of this specification, in essence, or the parts that contribute to the prior art, or parts of the technical solutions, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0205] The above description is merely a specific embodiment of this specification, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this specification should be included within the scope of protection of this specification. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A method for identifying transmission line galloping modes based on magnetic field induction, characterized in that, The method includes: The initial magnetic induction intensity of the magnetic dipole in each direction in the spatial coordinate system is obtained when the target overhead conductor is swaying; wherein, the initial magnetic induction intensity is measured by two magnetic induction sensors directly above the target overhead conductor; Based on the magnetic induction intensity in each direction, a nonlinear optimization method is used to invert the conductor motion; wherein, the conductor motion includes the vertical displacement and torsional angle of the target overhead conductor; Based on the vertical displacement and the torsion angle, the target mode of the target overhead conductor galloping is identified using the random subspace method.

2. The method according to claim 1, characterized in that, Based on the magnetic induction intensity in each direction, the motion of the conductor is inverted using a nonlinear optimization method, including: Based on the initial magnetic flux density, the target magnetic flux density of the magnetic dipole is determined; Construct an expression relating the target magnetic induction intensity to the magnetic moment of the target overhead conductor, the target unit vector from the target overhead conductor to the magnetic induction sensor, the unit vector components of the target unit vector in each direction of the spatial coordinate system, and the target distance between the target overhead conductor and the magnetic induction sensor; Based on the aforementioned relational expression, the vertical displacement and torsion angle of the target overhead conductor are determined.

3. The method according to claim 2, characterized in that, Let the spatial coordinates of the magnetic induction sensor in the spatial coordinate system be... The spatial coordinates of the measurement point when the target overhead conductor is oscillating are: The method further includes: Determine the target spatial vector (X, Y, Z) between the spatial coordinates of the measurement point when the target overhead conductor is galloping and the spatial coordinates of the magnetic induction sensor; where... , Indicates the vertical displacement of the target overhead conductor; Based on the spatial vector, calculate the target distance between the spatial coordinates of the measurement point when the target overhead conductor is swaying and the target spatial coordinates of the magnetic induction sensor. Divide the target space vector by the target distance to obtain the target unit vector.

4. The method according to claim 2, characterized in that, Based on the aforementioned relational expression, the vertical displacement and torsional angle of the target overhead conductor are determined, including: Constructing a joint objective function ;in, This indicates the vertical displacement of the target overhead conductor. This indicates the deflection angle of the target overhead conductor; where, The theoretical magnetic field value of the conductor in the k-th iteration on the i-th magnetic induction sensor is expressed as follows: ; The expression is ; Based on the Gauss-Newton method, iterative measurements of the vertical displacement and the deflection angle are performed until the number of iterations reaches a preset maximum number of iterations or the residual is less than a preset residual; wherein, In the formula, Represents the residual. Represents the Jacobian matrix, Indicates the step size.

5. The method according to claim 1, characterized in that, Based on the vertical displacement and the torsional angle, the target mode of the galloping of the target overhead conductor is identified using the random subspace method, including: A position vector is constructed based on the vertical displacement and torsional angle of each target measurement point on the target overhead conductor; and an output matrix is ​​solved based on the position vector and the state vector; wherein, the state vector is used to characterize the galloping information of the target overhead conductor; A Topletz matrix is ​​constructed based on the output matrix; and singular value decomposition is performed on the Topletz matrix; wherein, the singular value decomposition includes decomposing the Topletz matrix into a left singular vector matrix, a singular value matrix, and a right singular vector matrix; Construct a state matrix based on the left singular vector matrix and the singular value matrix; The state matrix is ​​decomposed into eigenvalues ​​to obtain an eigenvector matrix and an eigenvalue matrix; wherein the diagonal elements of the eigenvalue matrix are the eigenvalues ​​of the state matrix. Calculate the damping ratio corresponding to each of the aforementioned feature values, and when the damping ratio is negative, identify the mode corresponding to the feature value as the target mode of the target overhead conductor galloping.

6. The method according to claim 5, characterized in that, Constructing a state matrix based on the left singular vector matrix and the singular value matrix includes: Extracting the first n rows of the left singular vector matrix yields the first block matrix. ; and, extract the first n rows of the singular value matrix to obtain the second block matrix; where n is the number of states in the state vector; Multiply the square roots of the first block matrix and the second block matrix to obtain the observability matrix; The state matrix is ​​obtained by multiplying the pseudo-inverse of the observability matrix with the third block matrix; wherein the third block matrix is ​​obtained by removing 2N from the observability matrix, and N is the number of target measurement points on the target overhead conductor.

7. The method according to claim 5, characterized in that, The method further includes: Construct the true mode matrix; wherein the true mode matrix is ​​the product of the output matrix and the eigenvector matrix; Multiply the pseudo-inverse of the real mode matrix with the position vector to obtain the modal coordinates; Based on the modal coordinates, determine the complex modal coordinates of each mode; Based on the complex modal coordinates, the kinetic energy density and potential energy density of the target mode are calculated, and the sum of the kinetic energy density and the potential energy density is integrated over the length of the target overhead conductor to obtain the target energy corresponding to the target mode.

8. The method according to claim 7, characterized in that, The method further includes: Based on the relationship between mass data, stiffness data, and generalized damping, the generalized damping is determined; and based on the generalized damping and the current damping, the equivalent contribution damping of the target mode is determined. The equivalent contribution damping is added to the current damping so that the damping ratio of the target mode is greater than or equal to 0.

9. A device for identifying the galloping modes of transmission lines based on magnetic field induction, characterized in that, The transmission line galloping mode identification device based on magnetic field induction includes: The data acquisition module is used to acquire the initial magnetic induction intensity of the magnetic dipole in each direction in the spatial coordinate system when the target overhead conductor is swaying; wherein, the initial magnetic induction intensity is measured by two magnetic induction sensors directly above the target overhead conductor; The motion inversion module is used to invert the conductor motion using a nonlinear optimization method based on the magnetic induction intensity in each direction; wherein, the conductor motion includes the vertical displacement and torsional angle of the target overhead conductor; The galloping mode recognition module is used to identify the target mode of the galloping of the target overhead conductor based on the vertical displacement and the torsion angle using the random subspace method.

10. An electronic device, characterized in that, The method includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for identifying transmission line galloping modes based on magnetic field induction as described in any one of claims 1 to 8.

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