A method and system for adaptive control of aerial cable tension

Abstract

The application discloses a kind of ground wire pay-off tension self-adaptive control method and system, specifically related to automatic control technical field, for solving the problem that the control is inaccurate due to the inability to distinguish and peel off multi-span coupled vibration interference in existing tension control method;First, identify high-frequency fluctuation components by monitoring tension signal frequency band energy distribution, and then analyze the state space trajectory similarity and causal network topology of multi-point vibration signals, accurately determine whether the vibration is local or global coupling;Subsequently, the dominant vibration frequency and phase difference are extracted, and the vibration interference component is separated from the original tension signal through the adaptive filter of phase compensation accordingly;Finally, subtract the interference component from the original tension to obtain the true tension value, which is used for closed-loop regulation of tension equipment output;Accurate identification and dynamic compensation of vibration interference are realized, and the control accuracy and system stability of pay-off tension are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, and more specifically, to an adaptive control method and system for conductor and ground wire tension. Background Technology

[0002] In the construction of high-voltage transmission lines, the tension control of conductors and ground wires is a core aspect of ensuring construction quality and safety. Existing technologies typically use tension sensors installed at tensioning equipment to form a closed-loop control system, aiming to stabilize the tension of the conductors and ground wires at a set value. This control strategy based on single-point tension feedback plays a fundamental role in maintaining the static sag of the conductors and ground wires. The entire laying system involves traction equipment, tensioning equipment, and long-distance conductors and ground wires connected by multiple laying pulleys.

[0003] However, in actual field laying environments, conductors and ground wires are prone to vibration due to factors such as wind loads. This vibration can be transmitted and coupled between adjacent spans. The dynamic load formed by the coupled vibration of multiple spans will act on the tension sensing device in the tension control system, causing the detection signal to be mixed with non-real tension fluctuation components. Existing control methods are unable to distinguish and remove this vibration interference, causing the control system to adjust based on the distorted tension signal. This not only fails to effectively suppress real tension fluctuations but may also cause continuous oscillation of the system, ultimately affecting the accurate control and safety of conductor and ground wire sag during the laying process. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an adaptive control method and system for conductor and ground wire tension to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An adaptive control method for conductor and ground wire tension includes:

[0007] S1. Acquire the raw tension signal output by the tension sensor and the vibration signal output by the vibration sensor set at different positions on the conductor.

[0008] S2. Monitor the energy distribution of the original tension signal within the preset frequency band. When the energy appears simultaneously at multiple adjacent frequency points and continues to increase, it is determined that there is a high-frequency fluctuation component caused by multi-slot coupled vibration.

[0009] S3. When it is determined that there are high-frequency fluctuation components, the similarity of the state space trajectories reconstructed from vibration signals at different locations and the network topology constructed by the causal flow between vibration signals are analyzed to determine whether the high-frequency fluctuation components originate from local vibration or global coupled vibration.

[0010] S4, extracting dominant vibration frequency and phase difference information corresponding to the high-frequency fluctuation component from the vibration signal based on the determination result that the high-frequency fluctuation component is caused by local vibration or global coupling vibration;

[0011] S5, separating the vibration interference component from the original tension signal through an adaptive filter according to the dominant vibration frequency and the phase difference information;

[0012] S6, subtracting the vibration interference component from the original tension signal to obtain a compensated real tension value, and adjusting the output of the tension device according to the real tension value.

[0013] Further, the original tension signal output by the tension sensor and the vibration signal output by the vibration sensor arranged at different positions on the ground wire are obtained, comprising:

[0014] determining a plurality of equally spaced measuring point positions on the ground wire;

[0015] installing a vibration sensor at each measuring point position;

[0016] synchronously collecting the original tension signal output by the tension sensor and the vibration signal output by each vibration sensor at a preset sampling frequency;

[0017] normalizing the collected vibration signal output by each vibration sensor to obtain a vibration signal with consistent amplitude range.

[0018] Further, the energy distribution of the original tension signal in the preset frequency band is monitored, and when the energy simultaneously appears on a plurality of adjacent frequency points and continuously increases, it is determined that there is a high-frequency fluctuation component caused by multi-span coupling vibration, comprising:

[0019] determining the monitoring frequency band based on the span and material properties of the ground wire;

[0020] performing windowed Fourier transform on the original tension signal to obtain a time-frequency spectrum;

[0021] identify all frequency points with energy exceeding the background noise threshold in the monitoring frequency band as candidate frequency points;

[0022] If there are at least three adjacent candidate frequency points and their energy maintains a growing trend in a continuous time window, it is determined that there is a high-frequency fluctuation component caused by multi-span coupling vibration.

[0023] Further, when it is determined that there is a high-frequency fluctuation component, the similarity of the state space trajectory reconstructed from the vibration signals from different positions is analyzed, and the network topology structure constructed from the causal flow between the vibration signals is analyzed, to determine whether the high-frequency fluctuation component is caused by local vibration or global coupling vibration, comprising:

[0024] The normalized vibration signals are reconstructed in phase space respectively to obtain state space trajectories corresponding to positions of each measuring point;

[0025] The dynamic time warping distance between each pair of state space trajectories is calculated, and the average dynamic time warping distance is obtained;

[0026] The causal flow strength between different vibration signals is calculated based on the transfer entropy algorithm, and a causal flow matrix is constructed;

[0027] A directed network graph is generated according to the causal flow matrix, and the standard deviation of the node degree distribution of each node in the directed network graph is calculated;

[0028] If the average dynamic time warping distance is less than the similarity threshold and the standard deviation of the node degree distribution is lower than the network heterogeneity threshold, it is determined that the high-frequency fluctuation component is derived from global coupled vibration; otherwise, it is determined that the high-frequency fluctuation component is derived from local vibration.

[0029] Further, the causal flow strength between different vibration signals is calculated based on the transfer entropy algorithm, and a causal flow matrix is constructed, including: for each pair of normalized vibration signals, the transfer entropy value from the historical sequence of one vibration signal to the future sequence of another vibration signal is calculated, and the transfer entropy value is taken as the causal flow strength between the two vibration signals; all pairs of vibration signals are traversed, and the causal flow strength of each pair is filled into the corresponding position of the matrix to construct the causal flow matrix.

[0030] Further, a directed network graph is generated according to the causal flow matrix, and the standard deviation of the node degree distribution of each node in the directed network graph is calculated, including: the causal flow matrix is taken as a directed weighted adjacency matrix, wherein each measuring point position corresponds to a node, and a directed network graph is constructed; the node degree of each node in the directed network graph is calculated; based on the node degrees of all nodes, the standard deviation of the node degree distribution is calculated.

[0031] Further, based on the determination result that the high-frequency fluctuation component is derived from local vibration or global coupled vibration, dominant vibration frequency and phase difference information corresponding to the high-frequency fluctuation component are extracted from the vibration signal, including:

[0032] When the determination result is global coupled vibration, the normalized vibration signals are subjected to frequency spectrum analysis, and at least two frequencies with the maximum energy are extracted as the dominant vibration frequency;

[0033] When the determination result is local vibration, the vibration signal output by the vibration sensor closest to the vibration source is subjected to frequency spectrum analysis, and the frequency with the maximum energy is extracted as the dominant vibration frequency;

[0034] Taking the position of the tension sensor as the reference point, the phase difference between the vibration signal output by each vibration sensor and the original tension signal at the dominant vibration frequency is calculated as the phase difference information.

[0035] Further, according to the dominant vibration frequency and the phase difference information, a vibration interference component is separated from the original tension signal through an adaptive filter, including:

[0036] generating a reference signal containing sine wave and cosine wave components based on the dominant vibration frequency;

[0037] phase compensating the reference signal according to the phase difference information;

[0038] taking the phase-compensated reference signal as the input of the adaptive filter and taking the original tension signal as the expected signal;

[0039] adjusting the adaptive filter coefficients through a least mean square algorithm so that the adaptive filter output approximates the vibration interference component;

[0040] taking the output of the adaptive filter as the vibration interference component separated from the original tension signal.

[0041] Further, subtracting the vibration interference component from the original tension signal to obtain a compensated real tension value, and adjusting the output of the tension device according to the real tension value, including:

[0042] subtracting the original tension signal from the vibration interference component to obtain the compensated real tension value;

[0043] calculating a tension error between the real tension value and a preset tension setting value;

[0044] generating a tension device control signal based on the tension error using a proportional-integral-derivative control algorithm;

[0045] outputting the tension device control signal to the tension device to adjust the pay-off tension.

[0046] In another aspect, the present application provides a ground wire pay-off tension adaptive control system, including:

[0047] a signal acquisition module for acquiring an original tension signal output by a tension sensor and vibration signals output by vibration sensors arranged at different positions on the ground wire; wherein the signal acquisition module outputs the vibration signals acquired by it to a vibration determination module and an information extraction module respectively;

[0048] a high-frequency identification module for monitoring the energy distribution of the original tension signal in a preset frequency band, and determining that there is a high-frequency fluctuation component caused by multi-span coupled vibration when the energy simultaneously appears on multiple adjacent frequency points and continuously increases;

[0049] The vibration determination module is used for determining whether the high-frequency fluctuation component is derived from local vibration or global coupling vibration by analyzing the similarity of state space trajectories reconstructed from vibration signals from different positions and the network topology constructed by the causal flow among the vibration signals.

[0050] The information extraction module is used for extracting dominant vibration frequency and phase difference information corresponding to the high-frequency fluctuation component from the vibration signals based on the determination result of whether the high-frequency fluctuation component is derived from local vibration or global coupling vibration.

[0051] The component separation module is used for separating the vibration interference component from the original tension signal through an adaptive filter according to the dominant vibration frequency and phase difference information.

[0052] The tension output module is used for obtaining a compensated real tension value by subtracting the vibration interference component from the original tension signal, and adjusting the output of the tension device according to the real tension value.

[0053] Compared with the prior art, the present application has the following beneficial effects:

[0054] 1. By analyzing the space-time characteristics and causal correlation of multi-point vibration signals, the vibration interference source and its characteristics mixed in the tension signal can be accurately identified, not only the two different disturbance modes of local vibration and global coupling vibration are effectively distinguished, but also the accurate dominant vibration frequency and phase difference information are extracted, which provides a key basis for subsequent interference separation. This interference analysis strategy based on vibration source characteristic identification significantly improves the perception accuracy of the real tension state in a complex vibration environment.

[0055] 2. The adaptive filtering technology with phase compensation can dynamically separate the interference component synchronized with the vibration source from the original tension signal. This process realizes real-time online compensation of the tension measurement value, so that the control system finally depends on the "real tension" which can better reflect the actual force state of the ground wire, overcoming the signal distortion problem caused by vibration interference in the original control system, thereby greatly improving the accuracy of tension control and the stability of the system, and ensuring the accurate control of the ground wire sag during the pay-off process. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The flowchart of the ground wire pay-off tension adaptive control method of the present application;

[0057] Figure 2 The structural schematic diagram of the ground wire pay-off tension adaptive control system of the present application. DETAILED DESCRIPTION

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1: Figure 1 This invention provides an adaptive control method for conductor and ground wire tension, comprising:

[0060] S1. Acquire the raw tension signal output by the tension sensor and the vibration signal output by the vibration sensor set at different positions on the conductor.

[0061] S2. Monitor the energy distribution of the original tension signal within the preset frequency band. When the energy appears simultaneously at multiple adjacent frequency points and continues to increase, it is determined that there is a high-frequency fluctuation component caused by multi-slot coupled vibration.

[0062] S3. When it is determined that there are high-frequency fluctuation components, the similarity of the state space trajectories reconstructed from vibration signals at different locations and the network topology constructed by the causal flow between vibration signals are analyzed to determine whether the high-frequency fluctuation components originate from local vibration or global coupled vibration.

[0063] S4. Based on the determination result that the high-frequency wave component originates from local vibration or global coupled vibration, extract the dominant vibration frequency and phase difference information corresponding to the high-frequency wave component from the vibration signal.

[0064] S5. Based on the dominant vibration frequency and phase difference information, the vibration interference component is separated from the original tension signal using an adaptive filter;

[0065] S6. Subtract the vibration interference component from the original tension signal to obtain the compensated true tension value, and adjust the output of the tension device according to the true tension value.

[0066] To achieve precise adaptive control of the conductor tension, high-quality and consistent raw sensor data is first required. The specific implementation method of step S1 is described in detail below.

[0067] Determining the positions of multiple equidistantly distributed measuring points on the ground wire is the basis of data acquisition. The process of determining the positions of measuring points needs to consider the length of the entire section of the ground wire and the spatial resolution requirement of vibration monitoring. Specifically, the total length L of the section of the ground wire can be obtained according to the actual distance between the traction equipment and the tension equipment at the construction site. The determination of the number N of measuring points needs to balance the economy and the technical necessity, and N should generally be no less than 3, for example, N can be 4, 5 or 6, to ensure that the spatial distribution characteristics of the vibration along the ground wire can be effectively captured. The interval distance D between each measuring point can be calculated by the formula D = L / (N-1). The first measuring point is set at the closest point of the ground wire to the tension equipment, the last measuring point is set at the closest point of the ground wire to the traction equipment, and the remaining N-2 measuring points are equidistantly arranged between the two points according to the calculated interval distance D. For example, when L is 500 meters and N is set to 4, then D is equal to 500 / (4-1)≈166.7 meters, and the four measuring points are located at a distance of about 0 meters, 166.7 meters, 333.3 meters and 500 meters from the tension equipment respectively. This equidistant distribution mode aims to provide a uniform spatial sampling basis for subsequent analysis of the propagation of the vibration wave.

[0068] After completing the planning of the positions of the measuring points, a vibration sensor needs to be installed at each predetermined measuring point. The vibration sensor should be a high-sensitivity inertial measurement unit or an accelerometer suitable for field environment and capable of effectively detecting the transverse or longitudinal vibration of the ground wire. During installation, it is necessary to ensure that the vibration sensor housing is reliably fixed to the surface of the ground wire to avoid the introduction of measurement noise caused by relative sliding, for example, a special clamp can be used to tightly clamp the sensor on the ground wire. The model and performance parameters of all vibration sensors should be consistent to ensure the consistency of the collected data. The tension sensor is usually integrated in the tension equipment for detecting the real-time tension of the ground wire, and the output signal of the tension sensor is the original tension signal.

[0069] During the data acquisition stage, the original tension signal output by the tension sensor and the vibration signals output by each vibration sensor are synchronously acquired at a preset sampling frequency. The preset sampling frequency fs must comply with the Nyquist sampling theorem, that is, fs must be at least twice the highest vibration frequency component expected to be analyzed. Considering that the vibration frequency of the ground wire can be as high as tens of hertz, for example, if the highest analysis frequency is set to 50 hertz, then fs needs to be at least 100 hertz, and in order to reserve sufficient margin, fs can usually be set to 1000 hertz to ensure that high-frequency fluctuation components can be captured without distortion. Synchronous acquisition means that the starting time and sampling time interval of data acquisition of all sensor signals are completely consistent, which can be achieved by triggering multiple synchronous acquisition channels with a unified sampling clock signal, thereby ensuring that all signals have a strict time correspondence, laying a foundation for subsequent analysis of the phase difference and causal relationship between signals. The acquisition process continues and stores the data points of each signal in time sequence.

[0070] Due to the slight differences in installation position and response characteristics of each vibration sensor, the amplitude range of the original vibration signals output by each vibration sensor may not be the same. In order to eliminate the influence of amplitude differences on subsequent trajectory similarity comparison and other analyses, it is necessary to normalize the vibration signals output by each vibration sensor collected to obtain vibration signals with consistent amplitude ranges. Linear scaling method is used for normalization. Specifically, for a group of discrete time series signal data output by any vibration sensor, first calculate the absolute maximum value Vmax of the sequence signal. Then, divide each data point in the sequence by the calculated absolute maximum value Vmax. After this processing, the amplitude range of the data sequence of each vibration sensor output signal is scaled to the interval [-1, 1].

[0071] This step ensures that all vibration signals are in the same amplitude order of magnitude, and subsequent analysis will focus on the shape, frequency and phase characteristics of the signals, rather than the absolute amplitude size. After processing, the vibration signals with consistent amplitude ranges obtained will be used as input data for subsequent step analysis together with the original tension signals collected synchronously.

[0072] After successfully obtaining the original tension signals collected synchronously and the vibration signals with consistent amplitude ranges obtained after normalization processing, the next step is to monitor whether there are high-frequency fluctuation components caused by multi-span coupling vibration in the original tension signals. The specific implementation of step S2 is described in detail as follows.

[0073] Determining the monitoring frequency band based on the span and material properties of the ground wire is the premise of targeted frequency domain analysis. The basis for determining the monitoring frequency band is the basic vibration characteristics of the ground wire. The first-order vertical vibration frequency of a single-span ground wire, i.e. the base frequency fbase, can be estimated by the following formula: where Lspan represents the typical span length in meters, T represents the average operating tension of the ground wire in Newton, and μ represents the mass per unit length of the ground wire in kilograms per meter. Multi-span coupling vibration will excite high-order modes, and the frequency components will be distributed around integer multiples of the fundamental frequency. Therefore, the lower limit frequency flow of the monitoring frequency band can be set slightly higher than the fundamental frequency fbase, for example, flow = 1.2 x fbase, to avoid including too many low-frequency tension fluctuations. The upper limit frequency fhigh of the monitoring frequency band needs to cover enough high-order modes, for example, fhigh = 10 x fbase. For example, if the typical span length Lspan of an engineering project is 300 meters, the tension T is 20000 Newton, and the linear density μ is 1.5 kilograms per meter, and the fundamental frequency fbase is calculated to be about 0.2 Hz according to the calculation formula of the fundamental frequency, the monitoring frequency band can be set to 0.24 Hz to 2.0 Hz; wherein 0.24 Hz represents the lower limit frequency flow, and 2.0 Hz represents the upper limit frequency fhigh. It should be noted that this frequency band is an example, and the actual application needs to be calculated according to the specific line parameters. This monitoring frequency band will be used as the frequency range for subsequent energy analysis.

[0074] The original tension signal is subjected to windowed Fourier transform to obtain a time-frequency spectrum to observe the change of the frequency components of the signal over time. The windowed Fourier transform is performed in a sliding time window manner. First, a window function is selected, for example, a Hanning window, with a length of Nwindow sampling points, and the corresponding time length Twindow = Nwindow / fs, where fs is the preset sampling frequency set in step S1. The selection of the window length Twindow should take into account the frequency resolution and the time resolution, and it is usually required to be much greater than the period of the lowest frequency component of interest, for example, Twindow can be taken as 10 seconds. An overlapping part is provided between adjacent windows, for example, with an overlap ratio of 50%, to smooth the time-varying analysis results. For each time window, the window function is applied to the original tension signal data points in that time period, and then the windowed signal segment is subjected to fast Fourier transform to obtain the complex spectrum of the signal in that time window. The square of the amplitude of the spectrum is the energy spectrum density estimate of the signal in that time window. Moving the window in time sequence and repeating the above process can obtain the time-frequency spectrum of the original tension signal over time, which is a two-dimensional array with dimensions of frequency point number multiplied by time window sequence number.

[0075] After obtaining the time-frequency spectrum, all frequency points with energy exceeding the background noise threshold in the determined monitoring frequency band from the lower limit frequency flow to the upper limit frequency fhigh are identified as candidate frequency points. The background noise threshold Enoisethreshold is determined as follows: during a period of stable system operation without obvious external excitation, such as a windless period at night, a raw tension signal is recorded, the average energy spectrum density in the entire monitoring frequency band is calculated, and K times of this average value is set as the background noise threshold Enoisethreshold, K is a safety factor, for example, 3 to 5, which is determined according to the common statistical significance level. For each time window in the time-frequency spectrum, all discrete frequency points in the monitoring frequency band are scanned, and those frequency points with energy spectrum density values continuously exceeding Enoisethreshold are marked. These marked frequency points constitute the candidate frequency point set in the time window. The candidate frequency points represent potential vibration frequency components with energy significantly higher than the background noise at that moment.

[0076] Finally, based on the distribution and energy change trend of the candidate frequency points, a judgment is made. If in the continuous preset number of time windows, for example, 3 time windows, representing a continuous observation period, there are at least three adjacent candidate frequency points in the monitoring frequency band, that is, they are continuous or very close in the frequency axis, and the energy spectrum density values of these candidate frequency points increase in each subsequent time window compared to the previous time window, that is, they maintain a growth trend, it is determined that there is a high-frequency fluctuation component caused by multi-span coupled vibration. This pattern of coordinated energy growth of multiple adjacent frequency points is a typical feature of multi-span system modal coupling excitation, which is different from single frequency component fluctuation. Once this judgment condition is met, further analysis in step S3 is triggered; otherwise, step S2 of monitoring is continued.

[0077] When step S2 determines that there is a high-frequency fluctuation component caused by multi-span coupled vibration, further analysis of the vibration source characteristics of the component is required. The specific implementation of step S3 is to analyze the similarity of the state space trajectories reconstructed from vibration signals at different positions, and the network topology constructed from the causal flow between vibration signals, so as to determine whether the high-frequency fluctuation component is caused by local vibration or global coupled vibration.

[0078] The vibration signals with consistent amplitude range obtained in step S1 are reconstructed in phase space respectively to obtain state space trajectories corresponding to each measurement point position. The time delay method is used for phase space reconstruction. For the time series X of the vibration signal of each measurement point position, the series contains M data points and is expressed as X = {X1, X2,..., XM}, two key parameters need to be determined: embedding dimension m and time delay τ. The embedding dimension m can be determined by the false nearest neighbor algorithm, which determines the minimum sufficient embedding dimension by gradually increasing the dimension and observing that the proportion of false nearest neighbors is reduced to a sufficiently low value (e.g. less than 5%). The time delay τ can be determined by the autocorrelation function method, that is, the delay time of the first zero crossing point of the autocorrelation function of the time series is selected. For a vibration signal with a length of M, the reconstructed state space trajectory is composed of a series of m-dimensional vectors Yu, where Yu = [Xu, Xu+τ, Xu+2τ,..., Xu+(m-1)τ], and the value range of u is from 1 to M-(m-1)τ. Each measurement point position will obtain a state space trajectory representing the dynamic state evolution thereof.

[0079] The dynamic time warping distance between all pairs of state space trajectories is calculated, and the average dynamic time warping distance is calculated. The dynamic time warping algorithm is used to measure the similarity between two time series of different lengths or with nonlinear deformation. Specifically, for the state space trajectories of any two measurement points A and B, the Euclidean distance matrix between their corresponding m-dimensional vectors is first calculated. Then, the dynamic programming method is used to find the optimal path from the top left corner to the bottom right corner of the Euclidean distance matrix, which needs to satisfy the boundary conditions, monotonicity and continuity constraints. The minimum cumulative distance on the optimal path is the dynamic time warping distance DTW between the two state space trajectories. The dynamic time warping distance between each pair of trajectories is calculated by traversing all pairs of measurement points. The total number of pairs is calculated by the combination formula, and if the number of measurement points is 10, the total number of pairs is 10(10-1) / 2. Finally, the sum of all pairwise distances is divided by the total number of pairs to obtain the average dynamic time warping distance DTWmean, which reflects the overall similarity of the vibration patterns of all measurement points.

[0080] The strength of the causal flow direction between different vibration signals is calculated based on the transfer entropy algorithm, and a causal flow direction matrix is constructed. Transfer entropy is an information theory measure used to measure the flow of information from one time series to another. For each pair of vibration signals with consistent amplitude range, denoted as signal X and signal Y, the transfer entropy from X to Y is calculated. The transfer entropy is defined as the amount of uncertainty reduction in predicting the future of Y based on the history of X, given the history of Y itself. The calculation requires estimating the joint probability distribution, and when using the histogram method, the data needs to be discretized into B intervals, and B is selected based on the sample size, for example, according to the square root rule, B is taken as .

[0081] The calculation formula of transfer entropy is: ; wherein, represents the transfer entropy value from signal X to signal Y, representing the information flow intensity; represents the joint probability of , and ; represents the value of signal Y at future time t+1; represents the historical embedding vector of signal Y, containing the historical values backtracking k time steps from time t; represents the historical embedding vector of signal X, containing the historical values backtracking l time steps from time t; represents the conditional probability that and occur under the condition that ; represents the conditional probability that occurs under the condition that only ; k represents the dimension (time window length) of the historical embedding vector of signal Y; l represents the dimension (time window length) of the historical embedding vector of signal X.

[0082] A behavior vibration sensor quantity matrix is constructed, and the element in the ith row and jth column of the matrix is the causal flow intensity from the ith vibration signal to the jth vibration signal. The matrix is a causal flow matrix, which quantitatively describes the direction and intensity of vibration energy transmission between all measuring points.

[0083] A directed network graph is generated according to the causal flow matrix, and the standard deviation of the node degree distribution of each node in the directed network graph is calculated. The causal flow matrix is regarded as a directed weighted adjacency matrix, wherein each measuring point position corresponds to a node in the network graph, and the causal flow intensity in the matrix represents the weight of the directed edge from the source node to the target node. In order to highlight the significant causal relationship, a threshold needs to be set to filter weak connections. The threshold can be set as the median or average of all non-zero causal flow intensity values. The edges with weights greater than the threshold are retained, thereby generating a simplified directed network graph. In the directed network graph, the node degree of each node is calculated. The node degree is divided into out-degree and in-degree, and here the total node degree, i.e., the sum of the out-degree and in-degree of the node, is calculated. The out-degree refers to the number of edges (based on the thresholded connection) pointing from the node to other nodes, and the in-degree refers to the number of edges pointing from other nodes to the node. After obtaining the node degree values of all nodes, the standard deviation of the node degree values is calculated, denoted as σdegree. The standard deviation quantifies the dispersion degree of the node degree in the network, i.e., the heterogeneity of the network structure.

[0084] If the average dynamic time warping distance DTWmean is less than a similarity threshold and the standard deviation of the node degree distribution σdegree is lower than a network heterogeneity threshold, it is determined that the high-frequency fluctuation component is derived from global coupled vibration; otherwise, it is determined that the high-frequency fluctuation component is derived from local vibration. The similarity threshold θsimilarity is set according to the following basis: on the training data or simulation data known as global coupled vibration, the average dynamic time warping distance under multiple observations is calculated, and the statistical upper limit (for example, the mean plus one standard deviation) is taken as θsimilarity. The network heterogeneity threshold θheterogeneity is set similarly, based on the statistical upper limit of the typical value of the standard deviation of the node degree distribution under the global coupled vibration scenario. If both conditions are met, that is, DTWmean < θsimilarity and σdegree < θheterogeneity, it indicates that the vibration modes of all measurement points are highly similar and the causal network structure is uniform, which is a characteristic of global coupled vibration, so it is determined that the high-frequency fluctuation component is derived from global coupled vibration. If either condition is not met, for example, the trajectory similarity is low or there is a significant central node in the network, that is, a local vibration source, it is determined that the high-frequency fluctuation component is derived from local vibration. This determination result will directly guide the extraction strategy of the dominant vibration frequency and phase difference information in step S4.

[0085] After the determination of the source of the high-frequency fluctuation component (global coupled vibration or local vibration) is completed in step S3, the goal of step S4 is to accurately extract the dominant vibration frequency and phase difference information corresponding to the high-frequency fluctuation component from the vibration signal based on the determination result, providing key parameters for subsequent vibration interference separation.

[0086] Based on the determination result being global coupled vibration, the normalized vibration signal is subjected to frequency spectrum analysis, and at least two frequencies with the maximum energy are extracted as the dominant vibration frequency. Since global coupled vibration involves coordinated vibration of multiple spans, its energy is usually distributed on multiple modal frequencies. The specific operation is: select the same time period data in step S2 that is determined to have a high-frequency fluctuation component, and perform frequency spectrum analysis on the vibration signal output by each vibration sensor (corresponding to each measurement point position determined in step S1) with consistent amplitude range after normalization processing. Fast Fourier transform is used for frequency spectrum analysis.

[0087] For each vibration signal, its power spectral density is calculated. Then, the power spectral densities of all vibration sensors are averaged to obtain an average power spectral density curve. On this average power spectral density curve, all significant peaks within the monitoring frequency band determined in step S2 are identified. The criterion for a significant peak can be that the height of the peak exceeds the average height of the valley bottoms on both sides of the peak by a certain multiple, for example, 3 times. Then, from these significant peaks, the frequencies corresponding to the first P peaks with the largest power spectral density values are selected, P being an integer greater than or equal to 2, for example, P = 2 or P = 3. These selected frequencies are the dominant vibration frequencies under global coupled vibration, which represent the most important several vibration modes in the entire ground wire system.

[0088] Based on the determination result being local vibration, the vibration signal output by the vibration sensor closest to the vibration source is subjected to frequency spectrum analysis, and the frequency with the largest energy is extracted as the dominant vibration frequency. When the determination is local vibration, the vibration source is usually located at a certain specific position, and the signal received by the vibration sensor closest to the vibration source is the strongest and is least disturbed by vibrations from other parts. Determining the vibration sensor closest to the vibration source needs to combine the analysis result of step S3.

[0089] In step S3, if the determination is local vibration, there is usually a central node in the causal flow network topology whose node degree is significantly higher than those of other nodes, and the position of the node corresponding to the central node can be identified as the potential local vibration source position, and the vibration sensor installed at the position is the vibration sensor closest to the vibration source. The vibration signal output by the vibration sensor, which has been subjected to normalization processing to obtain a consistent amplitude range, is subjected to frequency spectrum analysis (also using fast Fourier transform to calculate the power spectral density). Within the monitoring frequency band determined in step S2, the frequency corresponding to the peak with the largest power spectral density is found. This single frequency is the dominant vibration frequency under local vibration.

[0090] The phase difference information is calculated as the phase difference between the vibration signal output by each vibration sensor and the original tension signal at the dominant vibration frequency, with the position of the tension sensor as the reference point. The phase difference information is needed to characterize the relative delay of the vibration wave propagation to the position of the tension sensor, regardless of whether the vibration source is global or local. The position of the tension sensor is fixed and usually located at the tension device. For each vibration sensor (i.e. each measurement point position), the following operations are performed: first, extract the data of the vibration sensor signal and the original tension signal obtained in step S1 in the same time period. Then, the vibration signal and the original tension signal are respectively band-pass filtered, and the center frequency of the filter is set to each dominant vibration frequency extracted in the previous step in turn, and the bandwidth is set to be narrow enough to highlight the frequency component, for example, 5% of the center frequency. For each dominant vibration frequency fdom, the phase difference ΔΦ between the filtered vibration signal and the filtered original tension signal at the frequency is calculated.

[0091] The calculation of the phase difference can use the Hilbert transform method: the filtered vibration signal and the filtered original tension signal are respectively subjected to Hilbert transform to obtain the respective analytic signals; the phase angle of the analytic signal is the instantaneous phase; the difference between the two instantaneous phases at the same time is calculated, and the average (or the mode of the main distribution area) of the difference value of this time sequence is taken to obtain the stable phase difference estimate ΔΦ. This phase difference ΔΦ reflects the phase shift corresponding to the time delay experienced by the vibration from the measurement point position to the position of the tension sensor. This phase difference is calculated for each vibration sensor and each dominant vibration frequency, thereby obtaining a complete set of phase difference information. This phase difference information will be used for phase compensation of the reference signal of the adaptive filter in step S5, to ensure that the interference component synchronized with the vibration source can be accurately separated.

[0092] After successfully extracting the dominant vibration frequency and the phase difference information in step S4, the core task of step S5 is to use these information to accurately separate the vibration interference component from the original tension signal through adaptive filtering technology.

[0093] The reference signal is generated based on the dominant vibration frequencies and contains sinusoidal and cosine components. For each dominant vibration frequency extracted in step S4, a pair of orthogonal sinusoidal and cosine components are generated respectively. The sinusoidal component is denoted as sin(2π fdom t) and the cosine component is denoted as cos(2π fdom t), where t is the continuous time variable, which is discretized as the sample point index multiplied by Ts in actual digital signal processing, Ts is the inverse of the preset sampling frequency fs set in step S1, i.e. the sampling period. The sinusoidal and cosine components corresponding to all dominant vibration frequencies are combined in order to form the reference signal vector X(n). For example, if two dominant vibration frequencies fdom1 and fdom2 are extracted, the reference signal vector at sampling time n is X(n) = [sin(2π fdom1 n Ts), cos(2π fdom1 n Ts), sin(2π fdom2 n Ts), cos(2π fdom2 n Ts)]. The reference signal vector contains all possible phase information of the vibration components and provides a basis for subsequent filtering.

[0094] The reference signal is phase compensated according to the phase difference information. In step S4, the phase difference ΔΦ between each vibration sensor signal and the original tension signal at each dominant vibration frequency fdom is calculated. The purpose of phase compensation is to adjust the phase of the reference signal to be in phase with the vibration interference component actually propagated to the tension sensor. For each dominant vibration frequency fdom, a representative phase difference ΔΦcomp needs to be selected for compensation. In the case of global coupled vibration, the phase differences of all measurement points at this frequency can be averaged as ΔΦcomp. In the case of local vibration, the phase difference calculated by the vibration sensor closest to the vibration source can be directly used as ΔΦcomp. Phase compensation is achieved by rotating the vector: for the orthogonal pair [sin(2π fdom n Ts), cos(2π fdom n Ts)] in the reference signal vector corresponding to the frequency fdom, it is multiplied by a rotation matrix. The rotation matrix is defined as [[cos(ΔΦcomp), -sin(ΔΦcomp)], [sin(ΔΦcomp), cos(ΔΦcomp)]]. After multiplication, a new orthogonal pair [sin(2π fdom n Ts + ΔΦcomp), cos(2π fdom n Ts + ΔΦcomp)] is obtained after phase compensation. This operation is performed on the components of all dominant vibration frequencies in turn to obtain the final phase-compensated reference signal vector Xcomp(n).

[0095] The phase-compensated reference signal is taken as the input of the adaptive filter, and the original tension signal is taken as the desired signal. The adaptive filter adopts a transversal filter structure, and its tap weight vector is denoted as W(n), which has the same dimension as the phase-compensated reference signal vector Xcomp(n). At each sampling time n, the output y(n) of the adaptive filter is calculated as the inner product of the tap weight vector W(n) and the phase-compensated reference signal vector Xcomp(n), that is, y(n) = W(n)T Xcomp(n). The original tension signal d(n) (i.e., the value of the original tension signal obtained in step S1 at the sampling time n) is set as the desired signal of the adaptive filter. The goal of the adaptive filtering is to adjust the weight vector W(n) so that the filter output y(n) approximates the component in the original tension signal d(n) that is related to the reference signal, i.e., the vibration interference component, as much as possible.

[0096] The adaptive filter coefficients are adjusted by the least mean square algorithm so that the adaptive filter output approximates the vibration interference component. The least mean square algorithm updates the filter weight vector W(n) by iteration to minimize the square of the instantaneous square error e(n), where e(n) = d(n) - y(n). The update formula of the weight vector is W(n+1) = W(n) + 2μ e(n) Xcomp(n), where μ is the step parameter, which is a key parameter for controlling the convergence speed and stability of the algorithm. The selection of the step parameter μ needs to satisfy the stability condition, and generally its value should be smaller than the reciprocal of the maximum eigenvalue of the autocorrelation matrix of the input signal Xcomp(n). In practical applications, a small positive value can be determined by experiments, for example, μ = 0.001, to ensure the smooth convergence of the algorithm. The error signal e(n) = d(n) - y(n) is calculated after each iteration and is used for the next weight update. Through continuous iteration, the weight vector W(n) of the adaptive filter will gradually converge to a set of optimal values, so that the filter output y(n) can best approximate the vibration interference component in the original tension signal.

[0097] The output of the adaptive filter is taken as the vibration interference component separated from the original tension signal. When the adaptive filter reaches a stable convergence state after a sufficient number of iterations, its output y(n) at each sampling time n is considered to be the estimated value of the vibration interference component separated from the original tension signal at the current time. The judgment criterion for the convergence state can be that the change norm of the weight vector W(n) in consecutive iterations is less than a preset minimum threshold. This separated vibration interference component y(n) accurately reflects the dominant vibration frequency component determined in step S4, and its phase is compensated and synchronized with the actual interference acting on the tension sensor. This vibration interference component will be directly input for tension compensation in step S6.

[0098] After successfully separating the vibration interference component from the original tension signal in step S5, the core task of step S6 is to complete the final compensation of tension and realize the closed-loop control of the pay-off tension, so as to suppress the influence of the vibration interference on the tension control accuracy.

[0099] The original tension signal is subtracted from the vibration interference component to obtain the compensated real tension value. This step is a direct data operation. At each sampling time p, the original tension signal obtained from step S1 is denoted as d(p), and the vibration interference component obtained from step S5 is denoted as y(p). The compensated real tension value s(p) is calculated by simple scalar subtraction: s(p) = d(p) - y(p). This operation is continuously performed for each sampling point, so that a sequence of real tension values that have compensated for the identified vibration interference is obtained. This real tension value s(p) is considered to be a more accurate estimate of the static or quasi-static tension actually borne by the ground wire, eliminating the high-frequency fluctuation components caused by multi-span coupling vibration or local vibration.

[0100] The tension error between the real tension value and the preset tension set value is calculated. The preset tension set value Tset is a constant determined in advance according to factors such as the construction specifications of the power transmission line, the type of the ground wire, and the pay-off speed, and its unit is kilonewton. This value is usually input into the control system by the operator before the pay-off operation begins. The tension error et(w) is calculated once in each control period w (the control period can be the same as the sampling period, or an integer multiple of the sampling period). The calculation method is to subtract the filtered real tension value sfiltered(w) (for example, taking a moving average to eliminate residual noise) in the current control period from the preset tension set value Tset. This error value et(w) reflects the size and direction of the deviation between the current actual tension and the desired tension, and is the direct basis for subsequent control decisions.

[0101] The tension device control signal is generated based on the tension error using a proportional-integral-derivative control algorithm. The proportional-integral-derivative control algorithm calculates the machine control quantity by weighted summation of the proportional term, the integral term, and the derivative term of the tension error. The control algorithm is executed once in each control period w.

[0102] The discrete position formula of the proportional-integral-derivative control algorithm is: u(w)=Kp x et(w)+Ki x Tc x∑et(j)+(Kd / Tc) x [et(w)-et(w-1)]; in the formula, u(w) is the control quantity calculated in the wth control period; et(w) is the tension error in the wth control period; et(w-1) is the tension error in the w-1th control period; ∑et(j) represents the accumulated sum of the tension errors from the 0th control period to the wth control period; Kp is a proportional gain coefficient; Ki is an integral gain coefficient; Kd is a differential gain coefficient; and Tc is a control period.

[0103] The proportional gain coefficient Kp is used to proportionally reflect the deviation signal et(w) of the control system, the greater the coefficient, the faster the system response, but too large will lead to system instability. The integral gain coefficient Ki is used to eliminate the steady-state error of the system and improve the error-free degree. The differential gain coefficient Kd reflects the change trend of the deviation signal and has predictability, which can predict the trend of the deviation change, thereby generating an advanced control effect. The three coefficients need to be adjusted according to the dynamic characteristics of the controlled object (tension device and ground wire system), for example, the Ziegler-Nichols tuning method or trial and error method can be used to determine the on-site debugging. The control period Tc needs to be matched with the response speed of the system, and is usually much larger than the sampling period, for example, it can be 100 milliseconds to 500 milliseconds. The calculated control quantity u(w) is subjected to amplitude limiting processing (for example, limited between 0 and 10 volts) and then used as the tension device control signal.

[0104] The tension device control signal is output to the tension device to adjust the pay-off tension. The tension device control signal u(w) is usually an analog voltage or current signal, or a digital communication protocol instruction. The signal is transmitted to the actuator of the tension device, such as a proportional valve of a hydraulic system or a driver of a motor, through the analog output channel of the data acquisition card or a dedicated control bus. The actuator linearly adjusts its output force or torque according to the size of the received control signal. For example, for a hydraulic tensioner, the control signal increases, the proportional valve opening increases, the hydraulic cylinder pressure rises, and the brake clamping force increases, thereby causing the pay-off tension to increase; vice versa. Through this continuous feedback control, the actual tension value s(p) will be automatically adjusted and stabilized near the preset tension set value Tset, thereby realizing high-precision adaptive control of the pay-off tension and effectively overcoming the influence of vibration interference. The entire control process forms a complete closed loop from signal acquisition, processing, compensation to final output.

[0105] Embodiment 2: Figure 2 A structure diagram of a ground wire pay-off tension adaptive control system is given, a ground wire pay-off tension adaptive control system, comprising:

[0106] The signal acquisition module is configured to acquire original tension signals output by the tension sensor and vibration signals output by vibration sensors arranged at different positions of the ground wire; wherein the signal acquisition module outputs the acquired vibration signals to the vibration determination module and the information extraction module respectively.

[0107] The high-frequency identification module is configured to monitor energy distribution of the original tension signals in a preset frequency band, and determine that a high-frequency fluctuation component caused by multi-span coupled vibration exists when the energy simultaneously appears on multiple adjacent frequency points and continuously increases.

[0108] The vibration determination module is configured to, when determining that the high-frequency fluctuation component exists, jointly analyze similarity of state space trajectories reconstructed from vibration signals at different positions and network topological structures constructed from causal flow among the vibration signals, and determine whether the high-frequency fluctuation component is derived from local vibration or global coupled vibration.

[0109] The information extraction module is configured to, based on a determination result that the high-frequency fluctuation component is derived from local vibration or global coupled vibration, extract dominant vibration frequency and phase difference information corresponding to the high-frequency fluctuation component from the vibration signals.

[0110] The component separation module is configured to separate a vibration interference component from the original tension signals through an adaptive filter according to the dominant vibration frequency and the phase difference information.

[0111] The tension output module is configured to subtract the vibration interference component from the original tension signals to obtain a compensated real tension value, and adjust an output of the tension device according to the real tension value.

[0112] In the embodiments, all calculations are de-dimensioned to obtain numerical values, and preset parameters and threshold values in the calculations are set by a person skilled in the art according to actual conditions.

[0113] It should be noted that the application can be deployed in the device itself to realize embedded application, or can be run on a PC terminal or other terminal with a user interface, so as to meet various hardware environments and use requirements.

[0114] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through wireless or wired transmission. The wired transmission includes optical fiber, twisted pair, coaxial cable, etc. The wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0115] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and module can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0116] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0117] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, which can be located in one place or distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0118] In addition, each functional module in the various embodiments of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0119] If the functions are implemented in the form of software function modules 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 the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0120] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0121] Finally: the above is only the preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for adaptive control of conductor tension during ground wire laying, characterized in that, include: S1. Acquire the raw tension signal output by the tension sensor and the vibration signal output by the vibration sensor set at different positions on the conductor. S2. Monitor the energy distribution of the original tension signal within the preset frequency band. When the energy appears simultaneously at multiple adjacent frequency points and continues to increase, it is determined that there is a high-frequency fluctuation component caused by multi-slot coupled vibration. S3. When high-frequency fluctuation components are determined to exist, collaboratively analyze the similarity of the state-space trajectories reconstructed from vibration signals at different locations, and the network topology constructed from the causal flow between vibration signals, to determine whether the high-frequency fluctuation components originate from local vibration or globally coupled vibration, including: The phase space of the normalized vibration signal is reconstructed to obtain the state space trajectory corresponding to each measuring point. Calculate the dynamic time warped distance between each pair of all state space trajectories, and obtain the average dynamic time warped distance; The causal flow direction intensity between different vibration signals is calculated based on the transfer entropy algorithm, and a causal flow direction matrix is ​​constructed. Generate a directed network graph based on the causal flow matrix, and calculate the standard deviation of the node degree distribution of each node in the directed network graph; If the average dynamic time warp distance is less than the similarity threshold and the standard deviation of the node degree distribution is less than the network heterogeneity threshold, then the high-frequency fluctuation component is determined to originate from global coupling vibration; otherwise, the high-frequency fluctuation component is determined to originate from local vibration. S4. Based on the determination result that the high-frequency wave component originates from local vibration or global coupled vibration, extract the dominant vibration frequency and phase difference information corresponding to the high-frequency wave component from the vibration signal. S5. Based on the dominant vibration frequency and phase difference information, the vibration interference component is separated from the original tension signal using an adaptive filter; S6. Subtract the vibration interference component from the original tension signal to obtain the compensated true tension value, and adjust the output of the tension device according to the true tension value.

2. The adaptive control method for conductor tension according to claim 1, characterized in that, Acquire the raw tension signal output by the tension sensor and the vibration signals output by vibration sensors located at different positions on the conductor, including: Determine the locations of multiple equally spaced measuring points on the conductor; Install a vibration sensor at each measuring point; The original tension signal output by the tension sensor and the vibration signal output by each vibration sensor are synchronously acquired at a preset sampling frequency. The vibration signals output from each vibration sensor are normalized to obtain vibration signals with a consistent amplitude range.

3. The adaptive control method for conductor and ground wire tension according to claim 1, characterized in that, Monitor the energy distribution of the original tension signal within a preset frequency band. When the energy appears simultaneously at multiple adjacent frequency points and continues to increase, it is determined that there is a high-frequency fluctuation component caused by multi-span coupled vibration, including: The monitoring frequency band is determined based on the conductor span and material properties; The time spectrum is obtained by performing a windowed Fourier transform on the original tension signal; All frequency points whose energy exceeds the background noise threshold within the monitoring frequency band are identified as candidate frequency points; If there are at least three adjacent candidate frequency points and their energy maintains an increasing trend within a continuous time window, then it is determined that there is a high-frequency fluctuation component caused by multi-slot coupled vibration.

4. The adaptive control method for conductor and ground wire tension according to claim 1, characterized in that, The causal flow intensity between different vibration signals is calculated based on the transfer entropy algorithm, and a causal flow matrix is ​​constructed. This includes: for each pair of normalized vibration signals, calculating the transfer entropy value from the historical sequence of one vibration signal to the future sequence of another vibration signal, and using the transfer entropy value as the causal flow intensity between the two vibration signals; traversing all vibration signal pairs, filling the corresponding positions of the matrix with the causal flow intensity of each pair, and constructing the causal flow matrix.

5. The adaptive control method for conductor and ground wire tension according to claim 1, characterized in that, A directed network graph is generated based on the causal flow matrix. The standard deviation of the node degree distribution of each node in the directed network graph is calculated, including: using the causal flow matrix as a directed weighted adjacency matrix, where each measurement point corresponds to a node, to construct a directed network graph; calculating the node degree of each node in the directed network graph; and calculating the standard deviation of the node degree distribution based on the node degree of all nodes.

6. The adaptive control method for conductor and ground wire tension according to claim 1, characterized in that, Based on the determination that the high-frequency wave component originates from local vibration or globally coupled vibration, the dominant vibration frequency and phase difference information corresponding to the high-frequency wave component are extracted from the vibration signal, including: When the determination result is global coupled vibration, the normalized vibration signal is subjected to spectrum analysis, and at least two frequencies with the highest energy are extracted as the dominant vibration frequencies. When the determination result is local vibration, the vibration signal output by the vibration sensor closest to the vibration source is subjected to spectrum analysis, and the frequency with the highest energy is extracted as the dominant vibration frequency. Using the location of the tension sensor as a reference point, the phase difference between the vibration signal output by each vibration sensor and the original tension signal at the dominant vibration frequency is calculated as the phase difference information.

7. The adaptive control method for conductor tension according to claim 1, characterized in that, Based on the dominant vibration frequency and phase difference information, the vibration interference component is separated from the original tension signal using an adaptive filter, including: A reference signal containing sine and cosine components is generated based on the dominant vibration frequency; Phase compensation is performed on the reference signal based on the phase difference information; The phase-compensated reference signal is used as the input to the adaptive filter, and the original tension signal is used as the desired signal. The coefficients of the adaptive filter are adjusted by the least mean square algorithm so that the output of the adaptive filter approximates the vibration disturbance component. The output of the adaptive filter is used as the vibration disturbance component separated from the original tension signal.

8. The adaptive control method for conductor and ground wire tension according to claim 1, characterized in that, The compensated true tension value is obtained by subtracting the vibration interference component from the original tension signal, and the output of the tension device is adjusted according to the true tension value, including: The original tension signal is subtracted from the vibration disturbance component to obtain the compensated true tension value. Calculate the tension error between the actual tension value and the preset tension setting value; Based on the tension error, a proportional-integral-derivative control algorithm is used to generate control signals for the tension equipment. The tension control signal is output to the tension device to adjust the wire tension.

9. A conductor-to-ground wire tension adaptive control system, used to implement the conductor-to-ground wire tension adaptive control method according to any one of claims 1-8, characterized in that, include: The signal acquisition module is used to acquire the raw tension signal output by the tension sensor and the vibration signal output by the vibration sensor set at different positions on the conductor; the signal acquisition module outputs the acquired vibration signal to the vibration determination module and the information extraction module respectively. The high-frequency identification module is used to monitor the energy distribution of the original tension signal within a preset frequency band. When the energy appears simultaneously at multiple adjacent frequency points and continues to increase, it is determined that there is a high-frequency fluctuation component caused by multi-span coupled vibration. The vibration determination module is used to collaboratively analyze the similarity of the state space trajectories reconstructed from vibration signals at different locations and the network topology constructed from the causal flow between vibration signals when high-frequency fluctuation components are determined to exist, and to determine whether the high-frequency fluctuation components originate from local vibration or global coupled vibration. The information extraction module is used to extract the dominant vibration frequency and phase difference information corresponding to the high-frequency vibration component from the vibration signal based on the determination result that the high-frequency wave component originates from local vibration or global coupled vibration. The component separation module is used to separate the vibration interference component from the original tension signal using an adaptive filter based on the dominant vibration frequency and phase difference information. The tension output module is used to subtract the vibration interference component from the original tension signal to obtain the compensated true tension value, and to adjust the output of the tension device according to the true tension value.

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