A power transmission conductor galloping monitoring method, device, equipment and storage medium

Abstract

The application discloses a power transmission conductor galloping monitoring method, device and equipment and a storage medium, relates to the technical field of power transmission monitoring, and the power transmission conductor galloping monitoring method comprises the following steps: acquiring inertial data and positioning data, and determining characteristic parameters of conductor galloping according to the inertial data; determining a target sampling frequency, target filtering parameters, a target fusion update frequency and filtering noise covariance according to the characteristic parameters; acquiring current inertial data based on the target sampling frequency, performing low-pass filtering on the current inertial data, and obtaining target inertial data; constructing an adaptive combined filtering model containing a kinematic constraint model; performing filtering calculation on the adaptive combined filtering model based on the target fusion update frequency, the target inertial data and the positioning data, and outputting a conductor monitoring node galloping monitoring result. The application can improve the accuracy of conductor galloping monitoring.

Description

Technical Field

[0001] This application relates to the field of power transmission monitoring technology, and in particular to a method, device, equipment, and storage medium for monitoring power transmission conductor galloping. Background Technology

[0002] When monitoring the galloping of power transmission lines, a combination of inertial measurement units (IMUs) and global navigation satellite systems (GNSS) is typically used. However, the use of fixed sampling frequencies and filtering parameters during monitoring leads to low data accuracy when encountering broadband galloping or sudden frequency changes in the conductors. Therefore, improving the accuracy of power transmission line galloping monitoring remains a problem that needs to be solved.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, device, and storage medium for monitoring the galloping of power transmission lines, aiming to solve the technical problem of how to improve the accuracy of monitoring the galloping of power transmission lines.

[0005] To achieve the above objectives, this application proposes a method for monitoring the galloping of transmission lines, the method comprising: The system acquires inertial data collected by an inertial measurement unit and positioning data output by a global navigation satellite system, and determines characteristic parameters of conductor galloping based on the inertial data. The inertial measurement unit is deployed at the monitoring location of the conductor. The target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance are determined based on the aforementioned feature parameters. The current inertial data is obtained based on the target sampling frequency, and the current inertial data is low-pass filtered based on the target filtering parameters to obtain the target inertial data. An adaptive combined filtering model incorporating kinematic constraints is constructed based on the feature parameters and the filter noise covariance. Based on the target fusion update frequency, the target inertial data, and the positioning data, the adaptive combined filtering model is filtered to output the galloping monitoring results of the conductor monitoring nodes.

[0006] In one embodiment, the characteristic parameters include the dominant frequency, vibration order, and amplitude, and the step of determining the characteristic parameters of conductor galloping based on the inertial data includes: Extract the radial acceleration time series of the conductor from the inertial data; Spectral analysis was performed on the acceleration time series to determine the dominant frequency, vibration order, and amplitude of the conductor galloping.

[0007] In one embodiment, the step of determining the target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance based on the feature parameters includes: The main frequency and amplitude are determined based on the aforementioned characteristic parameters; The undetermined sampling frequency is determined based on the main frequency and the first preset multiple, and the larger value between the undetermined sampling frequency and the preset minimum sampling frequency is determined as the target sampling frequency. The cutoff frequency of the low-pass filter is determined based on the main frequency and the second preset multiple, and the cutoff frequency is determined as the target filter parameter. The target fusion update frequency is determined based on the interval of the main frequency and the preset mapping relationship; The system noise covariance matrix and the observation noise covariance matrix are determined based on the amplitude and the positioning accuracy factor output by the global navigation satellite system, and the system noise covariance matrix and the observation noise covariance matrix are determined as the filtered noise covariance.

[0008] In one embodiment, the kinematic constraint model includes a nonlinear damped resonance constraint model, and the step of constructing an adaptive combined filtering model incorporating the kinematic constraint model based on the characteristic parameters and the filter noise covariance includes: The dominant frequency, vibration order, and amplitude are determined based on the aforementioned characteristic parameters. Determine the system noise covariance matrix and the observation noise covariance matrix based on the filtered noise covariance. An extended Kalman filter is used as the basic framework, and a system state vector is defined that includes attitude angle error, velocity error, position error, and zero bias of the inertial measurement unit. The system state equation is constructed based on the system state vector and the system noise covariance matrix. The system observation equation is constructed based on the system state vector and the observation noise covariance matrix. A nonlinear damped resonance constraint model is established based on the dominant frequency, the vibration order, and the amplitude. An adaptive combined filtering model incorporating kinematic constraints is obtained based on the system state equation, the system observation equation, and the nonlinear damped resonance constraint model.

[0009] In one embodiment, after the step of acquiring the inertial data collected by the inertial measurement unit and the positioning data output by the global navigation satellite system, the method further includes: The inertial data is subjected to system error correction to obtain corrected data; Abnormal bad values ​​in the corrected data are removed, and the removed data segments are completed using linear interpolation to obtain the completed data; The completed data is smoothed using a moving average filter to obtain preprocessed inertial data.

[0010] In one embodiment, after the step of outputting the galloping monitoring results of the wire monitoring node, the method further includes: Monitor the signal status of the global navigation satellite system; When the signal status is detected as signal loss, switch to dead reckoning mode, which uses the inertial data for galloping detection.

[0011] In one embodiment, after the step of outputting the galloping monitoring results of the wire monitoring node, the method further includes: Acquire historical main frequency data and determine the frequency mutation threshold based on the historical main frequency data; Continuously monitor the dominant frequency among the aforementioned characteristic parameters; When the detected main frequency mutation exceeds the frequency mutation threshold, the feature parameters are redefined.

[0012] Furthermore, to achieve the above objectives, this application also proposes a transmission line galloping monitoring device, which includes: The acquisition module is used to acquire inertial data collected by the inertial measurement unit and positioning data output by the global navigation satellite system, and to determine the characteristic parameters of the conductor galloping based on the inertial data. The determination module is used to determine the target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance based on the feature parameters. A filtering module is used to acquire current inertial data based on the target sampling frequency, and to perform low-pass filtering on the current inertial data based on the target filtering parameters to obtain target inertial data; A construction module is used to construct an adaptive combined filtering model containing a kinematic constraint model based on the feature parameters and the filter noise covariance; The output module is used to perform filtering calculations on the adaptive combined filtering model based on the target fusion update frequency, the target inertial data, and the positioning data, and output the galloping monitoring results of the conductor monitoring node.

[0013] In addition, to achieve the above objectives, this application also proposes a transmission line galloping monitoring device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the transmission line galloping monitoring method described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the transmission line galloping monitoring method described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of a transmission line galloping monitoring method as described above.

[0016] This application provides a method for monitoring the galloping of power transmission lines. The method acquires inertial data collected by an inertial measurement unit (IMU) and positioning data output by a global navigation satellite system (GNSS). Based on the inertial data, characteristic parameters of the galloping are determined. The IMU is deployed at the monitoring location of the power transmission line. A target sampling frequency, target filtering parameters, a target fusion update frequency, and a filter noise covariance are determined based on the characteristic parameters. Current inertial data is acquired based on the target sampling frequency, and low-pass filtered using the target filtering parameters to obtain target inertial data. An adaptive combined filtering model incorporating kinematic constraints is constructed based on the characteristic parameters and the filter noise covariance. The adaptive combined filtering model is then filtered using the target fusion update frequency, the target inertial data, and the positioning data to output the galloping monitoring results of the power transmission line monitoring node. This application updates the sampling frequency, low-pass filtering parameters, and covariance using the initially acquired galloping features, and then uses these updated parameters to construct the filtering model. This makes the final galloping monitoring results obtained from the filtered model more consistent with the actual situation, improving the accuracy of power transmission line galloping monitoring. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of a method for monitoring the galloping of transmission lines according to this application. Figure 2 This is a flowchart illustrating a second embodiment of a method for monitoring the galloping of transmission lines according to this application. Figure 3 This is a flowchart illustrating a third embodiment of a method for monitoring the galloping of transmission lines according to this application. Figure 4 A simplified flowchart illustrating a method for monitoring the galloping of transmission lines provided in Embodiment 1 of this application; Figure 5 This is a schematic diagram of the module structure of a power transmission line galloping monitoring device according to an embodiment of this application; Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in a power transmission line galloping monitoring method according to an embodiment of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] This application acquires inertial data collected by an inertial measurement unit (IMU) and positioning data output by a global navigation satellite system (GNSS), and determines characteristic parameters of conductor galloping based on the inertial data. The IMU is deployed at the monitoring location of the conductor. Based on the characteristic parameters, it determines the target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance. Based on the target sampling frequency, it acquires current inertial data and performs low-pass filtering on the current inertial data based on the target filtering parameters to obtain target inertial data. Based on the characteristic parameters and the filter noise covariance, it constructs an adaptive combined filtering model containing a kinematic constraint model. Based on the target fusion update frequency, the target inertial data, and the positioning data, it performs filtering calculations on the adaptive combined filtering model and outputs the galloping monitoring results of the conductor monitoring node.

[0024] When monitoring the galloping of power transmission lines, a combination of inertial measurement units (IMUs) and global navigation satellite systems (GNSS) is typically used. However, the use of fixed sampling frequencies and filtering parameters during monitoring leads to low data accuracy when encountering broadband galloping or sudden frequency changes in the conductors. Therefore, improving the accuracy of power transmission line galloping monitoring remains a problem that needs to be solved.

[0025] This application updates the acquisition frequency, low-pass filter parameters, and covariance parameters by using the galloping features acquired initially. Then, it uses these updated parameters to construct a filtering model, making the galloping monitoring results obtained by solving the filtering model more consistent with the actual situation and improving the accuracy of conductor galloping monitoring.

[0026] Based on this, embodiments of this application provide a method for monitoring the galloping of transmission lines, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of a method for monitoring the galloping of power transmission lines according to this application.

[0027] In this embodiment, the method for monitoring the galloping of transmission lines includes steps S10 to S50: Step S10: Acquire inertial data collected by the inertial measurement unit and positioning data output by the global navigation satellite system, and determine the characteristic parameters of the conductor galloping based on the inertial data. The inertial measurement unit is deployed at the monitoring position of the conductor. It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a power transmission line galloping monitoring device. The following description uses a power transmission line galloping monitoring device as an example to illustrate this embodiment and the subsequent embodiments.

[0028] It should be noted that conductor galloping refers to the low-frequency, large-amplitude self-excited vibration of a conductor under wind load, with a frequency range of 0.1Hz to 3Hz (normal frequency range) and an amplitude that can reach 5 to 300 times the conductor diameter. This can easily cause damage to power grid equipment and power outages. Therefore, it is necessary to monitor conductor galloping.

[0029] It should be noted that the Inertial Measurement Unit (IMU) can be used to acquire raw inertial data such as three-axis acceleration and three-axis angular velocity from monitoring nodes of power transmission lines. The Global Navigation Satellite System (GNSS) is used to acquire absolute positioning data such as three-dimensional position and three-dimensional velocity in the World Geodetic System 1984 (WGS84). In specific implementation, monitoring nodes integrating a six-axis IMU, a dual-frequency BeiDou / GNSS positioning module, an embedded edge computing unit, and a power supply module can be deployed at key locations to be measured on the power transmission line, such as the midpoint of the span or a quarter span, where galloping characteristics are significant. Through a hardware synchronization triggering mechanism, the timestamps of the IMU and the GNSS module are precisely aligned, and the raw inertial measurement data of three-axis acceleration and three-axis angular velocity output by the IMU, as well as the raw positioning data of three-dimensional position, three-dimensional velocity, and position dilution of precision (PDOP) in the WGS84 coordinate system output by the GNSS module are acquired synchronously. PDOP is used to characterize GNSS positioning accuracy; the larger the value, the lower the positioning accuracy.

[0030] It should be noted that when determining the characteristic parameters of conductor galloping based on the collected inertial data, the acceleration time series reflecting the radial vibration of the conductor can be extracted from the inertial data. Then, time-frequency analysis is performed on the acceleration time series to extract characteristic parameters such as the dominant frequency, vibration order, and amplitude of the conductor galloping. The time-frequency analysis method can be any of the following: spectral analysis (such as Fast Fourier Transform), time-frequency distribution analysis (such as Wavelet Transform, Hilbert-Huang Transform), or parametric spectral estimation (such as autoregressive models).

[0031] In one feasible approach, the characteristic parameters include the dominant frequency, vibration order, and amplitude. The step of determining the characteristic parameters of conductor galloping based on the inertial data includes: extracting the radial acceleration time series of the conductor from the inertial data; performing spectral analysis on the acceleration time series to determine the dominant frequency, vibration order, and amplitude of conductor galloping.

[0032] It should be noted that the characteristic parameters include the dominant frequency, vibration order, and amplitude. When calculating the characteristic parameters, the IMU acceleration data is first transformed from the carrier coordinate system to the Northeast Tiannao coordinate system using an attitude transformation matrix. Then, combined with the actual route of the transmission line, the acceleration time series in the radial direction of the conductor (perpendicular to the conductor's axis) is extracted. This acceleration time series will serve as the basic data for galloping frequency identification. Simultaneously, the inherent physical parameters of the transmission line are obtained, including span, conductor type, and rated tension. Based on the transmission conductor vibration theory, the natural frequencies of the conductor galloping are calculated to determine the theoretical effective frequency range. The formula for calculating the nth natural frequency of a single span conductor is shown below:

[0033] In the formula, Let be the nth natural frequency of the conductor, where n is the order of vibration and is a positive integer; L is the conductor span; and T is the rated horizontal tension of the conductor. Let S be the density of the conductor material and S be the cross-sectional area of ​​the conductor. The theoretical natural frequencies of the conductor's galloping can be calculated using the nth-order natural frequency calculation formula, thus defining the effective range for frequency identification, eliminating interference from irrelevant frequency components, and significantly improving the accuracy and anti-interference capability of frequency identification. Subsequently, a recursive spectrum analysis method is used to perform frequency domain decomposition on the radial acceleration time series. The preferred method is Recursive Fast Fourier Transform (RFFT) to extract the principal vibration frequency, harmonic frequencies, and corresponding amplitudes corresponding to the spectral peaks. The harmonic frequencies can be used as the final galloping monitoring data. The extracted principal vibration frequency and corresponding amplitude can be determined as the principal frequency and amplitude of the current conductor galloping. Then, the vibration order is determined by combining the frequencies calculated using the nth-order natural frequency calculation formula, thereby determining the principal frequency, vibration order, and amplitude range of the current conductor galloping. When it is necessary to improve the robustness of identification under frequency abrupt changes, frequency band overlap, or non-stationary conditions, further time-frequency enhancement analysis methods such as wavelet packet decomposition can be used for auxiliary judgment. Finally, a sliding time window with a fixed step size is used to recursively update the dancing feature parameters in real time to ensure the real-time performance and stability of the identification results. The dancing feature parameters at the current moment are output, providing the core input for subsequent adaptive adjustment of the full-link parameters.

[0034] In one feasible approach, after the step of acquiring the inertial data collected by the inertial measurement unit and the positioning data output by the global navigation satellite system, the method further includes: performing system error correction on the inertial data to obtain corrected data; removing abnormal bad values ​​from the corrected data and using linear interpolation to complete the removed data segments to obtain completed data; and using moving average filtering to smooth the completed data to obtain preprocessed inertial data.

[0035] It should be noted that when performing systematic error correction, the factory-calibrated zero bias, scale factor, and non-orthogonal error parameters of the inertial measurement unit can be used to compensate for and correct the original inertial data (triaxial acceleration and triaxial angular velocity), eliminating systematic deterministic errors and obtaining corrected inertial data. Then, the 3σ Laida criterion can be used to remove outlier values, and its criterion is shown in the following formula:

[0036] In the formula, For the i-th sampled data within the sliding time window, This represents the mean of the data within the sliding time window. The standard deviation of the data within the sliding time window is used to remove outlier data that exceeds the threshold range. Linear interpolation of adjacent valid data is used to complete the data segment. Finally, moving average filtering is used to smooth the initial noise of the data, resulting in preprocessed valid inertial and positioning data, which provides a reliable data foundation for subsequent frequency identification and positioning calculation.

[0037] Step S20: Determine the target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance based on the feature parameters; It should be noted that various adaptive parameters can be dynamically calculated based on the obtained dominant frequency and amplitude of the conductor galloping. Specifically, following the Nyquist sampling theorem, the target sampling frequency is set to the larger of a fixed multiple (e.g., 5 times) of the dominant frequency and a preset minimum sampling frequency threshold (e.g., 10Hz), not exceeding 250Hz. Furthermore, the low-pass filter cutoff frequency in the target filtering parameters is set to a fixed multiple (e.g., 2.5 times) of the dominant frequency. The target fusion update frequency is determined according to a preset mapping relationship based on the interval containing the dominant frequency. The filter noise covariance is then dynamically adjusted based on the galloping amplitude and the positioning accuracy factor output by the Global Navigation Satellite System, using the system noise covariance matrix Q and the observation noise covariance matrix R of the Extended Kalman Filter (EKF).

[0038] Step S30: Obtain the current inertial data based on the target sampling frequency, and perform low-pass filtering on the current inertial data based on the target filtering parameters to obtain the target inertial data; It should be noted that after determining the target sampling frequency and target filtering parameters, the sampling frequency of the inertial measurement unit can be set to the target sampling frequency, and the inertial data (three-axis acceleration and three-axis angular velocity) at the current moment can be re-acquired according to the target sampling frequency. Then, a Butterworth low-pass filter is used, with its cutoff frequency configured to the target filtering parameters (e.g., 2.5 times the main frequency), to perform low-pass filtering on the current inertial data, filtering out high-frequency random noise, fully preserving the effective components of the galloping signal, and outputting the filtered target inertial data. By dynamically adjusting the sampling frequency, the acquired data can be more comprehensive and sufficient, thereby improving the accuracy of subsequent galloping monitoring results.

[0039] Step S40: Construct an adaptive combined filtering model containing a kinematic constraint model based on the feature parameters and the filter noise covariance; It should be noted that after obtaining the feature parameters, a dynamic system model that can integrate multi-source data needs to be constructed. The dynamic system model includes state equations and observation equations, and introduces kinematic constraints for conductor galloping.

[0040] Step S50: Based on the target fusion update frequency, the target inertial data, and the positioning data, perform filtering calculation on the adaptive combined filtering model and output the galloping monitoring results of the conductor monitoring node. It should be noted that, according to the determined target fusion update frequency, the constructed adaptive combined filtering model is filtered using the obtained target inertial data and acquired Global Navigation Satellite System (GNSS) positioning data. The filtering solution includes two recursive stages: time prediction and measurement update. Within each filtering cycle, a time update is first performed based on the error propagation model of the inertial measurement unit (INS) to predict the current state estimate and its covariance. Then, when GNSS observation data arrives and kinematically constrained pseudo-observations are periodically used according to the fusion update frequency, a measurement update is performed to correct the state estimate. After the filtering solution is completed, the three-dimensional position, three-dimensional attitude, and three-dimensional velocity of the traverse monitoring node in the navigation coordinate system are output. Simultaneously, based on the continuously output position time series, core monitoring results such as the amplitude, frequency, trajectory, and direction of traverse galloping are further calculated, i.e., the galloping monitoring results. When the calculated galloping amplitude or frequency exceeds the preset safety threshold of the line, a galloping risk warning signal is automatically triggered.

[0041] In one feasible approach, after the step of outputting the galloping monitoring results of the conductor monitoring node, the method further includes: monitoring the signal status of the global navigation satellite system; when the signal status is detected as signal loss, switching to dead reckoning mode, wherein the dead reckoning mode uses the inertial data for galloping detection.

[0042] It should be noted that during real-time calculation, the system simultaneously monitors abrupt changes in GNSS signal status and galloping frequency. When GNSS signal loss is detected (i.e., no valid positioning data for three GNSS update cycles), the system automatically switches to IMU and galloping kinematic constraint dead reckoning mode. Based on the currently identified galloping frequency, the system updates the harmonic motion constraint model, using pseudo-observations to complete the filtered measurement update, effectively suppressing the accumulated error of the IMU. When the GNSS signal becomes valid again, a smooth transition algorithm switches back to the IMU and GNSS combined positioning mode to avoid abrupt changes in positioning results. Specifically, if the system detects no valid observations for three consecutive GNSS update cycles, it determines that the system has lost lock; it automatically switches to IMU + galloping constraint dead reckoning mode, i.e., GNSS observation updates are turned off, and galloping pseudo-observations are retained to use constraint displacement as the measurement update EKF, continuously suppressing zero-bias drift; during the 60-second loss period, the positioning error is controlled within 15cm with no significant divergence. Then, a galloping frequency mutation processing is performed (after 90 seconds). When the system detects that the dominant frequency band of the conductor has changed from the original low-frequency galloping state to a higher-frequency vibration state, the galloping feature identification is re-executed to obtain the new dominant frequency and corresponding amplitude parameters, and the system operating parameters are reconfigured accordingly. Specifically, the IMU sampling frequency is increased to meet the high-frequency vibration characterization requirements; the GNSS module maintains its available effective update frequency output; the filter performs state propagation according to the increased IMU frequency and performs asynchronous correction when the GNSS observation arrives; at the same time, the signal filtering cutoff frequency is increased accordingly, and the Q and R noise matrices are re-corrected based on the new dynamic intensity and observation quality, so that the filtering model can quickly adapt to the new galloping state and stably output the positioning results. Finally, after the GNSS signal is recovered, the system smoothly switches back to the IMU+GNSS combined positioning mode without position jumps; the galloping constraint model is reconstructed based on the new frequency, the filter quickly converges and stably outputs the high-frequency vibration trajectory, achieving continuous positioning under all working conditions.

[0043] In one feasible approach, after the step of monitoring the galloping results of the output conductor monitoring node, the method further includes: acquiring historical main frequency data and determining a frequency mutation threshold based on the historical main frequency data; continuously monitoring the main frequency in the characteristic parameters; and re-determining the characteristic parameters when a main frequency mutation is detected to exceed the frequency mutation threshold.

[0044] It should be noted that the dominant frequency of conductor galloping is not constant during transmission line galloping monitoring. With changes in environmental conditions and physical parameters such as wind speed, wind direction, ice thickness, and conductor tension, the galloping frequency may experience significant abrupt changes. For example, when wind speed suddenly increases from a low level, the galloping may jump from a first-order low-frequency vibration (e.g., 0.5Hz) to a second- or third-order high-frequency vibration (e.g., 1.5Hz or 2.5Hz); conversely, when ice detaches or wind speed decreases sharply, the frequency may also decrease rapidly. Therefore, it is essential to monitor changes in the dominant galloping frequency in real time. When a frequency mutation exceeding a certain threshold is detected, the identification of characteristic parameters and the adaptive adjustment of all-link parameters must be re-executed promptly. Specifically, during the operation of the monitoring node, the dominant galloping frequency value output in each calculation cycle is continuously recorded and stored in a fixed-length sliding time window (e.g., a window length of 60 seconds, or containing the most recent N=100 frequency sampling points). The historical dominant frequency sequence is read from the sliding time window. The mean and standard deviation of the historical dominant frequency sequence are calculated. Then, the dynamic frequency mutation threshold is determined based on the standard deviation and a preset multiplier (e.g., 3 times, following the 3σ criterion). The dynamic frequency mutation threshold is updated in real time with historical data, reflecting the maximum permissible deviation of the frequency within the normal fluctuation range. When the frequency change exceeds the dynamic frequency mutation threshold, it is determined to have occurred as a significant mutation.

[0045] In actual conductor galloping monitoring, the conductor parameters are assumed to be: span L = 3m, conductor cross-sectional area A = 70mm², mass per unit length ρA = 0.6kg / m, and rated tension T = 150N. The sensor module includes: a six-axis IMU (accelerometer range ±16g, angular velocity range ±2000° / s) and a BeiDou / GNSS dual-frequency positioning module. The initial default parameters include: IMU basic sampling frequency 50Hz, GNSS sampling frequency 10Hz, and low-pass filter initial cutoff frequency 10Hz. The galloping simulation settings are: platform output first-order galloping frequency 0.8Hz, galloping amplitude A = 0.5m, covering a wideband vibration simulation of 0.1~10Hz. The first step is to perform multi-source data acquisition and preprocessing. Specifically, hardware time synchronization is required between the IMU and GNSS, with a sampling time window length of 102 points and a sliding step size of 0.1s. After zero-bias correction of the raw IMU data, outliers are removed using the 3σ criterion: the mean μ and standard deviation σ of the acceleration data within the window are calculated, and |x| is removed. i The outliers μ|>3σ are identified and linearly interpolated for completion, followed by noise smoothing using a 5-point moving average filter. The second step is real-time identification of the galloping frequency. Specifically, the IMU acceleration is converted to the navigation coordinate system, and the radial vibration sequence of the conductor is extracted. Substituting the conductor parameters into the theoretical natural frequency formula yields a first-order natural frequency of 0.79Hz, with an effective frequency range of 0.5~10Hz. Recursive RFFT and 4th-order Daubechies (db4) wavelet packet decomposition are used to extract the dominant frequency of 0.8Hz, vibration order 1, and amplitude of 0.5m, updated in 0.1s steps. The third step is adaptive adjustment of the entire link parameters. Specifically, the dominant frequency and amplitude information obtained in the second step are used to adaptively configure the system operating parameters. For the low-frequency galloping condition in this embodiment, the IMU sampling frequency is set to an effective value not lower than a preset multiple of the main frequency, and determined to be 10Hz in combination with the minimum operating frequency requirement of the system; the GNSS module maintains an effective output of 10Hz; the low-pass filter cutoff frequency is adaptively set to approximately 2Hz according to the main frequency; the combined filter performs state propagation according to the IMU data, and performs asynchronous correction when the GNSS observation arrives. At the same time, based on the current condition characteristics of small galloping amplitude and normal GNSS observation quality, the system noise covariance matrix Q and the observation noise covariance matrix R are slightly adaptively corrected to ensure the stability of the filtering process and its adaptability to actual dynamics. The fourth step is the construction of the combined positioning filter model, specifically a 15-dimensional EKF filter model, in which the state vector includes attitude, velocity, position, and gyroscope / accelerator zero bias; the observation equation is constructed using the GNSS three-dimensional position and velocity as observation values; galloping motion constraints are introduced, and radial displacement is added to the filter as a pseudo-observation to suppress IMU integral drift. The fifth step is positioning calculation and result output. Specifically, EKF recursive calculation is performed to output the three-dimensional position, dancing trajectory, amplitude and frequency; the dancing amplitude safety threshold is set to 1.0m, and the current amplitude of 0.5m does not trigger an alarm; the positioning results are output in real time: horizontal positioning error ≤4.2cm, elevation error ≤8.7cm.

[0046] This embodiment acquires inertial data collected by an inertial measurement unit (IMU) and positioning data output by a global navigation satellite system (GNSS). Based on the inertial data, it determines the characteristic parameters of conductor galloping. The IMU is deployed at the monitoring location of the conductor. Based on the characteristic parameters, it determines the target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance. Based on the target sampling frequency, it acquires the current inertial data and performs low-pass filtering on the current inertial data based on the target filtering parameters to obtain target inertial data. Based on the characteristic parameters and the filter noise covariance, it constructs an adaptive combined filtering model containing a kinematic constraint model. Based on the target fusion update frequency, the target inertial data, and the positioning data, it performs filtering calculations on the adaptive combined filtering model and outputs the galloping monitoring results of the conductor monitoring node. This embodiment updates the acquisition frequency, low-pass filtering parameters, and covariance parameters using the initially acquired galloping features, and then uses these updated parameters to construct the filtering model. This makes the galloping monitoring results obtained from the final calculated filtering model more consistent with the actual situation, improving the accuracy of conductor galloping monitoring.

[0047] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S20 also includes steps S201 to S205: Step S201: Determine the main frequency and amplitude based on the characteristic parameters; It should be noted that the characteristic parameters include the main frequency and amplitude, which can be determined directly.

[0048] Step S202: Determine the sampling frequency to be determined based on the main frequency and the first preset multiple, and determine the larger value between the sampling frequency to be determined and the preset minimum sampling frequency as the target sampling frequency; It should be noted that when adaptively adjusting the sampling frequency, the Nyquist sampling law must be strictly followed. For the 0.1Hz-50Hz wideband vibration characteristics of the transmission line, a hierarchical adaptive sampling strategy is adopted, and the lower limit of the sensor sampling frequency is set to the first preset multiple (5 times) of the main vibration frequency.

[0049] In the formula, The sensor sampling frequency is adaptively adjusted. To identify the dominant frequency of the dance, This is the minimum sampling frequency threshold for the sensor. This rule enables dynamic matching between the sampling frequency and the main frequency of the dancing motion, ensuring the complete acquisition of the effective components of the dancing motion signal while avoiding power waste caused by oversampling.

[0050] Step S203: Determine the cutoff frequency of the low-pass filter based on the main frequency and the second preset multiple, and set the cutoff frequency as the target filter parameter; It should be noted that when adaptively adjusting the data filtering cutoff frequency, the low-pass filter cutoff frequency is set to a second preset multiple (e.g., 2.5 times) of the main frequency, as shown in the following formula:

[0051] In the formula, To achieve the adaptively adjusted low-pass filter cutoff frequency, a Butterworth low-pass filter is used to perform data filtering. This filters out high-frequency random noise while fully preserving the effective components of the dancing signal, thus avoiding signal distortion.

[0052] Step S204: Determine the target fusion update frequency based on the interval of the main frequency and the preset mapping relationship; It should be noted that when adaptively adjusting the filter main loop update frequency (i.e., the target fusion update frequency), since the combined positioning filter of this application adopts the working mechanism of "IMU high-frequency propagation, GNSS asynchronous correction, and constraint pseudo-observations participating in the update according to the main loop," the GNSS observation triggers the measurement update upon actual arrival and is not required to be completely consistent with the IMU sampling frequency or the filter main loop update frequency. Therefore, the fusion frequency can be set in stages according to the dynamic characteristics of the galloping condition. Specifically, when the galloping main frequency... When, the fusion update frequency is set to 10Hz; when At that time, the fusion update frequency is set to 50Hz; when At that time, the fusion update frequency was set to 100Hz to achieve precise matching between the fusion frequency and the dynamic characteristics of the dancing motion. Finally, the filtering noise model was adaptively adjusted.

[0053] Step S205: Determine the system noise covariance matrix and the observation noise covariance matrix based on the amplitude and the positioning accuracy factor output by the global navigation satellite system, and determine the system noise covariance matrix and the observation noise covariance matrix as the filtered noise covariance.

[0054] It should be noted that the filtered noise covariance is divided into the system noise covariance matrix and the observation noise covariance matrix. During calculation, the system noise covariance matrix Q and the observation noise covariance matrix R of the Kalman filter can be dynamically adjusted based on the galloping amplitude, dominant frequency, and GNSS positioning quality (i.e., positioning accuracy factor). The adjustment relationship can be expressed as:

[0055] In the formula, , The initial reference value for the noise matrix is ​​set in advance based on empirical values. The adaptive adjustment coefficient is A, where A is the identified dancing amplitude. , The weighting coefficients are preset, and PDOP is the GNSS positioning accuracy factor (Position Dilution of Precision).

[0056] This embodiment determines the main frequency and amplitude based on the characteristic parameters; it determines the undetermined sampling frequency based on the main frequency and a first preset multiple, and determines the larger of the undetermined sampling frequency and a preset minimum sampling frequency as the target sampling frequency; it determines the cutoff frequency of the low-pass filter based on the main frequency and a second preset multiple, and determines the cutoff frequency as the target filtering parameter; it determines the target fusion update frequency based on the interval where the main frequency is located and a preset mapping relationship; it determines the system noise covariance matrix and the observation noise covariance matrix based on the amplitude and the positioning accuracy factor output by the Global Navigation Satellite System, and determines the system noise covariance matrix and the observation noise covariance matrix as the filter noise covariance. This embodiment dynamically adjusts the sampling frequency, filtering parameters, fusion frequency, and noise covariance based on the galloping main frequency and amplitude, enabling the monitoring system to adaptively match different galloping conditions, reducing the inaccuracy of data caused by fixed parameters, thereby improving the integrity of data acquisition and the accuracy of filtering calculation.

[0057] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S40 also includes steps S401 to S407: Step S401: Determine the dominant frequency, vibration order, and amplitude based on the characteristic parameters; It should be noted that the characteristic parameters include the dominant frequency, vibration order, and amplitude, which can be determined directly.

[0058] Step S402: Determine the system noise covariance matrix and the observation noise covariance matrix based on the filtered noise covariance; It should be noted that the filtered noise covariance includes the system noise covariance matrix and the observation noise covariance matrix, which can be directly determined.

[0059] Step S403: Using extended Kalman filtering as the basic framework, define a system state vector that includes attitude angle error, velocity error, position error, and zero bias of the inertial measurement unit; It should be noted that when constructing the core positioning solution model based on the adaptively adjusted parameters, an Extended Kalman Filter (EKF) adapted to the computing power of embedded edge devices can be used as the basic filtering framework. Based on the IMU mechanical orchestration algorithm, a 15-dimensional system state vector is constructed, as shown in the following equation:

[0060] In the formula, This refers to the three-dimensional attitude angle error in the Northeast Tian navigation coordinate system. For three-dimensional velocity error, For three-dimensional position error, For the three-axis zero bias of the IMU accelerometer, This is the three-axis zero bias of the IMU gyroscope.

[0061] Step S404: Construct the system state equation based on the system state vector and the system noise covariance matrix; It should be noted that the system state equation is constructed based on the system state vector and the system noise covariance matrix, as shown in the following equation:

[0062] In the formula, That is, the state value of the system state vector at time t. Here is the system state transition matrix. The system noise driving matrix, It is the system noise covariance matrix, which is the adaptively adjusted Q matrix.

[0063] Step S405: Construct the system observation equation based on the system state vector and the observation noise covariance matrix; It should be noted that the system observation equation is constructed using the system state vector and the observation noise covariance matrix, with the three-dimensional position and three-dimensional velocity output by GNSS as the observed values, as shown in the following equation:

[0064] In the formula, That is, the state value of the system state vector at time k. The observation vector at time k is composed of the difference between the position and velocity output by the GNSS and the position and velocity calculated by the IMU mechanical orchestration. For the system observation matrix, The observation noise covariance matrix is ​​the adaptively adjusted R matrix.

[0065] Step S406: Establish a nonlinear damped resonance constraint model based on the dominant frequency, the vibration order, and the amplitude; It should be noted that, by introducing kinematic constraints on the galloping of transmission lines, and based on the boundary conditions of fixed ends of the transmission lines, combined with the real-time identified dominant galloping frequencies and vibration orders, a nonlinear damped resonance constraint model including the first two dominant frequencies is established, as shown in the following equation:

[0066] In the formula, Let A be the radial displacement of the conductor monitoring node, and A be the galloping amplitude. To activate the main frequency of the dance, For the initial phase, The empirical damping attenuation coefficient is... Let be the nth natural frequency of the conductor, where the vibration order is directly given by the galloping characteristic identification results; The estimation is based on the phase continuity of adjacent sliding time windows, or determined by the fitting results at the initial time. The parameters can be set based on the historical attenuation characteristics of the conductor, experimental calibration values, or online estimation results. The radial displacement constraint obtained from the nonlinear damped resonance constraint model is used as a pseudo-observation value and introduced into the system's observation equation to strengthen the constraint of the filtering model and effectively suppress the cumulative error of the IMU.

[0067] Step S407: Obtain an adaptive combined filtering model that includes a kinematic constraint model based on the system state equation, the system observation equation, and the nonlinear damped resonance constraint model.

[0068] It should be noted that the nonlinear damped resonance constraint model is used as the kinematic constraint model, and its output radial displacement pseudo-observation value is included in the system observation equations alongside the position and velocity observation values ​​of the global navigation satellite system as a source of measurement updates. Simultaneously, the system state equations and observation equations are integrated to form a complete adaptive combined filtering model that incorporates the kinematic constraint model.

[0069] In this embodiment, the dominant frequency, vibration order, and amplitude are determined based on the aforementioned characteristic parameters. The system noise covariance matrix and the observation noise covariance matrix are determined based on the filtered noise covariance. An extended Kalman filter is used as the basic framework, defining a system state vector that includes attitude angle error, velocity error, position error, and zero bias of the inertial measurement unit. The system state equation is constructed based on the system state vector and the system noise covariance matrix. The system observation equation is constructed based on the system state vector and the observation noise covariance matrix. A nonlinear damped resonance constraint model is established based on the dominant frequency, the vibration order, and the amplitude. An adaptive combined filtering model including a kinematic constraint model is obtained based on the system state equation, the system observation equation, and the nonlinear damped resonance constraint model. This embodiment directly configures the adaptively adjusted filtered noise covariance (including the system noise covariance matrix and the observation noise covariance matrix) into the state equation and observation equation of the filtering model, enabling the constructed combined filtering model to automatically match the optimal noise weights as the galloping conditions change, thereby avoiding the filtering divergence or accuracy degradation problems caused by fixed noise models when the conditions change.

[0070] For example, to help understand the implementation process of the transmission line galloping monitoring method obtained by combining this embodiment with the above-described embodiment one, please refer to... Figure 4 , Figure 4 A simplified flowchart of a method for monitoring conductor galloping is provided. Specifically: First, a data sensing layer acquires data from IMU sensors and a GNSS positioning module; then, the data is preprocessed, including outlier removal and noise smoothing; next, galloping frequency identification and frequency range constraints are performed; then, parameter adaptive scheduling is performed, including adaptive sampling frequency, adaptive filtering parameters, adaptive fusion frequency, and adaptive filtering noise; finally, data processing is performed, including IMU mechanical orchestration + frequency adaptive Kalman filtering, galloping kinematic constraints, and an adaptive switching mechanism for GNSS lockout or frequency mutation; finally, data is output, including three-dimensional position, attitude, and trajectory, galloping amplitude, frequency, and sag, and risk warning and data uploading are performed.

[0071] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the transmission line galloping monitoring method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0072] This application also provides a transmission line galloping monitoring device; please refer to [reference needed]. Figure 5 The aforementioned transmission line galloping monitoring device includes: The acquisition module 10 is used to acquire inertial data collected by the inertial measurement unit and positioning data output by the global navigation satellite system, and to determine the characteristic parameters of the conductor galloping based on the inertial data. The determination module 20 is used to determine the target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance based on the feature parameters. The filtering module 30 is used to acquire the current inertial data based on the target sampling frequency, and to perform low-pass filtering on the current inertial data based on the target filtering parameters to obtain the target inertial data; Construction module 40 is used to construct an adaptive combined filtering model containing a kinematic constraint model based on the feature parameters and the filter noise covariance; Output module 50 is used to perform filtering calculation on the adaptive combined filtering model based on the target fusion update frequency, the target inertial data and the positioning data, and output the galloping monitoring results of the conductor monitoring node.

[0073] This application acquires inertial data collected by an inertial measurement unit (IMU) and positioning data output by a global navigation satellite system (GNSS), and determines characteristic parameters of conductor galloping based on the inertial data. The IMU is deployed at the monitoring location of the conductor. Based on the characteristic parameters, it determines the target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance. It acquires current inertial data based on the target sampling frequency and performs low-pass filtering on the current inertial data based on the target filtering parameters to obtain target inertial data. Based on the characteristic parameters and the filter noise covariance, it constructs an adaptive combined filtering model including a kinematic constraint model. Based on the target fusion update frequency, the target inertial data, and the positioning data, it performs filtering calculations on the adaptive combined filtering model and outputs the galloping monitoring results of the conductor monitoring node. This application updates the acquisition frequency, low-pass filtering parameters, and covariance parameters using the initially acquired galloping features, and then uses these updated parameters to construct a filtering model. This makes the galloping monitoring results obtained from the final calculated filtering model more consistent with the actual situation, improving the accuracy of conductor galloping monitoring.

[0074] In one embodiment, the acquisition module 10 is further configured to extract the radial acceleration time series of the conductor from the inertial data; perform spectral analysis on the acceleration time series to determine the dominant frequency, vibration order, and amplitude of the conductor's galloping.

[0075] In one embodiment, the determining module 20 is further configured to: determine the main frequency and amplitude based on the feature parameters; determine the undetermined sampling frequency based on the main frequency and a first preset multiple, and determine the larger of the undetermined sampling frequency and a preset minimum sampling frequency as the target sampling frequency; determine the cutoff frequency of the low-pass filter based on the main frequency and a second preset multiple, and determine the cutoff frequency as the target filtering parameter; determine the target fusion update frequency based on the interval where the main frequency is located and a preset mapping relationship; determine the system noise covariance matrix and the observation noise covariance matrix based on the amplitude and the positioning accuracy factor output by the global navigation satellite system, and determine the system noise covariance matrix and the observation noise covariance matrix as the filter noise covariance.

[0076] In one embodiment, the construction module 40 is further configured to determine the dominant frequency, vibration order, and amplitude based on the characteristic parameters; Determine the system noise covariance matrix and the observation noise covariance matrix based on the filtered noise covariance. An extended Kalman filter is used as the basic framework. A system state vector is defined, which includes attitude angle error, velocity error, position error, and zero bias of the inertial measurement unit. A system state equation is constructed based on the system state vector and the system noise covariance matrix. A system observation equation is constructed based on the system state vector and the observation noise covariance matrix. A nonlinear damped resonance constraint model is established based on the dominant frequency, the vibration order, and the amplitude. An adaptive combined filtering model including a kinematic constraint model is obtained based on the system state equation, the system observation equation, and the nonlinear damped resonance constraint model.

[0077] In one embodiment, the acquisition module 10 is further configured to perform system error correction on the inertial data to obtain corrected data; remove abnormal bad values ​​from the corrected data and use linear interpolation to complete the removed data segments to obtain completed data; and use moving average filtering to smooth the completed data to obtain preprocessed inertial data.

[0078] In one embodiment, the output module 50 is further configured to monitor the signal status of the global navigation satellite system; when the signal status is detected as signal loss, it switches to dead reckoning mode, which uses the inertial data for galloping detection.

[0079] In one embodiment, the output module 50 is further configured to acquire historical main frequency data and determine a frequency mutation threshold based on the historical main frequency data; continuously monitor the main frequency in the feature parameters; and when a main frequency mutation is detected to exceed the frequency mutation threshold, re-determine the feature parameters.

[0080] This application provides a transmission line galloping monitoring device, which employs a transmission line galloping monitoring method described in the above embodiments, and can solve the technical problem of how to improve the accuracy of transmission line galloping monitoring. Compared with the prior art, the beneficial effects of the transmission line galloping monitoring device provided in this application are the same as those of the transmission line galloping monitoring method described in the above embodiments, and other technical features of the transmission line galloping monitoring device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0081] This application provides a transmission line galloping monitoring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a transmission line galloping monitoring method as described in Embodiment 1 above.

[0082] The following is for reference. Figure 6 This document illustrates a structural schematic diagram of a transmission line galloping monitoring device suitable for implementing embodiments of this application. The transmission line galloping monitoring device in this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle-mounted terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The power transmission line galloping monitoring device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0083] like Figure 6As shown, a power transmission line galloping monitoring device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. The RAM 1004 also stores various programs and data required for the operation of the power transmission line galloping monitoring device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows a power line galloping monitoring device to communicate wirelessly or wiredly with other devices to exchange data. Although a power line galloping monitoring device with various systems is shown in the figures, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0084] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0085] This application provides a transmission line galloping monitoring device, which employs a transmission line galloping monitoring method described in the above embodiments, and can solve the technical problem of how to improve the accuracy of transmission line galloping monitoring. Compared with the prior art, the beneficial effects of the transmission line galloping monitoring device provided in this application are the same as those of the transmission line galloping monitoring method provided in the above embodiments, and other technical features of this transmission line galloping monitoring device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0086] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0088] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute a transmission line galloping monitoring method according to the above embodiments.

[0089] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0090] The aforementioned computer-readable storage medium may be included in a power transmission line galloping monitoring device; or it may exist independently and not be assembled into a power transmission line galloping monitoring device.

[0091] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a power transmission line galloping monitoring device, the power transmission line galloping monitoring device: acquires inertial data collected by an inertial measurement unit and positioning data output by a global navigation satellite system, and determines characteristic parameters of the galloping of the power transmission line based on the inertial data, wherein the inertial measurement unit is deployed at the monitoring location of the power transmission line; determines a target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance based on the characteristic parameters; acquires current inertial data based on the target sampling frequency, and performs low-pass filtering on the current inertial data based on the target filtering parameters to obtain target inertial data; constructs an adaptive combined filtering model containing a kinematic constraint model based on the characteristic parameters and the filter noise covariance; performs filtering calculations on the adaptive combined filtering model based on the target fusion update frequency, the target inertial data, and the positioning data, and outputs the galloping monitoring results of the power transmission line monitoring node.

[0092] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0095] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for monitoring the galloping of transmission lines, thereby solving the technical problem of how to improve the accuracy of transmission line galloping monitoring. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the transmission line galloping monitoring method provided in the above embodiments, and will not be repeated here.

[0096] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for monitoring the galloping of power transmission lines.

[0097] The computer program product provided in this application can solve the technical problem of how to improve the accuracy of transmission line galloping monitoring. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the transmission line galloping monitoring method provided in the above embodiments, and will not be repeated here.

[0098] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for monitoring the galloping of power transmission lines, characterized in that, The method includes: The system acquires inertial data collected by an inertial measurement unit and positioning data output by a global navigation satellite system, and determines characteristic parameters of conductor galloping based on the inertial data. The inertial measurement unit is deployed at the monitoring location of the conductor. The target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance are determined based on the aforementioned feature parameters. The current inertial data is obtained based on the target sampling frequency, and the current inertial data is low-pass filtered based on the target filtering parameters to obtain the target inertial data. An adaptive combined filtering model incorporating kinematic constraints is constructed based on the feature parameters and the filter noise covariance. Based on the target fusion update frequency, the target inertial data, and the positioning data, the adaptive combined filtering model is filtered to output the galloping monitoring results of the conductor monitoring nodes.

2. The method as described in claim 1, characterized in that, The characteristic parameters include the dominant frequency, vibration order, and amplitude. The step of determining the characteristic parameters of conductor galloping based on the inertial data includes: Extract the radial acceleration time series of the conductor from the inertial data; Spectral analysis was performed on the acceleration time series to determine the dominant frequency, vibration order, and amplitude of the conductor galloping.

3. The method as described in claim 1, characterized in that, The steps of determining the target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance based on the feature parameters include: The main frequency and amplitude are determined based on the aforementioned characteristic parameters; The undetermined sampling frequency is determined based on the main frequency and the first preset multiple, and the larger value between the undetermined sampling frequency and the preset minimum sampling frequency is determined as the target sampling frequency. The cutoff frequency of the low-pass filter is determined based on the main frequency and the second preset multiple, and the cutoff frequency is determined as the target filter parameter. The target fusion update frequency is determined based on the interval of the main frequency and the preset mapping relationship; The system noise covariance matrix and the observation noise covariance matrix are determined based on the amplitude and the positioning accuracy factor output by the global navigation satellite system, and the system noise covariance matrix and the observation noise covariance matrix are determined as the filtered noise covariance.

4. The method as described in claim 1, characterized in that, The kinematic constraint model includes a nonlinear damped resonance constraint model, and the step of constructing an adaptive combined filtering model containing the kinematic constraint model based on the characteristic parameters and the filter noise covariance includes: The dominant frequency, vibration order, and amplitude are determined based on the aforementioned characteristic parameters. Determine the system noise covariance matrix and the observation noise covariance matrix based on the filtered noise covariance. An extended Kalman filter is used as the basic framework, and a system state vector is defined that includes attitude angle error, velocity error, position error, and zero bias of the inertial measurement unit. The system state equation is constructed based on the system state vector and the system noise covariance matrix. The system observation equation is constructed based on the system state vector and the observation noise covariance matrix. A nonlinear damped resonance constraint model is established based on the dominant frequency, the vibration order, and the amplitude. An adaptive combined filtering model incorporating kinematic constraints is obtained based on the system state equation, the system observation equation, and the nonlinear damped resonance constraint model.

5. The method as described in claim 1, characterized in that, Following the step of acquiring the inertial data collected by the inertial measurement unit and the positioning data output by the global navigation satellite system, the method further includes: The inertial data is subjected to system error correction to obtain corrected data; Abnormal bad values ​​in the corrected data are removed, and the removed data segments are completed using linear interpolation to obtain the completed data; The completed data is smoothed using a moving average filter to obtain preprocessed inertial data.

6. The method as described in claim 1, characterized in that, Following the step of monitoring the galloping results of the output conductor monitoring node, the method further includes: Monitor the signal status of the global navigation satellite system; When the signal status is detected as signal loss, switch to dead reckoning mode, which uses the inertial data for galloping detection.

7. The method as described in claim 1, characterized in that, Following the step of monitoring the galloping results of the output conductor monitoring node, the method further includes: Acquire historical main frequency data and determine the frequency mutation threshold based on the historical main frequency data; Continuously monitor the dominant frequency among the aforementioned characteristic parameters; When the detected main frequency mutation exceeds the frequency mutation threshold, the feature parameters are redefined.

8. A device for monitoring the galloping of power transmission lines, characterized in that, The device includes: The acquisition module is used to acquire inertial data collected by the inertial measurement unit and positioning data output by the global navigation satellite system, and to determine the characteristic parameters of the conductor galloping based on the inertial data. The determination module is used to determine the target sampling frequency, target filtering parameters, target fusion update frequency, and filter noise covariance based on the feature parameters. A filtering module is used to acquire current inertial data based on the target sampling frequency, and to perform low-pass filtering on the current inertial data based on the target filtering parameters to obtain target inertial data; A construction module is used to construct an adaptive combined filtering model containing a kinematic constraint model based on the feature parameters and the filter noise covariance; The output module is used to perform filtering calculations on the adaptive combined filtering model based on the target fusion update frequency, the target inertial data, and the positioning data, and output the galloping monitoring results of the conductor monitoring node.

9. A power transmission line galloping monitoring device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of a transmission line galloping monitoring method as claimed in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements a method for monitoring the galloping of transmission lines as described in any one of claims 1 to 7.

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