High-precision transmission line conductor galloping detection method

Abstract

The invention relates to a high-precision transmission line conductor galloping detection method and device, computer equipment and a readable storage medium. The method comprises the following steps: acquiring acceleration information and angular velocity information of a to-be-detected power transmission line; judging whether the to-be-detected power transmission line is in a zero-speed state in the current detection period according to the acceleration information and the angular velocity information; when the to-be-detected power transmission line is in the zero-speed state in the current detection period, determining the speed of the to-be-detected power transmission line in the current detection period as zero, and determining the current detection period as a zero-speed period; and according to the acceleration information and the angular velocity information in the detection period after the zero-speed period, determining the galloping amplitude and the galloping frequency of the power transmission line to be detected. By adopting the method, the transmission line conductor galloping detection reliability can be improved.

Description

[0001] Cross-references to related applications

[0002] This invention patent application claims priority to Chinese Patent Application No. 202510910793.9, filed on July 2, 2025, entitled "Method, Apparatus, Computer Equipment and Readable Storage Medium for Detecting Power Transmission Line Galloping", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of computer technology, and in particular to a high-precision method, apparatus, computer equipment, computer-readable storage medium, and computer program product for detecting conductor galloping in power transmission lines. Background Technology

[0004] Transmission lines are prone to low-frequency, large-amplitude galloping under the influence of meteorological factors such as wind and icing, which can lead to safety accidents such as broken strands and power outages in severe cases. Existing galloping detection methods mostly rely on inertial sensors to monitor the galloping trajectory and calculate the conductor's motion parameters through integration. However, due to sensor noise and integration errors, the detection results are easily deviated, affecting the accuracy of the galloping parameter calculations and resulting in poor reliability of galloping detection. Summary of the Invention

[0005] Therefore, it is necessary to provide a high-precision transmission line conductor galloping detection method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the reliability of high-precision transmission line conductor galloping detection.

[0006] Firstly, this application provides a high-precision method for detecting conductor galloping in transmission lines, including:

[0007] Obtain the acceleration and angular velocity information of the transmission line to be tested;

[0008] Based on the acceleration information and the angular velocity information, it is determined whether the transmission line to be tested is in a zero-speed state in the current testing cycle;

[0009] If the transmission line under test is in a zero-speed state during the current testing cycle, the speed of the transmission line under test during the current testing cycle is determined to be zero, and the current testing cycle is determined to be a zero-speed cycle.

[0010] Based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle, the galloping amplitude and galloping frequency of the transmission line to be tested are determined.

[0011] In one embodiment, determining whether the transmission line to be tested is in a zero-speed state in the current detection cycle based on the acceleration information and the angular velocity information includes:

[0012] Acquire multiple sampling information of the acceleration information and the angular velocity information in the current detection cycle;

[0013] Based on the multiple sampling information and the preset noise variance of the accelerometer and angular velocity meter, the zero-speed detection statistical value of the transmission line to be tested in the current detection cycle is determined;

[0014] If the zero-speed detection statistic is less than or equal to a preset threshold, the power transmission line to be tested is determined to be in a zero-speed state in the current detection cycle.

[0015] In one embodiment, determining the galloping amplitude and galloping frequency of the transmission line under test based on the acceleration and angular velocity information in the detection period following the zero-speed period includes:

[0016] The acceleration and angular velocity information are fused and corrected to obtain the corrected acceleration.

[0017] The corrected acceleration is filtered and integrated to obtain the initial velocity information;

[0018] Remove the linear trend error from the initial velocity information to obtain the target velocity information;

[0019] The target velocity information is integrated to obtain the initial displacement information;

[0020] Remove the linear trend error from the initial displacement information to obtain the target displacement information;

[0021] Based on the target displacement information, the galloping amplitude and galloping frequency of the transmission line to be detected are extracted.

[0022] In one embodiment, the attitude fusion and correction processing of the acceleration information and angular velocity information to obtain the corrected acceleration includes:

[0023] The acceleration information and the angular velocity information are fused using a complementary filtering model to obtain attitude data.

[0024] Based on the attitude data, the acceleration information and the angular velocity information are corrected to obtain the corrected acceleration.

[0025] In one embodiment, removing the linear trend error from the initial velocity information to obtain the target velocity information includes:

[0026] Construct a polynomial fitting model that is associated with velocity information;

[0027] The coefficient values ​​of the polynomial fitting model are determined with the goal of minimizing the error between the initial velocity information and the output value of the polynomial fitting model.

[0028] Based on the polynomial fitting model containing the coefficient values, the linear trend error in the initial velocity information is removed to obtain the target velocity information.

[0029] In one embodiment, the step of removing the linear trend error from the initial velocity information based on a polynomial fitting model containing the coefficient values ​​to obtain the target velocity information includes:

[0030] Based on the polynomial fitting model containing the coefficient values, a trend term data sequence corresponding to the initial velocity information in the time dimension is generated;

[0031] The target speed information is obtained by subtracting the trend data sequence point by point from the initial speed information.

[0032] Secondly, this application also provides a high-precision transmission line conductor galloping detection device, comprising:

[0033] The motion information acquisition module is used to acquire the acceleration and angular velocity information of the power transmission line to be detected;

[0034] The zero-speed state determination module is used to determine whether the transmission line to be tested is in a zero-speed state in the current detection cycle based on the acceleration information and the angular velocity information.

[0035] The zero-speed state determination module is used to determine the speed of the transmission line under test as zero in the current detection cycle and to determine the current detection cycle as a zero-speed cycle when the transmission line under test is in a zero-speed state in the current detection cycle.

[0036] The galloping information determination module is used to determine the galloping amplitude and galloping frequency of the transmission line to be tested based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle.

[0037] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0038] Obtain the acceleration and angular velocity information of the transmission line to be tested;

[0039] Based on the acceleration information and the angular velocity information, it is determined whether the transmission line to be tested is in a zero-speed state in the current testing cycle;

[0040] If the transmission line under test is in a zero-speed state during the current testing cycle, the speed of the transmission line under test during the current testing cycle is determined to be zero, and the current testing cycle is determined to be a zero-speed cycle.

[0041] Based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle, the galloping amplitude and galloping frequency of the transmission line to be tested are determined.

[0042] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0043] Obtain the acceleration and angular velocity information of the transmission line to be tested;

[0044] Based on the acceleration information and the angular velocity information, it is determined whether the transmission line to be tested is in a zero-speed state in the current testing cycle;

[0045] If the transmission line under test is in a zero-speed state during the current testing cycle, the speed of the transmission line under test during the current testing cycle is determined to be zero, and the current testing cycle is determined to be a zero-speed cycle.

[0046] Based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle, the galloping amplitude and galloping frequency of the transmission line to be tested are determined.

[0047] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0048] Obtain the acceleration and angular velocity information of the transmission line to be tested;

[0049] Based on the acceleration information and the angular velocity information, it is determined whether the transmission line to be tested is in a zero-speed state in the current testing cycle;

[0050] If the transmission line under test is in a zero-speed state during the current testing cycle, the speed of the transmission line under test during the current testing cycle is determined to be zero, and the current testing cycle is determined to be a zero-speed cycle.

[0051] Based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle, the galloping amplitude and galloping frequency of the transmission line to be tested are determined.

[0052] The aforementioned high-precision transmission line conductor galloping detection method, device, computer equipment, computer-readable storage medium, and computer program product first acquire the acceleration and angular velocity information of the transmission line under test. This comprehensively reflects the dynamic changes of the transmission line in space, providing basic data support for subsequent galloping state identification and parameter calculation, ensuring the timeliness and completeness of the detection. Next, based on the acceleration and angular velocity information, it determines whether the transmission line under test is in a zero-velocity state during the current detection cycle. This helps identify the motion state of the conductor within the detection cycle, avoiding misjudging a static state as a galloping state, thereby improving the accuracy of the detection process. Then, during the detection of the transmission line under test... When the power line is at zero speed during the current testing cycle, the speed of the power line under test is set to zero during the current testing cycle, and the current testing cycle is set as the zero-speed cycle. This can effectively suppress the cumulative error caused by the initial value error during the integration process and improve the stability of subsequent speed and displacement calculations. Finally, based on the acceleration and angular velocity information in the testing cycles after the zero-speed cycle, the galloping amplitude and galloping frequency of the power line under test are determined. By using the acceleration and angular velocity information after the zero-speed cycle to extract galloping parameters, relatively accurate galloping amplitude and frequency can be obtained with good error control, which is conducive to improving the reliability and usability of the galloping evaluation results. In the above method, the dynamic state of the conductor in each detection cycle can be accurately identified through the joint analysis of acceleration and angular velocity information. After identifying the zero-velocity cycle, the velocity is immediately reset to zero as the calibration starting point for subsequent integral calculations, thereby eliminating the influence of the initial error value on velocity and displacement. By extracting galloping parameters only from the acceleration and angular velocity data after the zero-velocity cycle, the interference of the static state on frequency and amplitude calculations is avoided, ensuring that the extracted galloping feature data has practical physical meaning. This achieves effective detection of the galloping state, balancing real-time performance and accuracy. It effectively solves the problem of galloping parameter drift caused by the accumulation of integral errors in inertial detection. It is suitable for long-term operation of transmission line condition monitoring systems and has good deployability, stability, and practical engineering value. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a high-precision transmission line conductor galloping detection method in one embodiment;

[0055] Figure 2This is a flowchart illustrating the steps for determining the amplitude and frequency of the dancing motion in one embodiment.

[0056] Figure 3 This is a flowchart illustrating a high-precision transmission line conductor galloping detection method in another embodiment;

[0057] Figure 4 This is a structural block diagram of a high-precision transmission line conductor galloping detection device in one embodiment;

[0058] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0060] In one embodiment, such as Figure 1 As shown, a high-precision method for detecting conductor galloping in transmission lines is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, and tablets. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. In this embodiment, the method includes the following steps:

[0061] Step S101: Obtain the acceleration and angular velocity information of the transmission line to be tested.

[0062] Among them, acceleration information refers to the three-axis acceleration data representing the linear motion state of the conductor, collected by an acceleration sensor installed on the transmission line; angular velocity information refers to the three-axis angular velocity data representing the rotational changes of the conductor around each axis, collected by a gyroscope sensor.

[0063] For example, the terminal can acquire the aforementioned acceleration and angular velocity data through an inertial measurement unit (IMU) deployed on the power transmission line itself or its attachment device. The terminal can acquire the dynamic information of the conductor within a set detection period through timed triggering or continuous sampling. The acquired data can be cached or uploaded to the upper-level processing module according to a set sampling frequency to support the entire process of subsequent attitude fusion, state judgment, and galloping feature calculation.

[0064] Step S102: Based on the acceleration and angular velocity information, determine whether the transmission line to be tested is in a zero-speed state in the current testing cycle.

[0065] The zero-speed state refers to the state in which the transmission line under test is stationary or nearly stationary during the current testing cycle, that is, the linear velocity is zero or close to zero. The zero-speed state not only includes the situation where the conductor is completely stationary during the testing cycle, but also usually occurs at the reversal point of motion in the galloping cycle, that is, the position where the conductor reaches the maximum offset amplitude and is about to swing in the opposite direction.

[0066] The detection period refers to the data time window used for status determination, which is usually a continuous sampling period of fixed length.

[0067] For example, the terminal can statistically process the acceleration and angular velocity information within the current detection cycle and combine it with a preset zero-velocity detection threshold to determine whether the transmission line is in a zero-velocity state. The terminal can use a residual combination method of acceleration and angular velocity to calculate its variance or fluctuation index within a time window. If the index is lower than a set threshold, the current detection cycle is determined to be a zero-velocity cycle. This judgment process helps to identify stable static periods of the conductor in a non-galloping state, providing a reliable starting point for subsequent integral error correction.

[0068] Step S103: When the transmission line to be tested is in a zero-speed state in the current testing cycle, the speed of the transmission line to be tested in the current testing cycle is determined to be zero, and the current testing cycle is determined to be a zero-speed cycle.

[0069] Among them, speed refers to the linear motion speed of the transmission line under test obtained by integrating acceleration within the current test cycle; zero speed cycle refers to the test cycle that is currently judged to be in a zero speed state, which is used as the integration initial point or reset basis for subsequent data processing.

[0070] For example, after determining that the current detection cycle is in a zero-speed state, the terminal can forcibly set the speed value corresponding to that cycle to zero, thereby clearing the speed integration error. Simultaneously, the terminal marks this detection cycle as a zero-speed cycle, which can be used as a segment boundary or reference node in subsequent galloping parameter calculations. This process helps to break the error accumulation chain during integration, reduce speed and displacement offsets, and improve the accuracy and stability of galloping amplitude and frequency extraction.

[0071] Step S104: Determine the galloping amplitude and galloping frequency of the transmission line to be tested based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle.

[0072] Among them, galloping amplitude refers to the maximum displacement of the transmission line relative to its static equilibrium position during galloping, which is used to measure the intensity of galloping; galloping frequency refers to the vibration frequency of the transmission line during periodic galloping, reflecting the speed at which galloping occurs; the detection period after the zero-speed period refers to a period of motion cycle data window that begins after the most recent detection period that was determined to be zero speed.

[0073] For example, after completing zero-velocity cycle identification and velocity zeroing, the terminal initiates analysis and processing of acceleration and angular velocity information for subsequent detection cycles. The terminal first obtains linear acceleration through attitude fusion and acceleration correction, then filters and integrates it to obtain velocity information. Further integration and removal of the linear trend term yields displacement information. Based on the displacement curve, the terminal extracts the maximum offset as the goofing amplitude and obtains the goofing frequency through periodic analysis of displacement over time (e.g., dominant frequency extraction). By starting parameter extraction after the zero-velocity cycle, high-precision goofing feature recognition can be achieved with minimal error interference.

[0074] In the aforementioned high-precision transmission line conductor galloping detection method, firstly, the acceleration and angular velocity information of the transmission line under test are acquired. This comprehensively reflects the dynamic changes of the transmission line in space, providing basic data support for subsequent galloping state identification and parameter calculation, ensuring the timeliness and completeness of the detection. Next, based on the acceleration and angular velocity information, it is determined whether the transmission line under test is in a zero-velocity state during the current detection cycle. This helps identify the motion state of the conductor within the detection cycle, avoiding misjudging a static state as a galloping state, thereby improving the accuracy of the detection process. Then, if the transmission line under test is in a zero-velocity state during the current detection cycle... In the case of zero speed, the speed of the transmission line under test is set to zero in the current test cycle, and the current test cycle is set as the zero speed cycle. This can effectively suppress the cumulative error caused by the initial value error during the integration process and improve the stability of subsequent speed and displacement calculations. Finally, based on the acceleration and angular velocity information in the test cycles after the zero speed cycle, the galloping amplitude and galloping frequency of the transmission line under test are determined. By using the acceleration and angular velocity information after the zero speed cycle to extract galloping parameters, relatively accurate galloping amplitude and frequency can be obtained with good error control, which is conducive to improving the reliability and usability of the galloping evaluation results. In the above method, the dynamic state of the conductor in each detection cycle can be accurately identified through the joint analysis of acceleration and angular velocity information. After identifying the zero-velocity cycle, the velocity is immediately reset to zero as the calibration starting point for subsequent integral calculations, thereby eliminating the influence of the initial error value on velocity and displacement. By extracting galloping parameters only from the acceleration and angular velocity data after the zero-velocity cycle, the interference of the static state on frequency and amplitude calculations is avoided, ensuring that the extracted galloping feature data has practical physical meaning. This achieves effective detection of the galloping state, balancing real-time performance and accuracy. It effectively solves the problem of galloping parameter drift caused by the accumulation of integral errors in inertial detection. It is suitable for long-term operation of transmission line condition monitoring systems and has good deployability, stability, and practical engineering value.

[0075] In an exemplary embodiment, step S102, which determines whether the transmission line to be tested is in a zero-speed state in the current detection cycle based on acceleration information and angular velocity information, further includes: acquiring multiple sampling information of acceleration information and angular velocity information in the current detection cycle; determining the zero-speed detection statistical value of the transmission line to be tested in the current detection cycle based on the multiple sampling information and the preset noise variance of the accelerometer and angular velocity meter; and determining that the transmission line to be tested is in a zero-speed state in the current detection cycle if the zero-speed detection statistical value is less than or equal to a preset threshold.

[0076] Among them, acceleration information refers to the three-axis acceleration data collected by the acceleration sensor, which characterizes the linear motion state of the transmission line; angular velocity information refers to the angular velocity data obtained by the gyroscope, which reflects the rotational changes of the transmission line around the three axes; zero-velocity state refers to the motion state of the transmission line where the velocity is zero or close to zero during the detection period; zero-velocity detection statistics refer to the normalized quantitative index that comprehensively reflects the fluctuation of acceleration and angular velocity during the detection period.

[0077] For example, within the current detection cycle, the terminal acquires multiple acceleration and angular velocity sample values ​​according to a set sampling frequency. The terminal calculates the average value of the acceleration samples within the cycle and calculates the deviation of each sample point relative to the mean; simultaneously, it calculates the magnitude of each angular velocity sample. The acceleration deviation and angular velocity magnitude are normalized using preset noise variances of the accelerometer and angular velocity meter, respectively, as weighting coefficients, to obtain a zero-velocity detection statistical value that fuses information from both types of sensors. The terminal compares this statistical value with a threshold set by the system; if the statistical value is less than or equal to the threshold, it determines that the current detection cycle is in a zero-velocity state.

[0078] In a specific example, the zero-speed detection statistic is:

[0079]

[0080] in, and , respectively, represent the noise variance of the accelerometer and the angular velocity meter; , respectively, represent the triaxial acceleration vector and triaxial angular velocity vector at time n; W is the detection window size, which is related to the sampling frequency; This represents the L2 norm; This represents the sample mean, specifically:

[0081]

[0082] In this embodiment, by fusing acceleration and angular velocity information, the true motion state of the conductor during the detection period can be reflected more accurately. Compared with the traditional method that relies solely on the magnitude of acceleration changes for judgment, this embodiment has stronger anti-interference capabilities and stability, effectively reducing the false judgment rate and improving the reliability of zero-velocity identification, thereby laying a more accurate foundation for subsequent integral error control and galloping parameter extraction.

[0083] In one exemplary embodiment, such as Figure 2 As shown, step S104 above, which determines the galloping amplitude and frequency of the transmission line to be tested based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle, can also be achieved through the following steps:

[0084] Step S201: Perform attitude fusion and correction processing on the acceleration information and angular velocity information to obtain the corrected acceleration;

[0085] Step S202: Filter and integrate the corrected acceleration to obtain the initial velocity information;

[0086] Step S203: Remove the linear trend error from the initial velocity information to obtain the target velocity information;

[0087] Step S204: Integrate the target velocity information to obtain the initial displacement information;

[0088] Step S205: Remove the linear trend error from the initial displacement information to obtain the target displacement information;

[0089] Step S206: Based on the target displacement information, extract the galloping amplitude and galloping frequency of the transmission line to be detected.

[0090] Among them, attitude fusion refers to calculating the attitude orientation of the transmission line during the detection period by fusing acceleration and angular velocity information, which is used to transform the data in the sensor coordinate system to the inertial reference coordinate system; corrected acceleration refers to linear acceleration data after removing gravity interference and attitude correction; initial velocity information refers to velocity data obtained by integrating the corrected acceleration; target velocity information refers to the velocity result after removing linear trend error; target displacement information refers to the net displacement data obtained after integrating the target velocity information and removing the displacement trend term; galloping amplitude refers to the maximum absolute value in the target displacement information; and galloping frequency refers to the frequency component of the dominant oscillation period in the target displacement signal.

[0091] For example, the terminal first processes the data after the zero-velocity cycle, using a complementary filtering algorithm to fuse acceleration and angular velocity information to obtain the attitude within the current detection cycle, and then converts the original acceleration data into linear acceleration in the inertial reference frame based on this attitude. Subsequently, the terminal filters the corrected acceleration (e.g., low-pass filtering or Kalman filtering) and integrates it to obtain initial velocity information. Considering the offset error during integration, the terminal constructs a polynomial fitting model to remove trend terms from the velocity data, obtaining the target velocity information. The target velocity information is then integrated, and the linear trend term in the displacement data is removed using the same method to obtain the target displacement information.

[0092] Finally, the terminal analyzes the temporal fluctuation characteristics of the target displacement information within the detection period based on the data sequence, and extracts the maximum displacement amplitude as the galloping amplitude. Specifically, the terminal iterates through the target displacement data, calculates its maximum and minimum values ​​within the detection period, and takes half of the absolute difference as the maximum displacement of the conductor from the equilibrium position within that period, which is used to measure the galloping intensity.

[0093] After extracting the amplitude of the galloping motion, the terminal further performs frequency domain analysis on the target displacement information to extract the corresponding galloping frequency. Specifically, the terminal can perform a spectral transformation on the target displacement sequence based on Fourier transform (e.g., Fast Fourier Transform) to obtain the spectral distribution of the displacement waveform. In the spectrum, the terminal identifies the dominant frequency component with the most concentrated energy and uses its corresponding frequency value as the galloping frequency in the current detection period. This dominant frequency usually reflects the main oscillation period of the conductor in that period, exhibiting good physical correspondence and stability. Since the displacement data has been processed to remove trend terms before frequency extraction, the interference of integral offset on frequency identification can be effectively eliminated, avoiding the phenomenon of galloping frequency shift and improving the accuracy of frequency extraction.

[0094] In this embodiment, by removing the linear trend term from the velocity and displacement data in stages and extracting the galloping feature parameters based on the target displacement, the influence of integral error on the calculation of galloping amplitude and frequency can be effectively suppressed, thereby improving the accuracy and robustness of the galloping detection results.

[0095] In an exemplary embodiment, step S201, which performs attitude fusion and correction processing on acceleration information and angular velocity information to obtain corrected acceleration, further includes: performing attitude fusion processing on acceleration information and angular velocity information through a complementary filtering model to obtain attitude data; and performing correction processing on acceleration information and angular velocity information based on the attitude data to obtain corrected acceleration.

[0096] Among them, attitude data refers to the attitude result representing the spatial direction or orientation of the transmission line, usually expressed in the form of quaternions or rotation matrices; complementary filtering model refers to the algorithm model that combines acceleration and angular velocity data for attitude estimation, which corrects low-frequency drift through acceleration and compensates for high-frequency dynamics through angular velocity, so as to achieve stable and reliable attitude calculation; corrected acceleration refers to linear acceleration data after removing the influence of gravity and undergoing attitude transformation in the inertial reference coordinate system.

[0097] For example, the terminal constructs a complementary filtering model (such as a Mahony filter) and uses the acquired acceleration and angular velocity information as inputs. Within the detection period, it fuses these two types of data to obtain the attitude data at the current moment. The attitude data can be represented using quaternions / rotation matrices to ensure the continuity and stability of the rotation transformation. Subsequently, the terminal uses this attitude data to transform the acceleration information in the sensor coordinate system to the inertial coordinate system and, combined with the gravity vector estimated from the attitude direction, corrects the acceleration information for errors. Specifically, it performs a cross product of the quaternion with the acquired acceleration and angular velocity information to correct the errors. Finally, the corrected acceleration, after removing the gravity component, is obtained for subsequent integration processing.

[0098] In this embodiment, attitude data is obtained by fusing acceleration and angular velocity information using a complementary filtering model. The original acceleration information is then corrected based on the attitude results, effectively eliminating gravitational interference and enabling the extraction of the true linear acceleration of the conductor. Compared to the traditional method of directly using the original acceleration for integration calculation, this embodiment exhibits higher stability and accuracy in attitude estimation and gravity correction, providing a more reliable input basis for subsequent velocity and displacement integration calculations. This improves the accuracy and error resistance of the entire gondola detection process.

[0099] In an exemplary embodiment, step S203 above, which removes the linear trend error from the initial velocity information to obtain the target velocity information, further includes: constructing a polynomial fitting model associated with the velocity information; determining the coefficient values ​​of the polynomial fitting model with the goal of minimizing the error between the initial velocity information and the output value of the polynomial fitting model; and removing the linear trend error from the initial velocity information based on the polynomial fitting model containing the coefficient values ​​to obtain the target velocity information.

[0100] Among them, the initial velocity information refers to the velocity data obtained by integrating the corrected acceleration, which usually contains a time-related linear offset trend; the polynomial fitting model refers to using a polynomial function to model the trend of the velocity data to characterize the low-frequency trend component of the velocity change over time; the target velocity information refers to the net velocity data after removing the trend term error, which is used for subsequent displacement calculation.

[0101] For example, the terminal first constructs a first-order or multi-order polynomial fitting model for the initial velocity information, with the fitting variable being the corresponding time series within the detection period, to establish a trend relationship between velocity and time. Then, the terminal uses the least squares method to solve for the fitting coefficients, aiming to minimize the mean square error between the initial velocity information and the output value of the polynomial fitting model. After obtaining the coefficient values, the terminal generates a trend term data sequence based on the polynomial model and subtracts it point by point from the initial velocity information to obtain the target velocity information after removing the linear trend error.

[0102] In a specific example, the polynomial fitting model can be: .

[0103] in The coefficients are polynomials, meaning the velocity P(t) is expressed as a polynomial dependent on time t, where... For the time linear term, the higher powers of t represent higher-order noise or nonlinear error.

[0104] The error function is constructed based on the least squares method, i.e.

[0105]

[0106] in Let be the velocity sample value (i.e., initial velocity information), and n be the number of samples. To minimize the error function S, it is necessary to solve a system of equations, which consists of the partial derivatives of the error function S with respect to each coefficient. That is, to solve for... , , ..., The system of linear equations formed.

[0107] Based on the above system of linear equations, construct The matrix solution is given, where T represents the matrix formed by the sampling times, Coff represents the coefficients to be determined, and V represents the matrix formed by the observed velocities. Gaussian elimination is used to solve the linear equations in the above matrix form, yielding the coefficients of the velocity model P(t).

[0108] In this embodiment, by fitting and removing the linear trend term in the initial velocity, the velocity drift caused by the offset error in the acceleration integration process can be effectively reduced, the accuracy of subsequent displacement calculation can be improved, and a more accurate data basis can be provided for the extraction of the dancing amplitude and frequency.

[0109] In an exemplary embodiment, the above-mentioned method of removing the linear trend error in the initial velocity information based on a polynomial fitting model containing coefficient values ​​to obtain the target velocity information further includes: generating a trend term data sequence corresponding to the initial velocity information in the time dimension based on the polynomial fitting model containing coefficient values; and subtracting the trend term data sequence from the initial velocity information point by point to obtain the target velocity information.

[0110] Among them, the trend term data sequence refers to the velocity trend value calculated based on the fitting model at each sampling time, reflecting the low-frequency shift trend of velocity over time; the time dimension correspondence refers to mapping each velocity data point to its corresponding sampling time, so that the output of the fitting model is completely aligned with the original velocity data on the time axis.

[0111] For example, based on the obtained polynomial fitting model coefficients, the terminal calculates the corresponding velocity trend term value for each sampling time point within the current detection cycle by substituting it into the fitting model, thereby constructing a complete trend term data sequence. This sequence represents the time-related systematic offset portion of the initial velocity data. Subsequently, the terminal performs point-by-point mapping processing between this trend term data sequence and the original initial velocity information, subtracting the trend term data from the original initial velocity information to obtain the target velocity information free from the influence of the time trend.

[0112] In this embodiment, by constructing a trend term sequence that is strictly aligned with the initial velocity data and performing a point-by-point elimination operation, the linear drift caused by the integration process or sensor deviation can be accurately removed, improving the authenticity and usability of the velocity data, thereby significantly enhancing the accuracy and stability of subsequent displacement calculation and galloping parameter extraction.

[0113] Furthermore, in an exemplary embodiment, step S205 removes the linear trend error in the initial displacement information to obtain the target displacement information. Specifically, it can also be implemented by removing the linear trend error in the initial velocity information as described in step S203 to obtain the target velocity information.

[0114] In another exemplary embodiment, such as Figure 3 As shown, this application provides a high-precision transmission line conductor galloping detection method, including:

[0115] Step 1: The inertial sensing unit, composed of accelerometers and angular velocity meters, measures and senses the attitude of the power transmission line.

[0116] Step 2: Real-time zero-velocity detection is performed on the measurement data using an optimal attitude detector that combines acceleration and angular velocity.

[0117] Step 3: The information collected by the accelerometer and angular velocity meter is fused using the Mahony complementary filtering algorithm to obtain a quaternion / rotation matrix. The quaternion is then cross-multiplied with the measurement value of the inertial sensing unit to correct the error and obtain the acceleration.

[0118] Step 4: Filter the acceleration value obtained in Step 3 using methods such as low-pass filtering and Kalman filtering, and integrate to obtain the velocity. Use Gaussian elimination and least squares method to remove linear terms from the above velocity expression, and then integrate again to obtain the displacement.

[0119] Step 5: The displacement result obtained in Step 4 introduces a time-dependent linear term again due to integration. Following the approach in Step 4, Gaussian elimination and least squares methods are used to remove the linear trend, resulting in high-precision dancing frequency and amplitude.

[0120] For example, in practical applications, when the transmission line is stationary or experiences only minor disturbances, the galloping detection method does not need to be activated and can remain in standby mode. When the line begins to exhibit significant galloping, the galloping detection process is automatically activated.

[0121] First, the sensor begins collecting acceleration and angular velocity data of the conductor, and combines these two types of data to determine whether it is currently in a zero-velocity state. When the first valid zero-velocity interval is detected, it usually means that the conductor is at the limit position of one swing, that is, reaching the maximum offset amplitude. At this point, this zero-velocity point is used as the starting point for analysis, and subsequent data sampling begins continuously.

[0122] Next, the attitude changes of the conductor are continuously tracked, the acceleration data is corrected, and the velocity and displacement information of the conductor are obtained through integration. To avoid errors caused by integration, trend terms in the velocity and displacement are removed to ensure more stable and accurate data.

[0123] Finally, based on the processed displacement data, the maximum amplitude and dominant frequency of the galloping segment are extracted as characteristic results of this galloping cycle, which are used for subsequent galloping analysis, early warning, or evaluation.

[0124] In this embodiment, an inertial sensing unit composed of accelerometers and angular velocity meters is constructed to sense the attitude information of the transmission line. By combining acceleration and angular velocity fusion processing, accurate identification of the conductor's zero-velocity state is achieved. The Mahony complementary filtering algorithm is used for attitude calculation, improving the dynamic response capability and stability of attitude estimation. Combined with the attitude-corrected acceleration, gravity interference can be effectively eliminated to obtain linear acceleration. During the acquisition of velocity and displacement, the acceleration is filtered and double-integrated. Simultaneously, Gaussian elimination and least squares methods are used to fit and remove linear trend terms in velocity and displacement, effectively suppressing the influence of accumulated errors during integration. This makes the final extracted galloping frequency and amplitude closer to the actual motion characteristics of the transmission line. In summary, this embodiment significantly improves the accuracy and stability of galloping detection data through multi-level trend removal, attitude fusion, and error correction. It possesses good anti-interference capabilities and engineering application feasibility, and is suitable for the accurate detection and evaluation of the galloping state of transmission line conductors in complex environments.

[0125] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0126] Based on the same inventive concept, this application also provides a high-precision transmission line conductor galloping detection device for implementing the high-precision transmission line conductor galloping detection method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations of one or more embodiments of the high-precision transmission line conductor galloping detection device provided below can be found in the limitations of the high-precision transmission line conductor galloping detection method described above, and will not be repeated here.

[0127] In one exemplary embodiment, such as Figure 4 As shown, a high-precision transmission line conductor galloping detection device is provided, comprising: a motion information acquisition module 401, a zero-speed state judgment module 402, a zero-speed state determination module 403, and a galloping information determination module 404, wherein:

[0128] The motion information acquisition module 401 is used to acquire the acceleration and angular velocity information of the power transmission line to be detected;

[0129] The zero-speed state determination module 402 is used to determine whether the transmission line to be tested is in a zero-speed state in the current detection cycle based on acceleration information and angular velocity information.

[0130] The zero-speed state determination module 403 is used to determine the speed of the transmission line under test as zero in the current detection cycle and to determine the current detection cycle as a zero-speed cycle when the transmission line under test is in a zero-speed state in the current detection cycle.

[0131] The galloping information determination module 404 is used to determine the galloping amplitude and galloping frequency of the transmission line to be tested based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle.

[0132] In one embodiment, the zero-speed state determination module 402 is further configured to acquire multiple sampling information of acceleration information and angular velocity information in the current detection cycle; determine the zero-speed detection statistical value of the transmission line to be tested in the current detection cycle based on the multiple sampling information and the preset noise variance of the accelerometer and angular velocity meter; and determine that the transmission line to be tested is in a zero-speed state in the current detection cycle if the zero-speed detection statistical value is less than or equal to a preset threshold.

[0133] In one embodiment, the aforementioned galloping information determination module 404 is further configured to perform attitude fusion and correction processing on the acceleration information and angular velocity information to obtain corrected acceleration; perform filtering and integration processing on the corrected acceleration to obtain initial velocity information; remove linear trend errors in the initial velocity information to obtain target velocity information; perform integration processing on the target velocity information to obtain initial displacement information; remove linear trend errors in the initial displacement information to obtain target displacement information; and extract the galloping amplitude and galloping frequency of the transmission line to be detected based on the target displacement information.

[0134] In one embodiment, the aforementioned dancing information determination module 404 is further configured to perform attitude fusion processing on acceleration information and angular velocity information through a complementary filtering model to obtain attitude data; and to perform correction processing on acceleration information and angular velocity information based on the attitude data to obtain corrected acceleration.

[0135] In one embodiment, the above-mentioned dancing information determination module 404 is further configured to construct a polynomial fitting model associated with the speed information; determine the coefficient values ​​of the polynomial fitting model with the goal of minimizing the error between the initial speed information and the output value of the polynomial fitting model; and remove the linear trend error in the initial speed information based on the polynomial fitting model containing the coefficient values ​​to obtain the target speed information.

[0136] In one embodiment, the aforementioned dancing information determination module 404 is further configured to generate a trend term data sequence corresponding to the initial velocity information in the time dimension based on a polynomial fitting model containing coefficient values; and to subtract the trend term data sequence from the initial velocity information point by point to obtain the target velocity information.

[0137] Each module in the aforementioned high-precision transmission line conductor galloping detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0138] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a high-precision method for detecting conductor galloping in power transmission lines. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0139] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0140] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0141] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0142] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0144] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0145] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0146] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A high-precision method for detecting conductor galloping in transmission lines, characterized in that, The method includes: Obtain the acceleration and angular velocity information of the transmission line to be tested; Based on the acceleration information and the angular velocity information, it is determined whether the transmission line to be tested is in a zero-speed state in the current testing cycle; If the transmission line under test is in a zero-speed state during the current testing cycle, the speed of the transmission line under test during the current testing cycle is determined to be zero, and the current testing cycle is determined to be a zero-speed cycle. Based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle, the galloping amplitude and galloping frequency of the transmission line to be tested are determined.

2. The method according to claim 1, characterized in that, The step of determining whether the transmission line to be tested is in a zero-speed state in the current detection cycle based on the acceleration information and the angular velocity information includes: Acquire multiple sampling information of the acceleration information and the angular velocity information in the current detection cycle; Based on the multiple sampling information and the preset noise variance of the accelerometer and angular velocity meter, the zero-speed detection statistical value of the transmission line to be tested in the current detection cycle is determined; If the zero-speed detection statistic is less than or equal to a preset threshold, the power transmission line to be tested is determined to be in a zero-speed state in the current detection cycle.

3. The method according to claim 1, characterized in that, The step of determining the galloping amplitude and frequency of the transmission line under test based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle includes: The acceleration and angular velocity information are fused and corrected to obtain the corrected acceleration. The corrected acceleration is filtered and integrated to obtain the initial velocity information; Remove the linear trend error from the initial velocity information to obtain the target velocity information; The target velocity information is integrated to obtain the initial displacement information; Remove the linear trend error from the initial displacement information to obtain the target displacement information; Based on the target displacement information, the galloping amplitude and galloping frequency of the transmission line to be detected are extracted.

4. The method according to claim 3, characterized in that, The attitude fusion and correction processing of the acceleration and angular velocity information to obtain the corrected acceleration includes: The acceleration information and the angular velocity information are fused using a complementary filtering model to obtain attitude data. Based on the attitude data, the acceleration information and the angular velocity information are corrected to obtain the corrected acceleration.

5. The method according to claim 3, characterized in that, The process of removing the linear trend error from the initial velocity information to obtain the target velocity information includes: Construct a polynomial fitting model that is associated with velocity information; The coefficient values ​​of the polynomial fitting model are determined with the goal of minimizing the error between the initial velocity information and the output value of the polynomial fitting model. Based on the polynomial fitting model containing the coefficient values, the linear trend error in the initial velocity information is removed to obtain the target velocity information.

6. The method according to claim 5, characterized in that, The step of removing the linear trend error from the initial velocity information based on a polynomial fitting model containing the coefficient values ​​to obtain the target velocity information includes: Based on the polynomial fitting model containing the coefficient values, a trend term data sequence corresponding to the initial velocity information in the time dimension is generated; The target speed information is obtained by subtracting the trend data sequence point by point from the initial speed information.

7. A high-precision transmission line conductor galloping detection device, characterized in that, The device includes: The motion information acquisition module is used to acquire the acceleration and angular velocity information of the power transmission line to be detected; The zero-speed state determination module is used to determine whether the transmission line to be tested is in a zero-speed state in the current detection cycle based on the acceleration information and the angular velocity information. The zero-speed state determination module is used to determine the speed of the transmission line under test as zero in the current detection cycle and to determine the current detection cycle as a zero-speed cycle when the transmission line under test is in a zero-speed state in the current detection cycle. The galloping information determination module is used to determine the galloping amplitude and galloping frequency of the transmission line to be tested based on the acceleration and angular velocity information in the detection cycle after the zero-speed cycle.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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