Method, device and medium for monitoring galloping of power transmission line

Abstract

The application provides a method and device for monitoring galloping of a power transmission line and a medium, and relates to the technical field of power transmission line monitoring. The method comprises the following steps: determining attitude time domain data of the power transmission line according to state time domain data of the power transmission line collected by an inertial measurement unit; performing Fourier transform on the attitude time domain data to obtain frequency domain spectrum data of the power transmission line; determining a spectrum leakage level of the frequency domain spectrum data according to a main peak amplitude and a secondary peak amplitude in the frequency domain spectrum data; determining a galloping frequency of the power transmission line based on the frequency domain spectrum data by using a frequency determination mode corresponding to the spectrum leakage level; and monitoring the power transmission line according to the galloping frequency to obtain a monitoring result. The spectrum leakage level of the frequency domain spectrum data is determined, the corresponding frequency determination mode is finely used for different degrees of frequency leakage, and the galloping frequency of the power transmission line is accurately determined, so that the galloping of the power transmission line is more accurately monitored.

Description

Technical Field

[0001] This application relates to the field of power transmission line monitoring technology, and more specifically, to a method, equipment, and medium for monitoring power transmission line galloping. Background Technology

[0002] Transmission line galloping is a low-frequency, large-amplitude self-excited vibration of transmission conductors under wind excitation. Long-term galloping of transmission lines can cause faults such as conductor breakage, hardware damage, and tower tilting. Therefore, transmission lines are a key monitoring target for power grid operation and maintenance.

[0003] In related technologies, attitude data is determined based on data collected by inertial measurement units, and spectrum analysis is performed based on this attitude angle data. The peak frequency of the spectrum analysis is directly used as the galloping frequency of the transmission line.

[0004] However, in related technologies, directly using the peak frequency of the spectrum in spectrum analysis as the galloping frequency of the transmission line can easily lead to inaccurate determination of the galloping frequency, resulting in inaccurate monitoring of transmission line galloping. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, and medium for monitoring transmission line galloping, in order to address the shortcomings of the prior art and solve the aforementioned technical problems in the related technologies.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a method for monitoring transmission line galloping, the method comprising: Based on the state time-domain data of the transmission line collected by the inertial measurement unit, the attitude time-domain data of the transmission line is determined. The frequency domain spectrum data of the transmission line is obtained by performing a Fourier transform on the attitude time domain data. The spectral leakage level of the frequency domain spectrum data is determined based on the amplitude of the main peak and the amplitude of the secondary peak in the frequency domain spectrum data. Using the frequency determination method corresponding to the spectrum leakage level, the galloping frequency of the transmission line is determined based on the frequency domain spectrum data; The transmission line is monitored based on the stated gyratory frequency, and the monitoring results are obtained.

[0007] Optionally, determining the attitude time-domain data of the transmission line based on the state time-domain data of the transmission line collected by the inertial measurement unit includes: Acquire the state time-domain data of multiple sampling points collected by the inertial measurement unit; The state time-domain data is compensated to obtain compensated state time-domain data; The compensated state time-domain data is smoothed and denoised to obtain denoised state time-domain data. Attitude calculation is performed based on the denoised state time-domain data to obtain the attitude time-domain data, wherein the attitude time-domain data includes: pitch angle time-domain data, roll angle time-domain data and yaw angle time-domain data.

[0008] Optionally, the state time-domain data includes: triaxial acceleration time-domain data and triaxial angular velocity time-domain data. The compensation processing of the state time-domain data to obtain compensated state time-domain data includes: Based on the triaxial acceleration time-domain data and the triaxial angular velocity time-domain data of the multiple sampling points, determine the partial triaxial angular velocity time-domain data when the transmission line is in a stationary state; Calculate the gyroscope zero bias based on the aforementioned partial triaxial angular velocity time-domain data; Based on the zero bias of the gyroscope, zero bias compensation is performed on the time-domain data of the three-axis angular velocity to obtain the compensated state time-domain data, which includes the compensated three-axis angular velocity time-domain data.

[0009] Optionally, determining partial triaxial angular velocity time-domain data of the transmission line when it is stationary, based on the triaxial acceleration time-domain data and the triaxial angular velocity time-domain data of the plurality of sampling points, includes: If the range of the triaxial acceleration time-domain data of multiple consecutive target sampling points is less than the first preset range, the range of the triaxial angular velocity time-domain data of multiple target sampling points is less than the second preset range, and the magnitude of the triaxial angular velocity time-domain data of multiple target sampling points is less than the preset magnitude, then the transmission line is determined to be in a stationary state. From the triaxial angular velocity time-domain data within a preset time period, determine a portion of the triaxial angular velocity time-domain data when the transmission line is in a stationary state.

[0010] Optionally, the step of calculating the gyroscope zero bias based on the partial three-axis angular velocity time-domain data includes: The mean value of the time-domain data of the partial three-axis angular velocity is used as the zero bias of the gyroscope.

[0011] Optionally, the step of performing a Fourier transform based on the attitude time-domain data to obtain the frequency domain spectrum data of the transmission line includes: The attitude time-domain data is processed using a preset window function to obtain windowed attitude time-domain data. The initial frequency domain spectrum data is obtained by performing a fast Fourier transform on the windowed attitude time-domain data. Based on a preset spectral amplitude threshold, the initial frequency domain spectral data is filtered to obtain the frequency domain spectral data.

[0012] Optionally, the step of determining the galloping frequency of the transmission line based on the frequency domain spectrum data using the frequency determination method corresponding to the spectrum leakage level includes: If the spectrum leakage level is mild spectrum leakage, then the frequency corresponding to the main peak in the frequency domain spectrum data is taken as the dancing frequency. If the spectrum leakage level is moderate, then a linear frequency modulation transformation is used to determine the dancing frequency based on the target frequency range and the preset number of points; wherein, the target frequency range is determined based on the frequency corresponding to the main peak in the frequency domain spectrum data.

[0013] Optionally, the step of determining the galloping frequency of the transmission line based on the frequency domain spectrum data using the frequency determination method corresponding to the spectrum leakage level includes: If the spectral leakage level is moderate to severe, determine the main peak and the adjacent discrete spectral lines of the main peak from the frequency domain spectral data; The dancing frequency is obtained by solving for the extreme value position through quadratic parabolic fitting based on the main peak and the adjacent discrete spectral lines.

[0014] If the spectrum leakage level is severe spectrum leakage, the dancing frequency is calculated based on the amplitude of the main peak and the amplitude of the secondary peak in the frequency domain spectrum data.

[0015] Secondly, embodiments of this application also provide a monitoring device for transmission line galloping, comprising: a memory and a processor, wherein the memory stores a computer program executable by the processor, and the processor executes the computer program to implement the transmission line galloping monitoring method described in any of the first aspects above.

[0016] Thirdly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when read and executed, enables the monitoring of transmission line galloping as described in any of the first aspects above.

[0017] The beneficial effects of this application are as follows: This application provides a method for monitoring transmission line galloping. The method may include: determining the attitude time-domain data of the transmission line based on the state time-domain data of the transmission line collected by an inertial measurement unit; performing a Fourier transform on the attitude time-domain data to obtain the frequency domain spectrum data of the transmission line; determining the spectral leakage level of the frequency domain spectrum data based on the main peak amplitude and secondary peak amplitude in the frequency domain spectrum data; determining the galloping frequency of the transmission line based on the frequency domain spectrum data using a frequency determination method corresponding to the spectral leakage level; and monitoring the transmission line according to the galloping frequency to obtain monitoring results. Based on the main peak amplitude and secondary peak amplitude in the frequency domain spectrum data, the degree of spectral leakage of the frequency domain spectrum data can be determined. For different degrees of frequency leakage, a corresponding frequency determination method is precisely applied to determine the galloping frequency of the transmission line, making the determined galloping frequency more accurate, thereby achieving more accurate monitoring of transmission line galloping. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 1 ; Figure 2 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 2 ; Figure 3 A waveform illustration of Z-axis angular velocity time-domain data provided in an embodiment of this application. Figure 1 ; Figure 4 A time-domain waveform diagram of the heading angle provided for the implementation of this application. Figure 1 ; Figure 5 A waveform diagram of time-domain data of Y-axis angular velocity provided in an embodiment of this application; Figure 6 A time-domain waveform diagram of a roll angle provided for the implementation of this application; Figure 7 This is a waveform diagram of X-axis angular velocity time-domain data provided in an embodiment of this application; Figure 8 A schematic diagram of a time-domain waveform of a pitch angle provided for the implementation of this application; Figure 9A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 3 ; Figure 10 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 4 ; Figure 11 A waveform illustration of Z-axis angular velocity time-domain data provided in an embodiment of this application. Figure 2 ; Figure 12 A waveform illustration of a heading angle provided in an embodiment of this application. Figure 2 ; Figure 13 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 5 ; Figure 14 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 6 ; Figure 15 A waveform diagram of pitch angle time-domain data provided in an embodiment of this application; Figure 16 A waveform illustration of pitch angle spectrum data provided in this application embodiment. Figure 1 ; Figure 17 A waveform illustration of pitch angle spectrum data provided in this application embodiment. Figure 2 ; Figure 18 A waveform diagram of roll angle time domain data provided in an embodiment of this application; Figure 19 A waveform illustration of roll angle spectrum data provided in this application embodiment. Figure 1 ; Figure 20 A waveform illustration of roll angle spectrum data provided in this application embodiment. Figure 2 ; Figure 21 A waveform diagram of heading angle time-domain data provided in an embodiment of this application; Figure 22 A waveform illustration of heading angle spectrum data provided in this application embodiment. Figure 1 ; Figure 23 A waveform illustration of heading angle spectrum data provided in this application embodiment. Figure 2 ; Figure 24 A schematic diagram of the structure of a monitoring device for transmission line galloping provided in an embodiment of this application; Figure 25This is a schematic diagram of the structure of a monitoring device for power transmission line galloping, provided as an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of this application, but not all embodiments.

[0021] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] In the description of this application, it should be noted that if the terms "upper", "lower", etc. appear to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that the product of this application is usually placed in, it is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0023] Furthermore, the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Additionally, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0025] This application provides a method for monitoring transmission line galloping, which is applied to a monitoring device for transmission line galloping. The monitoring device for transmission line galloping can be a server or a terminal device. The terminal device can be any of the following: computer equipment, laptop computer, tablet computer, or smartphone.

[0026] The following explains a method for monitoring transmission line galloping provided in the embodiments of this application.

[0027] Figure 1 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 1 ;like Figure 1 As shown, the method may include: S101. Determine the attitude time-domain data of the transmission line based on the state time-domain data of the transmission line collected by the inertial measurement unit.

[0028] The inertial measurement unit can be installed on the power transmission line.

[0029] In some implementations, the inertial measurement unit continuously collects the state time-domain data of the transmission line at a preset sampling frequency to obtain the state time-domain data of the transmission line collected by the inertial measurement unit; attitude calculation is performed based on the state time-domain data of the transmission line to determine the attitude time-domain data of the transmission line.

[0030] For example, the preset sampling frequency can be 100 Hz. In this embodiment, the state time-domain data of multiple sampling points in a single frame can be fixed, and the processes S102 to S105 described below can be executed based on this. For example, the number of multiple sampling points can be 4096 points.

[0031] In practical applications, a sampling frequency of 100 Hz and 4096 sampling points correspond to a frequency resolution of approximately 0.0244 Hz, which can accommodate the low-frequency galloping characteristics of transmission lines from 0.1 to 3 Hz. Of course, the sampling frequency and the number of sampling points can be set based on experience or actual needs. This application does not impose specific limitations in this regard.

[0032] S102. Perform Fourier transform on the attitude time-domain data to obtain the frequency domain spectrum data of the transmission line.

[0033] In some implementations, the attitude time-domain data can be preprocessed to obtain preprocessed attitude time-domain data, and a fast Fourier transform can be performed on the preprocessed attitude time-domain data to obtain the frequency domain spectrum data of the transmission line.

[0034] It should be noted that the frequency domain spectrum data contains complex results for each frequency point. The amplitude of each frequency point corresponds to the vibration intensity of the attitude at that frequency, while the phase reflects the temporal sequence.

[0035] S103. Determine the spectral leakage level of the frequency domain spectrum data based on the amplitude of the main peak and the amplitude of the secondary peak in the frequency domain spectrum data.

[0036] It is worth noting that transmission line galloping is a low-frequency, high-amplitude vibration. After performing a fast Fourier transform on the attitude time-domain data of a finite duration, the true frequency energy is easily diffused to adjacent frequency points, causing spectral peak shift and amplitude distortion. In other words, there is a problem of spectral leakage in the frequency domain spectrum data. Spectral leakage is the main source of error in low-frequency monitoring of transmission line galloping.

[0037] In some implementations, the amplitudes of the primary peak and the secondary peak in the frequency domain spectral data are determined, the ratio between the secondary peak amplitude and the primary peak amplitude is calculated, and the spectral leakage level of the frequency domain spectral data is determined based on this ratio. Wherein, the ratio R = secondary peak amplitude A2 / primary peak amplitude A1.

[0038] This involves pre-setting multiple preset ratio ranges and corresponding preset spectral leakage levels for each range. Different preset ratio ranges correspond to different preset spectral leakage levels. A target preset ratio range containing the calculated ratio is determined from these ranges, and the preset spectral leakage level corresponding to this target range is used as the spectral leakage level of the frequency domain spectral data.

[0039] For example, multiple preset ratio ranges may include: , , , . The corresponding preset spectrum leakage level is mild spectrum leakage. The corresponding preset spectrum leakage level is moderate spectrum leakage. The corresponding preset spectrum leakage level is moderate to severe spectrum leakage. The corresponding preset spectrum leakage level is severe spectrum leakage. The multiple preset ratio ranges shown here are merely an example and can be fine-tuned. Alternatively, multiple preset ratio ranges can be divided using asymmetric intervals, or multiple preset ratio ranges can be determined using a noise adaptive dynamic threshold; this application does not impose specific limitations on these methods.

[0040] In this embodiment, the degree of spectral leakage can be precisely and meticulously distinguished by calculating the ratio between the amplitude of the secondary peak and the amplitude of the primary peak. The spectral leakage level is used to reflect the degree of spectral leakage in the frequency domain spectral data.

[0041] S104. Using the frequency determination method corresponding to the spectrum leakage level, determine the galloping frequency of the transmission line based on the frequency domain spectrum data.

[0042] The frequency determination methods differ for different levels of spectrum leakage.

[0043] It is worth noting that different frequency determination methods are used depending on the degree of spectral leakage in the frequency domain spectrum data. Based on the frequency domain spectrum data, the galloping frequency of the transmission line can be determined in a targeted manner. This allows for reliable and accurate determination of the galloping frequency of the acquired transmission line.

[0044] In this embodiment, the degree of spectrum leakage is quantified by the ratio between the secondary peak amplitude and the primary peak amplitude. Different spectrum leakage levels correspond to different frequency determination methods, which can adapt to the spectrum leakage problem in all scenarios.

[0045] S105. Monitor the transmission line according to the galloping frequency and obtain the monitoring results.

[0046] In some implementations, the galloping level of the transmission line is determined based on the galloping frequency and the duration of the steady-state frequency. The steady-state frequency duration is the duration during which the line remains stable at the galloping frequency.

[0047] In practical applications, the galloping level of transmission lines can be any of the following: no galloping, slight galloping, steady-state galloping, or severe galloping. These galloping levels are merely examples and can be optimized based on them.

[0048] In addition, the monitoring results may include the galloping level of the transmission lines. Of course, the monitoring results may also include the galloping frequency and the duration of the steady-state frequency. The monitoring results can be displayed or read aloud.

[0049] In summary, this application provides a method for monitoring transmission line galloping. This method may include: determining the transmission line's attitude time-domain data based on the transmission line's state time-domain data collected by an inertial measurement unit; performing a Fourier transform on the attitude time-domain data to obtain the transmission line's frequency domain spectrum data; determining the spectral leakage level of the frequency domain spectrum data based on the main peak amplitude and secondary peak amplitude; determining the galloping frequency of the transmission line based on the frequency domain spectrum data using a frequency determination method corresponding to the spectral leakage level; and monitoring the transmission line based on the galloping frequency to obtain monitoring results. By using the main peak amplitude and secondary peak amplitude in the frequency domain spectrum data, the degree of spectral leakage can be determined. For different degrees of frequency leakage, a precise frequency determination method is used to determine the galloping frequency of the transmission line, making the determined galloping frequency more accurate, thereby achieving more accurate monitoring of transmission line galloping.

[0050] Optionally, Figure 2 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 2 ;like Figure 2As shown, the process of determining the attitude time-domain data of the transmission line based on the state time-domain data of the transmission line collected by the inertial measurement unit in S101 above may include: S201. Acquire the state time-domain data of multiple sampling points collected by the inertial measurement unit.

[0051] Here, "multiple sampling points" refers to the number of sampling points corresponding to a single frame; for example, the number of sampling points could be 4096. Of course, the sampling frequency could also be 80Hz or 125Hz, and the number of sampling points corresponding to a single frame could be 2048, thus reducing storage computational power. A sliding frame acquisition mode with a 512-point step size can also be used to achieve seamless data updates.

[0052] In practical applications, multiple inertial measurement units can be used to synchronously collect average values ​​at multiple points as state time-domain data, which can improve spatial anti-interference capability.

[0053] In this embodiment, the inertial measurement unit acquires the state time domain data, eliminating the need for external auxiliary data such as optical fibers, cameras, and weather sensors, thus ensuring the system's lightweight and low-power characteristics.

[0054] S202. Perform compensation processing on the state time domain data to obtain compensated state time domain data.

[0055] The state time-domain data includes: triaxial acceleration time-domain data and triaxial angular velocity time-domain data. The triaxial angular velocity time-domain data within the state time-domain data is compensated to obtain compensated triaxial angular velocity time-domain data. The compensated state time-domain data includes: compensated triaxial angular velocity time-domain data.

[0056] S203. Perform smoothing and denoising processing on the compensated state time-domain data to obtain denoised state time-domain data.

[0057] In some implementations, the compensated triaxial angular velocity time-domain data in the compensated state time-domain data is smoothed and denoised to obtain denoised triaxial angular velocity time-domain data. The denoised state time-domain data includes: the denoised triaxial angular velocity time-domain data.

[0058] For example, a smoothing denoising method can be performed using a five-point cubic smoothing denoising process, and the specific calculation formula is shown below:

[0059] in, For the compensated triaxial angular velocity time-domain data, This is the time-domain data of the three-axis angular velocity after denoising. A five-point cubic smoothing denoising method effectively filters out high-frequency random noise while fully preserving the waveform characteristics of the low-frequency dancing signal.

[0060] Of course, other smoothing and denoising methods include three-point quadratic smoothing, seven-point cubic smoothing, or five-point cubic smoothing combined with differential detrending, piecewise smoothing, and cascaded median filtering. Alternatively, an adaptive variable coefficient smoothing algorithm can be used to adapt to the denoising needs of dancing signals of varying intensities.

[0061] It should be noted that smoothing and denoising the compensated triaxial angular velocity time-domain data can filter out high-frequency random noise and pulse outliers while fully preserving the waveform characteristics of the low-frequency galloping of the transmission line, thus avoiding signal distortion caused by excessive denoising.

[0062] S204. Perform attitude calculation based on the denoised state time-domain data to obtain attitude time-domain data.

[0063] The attitude time-domain data includes pitch angle time-domain data, roll angle time-domain data, and yaw angle time-domain data.

[0064] In some implementations, quaternion attitude calculations are performed on the denoised state time-domain data to obtain attitude time-domain data such as pitch angle, roll angle, and yaw angle. Alternatively, first-order Euler integration or second-order Runge-Kutta integration can be used for attitude calculation. Single-axis galloping components can be extracted to simplify attitude calculation, multi-axis attitude data can be weighted and fused, or a high-pass filter can be superimposed after integration to remove ultra-low frequency drift components, thus obtaining the attitude time-domain data.

[0065] In practical applications, the independent attitude angle vibration signals corresponding to vertical, horizontal, and torsional galloping can be separated from the attitude time domain data to avoid multi-dimensional vibration coupling interference frequency detection, and the independent attitude angle vibration signals can be used as effective time domain signals for subsequent spectrum analysis.

[0066] Figure 3 A waveform illustration of Z-axis angular velocity time-domain data provided in an embodiment of this application. Figure 1 ,like Figure 3 As shown, the horizontal axis represents time, in seconds (s), and the vertical axis represents angular velocity, specifically the Z-axis angular velocity time-domain data, in degrees per second (deg / s). Red indicates the Z-axis angular velocity time-domain data from the state time-domain data, i.e., the original data; green indicates the denoised Z-axis angular velocity time-domain data, i.e., the smoothed data. Figure 3 It is evident that the denoised Z-axis angular velocity time-domain data effectively filters out high-frequency noise compared to the Z-axis angular velocity time-domain data in the state time-domain data.

[0067] Figure 4 A time-domain waveform diagram of the heading angle provided for the implementation of this application. Figure 1 ,like Figure 4As shown, the horizontal axis represents time in seconds (s), and the vertical axis represents the yaw angle in degrees (deg). The green waveform is the time-domain waveform of the yaw angle change, which is obtained by attitude calculation based on the denoised Z-axis angular velocity time-domain data.

[0068] Figure 5 The figure shows a waveform diagram of Y-axis angular velocity time-domain data provided in an embodiment of this application. The horizontal axis represents time (seconds), and the vertical axis represents angular velocity, specifically Y-axis angular velocity time-domain data (deg / s). Red indicates the Y-axis angular velocity time-domain data in the state time-domain data, i.e., the original data; green indicates the denoised Y-axis angular velocity time-domain data, i.e., the smoothed data. Figure 5 It is evident that the denoised Y-axis angular velocity time-domain data effectively filters out high-frequency noise compared to the Y-axis angular velocity time-domain data in the state time-domain data.

[0069] Figure 6 A time-domain waveform diagram of the roll angle provided for the implementation of this application is shown below. Figure 6 As shown, the horizontal axis represents time in seconds (s), and the vertical axis represents angle, specifically the roll angle in degrees (deg). The green waveform is the time-domain waveform of the roll angle change, which is obtained by attitude calculation based on the denoised Y-axis angular velocity time-domain data.

[0070] Figure 7 The figure shows a waveform diagram of X-axis angular velocity time-domain data provided in an embodiment of this application. The horizontal axis represents time (seconds), and the vertical axis represents angular velocity, specifically X-axis angular velocity time-domain data (deg / s). Red indicates the X-axis angular velocity time-domain data in the state time-domain data, i.e., the original data; green indicates the denoised X-axis angular velocity time-domain data, i.e., the smoothed data. Figure 7 It is evident that the denoised X-axis angular velocity time-domain data effectively filters out high-frequency noise compared to the X-axis angular velocity time-domain data in the state time-domain data.

[0071] Figure 8 A time-domain waveform diagram of a pitch angle provided for the implementation of this application, as shown below. Figure 8 As shown, the horizontal axis represents time in seconds (s), and the vertical axis represents angle, specifically the pitch angle in degrees (deg). The green waveform is the time-domain waveform of the pitch angle change, which is obtained by attitude calculation based on the denoised X-axis angular velocity time-domain data.

[0072] Optionally, Figure 9 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 3 ;like Figure 9 As shown, the state time-domain data includes: three-axis acceleration time-domain data and three-axis angular velocity time-domain data. The three axes include the x-axis, y-axis, and z-axis.

[0073] The process of compensating the state time-domain data in S202 above to obtain compensated state time-domain data may include: S301. Based on the triaxial acceleration time-domain data and triaxial angular velocity time-domain data of multiple sampling points, determine the partial triaxial angular velocity time-domain data when the transmission line is in a stationary state.

[0074] In some implementations, based on the time-domain data of triaxial acceleration and triaxial angular velocity of multiple consecutive target sampling points, it is determined whether the equipment where the inertial measurement unit is located is stationary within the time interval corresponding to the multiple target sampling points. If the equipment where the inertial measurement unit is located is stationary, it is determined that the transmission line is stationary within the time interval corresponding to the multiple target sampling points.

[0075] For example, the number of multiple target sampling points can be 50, 40, or 60, adapting to fast response or high stability scenarios. If the transmission line is stationary within the time interval corresponding to the first set of multiple target sampling points, and also stationary within the time interval corresponding to the second set of multiple target sampling points, the time-domain data of the triaxial angular velocity of the first set of multiple target sampling points and the time-domain data of the triaxial angular velocity of the second set of multiple target sampling points should be used as partial triaxial angular velocity time-domain data when the transmission line is stationary. In other words, all triaxial angular velocity time-domain data of the transmission line when it is stationary from the multiple sampling points should be used as partial triaxial angular velocity time-domain data when the transmission line is stationary.

[0076] S302. Calculate the gyroscope zero bias based on some three-axis angular velocity time-domain data.

[0077] S303. Based on the zero bias of the gyroscope, perform zero bias compensation on the time-domain data of the three-axis angular velocity to obtain the compensated state time-domain data.

[0078] The compensated state time-domain data includes: compensated triaxial angular velocity time-domain data.

[0079] In this embodiment, the gyroscope bias is subtracted from the triaxial angular velocity time-domain data of multiple sampling points to obtain compensated triaxial angular velocity time-domain data. Subtracting the gyroscope bias can eliminate gyroscope time-varying drift error and suppress attitude integral accumulation error.

[0080] Optionally, Figure 10 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 4 ;like Figure 10As shown, the process in S301 above, which determines the partial triaxial angular velocity time-domain data of the transmission line when it is stationary based on the triaxial acceleration time-domain data and triaxial angular velocity time-domain data from multiple sampling points, may include: S401. If the range of the triaxial acceleration time-domain data of multiple consecutive target sampling points is less than the first preset range, the range of the triaxial angular velocity time-domain data of multiple target sampling points is less than the second preset range, and the magnitude of the triaxial angular velocity time-domain data of multiple target sampling points is less than the preset magnitude, then the transmission line is determined to be in a stationary state.

[0081] The number of multiple target sampling points can be 50. Of course, it can also be set based on empirical values ​​or actual needs. This application embodiment does not impose specific limitations on this.

[0082] In practical applications, the first preset range can be 0.6 m / s² (meters per second squared), the second preset range can be 1.0 deg / s (degrees per second), and the preset modulus can be 1.0 deg / s. The values ​​provided here are merely examples; the first preset range, second preset range, or preset modulus can be increased or decreased according to actual needs. The modulus determination can be omitted for simplification.

[0083] In addition, the process described in S401 above can determine that the transmission line is in a stationary state within the time interval corresponding to multiple target sampling points.

[0084] S402. From the triaxial angular velocity time-domain data within a preset time period, determine a portion of the triaxial angular velocity time-domain data when the transmission line is in a stationary state.

[0085] In this embodiment of the application, within the time interval corresponding to multiple sampling points, there is at least one set of target sampling points whose corresponding time intervals also contain the transmission line in a stationary state. The triaxial angular velocity time-domain data of at least one set of target sampling points among the multiple sampling points are used as part of the triaxial angular velocity time-domain data when the transmission line is in a stationary state.

[0086] Each group of target sampling points includes multiple target sampling points.

[0087] Optionally, the process of calculating the gyroscope zero bias based on partial three-axis angular velocity time-domain data in S302 above may include: The mean of a portion of the three-axis angular velocity time-domain data is used as the gyroscope zero bias.

[0088] Specifically, the mean value of some three-axis angular velocity time-domain data when the transmission line is stationary is calculated, and this mean value is used as the zero bias of the gyroscope.

[0089] In the embodiments of this application, the median filtering and mean calculation can be performed on some three-axis angular velocity time-domain data, or the sliding mean can be used to iteratively update the gyroscope zero bias, or the independent zero bias compensation based on the split axis can be used. Alternatively, a simplified scheme of single-calibration zero bias at power-on can be adopted. The embodiments of this application do not impose specific limitations on this.

[0090] Figure 11 A waveform illustration of Z-axis angular velocity time-domain data provided in an embodiment of this application. Figure 2 ,like Figure 11 As shown, the horizontal axis represents time in seconds (s), and the vertical axis represents angular velocity, specifically the Z-axis angular velocity time-domain data in degrees per second (deg / s). Red indicates the Z-axis angular velocity time-domain data within the state time-domain data, i.e., the original data, while green indicates the denoised Z-axis angular velocity time-domain data, i.e., the smoothed data. There is a period of stillness at the beginning of the dance. In this still state, compared to the denoised Z-axis angular velocity time-domain data, the gyroscope zero bias has been subtracted.

[0091] Figure 12 A waveform illustration of a heading angle provided in an embodiment of this application. Figure 2 ,like Figure 12 As shown, the horizontal axis represents time in seconds (s), and the vertical axis represents angle, specifically the yaw angle in degrees (deg). The green waveform is the time-domain waveform of the yaw angle change, which is obtained from attitude calculation based on the denoised Z-axis angular velocity time-domain data. There is a period of stillness at the beginning of the galloping motion. During this stillness, because the gyroscope zero bias is subtracted from the denoised Z-axis angular velocity time-domain data, the yaw angle obtained from the attitude calculation does not diverge during this stillness period.

[0092] In summary, by using the combined acceleration range, angular velocity range, and angular velocity magnitude of triaxial acceleration time-domain data and triaxial angular velocity time-domain data from multiple target sampling points as a basis, the identification of a power transmission line in a stationary state can be significantly reduced, thereby making the zero-bias compensation of the triaxial angular velocity time-domain data more accurate.

[0093] Optionally, Figure 13 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 5 ;like Figure 13 As shown, the process in S102 above, which involves performing a Fourier transform on the attitude time-domain data to obtain the frequency domain spectrum data of the transmission line, may include: S501. Using a preset window function, the attitude time-domain data is processed to obtain windowed attitude time-domain data.

[0094] The preset window function is the Hanning window.

[0095] In some implementations, the Hanning window is used to perform weighted preprocessing on the pitch angle time-domain data, roll angle time-domain data, and yaw angle time-domain data in the attitude time-domain data. By smoothing the truncation effect of the attitude time-domain data through the Hanning window, the inherent fundamental spectrum leakage of the fast Fourier transform can be effectively suppressed from the source, reducing the energy diffusion and spectral peak distortion problems of low-frequency galloping signals.

[0096] S502. Perform a fast Fourier transform on the windowed attitude time-domain data to obtain the initial frequency domain spectrum data.

[0097] The initial frequency domain spectrum data is high-precision frequency domain spectrum data.

[0098] S503. Based on the preset spectral amplitude threshold, the initial frequency domain spectral data is filtered to obtain the frequency domain spectral data.

[0099] In this embodiment, the average amplitude of the spectrum in the initial frequency domain spectrum data is calculated, and a preset multiple of the average amplitude of the spectrum is used as a preset spectrum amplitude threshold. Spectral peaks in the initial frequency domain spectrum data that are greater than or equal to the preset spectrum amplitude threshold are removed. These spectral peaks are invalid pseudo-spectral peaks generated by environmental noise. The remaining spectral peaks in the initial frequency domain spectrum data are used as the frequency domain spectrum data.

[0100] It should be noted that the remaining spectral peaks in the initial frequency domain spectrum data are the spectral peaks corresponding to the actual galloping of the transmission line, which greatly improves the accuracy of determining the spectrum leakage level and galloping frequency.

[0101] In addition, 10% of the largest amplitude in the initial frequency domain spectrum data can be used to replace the average amplitude of the spectrum. In practical applications, multi-level screening can be used to filter noise spectral peaks, or an effective frequency band of 0.1 to 3 Hz can be set to remove invalid pseudo-spectral peaks caused by environmental noise and shield invalid frequency interference.

[0102] In summary, using the Hanning window to weight the attitude time-domain data effectively suppresses the inherent fundamental spectral leakage in the fast Fourier transform operation, reducing signal energy diffusion errors at the source. Combined with the frequency determination method corresponding to the spectral leakage level to determine the galloping frequency, the galloping of transmission lines can be monitored more accurately.

[0103] Optionally, the process of determining the galloping frequency of the transmission line based on frequency domain spectrum data using the frequency determination method corresponding to the spectrum leakage level in S104 above may include: If the spectrum leakage level is mild, then the frequency corresponding to the main peak in the frequency domain spectrum data will be used as the dancing frequency.

[0104] It should be noted that if the spectral leakage level is mild, it means that the spectral distortion of the frequency domain spectral data is low, and the main peak in the frequency domain spectral data can be directly extracted, with the frequency corresponding to the main peak used as the dancing frequency. The entire algorithm is efficient and has no redundancy.

[0105] In addition, for mild spectral leakage, the average output of multiple frequency points in the neighborhood can be used to determine the dancing frequency.

[0106] If the spectrum leakage level is moderate, then a linear frequency modulation transformation method is used to determine the dancing frequency based on the target frequency range and the preset number of points.

[0107] The target frequency range is determined based on the frequency corresponding to the main peak in the frequency domain spectrum data.

[0108] In practical applications, linear frequency modulation (LFM) transform is converted to LFM Z-transform, which can achieve local spectrum refinement and improve the frequency resolution of local frequency bands. The target frequency range is determined based on the frequency corresponding to the main peak in the frequency domain spectrum data. For example, the target frequency range can be... , This refers to the frequency corresponding to the main peak in the frequency domain spectrum data. The preset number of points can be set according to actual needs or empirical values; for example, the preset number of points can be 200 or 150.

[0109] In this embodiment, the inherent picket fence effect of the Fast Fourier Transform forces the true peak frequency to be quantized onto discrete spectral lines, resulting in a spectral peak shift error of up to half a frequency resolution, i.e., a spectral leakage problem. This application addresses this issue by using the frequency corresponding to the main peak... Within an extremely narrow range of 0.05 Hz before and after (i.e., a total bandwidth of only 0.1 Hz), linear frequency modulated Z-transform dense sampling is performed using 200 refined points. This is equivalent to compressing the originally sparse global spectrum grid into an ultra-fine scanning step of Δf = 0.1 / 200 = 0.0005 Hz in this local region, significantly reducing the maximum residual deviation between the true peak and the most recently calculated spectral line. The most recently calculated spectral line is the discrete frequency sampling point that best approximates the true peak after refinement.

[0110] Moreover, since the preset number of points is 200 and the calculation range is strictly locked within a narrow band, i.e. within the target frequency range, the linear frequency modulation Z-transform algorithm only requires a very small amount of multiplication and addition operations to complete the local spectrum reconstruction. There is no need to perform large-scale fast Fourier transform recalculation, which greatly reduces the interference of discrete grid on frequency positioning with low computing power overhead, accurately corrects the slight spectral peak shift error caused by the fence effect, and the obtained dancing frequency is more accurate.

[0111] In practical applications, With a target frequency range and 200 points, it can balance the need for slight offset correction with the computing power limitations of edge devices, and adapt to large-scale IoT deployments.

[0112] Optionally, Figure 14 A flowchart illustrating a method for monitoring transmission line galloping provided in this application embodiment. Figure 6 ;like Figure 14 As shown, the process of determining the galloping frequency of a transmission line based on frequency domain spectrum data using the frequency determination method corresponding to the spectrum leakage level in S104 above can include: S601. If the spectral leakage level is moderate to severe, determine the main peak and its adjacent discrete spectral lines from the frequency domain spectral data.

[0113] If the spectral leakage level is moderate to severe, the dancing frequency can be determined using pure parabolic interpolation subpixel method. Pure parabolic interpolation subpixel correction is based on frequency domain spectral data and solves for the subpixel-level true spectral peak positions through quadratic parabolic fitting, achieving high-precision frequency correction without spectral refinement.

[0114] In addition, the adjacent discrete spectral lines of the main peak include two discrete spectral lines that are adjacent to each other on the left and right.

[0115] S602. Based on the main peak and adjacent discrete spectral lines, the extreme value positions are solved by quadratic parabola fitting to obtain the dancing frequency.

[0116] The specific formula for calculating the dancing frequency is as follows:

[0117] It is worth noting that, For the frequency of the dance, This refers to the frequency corresponding to the main peak in the frequency domain spectrum data. The amplitude of the main peak, The preceding adjacent amplitude of the main peak in the frequency domain It is the next adjacent amplitude of the main peak in the frequency domain.

[0118] In practical applications, for moderate to severe spectral leakage, various pure interpolation variations can be used to determine the dancing frequency, such as three-point parabolic interpolation, five-point parabolic interpolation, amplitude-weighted interpolation, adaptive interpolation, iterative quadratic optimization interpolation, and interpolation after neighborhood denoising.

[0119] In summary, by solving for the extreme value positions through quadratic parabolic fitting, the dancing frequency can be obtained, thus resolving the problems of primary and secondary peak aliasing and spectral peak distortion shift. For moderate to severe spectral leakage, the computationally expensive linear frequency-modulated Z-transform is abandoned, and a pure parabolic interpolation subpixel method is used to determine the dancing frequency, eliminating the need for spectral refinement calculations and determining the dancing frequency with extremely low computational power.

[0120] If the spectrum leakage level is severe, the galloping frequency is calculated based on the amplitude of the main peak and the amplitude of the secondary peak in the frequency domain spectrum data.

[0121] In this embodiment of the application, if the spectrum leakage level is severe spectrum leakage, it indicates that the energy of the main peak and the secondary peak are seriously mixed. Based on the amplitude of the main peak and the amplitude of the secondary peak in the frequency domain spectrum data, a weighted average algorithm is used to fuse the dual frequency point information to calculate the dancing frequency, thereby avoiding the problem of single peak value failure.

[0122] Among them, the frequency of dancing , The amplitude of the main peak, The frequency corresponding to the main peak. The amplitude of the secondary peak. This represents the frequency corresponding to the secondary peak.

[0123] In addition, for severe spectral leakage, methods such as arithmetic average, coefficient-weighted average, and weighted secondary refinement can be used to fuse dual-frequency data to determine the galloping frequency.

[0124] It should be noted that, in the embodiments of this application, four frequency determination methods are provided, which can maximize the complementary advantages of various frequency determination methods.

[0125] In summary, the embodiments of this application quantify the spectrum leakage level and use different frequency determination methods to determine the galloping frequency for different spectrum leakage levels. This approach can adapt to spectrum leakage problems across all scenarios and significantly improve frequency monitoring accuracy. After determining the galloping frequency, additional methods such as frequency change rate auxiliary criteria, multi-frame moving average filtering, and R-value trend analysis can be added to optimize the galloping of transmission lines.

[0126] Figure 15 A waveform diagram of pitch angle time-domain data provided in an embodiment of this application; as shown Figure 15 As shown, the horizontal axis represents time in seconds (s), and the vertical axis represents angle, specifically the pitch angle in degrees (deg). Figure 16 A waveform illustration of pitch angle spectrum data provided in this application embodiment. Figure 1 ,like Figure 16 As shown, the horizontal axis represents frequency in Hz, and the vertical axis represents amplitude, specifically pitch angle amplitude in degrees. The amplitude corresponding to 0.710Hz is the main peak, and in related technologies, 0.710Hz is directly used as the pitch angle dancing frequency. Figure 17 A waveform illustration of pitch angle spectrum data provided in this application embodiment. Figure 2 ,like Figure 17As shown, the horizontal axis represents frequency in Hz, and the vertical axis represents amplitude, specifically pitch angle amplitude in deg. In this application, the 0.698991Hz corresponding to the blue dashed line is taken as the pitch angle dancing frequency.

[0127] Figure 18 A waveform diagram of roll angle time-domain data provided in an embodiment of this application; as shown Figure 18 As shown, the horizontal axis represents time in seconds (s), and the vertical axis represents angle, specifically roll angle in degrees (deg). Figure 19 A waveform illustration of roll angle spectrum data provided in this application embodiment. Figure 1 ,like Figure 19 As shown, the horizontal axis represents frequency in Hz, and the vertical axis represents amplitude, specifically the roll angle amplitude in deg. The amplitude corresponding to 0.710Hz is the main peak, and in related technologies, 0.710Hz is directly used as the rolling frequency. Figure 20 A waveform illustration of roll angle spectrum data provided in this application embodiment. Figure 2 ,like Figure 20 As shown, the horizontal axis represents frequency in Hz, and the vertical axis represents amplitude, specifically the roll angle amplitude in deg. In this application, the 0.698991 Hz corresponding to the blue dashed line is taken as the rolling frequency.

[0128] Figure 21 A waveform diagram of heading angle time-domain data provided in an embodiment of this application; as shown Figure 21 As shown, the horizontal axis represents time in seconds (s), and the vertical axis represents angle, specifically the heading angle in degrees (deg). Figure 22 A waveform illustration of heading angle spectrum data provided in this application embodiment. Figure 1 ,like Figure 22 As shown, the horizontal axis represents frequency in Hz, and the vertical axis represents amplitude, specifically the heading angle amplitude in deg. The amplitude corresponding to 0.710Hz is the main peak, and in related technologies, 0.710Hz is directly used as the heading angle dancing frequency. Figure 23 A waveform illustration of heading angle spectrum data provided in this application embodiment. Figure 2 ,like Figure 23 As shown, the horizontal axis represents frequency in Hz, and the vertical axis represents amplitude, specifically the heading angle amplitude in deg. In this application, the 0.698500Hz corresponding to the blue dashed line is taken as the heading angle gyration frequency.

[0129] In summary, the embodiments of this application completely solve the problem of insufficient frequency accuracy caused by low-frequency galloping spectrum leakage of transmission lines with extremely low computing power overhead. They take into account monitoring accuracy, real-time performance, and edge deployment capabilities. For different degrees of frequency leakage, corresponding frequency determination methods are precisely adopted to determine the galloping frequency of the transmission line, making the determined galloping frequency more accurate, thereby achieving more accurate monitoring of transmission line galloping.

[0130] The following describes the transmission line galloping monitoring device, transmission line galloping monitoring equipment, and storage medium used to implement the transmission line galloping monitoring method provided in this application. For the specific implementation process and technical effects, please refer to the relevant content of the transmission line galloping monitoring method mentioned above, and it will not be repeated below.

[0131] Figure 24 This is a schematic diagram of the structure of a monitoring device for transmission line galloping provided in an embodiment of this application, as shown below. Figure 24 As shown, the device may include: The first determining module 101 is used to determine the attitude time-domain data of the transmission line based on the state time-domain data of the transmission line collected by the inertial measurement unit. The transformation module 102 is used to perform Fourier transform on the attitude time-domain data to obtain the frequency domain spectrum data of the transmission line. The second determining module 103 is used to determine the spectral leakage level of the frequency domain spectral data based on the main peak amplitude and the secondary peak amplitude in the frequency domain spectral data; and to determine the galloping frequency of the transmission line based on the frequency domain spectral data using the frequency determining method corresponding to the spectral leakage level. The monitoring module 104 is used to monitor the transmission line according to the galloping frequency and obtain monitoring results.

[0132] Optionally, the first determining module 101 is specifically used to acquire the state time-domain data of multiple sampling points collected by the inertial measurement unit; perform compensation processing on the state time-domain data to obtain compensated state time-domain data; perform smoothing and denoising processing on the compensated state time-domain data to obtain denoised state time-domain data; and perform attitude calculation based on the denoised state time-domain data to obtain attitude time-domain data, wherein the attitude time-domain data includes: pitch angle time-domain data, roll angle time-domain data, and yaw angle time-domain data.

[0133] Optionally, the state time-domain data includes: triaxial acceleration time-domain data and triaxial angular velocity time-domain data. The first determining module 101 is specifically used to determine a portion of the triaxial angular velocity time-domain data when the transmission line is in a stationary state based on the triaxial acceleration time-domain data and the triaxial angular velocity time-domain data of the multiple sampling points; calculate the gyroscope zero bias based on the portion of the triaxial angular velocity time-domain data; and perform zero bias compensation on the triaxial angular velocity time-domain data based on the gyroscope zero bias to obtain the compensated state time-domain data, wherein the compensated state time-domain data includes: compensated triaxial angular velocity time-domain data.

[0134] Optionally, the first determining module 101 is specifically configured to determine that the transmission line is in a stationary state if the range of the triaxial acceleration time-domain data of a plurality of consecutive target sampling points among the plurality of sampling points is less than a first preset range, the range of the triaxial angular velocity time-domain data of the plurality of target sampling points is less than a second preset range, and the magnitude of the triaxial angular velocity time-domain data of the plurality of target sampling points is less than a preset magnitude; and determine the portion of the triaxial angular velocity time-domain data when the transmission line is in a stationary state from the triaxial angular velocity time-domain data within a preset time period.

[0135] Optionally, the first determining module 101 is specifically used to take the mean of the partial three-axis angular velocity time-domain data as the zero bias of the gyroscope.

[0136] Optionally, the transformation module 102 is specifically used to process the attitude time-domain data using a preset window function to obtain windowed attitude time-domain data; perform a fast Fourier transform on the windowed attitude time-domain data to obtain initial frequency domain spectrum data; and filter the initial frequency domain spectrum data based on a preset spectrum amplitude threshold to obtain the frequency domain spectrum data.

[0137] Optionally, the second determining module 103 is specifically used to determine the dancing frequency by taking the frequency corresponding to the main peak in the frequency domain spectrum data as the dancing frequency if the spectrum leakage level is mild spectrum leakage; and to determine the dancing frequency by using linear frequency modulation transformation based on the target frequency range and the preset number of points if the spectrum leakage level is moderate spectrum leakage; wherein the target frequency range is determined based on the frequency corresponding to the main peak in the frequency domain spectrum data.

[0138] Optionally, the second determining module 103 is specifically used to determine the main peak and its adjacent discrete spectral lines from the frequency domain spectrum data if the spectral leakage level is moderate to severe spectral leakage; and to obtain the galloping frequency by solving for the extreme value positions through quadratic parabolic fitting based on the main peak and the adjacent discrete spectral lines. If the spectral leakage level is severe spectral leakage, the galloping frequency is calculated based on the amplitude of the main peak and the amplitude of the secondary peak in the frequency domain spectrum data.

[0139] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0140] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0141] Figure 25 This is a schematic diagram of the structure of a monitoring device for transmission line galloping provided in an embodiment of this application, as shown below. Figure 25 As shown, the device includes: a processor 201 and a memory 202.

[0142] The memory 202 is used to store programs, and the processor 201 calls the programs stored in the memory 202 to execute the above method embodiments. The specific implementation and technical effects are similar, and will not be described in detail here.

[0143] In this application embodiment, the hardware deployment can adopt various architectures such as edge local full computing, cloud collaborative computing, solar offline storage, and multi-node distributed networking computing, all of which can achieve the core purpose of high-precision dancing frequency monitoring of this application.

[0144] Optionally, this application also provides a program product, such as a computer-readable storage medium, including a program that, when executed by a processor, performs the above-described method embodiments.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0146] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0148] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0149] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for monitoring transmission line galloping, characterized in that, The method includes: Based on the state time-domain data of the transmission line collected by the inertial measurement unit, the attitude time-domain data of the transmission line is determined. The frequency domain spectrum data of the transmission line is obtained by performing a Fourier transform on the attitude time domain data. The spectral leakage level of the frequency domain spectrum data is determined based on the amplitude of the main peak and the amplitude of the secondary peak in the frequency domain spectrum data. Using the frequency determination method corresponding to the spectrum leakage level, the galloping frequency of the transmission line is determined based on the frequency domain spectrum data; The transmission line is monitored based on the stated gyratory frequency, and the monitoring results are obtained.

2. The method according to claim 1, characterized in that, The step of determining the attitude time-domain data of the transmission line based on the state time-domain data of the transmission line collected by the inertial measurement unit includes: Acquire the state time-domain data of multiple sampling points collected by the inertial measurement unit; The state time-domain data is compensated to obtain compensated state time-domain data; The compensated state time-domain data is smoothed and denoised to obtain denoised state time-domain data. Attitude calculation is performed based on the denoised state time-domain data to obtain the attitude time-domain data, wherein the attitude time-domain data includes: pitch angle time-domain data, roll angle time-domain data and yaw angle time-domain data.

3. The method according to claim 2, characterized in that, The state time-domain data includes: triaxial acceleration time-domain data and triaxial angular velocity time-domain data. The compensation processing of the state time-domain data to obtain compensated state time-domain data includes: Based on the triaxial acceleration time-domain data and the triaxial angular velocity time-domain data of the multiple sampling points, determine the partial triaxial angular velocity time-domain data when the transmission line is in a stationary state; Calculate the gyroscope zero bias based on the aforementioned partial triaxial angular velocity time-domain data; Based on the zero bias of the gyroscope, zero bias compensation is performed on the time-domain data of the three-axis angular velocity to obtain the compensated state time-domain data, which includes the compensated three-axis angular velocity time-domain data.

4. The method according to claim 3, characterized in that, The step of determining partial triaxial angular velocity time-domain data of the transmission line when it is stationary, based on the triaxial acceleration time-domain data and the triaxial angular velocity time-domain data from the multiple sampling points, includes: If the range of the triaxial acceleration time-domain data of multiple consecutive target sampling points is less than the first preset range, the range of the triaxial angular velocity time-domain data of multiple target sampling points is less than the second preset range, and the magnitude of the triaxial angular velocity time-domain data of multiple target sampling points is less than the preset magnitude, then the transmission line is determined to be in a stationary state. From the triaxial angular velocity time-domain data within a preset time period, determine a portion of the triaxial angular velocity time-domain data when the transmission line is in a stationary state.

5. The method according to claim 4, characterized in that, The calculation of gyroscope zero bias based on the aforementioned partial triaxial angular velocity time-domain data includes: The mean value of the time-domain data of the partial three-axis angular velocity is used as the zero bias of the gyroscope.

6. The method according to claim 1, characterized in that, The step of performing a Fourier transform based on the attitude time-domain data to obtain the frequency domain spectrum data of the transmission line includes: The attitude time-domain data is processed using a preset window function to obtain windowed attitude time-domain data. The initial frequency domain spectrum data is obtained by performing a fast Fourier transform on the windowed attitude time domain data. Based on a preset spectral amplitude threshold, the initial frequency domain spectral data is filtered to obtain the frequency domain spectral data.

7. The method according to claim 1, characterized in that, The method of determining the frequency corresponding to the spectral leakage level, based on the frequency domain spectrum data, to determine the galloping frequency of the transmission line includes: If the spectrum leakage level is mild spectrum leakage, then the frequency corresponding to the main peak in the frequency domain spectrum data is taken as the dancing frequency. If the spectrum leakage level is moderate, then a linear frequency modulation transformation is used to determine the dancing frequency based on the target frequency range and the preset number of points; wherein, the target frequency range is determined based on the frequency corresponding to the main peak in the frequency domain spectrum data.

8. The method according to claim 1 or 7, characterized in that, The method of determining the frequency corresponding to the spectral leakage level, based on the frequency domain spectrum data, to determine the galloping frequency of the transmission line includes: If the spectral leakage level is moderate to severe, determine the main peak and the adjacent discrete spectral lines of the main peak from the frequency domain spectral data; The dancing frequency is obtained by solving for the extreme value position through quadratic parabolic fitting based on the main peak and the adjacent discrete spectral lines. If the spectrum leakage level is severe spectrum leakage, the dancing frequency is calculated based on the amplitude of the main peak and the amplitude of the secondary peak in the frequency domain spectrum data.

9. A monitoring device for transmission line galloping, characterized in that, include: A memory and a processor, wherein the memory stores a computer program executable by the processor, and the processor executes the computer program to implement the method for monitoring transmission line galloping as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when read and executed, enables the monitoring of transmission line galloping as described in any one of claims 1-8.

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