Low-noise OPGW (Optical Fiber Composite Overhead Ground Wire) galloping monitoring method and system based on characteristic value dynamic detection
By demodulating, modal decomposition and Hilbert transforming the array signal of the OPGW line, combined with the dynamic detection feature value correction of the time scale, the problem of insufficient accuracy of the dance monitoring of the low signal-to-noise ratio OPGW line is solved, and the dance monitoring signal processing with a high signal-to-noise ratio is achieved, which improves the accuracy and reliability of the monitoring.
Patent Information
- Application Number
- CN202410012092.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-04
- Publication Date
- 2025-07-25
AI Technical Summary
The prior art is difficult to effectively process OPGW line dancing monitoring signals with low signal to noise ratio, especially in extremely low frequencies, resulting in insufficient monitoring accuracy and reliability.
By obtaining the array signal of the OPGW dance monitoring system, demodulation and modal decomposition are performed, the sensor signal close to the tower is used as a reference to eliminate high-frequency random noise, perform Hilbert transformation, and dynamic detection characteristic values of the time scale are constructed, and the filtering results are corrected to obtain the low-noise dance monitoring signal.
It significantly improves the signal-to-noise ratio, improves the accuracy and reliability of OPGW line dancing monitoring, and enhances the accuracy of subsequent positioning and identification algorithms.
Smart Images
Figure CN120372186A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wire adjustment, and in particular, to a low-noise OPGW galloping monitoring method and system based on eigenvalue dynamic detection. Background Art
[0002] During the operation of transmission lines, changes in meteorological conditions and fluctuations in power loads may both cause the galloping of transmission lines. This phenomenon may not only cause mechanical damage to the lines, but is more likely to lead to serious faults such as line short circuits and disconnections, posing a threat to the stable operation of the power grid. Traditional galloping monitoring methods, such as manual inspections and the installation of tower sensors, are limited by the monitoring range, accuracy, and labor costs. As a new monitoring technology, fiber optic sensing technology has many advantages, but its current application in transmission lines mainly focuses on effectively obtaining monitoring signals. How to further process the low signal-to-noise ratio phase signals to obtain low-noise galloping monitoring signals remains a technical challenge. Currently, the signals under the galloping state of OPGW lines exhibit extremely low frequencies. Summary of the Invention
[0003] The purpose of this application is to overcome the above-mentioned prior art and provide a low-noise OPGW galloping monitoring method and system based on eigenvalue dynamic detection.
[0004] This application provides a low-noise OPGW galloping monitoring method based on eigenvalue dynamic detection, including:
[0005] Obtain the array signals of each grating in the OPGW galloping monitoring system;
[0006] Demodulate the array signals to obtain the phase change amounts generated by different sensors in response to changes in the external environment;
[0007] Use the sensor signal near the tower as the reference sensing phase, and the other array signals as the monitoring sensing phases, and perform modal decomposition on them respectively to obtain the modal functions;
[0008] Eliminate the modal functions containing high-frequency random signals in the modal functions to obtain the reference sensing signal and the monitoring sensing signal with high-frequency random noise removed;
[0009] Perform Hilbert transform on the reference sensing signal and the monitoring sensing signal with high-frequency random noise removed to obtain the filtering processing result;
[0010] Construct the dynamic detection eigenvalues of the time scale, and correct the filtering processing result based on the dynamic detection eigenvalues to obtain the low-noise galloping monitoring signal.
[0011] Optionally, the expressions for performing modal decomposition respectively are as follows:
[0012] The demodulated phase signal needs to be subjected to modal decomposition first, that is,
[0013]
[0014] wherein, is the phase signal obtained by demodulation, and IMF i is the intrinsic mode function obtained through decomposition, and r K is the difference remainder between the original demodulated phase signal and the mode function.
[0015] Optionally, modal decomposition is performed separately, including:
[0016] When the decomposed curve does not meet the transformation requirements, the output is used as the input signal again to continue calculating the next-layer intrinsic function;
[0017] Decompose multiple times until the final result is a constant, monotonic, or has only a single extreme value.
[0018] Optionally, it is characterized in that multiple decompositions are performed until the final result is a constant, monotonic, or has only a single extreme value, including: the final end condition is expressed as:
[0019]
[0020] where IMFi and IMFi-1 are the mode functions obtained through i and i-1 times of decomposition respectively.
[0021] Optionally, it is characterized in that the expression of the dynamic detection eigenvalue is as follows:
[0022]
[0023] In the formula, f = 1 to M is the low-frequency characteristic frequency band, and Hf is the result obtained by the Hilbert transform spectrum at the corresponding frequency.
[0024] This application also provides a low-noise OPGW galloping monitoring system based on eigenvalue dynamic detection, including:
[0025] An acquisition module for acquiring the array signals of each grating in the OPGW galloping monitoring system;
[0026] A phase module for demodulating the array signals to obtain the phase change amounts generated by different sensors in response to changes in the external environment;
[0027] A decomposition module for using the sensor signal close to the tower as the reference sensing phase, and other array signals as the monitoring sensing phase, and performing modal decomposition separately to obtain the mode functions;
[0028] A rejection module, configured to reject the mode functions containing high-frequency random signals in the mode functions, and obtain a reference sensing signal and a monitoring sensing signal with high-frequency random noise removed;
[0029] A filtering module, configured to perform Hilbert transform on the reference sensing signal and the monitoring sensing signal with high-frequency random noise removed, and obtain a filtering processing result;
[0030] A signal module, configured to construct a dynamic detection eigenvalue on a time scale, and correct the filtering processing result based on the dynamic detection eigenvalue to obtain a low-noise dancing monitoring signal.
[0031] Optionally, it includes: the expressions for the mode decomposition performed by the decomposition module are as follows:
[0032] It is necessary to first perform mode decomposition on the demodulation phase signal, that is
[0033]
[0034] Where is the phase signal obtained by demodulation, and IMF i is the intrinsic mode function obtained after decomposition, and r K is the difference remainder between the original demodulation phase signal and the mode function.
[0035] Optionally, the mode decomposition performed by the decomposition module respectively includes:
[0036] When the decomposed curve does not meet the transformation requirements, the output is used as the input signal again to continue calculating the next-layer intrinsic function;
[0037] Decompose multiple times until the final result is a constant, monotonic, or has only a single extreme value.
[0038] Optionally, the decomposition module decomposes multiple times until the final result is a constant, monotonic, or has only a single extreme value, including: the final end condition is expressed as:
[0039]
[0040] Where IMFi and IMFi-1 are the mode functions obtained after i and i-1 times of decomposition respectively.
[0041] Optionally, the expression of the dynamic detection eigenvalue is as follows:
[0042]
[0043] In the formula, f = 1 to M is the low-frequency characteristic frequency band, and Hf is the result obtained from the Hilbert transform spectrum at the corresponding frequency.
[0044] The beneficial effects of this application are:
[0045] The present application provides a low-noise OPGW galloping monitoring method based on eigenvalue dynamic detection, including: obtaining array signals of each grating in the OPGW galloping monitoring system; demodulating the array signals to obtain phase change amounts generated by different sensors due to changes in the external environment; using the sensor signals near the tower as the reference sensing phase, and other array signals as the monitoring sensing phase, and respectively performing modal decomposition to obtain modal functions; removing the modal functions containing high-frequency random signals in the modal functions to obtain a reference sensing signal and a monitoring sensing signal with high-frequency random noise removed; performing Hilbert transform on the reference sensing signal and the monitoring sensing signal with high-frequency random noise removed to obtain a filtering processing result; constructing a dynamic detection eigenvalue of the time scale, and correcting the filtering processing result based on the dynamic detection eigenvalue to obtain a low-noise galloping monitoring signal. By combining reference signal modal decomposition and eigenvalue dynamic detection technology, the present application obtains a galloping monitoring signal with high signal-to-noise ratio through a weak reflection grating sensing array. This optimized signal processing method can significantly improve the signal-to-noise ratio and enhance the accuracy of subsequent positioning and recognition algorithms. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] Figure 1 is a schematic diagram of low-noise OPGW galloping monitoring based on eigenvalue dynamic detection in the present application;
[0047] Figure 2 is a schematic diagram of low-noise OPGW galloping monitoring in the present application;
[0048] Figure 3 is a schematic diagram of the low-noise OPGW galloping monitoring process in the present application;
[0049] Figure 4 is a schematic diagram of the original demodulation phase in the present application;
[0050] Figure 5 is a schematic diagram of the IMF1 eigenfunction in the present application;
[0051] Figure 6 is a schematic diagram of the IMF2 eigenfunction in the present application;
[0052] Figure 7 is a schematic diagram of the IMF3 eigenfunction in the present application;
[0053] Figure 8 is a schematic diagram of the IMF4 eigenfunction in the present application;
[0054] Figure 9 is a schematic diagram of the dynamic eigenvalue outputting the low-noise galloping monitoring result in the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0055] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present application and be able to implement it.
[0056] The following content is all examples of the specific implementation process provided to detail the technical solution to be protected by the present application. However, the present application can also be implemented in other ways different from the descriptions herein. Those skilled in the art can, under the guidance of the concept of the present application, use different technical means to implement the present application. Therefore, the present application is not limited by the following specific embodiments.
[0057] Please refer to Figures 1 to 3 As shown, a low-noise OPGW galloping monitoring method based on eigenvalue dynamic detection, the steps of which include:
[0058] S101 Obtain the array signals of each grating in the OPGW galloping monitoring system.
[0059] First, ensure that all relevant devices such as a narrow-linewidth laser, a circulator, a grating sensing array, and an unbalanced interferometer are correctly connected and initialized.
[0060] Check and ensure that all optical paths are clear without optical loss or other obstructions.
[0061] In the present application, a narrow-linewidth laser emits pulsed light to ensure the quality and stability of the optical pulses. The pulsed light is guided through the circulator 1 into the span where the galloping monitoring array is located.
[0062] When the pulsed light irradiates the low-reflectivity grating sensing array (grating 1 to grating n), each grating generates a reflected light signal.
[0063] These reflected light signals (array signals) carry information about external environmental changes (such as galloping, vibration, etc.).
[0064] S102 Demodulate the array signals to obtain the phase change amounts generated by different sensors in response to external environmental changes.
[0065] In the present application, it is necessary to ensure that the optical signal output from the unbalanced interferometer has good quality without significant noise or other interference. If necessary, appropriate optical filters or other optical components can be used to preliminarily clean or amplify the signal.
[0066] Use a high-speed, high-sensitivity photodetector to convert the interfered optical signal into an electrical signal. The converted electrical signal may still be weak or accompanied by noise, so it needs to be amplified and conditioned.
[0067] Use a low-noise amplifier to amplify the signal and ensure that the bandwidth and gain of the amplifier are set appropriately to avoid signal distortion.
[0068] In this application, a phase demodulation method is selected to extract the phase change amount caused by external environmental changes by comparing the phase difference between the reference signal and the monitoring signal.
[0069] First, a stable reference signal is set as a benchmark. This is usually achieved by using a part of the optical signal that is not affected by the external environment as a reference.
[0070] Then, a phase comparator or correlator is used to compare the phase difference between the reference signal and the monitoring signal. This can be implemented through a hardware circuit or a digital signal processing algorithm.
[0071] After the phase comparison, a voltage or digital value proportional to the phase difference is output. This output value represents the phase change amount caused by external environmental changes. For different sensors (i.e., different gratings), this phase change amount will be different, reflecting the environmental change conditions at their respective positions.
[0072] S103 uses the sensor signal near the tower as the reference sensing phase, and the other array signals as the monitoring sensing phases, and performs modal decomposition on them respectively to obtain the modal functions.
[0073] In this application, the sensor signal near the tower is selected as the reference sensing phase. Since the position of the tower is relatively stable and less affected by the environment, its signal is relatively stable and can be used as a benchmark to compare the signals of other sensors.
[0074] All other array signals except the reference sensing phase are regarded as monitoring sensing phases. These sensors may be located at different span positions, so their signals will be affected by the environment to different degrees.
[0075] In this application, modal decomposition is a method for analyzing complex signals. It can decompose a complex signal into a series of relatively simple components, called modal functions. Each modal function represents a specific frequency or characteristic component in the signal.
[0076] An appropriate modal decomposition method (such as Empirical Mode Decomposition EMD, Variational Mode Decomposition VMD, etc.) is used to decompose the reference sensing phase. After decomposition, a series of reference modal functions are obtained, and these functions represent different frequencies or characteristic components in the reference sensing phase.
[0077] Similarly, the modal decomposition method is used to decompose each monitoring sensing phase. After decomposition, a set of modal functions corresponding to each monitoring sensing phase is obtained.
[0078] In this application, due to the different span positions of the sensors, the random noise generated by external environmental interference will show certain differences, and this part of the noise signal cannot be completely eliminated by the adaptive filter. However, considering that the random noise presents a high-frequency random signal state, after its modal decomposition, removing the intrinsic mode functions containing high-frequency signals will improve the correlation of adjacent reference gratings. For the icing galloping state of the OPGW transmission line, under the action of a complex natural environment, the phase results monitored by the sensors will present non-linear and non-stationary signals. To meet the transformation conditions, it is necessary to first perform modal decomposition on the demodulated phase signal, that is
[0079]
[0080] where φ(t) is the demodulated phase signal, and IMFi is the intrinsic mode function obtained after decomposition. rK is the difference remainder between the original demodulated phase signal and the IMF (Intrinsic Mode Function, IMF) function
[0081] During the modal decomposition process, it is necessary to satisfy that the number of local extreme points and the number of zero-crossing points of the decomposed curve are less than or equal to one within the transformation interval, and the mean values of the local upper envelope and the local lower envelope are zero. Therefore, when the decomposed curve does not meet the transformation requirements, the output can be used as the input signal again to calculate the next layer of intrinsic functions, and the decomposition is performed multiple times until the final result is a constant, monotonic, or has only a single extreme value S. The final end condition can be expressed as
[0082]
[0083] where IMFi and IMFi-1 are the modal functions obtained after the i-th and (i - 1)-th decompositions respectively
[0084] S104 Remove the modal functions containing high-frequency random signals from the modal functions to obtain a reference sensing signal and a monitoring sensing signal with high-frequency random noise removed
[0085] Perform spectral analysis on each modal function to observe its frequency distribution. High-frequency random signals usually appear as broadband noise in the high-frequency band on the spectrum. High-frequency random signals usually show fast and irregular fluctuations in the time domain, with large amplitude changes
[0086] Based on the observation results of spectral analysis and time-domain characteristics, set a suitable threshold for distinguishing high-frequency random signals from other valid signals. This threshold can be set based on frequency, amplitude, or other relevant features
[0087] Compare the characteristics of each modal function with the set threshold. If the characteristics of a certain modal function exceed the threshold, it is considered a modal function containing high-frequency random signals and is removed
[0088] After screening, the retained modal functions do not contain high-frequency random signals, and these modal functions represent the effective components in the signal. The retained effective modal functions are used for signal reconstruction, that is, these modal functions are combined to restore the complete reference sensing signal and monitoring sensing signal.
[0089] S105 performs Hilbert transform on the reference sensing signal and monitoring sensing signal after removing high-frequency random noise to obtain the filtering result.
[0090] The Hilbert Transform is a commonly used method in signal processing for obtaining the envelope and instantaneous phase information of a signal. For a real signal, the Hilbert transform can generate its corresponding imaginary part, thus constructing a complex signal. This complex signal can be used for further analysis and processing, such as calculating the envelope and instantaneous frequency of the signal.
[0091] Apply Hilbert transform to the reference sensing signal and monitoring sensing signal that have already removed high-frequency random noise respectively to generate their corresponding imaginary part signals.
[0092] Combine the original real part signal with the generated imaginary part signal to construct a complex signal. This complex signal contains the complete information of the signal, including amplitude and phase.
[0093] Then, by calculating the amplitude of the complex signal, the envelope of the signal can be obtained. The envelope represents the change of the signal amplitude and helps to further analyze the characteristics of the signal.
[0094] By calculating the phase of the complex signal, the instantaneous phase of the signal can be obtained. The instantaneous phase reflects the change of the signal phase over time and is of great significance for analyzing the frequency characteristics and phase characteristics of the signal.
[0095] Analyze the envelope and instantaneous phase in the time domain to observe the amplitude and phase changes of the signal at different time points. By comparing with the original signal, the influence of the filtering process on the signal characteristics can be evaluated.
[0096] Convert the envelope and instantaneous phase to the frequency domain for analysis to observe the frequency components and distribution of the signal. By comparing with the spectrum of the original signal, the influence of the filtering process on the signal frequency characteristics can be evaluated.
[0097] Performing Hilbert transform on the reference sensing signal and monitoring sensing signal after removing high-frequency random noise can obtain the filtering result. These results include the envelope and instantaneous phase information of the signal, which can be used for further analysis and processing, such as galloping monitoring, feature extraction, etc.
[0098] S106 constructs dynamic detection eigenvalues of a time scale, and modifies the filtering processing result based on the dynamic detection eigenvalues to obtain a low-noise galloping monitoring signal.
[0099] According to the requirements of galloping monitoring and the characteristics of the signal, select a suitable time scale. This time scale should be able to capture the changes in the galloping signal and distinguish between noise and effective signals.
[0100] Within each time scale, calculate some time-domain statistics of the signal, such as mean, standard deviation, skewness, kurtosis, etc. These statistics can describe the distribution and morphological characteristics of the signal within the time scale. Combine the calculated time-domain statistics to construct one or more dynamic detection eigenvalues. These eigenvalues can reflect the dynamic changes of the signal over the time scale and help distinguish between noise and galloping signals.
[0101] According to the distribution of the dynamic detection eigenvalues, set a suitable threshold for distinguishing between noise and galloping signals. This threshold can be determined based on statistical methods, empirical values, or other relevant methods.
[0102] Compare the filtering processing result within each time scale with the dynamic detection eigenvalues. If the filtering processing result within a certain time scale exceeds the set threshold, it is considered to be interfered by noise and needs to be corrected.
[0103] The correction method can be selected according to the specific situation. For example, methods such as threshold-based truncation, smoothing filtering, interpolation, etc. can be used to correct the filtering processing result that exceeds the threshold. The goal is to make the corrected signal closer to the real galloping signal while suppressing the influence of noise.
[0104] After correction, recombine the filtering processing results within each time scale to form a corrected complete signal. This signal has a lower noise level over the time scale and better retains the characteristics of the galloping signal.
[0105] In this application, the phase change amount obtained after demodulation of the weak reflection fiber optic sensing system is subjected to modal decomposition to obtain eigenfunctions, which still contain galloping signals and environmental noise signals. The noise signals are affected by external conditions such as climate, temperature, and ice coating thickness. By constructing dynamic detection eigenvalues of the time scale, the galloping monitoring effect of the transmission line can be further improved. The eigenvalues can be expressed as:
[0106]
[0107] Among them, f = 1 to M are low-frequency feature frequency bands, and Hf is the result obtained from the Hilbert transform spectrum at the corresponding frequency. When galloping does not occur in the OPGW transmission line, the Hilbert transform spectrum will approximately follow a Gaussian distribution over time. However, when a galloping event occurs, the distribution state will fluctuate significantly. Based on the sliding eigenvalue to correct the Hilbert transform result, a low-noise galloping monitoring signal will be obtained.
[0108] Verify and evaluate the corrected signal to ensure its quality and accuracy. Some metrics can be used to evaluate the denoising effect, such as signal-to-noise ratio (SNR), mean square error (MSE), etc. At the same time, it can also be compared with other methods or reference signals to verify the effectiveness of this method.
[0109] The technical solution proposed in this application inputs an analog signal formed by a low-frequency galloping signal superimposed with Gaussian noise, and this signal has a low signal-to-noise ratio. Figure 4 The original demodulated phase is shown, and it can be seen that the overall signal-to-noise ratio is low and the galloping period is not obvious. Through modal decomposition, this application obtains eigenfunctions at all levels, such as Figures 5 to 8 shown. After removing the high-frequency eigenfunctions, perform the Hilbert transform on the remaining eigenfunctions to obtain Figure 9 the light line part in, that is, the Hilbert spectrum signal. Next, this application calculates the dynamic eigenvalues based on the time scale and uses these eigenvalues to compensate the Hilbert spectrum signal to obtain Figure 9 the dark line part in. Since it is generally considered that the signals at each frequency of the Hilbert spectrum in the natural state follow a Gaussian distribution, this application will set the eigenvalues according to the probability of the sum of the mean and standard deviation of the Gaussian distribution and make further adjustments according to the actual usage environment.
[0110] This application also provides a low-noise OPGW galloping monitoring system based on dynamic eigenvalue detection, including:
[0111] An acquisition module for acquiring the array signals of each grating in the OPGW galloping monitoring system;
[0112] A phase module for demodulating the array signal to obtain the phase change amount generated by different sensors in response to changes in the external environment;
[0113] A decomposition module for using the sensor signal near the tower as the reference sensing phase, and other array signals as the monitoring sensing phase, and performing modal decomposition respectively to obtain the modal functions;
[0114] A rejection module for rejecting the modal functions containing high-frequency random signals in the modal functions to obtain the reference sensing signal and the monitoring sensing signal after removing high-frequency random noise;
[0115] A filtering module, configured to perform Hilbert transform on a reference sensing signal and a monitoring sensing signal from which high-frequency random noise has been removed, to obtain a filtering result;
[0116] A signal module, configured to construct a dynamic detection eigenvalue on a time scale, and correct the filtering result based on the dynamic detection eigenvalue to obtain a low-noise dancing monitoring signal.
Claims
1. A low-noise OPGW galloping monitoring method based on dynamic eigenvalue detection, characterized in that Including: Obtain the array signals of each grating in the OPGW galloping monitoring system; Demodulate the array signals to obtain the phase change amounts generated by different sensors due to external environment changes; Take the sensor signals near the tower as the reference sensing phase, and other array signals as the monitoring sensing phase, and perform modal decomposition respectively to obtain the modal functions; Eliminate the modal functions containing high-frequency random signals in the modal functions to obtain the reference sensing signal and the monitoring sensing signal with high-frequency random noise removed; Perform Hilbert transform on the reference sensing signal and the monitoring sensing signal with high-frequency random noise removed to obtain the filtering processing result; Construct the dynamic detection eigenvalue of the time scale, and correct the filtering processing result based on the dynamic detection eigenvalue to obtain the low-noise galloping monitoring signal.
2. The low-noise OPGW galloping monitoring method based on eigenvalue dynamic detection according to claim 1, wherein Including: The expressions for performing modal decomposition respectively are as follows: It is necessary to first perform modal decomposition on the demodulated phase signal, that is Among them, is the phase signal obtained by demodulation, and IMF i is the intrinsic mode function obtained through decomposition, and r K is the residual difference between the original demodulated phase signal and the mode function.
3. The low-noise OPGW galloping monitoring method based on eigenvalue dynamic detection according to claim 1, wherein Perform modal decomposition respectively, including: When the decomposed curve does not meet the transformation requirements, use the output as the input signal again to continue calculating the next-layer eigenfunction; Perform decomposition multiple times until the final result is a constant, monotonic, or has only a single extreme value.
4. The low-noise OPGW galloping monitoring method based on eigenvalue dynamic detection according to claim 3, wherein Perform decomposition multiple times until the final result is a constant, monotonic, or has only a single extreme value, including: The final end condition is expressed as: Among them, IMF i and IMF i-1 are the modal functions obtained through the i-th and (i - 1)-th decompositions respectively.
5. The low-noise OPGW galloping monitoring method based on eigenvalue dynamic detection according to claim 1, characterized in that, The expression of the dynamic detection eigenvalue is as follows: where f = 1 to M is the low-frequency characteristic frequency band, and H f is the result obtained from the Hilbert transform spectrum at the corresponding frequency.
6. A low-noise OPGW galloping monitoring system based on dynamic eigenvalue detection, characterized in that, Including: An acquisition module, used to obtain the array signals of each grating in the OPGW galloping monitoring system; A phase module, used to demodulate the array signals to obtain the phase change amounts generated by different sensors due to external environment changes; A decomposition module, used to take the sensor signals near the tower as the reference sensing phase, and other array signals as the monitoring sensing phase, and perform modal decomposition respectively to obtain the modal functions; An elimination module, used to eliminate the modal functions containing high-frequency random signals in the modal functions to obtain the reference sensing signal and the monitoring sensing signal with high-frequency random noise removed; A filtering module, used to perform Hilbert transform on the reference sensing signal and the monitoring sensing signal with high-frequency random noise removed to obtain the filtering processing result; A signal module, used to construct the dynamic detection eigenvalue of the time scale, and correct the filtering processing result based on the dynamic detection eigenvalue to obtain the low-noise galloping monitoring signal.
7. The low-noise OPGW galloping monitoring system based on eigenvalue dynamic detection according to claim 6, wherein Including: The expressions for the decomposition module to perform modal decomposition respectively are as follows: It is necessary to first perform modal decomposition on the demodulated phase signal, that is Among them, is the phase signal obtained by demodulation, and IMF i is the intrinsic mode function obtained through decomposition, and r K is the residual difference between the original demodulated phase signal and the mode function.
8. The low-noise OPGW galloping monitoring system based on eigenvalue dynamic detection according to claim 6, wherein, The decomposition module performs modal decomposition respectively, including: When the decomposed curve does not meet the transformation requirements, use the output as the input signal again to continue calculating the next-layer eigenfunction; Perform decomposition multiple times until the final result is a constant, monotonic, or has only a single extreme value.
9. The low-noise OPGW galloping monitoring system based on eigenvalue dynamic detection according to claim 8, wherein The decomposition module performs decomposition multiple times until the final result is a constant, monotonic, or has only a single extreme value, including: The final end condition is expressed as: Among them, IMF i and IMF i-1 are the mode functions obtained by decomposition for the i-th and (i - 1)-th times respectively.
10. The low-noise OPGW galloping monitoring system based on eigenvalue dynamic detection according to claim 6, wherein The expression of the dynamic detection eigenvalue is as follows: where \(f = 1\sim M\) is the low-frequency feature frequency band, and \(H\) f is the result obtained from the Hilbert transform spectrum at the corresponding frequency.