Seabed observation network photoelectric composite cable fault point positioning system fused with noise suppression

Abstract

The invention discloses a submarine observation network photoelectric composite cable fault point positioning system fused with noise suppression, and the system comprises sensing broadband fault signal modules which are disposed at the M end and the N end of a base station high-voltage source of a submarine observation network and two joints of a photoelectric composite cable respectively, and are used for capturing broadband fault signals generated by a fault point to be positioned in real time; the photoelectric composite cable line noise suppression module is used for carrying out specific suppression on the broadband fault signal through a two-stage series noise suppression strategy to obtain a clean broadband fault signal after noise reduction; the broadband fault signal feature detection module adopts Morse wavelets with excellent time-frequency aggregation to perform continuous wavelet transform, and performs feature extraction on clean broadband fault signals in combination with TT transform to obtain moments when the broadband fault signals reach an M end and an N end respectively; and the fault position output module calculates the fault distance according to the moments of reaching the M end and the N end in combination with the total length and the propagation speed of the photoelectric composite cable of the submarine observation network to realize fault point positioning.

Description

Technical Field

[0001] This invention belongs to the field of fault monitoring and maintenance technology for submarine observation networks, and specifically relates to a fault location system for submarine observation network optical-electric composite cables that integrates noise suppression. Background Technology

[0002] Submarine observation networks, powered by fiber optic cables laid on the seabed, provide power and transmit data to various underwater scientific instruments, serving as crucial infrastructure for long-term, in-situ, and real-time ocean observation. However, the marine environment in which these networks operate is extremely harsh. The fiber optic cables are subjected to high pressure, corrosion, ocean currents, and the effects of fishing activities, making them highly susceptible to electrical leakage due to insulation damage, leading to electrical faults. Such faults can paralyze the entire observation network, and the repair costs of submarine cables are extremely high. Therefore, the technology for rapid and accurate fault location is of paramount importance. Currently, several technical solutions have been proposed for fault location in submarine fiber optic cables, but they all have significant limitations in the complex and noisy marine environment. (1) Fault location method based on state estimation and parameter identification Existing technology 1 proposes a two-stage state estimation method based on power system topology and Kirchhoff's laws. While this method can locate fault points to some extent, its core flaw lies in its failure to fully consider the interference of complex marine electromagnetic noise on the measurement signal. Its positioning accuracy is highly dependent on the accuracy of the data collected by voltage and current sensors at the coastal base station and the seabed main base station. In actual marine environments, broadband fault signals, during long-distance transmission to the shore base station, are severely superimposed with marine environmental electromagnetic noise, equipment noise, and cross-interference, leading to a significant reduction in the signal-to-noise ratio of the measurement data. Under these low signal-to-noise ratio conditions, state estimation will amplify the measurement error, ultimately resulting in decreased fault location accuracy or even failure. In particular, when the fault point has high transition resistance, the fault characteristic signal is weak and more easily submerged by noise, making parameter identification methods difficult to respond accurately.

[0003] (2) Fault point localization method based on the combination of fault signal time domain features and machine learning Existing technology two discloses a method for fault point localization using the kurtosis coefficient, TK energy, and current polarity of the fault current as features, employing a Support Vector Machine (SVR) regression algorithm. The drawback of this method is that the stability of the time-domain features it relies on decreases sharply under extremely low signal-to-noise ratio (SNR) conditions. While features such as kurtosis and TK energy possess some inherent noise resistance, their computation is significantly affected by background noise. When broadband fault signals are submerged by strong ocean background noise, the extracted features become severely distorted or lose their discriminative power, preventing the trained SVR model from establishing an accurate mapping between features and location, thus degrading localization performance. Furthermore, this method relies on a very short time window (e.g., 1 ms) after the fault. Ensuring that the signal captured within this brief time window contains sufficient usable fault features rather than being dominated by noise in a low SNR environment presents a significant challenge.

[0004] (3) Fault location method based on traveling wave principle The traveling wave method is a commonly used technique for locating faults in terrestrial cables. It calculates the time difference between the arrival time of the traveling wave front generated by the fault at the measurement point. However, when applied to seabed observation networks, accurate detection of the traveling wave front becomes extremely difficult. Traveling waves propagating in long-distance fiber optic composite cables are distorted due to dispersion effects, resulting in a flattened wavefront. Furthermore, interference from complex electromagnetic noise in the ocean further obscures the sharp edges of the wavefront changes, making wavefront detection algorithms based on thresholding or traditional signal processing techniques prone to failure, introducing additional fault location errors.

[0005] (4) Limitations of signal processing techniques for noise suppression Traditional signal denoising techniques such as wavelet transform and empirical mode decomposition (EMD) have inherent limitations when facing the challenge of overlapping frequency bands between noise and fault features. The effectiveness of wavelet transform is highly dependent on the pre-selection of wavelet basis functions and threshold functions. For non-stationary, broadband fault signals and complex ocean noise, fixed basis functions and thresholds are insufficient to achieve optimal denoising, easily leading to signal distortion (over-smoothing of fault features) or noise residue. While EMD methods are adaptive, they suffer from mode aliasing and lack a solid mathematical foundation, resulting in poor stability when processing such signals and potentially inconsistent decomposition results, affecting the reliability of subsequent feature extraction. Some studies have attempted to use deep learning models such as the Swin-Transformer to mine deep features from fault signals. While these methods have shown potential, their model training heavily relies on a large amount of high-quality, labeled fault data samples. However, obtaining sufficient real-world data covering various fault types and noise scenarios in practical seabed observation network applications is extremely difficult and costly. Furthermore, existing algorithms are often not specifically optimized for noise suppression in fault location tasks, making it difficult to strike a balance between effectively removing noise and preserving the subtle fault features that are crucial for location with high fidelity.

[0006] In summary, existing technologies struggle to effectively suppress noise overlapping with fault characteristic frequency bands while faithfully preserving crucial, subtle fault feature information for accurate location in high-noise, complex marine environments. Developing a fault location system capable of actively combating noise and robustly extracting subtle fault features from strong interference is a critical technical challenge that urgently needs to be addressed to improve the reliability of seabed observation networks and reduce maintenance costs. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and propose a fault location system that integrates a noise suppression mechanism.

[0008] In view of this, the present invention proposes a fault location system for a submarine observation network's optical-electric composite cable integrating noise suppression, characterized by comprising: a broadband fault signal sensing module, an optical-electric composite cable line noise suppression module, a broadband fault signal feature detection module, and a fault location output module; wherein, The broadband fault signal sensing module is deployed at two connection points between the high-voltage source of the base station and the optical fiber composite cable of the submarine observation network: the M end and the N end, respectively, to capture the broadband fault signal generated by the fault point to be located in real time. The optoelectronic composite cable line noise suppression module is used to specifically suppress the characteristics of Gaussian white noise and narrowband interference through a two-stage series noise suppression strategy, so as to obtain a clean broadband fault signal after noise reduction. The broadband fault signal feature detection module is used to perform continuous wavelet transform using the Morse wavelet with excellent time-frequency aggregation, and to extract features from the clean broadband fault signal by combining it with the TT transform, so as to obtain the time when the broadband fault signal arrives at the M end and the N end respectively. The fault location output module is used to calculate the fault distance based on the arrival times at the M and N ends, combined with the total length and propagation speed of the submarine observation network's optical-electric composite cable, thereby achieving fault location.

[0009] As an improvement to the above system, the wideband fault signal sensing module has a flat amplitude-frequency characteristic with fluctuations not exceeding 3dB within the 10kHz~15MHz frequency band and a short-state convergence time of less than 0.1ms, enabling real-time capture of the wideband fault signal generated by the fault point to be located. .

[0010] As an improvement to the above system, the two-stage series noise suppression strategy includes: The first-level strategy is to deal with Gaussian white noise. Suppression, the second-level strategy is to suppress narrowband interference. inhibition.

[0011] As an improvement to the above system, the Gaussian white noise... Inhibition includes: Step S1: Generate a broadband fault signal for the fault point to be located. Perform a Fast Fourier Transform to obtain the spectrum. And normalize it; Step S2: Search for X local maxima points in the normalized spectrum, and determine the distance threshold by setting a threshold. Perform optimized filtering; Step S3: Divide the spectral boundary based on the selected maxima points and construct X empirical wavelet functions. ; Step S4: Through Based on improved empirical wavelet transform decomposition To obtain each modal component ; Step S5: Calculate each The kurtosis value K is used to reconstruct the modal components with K>3 to obtain the separated signal. Complete the task The inhibition.

[0012] As an improvement to the above system, the narrowband interference... Inhibition, including: Step T1: Separate the signal Perform a Fast Fourier Transform to obtain the spectrum. Search for its local maxima and determine the threshold. Identification center frequency ; Step T2: Construct a second-order notch filter for... Filtering is performed to output a clean, wideband fault signal after noise reduction. Complete the task The inhibition.

[0013] As an improvement to the above system, the processing procedure of the broadband fault signal feature detection module includes: Step V1: Calculate the denoised clean broadband fault signal using generalized Morse continuous wavelet transform. wavelet coefficients Then by and Convolution, taking its logarithmic modulus yields The time-frequency distribution matrix; Step V2: Calculate the time-frequency energy spectrum matrix to obtain the center frequency of the dominant frequency component. ; Step V3: Determine the infinity norm of the time-frequency energy spectrum matrix by calculating the infinity norm. main frequency components Then, the TT transform is used to generate the TT transform modulus matrix, and the time corresponding to the modulus maxima is extracted from the diagonal elements. , and ,in and These represent the arrival times of the initial broadband fault signal at the fault point at terminals M and N, respectively. The time it takes for the reflected signal of the initial broadband fault signal to reach terminal M at the fault point.

[0014] As an improvement to the above system, the fault distance Calculate according to the following formula:

[0015] in, This refers to the total length of the M and N ends of the fiber optic composite cable for the submarine observation network. v This represents the propagation speed of broadband fault signals in the cable.

[0016] As an improvement to the above system, the fault distance Calculate according to the following formula:

[0017] in, v This represents the propagation speed of broadband fault signals in the cable.

[0018] Compared with the prior art, the advantages of the present invention are: 1. More targeted and thorough noise suppression: By combining improved empirical wavelet transform (adaptive spectrum segmentation and kurtosis criterion reconstruction) with parameterized notch filter, specific suppression is performed on the characteristics of Gaussian white noise and narrowband interference respectively. Compared with single denoising methods, useful signals can be extracted more thoroughly from mixed noise, significantly improving the signal-to-noise ratio.

[0019] 2. High accuracy in fault feature extraction and more precise fault location: Continuous wavelet transform is performed using the Morse wavelet, which exhibits excellent time-frequency aggregation, and combined with the TT transform to accurately calibrate the arrival time of the wavefront. This method is extremely sensitive to abrupt changes in broadband fault signals, effectively reducing the impact of noise on abrupt change identification, thereby reducing timing errors and ultimately improving the accuracy of fault location.

[0020] 3. Enhanced system adaptability and reliability: The improved EWT has the ability to adaptively segment the spectrum, reducing the dependence of traditional methods on manually selecting wavelet basis functions and thresholds. This enables the system to better adapt to different line characteristics and changing environmental noise, improving the robustness of the method and its reliability in real complex marine environments. Attached Figure Description

[0021] Figure 1 This is a structural diagram of the submarine observation network optical-electric composite cable fault location system that integrates noise suppression, as described in this invention. Figure 2 It refers to the amplitude-frequency characteristics and transient characteristics of the wideband fault signal sensing module; Figure 3 This is a flowchart of a noise suppression method for fiber optic composite cable lines in a submarine observation network. Figure 4 This is a flowchart of the broadband fault signal feature detection module; Figure 5 This is a schematic diagram of the fault location principle in the fault location output module; Figure 6 It is a noise-filtered time-domain waveform sensed by a wideband fault signal sensing module installed at the M and N ends of the optical-electric composite cable at a fault point of 20km. Figure 7 These are the broadband fault signal characteristic detection results at the 20km fault location of the optical fiber composite cable, specifically the M and N ends. Detailed Implementation

[0022] This invention utilizes innovative signal processing technology to effectively extract weak fault characteristic signals in complex marine environmental noise backgrounds, thereby achieving precise location of fault points in optical-electric composite cables. It is applicable to seabed observation facilities that use optical-electric composite cables as the backbone of the power and information network.

[0023] Addressing the issues of insufficient noise suppression, easy loss of fault features, and low positioning accuracy in fault location of fiber optic composite cables in submarine observation networks, as pointed out in the background section, the core technical problem this invention aims to solve is: how to effectively suppress mixed noise (including Gaussian white noise and narrowband interference) in fiber optic composite cable lines under marine noise interference, while simultaneously preserving fault feature information with high fidelity, thereby achieving accurate fault location. First, fault features are easily lost. Complex marine noise (especially narrowband interference overlapping with fault feature frequency bands) can obscure weak fault features, and traditional denoising methods (such as standard wavelet transform or empirical mode decomposition) are prone to distortion or loss of fault feature information while filtering out noise. Second, noise suppression is incomplete. For broadband fault signals mixed with Gaussian white noise and narrowband interference at specific frequencies, a single denoising method is difficult to effectively address simultaneously, resulting in incomplete noise suppression and affecting the accuracy of subsequent fault feature extraction. Finally, it relies on manual experience and parameter adjustments. Existing methods (such as wavelet basis function selection and threshold setting for empirical mode decomposition) heavily rely on prior knowledge and human intervention, exhibiting poor adaptability and insufficient stability under different fault scenarios and noise backgrounds. This invention provides a fault location system for submarine observation network optical-electric composite cables that integrates noise suppression. This system achieves precise fault location through the collaborative work of multiple modules. The core innovation of the system lies in deeply embedding noise suppression into the fault location process, specifically achieved through the following technical means.

[0024] like Figure 1 The diagram shows the components of the positioning system, illustrating the connections and deployment relationships of its various modules, including a broadband fault signal sensing module, a broadband fault signal feature detection module, a fiber optic composite cable line noise suppression module, and a fault location output module. The broadband fault signal sensing module is deployed at two connection points between the high-voltage source of the submarine observation network's base station and the fiber optic composite cable: named end M and end N. These ends can be on land or deployed underwater by adding a waterproof enclosure to the broadband fault signal sensing module. It captures the broadband signal generated at fault point f in real time. S f+GWN+NIN ( t It possesses a flat amplitude-frequency characteristic with fluctuations not exceeding 3dB within the 10kHz~15MHz frequency band and a short-state convergence time of less than 0.1ms, ensuring timely and complete sensing of broadband signals. S f+GWN+NIN ( t A noise suppression module for optoelectronic composite cable lines is used to suppress noise in optoelectronic composite cable lines, specifically for broadband signals. S f+GWN+NIN ( t A two-stage series noise suppression strategy is adopted, with the first stage using Gaussian white noise. S GWN ( tSuppression, the second-level strategy is narrowband interference. S NIN ( t Inhibition. S GWN ( t Inhibition of ) : First step, for S f+GWN+NIN ( t The spectrum is obtained by performing a Fast Fourier Transform (FFT). S f+GWN+NIN ( w The first step is to normalize the normalized spectrum; the second step is to search for the M local maxima points of the normalized spectrum and determine the interval threshold by setting a distance threshold. δ w The third step involves optimizing and filtering the data; then, based on the selected maxima, the spectral boundaries are segmented, and M empirical wavelet functions are constructed. The fourth step is to... Compared with decomposition based on improved empirical wavelet transform (EWT) S f+GWN+NIN ( t ), thus obtaining each modal component. F m ( t Step 5: Calculate each F m ( t The kurtosis value K is used to reconstruct the modal components with K>3 to obtain the separated signal. S f+NIN ( t ), complete the S GWN ( t The inhibition of ). S NIN ( t Inhibition of ) : First step, for S f+NIN ( t Perform a Fast Fourier Transform to obtain the spectrum. S f+NIN (w), search for its local maxima, and determine the threshold. δ m Identification S NIN ( t The center frequency of ) f 0; The second step is to construct a second-order notch filter based on this, and then... S f+NIN ( t The signal is filtered to output a clean, wideband fault signal after noise reduction. S f ( t ), complete the SNIN ( t Suppression of fault signals. A wideband fault signal feature detection module is used to extract... S f ( t Fault characteristics (times corresponding to abrupt change points): The first step is to calculate using the generalized Morse continuous wavelet transform (CWT). S f ( t The wavelet coefficients W(a,τ) are then obtained from W(a,τ) and... S f ( t Convolution, taking its logarithmic modulus yields... S f ( t The time-frequency distribution matrix C M×N The second step is to calculate the time-frequency energy spectrum matrix E. M×N The center frequency of the dominant frequency component is obtained. f Domi Step 3: Calculate the time-frequency energy spectrum matrix E M×N The infinite norm is determined. S f ( t ) main frequency component S Domi Then, the TT transform is used to generate the TT transform modulus matrix, and the time corresponding to the modulus maxima is extracted from its diagonal elements. t M , t M2 and t N ,in t M and t N These represent the arrival times of the initial broadband fault signal at the fault point at terminals M and N, respectively. t M2 This is the time it takes for the reflected signal of the initial broadband fault signal to reach terminal M at the fault point. Fault location output module: based on the formula... or Calculate the fault distance, where L MN This refers to the total length of the M and N ends of the fiber optic composite cable for the submarine observation network. v This represents the propagation speed of broadband fault signals in the cable.

[0025] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0026] Example This embodiment provides a specific implementation scheme for a fault location system for a submarine observation network's optical-electric composite cable that integrates noise suppression. For example... Figure 1 As shown, the system consists of a broadband fault signal sensing module, a fiber optic composite cable line noise suppression module, a broadband fault signal feature detection module, and a fault location output module. Its workflow follows this pattern: signal sensing → noise suppression (suppressing white noise + suppressing narrowband interference) → signal feature detection → fault location calculation and output. Specifically: The first step is to deploy systems and sensing signals within the seabed observation network. For example... Figure 1 As shown, it will have the following features Figure 2 The broadband fault signal sensing modules, which exhibit amplitude-frequency and transient characteristics as shown, are installed at both the M and N ends. This ensures timely and complete sensing of the broadband fault signal generated at fault point f in the submarine observation network's optical-electric composite cable, and captures the mixed signal containing line noise propagating from the broadband fault signal to both the M and N ends. S f+GWN+NIN ( t ); The second step, through Figure 1 The signal transmission line enters the noise suppression module of the optoelectronic composite cable, triggering the module to implement a two-stage series noise suppression strategy. First, the first-stage strategy of Gaussian white noise is executed. S GWN ( t Suppress, then execute the second-level strategy narrowband interference. S NIN ( t Suppress, filter out sequentially S GWN ( t )and S NIN ( t ) obtain wideband fault signal S f ( t The process is as follows: Figure 3 As shown, a two-stage noise suppression strategy based on an improved EWT and a notch filter is described in detail; it should be noted that... Figure 3 The waveforms shown are for illustrative purposes only.

[0027] The third step, S f ( t The system enters the broadband fault signal feature detection module, triggering the module to perform fault signal feature detection and extracting the M-terminal signals respectively. S f ( t The moment corresponding to the maximum magnitude of the main frequency component) t M and N-end S f ( t The moment corresponding to the maximum magnitude of the main frequency component) t N Thent M and t N The value is sent to the fault location output module, which outputs the value according to the formula. or and known v The value is calculated to determine the fault point and output the specific fault location. The process is as follows: Figure 4 As shown, this illustrates the time relationship between the fault point and the detection end. The fault point location grid diagram used by the fault location output module is as follows: Figure 5 As shown, the principle of calculating the fault point of the optical-electric composite cable of the submarine observation network based on time difference is explained.

[0028] The noise suppression module for fiber optic composite cable lines, according to Figure 3 The process execution is as follows: The first step, the first-level strategy, is Gaussian white noise. S GWN ( t )inhibition.

[0029] Step 1: Data Import. This involves importing the mixed signal containing Gaussian white noise and narrowband interference captured by the wideband fault signal sensing module. S f+GWN+NIN ( t Import it into the noise suppression module of the optoelectronic composite cable line.

[0030] Step 2: Data Processing. First, [the data processing process is as follows]. S f+GWN+NIN ( t Perform FFT to obtain the spectrum S f+GWN+NIN ( w ), and normalize it (B100), then search the spectrum. S f+GWN+NIN ( w Local maxima of ) f m (B200), during the process, a local maximum interval threshold is set. δ w (B210) Set the local maximum spacing threshold. δ w Search for all maxima in the spectrum F ( w (B220), calculate F ( w The maximum value of ) F max If it exists F ( w Satisfy | F max - F (w )|< δ w Then set it to 0 (B230), F max Write f m Execute in a loop (B230); if F ( w If all values ​​are 0, the loop ends (B250); then, an empirical wavelet transform is performed to obtain... S f+GWN+NIN ( t The modal components of ) F m ( t (B300), during which two consecutive local maxima are calculated. f m The middle frequency serves as the boundary for dividing the spectrum. w m (m =1,2,…, M -1), in M +1 boundary ω m (include ω 0=0 and ω M = π )structure M An empirical wavelet, the empirical wavelet is derived from the empirical scaling function. With empirical wavelet function The structure is shown in equations (1) and (2), where, γ To ensure that two consecutive transformations do not overlap, the values ​​are as shown in equation (3). β ( x The polynomial that satisfies the transformations of equations (1) and (2) takes values ​​as shown in equation (4). The modal components obtained through empirical wavelet transform... F m ( t ) can be expressed as equation (5), where, To approximate the function, the empirical scaling function is used. and S f+GWN+NIN ( t Inner product is generated; For detail coefficients, derived from the empirical wavelet function and S f+GWN+NIN ( t The inner product is generated; then, based on the modal components... F m ( t Calculate the kurtosis value and reconstruct the separated signal from the kurtosis value. Sf+NIN ( t (B400), during which modal components are calculated. F m ( t The kurtosis value K of ) is given by formula (6), where, express S f+GWN+NIN ( t The mean of ) express S f+GWN+NIN ( t The standard deviation of Gaussian white noise and narrowband interference is used to reconstruct the signal from the modal components with kurtosis greater than 3, since the kurtosis values ​​are generally less than 3. S f+NIN ( t ), complete the S GWN ( t The inhibition of ).

[0031] (1) (2) (3) (4) (5) (6) The second step, the second-level strategy, is narrowband interference. S NIN ( t )inhibition.

[0032] Step 1: Search for and separate signals S f+NIN ( t The center frequency of narrowband interference in ) f 0(B500), during the process S f+NIN ( t Perform FFT to obtain the spectrum S f+NIN ( w ), search spectrum S f+NIN ( w Local maxima of ) w max Set a narrowband interference detection threshold δ m Calculate the mean of the spectrum around the local maximum point. w mean If | wmax -w mean |> δ m This local maximum point can be considered as the center frequency of the narrowband interference. f 0.

[0033] Step 2: Construct a second-order notch filter in the narrowband interference band, and use the second-order notch filter to reconstruct the separated signal. S f+NIN ( t ) Perform filtering to achieve global narrowband interference S NIN ( t By suppressing the fault signal, a clean broadband fault signal is obtained. S f ( t (B600), the system function of the second-order notch filter constructed in this process is shown in equation (7), where ω 0=2 πf 0 / f s The notch frequency, f 0 The center frequency of the frequency band where narrowband interference occurs. f s Sampling frequency, r The notch constant determines the stopband range and attenuation characteristics.

[0034] (7) Wideband fault signal feature detection module, according to Figure 4 The process execution is as follows: The first step is to S f ( t The wavelet coefficients are obtained by performing generalized Morse CWT. The formula for calculating the wavelet coefficients is shown in equation (8), where... These are Morse wavelet basis functions. S f ( t )and ψ ( t Convolution can achieve S f ( t Multi-scale subdivision, which is subdivided into individual components located in different frequency bands. W ( a , τ The amplitude of ) represents S f ( t The greater the correlation between Morse and Morse, the higher the magnitude of the correlation.S f ( t The more pronounced the local mutations, the more favorable it is. S f ( t Fault feature extraction.

[0035] (8) The second step is to use equation (9) to... Taking the logarithm modulus S f ( t The time-frequency distribution matrix of ) C M×N , where M is S f ( t The total number of components in different frequency bands, where N is the number of time-domain sampling points, | c ij |for S f ( t ) No. i The frequency band component in the first j Logarithmic modulus of wavelet coefficients at each time-domain sampling point. Time-frequency distribution matrix C M×N It can be fully represented S f ( t The distribution of fault information in the time-frequency domain.

[0036] The third step is to calculate the wavelet coefficients. Each element of the amplitude matrix corresponds to the center frequency of the frequency band of the wavelet coefficient. f M The product of these terms, represented by equation (10) to characterize the energy of each time-frequency block, can be used to obtain the energy. S f ( t The time-frequency energy spectrum matrix E M×N E fM for S f ( t The time-frequency energy of the mid-frequency band M, f M for S f ( t The mid-frequency band M corresponds to the center frequency.

[0037] (9) (10) The fourth step is to calculate the time-frequency energy spectrum matrix. E M×NThe infinite norm of the matrix is ​​the row vector of the matrix whose norm is required. S f ( t ) main frequency component S Domi The infinite norm and the center frequency of the dominant frequency component are calculated according to equations (11) and (12), where f i ∈{ f 1, f 2, ..., f M}, f Domi The main frequency component corresponds to the center frequency.

[0038] (11) (12) Fifth step, for S f ( t ) main frequency component I Domi Perform a time-to-time transformation (TT transform) to obtain the TT transform modulus matrix. Extract the diagonal elements of the matrix and pinpoint the time corresponding to the first modulus maxima to obtain the result. t M and t N .

[0039] The fault location output module is specifically as follows: Figure 5 As shown, according to S f ( t Propagation speed in optical fiber composite cable v Japanese style or Calculate the location of the fault and then output the fault point.

[0040] Figure 6 The system uses broadband fault signal sensing modules installed at the M and N ends to capture the noise-filtered time-domain waveforms of a 20km fault point on the submarine observation network's optical-electric composite cable. This demonstrates the system's ability to capture broadband fault signals and effectively filter noise. The waveforms, after passing through the optical-electric composite cable line noise suppression module and the broadband fault signal feature detection module, are shown below. Figure 7 The fault signal feature extraction results shown demonstrate that the system can accurately extract features. According to... v The fault location was calculated based on 189 m / us. L f =20.06425km (with an error of 64.425m) or L f=19.98675km (with an error of 13.25m).

[0041] It is worth noting that in the embodiments of the above system, the modules included are divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional module are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A fault location system for an optical-electrical hybrid cable for an observatory network with noise suppression, characterized in that, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

2. The ocean observatory network optical and electrical hybrid cable fault location system with fusion noise suppression of claim 1, wherein, The wideband fault signal sensing module has a flat amplitude-frequency characteristic fluctuating no more than 3dB within a 10kHz-15MHz frequency band and a short transient convergence time less than 0.1ms, and can capture in real time a wideband fault signal generated by a fault point to be located .

3. The fusion noise-repressed OBS optical and electrical composite cable fault location system according to claim 1, characterized in that, The method comprises the following steps: The first stage strategy is to suppress a Gaussian white noise and the second stage strategy is to suppress a narrowband interference .

4. The fusion noise-repressed OBS optical and electrical composite cable fault location system according to claim 3, characterized in that, The pair of Gaussian white noise The inhibiting includes: Step S1: generating a broadband fault signal for the fault point to be located performing fast Fourier transform to obtain a frequency spectrum and normalizing the frequency spectrum; Step S2: search X local maximum points of the normalized spectrum, set a distance threshold Optimization screening is performed; Step S3: dividing the spectrum boundary according to the screened maximum points, and constructing X empirical wavelet functions ; Step S4: obtaining each modal component by decomposing the improved empirical wavelet transform ;​​ Step S5: Calculate the kurtosis value K of each , reconstruct the modal component with K>3, and obtain the separated signal , and complete the suppression of .

5. The fusion noise-repressed OBS optical and electrical composite cable fault location system according to claim 4, characterized in that, The pair of narrowband interference Inhibiting, comprising: Step T1: Separate the signal Perform a Fast Fourier Transform to obtain the spectrum. Search for its local maxima and determine the threshold. Identification center frequency ; Step T2: Constructing a second-order notch filter, filtering the output of step T1, outputting a clean wideband fault signal after noise reduction, and completing the suppression of step T1. ​​​ 6. 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The fusion noise-repressed OBS optical and electrical composite cable fault location system according to claim 6, characterized in that, the fault distance is calculated according to the formula: wherein, Ltotai is the total length of the M end and N end of the ocean observatory network electro-optical hybrid cable, v C is the propagation velocity of the broadband fault signal in the cable.

8. The fusion noise-repressed OBS optical and electrical composite cable fault location system of claim 6, wherein, the fault distance is calculated according to the formula: wherein v is the propagation velocity of the broadband fault signal in the cable.

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