Underground cable fault positioning method and device based on vibration attenuation

Abstract

The invention relates to the technical field of cable fault diagnosis, and discloses an underground cable fault positioning method and device based on vibration attenuation. The method comprises the following steps: acquiring vibration data of a cable by using a plurality of sensors to form a first time sequence; decomposing the first time sequence into a plurality of intrinsic mode function components by adopting an empirical mode decomposition method; according to each intrinsic mode function component, calculating a plurality of characteristic values of the first time sequence; determining a fault position according to the position data and the characteristic value of each sensor by using a preset vibration positioning model; and matching the characteristic value corresponding to the fault position in a preset database to obtain a fault type corresponding to the fault position. The method improves the efficiency and accuracy of underground cable fault detection, guarantees the operation stability of a power system, and remarkably reduces the economic loss and safety risk caused by fault delay.

Description

Technical Field

[0001] The present invention relates to the technical field of cable fault diagnosis, and in particular to a method and a device for locating underground cable faults based on vibration attenuation. Background Art

[0002] With the rapid development of my country's economy and the advancement of urbanization, overhead cable lines have been gradually replaced by underground power cables, bringing significant benefits to the beauty and safety of cities. However, the challenges faced by underground cables have also increased, especially due to the acceleration of urban construction and renovation, and the risk of cable damage caused by frequent earthwork construction has increased significantly. Statistics show that one of the main causes of cable accidents is external force damage, among which the physical damage caused by road excavation projects is the most serious.

[0003] At present, underground cables are mostly laid directly in the ground. Although this method is simple and low-cost, it is extremely susceptible to external construction. At present, the maintenance and inspection of cable systems often adopts the method of manual collaborative inspection, which is time-consuming and labor-intensive, and it is difficult to achieve real-time monitoring; the method of using robot inspection has obvious limitations for underground direct buried cables, tunnel laying or cable trench laying, and cannot be effectively detected. At present, the monitoring scheme for cables mostly adopts the deployment of monitoring equipment and the use of image processing technology to detect the distance of hidden danger objects, but in actual applications, it is easily affected by ambient light and there may be monitoring blind spots, which is difficult to promote widely; and the method of using optical cables to detect vibration information along the monitoring area usually has a higher initial installation cost than other types of monitoring systems, and is sensitive to temperature and humidity, which affects its performance and accuracy. Summary of the invention

[0004] The present invention provides a method and device for locating underground cable faults based on vibration attenuation, which improves the efficiency and accuracy of underground cable fault detection, ensures the operational stability of the power system, and significantly reduces the economic losses and safety risks caused by fault delays.

[0005] In order to solve the above technical problems, the present invention provides a method for locating underground cable faults based on vibration attenuation, comprising:

[0006] Using a plurality of sensors to collect vibration data of the cable to form a first time series; wherein the spacing distances between the sensors are equal;

[0007] Decomposing the first time series into a plurality of intrinsic mode function components by using an empirical mode decomposition method;

[0008] Calculating a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components; wherein the eigenvalues ​​correspond to the sensors one by one;

[0009] Using a preset vibration location model, the fault location is determined according to the location data and characteristic values ​​of each of the sensors;

[0010] The characteristic value corresponding to the fault position is matched in a preset database to obtain the fault type corresponding to the fault position.

[0011] Furthermore, the empirical mode decomposition method is used to decompose the first time series into a number of intrinsic mode function components, specifically:

[0012] Determine the first time series as an original signal;

[0013] cyclically decomposing the original signal into an approximate trend term and a residual component until the residual component is a monotonic function;

[0014] The approximate trend term obtained from each decomposition is determined as the intrinsic mode function component.

[0015] Further, the decomposition of the original signal into an approximate trend term and a residual component is specifically as follows:

[0016] Acquire a plurality of maximum value points and a plurality of minimum value points in the original signal;

[0017] Interpolating a number of the maximum value points to obtain an upper envelope, and interpolating a number of the minimum value points to obtain a lower envelope;

[0018] Calculate the mean of the upper envelope and the lower envelope to obtain the approximate trend term:

[0019] Subtracting the approximate trend term from the original signal to obtain a residual component;

[0020] Determining whether the residual component is a monotonic function;

[0021] If the residual component is not a monotonic function, the residual component is determined to be the original signal.

[0022] Further, the calculating of a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components further includes:

[0023] According to each of the intrinsic mode function components, several energy entropies of the first time series are calculated, specifically:

[0024]

[0025] In the formula, H i is the energy entropy of the i-th sensor; p ijis the ratio of the energy contained in the jth intrinsic mode function component of the i-th sensor to the total energy; m is the number of intrinsic mode function components;

[0026] Alternatively, several waveform factors of the first time series are calculated according to each of the intrinsic mode function components, specifically:

[0027]

[0028] Where WF i is the waveform factor of the i-th sensor; a i (t) is the vibration signal of the i-th sensor; N is the signal a i The total number of sampling points of (t);

[0029] Alternatively, according to each of the intrinsic mode function components, several kurtosis of the first time series are calculated, specifically:

[0030]

[0031] In the formula, K i is the kurtosis of the i-th sensor; a i (t) is the vibration signal of the i-th sensor; N is the signal a i The total number of sampling points of (t); is the mean value of the vibration signal; s is the standard deviation of the vibration signal;

[0032] Alternatively, according to each of the intrinsic mode function components, several centroid frequencies of the first time series are calculated, specifically:

[0033]

[0034] Where CF i is the centroid frequency of the ith sensor; K is the total number of frequency components; f k is the kth frequency component; E ik is the energy of the frequency component of the i-th sensor in the vibration signal.

[0035] Furthermore, the preset vibration location model is used to determine the fault location according to the location data and characteristic values ​​of each sensor, specifically:

[0036] Using the position data and characteristic values ​​of each of the sensors, a preset vibration positioning model is fitted to obtain a relationship function between the characteristic value and the position;

[0037] Based on the relationship function, calculating the theoretical characteristic value corresponding to each of the sensors according to the position data of each of the sensors;

[0038] Obtain candidate position data corresponding to the two sensors with the largest theoretical eigenvalues;

[0039] The middle value of the two candidate position data is determined as the fault position.

[0040] Furthermore, the vibration positioning model is specifically:

[0041] f(x)=a·e -bx +c

[0042] Where f(x) is the characteristic value of the sensor; x is the position data of the sensor; a, b and c are the parameters to be fitted.

[0043] Furthermore, the characteristic value corresponding to the fault position is matched in a preset database to obtain the fault type corresponding to the fault position, specifically:

[0044] In the first time series, obtaining a characteristic value corresponding to the fault position;

[0045] Comparing the characteristic value corresponding to the fault position with a plurality of data thresholds in a preset database to obtain a plurality of similarities;

[0046] Obtaining a first fault type corresponding to a data threshold with the highest similarity;

[0047] The first fault type is determined as the fault type corresponding to the fault location.

[0048] Furthermore, the use of a plurality of sensors to collect vibration data of the cable to form a first time series is specifically as follows:

[0049] Obtain the installation starting point position and preset interval distance of the starting sensor;

[0050] Calculating position data of a plurality of sensors according to the installation starting point position of the starting sensor and the preset interval distance;

[0051] According to the position data of each sensor, each sensor is arranged along the longitudinal direction of the cable;

[0052] The sensors are used to collect vibration data of the cable to form a first time series.

[0053] Furthermore, the vibration data of the cable is collected by each of the sensors to form a first time series, specifically:

[0054] After collecting the vibration data of the cable by using each of the sensors, performing noise removal processing on the vibration data by using an adaptive filtering algorithm;

[0055] The denoised vibration data is subjected to data standardization processing to form a first time series.

[0056] The present invention provides an underground cable fault location method based on vibration attenuation, which uses several sensors to collect vibration data of the cable to form a first time series; uses the empirical mode decomposition method to decompose the first time series into several intrinsic mode function components; calculates several eigenvalues ​​of the first time series according to each intrinsic mode function component; uses a preset vibration location model to determine the fault location according to the position data and eigenvalues ​​of each sensor; matches the eigenvalues ​​corresponding to the fault location in a preset database to obtain the fault type corresponding to the fault location. The present invention can effectively issue an early warning when the underground direct buried cable is subjected to external impact, ensuring the stable operation of the power system and the normal operation of the social economy; improving the efficiency and accuracy of underground cable fault detection; and significantly reducing the economic losses and safety risks caused by fault delays, and has extremely high practical value and promotion prospects.

[0057] Accordingly, the present invention provides an underground cable fault location device based on vibration attenuation, comprising: a continuous acquisition module, a decomposition module, a calculation module, a positioning module and a matching module;

[0058] The acquisition module is used to collect vibration data of the cable using a plurality of sensors to form a first time series; wherein the interval distances between the sensors are equal;

[0059] The decomposition module is used to decompose the first time series into a plurality of intrinsic mode function components by using an empirical mode decomposition method;

[0060] The calculation module is used to calculate a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components; wherein the eigenvalues ​​correspond to the sensors one by one;

[0061] The positioning module is used to determine the fault location according to the position data and characteristic values ​​of each sensor using a preset vibration positioning model;

[0062] The matching module is used to match the characteristic value corresponding to the fault position in a preset database to obtain the fault type corresponding to the fault position.

[0063] The present invention provides an underground cable fault location device based on vibration attenuation. Based on the organic combination of modules, several sensors are used to collect the vibration data of the cable to form a first time series; the first time series is decomposed into several intrinsic mode function components by using the empirical mode decomposition method; several eigenvalues ​​of the first time series are calculated according to each intrinsic mode function component; the fault location is determined according to the position data and eigenvalues ​​of each sensor by using a preset vibration location model; the eigenvalues ​​corresponding to the fault location are matched in a preset database to obtain the fault type corresponding to the fault location. The present invention can effectively issue an early warning when the underground direct buried cable is subjected to external impact, ensuring the stable operation of the power system and the normal operation of the social economy; improving the efficiency and accuracy of underground cable fault detection; and significantly reducing the economic losses and safety risks caused by fault delays, and has extremely high practical value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic flow chart of an embodiment of an underground cable fault location method based on vibration attenuation provided by the present invention;

[0065] Figure 2 A schematic structural diagram of an embodiment of a vibration data acquisition system provided by the present invention;

[0066] Figure 3 A schematic diagram of the time series provided by the present invention;

[0067] Figure 4 A schematic structural diagram of an embodiment of an underground cable fault location system based on vibration attenuation provided by the present invention;

[0068] Figure 5 A schematic flow chart of another embodiment of the underground cable fault location method based on vibration attenuation provided by the present invention;

[0069] Figure 6 A schematic structural diagram of an embodiment of an underground cable fault locating device based on vibration attenuation provided by the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0072] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0073] Example 1

[0074] See also Figure 1 , is a flow chart of an embodiment of an underground cable fault location method based on vibration attenuation provided by the present invention, the method includes steps 101 to 105, each step is specifically as follows:

[0075] Step 101: using a plurality of sensors to collect vibration data of the cable to form a first time series; wherein the spacing distances between the sensors are equal.

[0076] Furthermore, in the first embodiment of the present invention, a plurality of sensors are used to collect vibration data of the cable to form a first time series, specifically:

[0077] Obtain the installation starting point position and preset interval distance of the starting sensor;

[0078] Calculating position data of a plurality of sensors according to the installation starting point position of the starting sensor and the preset interval distance;

[0079] According to the position data of each sensor, each sensor is arranged along the longitudinal direction of the cable;

[0080] The sensors are used to collect vibration data of the cable to form a first time series.

[0081] In the first embodiment of the present invention, see Figure 2 , is a structural schematic diagram of an embodiment of the vibration data acquisition system provided by the present invention. By configuring a high-sensitivity and high-antimagnetic accelerometer sensor, the acceleration or vibration generated by the cable under the action of an external force can be measured. Therefore, a group of multiple accelerometer sensors are evenly arranged on the underground direct-buried cable so that they are arranged at intervals along the longitudinal direction of the cable. Each accelerometer sensor is attached to the surface of the cable to ensure efficient transmission of the vibration signal and minimal signal attenuation. Among them, the installation position of each accelerometer sensor can be determined by the following equal spacing formula:

[0082] L i =L 0 +(i-1)·d

[0083] Where, L i is the installation position of the i-th accelerometer sensor; L 0 is the installation starting point of the first sensor; d is the spacing distance between adjacent sensors.

[0084] In the first embodiment of the present invention, a time series can be formed after vibration data is collected by using sensors arranged along the longitudinal direction of the cable. Figure 3 , a schematic diagram of the time series provided by the present invention, the data on the coordinate system in the figure is obtained by the data acquisition sensor, the horizontal axis is the distance data of each data acquisition sensor along the cable, and the vertical axis is the signal amplitude collected by the data acquisition sensor at each corresponding distance, thereby constructing a first time series. The present invention uses sensors to collect vibration data of the cable in real time, which helps to identify cable loss or failure in advance, provides a scientific basis for maintenance work, and ensures the stability and continuity of power grid operation.

[0085] Furthermore, in the first embodiment of the present invention, the vibration data of the cable is collected by using the sensors to form a first time series, specifically:

[0086] After collecting the vibration data of the cable by using each of the sensors, performing noise removal processing on the vibration data by using an adaptive filtering algorithm;

[0087] The denoised vibration data is subjected to data standardization processing to form a first time series.

[0088] In the first embodiment of the present invention, after the vibration data is acquired at various positions of the cable by using the sensor, the acquired data is denoised by using the adaptive filtering algorithm to remove the noise interference of the vibration signal, and the data is standardized to ensure the consistency and comparability of the data during the analysis process. The processed vibration data is recorded as a time series a i (t) to achieve accurate extraction of vibration characteristics and provide an accurate and stable data basis for subsequent fault analysis.

[0089] Step 102: Decomposing the first time series into a plurality of intrinsic mode function components by using an empirical mode decomposition method.

[0090] Furthermore, in the first embodiment of the present invention, an empirical mode decomposition method is used to decompose the first time series into a number of intrinsic mode function components, specifically:

[0091] Determine the first time series as an original signal;

[0092] cyclically decomposing the original signal into an approximate trend term and a residual component until the residual component is a monotonic function;

[0093] The approximate trend term obtained from each decomposition is determined as the intrinsic mode function component.

[0094] Further, in the first embodiment of the present invention, the original signal is decomposed into an approximate trend term and a residual component, specifically:

[0095] Acquire a plurality of maximum value points and a plurality of minimum value points in the original signal;

[0096] Interpolating a number of the maximum value points to obtain an upper envelope, and interpolating a number of the minimum value points to obtain a lower envelope;

[0097] Calculate the mean of the upper envelope and the lower envelope to obtain the approximate trend term:

[0098] Subtracting the approximate trend term from the original signal to obtain a residual component;

[0099] Determining whether the residual component is a monotonic function;

[0100] If the residual component is not a monotonic function, the residual component is determined to be the original signal.

[0101] In the first embodiment of the present invention, the empirical mode decomposition method is used to decompose the time series into multiple intrinsic mode function components (IMF), denoted as e ij (t), j is the corresponding IMF number. The decomposition process of the time series is as follows: first determine the signal a i All the maximum and minimum points in (t) are interpolated to obtain the upper envelope e max (t), use the minimum point interpolation to get the lower envelope e min (t). After obtaining the upper and lower envelopes, calculate the mean value m(t) of the upper and lower envelopes and use it as the approximate trend term of the signal: By obtaining the signal a i The details are extracted by the difference between (t) and the approximate trend term: d(t) = a(t)-m(t), where d(t) is the residual component. After each decomposition, it is determined whether the residual component does not contain the intrinsic mode function component or whether it is a monotonic function r(t). If the residual component is not a monotonic function and can still be decomposed, it is continued to be iteratively decomposed in the above manner. When the residual component is a monotonic function, the time series a can be sorted. i The mathematical form of (t) is: In the formula, m i(t) is the intrinsic mode function component of the i-th sensor after each decomposition, and r(t) is the residual component. According to the above decomposition method, the time series can be converted into intrinsic mode function components. Each intrinsic mode function component corresponds to a specific frequency component in the vibration signal, thereby revealing the intrinsic structure and properties of the signal in detail to reflect the vibration characteristics of the cable and provide key information for identifying cable state changes.

[0102] Step 103: Calculate a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components; wherein the eigenvalues ​​correspond to the sensors one by one.

[0103] Furthermore, in the first embodiment of the present invention, the step of calculating a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components further includes:

[0104] According to each of the intrinsic mode function components, several energy entropies of the first time series are calculated, specifically:

[0105]

[0106] In the formula, H i is the energy entropy of the i-th sensor; p ij is the ratio of the energy contained in the jth intrinsic mode function component of the i-th sensor to the total energy; m is the number of intrinsic mode function components;

[0107] Alternatively, several waveform factors of the first time series are calculated according to each of the intrinsic mode function components, specifically:

[0108]

[0109] Where WF i is the waveform factor of the i-th sensor; a i (t) is the vibration signal of the i-th sensor; N is the signal a i The total number of sampling points of (t);

[0110] Alternatively, according to each of the intrinsic mode function components, several kurtosis of the first time series are calculated, specifically:

[0111]

[0112] In the formula, K i is the kurtosis of the i-th sensor; a i (t) is the vibration signal of the i-th sensor; N is the signal a i The total number of sampling points of (t); is the mean value of the vibration signal; s is the standard deviation of the vibration signal;

[0113] Alternatively, according to each of the intrinsic mode function components, several centroid frequencies of the first time series are calculated, specifically:

[0114]

[0115] Where CF i is the centroid frequency of the ith sensor; K is the total number of frequency components; f k is the kth frequency component; E ik is the energy of the frequency component of the i-th sensor in the vibration signal.

[0116] In the first embodiment of the present invention, for each sensor, the intrinsic mode function component decomposed by the empirical mode decomposition method can calculate the characteristic value of its vibration signal, and then the fault location and fault type are determined according to the characteristic value. The characteristic value of the vibration signal includes the energy entropy H i , Form Factor WF i , Kurtosis K i and the center of gravity frequency CF i Energy entropy H i Characterizes the uniformity of signal energy distribution and reflects the complexity and randomness of the vibration signal; the waveform factor WF i Reflects the sharpness of the signal shape; Kurtosis K i quantifies the thickness of the tail of the signal probability distribution, and the center of gravity frequency indicates the CF i The location where signal energy is concentrated in the frequency domain.

[0117] Step 104: using a preset vibration location model, determine the fault location according to the location data and characteristic values ​​of each sensor.

[0118] Furthermore, in the first embodiment of the present invention, a preset vibration location model is used to determine the fault location according to the location data and characteristic values ​​of each sensor, specifically:

[0119] Using the position data and characteristic values ​​of each of the sensors, a preset vibration positioning model is fitted to obtain a relationship function between the characteristic value and the position;

[0120] Based on the relationship function, calculating the theoretical characteristic value corresponding to each of the sensors according to the position data of each of the sensors;

[0121] Obtain candidate position data corresponding to the two sensors with the largest theoretical eigenvalues;

[0122] The middle value of the two candidate position data is determined as the fault position.

[0123] Furthermore, in the first embodiment of the present invention, the vibration positioning model is specifically:

[0124] f(x)=a·e -bx +c

[0125] Where f(x) is the characteristic value of the sensor; x is the position data of the sensor; a, b and c are the parameters to be fitted.

[0126] In the first embodiment of the present invention, when performing underground cable fault location analysis, an empirical mathematical model can be used to quantitatively describe the attenuation law of the vibration characteristic quantity with distance. The mathematical model can be expressed as a function f(x) about the distance x: f(x) = a·e -bx +c, where a, b and c are key model parameters, which reveal the intrinsic connection between the physical phenomena related to the vibration characteristics and the cable fault location. Among them, the parameter a represents the initial intensity of the vibration characteristics at the vibration source (i.e., the possible cable fault point), which represents the starting amplitude of the vibration signal and is closely related to the response amplitude of the cable when it is disturbed by external forces. A high a value usually means a larger interference source, indicating more serious cable damage or fault. Parameter b reflects the attenuation rate of the vibration characteristics as the distance increases, which reflects the attenuation characteristics of the characteristics such as frequency and amplitude that change with the distance during the propagation process. A larger b value means that the vibration signal decays faster. By comparing and analyzing the attenuation rates at different distances, the range of the fault location can be accurately determined. Parameter c represents the level of the vibration characteristics that can remain unchanged when the vibration propagates to a longer distance, that is, the residual vibration level. It reflects that no matter how far the distance is, there is always a certain degree of vibration characteristics that can be detected, which is related to the conductivity of the medium or the structure of the cable itself.

[0127] In the first embodiment of the present invention, after extracting the eigenvalues ​​corresponding to each sensor, the position data and eigenvalues ​​of each sensor are used to fit the function f(x) described above, and the values ​​of parameters a, b and c are obtained. Then the position data of each sensor is substituted into the function f(x), the size of the eigenvalue corresponding to each sensor is calculated, and the two largest eigenvalues ​​are selected, and the middle value of the position where the first largest eigenvalue and the second largest eigenvalue are located is determined as the cable fault position. The present invention realizes fault location according to the vibration attenuation and characteristic quantity attenuation law, and is suitable for identifying and locating cable faults caused by external factors, such as mechanical excavation, manual construction, pile driver operation, etc. By analyzing the abnormal vibration characteristic quantity and its attenuation mode recorded in the test, the fault source and fault location can be determined quickly and accurately.

[0128] Step 105: Match the characteristic value corresponding to the fault position in a preset database to obtain the fault type corresponding to the fault position.

[0129] Furthermore, in the first embodiment of the present invention, the characteristic value corresponding to the fault position is matched in a preset database to obtain the fault type corresponding to the fault position, specifically:

[0130] In the first time series, obtaining a characteristic value corresponding to the fault position;

[0131] Comparing the characteristic value corresponding to the fault position with a plurality of data thresholds in a preset database to obtain a plurality of similarities;

[0132] Obtaining a first fault type corresponding to a data threshold with the highest similarity;

[0133] The first fault type is determined as the fault type corresponding to the fault location.

[0134] In the first embodiment of the present invention, the preset database stores vibration data of the cable under different working conditions. The data stored in the preset database is obtained through large-scale experimental research, and the experiment includes capturing the vibration characteristics of the cable under various predefined states to ensure that the obtained data covers various working conditions that the cable may encounter. After the vibration data is obtained through experimental research, the data is preprocessed and analyzed to construct a comprehensive vibration feature matching library. The vibration feature matching library contains the feature data of various vibration modes and their corresponding cable status information.

[0135] In the first embodiment of the present invention, after the fault location is determined, the characteristic value corresponding to the fault location is obtained, and the characteristic value is compared and analyzed with the characteristic value pre-stored in the database. By setting a threshold or a fluctuation range, it is possible to identify which fault type the characteristic value corresponding to the fault location belongs to, thereby determining the fault type of the fault location.

[0136] In the first embodiment of the present invention, compared with sending all the feature quantities extracted by sensor measurements at different positions to the database for matching when the fault position is not determined, the fault position is first determined and then the feature values ​​of the fault position are matched with the database. This can improve the efficiency of data matching and obtain more accurate fault type matching results.

[0137] As an example of the first embodiment of the present invention, see Figure 4, is a structural schematic diagram of an embodiment of an underground cable fault location system based on vibration attenuation provided by the present invention. The vibration sensor is used to capture the vibration characteristics of the cable in real time, including key parameters such as frequency, amplitude and duration. The data collected by each sensor is parsed and analyzed using multiple analog front-end circuits to obtain a first time series. The first time series is transmitted to the data terminal through the analog-to-digital conversion circuit and the single-chip microcomputer controller, so that the data terminal analyzes the vibration response of the cable to identify potential abnormal vibration modes, and then conducts real-time evaluation of the operating status of the cable. The underground cable fault location system can continuously monitor the cable and provide fault warnings by analyzing the vibration response of the cable, thereby ensuring the safety and reliability of power transmission.

[0138] As an example of the first embodiment of the present invention, see Figure 5 , is a flow chart of an embodiment of an underground cable fault location method based on vibration attenuation provided by the present invention. Vibration data is collected by sensors, the collected data is further subjected to adaptive filtering and denoising processing, and then the data is standardized or normalized to accurately record the vibration data of the cable in a time series manner. The vibration data is decomposed by applying a variational modal algorithm to output multiple intrinsic modes (IMFs), and time-frequency analysis is performed on each intrinsic mode to extract characteristic quantities such as frequency and amplitude. The type of vibration and its attenuation law are analyzed by constructing a vibration location model, and the cable fault is located and analyzed based on the attenuation law of the vibration signal to obtain the fault location. After obtaining the fault location, the characteristic quantity of the fault location is matched with the characteristic quantities of different types of faults recorded in the database to obtain the fault type. The present invention does not send all the characteristic quantities measured and extracted by sensors at different locations into the database for matching, but sends the unattenuated characteristic quantities or the characteristic quantities with little attenuation of the fault location into the database for matching, thereby improving the data matching method and obtaining a more accurate fault type.

[0139] In summary, the first embodiment of the present invention provides an underground cable fault location method based on vibration attenuation, using several sensors to collect vibration data of the cable to form a first time series; using the empirical mode decomposition method, the first time series is decomposed into several intrinsic mode function components; according to each intrinsic mode function component, several eigenvalues ​​of the first time series are calculated; using a preset vibration location model, the fault location is determined according to the position data and eigenvalues ​​of each sensor; the eigenvalues ​​corresponding to the fault location are matched in a preset database to obtain the fault type corresponding to the fault location. The present invention can effectively issue an early warning when the underground direct buried cable is subjected to external impact, ensuring the stable operation of the power system and the normal operation of the social economy; improving the efficiency and accuracy of underground cable fault detection; and significantly reducing the economic losses and safety risks caused by fault delays, and has extremely high practical value and promotion prospects.

[0140] Example 2

[0141] See also Figure 6 , is a schematic structural diagram of an embodiment of an underground cable fault location device based on vibration attenuation provided by the present invention, the device includes a collection module 201, a decomposition module 202, a calculation module 203, a positioning module 204 and a matching module 205;

[0142] The acquisition module 201 is used to collect the vibration data of the cable using a plurality of sensors to form a first time series; wherein the interval distances between the sensors are equal;

[0143] The decomposition module 202 is used to decompose the first time series into a plurality of intrinsic mode function components by using an empirical mode decomposition method;

[0144] The calculation module 203 is used to calculate a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components; wherein the eigenvalues ​​correspond to the sensors one by one;

[0145] The positioning module 204 is used to determine the fault location according to the position data and characteristic values ​​of each sensor using a preset vibration positioning model;

[0146] The matching module 205 is used to match the characteristic value corresponding to the fault position in a preset database to obtain the fault type corresponding to the fault position.

[0147] Furthermore, in the second embodiment of the present invention, an empirical mode decomposition method is used to decompose the first time series into a number of intrinsic mode function components, specifically:

[0148] Determine the first time series as an original signal;

[0149] cyclically decomposing the original signal into an approximate trend term and a residual component until the residual component is a monotonic function;

[0150] The approximate trend term obtained from each decomposition is determined as the intrinsic mode function component.

[0151] Furthermore, in the second embodiment of the present invention, the original signal is decomposed into an approximate trend term and a residual component, specifically:

[0152] Acquire a plurality of maximum value points and a plurality of minimum value points in the original signal;

[0153] Interpolating a number of the maximum value points to obtain an upper envelope, and interpolating a number of the minimum value points to obtain a lower envelope;

[0154] Calculate the mean of the upper envelope and the lower envelope to obtain the approximate trend term:

[0155] Subtracting the approximate trend term from the original signal to obtain a residual component;

[0156] Determining whether the residual component is a monotonic function;

[0157] If the residual component is not a monotonic function, the residual component is determined to be the original signal.

[0158] Furthermore, in the second embodiment of the present invention, the step of calculating a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components further includes:

[0159] According to each of the intrinsic mode function components, several energy entropies of the first time series are calculated, specifically:

[0160]

[0161] In the formula, H i is the energy entropy of the i-th sensor; p ij is the ratio of the energy contained in the jth intrinsic mode function component of the i-th sensor to the total energy; m is the number of intrinsic mode function components;

[0162] Alternatively, several waveform factors of the first time series are calculated according to each of the intrinsic mode function components, specifically:

[0163]

[0164] Where WF i is the waveform factor of the i-th sensor; a i (t) is the vibration signal of the i-th sensor; N is the signal a i The total number of sampling points of (t);

[0165] Alternatively, several kurtosis of the first time series are calculated according to each of the intrinsic mode function components, specifically:

[0166]

[0167] In the formula, K i is the kurtosis of the i-th sensor; a i (t) is the vibration signal of the i-th sensor; N is the signal a i The total number of sampling points of (t); is the mean value of the vibration signal; s is the standard deviation of the vibration signal;

[0168] Alternatively, according to each of the intrinsic mode function components, several centroid frequencies of the first time series are calculated, specifically:

[0169]

[0170] Where CF i is the centroid frequency of the ith sensor; K is the total number of frequency components; f k is the kth frequency component; E ik is the energy of the frequency component of the i-th sensor in the vibration signal.

[0171] Furthermore, in the second embodiment of the present invention, a preset vibration location model is used to determine the fault location according to the location data and characteristic values ​​of each sensor, specifically:

[0172] Using the position data and characteristic values ​​of each of the sensors, a preset vibration positioning model is fitted to obtain a relationship function between the characteristic value and the position;

[0173] Based on the relationship function, calculating the theoretical characteristic value corresponding to each of the sensors according to the position data of each of the sensors;

[0174] Obtain candidate position data corresponding to the two sensors with the largest theoretical eigenvalues;

[0175] The middle value of the two candidate position data is determined as the fault position.

[0176] Furthermore, in the second embodiment of the present invention, the vibration positioning model is specifically:

[0177] f(x)=a·e -bx +c

[0178] Where f(x) is the characteristic value of the sensor; x is the position data of the sensor; a, b and c are the parameters to be fitted.

[0179] Furthermore, in the second embodiment of the present invention, the characteristic value corresponding to the fault position is matched in a preset database to obtain the fault type corresponding to the fault position, specifically:

[0180] In the first time series, obtaining a characteristic value corresponding to the fault position;

[0181] Comparing the characteristic value corresponding to the fault position with a plurality of data thresholds in a preset database to obtain a plurality of similarities;

[0182] Obtaining a first fault type corresponding to a data threshold with the highest similarity;

[0183] The first fault type is determined as the fault type corresponding to the fault location.

[0184] Furthermore, in the second embodiment of the present invention, a plurality of sensors are used to collect vibration data of the cable to form a first time series, specifically:

[0185] Obtain the installation starting point position and preset interval distance of the starting sensor;

[0186] Calculating position data of a plurality of sensors according to the installation starting point position of the starting sensor and the preset interval distance;

[0187] According to the position data of each sensor, each sensor is arranged along the longitudinal direction of the cable;

[0188] The sensors are used to collect vibration data of the cable to form a first time series.

[0189] Furthermore, in the second embodiment of the present invention, the vibration data of the cable is collected by using the sensors to form a first time series, specifically:

[0190] After collecting the vibration data of the cable by using each of the sensors, performing noise removal processing on the vibration data by using an adaptive filtering algorithm;

[0191] The denoised vibration data is subjected to data standardization processing to form a first time series.

[0192] In summary, the second embodiment of the present invention provides an underground cable fault location device based on vibration attenuation, which is based on the organic combination of modules, uses several sensors to collect the vibration data of the cable to form a first time series; uses the empirical mode decomposition method to decompose the first time series into several intrinsic mode function components; calculates several eigenvalues ​​of the first time series according to each intrinsic mode function component; uses a preset vibration location model to determine the fault location according to the position data and eigenvalues ​​of each sensor; matches the eigenvalues ​​corresponding to the fault location in a preset database to obtain the fault type corresponding to the fault location. The present invention can effectively issue an early warning when the underground direct buried cable is subjected to external impact, ensuring the stable operation of the power system and the normal operation of the social economy; improving the efficiency and accuracy of underground cable fault detection; and significantly reducing the economic losses and safety risks caused by fault delays, and has extremely high practical value and promotion prospects.

[0193] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for locating underground cable faults based on vibration attenuation, characterized in that: include: Using a plurality of sensors to collect vibration data of the cable to form a first time series; wherein the spacing distances between the sensors are equal; Decomposing the first time series into a plurality of intrinsic mode function components by using an empirical mode decomposition method; Calculating a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components; wherein the eigenvalues ​​correspond to the sensors one by one; Using a preset vibration location model, the fault location is determined according to the location data and characteristic values ​​of each of the sensors; The characteristic value corresponding to the fault position is matched in a preset database to obtain the fault type corresponding to the fault position.

2. The underground cable fault location method based on vibration attenuation according to claim 1 is characterized in that: The empirical mode decomposition method is used to decompose the first time series into a number of intrinsic mode function components, specifically: Determine the first time series as an original signal; cyclically decomposing the original signal into an approximate trend term and a residual component until the residual component is a monotonic function; The approximate trend term obtained from each decomposition is determined as the intrinsic mode function component.

3. The underground cable fault location method based on vibration attenuation according to claim 2 is characterized in that: The decomposition of the original signal into an approximate trend term and a residual component is specifically as follows: Acquire a plurality of maximum value points and a plurality of minimum value points in the original signal; Interpolating a number of the maximum value points to obtain an upper envelope, and interpolating a number of the minimum value points to obtain a lower envelope; Calculate the mean of the upper envelope and the lower envelope to obtain the approximate trend term: Subtracting the approximate trend term from the original signal to obtain a residual component; Determining whether the residual component is a monotonic function; If the residual component is not a monotonic function, the residual component is determined to be the original signal.

4. The underground cable fault location method based on vibration attenuation according to claim 1, characterized in that: The step of calculating a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components further includes: According to each of the intrinsic mode function components, several energy entropies of the first time series are calculated, specifically: In the formula, H i is the energy entropy of the i-th sensor; p ij is the ratio of the energy contained in the jth intrinsic mode function component of the i-th sensor to the total energy; m is the number of intrinsic mode function components; Alternatively, several waveform factors of the first time series are calculated according to each of the intrinsic mode function components, specifically: Where WF i is the waveform factor of the i-th sensor; a i (t) is the vibration signal of the i-th sensor; N is the signal a i The total number of sampling points of (t); Alternatively, several kurtosis of the first time series are calculated according to each of the intrinsic mode function components, specifically: In the formula, K i is the kurtosis of the i-th sensor; a i (t) is the vibration signal of the i-th sensor; N is the signal a i The total number of sampling points of (t); is the mean value of the vibration signal; s is the standard deviation of the vibration signal; Alternatively, according to each of the intrinsic mode function components, several centroid frequencies of the first time series are calculated, specifically: Where, CF i is the centroid frequency of the i-th sensor; K is the total number of frequency components; f k is the kth frequency component; E ik is the energy of the frequency component of the i-th sensor in the vibration signal.

5. The underground cable fault location method based on vibration attenuation according to claim 1, characterized in that: The preset vibration location model is used to determine the fault location according to the location data and characteristic values ​​of each sensor, specifically: Using the position data and characteristic values ​​of each of the sensors, a preset vibration positioning model is fitted to obtain a relationship function between the characteristic value and the position; Based on the relationship function, calculating the theoretical characteristic value corresponding to each of the sensors according to the position data of each of the sensors; Obtain candidate position data corresponding to the two sensors with the largest theoretical eigenvalues; The middle value of the two candidate position data is determined as the fault position.

6. The underground cable fault location method based on vibration attenuation according to claim 5 is characterized in that: The vibration positioning model is specifically: f(x)=a·e -bx +c Where f(x) is the characteristic value of the sensor; x is the position data of the sensor; a, b and c are the parameters to be fitted.

7. The underground cable fault location method based on vibration attenuation according to claim 1, characterized in that: The characteristic value corresponding to the fault position is matched in a preset database to obtain the fault type corresponding to the fault position, specifically: In the first time series, obtaining a characteristic value corresponding to the fault position; Comparing the characteristic value corresponding to the fault position with a plurality of data thresholds in a preset database to obtain a plurality of similarities; Obtaining a first fault type corresponding to a data threshold with the highest similarity; The first fault type is determined as the fault type corresponding to the fault location.

8. The underground cable fault location method based on vibration attenuation according to claim 1, characterized in that: The method of collecting the vibration data of the cable using a plurality of sensors to form a first time series is specifically as follows: Obtain the installation starting point position and preset interval distance of the starting sensor; Calculating position data of a plurality of sensors according to the installation starting point position of the starting sensor and the preset interval distance; According to the position data of each sensor, each sensor is arranged along the longitudinal direction of the cable; The sensors are used to collect vibration data of the cable to form a first time series.

9. The underground cable fault location method based on vibration attenuation according to claim 8, characterized in that: The use of each of the sensors to collect the vibration data of the cable to form a first time series is specifically as follows: After collecting the vibration data of the cable by using each of the sensors, performing noise removal processing on the vibration data by using an adaptive filtering algorithm; The denoised vibration data is subjected to data standardization processing to form a first time series.

10. An underground cable fault location device based on vibration attenuation, characterized in that: include: Acquisition module, decomposition module, calculation module, positioning module and matching module; The acquisition module is used to collect vibration data of the cable using a plurality of sensors to form a first time series; wherein the interval distances between the sensors are equal; The decomposition module is used to decompose the first time series into a plurality of intrinsic mode function components by using an empirical mode decomposition method; The calculation module is used to calculate a plurality of eigenvalues ​​of the first time series according to each of the intrinsic mode function components; wherein the eigenvalues ​​correspond to the sensors one by one; The positioning module is used to determine the fault location according to the position data and characteristic values ​​of each sensor using a preset vibration positioning model; The matching module is used to match the characteristic value corresponding to the fault position in a preset database to obtain the fault type corresponding to the fault position.

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