Power cable fault positioning system and method based on Beidou and sound arrival angle

Abstract

The invention discloses a power cable fault positioning system based on Beidou and a sound arrival angle. The power cable fault positioning system comprises a pulse generator and a measuring station, the pulse generator periodically applies pulse signals to the power cable to excite a fault point of the power cable to emit sound signals; the measuring station is movably installed in a cable trench, the coordinate position of the measuring station is obtained through a satellite signal receiver after the measuring station is movably installed and fixed every time, a sound array signal generated when a high-voltage direct-current pulse signal is applied to a power cable by a pulse generator is collected in real time through an MEMS microphone array, and the sound array signal is timestamped. And further determining the final position of the fault point in the power cable by using a data calculation module according to the coordinate position obtained after each time of movable installation and fixation and the sound array signal with the timestamp. By implementing the method, the defects existing in positioning the fault point of the power cable by an existing acoustic-magnetic synchronization method can be overcome, so that the fault point of the power cable can be quickly and accurately positioned.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a power cable fault location system and method based on Beidou and sound arrival angle. Background Art

[0002] Power cables are an important part of the power system and an important guarantee for the development of the modern national economy and people's lives. Especially in urban areas with high population density, the scale of power cable laying is getting larger and larger as the power system expands with the urbanization process. In order to save the limited land area in the city and beautify the environment, most power cables are buried underground or in cable trenches, which has become the main way of cable laying. With the extension of the service life, power cable failures are inevitable. Although regular power cable safety inspection and troubleshooting can effectively reduce the probability of cable failure, if the fault point of the power cable cannot be located in time when the fault occurs, it will cause great inconvenience and economic losses to the surrounding factories and residents. Therefore, it is necessary for power cable managers to quickly and accurately find the fault point of the faulty cable, thereby improving the reliability of power cable power supply and reducing losses and maintenance costs. Therefore, fast and accurate power cable fault location not only has social and practical significance, but also has engineering practical value.

[0003] The environment in which buried power cables are located is relatively complex, which makes it relatively difficult to detect and locate power cable faults. The power cable fault detection operation steps can be divided into determining the nature of the power cable fault, roughly measuring the fault distance, finding the path of the faulty power cable, and accurately determining the location. The so-called accurate location is to limit the location of the fault point to a very small range, thereby avoiding the impact of large-scale excavation on the surrounding and other pipelines, and at the same time improving the efficiency of troubleshooting.

[0004] At present, the current power cable fault troubleshooting method mainly uses the acoustic-magnetic synchronization method to determine the exact location of the power cable fault point. Specifically, a pulse generator is used to apply a high-voltage DC pulse signal to the faulty power cable. Under the action of the DC pulse signal, the fault point produces a discharge phenomenon and releases sound signals and magnetic field signals to the surrounding environment. These two signals are collected and analyzed respectively, and then the fault point is accurately located.

[0005] However, the above-mentioned method of using the acoustic-magnetic synchronization method to determine the fault point of the power cable has the following defects, which are: (1) The sound signal will propagate along the power cable. Due to its fast propagation speed and slow attenuation, the overall accuracy of using the signal to locate the fault point is low; (2) When the sound signal propagates in the groove, there will be extremely serious multipath propagation phenomenon. Under the superposition of multipath effects, the sound signal will be strengthened in the groove and propagate farther, which introduces a large error in distance judgment and reduces the accuracy of fault point positioning; (3) Near the fault point, the true path of the fault sound signal is submerged in the superposition of the power cable propagation path and the groove multipath, making it impossible to accurately determine the true position of the fault point.

[0006] Therefore, there is an urgent need for a new power cable fault location method that can solve the defects of the existing acoustic-magnetic synchronous method for locating power cable fault points, thereby achieving rapid and accurate positioning of power cable fault points. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a power cable fault location system and method based on Beidou and sound arrival angle, which can solve the defects of the existing acoustic and magnetic synchronization method in locating the power cable fault point, thereby realizing rapid and accurate positioning of the power cable fault point.

[0008] In order to solve the above technical problems, the embodiment of the present invention provides a system for implementing a power cable fault location system based on Beidou and sound arrival angle, including a pulse generator and a measuring station; wherein,

[0009] The pulse generator is loaded on the power cable in the cable trench and is used to periodically apply a high-voltage DC pulse signal to the power cable so as to stimulate the fault point to emit a sound signal when there is a fault point in the power cable;

[0010] The measuring station is movably installed in the cable trench, and is provided with a satellite signal receiver, a MEMS microphone array and a data calculation module. The measuring station is used to obtain its own coordinate position by using the satellite signal receiver after each active installation and fixation, and to timestamp the sound array signal generated by the power cable when the pulse generator applies a high-voltage DC pulse signal to the power cable in real time by using the MEMS microphone array. Furthermore, the data calculation module is used to determine the final position of the fault point in the power cable based on the coordinate position obtained after each active installation and fixation and the sound array signal with the timestamp.

[0011] Wherein, the data calculation module includes:

[0012] A coordinate position determination submodule, used to determine that the total number of times the measuring station collects sound array signals is k, and based on the coordinate position obtained after each active installation and fixation of the measuring station, obtain the k coordinate positions corresponding to the sound array signal collection performed by the measuring station 1 to k times, and combine the initial position of the MEMS microphone array installed at the measuring station to obtain the k coordinate positions corresponding to the MEMS microphone array when the measuring station collects sound array signals 1 to k times; wherein k is a positive integer greater than 1;

[0013] A sound signal decomposition submodule is used to use a preset complementary set empirical mode decomposition (CEEMD) algorithm to directly decompose the sound array signals with time stamps collected by the measuring station at 1 to k times to obtain k first eigenmode function groups; and first extract the sound signals belonging to the central array element from the sound array signals with time stamps collected by the measuring station at 1 to k times, and then use the CEEMD algorithm to decompose the k sound signals extracted from the central array element to obtain k second eigenmode function groups;

[0014] An angle estimation submodule, used to calculate k angle estimates of the fault points corresponding to the measurement station at times 1 to k relative to the MEMS microphone array using a MUSIC algorithm based on the k first eigenmode function groups output by the sound signal decomposition submodule;

[0015] A distance estimation submodule, for extracting only soil propagation path signals from the k second eigenmode function groups output by the sound signal decomposition submodule, and further calculating the strengths of the k extracted soil propagation path signals, and calculating k distance estimates between the fault points corresponding to the measurement station at times 1 to k and the measurement station in combination with a preset RSSI model;

[0016] A time delay estimation submodule, for simultaneously extracting soil propagation path signals and cable propagation path signals from the k second eigenmode function groups output by the sound signal decomposition submodule, and calculating k time delay differences corresponding to 1 to k times when the sound signal propagates in the power cable and the soil at the measuring station according to the soil propagation path signals and the cable propagation path signals extracted simultaneously each time;

[0017] The fault point locating submodule is used to construct an objective function according to the k coordinate positions, k angle estimates, k distance estimates and k time delay differences corresponding to the k times determined by the measuring station, and the k coordinate positions corresponding to the MEMS microphone array, and use the particle swarm optimization algorithm and the Levenberg-Marquardt algorithm to find the optimal solution of the objective function, and the optimal solution obtained is output as the final location of the fault point in the power cable.

[0018] Among them, the objective function is in, is the estimated position of the fault point in the power cable; p is the quantity to be estimated at the position of the fault point; p i and p j are the coordinate positions of the measuring station at the i-th and j-th times respectively; is the coordinate position of the MEMS microphone array when the sound array signal is collected for the i-th time; subscripts i and j are the measurement times, and i=1,2,...,k, j=1,2,...,k; || is the modulus of the vector; ||2 is the 2-norm of the vector; An estimated angle calculated by the measuring station at the i-th correspondence; Calculate the estimated distance between the fault point and the measuring station for the measuring station at the i-th correspondence; Calculate the time delay difference of the sound signal propagating in the power cable and the soil for the i-th time for the measuring station; The time delay difference of the sound signal propagating in the power cable and the soil is calculated for the measuring station for the jth time.

[0019] The expression of the time-stamped sound array signal is: Wherein, M is the number of array elements of the MEMS microphone array; is the sound signal of the mth array element in the MEMS microphone array when the measuring station collects the sound array signal for the kth time; The sound signal belonging to the central array element in the MEMS microphone array when the measuring station collects the sound array signal for the kth time;

[0020] The expression of the first eigenmode function group corresponding to the kth time at the measuring station is: The corresponding expression of the second eigenmode function group is Where N is the maximum number of eigenmode functions.

[0021] The angle estimation of the fault point corresponding to the kth time at the measuring station compared to the MEMS microphone array is based on the eigenmode function And through the formula To calculate; among them,

[0022] t=3,4,5; Based on the spatial spectrum function The largest peak found in ; Based on the matrix The corresponding sample covariance matrix in After eigendecomposition, the eigenvector corresponding to the minimum eigenvalue is obtained; the superscript H is the conjugate transpose operation; a(φ,θ) is the steering vector of the spatial spectrum function.

[0023] The k distance estimates between the kth corresponding fault point and the measuring station are based on the eigenmode function And combined with the RSSI model And formula To calculate; among them,

[0024] q=3,4,5; RSSI is the received signal strength, which is the eigenmode function of the sound signal RSSI0 is the strength under the condition of distance d0, which is a fixed value; η is the path loss index, which is a fixed value; x is the flat fading index, which is a fixed value.

[0025] Among them, through the formula The time delay difference formed by the sound signal propagating in the power cable and the soil at the measuring station for the kth time is calculated; wherein,

[0026] Through the threshold method, we get represents the k-th time delay of the measuring station on the power cable propagation path, represents the k-th time delay of the measuring station on the soil propagation path; f s is a fixed threshold parameter.

[0027] The embodiment of the present invention further provides a method for locating a power cable fault based on Beidou and sound arrival angle, which is implemented on the aforementioned power cable fault locating system based on Beidou and sound arrival angle, and the method comprises the following steps:

[0028] The pulse generator periodically applies a high-voltage DC pulse signal to the power cable, so that when there is a fault point in the power cable, the fault point can be stimulated to emit a sound signal;

[0029] The measuring station obtains its own coordinate position after each activity and installation, and timestamps the sound array signal generated when the high-voltage DC pulse signal is applied to the power cable by the pulse generator in real time, and further determines the final position of the fault point in the power cable based on the coordinate position obtained after each activity and installation and the sound array signal with the timestamp.

[0030] The measuring station obtains its own coordinate position after each active installation and fixation, and timestamps the sound array signal generated when the high-voltage DC pulse signal is applied to the power cable by the pulse generator in real time, and further determines the final position of the fault point in the power cable according to the coordinate position obtained after each active installation and fixation and the sound array signal with the timestamp, including the following specific steps:

[0031] Determine that the total number of times the measuring station collects sound array signals is k, and based on the coordinate position obtained after each active installation and fixation of the measuring station, obtain the k coordinate positions corresponding to the 1-k times of sound array signal collection by the measuring station, and combine the initial position of the MEMS microphone array installed at the measuring station to obtain the k coordinate positions corresponding to the MEMS microphone array when the measuring station collects sound array signals 1-k times; wherein k is a positive integer greater than 1;

[0032] Using a preset complementary set empirical mode decomposition (CEEMD) algorithm, the sound array signals with time stamps collected by the measuring station at 1 to k times are directly decomposed to obtain k first eigenmode function groups; and firstly, the sound signals belonging to the central array element are respectively extracted from the sound array signals with time stamps collected by the measuring station at 1 to k times, and then the CEEMD algorithm is used to decompose the k sound signals extracted from the central array element to obtain k second eigenmode function groups;

[0033] According to the k first eigenmode function groups, using a MUSIC algorithm, k angle estimates of the fault points corresponding to the measurement station at times 1 to k compared to the MEMS microphone array are calculated;

[0034] From the k second eigenmode function groups, only soil propagation path signals are extracted, and the strengths of the k extracted soil propagation path signals are further calculated, and in combination with a preset RSSI model, k distance estimates between the fault points corresponding to the measuring station at times 1 to k are calculated;

[0035] From the k second eigenmode function groups, soil propagation path signals and cable propagation path signals are simultaneously extracted, and according to the soil propagation path signals and cable propagation path signals extracted simultaneously each time, k time delay differences corresponding to the sound signal propagating in the power cable and the soil at the measuring station are calculated at 1 to k times;

[0036] According to the k coordinate positions, k angle estimates, k distance estimates and k time delay differences determined by the measuring station at 1 to k times, and the k coordinate positions corresponding to the MEMS microphone array, an objective function is constructed, and the particle swarm optimization algorithm and the Levenberg-Marquardt algorithm are used to find the optimal solution of the objective function, and the optimal solution obtained is output as the final position of the fault point in the power cable.

[0037] Implementing the embodiments of the present invention has the following beneficial effects:

[0038] 1. The present invention realizes the rapid positioning of cable fault points based on satellite systems (such as Beidou) and sound technology, and only relies on MEMS microphone array to realize the rapid positioning of fault points. It is not only simple in structure and easy to carry, but also greatly reduces the system complexity and system power consumption, thereby solving the defects of the existing acoustic and magnetic synchronous method for locating power cable fault points;

[0039] 2. The present invention converts the fault point location estimation problem in the objective function into a nonlinear weighted least squares problem, and then uses a heuristic algorithm to solve it to gradually improve the fault point location accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0041] Figure 1 A schematic structural diagram of a power cable fault location system based on Beidou and sound arrival angle provided by an embodiment of the present invention;

[0042] Figure 2 A functional structure diagram of a data calculation module included in a measuring station in a power cable fault location system based on Beidou and sound arrival angle provided by an embodiment of the present invention;

[0043] Figure 3 A flowchart of a method for locating power cable faults based on Beidou and sound arrival angle is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] like Figure 1 As shown, in an embodiment of the present invention, a power cable fault location system based on Beidou and sound arrival angle is proposed, including a pulse generator 1 and a measuring station 2; wherein,

[0046] The pulse generator 1 is loaded on the power cable L in the cable trench, and is used to periodically (e.g., once every 30 seconds) apply a high-voltage DC pulse signal to the power cable L, so that when there is a fault point in the power cable L, the fault point can be stimulated to emit a sound signal;

[0047] The measuring station 2 is movably installed in the cable trench, and is provided with a satellite signal receiver 21, a MEMS microphone array 22 and a data calculation module 23. After each active installation and fixation, the measuring station 2 uses the satellite signal receiver 21 to communicate with the Beidou satellite to obtain its own coordinate position, and uses the MEMS microphone array 22 to collect in real time the sound array signal generated when the power cable L is applied with a high-voltage DC pulse signal by the pulse generator 1, and timestamps it. Furthermore, based on the coordinate position obtained after each active installation and fixation and the sound array signal with the timestamp, the data calculation module 23 is used to determine the final position of the fault point in the power cable L.

[0048] It is understandable that, firstly, after the fault is roughly located, the fault type and the fault point location have been roughly determined, and the measurement location is randomly selected (as close to the fault point as possible); secondly, the location of the measurement station 2 can be changed multiple times. Each time the location is changed, the measurement station 2 will obtain the coordinate location information through the satellite signal receiver 21, and at the same time, it is necessary to monitor the strength of the sound signal emitted by the fault point. If it is too weak, the measurement location needs to be changed. Of course, if the strength of the sound signal is strong, multiple measurements can be made at the same location.

[0049] In the embodiment of the present invention, after each communication measurement is completed, the data collected by the measurement station 2 is used in the data calculation module 23 to perform a fast location estimation of the fault point.

[0050] like Figure 2 As shown, the data calculation module 23 includes a coordinate position determination submodule 231 , a sound signal decomposition submodule 232 , an angle estimation submodule 233 , a distance estimation submodule 234 , a delay estimation submodule 235 and a fault point location submodule 236 .

[0051] At this time, the coordinate position determination submodule 231 is used to determine that the total number of times the measuring station 2 collects sound array signals is k, and based on the coordinate position obtained after each active installation and fixation of the measuring station 2, obtains the k coordinate positions corresponding to the measuring station when the sound array signal is collected 1 to k times, and combines the MEMS microphone array 22 installed in the initial position of the measuring station 2 to obtain the k coordinate positions corresponding to the MEMS microphone array when the measuring station 2 collects sound array signals 1 to k times; wherein k is a positive integer greater than 1.

[0052] For example, for the kth measurement, the measuring station 2 is positioned by the satellite signal receiver 21, and the coordinate position obtained by the measuring station 2 at the kth time is recorded as p k Since the MEMS microphone array 22 is fixedly mounted on the measuring station 2, the coordinate position of the MEMS microphone array 22 changes each time the position of the measuring station 2 changes, so that the coordinate position of the MEMS microphone array corresponding to the sound array signal collection can be obtained as p0. k .

[0053] At this time, the sound signal decomposition submodule 232 is used to use the preset complementary set empirical mode decomposition (CEEMD) algorithm to directly decompose the time-stamped sound array signals collected by the measuring station 2 at 1 to k times to obtain k first eigenmode function groups; and first extract the sound signals belonging to the central array element from the time-stamped sound array signals collected by the measuring station 2 at 1 to k times, and then use the above-mentioned CEEMD algorithm to decompose the k sound signals extracted from the central array element to obtain k second eigenmode function groups.

[0054] For example, for the kth measurement, the expression of the sound array signal with the timestamp is Wherein, M is the number of array elements of the MEMS microphone array; is the sound signal of the mth array element in the MEMS microphone array when the measuring station collects the sound array signal for the kth time; It is the sound signal belonging to the central array element in the MEMS microphone array when the measuring station collects the sound array signal for the kth time.

[0055] At this time, the propagation path of the sound signal is mainly divided into the power cable propagation path and the ground propagation path. The propagation speed of the sound signal in the cable is greater than 5000m / s, while the propagation speed in the soil is about 1240m / s. Therefore, the measurement station will first receive the signal on the power cable propagation path, and then the line-of-sight propagation path signal in the soil. There is a certain delay difference between the two. When the measurement station gradually approaches the fault point, the delay difference is gradually reduced and becomes difficult to separate. This is also the biggest difficulty and challenge currently faced by the acoustic-magnetic synchronization method.

[0056] Since the acoustic signal excited by the fault point is a typical pulse signal with broadband characteristics, the attenuation degree of the high-frequency component of the broadband signal in the soil is much greater than that in the metal medium, so the cable propagation path signal and the atmospheric propagation path signal can be separated by performing empirical mode decomposition (EMD) on it. In order to further improve the decomposition accuracy and avoid aliasing, the complementary integrated empirical mode decomposition method (CEEMD) can be used to achieve this.

[0057] For sound array signals Decompose it and get the expression of the first eigenmode function group corresponding to the kth time of the measuring station: Where N is the maximum number of eigenmode functions; for the sound signal belonging to the center element Decompose it and get the expression of the second eigenmode function group corresponding to the kth measurement station:

[0058] It should be noted that the maximum number of iterations of the EEMD is predefined to be 100, the added noise signal-to-noise ratio is 20 dB, and the maximum number of decomposition layers of the eigenmode function group is 10 layers.

[0059] At this time, the angle estimation submodule 233 is used to calculate k angle estimates of the fault points corresponding to the measurement station 2 at times 1 to k compared to the MEMS microphone array 22 using the MUSIC algorithm based on the k first eigenmode function groups output by the sound signal decomposition submodule 232.

[0060] For example, for the kth measurement, a beamforming or MUSIC algorithm is used to estimate the angle of the fault point relative to the MEMS microphone array.

[0061] Taking the MUSIC algorithm as an example, based on the eigenmode function And through the formula To calculate the angle estimation of the fault point corresponding to the kth time at the measurement station compared to the MEMS microphone array The details are as follows:

[0062] (1) Based on the first eigenmode function group The eigenmode function in Constructing the Matrix

[0063]

[0064] Among them, t = 3, 4, 5;

[0065] (2) Based on matrix Calculate the sample covariance matrix

[0066] (3) Yes Perform eigendecomposition and obtain the eigenvector corresponding to the minimum eigenvalue: And traverse the steering vector a(φ,θ), for the spatial spectrum function Calculate and solve for the maximum peak, which is the signal angle Wherein, the superscript H is the conjugate transpose operation;

[0067] (4) Through the formula Calculate the angle estimation of the measurement station at the kth fault point relative to the MEMS microphone array

[0068] The distance estimation submodule 234 is used to extract only the soil propagation path signal from the k second eigenmode function groups output by the sound signal decomposition submodule 232, and further calculate the strength of the k extracted soil propagation path signals, and calculate the k distance estimates between the fault points corresponding to the measuring station 2 at times 1 to k and the measuring station 2 in combination with the preset RSSI model.

[0069] For example, for the kth measurement, first, based on the second eigenmode function group The eigenmode function in Only the starting point of the soil propagation path signal is extracted and from Take a 200ms segment of the eigenmode function to calculate the total energy of the segment

[0070]

[0071] Secondly, combined with the RSSI model To estimate the distance Where q = 3, 4, 5; RSSI is the received signal strength, which is the eigenmode function of the sound signal The strength of RSSI0 is the strength under the condition of distance d0, which is a fixed value; η is the path loss index, which is a fixed value; x is the flat fading index, which is a fixed value;

[0072] Finally, through the formula Calculate the estimated distance between the measuring station and the fault point corresponding to the kth time

[0073] The time delay estimation submodule 235 is used to simultaneously extract the soil propagation path signal and the cable propagation path signal from the k second eigenmode function groups output by the sound signal decomposition submodule 232, and calculate the k time delay differences corresponding to 1 to k times formed when the measuring station 2 propagates in the power cable and the soil based on the soil propagation path signal and the cable propagation path signal extracted simultaneously each time.

[0074] For example, for the kth measurement, Usually, the high-frequency components in the signal are included, but in the signal denoising application, it is considered to carry a large amount of high-frequency noise sound signals. In the present invention, since the pulse signal is a broadband signal, the sound signal propagated along the power cable path will be Therefore, Extract the cable propagation path signal corresponding to the kth measurement station.

[0075] Since soil propagation attenuates the high-frequency components of sound signals quickly, The soil propagation path signal with stronger energy is observed, thus achieving the separation of the power cable propagation path signal and the atmospheric propagation path signal;

[0076] Secondly, the threshold method formula (1) is used to obtain the k-th time delay of the measuring station on the power cable propagation path: and the kth time delay of the measuring station on the soil propagation path

[0077]

[0078] Among them, f s is a fixed threshold parameter, usually 0.8;

[0079] Finally, through the formula The time delay difference formed by the sound signal propagating in the power cable and the soil at the measuring station for the kth time is calculated.

[0080] The fault point locating submodule 236 is used to construct an objective function based on the k coordinate positions, k angle estimates, k distance estimates and k time delay differences corresponding to the measurement station determined at 1 to k times, and the k coordinate positions corresponding to the MEMS microphone array, and use the particle swarm optimization algorithm and the Levenberg-Marquardt algorithm to find the optimal solution for the objective function. The optimal solution is output as the final location of the fault point in the power cable.

[0081] The objective function is shown in the following formula (2):

[0082]

[0083] in, is the estimated position of the fault point in the power cable; p is the quantity to be estimated at the fault point; p i and p j are the coordinate positions of the measuring station at the i-th and j-th times respectively; is the coordinate position of the MEMS microphone array when collecting the sound array signal for the i-th time; the subscripts i and j are the measurement times, and i=1,2,...,k, j=1,2,...,k; || is the modulus of the vector; ||2 is the 2-norm of the vector; is the angle estimate calculated by the measuring station at the i-th correspondence; Calculate the estimated distance between the fault point and the measuring station for the measuring station at the i-th correspondence; Calculate the time delay difference of the sound signal propagating in the power cable and the soil for the measuring station at the i-th correspondence; Calculate the time delay difference of the sound signal propagating in the power cable and the soil for the measuring station at the jth time.

[0084] At this time, the inventor found that the method of estimating the position through the objective function can be converted into a nonlinear least squares problem. Since the measurement information will increase with the increase of the number of measurements, it is necessary to use an intelligent search algorithm, Newton descent method or Levenberg-Marquardt algorithm to solve it. Therefore, the particle swarm optimization algorithm and the Levenberg-Marquardt algorithm are used to find the optimal solution to the objective function, and the optimal solution output is the final position of the fault point in the power cable.

[0085] The application scenario of a power cable fault location system based on Beidou and sound arrival angle in an embodiment of the present invention is further described as follows:

[0086] To locate the cable fault point in the urban cable trench, a total of 1 measuring station and 1 pulse generator are used. At this time, the pulse generator is used to pass a high-voltage pulse electrical signal to the faulty cable at a fixed period to stimulate the fault point to emit a sound signal.

[0087] The coordinates of the measuring station do not need to be manually calibrated, and are directly given in the geodetic coordinate system by the satellite signal receiver connected to the Beidou positioning system. The measuring station is carried by the operator, and after measuring 3 to 5 times at a single measuring position, it moves a distance in the direction of the fault point. After 2 to 3 transformations, the precise location of the fault point can be obtained.

[0088] First, in the kth measurement process, the coordinates of the measuring station are recorded as p k . The pulse generator generates a high-voltage pulse in turn at an interval of 30 seconds to stimulate the fault point to emit an acoustic signal. The measurement station collects the sound array signal through the MEMS microphone array, and the sampling frequency of the sound array signal is 48kHz. The MEMS microphone array adopts a "cross" array with 17 array elements and an array element spacing of 2cm. When performing RSSI distance estimation and delay difference estimation, only the data collected by the central array element is processed. The measurement station judges the validity of the collected sound array signal, and performs signal processing and position estimation.

[0089] Furthermore, based on the MUSIC algorithm, the eigenmode function Calculate and use the formula To calculate the angle estimation of the fault point corresponding to the kth time at the measurement station compared to the MEMS microphone array

[0090] Furthermore, the eigenmode function is calculated Total Energy Then get the distance based on the RSSI model obtained in the experiment q=3,4,5, by formula Calculate the estimated distance between the measuring station and the fault point corresponding to the kth time

[0091] Furthermore, the cable propagation path delay information at the measurement site is obtained according to formula (1): and soil propagation path delay information Then the time delay difference of the sound signal propagating through the power cable and soil in the measuring station is obtained. Threshold factor f s Take 0.8.

[0092] Finally, the fault point location is estimated based on equation (2). The nonlinear least squares problem can be accurately solved based on the LM algorithm to obtain the coordinates of the fault point at the kth moment:

[0093] like Figure 3 As shown in the figure, a method for locating a power cable fault based on Beidou and sound arrival angle is provided in an embodiment of the present invention. The method is implemented on a power cable fault locating system based on Beidou and sound arrival angle in an embodiment of the present invention. The method comprises the following steps:

[0094] Step S1, the pulse generator periodically applies a high-voltage DC pulse signal to the power cable, so that when there is a fault point in the power cable, the fault point can be stimulated to emit a sound signal;

[0095] Step S2: After each active installation and fixation, the measuring station obtains its own coordinate position, and timestamps the sound array signal generated when the high-voltage DC pulse signal is applied to the power cable by the pulse generator in real time, and further determines the final position of the fault point in the power cable based on the coordinate position obtained after each active installation and fixation and the sound array signal with the timestamp.

[0096] The specific process is as follows: in step S1, the pulse generator periodically (eg, once every 30 seconds) applies a high-voltage DC pulse signal to the power cable, so that when a fault point exists in the power cable, the fault point can be stimulated to emit a sound signal.

[0097] In step S2, first, the total number of times the measuring station collects sound array signals is determined to be k, and based on the coordinate positions obtained after each active installation and fixation of the measuring station, the k coordinate positions corresponding to the measuring station collecting sound array signals 1 to k times are obtained, and combined with the initial position of the MEMS microphone array installed at the measuring station, the k coordinate positions corresponding to the MEMS microphone array when the measuring station collects sound array signals 1 to k times are obtained; wherein k is a positive integer greater than 1;

[0098] Next, the preset complementary set empirical mode decomposition (CEEMD) algorithm is used to directly decompose the time-stamped sound array signals collected by the measuring station at 1 to k times to obtain k first eigenmode function groups; and first, the sound signals belonging to the central array element are respectively extracted from the time-stamped sound array signals collected by the measuring station at 1 to k times, and then the CEEMD algorithm is used to decompose the k sound signals extracted from the central array element to obtain k second eigenmode function groups;

[0099] Then, based on the k first eigenmode function groups, the MUSIC algorithm is used to calculate k angle estimates of the fault points corresponding to the 1st to kth times of the measurement station relative to the MEMS microphone array;

[0100] Then, from the k second eigenmode function groups, only soil propagation path signals are extracted, and the strengths of the k extracted soil propagation path signals are further calculated, and combined with the preset RSSI model, k distance estimates between the fault points corresponding to the measuring station at times 1 to k are calculated to the measuring station;

[0101] Next, soil propagation path signals and cable propagation path signals are simultaneously extracted from the k second eigenmode function groups, and k time delay differences corresponding to the sound signal propagating in the power cable and the soil at the measuring station at 1 to k times are calculated based on the soil propagation path signals and cable propagation path signals extracted simultaneously each time;

[0102] Finally, the objective function is constructed based on the k coordinate positions, k angle estimates, k distance estimates and k time delay differences determined by the measuring station at times 1 to k, as well as the k coordinate positions corresponding to the MEMS microphone array. The particle swarm optimization algorithm and Levenberg-Marquardt algorithm are used to find the optimal solution of the objective function. The optimal solution is output as the final location of the fault point in the power cable.

[0103] Implementing the embodiments of the present invention has the following beneficial effects:

[0104] 1. The present invention realizes the rapid positioning of cable fault points based on satellite systems (such as Beidou) and sound technology, and only relies on MEMS microphone array to realize the rapid positioning of fault points. It is not only simple in structure and easy to carry, but also greatly reduces the system complexity and system power consumption, thereby solving the defects of the existing acoustic and magnetic synchronous method for locating power cable fault points;

[0105] 2. The present invention converts the fault point location estimation problem in the objective function into a nonlinear weighted least squares problem, and then uses a heuristic algorithm to solve it to gradually improve the fault point location accuracy.

[0106] It is worth noting that in the above system embodiment, the various system modules included are only 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 the various functional modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0107] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, disk, CD-ROM, etc.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A power cable fault location system based on Beidou and sound arrival angle, characterized in that: It includes a pulse generator and a measuring station; wherein, The pulse generator is loaded on the power cable in the cable trench and is used to periodically apply a high-voltage DC pulse signal to the power cable so as to stimulate the fault point to emit a sound signal when there is a fault point in the power cable; The measuring station is movably installed in the cable trench, and is provided with a satellite signal receiver, a MEMS microphone array and a data calculation module. The measuring station is used to obtain its own coordinate position by using the satellite signal receiver after each active installation and fixation, and to timestamp the sound array signal generated by the power cable when the pulse generator applies a high-voltage DC pulse signal to the power cable in real time by using the MEMS microphone array. Furthermore, the data calculation module is used to determine the final position of the fault point in the power cable based on the coordinate position obtained after each active installation and fixation and the sound array signal with the timestamp.

2. The power cable fault location system based on Beidou and sound arrival angle as claimed in claim 1, characterized in that: The data calculation module includes: A coordinate position determination submodule, used to determine that the total number of times the measuring station collects sound array signals is k, and based on the coordinate position obtained after each active installation and fixation of the measuring station, obtain the k coordinate positions corresponding to the sound array signal collection performed by the measuring station 1 to k times, and combine the initial position of the MEMS microphone array installed at the measuring station to obtain the k coordinate positions corresponding to the MEMS microphone array when the measuring station collects sound array signals 1 to k times; wherein k is a positive integer greater than 1; A sound signal decomposition submodule is used to use a preset complementary set empirical mode decomposition (CEEMD) algorithm to directly decompose the sound array signals with time stamps collected by the measuring station at 1 to k times to obtain k first eigenmode function groups; and first extract the sound signals belonging to the central array element from the sound array signals with time stamps collected by the measuring station at 1 to k times, and then use the CEEMD algorithm to decompose the k sound signals extracted from the central array element to obtain k second eigenmode function groups; An angle estimation submodule, used to calculate k angle estimates of the fault points corresponding to the measurement station at times 1 to k relative to the MEMS microphone array using a MUSIC algorithm based on the k first eigenmode function groups output by the sound signal decomposition submodule; A distance estimation submodule, for extracting only soil propagation path signals from the k second eigenmode function groups output by the sound signal decomposition submodule, and further calculating the strengths of the k extracted soil propagation path signals, and calculating k distance estimates between the fault points corresponding to the measurement station at times 1 to k and the measurement station in combination with a preset RSSI model; A time delay estimation submodule, for simultaneously extracting soil propagation path signals and cable propagation path signals from the k second eigenmode function groups output by the sound signal decomposition submodule, and calculating k time delay differences corresponding to 1 to k times when the sound signal propagates in the power cable and the soil at the measuring station according to the soil propagation path signals and the cable propagation path signals extracted simultaneously each time; The fault point locating submodule is used to construct an objective function according to the k coordinate positions, k angle estimates, k distance estimates and k time delay differences corresponding to the k times determined by the measuring station, and the k coordinate positions corresponding to the MEMS microphone array, and use the particle swarm optimization algorithm and the Levenberg-Marquardt algorithm to find the optimal solution of the objective function, and the optimal solution obtained is output as the final location of the fault point in the power cable.

3. The power cable fault location system based on Beidou and sound arrival angle as claimed in claim 2, characterized in that: The objective function is in, is the estimated position of the fault point in the power cable; p is the quantity to be estimated at the position of the fault point; p i and p j are the coordinate positions of the measuring station at the i-th and j-th times respectively; is the coordinate position of the MEMS microphone array when the sound array signal is collected for the i-th time; subscripts i and j are the measurement times, and i=1,2,...,k, j=1,2,...,k; || is the modulus of the vector; ||2 is the 2-norm of the vector; An estimated angle calculated by the measuring station at the i-th correspondence; Calculate the estimated distance between the fault point and the measuring station for the measuring station at the i-th correspondence; Calculate the time delay difference of the sound signal propagating in the power cable and the soil for the i-th time for the measuring station; The time delay difference of the sound signal propagating in the power cable and the soil is calculated for the measuring station for the jth time.

4. The power cable fault location system based on Beidou and sound arrival angle as described in claim 3, characterized in that: The expression of the time-stamped sound array signal is: Wherein, M is the number of array elements of the MEMS microphone array; is the sound signal of the mth array element in the MEMS microphone array when the measuring station collects the sound array signal for the kth time; The sound signal belonging to the central array element in the MEMS microphone array when the measuring station collects the sound array signal for the kth time; The expression of the first eigenmode function group corresponding to the kth time at the measuring station is: The corresponding expression of the second eigenmode function group is Where N is the maximum number of eigenmode functions.

5. The power cable fault location system based on Beidou and sound arrival angle as described in claim 4, characterized in that: The angle estimation of the fault point corresponding to the kth time at the measuring station relative to the MEMS microphone array is based on the eigenmode function And through the formula To calculate; among them, t=3,4,5; Based on the spatial spectrum function The largest peak found in ; Based on the matrix The corresponding sample covariance matrix in After eigendecomposition, the eigenvector corresponding to the minimum eigenvalue is obtained; the superscript H is the conjugate transpose operation; a(φ,θ) is the steering vector of the spatial spectrum function.

6. The power cable fault location system based on Beidou and sound arrival angle as described in claim 5, characterized in that: The k distance estimates between the kth corresponding fault point and the measuring station are based on the eigenmode function And combined with the RSSI model And formula To calculate; among them, q=3,4,5; RSSI is the received signal strength, which is the eigenmode function of the sound signal RSSI0 is the strength under the condition of distance d0, which is a fixed value; η is the path loss index, which is a fixed value; x is the flat fading index, which is a fixed value.

7. The power cable fault location system based on Beidou and sound arrival angle as described in claim 6, characterized in that: By formula The time delay difference formed by the sound signal propagating in the power cable and the soil at the measuring station for the kth time is calculated; wherein, Through the threshold method, we get represents the k-th time delay of the measuring station on the power cable propagation path, represents the k-th time delay of the measuring station on the soil propagation path; f s is a fixed threshold parameter.

8. A method for locating power cable faults based on Beidou and sound arrival angle, characterized in that: It is implemented on the power cable fault location system based on Beidou and sound arrival angle as described in claim 7, and the method comprises the following steps: The pulse generator periodically applies a high-voltage DC pulse signal to the power cable, so that when there is a fault point in the power cable, the fault point can be stimulated to emit a sound signal; The measuring station obtains its own coordinate position after each activity and installation, and timestamps the sound array signal generated when the high-voltage DC pulse signal is applied to the power cable by the pulse generator in real time, and further determines the final position of the fault point in the power cable based on the coordinate position obtained after each activity and installation and the sound array signal with the timestamp.

9. The method for locating power cable faults based on Beidou and sound arrival angle as claimed in claim 8, characterized in that: The measuring station obtains its own coordinate position after each active installation and fixation, and timestamps the sound array signal generated when the high-voltage DC pulse signal is applied to the power cable by the pulse generator in real time, and further determines the final position of the fault point in the power cable according to the coordinate position obtained after each active installation and fixation and the sound array signal with the timestamp, and the specific steps include: Determine that the total number of times the measuring station collects sound array signals is k, and based on the coordinate position obtained after each active installation and fixation of the measuring station, obtain the k coordinate positions corresponding to the 1-k times of sound array signal collection by the measuring station, and combine the initial position of the MEMS microphone array installed at the measuring station to obtain the k coordinate positions corresponding to the MEMS microphone array when the measuring station collects sound array signals 1-k times; wherein k is a positive integer greater than 1; Using a preset complementary set empirical mode decomposition (CEEMD) algorithm, the sound array signals with time stamps collected by the measuring station at 1 to k times are directly decomposed to obtain k first eigenmode function groups; and firstly, the sound signals belonging to the central array element are respectively extracted from the sound array signals with time stamps collected by the measuring station at 1 to k times, and then the CEEMD algorithm is used to decompose the k sound signals extracted from the central array element to obtain k second eigenmode function groups; According to the k first eigenmode function groups, using a MUSIC algorithm, k angle estimates of the fault points corresponding to the measurement station at times 1 to k compared to the MEMS microphone array are calculated; From the k second eigenmode function groups, only soil propagation path signals are extracted, and the strengths of the k extracted soil propagation path signals are further calculated, and in combination with a preset RSSI model, k distance estimates between the fault points corresponding to the measuring station at times 1 to k are calculated; From the k second eigenmode function groups, soil propagation path signals and cable propagation path signals are simultaneously extracted, and according to the soil propagation path signals and cable propagation path signals extracted simultaneously each time, k time delay differences corresponding to the sound signal propagating in the power cable and the soil at the measuring station are calculated at 1 to k times; According to the k coordinate positions, k angle estimates, k distance estimates and k time delay differences determined by the measuring station at 1 to k times, and the k coordinate positions corresponding to the MEMS microphone array, an objective function is constructed, and the particle swarm optimization algorithm and the Levenberg-Marquardt algorithm are used to find the optimal solution of the objective function, and the optimal solution obtained is output as the final position of the fault point in the power cable.

Images