Power cable fault location system and method based on beidou and angle of arrival

Abstract

The application discloses a power cable fault positioning system based on Beidou and a sound arrival angle, which comprises a pulse generator and a measuring station; the pulse generator periodically applies a pulse signal to a power cable to excite a sound signal emitted by a fault point of the power cable; the measuring station is movably installed in a cable trench, and after being fixed after each movement, a satellite signal receiver is used to obtain a coordinate position where the measuring station is located, and a MEMS microphone array is used to collect a sound array signal generated by the power cable when the power cable is applied with a high-voltage direct-current pulse signal by the pulse generator and to time stamp the sound array signal; and further, according to the coordinate position obtained after each movement and the time-stamped sound array signal, a data calculation module is used to determine a final position of the fault point in the power cable. By implementing the application, defects existing in a current acoustic-magnetic synchronous method for positioning a fault point of a power cable can be solved, so that fast and accurate positioning of the fault point of the power cable is realized.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a power cable fault location system and method based on BeiDou and sound arrival angle. Background Technology

[0002] Power cables are a crucial component of power systems and a vital guarantee for modern national economic development and people's lives. Especially in densely populated urban areas, the scale of power cable laying has become increasingly massive with the expansion of urbanization. To conserve limited urban land and maintain environmental aesthetics, most power cables are buried underground or in cable trenches, becoming the primary method of cable laying. As their service life extends, power cable faults are inevitable. While regular safety inspections and troubleshooting can effectively reduce the probability of cable failures, the inability to promptly locate fault points when they occur can cause significant inconvenience and economic losses to surrounding factories and residents. Therefore, power cable managers need to be able to quickly and accurately locate faulty cables to improve the reliability of power supply and reduce losses and maintenance costs. Thus, rapid and accurate power cable fault location not only has practical social significance but also possesses engineering value.

[0003] The environment in which buried power cables are located is relatively complex, making fault detection and location relatively difficult. The steps for power cable fault detection can be divided into three parts: determining the nature of the cable fault, roughly measuring the fault distance, tracing the path of the faulty power cable, and precisely pinpointing the location. Precise pinpointing means restricting the location of the fault to a very small area, thus avoiding the impact of large-scale excavation on surrounding areas and other pipelines, while improving the efficiency of fault removal.

[0004] Currently, the main method for troubleshooting power cable faults is the acoustic-magnetic synchronization method, which involves using a pulse generator to apply a high-voltage DC pulse signal to the faulty power cable. Under the action of the DC pulse signal, the fault point generates a discharge phenomenon and releases sound and magnetic field signals into the surrounding environment. These two signals are collected and analyzed to accurately locate the fault point.

[0005] However, the method for determining the fault point of the power cable by using the acoustic-magnetic synchronous method has the following defects: (1) the sound signal propagates along the power cable, and due to the fast propagation speed and slow attenuation, the overall positioning accuracy is low when the signal is used for fault point positioning; (2) the sound signal has a very serious multipath propagation phenomenon when propagating in the trench, and under the superposition of the multipath effect, the sound signal can be strengthened and propagate far away, which introduces a large error for distance discrimination and reduces the positioning accuracy of the fault point; (3) near the fault point, the real path of the fault sound signal is submerged in the power cable propagation path and the trench multipath superposition, so that the real position of the fault point cannot be accurately determined.

[0006] Therefore, there is an urgent need for a new power cable fault positioning method to solve the defects of the existing acoustic-magnetic synchronous method for positioning the fault point of the power cable, so as to realize fast and accurate positioning of the fault point of the power cable. SUMMARY

[0007] The technical problem to be solved by the present application is to provide a power cable fault positioning system and method based on Beidou and sound arrival angle, which can solve the defects of the existing acoustic-magnetic synchronous method for positioning the fault point of the power cable, so as to realize fast and accurate positioning of the fault point of the power cable.

[0008] In order to solve the above technical problems, the embodiment of the present application provides an implementation system of a power cable fault positioning system based on Beidou and sound arrival angle, comprising a pulse generator and a measurement 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 direct-current pulse signal to the power cable, so as to excite the fault point to emit a sound signal when the power cable has a fault point;

[0010] The measurement 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, which is used to obtain the coordinate position of itself by using the satellite signal receiver after each movable installation and fixation, and to give a timestamp to the sound array signal generated by the power cable when the high-voltage direct-current pulse signal is applied by the pulse generator, and further to determine the final position of the fault point in the power cable by using the data calculation module according to the coordinate position obtained after each movable installation and fixation and the sound array signal with the timestamp.

[0011] The data calculation module comprises:

[0012] The coordinate position determining submodule is configured to determine that the total number of times of sound array signal collection performed by the measurement station is k, and obtain k coordinate positions corresponding to the sound array signal collection performed by the measurement station for 1 to k times based on the coordinate positions obtained after the measurement station is installed fixedly each time, and combine the initial position of the MEMS microphone array installed on the measurement station to obtain k coordinate positions corresponding to the MEMS microphone array during the sound array signal collection performed by the measurement station for 1 to k times; wherein k is a positive integer greater than 1.

[0013] The sound signal decomposition submodule is configured to directly decompose the time-stamped sound array signals collected by the measurement station for 1 to k times by using a preset complementary ensemble empirical mode decomposition (CEEMD) algorithm to obtain k first intrinsic mode function groups, and extract sound signals belonging to the central array element from the time-stamped sound array signals collected by the measurement station for 1 to k times, and then decompose the k sound signals belonging to the central array element by using the CEEMD algorithm to obtain k second intrinsic mode function groups.

[0014] The angle estimation submodule is configured to calculate k angle estimates of the fault point corresponding to the measurement station for 1 to k times relative to the MEMS microphone array by using a multiple signal classification (MUSIC) algorithm based on the k first intrinsic mode function groups output by the sound signal decomposition submodule.

[0015] The distance estimation submodule is configured to extract soil propagation path signals from the k second intrinsic mode function groups output by the sound signal decomposition submodule, further calculate the intensities of the k extracted soil propagation path signals, and calculate k distance estimates between the fault point corresponding to the measurement station for 1 to k times and the measurement station by combining a preset received signal strength indicator (RSSI) model.

[0016] The time delay estimation submodule is configured to extract soil propagation path signals and cable propagation path signals from the k second intrinsic mode function groups output by the sound signal decomposition submodule, and calculate k time delay differences corresponding to the measurement station for 1 to k times based on the sound signal propagation in the power cable and the soil by using the soil propagation path signals and the cable propagation path signals extracted each time.

[0017] The fault point positioning submodule is configured to construct an objective function based on the k coordinate positions, the k angle estimates, the k distance estimates and the k time delay differences corresponding to the measurement station for 1 to k times, and the k coordinate positions corresponding to the MEMS microphone array, and use a particle swarm optimization algorithm and a Levenberg-Marquardt algorithm to obtain an optimal solution of the objective function, and output the optimal solution as the final position of the fault point in the power cable.

[0018] wherein the objective function is wherein, is the estimated location of the fault point in the power cable; p is the to-be-estimated quantity of the fault point location; 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 performing sound array signal collection at the i-th time; the subscripts i and j are both measurement times, and i = 1, 2,..., k, j = 1, 2,..., k; || is the modulus of a vector; ||2 is the 2-norm of a vector; is the corresponding calculated angle estimate of the measuring station at the i-th time; is the corresponding calculated distance estimate between the fault point and the measuring station of the measuring station at the i-th time; is the corresponding calculated time delay difference of the sound signal when propagating in the power cable and the soil of the measuring station at the i-th time; is the calculated time delay difference of the sound signal when propagating in the power cable and the soil of the measuring station at the j-th time.

[0019] wherein the expression of the time-stamped sound array signal is wherein M is the number of elements of the MEMS microphone array; is the sound signal of the m-th element in the MEMS microphone array when the measuring station performs sound array signal collection at the k-th time; is the sound signal belonging to the central element in the MEMS microphone array when the measuring station performs sound array signal collection at the k-th time;

[0020] the expression of the corresponding first eigenmode function group of the measuring station at the k-th time is the expression of the corresponding second eigenmode function group is wherein N is the maximum number of eigenmode functions.

[0021] wherein the angle estimate of the corresponding fault point of the measuring station at the k-th time compared to the MEMS microphone array is calculated based on the eigenmode function and obtained through the formula wherein,

[0022] t = 3, 4, 5; is the maximum peak solved based on the spatial spectrum function is the corresponding sample covariance matrix based on the matrix ​After eigen decomposition, the minimum eigenvalue corresponding to the eigenvector is obtained; the superscript H is a conjugate transpose operation; a (phi, theta) is a steering vector of a spatial spectrum function.

[0023] Wherein, the k distance estimation between the kth corresponding fault point to the measuring station of the measuring station is based on the eigenfunction And combined with the RSSI model is And the formula Is calculated; wherein,

[0024] Q=3,4,5; RSSI is the received signal strength, that is, the intensity of the sound signal eigenfunction ; RSSI0 is the intensity under the condition of d0 distance, which is a fixed value; eta is the path loss exponent, which is a fixed value; x is the flat fading exponent, which is a fixed value.

[0025] Wherein, the time delay difference of the measuring station at the kth time based on the sound signal formed when propagating in the power cable and the soil is calculated by the formula ; wherein,

[0026] By threshold method, the formula The time delay of the measuring station on the power cable propagation path at the kth time is represented by The time delay of the measuring station on the soil propagation path at the kth time is represented by f s Is a fixed threshold parameter.

[0027] The embodiment of the application also provides a power cable fault positioning method based on Beidou and sound arrival angle, which is realized on the power cable fault positioning system based on Beidou and sound arrival angle, and the method comprises the following steps:

[0028] The pulse generator periodically applies high-voltage direct current pulse signals to the power cable, so that the fault point can emit sound signals when the power cable has a fault point;

[0029] After the measuring station is installed and fixed each time, the coordinate position of the measuring station is obtained, and the sound array signal generated when the power cable is applied with high-voltage direct current pulse signals by the pulse generator is time stamped, and further, according to the coordinate position obtained after each time of active installation and fixation and the time stamped sound array signal, the final position of the fault point in the power cable is determined.

[0030] The specific steps for determining the final position of the fault point in the power cable based on the coordinate position obtained after each active installation and fixation of the measuring station and the time-stamped sound array signal include:

[0031] The total number of times of sound array signal collection by the measuring station is determined as k, and based on the coordinate position obtained after each active installation and fixation of the measuring station, k coordinate positions corresponding to the sound array signal collection by the measuring station from the first time to the kth time are obtained, and in combination with the initial position of the MEMS microphone array installed on the measuring station, k coordinate positions corresponding to the MEMS microphone array during the sound array signal collection from the first time to the kth time are obtained; wherein k is a positive integer greater than 1.

[0032] The time-stamped sound array signals collected by the measuring station from the first time to the kth time are directly decomposed using a preset complementary ensemble empirical mode decomposition (CEEMD) algorithm to obtain k first intrinsic mode function groups; and the sound signals belonging to the central elements are extracted from the time-stamped sound array signals collected by the measuring station from the first time to the kth time, and the k sound signals belonging to the central elements are decomposed using the CEEMD algorithm to obtain k second intrinsic mode function groups.

[0033] According to the k first intrinsic mode function groups, the MUSIC algorithm is used to calculate k angle estimates of the fault point corresponding to the measuring station from the first time to the kth time relative to the MEMS microphone array.

[0034] From the k second intrinsic mode function groups, only the soil propagation path signals are extracted, and the intensities 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 point corresponding to the measuring station from the first time to the kth time and the measuring station are calculated.

[0035] From the k second intrinsic mode function groups, both the soil propagation path signals and the cable propagation path signals are extracted, and based on the soil propagation path signals and the cable propagation path signals extracted each time, k time delay differences corresponding to the sound signal propagation in the power cable and the soil by the measuring station from the first time to the kth time are calculated.

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

[0037] The embodiment of the present application has the following beneficial effects:

[0038] 1. The present application realizes the rapid positioning of the fault point in the power cable based on the satellite system (such as Beidou) and the sound technology, and only relies on the MEMS microphone array to realize the rapid positioning of the fault point, so that the structure is simple, the carrying is convenient, the system complexity and the system power consumption are greatly reduced, and the defects of the existing acoustic magnetic synchronous method for positioning the fault point in the power cable are solved.

[0039] 2. The present application converts the fault point position estimation problem in the target function into a nonlinear weighted least square problem, and then uses a heuristic algorithm to solve the problem, so that the positioning accuracy of the fault point is gradually improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative labor.

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

[0042] Figure 2 A functional structural schematic diagram of a data calculation module contained in a measuring station in a power cable fault positioning system based on Beidou and sound angle of arrival provided by the embodiment of the present application;

[0043] Figure 3 A flowchart of a power cable fault positioning method based on Beidou and sound angle of arrival provided by the embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical scheme and advantages of the present application more clear, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0045] As Figure 1 shown in the figure, in the embodiment of the application, a power cable fault positioning system based on Beidou and sound arrival angle is provided, which comprises 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, which is used to periodically (such as once every 30 seconds) apply a high-voltage direct-current pulse signal to the power cable L, so as to excite the fault point to emit a sound signal when the power cable L has a fault point;

[0047] The measuring station 2 is movably installed in the cable trench, which is provided with a satellite signal receiver 21, a MEMS microphone array 22 and a data calculation module 23, which is used to obtain the coordinate position of itself by communicating with the Beidou satellite through the satellite signal receiver 21 after each movable installation and fixation, and to give a time stamp to the sound array signal generated by the power cable L when the pulse generator 1 applies a high-voltage direct-current pulse signal, and further to determine the final position of the fault point in the power cable L by using the data calculation module 23 according to the coordinate position obtained after each movable installation and fixation and the sound array signal with a time stamp.

[0048] It can be understood that, first of all, after rough fault positioning, the fault type and fault point position have been roughly determined, and the measurement position is randomly selected (as close to the fault point as possible); secondly, the position of the measuring station 2 can be changed many times, and the measuring station 2 will obtain the coordinate position information through the satellite signal receiver 21 every time the position is changed, and at the same time, the intensity of the sound signal emitted by the fault point needs to be monitored, if it is too weak, the measurement position needs to be changed. Of course, if the intensity of the sound signal is strong, it can be measured many times at the same position.

[0049] In the embodiment of the application, after each communication measurement is completed, the data collected by the measuring station 2 will be subjected to rapid position estimation of the fault point in the data calculation module 23.

[0050] As Figure 2 shown in the figure, the data calculation module 23 comprises a coordinate position determination sub-module 231, a sound signal decomposition sub-module 232, an angle estimation sub-module 233, a distance estimation sub-module 234, a time delay estimation sub-module 235 and a fault point positioning sub-module 236.

[0051] At this time, the coordinate position determination submodule 231 is configured to determine that the total number of times of sound array signal collection performed by the measurement station 2 is k, and based on the coordinate position obtained after each time the measurement station 2 is installed and fixed, obtain k coordinate positions corresponding to the sound array signal collection performed by the measurement station 1-k times, and combine the initial position of the MEMS microphone array 22 installed on the measurement station 2 to obtain k coordinate positions corresponding to the MEMS microphone array during the sound array signal collection performed by the measurement station 1-k times; wherein k is a positive integer greater than 1.

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

[0053] At this time, the sound signal decomposition submodule 232 is configured to directly decompose the time-stamped sound array signals collected by the measurement station 2 1-k times using a preset complementary ensemble empirical mode decomposition (CEEMD) algorithm to obtain k first intrinsic mode function groups, and first extract the sound signals belonging to the central element from the time-stamped sound array signals collected by the measurement station 2 1-k times, and then decompose the k sound signals belonging to the central element using the above-mentioned CEEMD algorithm to obtain k second intrinsic mode function groups.

[0054] For example, for the kth measurement, the expression of the time-stamped sound array signal is denoted as wherein M is the number of elements of the MEMS microphone array; is the sound signal of the mth element of the MEMS microphone array when the measurement station collects the sound array signal for the kth time; is the sound signal belonging to the central element of the MEMS microphone array when the measurement station collects the sound array signal for the kth time.

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

[0056] Since the acoustic signal excited by the fault point is a typical pulse signal with wideband characteristics. The high-frequency component of the wideband signal is attenuated to a much greater extent in soil than in metal media, so the cable propagation path signal and the atmospheric propagation path signal can be separated by empirical mode decomposition (EMD). In order to further improve the decomposition accuracy and avoid aliasing, a complementary ensemble empirical mode decomposition method (CEEMD) can be used to achieve this.

[0057] The sound array signal is decomposed to obtain the expression of the first intrinsic mode function group corresponding to the kth measurement station where N is the maximum number of intrinsic mode functions; the sound signal belonging to the central element is decomposed to obtain the expression of the second intrinsic mode function group corresponding to the kth measurement station

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

[0059] At this time, the angle estimation submodule 233 is configured to calculate the k angle estimates of the fault point corresponding to the kth measurement station relative to the MEMS microphone array 22 based on the k first intrinsic mode function groups output by the sound signal decomposition submodule 232 using the MUSIC algorithm.

[0060] For example, for the kth measurement, the 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, the intrinsic mode function is calculated based on the intrinsic mode function to obtain the angle estimate of the fault point corresponding to the kth measurement station relative to the MEMS microphone array Specifically as follows:

[0062] (1) Based on the first intrinsic mode function group , the intrinsic mode function is constructed into a matrix

[0063]

[0064] where t = 3, 4, 5;

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

[0066] (3) Perform eigenvalue decomposition to obtain the eigenvector corresponding to the smallest eigenvalue. And iterate through the guiding vector a(φ,θ) for the spatial spectral function. The maximum peak obtained through calculation is the signal angle. Wherein, the superscript H represents the conjugate transpose operation;

[0067] (4) Through the formula The estimated angle of the fault point at the k-th measurement station relative to the MEMS microphone array was calculated.

[0068] The distance estimation submodule 234 is used to extract only the soil propagation path signal from the k second intrinsic mode function groups output by the sound signal decomposition submodule 232, and further calculate the intensity of the extracted k soil propagation path signals. Combined with the preset RSSI model, it calculates the k distance estimates between the measurement station 2 and the corresponding fault points from the measurement station 2 in the 1st to kth times.

[0069] For example, for the k-th measurement, firstly, based on the second intrinsic modulus function set... Intrinsic modulus function Only the starting point of the soil propagation path signal was extracted. and from The total energy of the fragment is calculated by extracting a 200ms segment of the intrinsic mode function.

[0070] Secondly, combining the RSSI model for To estimate distance Where q = 3, 4, 5; RSSI is the received signal strength, which is the intrinsic mode function of the sound signal. The intensity of d0 is the intensity under distance d0, which is a fixed value; η is the path loss exponent, which is a fixed value; x is the flat fading exponent, which is a fixed value.

[0071] Finally, through the formula The estimated distance between the measuring station and the fault point corresponding to the k-th measurement is calculated.

[0072] 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 intrinsic mode function groups output by the sound signal decomposition submodule 232, and calculate the k time delay differences formed by the measuring station 2 in the 1 to k times based on the sound signal propagation in the power cable and soil.

[0073] For example, for the kth measurement, Generally, the high frequency components in the signal are contained in the signal, and in the signal denoising application, it is considered to carry a large amount of high frequency noise signal. In the present application, since the pulse signal is a wideband signal, the sound signal propagated in the power cable path will mainly contain the signal in Therefore, the kth corresponding cable propagation path signal of the measurement station is extracted from .

[0074] Since the soil propagation attenuates the high frequency components in the sound signal quickly, the soil propagation path signal with strong energy can be observed in , thereby realizing the separation of the power cable propagation path and the atmospheric propagation path signal;

[0075] Secondly, the threshold method formula (1) is used to obtain the time delay of the kth measurement station on the power cable propagation path and the time delay of the kth measurement station on the soil propagation path

[0076]

[0077] Where f s is a fixed threshold parameter, usually 0.8;

[0078] Finally, the time delay difference of the kth measurement station based on the sound signal propagation in the power cable and the soil is calculated by formula .

[0079] The fault point positioning submodule 236 is configured to construct a target function according to the k coordinate positions, the k angle estimates, the k distance estimates and the k time delay differences corresponding to the 1th to kth measurements of the measurement station, and the k coordinate positions corresponding to the MEMS microphone array, and use a particle swarm optimization algorithm and a Levenberg-Marquardt algorithm to obtain an optimal solution of the target function. The optimal solution is output as the final position of the fault point in the power cable.

[0080] Wherein, the target function is shown in formula (2) as follows:

[0081]

[0082] Wherein, is the estimated position of the fault point in the power cable; p is the to-be-estimated quantity of the fault point position; p i and p j are the coordinate positions of the measurement station in the ith and jth times, respectively. is the coordinate position of the MEMS microphone array when the sound array signal is collected for the i-th time; both subscripts i and j are measurement times, and i = 1, 2,..., k, j = 1, 2,..., k; || is a vector norm; and || 2 is a 2-norm of a vector; is the corresponding calculated angle estimate of the measurement station for the i-th time; is the corresponding calculated distance estimate between the fault point and the measurement station for the i-th time; is the corresponding calculated time delay difference of the sound signal when propagating in the power cable and the soil for the i-th time; is the calculated time delay difference of the sound signal when propagating in the power cable and the soil for the j-th time.

[0083] At this time, the inventor finds that the method of estimating the position by the objective function can be converted into a nonlinear least squares problem, and since the measurement information increases with the increase of the measurement times, it is necessary to use an intelligent search algorithm, a Newton descent method or a Levenberg-Marquardt algorithm for solving. Therefore, the particle swarm optimization algorithm and the Levenberg-Marquardt algorithm are used to solve the optimal solution of the objective function, and the obtained optimal solution is output as the final position of the fault point in the power cable.

[0084] The application scene of one of the embodiments of the application, a power cable fault positioning system based on Beidou and sound arrival angle, is further described as follows:

[0085] For positioning the cable fault point in the urban cable trench, one measurement station and one pulse generator are used. At this time, the pulse generator is used to pass a high-voltage pulse electrical signal to the fault cable according to a fixed period, so as to excite the fault point to emit a sound signal.

[0086] The coordinates of the measurement station do not need to be manually calibrated, but are directly given in the geodetic coordinate system by the satellite signal receiver connected to the Beidou positioning system. The measurement station is carried by an operator, and after measuring 3-5 times at a single measurement position, the operator moves a distance in the direction of the fault point, and after 2-3 times of transformation, the accurate position of the fault point can be obtained.

[0087] First, in the k-th measurement process, the coordinates of the measurement station are denoted as p k The pulse generating device generates a high-voltage pulse once at an interval of 30 seconds in turn to excite the fault point to emit a sound signal. The measurement station collects the sound array signal through the MEMS microphone array, and the sampling frequency of the sound array signal is 48 kHz. The MEMS microphone array adopts a "cross" array, the number of array elements is 17, and the array element spacing is 2 cm. When performing RSSI distance estimation and time delay difference estimation, only the data collected by the center array element is processed. The measurement station performs validity discrimination on the collected sound array signal, and performs signal processing and position estimation.

[0088] Further, the eigenmode function is calculated based on the MUSIC algorithm, and the angle estimation of the kth time corresponding to the fault point of the measurement station relative to the MEMS microphone array is calculated by formula

[0089] Further, the total energy of the eigenmode function is calculated Further, the distance q=3,4,5 is calculated based on the RSSI model obtained by the experiment, and the distance estimation of the kth time corresponding to the fault point of the measurement station is calculated by formula

[0090] Further, the cable propagation path delay information and the soil propagation path delay information at the measurement station are obtained according to formula (1) respectively, and the delay difference of the sound signal propagating through the power cable and the soil at the measurement station is obtained The threshold factor f s is 0.8.

[0091] Finally, the fault point position is estimated based on formula (2), and the nonlinear least squares problem can be accurately solved based on the LM algorithm to obtain the coordinates of the kth time fault point

[0092] As shown in Figure 3 , a power cable fault positioning method based on Beidou and sound arrival angle is provided in the embodiment of the application, which is implemented on a power cable fault positioning system based on Beidou and sound arrival angle in the embodiment of the application. The method comprises the following steps:

[0093] Step S1, the pulse generator periodically applies a high-voltage direct current pulse signal to the power cable, so that when there is a fault point in the power cable, the fault point can be excited to emit a sound signal;

[0094] Step S2, after each active installation and fixation, the coordinate position of the measurement station is obtained, and the sound array signal generated when the power cable is applied with a high-voltage direct current pulse signal by the pulse generator is time-stamped, and further, according to the coordinate position obtained after each active installation and fixation and the time-stamped sound array signal, the final position of the fault point in the power cable is determined.

[0095] ​​The specific process is that in step S1, the pulse generator periodically (for example, once every 30 seconds) applies a high-voltage direct-current pulse signal to the power cable, so that when there is a fault point in the power cable, the fault point can be excited to emit a sound signal.

[0096] In step S2, first, the total number of times k of sound array signal collection by the measuring station is determined, and based on the coordinate positions obtained after each time the measuring station is installed and fixed, k coordinate positions corresponding to the sound array signal collection by the measuring station 1-k times are obtained, and the initial position of the MEMS microphone array installed on the measuring station is combined to obtain k coordinate positions corresponding to the MEMS microphone array during the sound array signal collection by the measuring station 1-k times; wherein k is a positive integer greater than 1.

[0097] Then, using the preset complementary ensemble empirical mode decomposition (CEEMD) algorithm, the time-stamped sound array signals collected by the measuring station 1-k times are directly decomposed to obtain k first intrinsic mode function groups; and the sound signals belonging to the central elements are extracted from the time-stamped sound array signals collected by the measuring station 1-k times, and the k sound signals belonging to the central elements are decomposed using the above-mentioned CEEMD algorithm to obtain k second intrinsic mode function groups.

[0098] Then, according to the k first intrinsic mode function groups, the MUSIC algorithm is used to calculate k angle estimates of the fault point corresponding to the measuring station 1-k times compared with the MEMS microphone array;

[0099] Then, from the k second intrinsic mode function groups, only the soil propagation path signals are extracted, and the intensities of the k extracted soil propagation path signals are further calculated, and the k distance estimates between the fault point corresponding to the measuring station 1-k times and the measuring station are calculated based on the preset RSSI model;

[0100] Then, from the k second intrinsic mode function groups, the soil propagation path signals and the cable propagation path signals are simultaneously extracted, and based on the soil propagation path signals and the cable propagation path signals simultaneously extracted each time, k time delay differences corresponding to the sound signal propagation in the power cable and the soil by the measuring station 1-k times are calculated.

[0101] Finally, based on the k coordinate positions, the k angle estimates, the k distance estimates and the k time delay differences corresponding to the measuring station 1-k times, and the k coordinate positions corresponding to the MEMS microphone array, a target function is constructed, and the particle swarm optimization algorithm and the Levenberg-Marquardt algorithm are used to find the optimal solution of the target function, and the optimal solution output is the final position of the fault point in the power cable.

[0102] The embodiment of the present application has the following beneficial effects:

[0103] 1、The present application realizes the rapid positioning of the cable fault point based on the satellite system (such as Beidou) and sound technology, and only relies on the MEMS microphone array to realize the rapid positioning of the fault point, which not only has simple structure and is convenient to carry, but also greatly reduces the system complexity and system power consumption, thereby solving the defects of the existing acoustic magnetic synchronous method for positioning the power cable fault point.

[0104] 2、The present application converts the fault point position 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 positioning accuracy.

[0105] It is worth noting that the various system modules in the above system embodiment are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional module are only for easy mutual differentiation, and are not used to limit the protection scope of the present application.

[0106] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc.

[0107] The above only describes the preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A power cable fault location system based on Beidou and angle of arrival of sound, characterized in that, The utility model relates to a kind of power cable fault locating system, including pulse generator and measuring station;Wherein, The pulse generator is loaded on the power cable in the cable trench, for periodically applying high-voltage direct current pulse signal to the power cable, to excite the fault point to emit sound signal when the power cable exists fault point; The measuring station is installed in the cable trench, and satellite signal receiver, MEMS microphone array and data calculation module are arranged on it, for obtaining the coordinate position where itself is after each active installation fixation using the satellite signal receiver, and using the MEMS microphone array to collect the sound array signal generated by the power cable when the high-voltage direct current pulse signal is applied by the pulse generator Time stamping, and further according to the coordinate position obtained after each active installation fixation and the sound array signal stamped with time, the final position of the fault point in the power cable is determined using the data calculation module; Wherein, the data calculation module includes: coordinate position determination submodule, for determining the total number of sound array signal collection of the measuring station is k, and based on the coordinate position obtained after each active installation fixation of the measuring station, the k coordinate positions corresponding to the sound array signal collection of the measuring station from 1 to k are obtained, and the initial position of the MEMS microphone array installed in the measuring station is combined to obtain the k coordinate positions corresponding to the MEMS microphone array when the measuring station carries out sound array signal collection from 1 to k;Wherein, k is a positive integer greater than 1; Sound signal decomposition submodule, for using the preset complementary set empirical mode decomposition CEEMD algorithm, directly decomposing the sound array signal collected by the measuring station from 1 to k stamped with time to obtain k first intrinsic mode function groups;And, first, the sound signal belonging to the center element is extracted from the sound array signal collected by the measuring station from 1 to k stamped with time, and then the CEEMD algorithm is used to decompose the k sound signals belonging to the center element to obtain k second intrinsic mode function groups; Angle estimation submodule, for calculating the k angle estimates of the fault point corresponding to the measuring station from 1 to k compared with the MEMS microphone array using MUSIC algorithm according to the k first intrinsic mode function groups output by the sound signal decomposition submodule; Distance estimation submodule, for extracting soil propagation path signal from the k second intrinsic mode function groups output by the sound signal decomposition submodule, and further calculating the intensity of the extracted k soil propagation path signals, and combining the preset RSSI model, the k distance estimates between the fault point corresponding to the measuring station from 1 to k and the measuring station are calculated. a time delay estimation sub-module, configured to extract soil propagation path signals and cable propagation path signals from each of the k sets of second intrinsic mode functions output by the sound signal decomposition sub-module, and calculate k time delay differences corresponding to the k times of sound signal propagation in the power cable and the soil based on the soil propagation path signals and the cable propagation path signals extracted each time; a fault point positioning sub-module, configured to construct a target function based on the k coordinate positions, the k angle estimates, the k distance estimates and the k time delay differences corresponding to the k times of sound signal propagation in the power cable and the soil, and the k coordinate positions corresponding to the MEMS microphone array, and use a particle swarm optimization algorithm and a Levenberg-Marquardt algorithm to obtain an optimal solution of the target function, and output the optimal solution as the final position of the fault point in the power cable. 2.The power cable fault locating system based on Beidou and sound arrival angle according to claim 1, wherein, The objective function is wherein, is the estimated location of the fault point in the power cable; p is the to-be-estimated quantity of the location 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 performing sound array signal collection at the i-th time; the subscripts i and j are both measurement times, and i = 1, 2, …, k, j = 1, 2, …, k; || is the modulus of a vector; ||2 is the 2-norm of a vector; is the corresponding calculated angle estimate of the measuring station at the i-th time; is the corresponding calculated distance estimate between the fault point and the measuring station of the measuring station at the i-th time; is the corresponding calculated time delay difference of the sound signal when propagating in the power cable and the soil of the measuring station at the i-th time; is the calculated time delay difference of the sound signal when propagating in the power cable and the soil of the measuring station at the j-th time. 3.The power cable fault locating system based on Beidou and sound arrival angle according to claim 2, characterized in that, The expression of the timestamped sound array signal is 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 measurement station performs sound array signal collection for the kth time; is the sound signal of the central array element in the MEMS microphone array when the measurement station performs sound array signal collection for the kth time; The expression of the corresponding first eigenmode function group of the measuring station at the kth time is The expression of the corresponding second eigenmode function group is Wherein, N is the maximum number of eigenmode functions.

4. The Beidou and angle of arrival based power cable fault location system as claimed in claim 3, wherein, The angle estimation of the corresponding fault point corresponding to the kth time of the measuring station compared to the MEMS microphone array is based on the eigenfunction and is calculated by the formula ; wherein, for angle estimation, t = 3, 4, 5; based on the spatial spectrum function the maximum peak solved in based on the matrix the sample covariance matrix corresponding to the eigenvector corresponding to the minimum eigenvalue after eigenvalue decomposition; the superscript H is the conjugate transpose operation; a(φ, θ) is the steering vector of the spatial spectrum function.

5. The Beidou and angle of arrival based power cable fault location system as claimed in claim 4, wherein, The k distance estimates between the k corresponding fault points to the measurement station are based on eigenmode functions and combined with the RSSI model and the formula are calculated; wherein, for distance estimation, for distance, q = 3, 4, 5; RSSI is the received signal strength, i.e. the intensity of the sound signal eigenmode function RSSI0 is the intensity at a distance d0, which is a fixed value; η is the path loss exponent, which is a fixed value; x is the flat fading exponent, which is a fixed value.

6. The Beidou and angle of arrival based power cable fault location system as claimed in claim 5, wherein, The time delay difference formed by the measuring station when the sound signal propagates in the power cable and the soil for the kth time is calculated by the formula ; wherein, By threshold method, we get representing the time delay of the kth measurement station on the power cable propagation path, representing the time delay of the kth measurement station on the soil propagation path; f s is a fixed threshold parameter.

7. A power cable fault locating method based on Beidou and angle of arrival, characterized in that, The method is implemented on the power cable fault positioning system based on Beidou and sound angle of arrival as claimed in claim 6, and comprises the following steps: The pulse generator periodically applies a high-voltage direct-current pulse signal to the power cable to excite the fault point to emit a sound signal when the fault point exists in the power cable; After each time of active installation and fixation, the measurement station obtains a coordinate position thereof, timestamps a sound array signal generated by the power cable when the power cable is subjected to the high-voltage direct-current pulse signal applied by the pulse generator, and further determines the final position of the fault point in the power cable based on the coordinate position obtained after each time of active installation and fixation and the timestamped sound array signal. 8.The power cable fault locating method based on Beidou and sound arrival angle of claim 7, wherein, The specific steps of obtaining, after each time of active installation and fixation of the measurement station, a coordinate position thereof, timestamping a sound array signal generated by the power cable when the power cable is subjected to the high-voltage direct-current pulse signal applied by the pulse generator, and further determining the final position of the fault point in the power cable based on the coordinate position obtained after each time of active installation and fixation and the timestamped sound array signal include: determining a total number of times of sound array signal collection by the measurement station as k, obtaining k coordinate positions corresponding to the k times of sound array signal collection by the measurement station based on the coordinate position obtained after each time of active installation and fixation of the measurement station, and obtaining k coordinate positions corresponding to the MEMS microphone array based on an initial position of the MEMS microphone array installed on the measurement station, so as to obtain the k coordinate positions corresponding to the MEMS microphone array during the k times of sound array signal collection by the measurement station; wherein k is a positive integer greater than 1; directly decomposing the timestamped sound array signals collected by the measurement station during the k times by using a preset complementary ensemble empirical mode decomposition (CEEMD) algorithm to obtain k sets of first intrinsic mode functions, and extracting k sound signals belonging to a central element from the timestamped sound array signals collected by the measurement station during the k times, and then decomposing the k sound signals by using the CEEMD algorithm to obtain k sets of second intrinsic mode functions; and According to the k first eigenmode function groups, a MUSIC algorithm is used to calculate k angle estimations of the measurement station corresponding to the fault points in 1-k times relative to the MEMS microphone array; From the k second eigenmode function groups, only soil propagation path signals are extracted, and the intensities of the k extracted soil propagation path signals are further calculated, and combined with a preset RSSI model, k distance estimations between the measurement station and the fault points in 1-k times are calculated; From the k second eigenmode function groups, soil propagation path signals and cable propagation path signals are extracted at the same time, and according to the soil propagation path signals and cable propagation path signals extracted at the same time each time, k time delay differences corresponding to the measurement station based on the sound signal propagation in the power cable and the soil in 1-k times are calculated; According to the k coordinate positions, k angle estimations, k distance estimations and k time delay differences corresponding to the measurement station in 1-k times, and the k coordinate positions corresponding to the MEMS microphone array, a target function is constructed, and a particle swarm optimization algorithm and a Levenberg-Marquardt algorithm are used to solve the optimal solution of the target function, and the obtained optimal solution is output as the final position of the fault point in the power cable.

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