A method, system and readable storage medium for locating faults in high-voltage cable lines

Abstract

The embodiment of the present application provides a method, system and readable storage medium for locating faults in a high-voltage cable line. The method includes obtaining the sheath current generated at multiple preset sampling points in the same cable channel, wherein, for a high-voltage cable line with a cross-interconnected structure, the sampling points include a ground terminal and a cross-interconnected intermediate joint; constructing a corresponding high-dimensional data matrix based on the multiple sheath currents obtained by sampling; performing cluster analysis based on the mutually reachable distances between the data objects in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class; finding the target feature cluster points of the two branch clusters with the smallest density level from the target data class, and locating the fault according to the time tags corresponding to the target feature cluster points. The implementation of this method can achieve more accurate fault location for high-voltage cable lines.

Description

Technical Field

[0001] The present application relates to the technical field of line fault location, and in particular to a method and system for locating a high-voltage cable line fault and a readable storage medium. Background Art

[0002] Fault location in power grids is of great significance to improving the sustainability of power systems. Accurate and reliable fault location methods can not only reduce the manual workload of line inspectors, but also help troubleshoot and restore power grid operations in a timely and rapid manner to reduce economic losses caused by faults. At present, online fault location methods for power systems are mainly based on impedance and traveling waves. Among them, the impedance-based method mainly uses relay protection devices to collect fault voltage / current data and locates faults based on parameter identification of system impedance. Since the impedance monitored by this method may have a nonlinear relationship with the distance between the fault point, it is difficult to directly and accurately locate the fault through the distance relationship between the impedance and the fault point. In addition, the traveling wave-based method mainly locates the fault by identifying the propagation time of the fault traveling wave. However, when the line distribution parameters are large or uneven, it will be difficult to accurately extract the wave head and / or arrival time of the traveling wave. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a high-voltage cable line fault location method, system and readable storage medium, which can achieve more accurate fault location for the high-voltage cable line.

[0004] The present invention provides a method for locating a high-voltage cable line fault, comprising the following steps:

[0005] S1. Obtaining sheath current generated at a plurality of preset sampling points in the same cable channel, wherein for a high-voltage cable line adopting a cross-connection structure, the sampling points include a ground terminal and a cross-connection intermediate joint;

[0006] S2. constructing a corresponding high-dimensional data matrix according to the multiple sheath currents obtained by sampling;

[0007] S3, performing cluster analysis based on the mutually reachable distances between the data objects in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class;

[0008] S4. Find target feature cluster points of two branch clusters with the smallest density level from the target data class, and locate the fault according to the time tags corresponding to the target feature cluster points.

[0009] In a second aspect, an embodiment of the present application provides a high-voltage cable line fault location system, the system comprising a data acquisition module, a data processing module, a cluster analysis module and a fault location module, wherein:

[0010] The data acquisition module is used to acquire the sheath current generated at a plurality of preset sampling points in the same cable channel, wherein, for a high-voltage cable line adopting a cross-connection structure, the sampling points include a ground terminal and a cross-connection intermediate joint;

[0011] The data processing module is used to construct a corresponding high-dimensional data matrix according to the multiple sheath currents obtained by sampling;

[0012] The cluster analysis module is used to perform cluster analysis based on the mutual reachable distances between the data objects in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class;

[0013] The fault location module is used to find the target feature cluster points of the two branch clusters with the smallest density level from the target data class, and perform fault location according to the time tags corresponding to the target feature cluster points.

[0014] In a third aspect, an embodiment of the present application provides a readable storage medium, which includes a high-voltage cable line fault locating method program. When the high-voltage cable line fault locating method program is executed by a processor, the steps of a high-voltage cable line fault locating method as described in any one of the above items are implemented.

[0015] As can be seen from the above, a high-voltage cable line fault location method, system and readable storage medium provided by the embodiment of the present application can accurately identify the arrival time of the fault traveling wave through the sheath current generated at multiple sampling points. Since the error of the two-terminal traveling wave method mainly comes from the identification of the arrival time at both ends of the cable, and the main source of the identification error is randomness, such as synchronization delay, noise interference, etc., more sampling points help to reduce random errors and thus improve accuracy. In addition, in order to reduce random errors, it is considered to monitor the sheath current of the three-phase cable and the adjacent cables in the same cable channel, and construct a multidimensional data matrix based on the sheath current data obtained from the monitoring. By analyzing the multidimensional matrix, the arrival time identification of the traveling wave is improved, so that it can more accurately and effectively identify the arrival time of the traveling wave.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a high-voltage cable line fault location method provided in an embodiment of the present application;

[0019] Figure 2 It is a schematic diagram of the layout of a high-voltage cable line adopting a cross-interconnection structure;

[0020] Figure 3 Schematic diagram of the number of samples of each data class obtained by clustering;

[0021] Figure 4 Schematic diagram for identification of arrival time of traveling waves;

[0022] Figure 5 A schematic structural diagram of a high-voltage cable line fault locating system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0024] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0025] Please refer to Figure 1 , Figure 1This is a flow chart of a high-voltage cable line fault location method in some embodiments of the present application. Taking the method applied to a computer device (the computer device can be a terminal or a server, and the terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server can be an independent server or a server cluster composed of multiple servers) as an example, the method includes the following steps:

[0026] Step S1, obtaining sheath current generated at a plurality of preset sampling points in the same cable channel, wherein for a high-voltage cable line adopting a cross-connection structure, the sampling points include a ground terminal and a cross-connection intermediate joint.

[0027] Step S2, constructing a corresponding high-dimensional data matrix according to the multiple sheath currents obtained by sampling.

[0028] Step S3, performing cluster analysis based on the mutual reachable distances between the data objects in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class.

[0029] Step S4, finding target feature cluster points of two branch clusters with the smallest density level from the target data class, and locating the fault according to the time tags corresponding to the target feature cluster points.

[0030] As can be seen from the above, a high-voltage cable line fault location method provided by an embodiment of the present application accurately identifies the arrival time of the fault traveling wave through the sheath current generated at multiple sampling points. Since the error of the two-end traveling wave method mainly comes from the identification of the arrival time at both ends of the cable, and the main source of the identification error is randomness, such as synchronization delay, noise interference, etc., more sampling points help to reduce random errors and thus improve accuracy. In addition, in order to reduce random errors, it is considered to monitor the sheath current of the three-phase cable and the adjacent cables in the same cable channel, and construct a multidimensional data matrix based on the sheath current data obtained from the monitoring. By analyzing the multidimensional matrix, the arrival time identification of the traveling wave is improved, so that it can more accurately and effectively identify the arrival time of the traveling wave.

[0031] In one embodiment, please refer to Figure 2 In step S1, the sheath currents of the three-phase cable lines and adjacent cable lines in the same cable channel are taken as monitoring objects, wherein: the sheath current at the grounding terminal is collected by a grounding box connected to the corresponding sampling point, and the sheath current at the middle joint is collected by a cross-connection box connected to the corresponding sampling point, wherein the direct grounding box and the cross-connection box are both provided with current transformers.

[0032] Specifically, Figure 2As shown, in the actual high-voltage cross-link cable system, it can be considered to be in the grounding box (i.e. Figure 2 The sheath grounding current is monitored at the direct grounding box 1 and direct grounding box 2) and the cross-connection box shown. The specific reason is that usually there are multiple cables sharing the same channel in the high-voltage cross-linked cable system. If a short circuit occurs in one of the lines, an induction signal will also appear on the metal sheath of the adjacent line. The time when the induction signal appears is the same as the time when the fault occurs, and the propagation characteristics obey the transmission line equation.

[0033] Therefore, in the current embodiment, the sheath currents of the three-phase cable line and the adjacent cable line are analyzed simultaneously, which helps to improve the recognition efficiency and recognition accuracy of the fault traveling wave arrival time.

[0034] In one embodiment, in step S2, constructing a corresponding high-dimensional data matrix according to the multiple sheath currents obtained by sampling includes:

[0035] Step S21, determining the distribution of sampling points.

[0036] Specifically, the distribution of the sampling points is determined, that is, the arrangement order of the sampling points is determined. Subsequently, the computer equipment will sort the multiple sheath currents obtained by sampling in sequence according to the arrangement order of the sampling points, and further construct a corresponding high-dimensional data matrix.

[0037] Step S22, constructing a corresponding high-dimensional data matrix according to the multiple sheath currents obtained by sampling and the distribution of the sampling points, wherein the matrix row vectors are determined according to the amplitude of the sheath current and the number of matrix columns is determined according to the total number of sampling points.

[0038] Specifically, in Figure 2 In a typical complete high-voltage cross-connection section shown in FIG, the 12 sheath grounding current amplitudes recorded at different positions are quite different. These 12 sets of currents constitute the input of the elements of the high-dimensional matrix I, as shown in the following formula (1):

[0039] I=[I a1 I b1 I c1 ... I c3 ] T (1)

[0040] In the above embodiment, a plurality of monitored sheath grounding currents are analyzed together, which can improve the recognition accuracy of the arrival time and improve the analysis efficiency.

[0041] In one embodiment, in step S3, cluster analysis is performed based on the mutual reachable distances between data objects in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class, including:

[0042] Step S31, determining the mutual reachable distances between data objects in the high-dimensional data matrix.

[0043] Specifically, since the Euclidean distances of all data objects in the high-dimensional space tend to be equal, in the current embodiment, the computer device performs cluster analysis based on the mutually reachable distances between the data objects. Since, under the expression of the mutually reachable distance, dense objects with small core distances maintain the same distance from each other, while sparse objects have been dispersed to at least the core distance of any other object, the clustering accuracy can be further improved, and the recognition accuracy of the traveling wave arrival time can be improved.

[0044] Step S32: taking each of the data objects as a point and taking the mutual reachable distance between the data objects as the weight of the connecting edge, and constructing a minimum spanning tree based on a weight threshold comparison method.

[0045] Specifically, the above-mentioned weight threshold comparison method, that is, the computer device compares the weights of each connected edge determined with the preset weight threshold parameter, and based on the comparison result, deletes the edge with a weight higher than the weight threshold parameter. Among them, the weight threshold parameter starts from a relatively large value and has been steadily decreasing, and this change trend is an important step of this method and an important idea for realizing clustering. Its purpose is to give a criterion for merging "points" and deleting "edges". The reason why the weight threshold parameter is limited to change from large to small is to ensure that the edges with too large weights can be deleted first.

[0046] Step S33: convert the minimum spanning tree into a hierarchy of connected components to make the clustering hierarchy flat.

[0047] Specifically, since the minimum spanning tree of a high-dimensional data object is still high-dimensional, it needs to be converted into a hierarchy of connected components to make the clustering hierarchy "flat".

[0048] Step S34, during the cluster analysis process, MinClustSize-minimum data class size is used to limit possible data classes, and the accuracy of the data class is determined based on at least one of the quality, relative quality and stability of the data class, and the most stable target data class is outputted.

[0049] Specifically, the first step of cluster extraction is to compress the large and complex cluster hierarchy into a smaller tree and attach more data to each node. In the current embodiment, no fixed parameter for the mutual reachability distance is set, but MinClustSize is used to limit the possible data classes. Therefore, when traversing the hierarchy, each split will determine whether the number of points in one of the new data classes created by each split is less than MinClustSize, where:

[0050] (1) If the number of data objects is less than MinClustSize, the data object falls outside the data class, and the larger data class retains the data class identity of the parent node. Then, the data objects that fall outside the cluster with a mutual reachable distance are marked.

[0051] (2) If the split is into two data classes and each data class is at least as large as MinClustSize, the split will persist in the tree.

[0052] The implementation of the above embodiment can make the data class easier to view and process, especially for data class problems with simple data structures. However, it should be noted that the computer device still needs to select the data class as a flat data class.

[0053] It should be noted that in order to illustrate the concept of stability, several important mathematical measurement parameters are currently introduced:

[0054] (1) First, different metrics are needed to represent the persistence of the cluster, as shown in (2):

[0055]

[0056] Here, λ represents the density level.

[0057] (2) For data class C i , C i The quality of E(C i ) can be defined by the following formula (3):

[0058]

[0059] Where f(x) is the density function, which represents the density of each data class. x is the data object, that is, the variable that needs to be substituted into formula (3) for calculation.

[0060] (3) Data Class C i In λ min (C i The relative mass at ) can be defined by the following formula (4):

[0061]

[0062] Among them, λ max (x,C i )=min{f(x),λ max (C i )} and λ max (C i ) is the data class C i Density levels that split or disappear.

[0063] (4) Data Class C i The stability of can be defined by the following formula (5):

[0064]

[0065] Among them, λ min (C i ) is the data class C i The minimum density level that exists, λ max (x j ,C i ) is the density level above which object x j Will no longer belong to cluster C i .

[0066] Therefore, in the current embodiment, the above steps can be regarded as an optimization problem, the goal of which is to maximize the overall stability of the extracted clusters. After several rounds of optimization, the most stable target data class is the output.

[0067] In one embodiment, in step S31, determining the mutual reachable distance between data objects in the high-dimensional data matrix includes:

[0068] Step S311, obtaining the Euclidean distance between each data object in the high-dimensional data matrix.

[0069] Specifically, the Euclidean distance is also called the Euclidean distance. In n-dimensional space, the length of the shortest line is its Euclidean distance. It is a commonly used distance definition, which is the true distance between two points in m-dimensional space.

[0070] In one embodiment, the Euclidean distance in two-dimensional and three-dimensional space is the distance between two points, wherein the calculation formula in two-dimensional space can be:

[0071] d = sqrt((x 1 -x 2 )^+(y 1 -y 2 )^).

[0072] Its calculation formula in three-dimensional space can be:

[0073] d = sqrt((x 1 -x 2 )^+(y 1 -y 2 )^+(z 1 -z 2 )^).

[0074] If extended to n-dimensional space, the calculation formula of Euclidean distance can be:

[0075] d=sqrt(∑(x i1 -x i2 )^);

[0076] Where i = 1, 2..n; x i1 Represents the i-th dimension coordinate of the first point, x i2 Represents the i-th dimension coordinate of the second point.

[0077] It should be noted that the n-dimensional Euclidean space is a set of points, each of which can be represented by (x(1), x(2), ... x(n)), where x(i) (i = 1, 2 ... n) is a real number, called the i-th coordinate of x, and the distance d(x, y) between two points x and y = (y(1), y(2) ... y(n)) is defined as the above formula.

[0078] Step S312, determining whether the number of objects included in the ε-domain is within the range defined by a preset sample quantity threshold, and thereby determining the core objects corresponding to each data object, wherein ε represents the value of the minimum radius.

[0079] Specifically, if a data object x p If the ε-domain contains at least MinPts (i.e., the sample number threshold) objects, then the object is called a core object.

[0080] In one embodiment, the data object x p The core distance d core (x p ) is defined as p The Euclidean distance to its MinPts nearest neighbor.

[0081] Step S313: determining the mutual reachable distances between the data objects according to the core distances between the data objects and the corresponding core objects and the Euclidean distances between the data objects.

[0082] Specifically, two data objects x p and x q The mutual reachability distance between can be defined as:

[0083] dmreach (x p , x q )=max{d core (x p ), d core (x q ), d(x p , x q )}.

[0084] Among them, d(x p ,x q ) is x p and x q The Euclidean distance between .

[0085] It should be noted that, under the above expression of mutually reachable distance, dense objects with small core distances maintain the same distance from each other, while sparse objects have been dispersed to at least the core distance of any other object. It should be noted that the effectiveness depends on the value of MinPts. Therefore, in the current embodiment, the larger the value of MinPts, the more data objects are affected by the measurement, and the value of MinPts cannot be too large, otherwise the number of final clusters will be very small.

[0086] In one embodiment, in step S32, constructing a minimum spanning tree based on a weight threshold comparison method includes:

[0087] Step S321, comparing the determined weights of each connecting edge with a preset weight threshold parameter to obtain a corresponding comparison result.

[0088] Step S322: According to the comparison result, first connection edges with weights higher than the threshold parameter are deleted from the connection edges, and a minimum spanning tree is constructed based on the remaining second connection edges.

[0089] In one embodiment, in step S33, converting the minimum spanning tree into a hierarchical structure of connected components includes:

[0090] Step S331, sorting the edges of the minimum spanning tree from large to small according to the obtained weight values.

[0091] Specifically, the computer device can sort the edges of the minimum spanning tree according to at least one sorting method including bubble sort, selection sort, insertion sort, etc. The embodiment of the present application does not limit the specific sorting method.

[0092] Step S332, based on the sorted target minimum spanning tree, traverse all branches in the target minimum spanning tree, and during the traversal process, create a corresponding merged data class for each edge through the union-find data structure, so that each data object in the hierarchical structure has a corresponding starting point and end point.

[0093] Specifically, when creating a corresponding merged data class for each edge through the union-find data structure, the computer device will first traverse all branches (because only by traversing all branches can the neighborhood radius be compressed to achieve hierarchical search). After that, a new merged data class is created for each edge (the merged data class can be understood as a data set with hierarchical labels. The purpose of this step is to transform the original high-dimensional data structure into a weighted tree structure). In this way, each data object has a clear corresponding starting point and end point in the hierarchical structure (with the mutually reachable distance as the vertical coordinate), and can be arbitrarily clustered by cutting branches with a single horizontal line. Afterwards, the computer device also needs to find a reasonable horizontal line for clustering, rather than just performing clustering analysis by setting fixed parameters of the mutually reachable distance.

[0094] The above embodiment provides a minimum spanning tree path for high-dimensional data, which does not use the mutual reachable distance and MinPts to describe the closeness of sample distribution in the neighborhood. Instead, it uses the parameters MinPts and MinClustSize (i.e., minimize the cluster size) to limit possible clusters and make the high-dimensional clustering problem an optimization problem, so that the fault location accuracy can be improved while reducing random errors.

[0095] Please refer to Figure 3-Figure 4 , in order to make the distinction between clusters more significant, a relatively large MinClustSize and a relatively small MinPts are required. In this case, the computer device sets MinClustSize and MinPts to 12 and 7, respectively. Based on the above clustering analysis steps, a condensed clustering tree can be obtained. In one embodiment, the resulting data classes 1-9 represent the final stable classes, and data class 0 represents "noise". It should be noted that cluster 0 is not necessarily true noise in practice. As described above, object x p Whether it is labeled as "noise" depends only on the hierarchical structure of the data, which is related to the density level and stability of the high-dimensional matrix. In this case, the travel wave arrival time points that need to be extracted have similar characteristics to "noise". Therefore, data class 0 should be analyzed together with ordinary clustering. For further analysis, Figure 3 The number of samples in each of the 10 data classes is shown.

[0096] Since the traveling wave propagates in opposite directions to both ends of the cable and is roughly mirrored on both sides of the fault, the monitoring location closest to the fault records the traveling wave first. In addition, when the fault occurs, there are signal steps in the sheath grounding current, which correspond exactly to the arrival time of the traveling wave. For the density-based hierarchical clustering method, these are the two branch nodes with the smallest λ value. In this case, it is noted that the fault is closer to J1 (for details, please refer to Figure 2 ),correspond Figure 3 Therefore, data classes 4 and 0 can be analyzed together. Figure 4 As shown, the timestamp of the only sample in data class 4 labeled as cluster 0 is 1.35 μs. This is exactly the expected time of the first arriving traveling wave recorded at J1, corresponding to the sheath current I a2 .

[0097] Please refer to Figure 5 As shown, a high-voltage cable line fault location system 500 provided in an embodiment of the present application includes a data acquisition module 501, a data processing module 502, a cluster analysis module 503 and a fault location module 504, wherein:

[0098] The data acquisition module 501 is used to acquire the sheath current generated at multiple preset sampling points in the same cable channel, wherein for a high-voltage cable line with a cross-connected structure, the sampling points include a ground terminal and a cross-connected intermediate joint.

[0099] The data processing module 502 is used to construct a corresponding high-dimensional data matrix according to the multiple sheath currents obtained by sampling.

[0100] The cluster analysis module 503 is used to perform cluster analysis based on the mutual reachable distances between the data objects in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class.

[0101] The fault location module 504 is used to find target feature cluster points of two branch clusters with the smallest density level from the target data class, and perform fault location according to the time tags corresponding to the target feature cluster points.

[0102] In one embodiment, the data processing module 502 is also used to determine the distribution of sampling points; based on the multiple sheath currents obtained by sampling and according to the distribution of the sampling points, a corresponding high-dimensional data matrix is ​​constructed, wherein the matrix row vectors are determined according to the amplitude of the sheath current, and the number of matrix columns is determined according to the total number of sampling points.

[0103] In one embodiment, the clustering analysis module 503 is also used to determine the mutually reachable distance between each data object in the high-dimensional data matrix; taking each data object as a point and the mutually reachable distance between each data object as the weight of the connecting edge, and constructing a minimum spanning tree based on the weight threshold comparison method; converting the minimum spanning tree into a hierarchy of connected components to make the clustering hierarchy flat; during the clustering analysis process, using MinClustSize-minimum data class size to limit possible data classes, and determining the accuracy of the data class based on at least one of the quality, relative quality and stability of the data class, and outputting the most stable target data class.

[0104] In one embodiment, the cluster analysis module 503 is also used to obtain the Euclidean distance between each data object in the high-dimensional data matrix; determine whether the number of objects contained in the ε-domain is within the range defined by a preset sample quantity threshold, and thereby determine the core objects corresponding to each data object, wherein ε represents the value of the minimum radius; and determine the mutually reachable distance between each data object based on the core distance between each data object and the corresponding core object, and the Euclidean distance between each data object.

[0105] In one embodiment, the clustering analysis module 503 is also used to compare the weights of each determined connection edge with a preset weight threshold parameter to obtain a corresponding comparison result; based on the comparison result, the first connection edge with a weight higher than the threshold parameter is deleted from the connection edges, and a minimum spanning tree is constructed based on the remaining second connection edges.

[0106] In one of the embodiments, the clustering analysis module 503 is also used to sort the edges of the minimum spanning tree from large to small according to the obtained weight values; based on the sorted target minimum spanning tree, all branches in the target minimum spanning tree are traversed, and during the traversal process, a corresponding merged data class is created for each edge through the union-find data structure, so that each data object in the hierarchical structure has a corresponding starting point and end point.

[0107] The above-mentioned high-voltage cable line fault location system accurately identifies the arrival time of the fault traveling wave through the sheath current generated at multiple sampling points. Since the error of the two-terminal traveling wave method mainly comes from the identification of the arrival time at both ends of the cable, and the main source of the identification error is randomness, such as synchronization delay, noise interference, etc., more sampling points help to reduce random errors and thus improve accuracy. In addition, in order to reduce random errors, it is considered to monitor the sheath current of the three-phase cable and the adjacent cables in the same cable channel, and construct a multidimensional data matrix based on the sheath current data obtained from the monitoring. By analyzing the multidimensional matrix, the identification of the arrival time of the traveling wave is improved, so that it can more accurately and effectively identify the arrival time of the traveling wave.

[0108] The embodiment of the present application provides a storage medium, and when the computer program is executed by the processor, the method in any optional implementation of the above embodiment is executed. Wherein, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk.

[0109] The above storage medium accurately identifies the arrival time of the fault traveling wave through the sheath current generated at multiple sampling points. Since the error of the two-terminal traveling wave method mainly comes from the identification of the arrival time at both ends of the cable, and the main source of the identification error is randomness, such as synchronization delay, noise interference, etc., more sampling points help to reduce random errors and thus improve accuracy. In addition, in order to reduce random errors, it is considered to monitor the sheath current of the three-phase cable and the adjacent cables in the same cable channel, and construct a multidimensional data matrix based on the sheath current data obtained from the monitoring. By analyzing the multidimensional matrix, the identification of the arrival time of the traveling wave is improved, so that it can more accurately and effectively identify the arrival time of the traveling wave.

[0110] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0111] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0112] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0113] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0114] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for locating a high-voltage cable line fault. It is characterized in that The following steps are involved: S1. Obtaining sheath current generated at a plurality of preset sampling points in the same cable channel, wherein for a high-voltage cable line adopting a cross-connection structure, the sampling points include a ground terminal and a cross-connection intermediate joint; S2. constructing a corresponding high-dimensional data matrix according to the multiple sheath currents obtained by sampling; S3, performing cluster analysis based on the mutually reachable distances between the data objects in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class; S4, finding target feature cluster points of two branch clusters with the smallest density level from the target data class, and locating the fault according to the time tags corresponding to the target feature cluster points; In step S3, cluster analysis is performed based on the mutual reachable distances between the data objects in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class, including: S31, determining the mutual reachable distance between each data object in the high-dimensional data matrix; S32, taking each data object as a point, and taking the mutual reachable distance between each data object as the weight of the connecting edge, and constructing a minimum spanning tree based on a weight threshold comparison method; S33, converting the minimum spanning tree into a hierarchy of connected components so that the clustering hierarchy is flat; S34. During cluster analysis, use MinClustSize - a minimum data class size to limit possible data classes, and determine the accuracy of the data class based on at least one of the quality, relative quality and stability of the data class, and output the most stable target data class; In step S31, determining the mutual reachable distance between each data object in the high-dimensional data matrix includes: S311, obtaining the Euclidean distance between each data object in the high-dimensional data matrix; S312, judging whether the number of objects included in the ε-domain is within the range defined by a preset sample quantity threshold, and thereby determining the core objects corresponding to each data object, wherein ε represents the value of the minimum radius; S313: Determine the mutual reachable distance between each data object according to the core distance between each data object and the corresponding core object and the Euclidean distance between each data object.

2. The method according to claim 1, It is characterized in that In step S1, the sheath current of the three-phase cable line and the adjacent cable line in the same cable channel is taken as the monitoring object, wherein: The sheath current at the grounding terminal is collected by a grounding box connected to the corresponding sampling point, and the sheath current at the intermediate joint is collected by a cross-connection box connected to the corresponding sampling point, wherein the grounding box and the cross-connection box are both provided with current transformers.

3. The method according to claim 1, It is characterized in that In step S2, the corresponding high-dimensional data matrix is ​​constructed according to the multiple sheath currents obtained by sampling, including: S21, determining the distribution of sampling points; S22. According to the multiple sheath currents obtained by sampling and the distribution of the sampling points, a corresponding high-dimensional data matrix is ​​constructed, wherein the row vectors of the matrix are determined according to the amplitude of the sheath current, and the number of matrix columns is determined according to the total number of sampling points.

4. The method according to claim 1, It is characterized in that In step S32, the minimum spanning tree is constructed based on the weight threshold comparison method, including: S321, comparing the determined weights of each connecting edge with a preset weight threshold parameter to obtain a corresponding comparison result; S322: According to the comparison result, delete the first connection edges whose weights are higher than the threshold parameter from the connection edges, and construct a minimum spanning tree based on the remaining second connection edges.

5. The method according to claim 1, It is characterized in that In step S33, converting the minimum spanning tree into a hierarchical structure of connected components includes: S331, sorting the edges of the minimum spanning tree from large to small according to the obtained weight values; S332, based on the sorted target minimum spanning tree, traverse all branches in the target minimum spanning tree, and in the traversal process, union-find The data structure creates a corresponding merged data class for each edge, and enables each data object to have a corresponding starting point and end point in the hierarchical structure.

6. A high voltage cable line fault location system, It is characterized in that The system includes a data acquisition module, a data processing module, a cluster analysis module and a fault location module, wherein: The data acquisition module is used to acquire the sheath current generated at a plurality of preset sampling points in the same cable channel, wherein, for a high-voltage cable line adopting a cross-connection structure, the sampling points include a ground terminal and a cross-connection intermediate joint; The data processing module is used to construct a corresponding high-dimensional data matrix according to the multiple sheath currents obtained by sampling; The cluster analysis module is used to perform cluster analysis based on the mutual reachable distances between the data objects in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class; The fault location module is used to find the target feature cluster points of the two branch clusters with the smallest density level from the target data class, and perform fault location according to the time tags corresponding to the target feature cluster points; The cluster analysis module performs cluster analysis based on the mutual reachable distance between each data object in the high-dimensional data matrix to maximize the overall stability of the clustering and output the most stable target data class. The specific implementation is: Determine the mutual reachability distances between data objects in a high-dimensional data matrix; Each data object is regarded as a point, and the mutual reachable distance between each data object is regarded as the weight of the connecting edge. Based on the weight threshold comparison method, a minimum spanning tree is constructed; Convert the minimum spanning tree into a hierarchy of connected components so that the clustering hierarchy is flat; In cluster analysis, we use MinClustSize - a minimum data class size to limit possible data classes, and determine the accuracy of the data class based on at least one of the quality, relative quality and stability of the data class, and output the most stable target data class; The specific implementation of the cluster analysis module determining the mutual reachable distance between each data object in the high-dimensional data matrix includes: Obtaining the Euclidean distance between each data object in the high-dimensional data matrix; Determine whether the number of objects contained in the ε-domain is within the range defined by a preset sample quantity threshold, and thereby determine the core objects corresponding to each data object, where ε represents the value of the minimum radius; The mutual reachable distance between each data object is determined according to the core distance between each data object and the corresponding core object and the Euclidean distance between each data object.

7. The system according to claim 6, wherein the data processing module is further used to determine the distribution of sampling points; construct a corresponding high-dimensional data matrix according to the plurality of sheath currents obtained by sampling and the distribution of the sampling points, in, The row vector of the matrix is ​​determined according to the amplitude of the sheath current, and the number of matrix columns is determined according to the total number of sampling points.

8. A readable storage medium, It is characterized in that The readable storage medium includes a high-voltage cable line fault locating method program, and when the high-voltage cable line fault locating method program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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