A power cable fault location method and intelligent power distribution system

Abstract

The present application provides a method for locating power cable faults and an intelligent power distribution system, which collects current signals of each fault monitoring node of the target power cable in the intelligent power distribution system; determines the signal correlation feature sequence of each fault monitoring node, and further determines the adjacent fault discrimination amount of the signal at each fault monitoring node; identifies the implicit fault interval of the target power cable based on each monitoring time span and all adjacent fault discrimination amounts; determines the fault response point on the high-frequency band and the non-fault response point on the low-frequency band of the target power cable when it is pulse-excited according to the fault response coefficient and the implicit fault interval of the target power cable; determines the explicit fault interval of the target power cable based on the fault response point and the non-fault response point. The above scheme determines the explicit fault interval of the target power cable based on the fault response point and the non-fault response point, and can locate the fault interval of the power cable under mixed reflected waves.

Description

Technical Field

[0001] The present application relates to the technical field of cable fault location, and more specifically, to a power cable fault location method and an intelligent power distribution system. Background Art

[0002] Cable fault location in intelligent distribution system is one of the key technologies to ensure the stable operation of distribution system. With the continuous growth of modern society's demand for electricity and communication, the number and length of cables in the distribution system are also increasing rapidly. Therefore, the importance of cable fault location technology is becoming more and more prominent. The causes of cable failure are various, including mechanical damage, insulation aging, environmental factors (such as moisture, corrosion), overvoltage and manufacturing defects of the cable itself. These faults will not only cause power outages and equipment damage, but may also cause serious safety accidents such as fire and explosion. Therefore, timely and accurate detection and location of cable faults has become an important task in the maintenance and management of intelligent distribution systems. Traditional cable fault location methods mainly rely on manual experience and simple electrical measurement methods, such as insulation resistance testing and DC high voltage testing. However, these methods usually require the cable to be completely shut down for testing, and the detection efficiency and accuracy are low. With the development of science and technology, modern cable fault location technology is gradually developing towards intelligence, automation and online detection.

[0003] Generally, the realization of cable fault location is mainly based on the reflection of electromagnetic waves and the propagation characteristics of electrical signals. The fault location is determined by measuring and analyzing the changes in electrical characteristics between the fault point and the detection point. Common methods include time domain reflection method and frequency domain reflection method. The time domain reflection method determines the fault location by injecting a pulse signal into the cable and measuring the time difference of its reflection back at the fault point; the frequency domain reflection method locates the fault by analyzing the changes in the impedance characteristics of the cable at different frequencies; however, in the fault location of power cables, the joints, branches and other irregular structures inside the power cables will cause multiple reflections, thereby generating complex multiple reflection waves. These multiple reflection waves are mixed with the reflection waves of the fault point to form a phenomenon of mixed reflection waves, thereby reducing the accuracy of the power cable fault interval positioning. Therefore, how to locate the fault interval of the power cable in the intelligent distribution system under the condition of mixed reflection waves, thereby improving the accuracy of the power cable fault interval positioning in the intelligent distribution system has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a power cable fault location method and an intelligent power distribution system, which can locate the fault section of the power cable under mixed reflected waves, thereby improving the accuracy of power cable fault section location in the intelligent power distribution system.

[0005] In a first aspect, the present application provides a method for locating a power cable fault, comprising the following steps:

[0006] Pulse excitation is performed on the target power cable in the intelligent power distribution system, and current signals of each fault monitoring node are collected when the target power cable is pulse-excited;

[0007] Perform signal propagation correlation between the current signal of each fault monitoring node and the current signal of its adjacent fault monitoring node, and then obtain the signal correlation feature sequence of each fault monitoring node;

[0008] According to all signal correlation feature sequences, fault-end adjacent discrimination is performed on each fault monitoring node, and then the adjacent fault discrimination amount of each fault monitoring node is obtained. Based on the adjacent fault discrimination amount of each fault monitoring node and the monitoring time span of each fault monitoring node, the hidden fault interval when the target power cable in the intelligent distribution system fails is determined;

[0009] Determine a fault response coefficient of a current signal when a fault occurs in a target power cable, and determine a fault response point in a high frequency band and a non-fault response point in a low frequency band when the target power cable is pulse excited according to the fault response coefficient and the hidden fault interval;

[0010] The explicit fault interval of the target power cable in the intelligent distribution system is determined based on the fault response point in the high-frequency band and the non-fault response point in the low-frequency band when the target power cable is pulse excited.

[0011] In some embodiments, performing signal propagation correlation between the current signal of each fault monitoring node and the current signal of its adjacent fault monitoring node to obtain the signal correlation feature sequence of each fault monitoring node specifically includes:

[0012] Perform signal alignment on the current signals of all fault monitoring nodes;

[0013] Combining the aligned current signals of each fault monitoring node with the current signals of its adjacent fault monitoring nodes to obtain current signal pairs of each fault monitoring node;

[0014] A current signal pair of a fault monitoring node is selected, all current signals in the current signal pair are correlated and mapped, and each current signal after the correlation mapping is correspondingly calculated to obtain a signal correlation feature sequence of the fault monitoring node;

[0015] Repeat the above steps to obtain the signal correlation feature sequences of the remaining fault monitoring nodes.

[0016] In some embodiments, the fault end adjacent determination is performed on each fault monitoring node according to all signal correlation feature sequences, and the adjacent fault determination amount of each fault monitoring node is obtained, which specifically includes:

[0017] Obtaining signal correlation feature sequences of each fault monitoring node;

[0018] Selecting a signal correlation feature sequence of a fault monitoring node, and extracting all different signal correlation feature values ​​in the signal correlation feature sequence;

[0019] Determine the distribution of the eigenvalues ​​associated with each of the different signals;

[0020] Determine the adjacent fault discrimination amount of the fault monitoring node according to the distribution amount of all the different signal correlation characteristic values;

[0021] Repeat the above steps to obtain the adjacent fault discrimination quantities of the remaining fault monitoring nodes.

[0022] In some embodiments, determining the hidden fault interval when a target power cable in the intelligent power distribution system fails based on the adjacent fault discrimination amount of each fault monitoring node and the monitoring time span of each fault monitoring node specifically includes:

[0023] Obtain the monitoring time span and adjacent fault discrimination amount of each fault monitoring node;

[0024] Arrange all adjacent fault discrimination quantities in ascending order;

[0025] Determine the fault sign reference point according to all adjacent fault discrimination quantities arranged in ascending order;

[0026] The monitoring time span corresponding to the fault sign reference point is used as the hidden fault interval when a target power cable fails in the intelligent power distribution system.

[0027] In some embodiments, determining the fault response coefficient of the current signal when a fault occurs in the target power cable specifically includes:

[0028] Acquire the current signal when a fault occurs in the target power cable;

[0029] determining a fluctuation margin of the current signal;

[0030] Performing margin compensation on each signal value in the current signal according to the fluctuation margin to obtain a signal margin compensation value corresponding to each signal value;

[0031] Determine a signal change characteristic value when a target power cable fails according to all signal margin compensation values;

[0032] A fault response coefficient of the current signal when a fault occurs in the target power cable is determined according to the signal change characteristic value and the reference signal change characteristic value of the target power cable.

[0033] In some embodiments, the propagation association represents a process of measuring the deviation between signals of current signals collected by adjacent fault monitoring nodes.

[0034] In some embodiments, current signals of each fault monitoring node are collected by current sensors when the target power cable is pulse excited.

[0035] In a second aspect, the present application provides an intelligent power distribution system, including a power cable fault locating unit, wherein the power cable fault locating unit includes:

[0036] The acquisition module is used to pulse excite the target power cable in the intelligent power distribution system and collect the current signals of each fault monitoring node when the target power cable is pulse excited;

[0037] A processing module, used to perform signal propagation correlation between the current signal of each fault monitoring node and the current signal of its adjacent fault monitoring node, thereby obtaining a signal correlation feature sequence of each fault monitoring node;

[0038] The processing module is further used to perform fault-end adjacent discrimination on each fault monitoring node according to all signal correlation feature sequences, thereby obtaining adjacent fault discrimination amounts of each fault monitoring node, and determining the hidden fault interval when a target power cable in the intelligent power distribution system fails based on the adjacent fault discrimination amounts of each fault monitoring node and the monitoring time span of each fault monitoring node;

[0039] The processing module is further used to determine a fault response coefficient of a current signal when a fault occurs in the target power cable, and determine a fault response point in a high frequency band and a non-fault response point in a low frequency band when the target power cable is pulse excited according to the fault response coefficient and the hidden fault interval;

[0040] The execution module is used to determine the explicit fault interval of the target power cable in the intelligent power distribution system based on the fault response point in the high frequency band and the non-fault response point in the low frequency band when the target power cable is pulse excited.

[0041] In a third aspect, the present application provides a computer device, the computer device comprising a memory and a processor, the memory storing codes, the processor being configured to obtain the codes and execute the above-mentioned power cable fault locating method.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned power cable fault location method is implemented.

[0043] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0044] In the power cable fault location method and intelligent power distribution system provided by the present application, firstly, a pulse excitation is performed on the target power cable in the intelligent power distribution system, and the current signal of each fault monitoring node is collected when the target power cable is pulsed; secondly, the current signal of each fault monitoring node is correlated with the current signal of its adjacent fault monitoring node through signal propagation, thereby obtaining a signal correlation feature sequence of each fault monitoring node; further, the fault end adjacent judgment is performed on each fault monitoring node according to all the signal correlation feature sequences, thereby obtaining the adjacent fault judgment amount of each fault monitoring node, and based on each fault monitoring node, the adjacent fault judgment amount of each fault monitoring node is obtained. The adjacent fault discrimination amount of the node and the monitoring time span of each fault monitoring node determine the implicit fault interval when the target power cable fails in the intelligent distribution system; then, the fault response coefficient of the current signal when the target power cable fails is determined, and the fault response point on the high-frequency band and the non-fault response point on the low-frequency band of the target power cable when it is pulsed excited are determined according to the fault response coefficient and the implicit fault interval; finally, the explicit fault interval of the target power cable in the intelligent distribution system is determined based on the fault response point on the high-frequency band and the non-fault response point on the low-frequency band when the target power cable is pulsed excited.

[0045] It can be seen that the present application firstly excites the target power cable through a high-voltage pulse to obtain a current signal with a high credibility, so as to avoid the influence caused by the complex waveform caused by the joints, branches and other irregular structures inside the power cable; secondly, the deviation between the signal values ​​of the current signals of all adjacent fault monitoring nodes is measured to identify the current signal characteristics of the fault monitoring nodes, thereby avoiding the detection error caused by the mixing of reflected waves; further, the hidden fault interval is determined according to the adjacent fault discrimination amount of each fault monitoring node, so as to preliminarily identify the time period when the power cable may have a fault but has not yet been obviously manifested, thereby avoiding greater losses and power outages caused by fault escalation; Then, based on the fault response coefficient of the current signal when a fault occurs in the target power cable and the hidden fault interval, the fault response point on the high-frequency band and the non-fault response point on the low-frequency band when the target power cable is pulse-excited are determined to further find the interval where the target power cable has a fault; finally, the interval in which the power cable obviously exhibits fault characteristics is refined according to the fault response point on the high-frequency band and the non-fault response point on the low-frequency band, thereby improving the accuracy of locating the fault interval of the power cable; in summary, the scheme of the present application can realize the positioning of the fault interval of the power cable under the mixing of reflected waves, thereby improving the accuracy of locating the fault interval of the power cable in the intelligent distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is an exemplary flow chart of a power cable fault location method according to some embodiments of the present application;

[0047] Figure 2 is an exemplary flow chart for determining adjacent fault discrimination amounts according to some embodiments of the present application;

[0048] Figure 3 is an exemplary flow chart of determining a fault response coefficient according to some embodiments of the present application;

[0049] Figure 4 is a schematic diagram of exemplary hardware and / or software of a power cable fault locating unit according to some embodiments of the present application;

[0050] Figure 5 It is a structural schematic diagram of a computer device for implementing a power cable fault locating method according to some embodiments of the present application. DETAILED DESCRIPTION

[0051] The core of the present application is to firstly pulse excite the target power cable and collect the current signals of each fault monitoring node in the intelligent distribution system when the target power cable is pulse excited; secondly, perform signal propagation correlation between the current signal of each fault monitoring node and the current signal of its adjacent fault monitoring node, and then obtain the signal correlation feature sequence of each fault monitoring node; further, perform fault end adjacent discrimination on each fault monitoring node according to all signal correlation feature sequences, and then obtain the adjacent fault discrimination amount of each fault monitoring node, and determine the intelligent distribution system based on the adjacent fault discrimination amount of each fault monitoring node and the monitoring time span of each fault monitoring node. The implicit fault interval when the target power cable fails is determined; then, the fault response coefficient of the current signal when the target power cable fails is determined, and the fault response point on the high-frequency band and the non-fault response point on the low-frequency band of the target power cable when the target power cable is pulse-excited are determined according to the fault response coefficient and the implicit fault interval; finally, the explicit fault interval of the target power cable in the intelligent distribution system is determined based on the fault response point on the high-frequency band and the non-fault response point on the low-frequency band when the target power cable is pulse-excited, which can realize the positioning of the fault interval of the power cable under the mixed reflected wave, thereby improving the accuracy of positioning the fault interval of the power cable in the intelligent distribution system.

[0052] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a power cable fault location method according to some embodiments of the present application. The power cable fault location method 100 mainly includes the following steps:

[0053] In step 101, a pulse excitation is performed on a target power cable in the intelligent power distribution system, and current signals of various fault monitoring nodes are collected when the target power cable is pulse excitation.

[0054] It should be noted that the power cable in this application refers to a power cable using cross-linked polyethylene (XLPE) as the insulating material, also called a cross-linked polyethylene insulated power cable. Compared with ordinary power cables, cross-linked polyethylene insulated power cables have higher heat resistance, mechanical strength and insulation performance.

[0055] In specific implementation, a high-voltage pulse generator is started to inject a pulse excitation signal into the target power cable in the intelligent power distribution system for pulse excitation, and the current signal of each fault monitoring node when the target power cable is pulsed is collected through a current sensor. The current signal in this application represents a signal generated by the change of current passing through the target power cable when a pulse excitation is injected into the target power cable. The current signal changes with time, effectively reflecting the distribution, change and characteristics of the current in the power cable.

[0056] It should be noted that a high-voltage pulse generator is usually used to inject a pulse excitation signal into a target power cable to generate a desired signal, such as a current signal, to detect and locate power cable faults. In addition, the pulse excitation signal is a short, high-energy signal, usually consisting of a burst of voltage or current pulses. This signal has a very short duration in time, usually in the order of nanoseconds to milliseconds. Pulse excitation signals are often used to test, excite or stimulate the system or equipment under test, and have different uses in different applications.

[0057] It should also be noted that in the present application, the fault monitoring nodes are evenly installed on the target power cable, which will not be elaborated here.

[0058] In step 102, the current signal of each fault monitoring node is correlated with the current signal of its adjacent fault monitoring node through signal propagation, thereby obtaining a signal correlation feature sequence of each fault monitoring node.

[0059] In some embodiments, the current signal of each fault monitoring node is correlated with the current signal of its adjacent fault monitoring node through signal propagation, and then the signal correlation feature sequence of each fault monitoring node is obtained by specifically adopting the following steps, namely:

[0060] Perform signal alignment on the current signals of all fault monitoring nodes;

[0061] Combining the aligned current signals of each fault monitoring node with the current signals of its adjacent fault monitoring nodes to obtain current signal pairs of each fault monitoring node;

[0062] A current signal pair of a fault monitoring node is selected, all current signals in the current signal pair are correlated and mapped, and each current signal after the correlation mapping is correspondingly calculated to obtain a signal correlation feature sequence of the fault monitoring node;

[0063] Repeat the above steps to obtain the signal correlation feature sequences of the remaining fault monitoring nodes.

[0064] In specific implementation, the current signals of all fault monitoring nodes are aligned, that is: the time axis of the longest current signal among all current signals is used as the reference time axis, and for the current signal of each fault monitoring node, the current signal is interpolated based on the reference time axis, and then the current signals of all fault monitoring nodes after the interpolation processing are aligned on the reference time axis, thereby completing the signal alignment of the current signals of all fault monitoring nodes.

[0065] It should be noted that, in this embodiment, signal alignment refers to the process of synchronizing multiple current signals in time, that is, the process of aligning the signal values ​​of multiple current signals, which will not be repeated here. In addition, in this embodiment, the longest current signal refers to the current signal with the largest number of sampling points. In addition, the reference time axis in this application refers to a time axis used as a reference.

[0066] In the specific implementation, the current signal is interpolated based on the reference time axis using the linear interpolation method in the prior art, which will not be repeated here. The purpose of the interpolation is to align each signal value on the reference time axis. In addition, in other embodiments, other interpolation methods may be used to interpolate the current signal, which is not limited here.

[0067] In specific implementation, the aligned current signals of each fault monitoring node are combined with the current signals of its adjacent fault monitoring nodes to obtain current signal pairs of each fault monitoring node, that is: for each fault monitoring node, the fault monitoring node and the next fault monitoring node are regarded as a pair of adjacent fault monitoring nodes, and the current signal of the fault monitoring node is combined with the current signal of the next fault monitoring node to obtain a current signal pair of the fault monitoring node. It should be noted that the current signal pair in this embodiment represents a combination of two current signals.

[0068] In a specific implementation, all current signals in the current signal pair are associated and mapped, that is: a two-dimensional coordinate system is established with the signal value of the current signal in the current signal pair as the vertical coordinate and the signal value sequence number of the current signal in the current signal pair as the horizontal coordinate, and all signal values ​​of each current signal in the current signal pair are mapped in the two-dimensional coordinate system to complete the associated mapping of each current signal in the current signal pair.

[0069] It should be noted that, in this embodiment, the signal value serial number of the current signal represents the serial number of the signal value in the current signal, that is, starting from the starting signal value in the current signal, the numbers are numbered from small to large, and each signal value corresponds to a serial number.

[0070] It should also be noted that the association mapping in this embodiment represents the process of mapping each current signal in the current signal pair to the same coordinate system, that is, the process of mapping the current signals of adjacent fault monitoring nodes in this application to the same coordinate system, which will not be repeated here.

[0071] In the specific implementation, the difference calculation is performed on each current signal after the association mapping, and then the signal association characteristic sequence of the fault monitoring node is obtained, that is: each signal value number after mapping is obtained, and the difference calculation is performed on each signal value corresponding to each signal value number to obtain the signal association characteristic value corresponding to each signal value number, and all the signal association characteristic values ​​are combined to obtain the signal association characteristic sequence of the fault monitoring node. In addition, in other embodiments, other calculation methods can also be used to obtain signal association characteristic values, which are not limited here.

[0072] It should be noted that all current signals in the current signal pair are a combination of two current signals of adjacent fault monitoring nodes. Therefore, there are only two current signals when mapping in the two-dimensional coordinate system, and there are two signal values ​​of the current signals corresponding to each signal value number. In addition, the difference calculation in this embodiment is the difference calculation between the maximum signal value and the minimum signal value at the same signal value number, which will not be repeated here.

[0073] It should also be noted that the signal association feature sequence includes multiple signal association feature values. In the present application, the signal association feature value represents the association feature between the signal values ​​of two current signals. In the present application, the signal deviation between the signal values ​​of the two current signals is used as the signal association feature value. In the fault detection of the power cable, it is usually necessary to perform pulse excitation on the target power cable to obtain the current signal. However, in the acquired current signal, there will be inconsistencies in the signal features for the current signals on both sides of the fault point, and there will be deviations in the signal features, such as waveform feature deviations. The current signals of the fault monitoring nodes on the same side show similar signal features (that is, they are divided into left-side fault monitoring nodes and right-side fault monitoring nodes with the fault point as the center). Therefore, based on the fact that the current signals on the same side have similar signal features, while the current signals on the left and right sides of the fault point will have deviations in signal features, the deviation between the signal values ​​of the current signals of adjacent fault monitoring nodes can be calculated and used as the association feature of signal propagation. By determining the signal association feature value, the location of the fault point of the power cable can be preliminarily identified.

[0074] In addition, it should be noted that the propagation association in the present application represents the process of measuring the deviation between the current signals collected by adjacent fault monitoring nodes, which will not be described in detail here.

[0075] In step 103, fault-end adjacent discrimination is performed on each fault monitoring node according to all signal correlation feature sequences, thereby obtaining the adjacent fault discrimination amount of each fault monitoring node, and based on the adjacent fault discrimination amount of each fault monitoring node and the monitoring time span of each fault monitoring node, the hidden fault interval when a target power cable fails in the intelligent distribution system is determined.

[0076] In some embodiments, the following steps may be used to perform fault-end adjacent discrimination on each fault monitoring node according to all signal correlation feature sequences, and then obtain adjacent fault discrimination amounts of each fault monitoring node, namely:

[0077] In some embodiments, reference Figure 2 As shown in FIG. 1 , this figure is an exemplary flow chart of determining the adjacent fault discrimination amount according to some embodiments of the present application. In this embodiment, the adjacent fault end discrimination is performed on each fault monitoring node according to all signal correlation feature sequences, and then the adjacent fault discrimination amount of each fault monitoring node is obtained, which can be implemented by the following steps:

[0078] First, in step 1031, the signal correlation feature sequence of each fault monitoring node is obtained;

[0079] Secondly, in step 1032, a signal correlation feature sequence of a fault monitoring node is selected, and all different signal correlation feature values ​​in the signal correlation feature sequence are extracted;

[0080] Further, in step 1033, the distribution amount of each different signal correlation characteristic value is determined;

[0081] Then, in step 1034, the adjacent fault discrimination amount of the fault monitoring node is determined according to the distribution amount of all the different signal correlation characteristic values;

[0082] Finally, in step 1035, the above steps are repeated to obtain adjacent fault discrimination quantities of the remaining fault monitoring nodes.

[0083] In the specific implementation, a signal association feature sequence of a fault monitoring node is selected, and different signal association feature values ​​in the signal association feature sequence are extracted, that is, a signal association feature sequence of a fault monitoring node is selected, and signal association feature values ​​with different numerical values ​​in the signal association feature sequence are extracted, and then all different signal association feature values ​​are obtained, and the different signal association feature values ​​represent signal association feature values ​​with different numerical values ​​in the signal association feature sequence.

[0084] In specific implementation, the distribution amount of each different signal-related characteristic value is determined, that is, the total number of signal-related characteristic values ​​of the signal-related characteristic sequence is obtained, the frequency of occurrence of each different signal-related characteristic value is obtained, and the frequency of occurrence of each different signal-related characteristic value is respectively calculated by ratio calculation to the total number of signal-related characteristic values ​​to obtain the distribution amount of each different signal-related characteristic value. It should be noted that the distribution amount of different signal-related characteristic values ​​in this embodiment represents the occupancy of different signal-related characteristic values ​​in the entire signal-related characteristic sequence, that is, the higher the distribution amount of different signal-related characteristic values, the greater the occupancy of the different signal-related characteristic value in the entire signal-related characteristic sequence, and vice versa.

[0085] In specific implementation, the distribution of all different signal-related characteristic values ​​can be calculated through the information entropy model to determine the adjacent fault discrimination amount of the signal at the fault monitoring node, that is, the distribution of all different signal-related characteristic values ​​is used as the input parameter of the information entropy model, and finally the adjacent fault discrimination amount of the signal at the fault monitoring node is output according to the information entropy model. In addition, in other embodiments, other entropy calculation models can also be used to calculate the adjacent fault discrimination amount, which is not limited here.

[0086] It should be noted that the adjacent fault discrimination amount in the present application represents the amount of proximity between the fault monitoring node and the fault point of the target power cable. The present application uses the information entropy of the signal correlation characteristic value as the adjacent fault discrimination amount of the fault monitoring node. When the adjacent fault discrimination amount is larger, the degree of proximity between the fault monitoring node and the fault point of the target power cable is greater. When the adjacent fault discrimination amount is smaller, the degree of proximity between the fault monitoring node and the fault point of the target power cable is greater. In power cable fault monitoring, faults in the power cable usually cause significant changes in the current signal near the fault point. By calculating the adjacent fault discrimination amounts at different fault monitoring nodes, the nodes with the most significant changes can be effectively identified, thereby helping to locate the fault point. The fault monitoring node with a larger adjacent fault discrimination amount is usually closer to the fault point, that is, the current signal characteristic at the fault monitoring node changes the most.

[0087] In some embodiments, the following steps may be specifically used to determine the hidden fault interval when a target power cable in the intelligent power distribution system fails based on the adjacent fault discrimination amount of each fault monitoring node and the monitoring time span of each fault monitoring node, namely:

[0088] Obtain the monitoring time span and adjacent fault discrimination amount of each fault monitoring node;

[0089] Arrange all adjacent fault discrimination quantities in ascending order;

[0090] Determine the fault sign reference point according to all adjacent fault discrimination quantities arranged in ascending order;

[0091] The monitoring time span corresponding to the fault sign reference point is used as the hidden fault interval when a target power cable fails in the intelligent power distribution system.

[0092] In specific implementation, the monitoring time span of each fault monitoring node can be obtained through the power cable monitoring database. The monitoring time span represents the time length between adjacent fault monitoring nodes on the power cable, that is, the time period from when the previous fault monitoring node monitors the current signal to when the current fault monitoring node monitors the current signal. This time period is the monitoring time span. The monitoring time span determines the coverage and accuracy of fault monitoring. A smaller monitoring time span can provide higher fault location accuracy. When a fault occurs, the signal change can be detected more quickly by the adjacent fault monitoring nodes. A larger monitoring time span may lead to reduced positioning accuracy. When the fault signal propagates over a longer distance, it may be attenuated or masked. The monitoring time span plays a vital role in power cable fault monitoring. A reasonable monitoring time span can ensure the efficiency and economy of the monitoring system.

[0093] It should be noted that the power cable monitoring database in this embodiment is a system or platform for storing, managing and analyzing power cable monitoring data. It contains various data collected from monitoring nodes, including current signal data, environmental data, fault records, etc. This database is an important part of the power cable health monitoring and fault management system, and provides the infrastructure for data storage, retrieval and analysis.

[0094] In a specific implementation, all adjacent fault discrimination quantities are arranged in ascending order, that is, all adjacent fault discrimination quantities are arranged from small to large.

[0095] In specific implementation, the fault sign reference point is determined based on all adjacent fault discrimination quantities in ascending order, that is, the fault monitoring node corresponding to the last adjacent fault discrimination quantity in ascending order is used as the fault sign reference point. It should be noted that the fault sign reference point in this embodiment represents the key feature point for judging the potential fault of the power cable, that is, the interval where the potential fault of the power cable occurs can be identified through the fault sign reference point.

[0096] It should be noted that in the present application, one fault monitoring node corresponds to one monitoring time span. Since the fault sign reference point is the fault monitoring node corresponding to the last adjacent fault discrimination quantity after ascending arrangement, the fault sign reference point corresponds to one monitoring time span.

[0097] It should be noted that the hidden fault interval in the present application represents a time period when a fault may exist in the power cable but has not yet manifested itself obviously. This interval includes the time period when the power cable shows signs of abnormality. The hidden fault interval also includes a current signal. The determination of the hidden fault interval lies in identifying the interval where the power cable may show signs of abnormality. These abnormal signs may include sudden changes in current and voltage, increased local discharge, etc. By timely capturing and analyzing these signs, the system can detect possible fault risks in the cable in advance, thereby taking preventive maintenance measures to avoid greater losses and power outages caused by fault escalation.

[0098] In step 104, a fault response coefficient of a current signal when a fault occurs in a target power cable is determined, and a fault response point in a high frequency band and a non-fault response point in a low frequency band when the target power cable is pulse excited are determined based on the fault response coefficient and the hidden fault interval.

[0099] In some embodiments, reference Figure 3 As shown in FIG. 1 , this figure is an exemplary flow chart of determining a fault response coefficient according to some embodiments of the present application. In this embodiment, determining the fault response coefficient of the current signal when a fault occurs in the target power cable can be implemented by the following steps:

[0100] First, in step 1041, a current signal when a target power cable fails is obtained;

[0101] Next, in step 1042, the fluctuation margin of the current signal is determined;

[0102] Further, in step 1043, margin compensation is performed on each signal value in the current signal according to the fluctuation margin to obtain a signal margin compensation value corresponding to each signal value;

[0103] Then, in step 1044, a signal change characteristic value when a fault occurs in the target power cable is determined according to all signal margin compensation values;

[0104] Finally, in step 1045, a fault response coefficient of the current signal when a fault occurs in the target power cable is determined according to the signal change characteristic value and the reference signal change characteristic value of the target power cable.

[0105] In specific implementation, the current signal when the target power cable fails can be obtained through the power cable monitoring database. The current signal when the power cable fails is the current signal collected when the power cable fails. The current signal reflects the change of the current when the power cable fails. The current signal records the abnormal waveform when the fault occurs. The current signal when the power cable fails in the intelligent distribution system contains significant features of the fault. By acquiring the signal, the characteristic form of the fault type can be extracted, for example, the variation length of the signal waveform, and then the target power cable is analyzed, located and diagnosed according to the characteristic form.

[0106] In specific implementation, the fluctuation margin of the current signal is determined, that is, the root mean square value of the current signal is calculated, and the root mean square value calculation result is used as the fluctuation margin of the current signal. In addition, in other embodiments, other calculation methods can be used to calculate the fluctuation margin of the current signal, which is not limited here.

[0107] It should be noted that, in this embodiment, the fluctuation margin indicates the allowable variation range of the current signal within a certain time range, and is used to reflect the stability and variation of the signal.

[0108] In a specific implementation, margin compensation is performed on each signal value in the current signal according to the fluctuation margin to obtain a signal margin compensation value corresponding to each signal value, that is, the fluctuation margin is summed with each signal value in the current signal to obtain a signal margin compensation value corresponding to each signal value. In addition, in other embodiments, other calculation methods can be used to calculate the signal margin compensation value, which is not limited here.

[0109] It should be noted that the signal margin compensation value in this embodiment represents the value obtained after performing fluctuation margin compensation on the signal value. In the fault detection of the power cable, determining the signal margin compensation value can effectively ensure the stability of the system when facing fluctuations and interference. By performing margin compensation on the signal, sufficient buffer space can be reserved to prevent the system from becoming unstable due to sudden signal changes.

[0110] In specific implementation, the signal change characteristic value when the target power cable fails is determined based on all signal margin compensation values, that is: first, each signal margin compensation value is compared with the preset signal margin compensation value, and secondly, all signal margin compensation values ​​greater than the preset signal margin compensation value are extracted. Finally, the ratio of the extracted minimum signal margin compensation value to the maximum signal margin compensation value is used as the signal change characteristic value when the target power cable fails. All signal margin compensation values ​​that are less than or equal to the preset signal margin compensation value are not processed. In addition, in other embodiments, other calculation methods can also be used to calculate, which is not limited here.

[0111] It should be noted that the signal margin compensation value greater than the preset signal margin compensation value is the value of the abnormal signal band in the current signal, and the signal margin compensation value less than or equal to the preset signal margin compensation value is the value of the normal signal band in the current signal.

[0112] It should also be noted that the signal change characteristic value in this embodiment represents an indicator reflecting the health status of the power cable when a power cable fault occurs. By determining the signal change characteristic value, the change characteristics of the current signal when a power cable fault occurs can be identified in advance, thereby improving the fault judgment of the current target power cable to save time and resources.

[0113] In a specific implementation, the fault response coefficient of the current signal when a fault occurs in the target power cable is determined based on the signal change characteristic value and the reference signal change characteristic value of the target power cable, that is, the fault response coefficient of the current signal when a fault occurs in the target power cable is determined based on the ratio of the signal change characteristic value to the reference signal change characteristic value of the target power cable. In addition, in other implementations, other calculation methods can also be used to calculate the fault response coefficient of the current signal when a fault occurs in the target power cable, which is not limited here.

[0114] It should be noted that the reference signal change characteristic value in this embodiment is a pre-set standard signal change characteristic value, that is, the signal change characteristic value of the current signal when the target power cable is not faulty. It can be specifically set by analyzing the current signal of the target power cable when it is not faulty based on machine learning, and will not be repeated here.

[0115] It should be noted that the fault response coefficient in the present application is an indicator used to describe the degree of change of the current signal relative to the normal state when a fault occurs in the power cable. It is usually determined based on the signal change before and after the fault. The fault response coefficient can quantitatively describe the change characteristics of the current signal when a fault occurs. By determining the fault response coefficient, the fault point in the cable can be located more accurately. Different fault locations will cause the abnormal signal band to have different response degrees at the monitoring point. This feature can be used to narrow the scope of fault investigation and improve the detection accuracy of the power cable fault location.

[0116] In some embodiments, the following steps may be specifically used to determine the fault response point on the high frequency band and the non-fault response point on the low frequency band of the target power cable when the target power cable is pulse excited according to the fault response coefficient and the hidden fault interval, namely:

[0117] Acquiring a current signal of the hidden fault interval;

[0118] determining a signal response coefficient of the current signal;

[0119] The fault response coefficient is compared with the signal response coefficient. If the signal response coefficient is greater than or equal to the fault response coefficient, the current signal in the hidden fault interval is frequency-band segmented to obtain a high-frequency current signal and a low-frequency current signal when the target power cable is pulse-excited. If the signal response coefficient is less than the fault response coefficient, the current signal in the hidden fault interval is not processed.

[0120] Determining the fault response point of the target power cable in the high frequency band when the target power cable is pulse excited by the high frequency current signal;

[0121] The non-fault response point of the target power cable in the low-frequency band when the target power cable is pulse excited is determined by the high-frequency current signal.

[0122] In some embodiments, the signal response coefficient of the current signal may be determined by the following steps:

[0123] Determining the concentration trend of all signal peaks and the concentration trend of all signal troughs in the current signal;

[0124] The signal response coefficient of the current signal is obtained by calculating the ratio of the concentration trend of all signal troughs to the concentration trend of all signal peaks.

[0125] In some embodiments, the following steps may be used to determine the concentration trend of all signal peaks and the concentration trend of all signal troughs in the current signal, namely:

[0126] Obtaining all signal peaks and all signal troughs from the current signal;

[0127] Calculate the average value of the signal values ​​corresponding to all signal peaks to obtain the concentration trend of all signal peaks in the current signal;

[0128] The signal values ​​corresponding to all signal troughs are averaged to obtain the concentration trend of all signal troughs in the current signal.

[0129] In specific implementation, signal processing software, such as Python's SciPy library, can be used to obtain all signal peaks and all signal troughs from the current signal. The signal peak is when the amplitude of the signal in the signal waveform of the current signal reaches the highest value among adjacent data points, and the signal peak is when the amplitude of the signal in the signal waveform of the current signal reaches the lowest value among adjacent data points.

[0130] It should be noted that the concentration trend of all signal peaks in the current signal in this embodiment represents the trend of concentrated changes in all signal peaks in the current signal, and the concentration trend of all signal troughs in the current signal in this embodiment represents the trend of concentrated changes in all signal troughs in the current signal.

[0131] It should also be noted that in other embodiments, other calculation methods can also be used to calculate the signal response coefficient, which is not limited here. The signal response coefficient in the present application represents a response index of the signal value of the current signal in the hidden fault interval changing with time, and is used to evaluate the degree of influence of the current signal in the hidden fault interval on the cable system. By determining the signal response coefficient, the possibility of a fault in the hidden fault interval can be effectively determined, so as to quickly and accurately identify and locate the fault point. In the power cable fault location, the collected current signal waveform will fluctuate to varying degrees due to the influence of external environmental factors and the properties of the acquisition equipment itself. The current signal waveform collected near the fault point will fluctuate greatly, which will be different from the normal fluctuation state, especially in the changes in the peaks and troughs of the current signal waveform. Therefore, the present application uses signal peaks and signal troughs to measure whether the current signal in the hidden fault interval has abnormal fluctuations, which can effectively locate the accurate fault position of the power cable.

[0132] In specific implementation, if the signal response coefficient is greater than or equal to the fault response coefficient, it means that the power cable is more likely to have a fault in the hidden fault interval, that is, the acquired hidden fault interval is more accurate and has the authenticity of the fault judgment, then the high-pass filter and the low-pass filter in the prior art are used to perform frequency band segmentation on the current signal in the hidden fault interval respectively, so as to obtain the high-frequency current signal and the low-frequency current signal when the target power cable is pulse-excited, which will not be repeated here. If the signal response coefficient is less than the fault response coefficient, it means that the power cable is less likely to have a fault in the hidden fault interval, that is, the acquired hidden fault interval is inaccurate and has the unreliability of fault judgment, then the current signal in the hidden fault interval is not processed, and the hidden fault interval needs to be further determined before the current signal in the hidden fault interval is processed. In addition, in other embodiments, other filters can also be used to perform frequency band segmentation on the current signal in the hidden fault interval, which is not limited here.

[0133] In specific implementation, the high-frequency current signal is used to determine the fault response point in the high-frequency band when the target power cable is pulse-excited, that is, the maximum signal value in the high-frequency current signal is extracted, and the time point corresponding to the maximum signal value is used as the fault response point in the high-frequency band when the target power cable is pulse-excited. It should be noted that the fault response point in the present application represents the moment when the current signal shows a significant change when a fault occurs in the target power cable, that is, the time point when the signal value in the high-frequency current signal when the target power cable is pulse-excited in the present application is most sensitive to the change. The high-frequency current signal usually includes higher frequency components than low-order harmonics. These components may originate from equipment failure, switching action, arc discharge, partial discharge, etc. Therefore, the time point corresponding to the signal value in the high-frequency current signal when the target power cable is pulse-excited can be used as the fault response point in the high-frequency band.

[0134] In specific implementation, the non-fault response point on the high-frequency band when the target power cable in the intelligent distribution system is pulse-excited is determined by the low-frequency current signal, that is, the maximum signal value in the low-frequency current signal is extracted, and the time point corresponding to the maximum signal value is used as the non-fault response point on the low-frequency band when the target power cable is pulse-excited. It should be noted that the non-fault response point in the present application represents the moment when the current signal does not show a significant change when a fault occurs in the target power cable, that is, the time point when the signal value in the low-frequency current signal when the target power cable is pulse-excited in the present application is most sensitive to the change. The low-frequency current signal is usually relatively stable, reflecting the normal operating state and load conditions of the system. When a fault occurs, the low-frequency current signal may show some changes, such as an increase or decrease in amplitude, a change in phase, etc. Therefore, the time point corresponding to the signal value in the low-frequency current signal when the target power cable is pulse-excited can be used as the non-fault response point on the low-frequency band.

[0135] In step 105, the explicit fault interval of the target power cable in the intelligent power distribution system is determined based on the fault response point on the high frequency band and the non-fault response point on the low frequency band when the target power cable is pulse excited.

[0136] In some embodiments, the following steps may be specifically adopted to determine the explicit fault interval of the target power cable in the intelligent power distribution system based on the fault response point in the high frequency band and the non-fault response point in the low frequency band when the target power cable is pulse excited, namely:

[0137] The fault response point on the high frequency band and the non-fault response point on the low frequency band of the target power cable when the target power cable is pulse excited are mapped on the same time axis, and the time period between the fault response point and the non-fault response point after mapping is used as the explicit fault interval of the target power cable in the intelligent power distribution system.

[0138] In specific implementation, the fault response point on the high-frequency band and the non-fault response point on the low-frequency band of the target power cable when the target power cable is pulse-excited are mapped on the same time axis, that is, the signal length of the current signal in the hidden fault interval is used as the time axis, and the fault response point and the non-fault response point are marked on the time axis accordingly to complete the same time axis mapping.

[0139] It should be noted that, in this embodiment, the same time axis mapping refers to the process of mapping the values ​​to be mapped onto the same time axis, which will not be described in detail here.

[0140] It should be noted that the explicit fault interval in the present application refers to the time period during which the power cable clearly exhibits fault characteristics. The explicit fault interval reflects the significant changes and abnormal performance of the power cable in the intelligent distribution system during the fault period, such as sudden current fluctuations, increased harmonics or spectral distortion. The identification and positioning of the explicit fault interval is crucial for fault diagnosis and system maintenance. By analyzing the explicit fault interval, the position of the fault point can be quickly located.

[0141] In addition, in another aspect of the present application, in some embodiments, the present application provides an intelligent power distribution system. When implemented specifically, the intelligent power distribution system includes a power cable fault locating unit. Figure 4 , which is a schematic diagram of exemplary hardware and / or software of a power cable fault location unit according to some embodiments of the present application, the power cable fault location unit 200 includes: a collection module 201, a processing module 202 and an execution module 203, which are described as follows:

[0142] The acquisition module 201 in the present application is mainly used to pulse excite the target power cable in the intelligent power distribution system, and to collect the current signals of each fault monitoring node when the target power cable is pulse excited;

[0143] Processing module 202, in the present application, the processing module 202 is mainly used to perform signal propagation correlation between the current signal of each fault monitoring node and the current signal of its adjacent fault monitoring node, thereby obtaining a signal correlation feature sequence of each fault monitoring node;

[0144] The processing module 202 is further used to perform fault-end adjacent discrimination on each fault monitoring node according to all signal correlation feature sequences, thereby obtaining adjacent fault discrimination amounts of each fault monitoring node, and determining the hidden fault interval when a target power cable in the intelligent power distribution system fails based on the adjacent fault discrimination amounts of each fault monitoring node and the monitoring time span of each fault monitoring node;

[0145] In addition, the processing module 202 is further used to determine a fault response coefficient of a current signal when a fault occurs in the target power cable, and determine a fault response point in a high frequency band and a non-fault response point in a low frequency band when the target power cable is pulse excited according to the fault response coefficient and the hidden fault interval;

[0146] Execution module 203, in the present application, execution module 203 is mainly used to determine the explicit fault interval of the target power cable in the intelligent distribution system based on the fault response point in the high-frequency band and the non-fault response point in the low-frequency band when the target power cable is pulse-excited.

[0147] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned power cable fault locating method.

[0148] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a power cable fault location method according to some embodiments of the present application. The power cable fault location method in the above embodiment can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0149] The processor 301 may be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more processors for controlling the execution of the power cable fault location method in the present application.

[0150] The communication bus 302 may be used to transmit information between the above-mentioned components.

[0151] The memory 303 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0152] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The determination of the power cable fault location method in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0153] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0154] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0155] The above-mentioned computer device can be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0156] In addition, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned power cable fault location method is implemented.

[0157] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0158] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for locating a power cable fault, characterized in that: The steps include: Pulse excitation is performed on the target power cable in the intelligent power distribution system, and current signals of each fault monitoring node are collected when the target power cable is pulse-excited; Perform signal propagation correlation between the current signal of each fault monitoring node and the current signal of its adjacent fault monitoring node, and then obtain the signal correlation feature sequence of each fault monitoring node; According to all signal correlation feature sequences, fault-end adjacent discrimination is performed on each fault monitoring node, and then the adjacent fault discrimination amount of each fault monitoring node is obtained. Based on the adjacent fault discrimination amount of each fault monitoring node and the monitoring time span of each fault monitoring node, the hidden fault interval when the target power cable in the intelligent distribution system fails is determined; Determine a fault response coefficient of a current signal when a fault occurs in a target power cable, and determine a fault response point in a high frequency band and a non-fault response point in a low frequency band when the target power cable is pulse excited according to the fault response coefficient and the hidden fault interval; Determine the dominant fault interval of the target power cable in the intelligent power distribution system based on the fault response point in the high frequency band and the non-fault response point in the low frequency band when the target power cable is pulse excited; Among them, the adjacent fault discrimination amount represents the degree of proximity between the fault monitoring node and the fault point of the target power cable. According to all signal correlation feature sequences, the adjacent fault end of each fault monitoring node is judged, and the adjacent fault discrimination amount of each fault monitoring node is obtained, which specifically includes: Obtaining signal correlation feature sequences of each fault monitoring node; Selecting a signal correlation feature sequence of a fault monitoring node, and extracting all different signal correlation feature values ​​in the signal correlation feature sequence; Determine the distribution of the eigenvalues ​​associated with each of the different signals; Determine the adjacent fault discrimination amount of the fault monitoring node according to the distribution amount of all the different signal correlation characteristic values; Repeat the above steps to obtain the adjacent fault discrimination quantities of the remaining fault monitoring nodes; Among them, the hidden fault interval when the target power cable fails in the intelligent distribution system is determined based on the adjacent fault discrimination amount of each fault monitoring node and the monitoring time span of each fault monitoring node, specifically including: Obtain the monitoring time span and adjacent fault discrimination amount of each fault monitoring node; Arrange all adjacent fault discrimination quantities in ascending order; Determine the fault sign reference point according to all adjacent fault discrimination quantities arranged in ascending order; The monitoring time span corresponding to the fault sign reference point is used as the hidden fault interval when a target power cable fails in the intelligent power distribution system; Among them, the fault response coefficient is an indicator used to describe the degree of change of the current signal relative to the normal state when a power cable fails. The fault response coefficient of the current signal when a target power cable fails specifically includes: Acquire the current signal when a fault occurs in the target power cable; determining a fluctuation margin of the current signal; Performing margin compensation on each signal value in the current signal according to the fluctuation margin to obtain a signal margin compensation value corresponding to each signal value; Determine a signal change characteristic value when a target power cable fails according to all signal margin compensation values; Determining a fault response coefficient of the current signal when a fault occurs in the target power cable according to the signal change characteristic value and the reference signal change characteristic value of the target power cable; The following steps are specifically used to determine the fault response point on the high frequency band and the non-fault response point on the low frequency band of the target power cable when the target power cable is pulse excited according to the fault response coefficient and the hidden fault interval: Acquiring a current signal of the hidden fault interval; determining a signal response coefficient of the current signal; The fault response coefficient is compared with the signal response coefficient. If the signal response coefficient is greater than or equal to the fault response coefficient, the current signal in the hidden fault interval is frequency-band segmented to obtain a high-frequency current signal and a low-frequency current signal when the target power cable is pulse-excited. If the signal response coefficient is less than the fault response coefficient, the current signal in the hidden fault interval is not processed. Determining the fault response point of the target power cable in the high frequency band when the target power cable is pulse excited by the high frequency current signal; The non-fault response point of the target power cable in the low-frequency band when the target power cable is pulse excited is determined by the low-frequency current signal.

2. The method according to claim 1, characterized in that The current signal of each fault monitoring node is correlated with the current signal of its adjacent fault monitoring node through signal propagation, and then the signal correlation feature sequence of each fault monitoring node is obtained, which specifically includes: Perform signal alignment on the current signals of all fault monitoring nodes; Combining the aligned current signals of each fault monitoring node with the current signals of its adjacent fault monitoring nodes to obtain current signal pairs of each fault monitoring node; A current signal pair of a fault monitoring node is selected, all current signals in the current signal pair are correlated and mapped, and each current signal after the correlation mapping is correspondingly calculated to obtain a signal correlation feature sequence of the fault monitoring node; Repeat the above steps to obtain the signal correlation feature sequences of the remaining fault monitoring nodes.

3. The method according to claim 1, characterized in that The propagation association represents the process of measuring the deviation between the current signals collected by adjacent fault monitoring nodes.

4. The method according to claim 1, characterized in that The current signal of each fault monitoring node is collected by the current sensor when the target power cable is pulse excited.

5. An intelligent power distribution system, which adopts the method according to any one of claims 1 to 4 to locate power cable faults, and the intelligent power distribution system comprises a power cable fault location unit, characterized in that: The power cable fault locating unit comprises: The acquisition module is used to pulse excite the target power cable in the intelligent power distribution system and collect the current signals of each fault monitoring node when the target power cable is pulse excited; A processing module, used to perform signal propagation correlation between the current signal of each fault monitoring node and the current signal of its adjacent fault monitoring node, thereby obtaining a signal correlation feature sequence of each fault monitoring node; The processing module is further used to perform fault-end adjacent discrimination on each fault monitoring node according to all signal correlation feature sequences, thereby obtaining adjacent fault discrimination amounts of each fault monitoring node, and determining the hidden fault interval when a target power cable in the intelligent power distribution system fails based on the adjacent fault discrimination amounts of each fault monitoring node and the monitoring time span of each fault monitoring node; The processing module is further used to determine a fault response coefficient of a current signal when a fault occurs in the target power cable, and determine a fault response point in a high frequency band and a non-fault response point in a low frequency band when the target power cable is pulse excited according to the fault response coefficient and the hidden fault interval; The execution module is used to determine the explicit fault interval of the target power cable in the intelligent power distribution system based on the fault response point in the high frequency band and the non-fault response point in the low frequency band when the target power cable is pulse excited.

6. A computer device, characterized in that: The computer device comprises a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the power cable fault locating method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the power cable fault locating method according to any one of claims 1 to 4 is implemented.

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