Distributed cable fault traveling wave feature acquisition and positioning method and system

Abstract

The invention discloses a distributed cable fault traveling wave characteristic acquisition and positioning method, and relates to the technical field of cable faults, and the method comprises the steps: collecting traveling wave characteristic information through a branch point sensing unit, and sharing the collected traveling wave characteristic information through a communication network; constructing a dynamic topology awareness and network reconstruction framework, wherein weighted averaging comprises the step of allocating weights to data sources based on a signal-to-noise ratio, a topology distance and time consistency; and establishing a propagation path model of the traveling wave signal in the distributed cable, and defining a cable fault position positioning coordinate by using a hyperbolic positioning rule in combination with the propagation path model. The distributed cooperation capability of the system is enhanced, the whole scheme has good expansibility and adaptability while improving the positioning precision, the method can be widely applied to fault monitoring and management of a large-scale distributed cable network, the troubleshooting time is effectively shortened, the troubleshooting cost is effectively reduced, and powerful support is provided for intelligent operation and maintenance of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of cable faults, and in particular to a method and system for collecting and locating traveling wave characteristics of distributed cable faults. Background Art

[0002] With the rapid development of the global energy Internet, the power system is gradually evolving towards a distributed and intelligent direction. As an important carrier of power transmission, the cable network is becoming increasingly complex and large in scale. With the acceleration of urbanization and the increasing access to new energy sources, the frequency of cable failures has increased significantly, especially in distributed power grids. Due to the wide coverage of cable networks and the large number of branch nodes, traditional fault detection methods can no longer meet the needs of rapid positioning and efficient recovery.

[0003] Existing traveling wave acquisition systems usually rely on fixed sampling strategies and a single signal processing algorithm. When faced with multi-branch structures in distributed cable networks, they are unable to effectively analyze the complex traveling wave reflection and refraction phenomena, resulting in a decrease in positioning accuracy and affecting the scientific nature of maintenance decisions. Traditional methods have limited ability to extract traveling wave characteristics of distributed cable faults and are prone to losing key information in long-distance transmission or high-noise environments. Summary of the invention

[0004] In view of the problems existing in the existing distributed cable fault traveling wave feature acquisition and positioning method and system, the present invention is proposed.

[0005] Therefore, in order to address the defects of the traveling wave acquisition system in the prior art in the multi-branch structure of the distributed cable network, namely, low positioning accuracy and difficulty in analyzing complex traveling wave reflection and refraction phenomena, the present invention deploys branch point sensing units to collect traveling wave characteristic information and combines weighted average and hyperbolic positioning rules to construct a dynamic topology perception and network reconstruction framework to achieve high-precision cable fault positioning.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for collecting and locating traveling wave characteristics of a distributed cable fault, which includes deploying branch point sensing units of a cable network, collecting traveling wave characteristic information through the branch point sensing units, and sharing the collected traveling wave characteristic information through a communication network; The branch point sensing unit collects the traveling wave characteristic information and the distributed cable adjacent node data for weighted averaging to build a dynamic topology perception and network reconstruction framework. The weighted averaging includes assigning weights to data sources based on signal-to-noise ratio, topological distance and time consistency. The traveling wave characteristic information collected by the branch point sensor unit and the data of adjacent nodes of the distributed cable are linearly combined according to the assigned weight values ​​to establish a propagation path model of the traveling wave signal in the distributed cable. The hyperbolic positioning rule is used in combination with the propagation path model to define the cable fault location coordinates.

[0007] As a preferred solution of the distributed cable fault traveling wave feature acquisition and positioning method of the present invention, wherein: the acquisition of traveling wave feature information includes using a branch point sensor unit equipped with a multi-channel data acquisition module, introducing a scale sampling technology, and capturing high-frequency components and low-frequency components of the traveling wave signal; The scale sampling technology includes building an adaptive threshold detection model by fusing multi-source features of the traveling wave signal, wherein the multi-source features of the fused traveling wave signal include the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal; The construction of the adaptive threshold detection model includes establishing a double-layer threshold structure through the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal, and the establishment of the double-layer threshold structure includes setting independent detection thresholds for high-frequency components and low-frequency components.

[0008] As a preferred solution of the distributed cable fault traveling wave feature acquisition and positioning method of the present invention, wherein: the sharing of the collected traveling wave feature information includes using a communication network to transmit each branch point sensing unit to an adjacent node of the traveling wave feature information; The method of transmitting the traveling wave characteristic information of each branch point sensor unit to the adjacent node includes dividing the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal into data packets, adding timestamps, node identifiers and metadata to the data packets, and establishing a time synchronization mechanism based on the time series characteristics. The time synchronization mechanism includes introducing a time protocol and a positioning system synchronization technology to make the time reference of each branch point sensor unit and the adjacent node consistent, thereby completing the construction of a transmission path and a distributed collaborative perception network model.

[0009] As a preferred solution of the distributed cable fault traveling wave feature acquisition and positioning method of the present invention, wherein: the distributed cable adjacent node data includes data transmitted from the adjacent node to the current node through the communication network; The weighted average includes processing the data of the current node by collecting the traveling wave characteristic information, and the processing of the data of the current node includes calculating the signal-to-noise ratio of the data of the current node and the data of the adjacent nodes of the distributed cable, and the calculation formula of the signal-to-noise ratio is: ; in, represents the signal-to-noise ratio, Indicates the dominant frequency region of the signal, represents the non-negative part of the signal power, represents the total noise power in the noise-dominated frequency region, represents historical noise data; The topological distance includes introducing a distance attenuation factor according to the physical distance between the adjacent node data of the distributed cable and the current node data, and adjusting the weight of the adjacent node data; The distance attenuation factor is , represents the topological distance, represents the attenuation coefficient; When the change in physical distance is decreasing, the weight of the adjacent node data is adjusted to increase; When the change in physical distance increases, the weight of the neighboring node data is adjusted to decrease; The temporal consistency includes comparing the timestamps of the current node data with the adjacent node data to evaluate the temporal consistency of the two; When the timestamp data of the comparison keeps decreasing, the weight of the adjacent node data is adjusted to increase; When the timestamp data for comparison increases continuously, the weight of the adjacent node data is adjusted to decrease.

[0010] As a preferred solution of the distributed cable fault traveling wave feature acquisition and positioning method of the present invention, wherein: the weighting of the data source includes secondary weighting of the weights adjusted by the topological distance and the time consistency according to the signal-to-noise ratio; The secondary distribution weight includes comprehensively optimizing the weights adjusted by the topological distance and the time consistency based on the signal-to-noise ratio, and expressing the weights adjusted by the topological distance and the time consistency based on the signal-to-noise ratio as two independent weight vectors respectively; The two independent weight vectors are as follows: ; ; in, represents the weight vector after topological distance adjustment, represents the weight vector after time consistency adjustment, Represents the weight vector after topological distance adjustment of the Nth data source, represents the weight vector after time consistency adjustment of the Nth data source; Based on two independent weight vectors, weight fusion is performed to define the function after weight fusion. The calculation formula of the weight fusion is: ; in, represents the weight fusion function, , and represents the weight coefficient; The function after defining weight fusion includes defining a membership function for the weight value of each dimension and mapping it to a standardized interval ; The definition includes: Increasing the signal-to-noise ratio corresponds to increasing the membership, and decreasing the signal-to-noise ratio corresponds to decreasing the membership; A reduced topological distance corresponds to an increased degree of membership, and an increased topological distance corresponds to a decreased degree of membership; Increasing temporal consistency corresponds to increasing membership, and decreasing temporal consistency corresponds to decreasing membership.

[0011] As a preferred solution of the distributed cable fault traveling wave feature acquisition and positioning method of the present invention, wherein: the linear combination according to the assigned weight values ​​includes weighted linear combination of the traveling wave feature information collected by the branch point sensor unit and the distributed cable adjacent node data according to the weight value after secondary allocation, and the formula of the weighted linear combination is: ; in, represents the comprehensive eigenvector of the weighted linear combination, Indicates The traveling wave eigenvector of the data source.

[0012] As a preferred solution of the distributed cable fault traveling wave feature acquisition and positioning method of the present invention, wherein: the establishment of the propagation path model of the traveling wave signal in the distributed cable includes constructing the topological structure of the cable network according to the comprehensive feature vector of the weighted linear combination, and the construction of the topological structure of the cable network includes using the branch point sensing unit of the adjacent node data and the edge as the communication link between the nodes to output the hyperbolic positioning rule; The output hyperbola positioning rule includes using the adjacent nodes of the comprehensive feature vector of the weighted linear combination as the node input, the topological distance and time consistency information of the adjacent nodes as the edge feature input, and outputting the cable fault position positioning coordinates.

[0013] In a second aspect, an embodiment of the present invention provides a distributed cable fault traveling wave feature acquisition and positioning system, which includes: A collection module deploys branch point sensing units of the cable network, collects traveling wave characteristic information through the branch point sensing units, and shares the collected traveling wave characteristic information through a communication network; A construction module collects traveling wave characteristic information and distributed cable adjacent node data through branch point sensing units for weighted averaging to build a dynamic topology perception and network reconstruction framework; The positioning module combines the traveling wave characteristic information collected by the branch point sensor unit and the data of the adjacent nodes of the distributed cable with the propagation path model and uses the hyperbolic positioning rule to define the positioning coordinates of the cable fault position.

[0014] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned distributed cable fault traveling wave feature acquisition and positioning method is implemented.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for collecting and locating traveling wave characteristics of distributed cable faults is implemented.

[0016] The beneficial effects of the present invention are as follows: the present invention realizes efficient collection and sharing of traveling wave characteristic information in distributed cable networks by deploying branch point sensing units and communication networks, improves the fault location accuracy under complex multi-branch structures, constructs a propagation path model based on weighted linear combinations, and combines hyperbolic positioning rules to achieve rapid and accurate positioning of cable fault locations, providing a scientific basis for maintenance decisions. The time synchronization mechanism of the present invention ensures the consistency of data between nodes and enhances the distributed collaboration capabilities of the system. While improving positioning accuracy, the overall solution also has good scalability and adaptability. It can be widely used in fault monitoring and management of large-scale distributed cable networks, effectively reducing troubleshooting time and cost, and providing strong support for intelligent operation and maintenance of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 A flow chart of a method and system for collecting and locating traveling wave characteristics of a distributed cable fault provided by an embodiment of the present invention.

[0018] Figure 2 A schematic diagram of data source weight allocation for a distributed cable fault traveling wave feature acquisition and positioning method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0023] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0024] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] Example 1 Reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a method for collecting and locating traveling wave characteristics of a distributed cable fault, comprising: S1: Deploy branch point sensor units of the cable network, collect traveling wave characteristic information through the branch point sensor units, and share the collected traveling wave characteristic information through the communication network.

[0026] Among them, collecting the characteristic information of the traveling wave includes using the branch point sensor unit equipped with a multi-channel data acquisition module, introducing scale sampling technology, and capturing the high-frequency component and low-frequency component of the traveling wave signal; The scale sampling technology includes building an adaptive threshold detection model by fusing the multi-source features of the traveling wave signal, and the multi-source features of the fused traveling wave signal include the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal; Constructing an adaptive threshold detection model includes establishing a double-layer threshold structure through the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal. Establishing the double-layer threshold structure includes setting independent detection thresholds for high-frequency components and low-frequency components.

[0027] S1.1: Sharing the collected traveling wave characteristic information includes transmitting each branch point sensing unit to an adjacent node of the traveling wave characteristic information by using a communication network; Transmitting the traveling wave characteristic information of the sensor units at each branch point to the adjacent nodes includes dividing the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal into data packets, adding timestamps, node identifiers and metadata to the data packets, and establishing a time synchronization mechanism based on the time series characteristics. The time synchronization mechanism includes introducing time protocols and positioning system synchronization technology to make the time references of the sensor units at each branch point and the adjacent nodes consistent, thereby completing the construction of the transmission path and distributed collaborative perception network model.

[0028] Furthermore, after the sensor units are deployed at the branch points of the cable network, a multi-channel data acquisition module is equipped and a scale sampling technology is introduced to capture the high-frequency and low-frequency components in the traveling wave signal. The detection threshold of the high-frequency component is set to a signal amplitude exceeding 1.5 times the average amplitude and the frequency is in the range of 20kHz to 100kHz, and the detection threshold of the low-frequency component is set to a signal amplitude lower than 0.8 times the average amplitude and the frequency is in the range of 1Hz to 10kHz. Combined with the standards of phase change rate greater than 0.05rad / ms and time series characteristic fluctuation amplitude less than 0.1, an adaptive A threshold detection model is proposed. The model dynamically adjusts the threshold range to adapt to the signal characteristics in different environments by integrating and analyzing the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal. After the traveling wave characteristic information is collected, the data of the sensor unit at each branch point is transmitted to the adjacent node via the communication network. In this process, the traveling wave signal containing the amplitude, phase, frequency distribution and time series characteristics is divided into data packets of fixed length. An accurate timestamp is added to each data packet to ensure timing consistency, and a unique node identifier and metadata are attached to indicate the source of the data. By introducing time protocols such as the IEEE 1588 precision time protocol and positioning system synchronization technology, the time reference difference between the sensor unit at each branch point and the adjacent node is calibrated to control the time deviation within microseconds, thereby establishing a stable time synchronization mechanism, and finally completing the transmission path optimization and the construction of a distributed collaborative perception network model to ensure the efficiency and accuracy of data sharing.

[0029] S2: The branch point sensor unit collects the traveling wave characteristic information and the distributed cable adjacent node data for weighted averaging to build a dynamic topology perception and network reconstruction framework. The weighted averaging includes assigning weights to data sources based on signal-to-noise ratio, topological distance and time consistency.

[0030] Wherein, the distributed cable adjacent node data includes data transmitted from the adjacent node to the current node via the communication network; The weighted average includes processing the data of the current node by collecting the traveling wave characteristic information. The processing of the data of the current node includes calculating the signal-to-noise ratio of the data of the current node and the data of the adjacent nodes of the distributed cable. The calculation formula of the signal-to-noise ratio is: ; in, represents the signal-to-noise ratio, Indicates the dominant frequency region of the signal, represents the non-negative part of the signal power, represents the total noise power in the noise-dominated frequency region, represents historical noise data; The topological distance includes introducing a distance attenuation factor and adjusting the weight of the adjacent node data according to the physical distance between the adjacent node data of the distributed cable and the current node data; The distance attenuation factor is , represents the topological distance, represents the attenuation coefficient; When the change in physical distance is decreasing, the weight of the adjacent node data is adjusted to increase; When the change in physical distance increases, the weight of the neighboring node data is adjusted to decrease; Temporal consistency involves comparing the timestamps of the current node data with the adjacent node data to evaluate the temporal consistency of the two; When the timestamp data of the comparison keeps decreasing, the weight of the adjacent node data is adjusted to increase; When the timestamp data for comparison increases continuously, the weight of the adjacent node data is adjusted to decrease.

[0031] Furthermore, when performing weighted averaging, the signal-to-noise ratio of the current node and the adjacent node data is first calculated. Assuming that the signal-dominant frequency area is 50kHz, the non-negative part of the signal power is 120dB, the total noise power in the noise-dominant frequency area is 30dB, and the historical noise data is 10dB, the signal-to-noise ratio is approximately 3.67 according to the formula. The weight is adjusted based on the topological distance. If the physical distance between the current node and the adjacent node is 200 meters, the attenuation coefficient is set to 0.01, and the distance attenuation factor is calculated to be 0.819. When the physical distance decreases from 200 meters to 100 meters, the weight increases from 0.5 to 0.7; conversely, if the distance increases to 300 meters, the weight drops to 0.4. In the time consistency assessment, if the difference between the timestamps of the two nodes decreases from 10 microseconds to 5 microseconds, the weight of the adjacent node increases from 0.6 to 0.8; if the timestamp difference increases to 15 microseconds, the weight drops to 0.4, thereby realizing dynamic adjustment of the weight of the adjacent node data to ensure the accuracy and reliability of data fusion. The dynamic adjustment parameters of node data weight are shown in Table 1 below: Table 1 Node data weight dynamic adjustment parameter table Parameter Type Initial Value After change Weight changes Signal-to-noise ratio calculation 0 0 3.67 Topological distance (meters) 200 100 0.5 → 0.7 Topological distance (meters) 200 300 0.5 → 0.4 Time consistency (microseconds) 10 5 0.6 → 0.8 Time consistency (microseconds) 10 15 0.6 → 0.4 Among them, according to the content of Table 1, the dynamic adjustment of data weights between nodes is shown, and the weight change range is quantitatively analyzed through signal-to-noise ratio calculation, topological distance change and time consistency difference.

[0032] S2.1: Assigning weights to data sources includes secondary weight assignment based on the weights adjusted by topological distance and time consistency according to the signal-to-noise ratio; The secondary weight allocation includes comprehensively optimizing the weights adjusted by the topological distance and the time consistency based on the signal-to-noise ratio, and expressing the weights adjusted by the topological distance and the time consistency based on the signal-to-noise ratio as two independent weight vectors; The two separate weight vectors are shown below: ; ;

[0033] in, represents the weight vector after topological distance adjustment, represents the weight vector after time consistency adjustment, Represents the weight vector after topological distance adjustment of the Nth data source, represents the weight vector after time consistency adjustment of the Nth data source; Based on two independent weight vectors, weight fusion is performed and the function after weight fusion is defined. The calculation formula of weight fusion is:

[0034] in, represents the weight fusion function, , and represents the weight coefficient; Defining the weight fusion function includes defining the membership function for the weight values ​​of each dimension and mapping it to the standardized interval ; Definitions include: Increasing the signal-to-noise ratio corresponds to increasing the membership, and decreasing the signal-to-noise ratio corresponds to decreasing the membership; A reduced topological distance corresponds to an increased degree of membership, and an increased topological distance corresponds to a decreased degree of membership; Increasing temporal consistency corresponds to increasing membership, and decreasing temporal consistency corresponds to decreasing membership.

[0035] S3: Linearly combine the traveling wave characteristic information collected by the branch point sensor unit and the data of the adjacent nodes of the distributed cable according to the assigned weight values, establish a propagation path model of the traveling wave signal in the distributed cable, and use the hyperbolic positioning rule in combination with the propagation path model to define the cable fault location coordinates.

[0036] Among them, performing linear combination according to the assigned weight values ​​includes performing weighted linear combination of the traveling wave characteristic information collected by the branch point sensor unit and the distributed cable adjacent node data according to the weight values ​​after secondary distribution. The formula of the weighted linear combination is: ; in, represents the comprehensive eigenvector of the weighted linear combination, Indicates The traveling wave eigenvector of the data source.

[0037] Preferably, establishing a propagation path model of a traveling wave signal in a distributed cable includes constructing a topological structure of a cable network according to a comprehensive feature vector of a weighted linear combination, and constructing the topological structure of the cable network includes outputting a hyperbolic positioning rule for a communication link between nodes using a branch point sensing unit of adjacent node data and an edge; The output hyperbolic location rule includes using the neighboring nodes of the comprehensive feature vector of the weighted linear combination as the input of the node, the topological distance and time consistency information of the neighboring nodes as the input of the edge feature, and outputting the cable fault location coordinates.

[0038] In a preferred embodiment, a distributed cable fault traveling wave feature acquisition and positioning system includes an acquisition module, which deploys branch point sensor units of the cable network, collects traveling wave feature information through the branch point sensor units, and shares the collected traveling wave feature information through a communication network; a construction module, which collects traveling wave feature information through the branch point sensor units and performs weighted averaging with distributed cable adjacent node data to construct a dynamic topology perception and network reconstruction framework; a positioning module, which combines the traveling wave feature information collected by the branch point sensor units and the distributed cable adjacent node data with a propagation path model and uses a hyperbolic positioning rule to define the cable fault location coordinates.

[0039] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0040] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0041] In summary, the present invention realizes the efficient collection and sharing of traveling wave characteristic information in distributed cable networks by deploying branch point sensing units and communication networks, improves the fault location accuracy under complex multi-branch structures, constructs a propagation path model based on weighted linear combinations, and combines hyperbolic positioning rules to achieve rapid and accurate positioning of cable fault locations, providing a scientific basis for maintenance decisions. The time synchronization mechanism of the present invention ensures the consistency of data between nodes and enhances the distributed collaboration capabilities of the system. While improving positioning accuracy, the overall solution also has good scalability and adaptability. It can be widely used in fault monitoring and management of large-scale distributed cable networks, effectively reducing troubleshooting time and cost, and providing strong support for intelligent operation and maintenance of power systems.

[0042] Example 2 Reference Figure 1 and Figure 2 , which is the second embodiment of the present invention, and this embodiment provides a method for collecting and locating traveling wave characteristics of distributed cable faults. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0043] In the secondary weight distribution process, the signal-to-noise ratio of the current node is 4.0. Based on this, the weights after topological distance and time consistency adjustment are optimized. The weights after topological distance adjustment of the first data source are set to 0.6, 0.7, and 0.8, and the weights after time consistency adjustment are set to 0.5, 0.7, and 0.9. When these weight values ​​are fused and calculated, the weight corresponding to the signal-to-noise ratio is set to 40%, the topological distance to 30%, and the time consistency to 30%. Through specific calculations, the fused weight values ​​are finally obtained to be 0.55, 0.70, and 0.85, respectively. In the membership function, when the signal-to-noise ratio increases from 4.0 to 5.0, the membership increases from 0.7 to 0.9; when the topological distance decreases from 200 meters to 100 meters, the membership increases from 0.6 to 0.8; when the time consistency increases from 10 microseconds to 5 microseconds, the membership increases from 0.6 to 0.9, thereby realizing the standardized mapping and dynamic adjustment of the weight value. The comparison between the present invention and the prior art is shown in Table 2 below: Table 2 Comparison table between the present invention and the prior art Comparison Dimensions Prior art Technical solution of the present invention Advantages Positioning accuracy Low precision, poor performance in complex scenes High precision, adaptable to complex networks Improve positioning accuracy Data collection capabilities Easy to lose high frequency / low frequency information Comprehensive capture of traveling wave characteristics Enhanced signal integrity Data sharing capabilities Time synchronization is difficult Distributed Collaborative Sensing Network Improve sharing efficiency and timing consistency Noise immunity Sensitive to noise Optimize weight distribution and enhance noise resistance Improve noise immunity Applicability and scalability Limited scalability Support for large-scale distributed networks Greater applicability and scalability Computational efficiency The calculation process is complicated Simplify the calculation process Improve computational efficiency Among them, Table 2 shows that by introducing technologies such as weighted averaging, dynamic topology perception and distributed collaborative networks, the positioning accuracy, signal integrity and system noise resistance are significantly improved. At the same time, the calculation process is optimized, the applicability and scalability of large-scale distributed networks are enhanced, showing obvious technical advantages.

[0044] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for collecting and locating traveling wave characteristics of distributed cable faults, characterized in that: include, Deploy branch point sensing units of the cable network, collect traveling wave characteristic information through the branch point sensing units, and share the collected traveling wave characteristic information through a communication network; The branch point sensing unit collects the traveling wave characteristic information and the distributed cable adjacent node data for weighted averaging to build a dynamic topology perception and network reconstruction framework. The weighted averaging includes assigning weights to data sources based on signal-to-noise ratio, topological distance and time consistency. The traveling wave characteristic information collected by the branch point sensor unit and the data of adjacent nodes of the distributed cable are linearly combined according to the assigned weight values ​​to establish a propagation path model of the traveling wave signal in the distributed cable. The hyperbolic positioning rule is used in combination with the propagation path model to define the cable fault location coordinates.

2. The method for collecting and locating the traveling wave characteristics of a distributed cable fault according to claim 1, characterized in that: The collecting of the traveling wave characteristic information includes using a branch point sensing unit equipped with a multi-channel data acquisition module, introducing a scale sampling technology, and capturing high-frequency components and low-frequency components of the traveling wave signal; The scale sampling technology includes building an adaptive threshold detection model by fusing multi-source features of the traveling wave signal, wherein the multi-source features of the fused traveling wave signal include the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal; The construction of the adaptive threshold detection model includes establishing a double-layer threshold structure through the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal, and the establishment of the double-layer threshold structure includes setting independent detection thresholds for high-frequency components and low-frequency components.

3. The method for collecting and locating the traveling wave characteristics of a distributed cable fault according to claim 2, characterized in that: The sharing of the collected traveling wave characteristic information includes transmitting each branch point sensing unit to an adjacent node of the traveling wave characteristic information by using a communication network; The method of transmitting the traveling wave characteristic information of each branch point sensor unit to the adjacent node includes dividing the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal into data packets, adding timestamps, node identifiers and metadata to the data packets, and establishing a time synchronization mechanism based on the time series characteristics. The time synchronization mechanism includes introducing a time protocol and a positioning system synchronization technology to make the time reference of each branch point sensor unit and the adjacent node consistent, thereby completing the construction of a transmission path and a distributed collaborative perception network model.

4. The method for collecting and locating the traveling wave characteristics of a distributed cable fault according to claim 3, characterized in that: The distributed cable adjacent node data includes data transmitted from an adjacent node to a current node via a communication network; The weighted average includes processing the data of the current node by collecting the traveling wave characteristic information, and the processing of the data of the current node includes calculating the signal-to-noise ratio of the data of the current node and the data of the adjacent nodes of the distributed cable, and the calculation formula of the signal-to-noise ratio is: ； in, represents the signal-to-noise ratio, Indicates the dominant frequency region of the signal, represents the non-negative part of the signal power, represents the total noise power in the noise-dominated frequency region, represents historical noise data; The topological distance includes introducing a distance attenuation factor according to the physical distance between the adjacent node data of the distributed cable and the current node data, and adjusting the weight of the adjacent node data; The distance attenuation factor is , represents the topological distance, represents the attenuation coefficient; When the change in physical distance is decreasing, the weight of the adjacent node data is adjusted to increase; When the change in physical distance increases, the weight of the neighboring node data is adjusted to decrease; The temporal consistency includes comparing the timestamps of the current node data and the adjacent node data to evaluate the temporal consistency of the two; When the timestamp data of the comparison keeps decreasing, the weight of the adjacent node data is adjusted to increase; When the timestamp data for comparison increases continuously, the weight of the adjacent node data is adjusted to decrease.

5. The method for collecting and locating the traveling wave characteristics of a distributed cable fault according to claim 4, characterized in that: The weighting of the data sources includes performing secondary weighting according to the weights adjusted by the topological distance and the time consistency based on the signal-to-noise ratio; The secondary distribution weight includes comprehensively optimizing the weights adjusted by the topological distance and the time consistency based on the signal-to-noise ratio, and expressing the weights adjusted by the topological distance and the time consistency based on the signal-to-noise ratio as two independent weight vectors respectively; The two independent weight vectors are as follows: ； ； in, represents the weight vector after topological distance adjustment, represents the weight vector after time consistency adjustment, Represents the weight vector after topological distance adjustment of the Nth data source, represents the weight vector after time consistency adjustment of the Nth data source; Based on two independent weight vectors, weight fusion is performed to define the function after weight fusion. The calculation formula of the weight fusion is: ； in, represents the weight fusion function, , and represents the weight coefficient; The function after defining weight fusion includes defining a membership function for the weight value of each dimension and mapping it to a standardized interval ; The definition includes: Increasing the signal-to-noise ratio corresponds to increasing the membership, and decreasing the signal-to-noise ratio corresponds to decreasing the membership; A reduced topological distance corresponds to an increased degree of membership, and an increased topological distance corresponds to a decreased degree of membership; Increasing temporal consistency corresponds to increasing membership, and decreasing temporal consistency corresponds to decreasing membership.

6. The method for collecting and locating the traveling wave characteristics of a distributed cable fault according to claim 5, characterized in that: The linear combination according to the assigned weight values ​​includes performing weighted linear combination on the traveling wave characteristic information collected by the branch point sensor unit and the distributed cable adjacent node data according to the weight values ​​after secondary distribution, and the formula of the weighted linear combination is: ； in, represents the comprehensive eigenvector of the weighted linear combination, Indicates The traveling wave eigenvector of the data source.

7. The method for collecting and locating the traveling wave characteristics of a distributed cable fault according to claim 6, characterized in that: The establishment of the propagation path model of the traveling wave signal in the distributed cable includes constructing the topological structure of the cable network according to the comprehensive characteristic vector of the weighted linear combination, and the construction of the topological structure of the cable network includes outputting a hyperbolic positioning rule for the communication link between the branch point sensing units and the edges of the adjacent node data; The output hyperbola positioning rule includes using the adjacent nodes of the comprehensive feature vector of the weighted linear combination as the node input, the topological distance and time consistency information of the adjacent nodes as the edge feature input, and outputting the cable fault position positioning coordinates.

8. A distributed cable fault traveling wave feature acquisition and positioning system, based on the distributed cable fault traveling wave feature acquisition and positioning method according to any one of claims 1 to 7, characterized in that: include, A collection module deploys branch point sensing units of the cable network, collects traveling wave characteristic information through the branch point sensing units, and shares the collected traveling wave characteristic information through a communication network; A construction module collects traveling wave characteristic information and distributed cable adjacent node data through branch point sensing units for weighted averaging to build a dynamic topology perception and network reconstruction framework; The positioning module combines the traveling wave characteristic information collected by the branch point sensor unit and the data of the adjacent nodes of the distributed cable with the propagation path model and uses the hyperbolic positioning rule to define the positioning coordinates of the cable fault position.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for collecting and locating traveling wave characteristics of distributed cable faults described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for collecting and locating traveling wave characteristics of a distributed cable fault as described in any one of claims 1 to 7 are implemented.

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