A distributed cable fault traveling wave feature acquisition and positioning method and system

Abstract

The present invention discloses a method for collecting and locating traveling wave characteristics of distributed cable faults, which relates to the field of cable fault technology. The method includes collecting traveling wave characteristic information through branch point sensing units and sharing the collected traveling wave characteristic information through a communication network; constructing a dynamic topology perception and network reconstruction framework, wherein weighted averaging includes assigning weights to data sources based on signal-to-noise ratio, topological distance, and time consistency; establishing a propagation path model of traveling wave signals in distributed cables, and using hyperbolic positioning rules in combination with the propagation path model to define the cable fault location coordinates. The present invention enhances the distributed collaboration capability of the system. While improving positioning accuracy, the overall solution 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 the intelligent operation and maintenance of power systems.

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, power systems are gradually evolving towards distributed and intelligent directions. As an important carrier of power transmission, cable networks are becoming increasingly complex and large-scale. With the acceleration of urbanization and the increasing access to new energy sources, the frequency of cable faults 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 typically rely on fixed sampling strategies and single signal processing algorithms. When faced with the multi-branch structure of distributed cable networks, they are unable to effectively analyze the complex traveling wave reflection and refraction phenomena, resulting in reduced positioning accuracy and affecting the scientific nature of maintenance decisions. Traditional methods have limited ability to extract the traveling wave characteristics of distributed cable faults and are prone to losing key information over long distances or in 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, the present invention addresses the defects of the existing traveling wave acquisition system in the multi-branch structure of the distributed cable network, such as 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 hyperbola 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:

[0007] 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 at the 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;

[0008] The branch point sensing unit collects traveling wave characteristic information and performs weighted averaging on the data of adjacent nodes in the distributed cable 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.

[0009] The traveling wave characteristic information collected by the branch point sensing unit and the data of the 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. Combined with the propagation path model, the hyperbola positioning rule is used to define the cable fault location coordinates.

[0010] As a preferred solution of the distributed cable fault traveling wave feature acquisition and location 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 scale sampling technology, and capturing the high-frequency and low-frequency components of the traveling wave signal;

[0011] 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;

[0012] 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.

[0013] As a preferred solution of the distributed cable fault traveling wave feature acquisition and location method of the present invention, wherein: the sharing of the acquired traveling wave feature information includes transmitting the traveling wave feature information of each branch point sensing unit to the adjacent nodes using a communication network;

[0014] The method of transmitting the traveling wave characteristic information of each branch point sensing 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 time protocols and positioning system synchronization technology to make the time bases of each branch point sensing unit and the adjacent nodes consistent, thereby completing the construction of the transmission path and distributed collaborative perception network model.

[0015] 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 via the communication network;

[0016] The weighted averaging includes processing the data of the current node by collecting 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 adjacent nodes of the distributed cable. The calculation formula of the signal-to-noise ratio is:

[0017] ;

[0018] in, represents the signal-to-noise ratio, Indicates the signal dominant frequency region, represents the non-negative part of the signal power, represents the total noise power in the noise-dominated frequency region, represents historical noise data;

[0019] 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;

[0020] The distance attenuation factor is , represents the topological distance, represents the attenuation coefficient;

[0021] When the change in physical distance decreases, the weight of the adjacent node data is adjusted to increase;

[0022] When the change in physical distance increases, the weight of the adjacent node data is adjusted to decrease;

[0023] 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;

[0024] When the timestamp data of the comparison continues to decrease, the weight of the adjacent node data is adjusted to increase;

[0025] When the timestamp data of the comparison increases continuously, the weight of the adjacent node data is adjusted to decrease.

[0026] 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 based on the weights adjusted by the topological distance and time consistency according to the signal-to-noise ratio;

[0027] The secondary weight distribution 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;

[0028] The two independent weight vectors are as follows:

[0029] ;

[0030] ;

[0031] 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;

[0032] Based on two independent weight vectors, weight fusion is performed and the function after weight fusion is defined. The calculation formula of the weight fusion is:

[0033] ;

[0034] in, represents the weight fusion function, 、 and represents the weight coefficient;

[0035] The function after defining weight fusion includes defining a membership function for the weight value of each dimension and mapping it to the standardized interval ; The definition includes:

[0036] Increasing the signal-to-noise ratio corresponds to increasing the membership, and decreasing the signal-to-noise ratio corresponds to decreasing the membership;

[0037] A decreasing topological distance corresponds to an increased degree of membership, and an increasing topological distance corresponds to a decreased degree of membership;

[0038] Increasing temporal consistency corresponds to increasing membership, and decreasing temporal consistency corresponds to decreasing membership.

[0039] As a preferred solution of the distributed cable fault traveling wave feature acquisition and location method of the present invention, the linear combination according to the assigned weight values ​​includes performing a weighted linear combination of the traveling wave feature information collected by the branch point sensing unit and the distributed cable adjacent node data according to the secondary assigned weight values. The formula of the weighted linear combination is:

[0040] ;

[0041] in, represents the comprehensive eigenvector of the weighted linear combination, Indicates the The traveling wave eigenvector of the data source.

[0042] As a preferred embodiment of the method for collecting and locating traveling wave characteristics of distributed cable faults according to the present invention, the method includes: establishing a propagation path model of the traveling wave signal in the distributed cable includes constructing a topological structure of the cable network based on a comprehensive characteristic vector of a weighted linear combination, and the method includes outputting a hyperbolic positioning rule for the communication link between the branch point sensing units and the edges of the adjacent node data;

[0043] The output hyperbola positioning rule includes using adjacent nodes of the comprehensive feature vector of the weighted linear combination as node input, topological distance and time consistency information of the adjacent nodes as edge feature input, and outputting the cable fault position positioning coordinates.

[0044] In a second aspect, an embodiment of the present invention provides a distributed cable fault traveling wave feature acquisition and positioning system, comprising:

[0045] 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;

[0046] A construction module collects traveling wave characteristic information and distributed cable adjacent node data through branch point sensing units, performs weighted averaging, and builds a dynamic topology perception and network reconstruction framework;

[0047] 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 cable fault location coordinates.

[0048] In a third aspect, an embodiment of the present invention provides a computer device comprising 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 method for collecting and locating traveling wave characteristics of distributed cable faults is implemented.

[0049] 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.

[0050] The beneficial effects of the present invention are as follows: the present invention realizes the efficient collection and sharing of traveling wave characteristic information in the distributed cable network by deploying branch point sensing units and communication networks, thereby improving the fault location accuracy under complex multi-branch structures, constructing a propagation path model based on weighted linear combination, and combining it with the hyperbolic positioning rule to achieve rapid and accurate positioning of the cable fault position, providing a scientific basis for maintenance decision-making. The time synchronization mechanism of the present invention ensures the consistency of data between nodes and enhances the distributed collaboration capability 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 the time and cost of troubleshooting, and providing strong support for the intelligent operation and maintenance of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] 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. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0052] Figure 1 A flowchart of a method and system for collecting and locating traveling wave characteristics of distributed cable faults provided by one embodiment of the present invention.

[0053] Figure 2 A schematic diagram of data source weight allocation for a distributed cable fault traveling wave feature acquisition and location method and system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0054] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. 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.

[0056] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0057] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0058] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0059] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0060] Example 1

[0061] 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:

[0062] S1: 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 the communication network.

[0063] The collection of traveling wave characteristic information includes using a branch point sensing unit equipped with a multi-channel data acquisition module and introducing scale sampling technology to capture the high-frequency and low-frequency components of the traveling wave signal;

[0064] The scale sampling technology includes building an adaptive threshold detection model by fusing the multi-source features of the traveling wave signal, including the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal;

[0065] 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.

[0066] S1.1: Sharing the collected traveling wave characteristic information includes using a communication network to transmit the sensing units of each branch point to adjacent nodes of the traveling wave characteristic information;

[0067] Transmitting the traveling wave characteristic information of the sensing unit 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 bases of the sensing units at each branch point and the adjacent nodes consistent, thereby completing the construction of the transmission path and distributed collaborative perception network model.

[0068] Furthermore, after deploying the sensing unit at the branch point 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 a frequency within 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 a frequency within the range of 1Hz to 10kHz. Combined with the standards of a phase change rate greater than 0.05rad / ms and a time series characteristic fluctuation amplitude less than 0.1, an adaptive A threshold detection model is proposed. This model integrates and analyzes the amplitude, phase, frequency distribution and time series characteristics of the traveling wave signal, and dynamically adjusts the threshold range to adapt to the signal characteristics in different environments. After the traveling wave characteristic information is collected, the data of the sensor units at each branch point is transmitted to the adjacent nodes 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 data source. By introducing time protocols such as the IEEE 1588 precision time protocol and positioning system synchronization technology, the time base difference between the sensor units at each branch point and the adjacent nodes is calibrated, so that the time deviation is controlled within the microsecond level, 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.

[0069] S2: The branch point sensing unit collects the traveling wave characteristic information and the data of the adjacent nodes of the distributed cable for weighted averaging to build a dynamic topology perception and network reconstruction framework. The weighted averaging includes assigning weights to data sources based on the signal-to-noise ratio, topological distance and time consistency.

[0070] Wherein, the distributed cable adjacent node data includes data transmitted from the adjacent node to the current node via the communication network;

[0071] 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:

[0072] ;

[0073] in, represents the signal-to-noise ratio, Indicates the signal dominant frequency region, represents the non-negative part of the signal power, represents the total noise power in the noise-dominated frequency region, represents historical noise data;

[0074] The topological distance includes introducing a distance attenuation factor based on 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;

[0075] The distance attenuation factor is , represents the topological distance, represents the attenuation coefficient;

[0076] When the change in physical distance decreases, the weight of the adjacent node data is adjusted to increase;

[0077] When the change in physical distance increases, the weight of the adjacent node data is adjusted to decrease;

[0078] Temporal consistency involves comparing the timestamps of the current node data with those of the adjacent node data to evaluate the temporal consistency of the two;

[0079] When the timestamp data of the comparison continues to decrease, the weight of the adjacent node data is adjusted to increase;

[0080] When the timestamp data of the comparison increases continuously, the weight of the adjacent node data is adjusted to decrease.

[0081] Furthermore, when performing weighted averaging, the signal-to-noise ratio (SNR) of the data between the current node and adjacent nodes is first calculated. Assuming the signal-dominant frequency region is 50 kHz, the non-negative part of the signal power is 120 dB, the total noise power in the noise-dominant frequency region is 30 dB, and the historical noise data is 10 dB, the SNR 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 and the attenuation coefficient is set to 0.01, the calculated distance attenuation factor is 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 decreases to 0.4. In the time consistency assessment, if the timestamp difference between 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 decreases to 0.4, thereby achieving 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:

[0082] Table 1 Node data weight dynamic adjustment parameter table

[0083] Parameter Type Initial value 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

[0084] Among them, according to the content of Table 1, the dynamic adjustment of data weights between nodes is shown. The range of weight changes is quantitatively analyzed through signal-to-noise ratio calculation, topological distance changes, and time consistency differences.

[0085] S2.1: Assigning weights to data sources includes adjusting weights based on topological distance and temporal consistency according to the signal-to-noise ratio, and performing secondary weight assignment;

[0086] The secondary weight allocation includes comprehensively optimizing the weights adjusted by the topological distance and time consistency based on the signal-to-noise ratio, and expressing the weights adjusted by the topological distance and time consistency based on the signal-to-noise ratio as two independent weight vectors respectively;

[0087] The two independent weight vectors are as follows:

[0088] ;

[0089] ;

[0090] 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;

[0091] 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:

[0092]

[0093] in, represents the weight fusion function, 、 and represents the weight coefficient;

[0094] The function after weight fusion is defined, including defining the membership function of the weight value of each dimension and mapping it to the standardized interval ; Definitions include:

[0095] Increasing the signal-to-noise ratio corresponds to increasing the membership, and decreasing the signal-to-noise ratio corresponds to decreasing the membership;

[0096] A decreasing topological distance corresponds to an increased degree of membership, and an increasing topological distance corresponds to a decreased degree of membership;

[0097] Increasing temporal consistency corresponds to increasing membership, and decreasing temporal consistency corresponds to decreasing membership.

[0098] S3: The traveling wave characteristic information collected by the branch point sensor unit and the data of the 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 cable fault location coordinates are defined using the hyperbola positioning rule in combination with the propagation path model.

[0099] 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 sensing unit and the data of the adjacent nodes of the distributed cable according to the weight values ​​after secondary distribution. The formula of the weighted linear combination is:

[0100] ;

[0101] in, represents the comprehensive eigenvector of the weighted linear combination, Indicates the The traveling wave eigenvector of the data source.

[0102] Preferably, establishing a propagation path model of a traveling wave signal in a distributed cable includes constructing a topological structure of the cable network based on a comprehensive characteristic 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 branch point sensing units and edges of adjacent node data;

[0103] The output hyperbola location 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 input of the edge feature, and outputting the cable fault location coordinates.

[0104] In a preferred embodiment, a distributed cable fault traveling wave feature acquisition and positioning system includes an acquisition module, which deploys branch point sensing units of the cable network, collects traveling wave feature information through the branch point sensing 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 sensing units and performs weighted averaging on the distributed cable adjacent node data to construct a dynamic topology perception and network reconstruction framework; and a positioning module, which combines the traveling wave feature information collected by the branch point sensing units and the distributed cable adjacent node data with a propagation path model and uses a hyperbola positioning rule to define the cable fault location coordinates.

[0105] The above-mentioned unit modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0106] The computer device may be a terminal, comprising 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 comprises 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 may 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 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 button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0107] In summary, the present invention achieves efficient collection and sharing of traveling wave characteristic information in distributed cable networks by deploying branch point sensing units and communication networks, thereby improving the fault location accuracy under complex multi-branch structures. Based on the weighted linear combination, the propagation path model is constructed, and combined with the hyperbolic positioning rule, the cable fault position is quickly and accurately located, 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 the time and cost of troubleshooting, and providing strong support for the intelligent operation and maintenance of power systems.

[0108] Example 2

[0109] Reference Figure 1 and Figure 2 This is the second embodiment of the present invention, which 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.

[0110] 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 accounts for 30%, and the time consistency accounts for 30%. Through specific calculations, the final fused weight values ​​are 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 standardized mapping and dynamic adjustment of weight values. The comparison between the present invention and the prior art is shown in Table 2 below:

[0111] Table 2 Comparison between the present invention and the prior art

[0112] Comparison Dimension Existing technology 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 Comprehensively capture the characteristics of traveling waves 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 Complex calculation process Simplify the calculation process Improve computing efficiency

[0113] 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, and obvious technical advantages are demonstrated.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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 may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all 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 by: include, Deploy branch point sensing units at the cable network, collect traveling wave characteristic information through the branch point sensing units, and share the collected traveling wave characteristic information through the communication network; The branch point sensing unit collects traveling wave characteristic information and performs weighted averaging on the data of adjacent nodes in the distributed cable 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 the 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 cable fault location coordinates are defined using the hyperbola positioning rule in combination with the propagation path model. The distributed cable adjacent node data includes data transmitted from adjacent nodes to the current node via a communication network; The weighted averaging includes processing the data of the current node by collecting 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 adjacent nodes of the distributed cable. The calculation formula of the signal-to-noise ratio is: Among them, SNR i represents the signal-to-noise ratio, F sig Indicates the signal dominant frequency region, (S i (f)-α·N i (f)) + represents the non-negative part of the signal power, Indicates the total noise power in the noise-dominated frequency region, F noise 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; Among them, the distance attenuation factor is e -αd , d represents the topological distance, α represents the attenuation coefficient; When the change in physical distance decreases, the weight of the adjacent node data is adjusted to increase; When the change in physical distance increases, the weight of the adjacent 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 continues to decrease, the weight of the adjacent node data is adjusted to increase; When the timestamp data of the comparison increases continuously, the weight of the adjacent node data is adjusted to decrease; The weighting of the data sources includes performing secondary weighting based on the weights adjusted by the topological distance and the time consistency according to the signal-to-noise ratio; The secondary weight distribution 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 independent weight vectors are as follows: IN Distance =[in Distance，1 ,In Distance，2 ,...,In Distance，N ] IN Time =[in Time，1 ,In Time，2 ,...,In Time，N ] Among them, W Distance Represents the weight vector after topological distance adjustment, W Time represents the weight vector after time consistency adjustment, w Distance，N Represents the weight vector after topological distance adjustment of the Nth data source, w Time，N 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 the weight fusion is: Among them, W Final，i represents the weight fusion function, α, β and γ represent the weight coefficients; The function after weight fusion is defined includes defining a membership function for the weight value of each dimension and mapping it to the standardized interval [x, y]. 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 decreasing topological distance corresponds to an increased degree of membership, and an increasing topological distance corresponds to a decreased degree of membership; Increasing temporal consistency corresponds to increasing membership, and decreasing temporal consistency corresponds to decreasing membership.

2. The method for collecting and locating 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 the high-frequency component and the low-frequency component 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 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 sensing 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 time protocols and positioning system synchronization technology to make the time bases of each branch point sensing unit and the adjacent nodes consistent, thereby completing the construction of the transmission path and distributed collaborative perception network model.

4. The method for collecting and locating traveling wave characteristics of a distributed cable fault according to claim 3, characterized in that: The performing of linear combination according to the assigned weight values ​​includes performing a weighted linear combination of the traveling wave characteristic information collected by the branch point sensing unit and the distributed cable adjacent node data according to the secondary assigned weight values. The formula of the weighted linear combination is: Among them, F Combined represents the comprehensive eigenvector of the weighted linear combination, F i Represents the traveling wave eigenvector of the i-th data source.

5. The method for collecting and locating traveling wave characteristics of a distributed cable fault according to claim 4, characterized in that: The establishing of a propagation path model of a traveling wave signal in a distributed cable includes constructing a topological structure of the cable network based on a comprehensive characteristic vector of a weighted linear combination, wherein the constructing of the topological structure of the cable network includes outputting a hyperbolic positioning rule for a communication link between nodes using branch point sensing units and edges of adjacent node data; The output hyperbola positioning rule includes using adjacent nodes of the comprehensive feature vector of the weighted linear combination as node input, topological distance and time consistency information of the adjacent nodes as edge feature input, and outputting the cable fault position positioning coordinates.

6. A distributed cable fault traveling wave feature acquisition and location system, based on the distributed cable fault traveling wave feature acquisition and location method according to any one of claims 1 to 5, 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, performs weighted averaging, and builds 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 cable fault location coordinates.

7. 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 a distributed cable fault as claimed in any one of claims 1 to 5 are implemented.

8. 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 according to any one of claims 1 to 5 are implemented.

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