A method and system for fault location of cables

Abstract

This invention discloses a method and system for locating cable faults, applied in the field of fault detection technology. The method includes sending a first pulse signal to a faulty cable line and acquiring first pulse reflected wave information of the faulty cable line; extracting voltage waveform data from the first pulse reflected wave information and performing peak detection processing on the voltage waveform data to obtain first pulse reflection extreme value information; determining the faulty cable section based on the topology information of the faulty cable line and the first pulse reflection extreme value information; sending second discharge information to the faulty cable line and acquiring second discharge acoustic wave information of the faulty cable section during the discharge process; extracting instantaneous acoustic wave features from the second discharge acoustic wave information and performing feature quantization processing on the instantaneous acoustic wave features to obtain acoustic location parameters; and determining the line fault point of the faulty cable line based on the acoustic location parameters. This method enables rapid and accurate location of cable fault points.

Description

Technical Field

[0001] This invention relates to the field of fault detection technology, and in particular to a method and system for locating cable faults. Background Technology

[0002] Power cables are critical carriers of electrical energy transmission. During long-term operation, they are susceptible to factors such as insulation aging and construction damage, often leading to grounding or short-circuit faults. Timely fault location helps maintenance personnel quickly isolate and repair faults, ensuring the safe and stable operation of the power grid.

[0003] In existing technologies, fault location methods require maintenance personnel to traverse every node of the cable line and collect current data at each node. They then rely on their personal experience to comprehensively analyze all the current data to pinpoint the fault location. However, this manual data collection method is labor-intensive, time-consuming, and can easily delay the repair progress. Furthermore, the subjective nature of fault point analysis based on human experience makes it difficult to accurately identify the fault characteristics reflected in the current data, potentially leading to misjudgments or omissions of cable fault locations, which is detrimental to the safe and reliable operation of the power grid. Summary of the Invention

[0004] This invention provides a method and system for locating cable faults, which solves the technical problems of time-consuming and labor-intensive manual current data collection and the inability of human experience to accurately locate fault points, thereby achieving the effect of rapid and accurate location of cable fault points.

[0005] To address the aforementioned technical problems, the present invention provides a method for locating cable faults, comprising: Send a first pulse signal to the faulty cable line and obtain the first pulse reflected wave information of the faulty cable line; Extract the voltage waveform data from the first pulse reflection wave information, and perform peak detection processing on the voltage waveform data to obtain the first pulse reflection extreme value information; Based on the topology information of the faulty cable line and the extreme value information of the first pulse reflection, the faulty cable section of the faulty cable line is determined. Send a second discharge information to the faulty cable line and acquire the second discharge acoustic wave information of the faulty cable section during the discharge process; The instantaneous acoustic wave features are extracted from the second discharge acoustic wave information, and the instantaneous acoustic wave features are subjected to feature quantization processing to obtain acoustic positioning parameters. The fault point of the faulty cable line is determined based on the acoustic positioning parameters.

[0006] As one preferred embodiment, the step of extracting voltage waveform data from the first pulse reflection wave information and performing peak detection processing on the voltage waveform data to obtain the first pulse reflection extreme value information includes: Based on the Fourier transform algorithm, waveform features are extracted from the first pulse reflection wave information to obtain the voltage waveform data; Based on the bandpass filtering algorithm, the voltage waveform data is denoised to obtain the voltage waveform data to be detected. The peak detection processing is performed on the voltage waveform data to be detected to obtain the first pulse reflection extreme value information.

[0007] As one preferred embodiment, determining the faulty cable section of the faulty cable line based on the topology information of the faulty cable line and the first pulse reflection extreme value information includes: Extract the topological features from the topological information and perform quantitative analysis on the topological features to obtain the topological structure feature vector; By combining the first pulse reflection extreme value information and the topology feature vector, a first fault distance analysis is performed to obtain the line fault distance information; The fault distance information of the line is analyzed in a second fault section to obtain the fault cable section of the faulty cable line.

[0008] As one preferred embodiment, the step of extracting instantaneous acoustic features from the second discharge acoustic wave information and performing feature quantization on the instantaneous acoustic wave features to obtain acoustic positioning parameters includes: Based on the empirical mode decomposition algorithm, acoustic wave feature extraction processing is performed on the second discharge acoustic wave information to obtain the instantaneous acoustic wave features; Based on a convolutional neural network model, feature enhancement processing is performed on the instantaneous acoustic wave features to obtain the instantaneous acoustic wave features to be quantized; The instantaneous acoustic wave features to be quantized are subjected to feature quantization processing to obtain the acoustic positioning parameters.

[0009] As one preferred embodiment, before sending the second discharge information to the faulty cable line, the method further includes: Impedance analysis is performed on the first pulse reflection extreme value information to obtain the equivalent impedance characteristics; Based on the equivalent impedance characteristics, the fault type of the faulty cable line is determined; Based on the fault type, the voltage amplitude of the second discharge information is determined.

[0010] Another aspect of the present invention provides a cable fault location system, comprising: The first pulse signal transmitting module is used to send a first pulse signal to the faulty cable line and to obtain the first pulse reflected wave information of the faulty cable line. The first pulse reflection wave information processing module is used to extract voltage waveform data from the first pulse reflection wave information and perform peak detection processing on the voltage waveform data to obtain the first pulse reflection extreme value information. The faulty cable section determination module is used to determine the faulty cable section of the faulty cable line based on the topology information of the faulty cable line and the first pulse reflection extreme value information. The second discharge signal transmitting module is used to transmit a second discharge signal to the faulty cable line and acquire the second discharge acoustic wave information of the faulty cable section during the discharge process. The instantaneous acoustic wave feature analysis module is used to extract instantaneous acoustic wave features from the second discharge acoustic wave information and perform feature quantization processing on the instantaneous acoustic wave features to obtain acoustic positioning parameters. The line fault location module is used to determine the line fault point of the faulty cable line based on the acoustic location parameters.

[0011] As one preferred embodiment, the first pulse reflection wave information processing module includes: The waveform feature extraction unit is used to extract waveform features from the first pulse reflection wave information based on the Fourier transform algorithm to obtain the voltage waveform data; The data denoising unit is used to perform denoising processing on the voltage waveform data based on the bandpass filtering algorithm to obtain the voltage waveform data to be detected. A peak detection unit is used to perform peak detection processing on the voltage waveform data to be detected to obtain the first pulse reflection extreme value information.

[0012] As one preferred embodiment, the faulty cable section determination module includes: The feature quantization unit is used to extract topological features from the topological information and perform quantization analysis on the topological features to obtain a topological structure feature vector. The first fault distance analysis unit is used to perform a first fault distance analysis by combining the first pulse reflection extreme value information and the topology feature vector to obtain line fault distance information. The second fault section analysis unit is used to perform a second fault section analysis on the line fault distance information to obtain the fault cable section of the faulty cable line.

[0013] As one preferred embodiment, the instantaneous acoustic wave feature analysis module includes: The acoustic feature extraction unit is used to perform acoustic feature extraction processing on the second discharge acoustic wave information based on the empirical mode decomposition algorithm to obtain the instantaneous acoustic wave features. The feature enhancement unit is used to perform feature enhancement processing on the instantaneous acoustic wave features based on a convolutional neural network model to obtain the instantaneous acoustic wave features to be quantized. An acoustic positioning parameter determination unit is used to perform feature quantization processing on the instantaneous acoustic wave features to be quantized to obtain the acoustic positioning parameters.

[0014] As one preferred embodiment, before the second discharge signal transmitting module, the system further includes: Impedance analysis unit is used to perform impedance analysis on the extreme value information of the first pulse reflection to obtain the equivalent impedance characteristics; The fault type analysis unit is used to determine the fault type of the faulty cable line based on the equivalent impedance characteristics. A voltage amplitude determination unit is used to determine the voltage amplitude of the second discharge information based on the fault type.

[0015] Compared to existing technologies, the beneficial effects of the embodiments of the present invention are at least one of the following: The present invention, by sending a first pulse signal and a second discharge signal to the faulty cable line and acquiring corresponding first pulse reflection wave information and second discharge acoustic wave information, solves the technical problem in the prior art where cable line nodes are scattered and manual collection of current data node by node is time-consuming and labor-intensive, enabling rapid location of cable fault points; the present invention, by combining cable topology information and first pulse reflection extreme value information, determines the faulty cable section, limiting subsequent acoustic wave acquisition to a small path range, avoiding blind large-scale searches; the present invention performs instantaneous acoustic wave feature extraction and quantification analysis on the discharge acoustic wave information, transforming subjective listening into objective acoustic parameters, reducing reliance on manual experience, and improving the accuracy of fault point location; the present invention, by fusing electrical and acoustic measurement information and through feature quantization processing, effectively distinguishes between real discharge signals and environmental interference, significantly reducing the probability of false or missed fault detection. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a cable fault location method according to one embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a cable fault location system according to one embodiment of the present invention; Figure label: Among them, 11, first pulse signal transmission module; 12, first pulse reflected wave information processing module; 13, fault cable section determination module; 14, second discharge signal transmission module; 15, instantaneous acoustic wave characteristic analysis module; 16, line fault point location module. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0020] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] One embodiment of the present invention provides a method for locating cable faults. For details, please refer to [link to documentation]. Figure 1 , Figure 1 The diagram shown illustrates a cable fault location method according to one embodiment of the present invention, the method comprising steps S1 to S6: S1. Send a first pulse signal to the faulty cable line and obtain the first pulse reflected wave information of the faulty cable line; S2. Extract the voltage waveform data from the first pulse reflection wave information, and perform peak detection processing on the voltage waveform data to obtain the first pulse reflection extreme value information; S3. Based on the topology information of the faulty cable line and the first pulse reflection extreme value information, determine the faulty cable section of the faulty cable line; S4. Send a second discharge information to the faulty cable line and acquire the second discharge acoustic wave information of the faulty cable section during the discharge process; S5. Extract the instantaneous acoustic wave features from the second discharge acoustic wave information, and perform feature quantization processing on the instantaneous acoustic wave features to obtain acoustic positioning parameters; S6. Determine the fault point of the faulty cable line based on the acoustic positioning parameters.

[0022] In existing technologies, fault location methods first require maintenance personnel to traverse every node of the cable line and collect current data at each node. Then, based on the personnel's experience, all collected current data is comprehensively analyzed to determine the fault point. Cable lines are typically buried underground, and their nodes are often distributed in streets, green belts, or fields. Some cable line nodes are located under traffic lanes or inside narrow trenches, and the distance between nodes is often considerable. Maintenance personnel need to search for these nodes one by one in complex environments, resulting in extremely high manpower and material costs. Furthermore, the internal space of electrical equipment at nodes is confined, and live parts are densely packed. Manually collecting current data at nodes not only poses a risk of electric shock, but the electromagnetic fields of surrounding live equipment can also cause severe electromagnetic interference to the weak fault signals collected, leading to data distortion. To avoid the shortcomings of manual data collection, this invention uses a method of sending pulse signals to the faulty cable line and receiving feedback information to replace manual data collection, thereby improving the accuracy of subsequent fault location.

[0023] In the cable fault location process of this invention, the primary task is to determine the faulty section of the line. If the fault distance information cannot be obtained quickly and accurately, the subsequent location work will become blind, causing maintenance personnel to conduct large-area searches on long-distance lines, which is extremely inefficient. Therefore, in step S1, a first pulse signal is sent to the faulty cable line, and the first pulse reflection wave information of the faulty cable line is obtained. Specifically, a pulse signal is quickly sent from any end of the cable. The pulse propagates along the cable to the terminal at a fixed speed. If a fault point is encountered, the pulse will turn back at the fault point and generate a reflected pulse, which is the first pulse reflection wave information.

[0024] Preferably, step S1 can also be implemented using a cable fault tester integrating time-domain reflectometry (TDR) functionality. This instrument typically includes a microprocessor, a pulse generation circuit, a coupler, a high-speed data acquisition system, and a display module. Specifically, the output port of the tester is first connected to the faulty cable via test leads. After the test is started, the microprocessor controls the pulse generation circuit to generate a low-voltage electrical pulse with an extremely narrow width (typically adjustable between 0.1μs and 5μs) and a low amplitude (e.g., 5V to 50V). This pulse is injected into the cable core via a wideband coupler to avoid secondary damage to the cable and to isolate power frequency high-voltage interference on the cable. At the moment the pulse is emitted, the microprocessor synchronously triggers the high-speed data acquisition system (sampling rate up to 100MHz or higher) to start working. The acquisition system monitors and digitizes the voltage signal in the cable in real time, continuously records the complete waveform data from the emitted pulse to the possible reflected pulse, and stores it in an internal buffer.

[0025] The technical principle of TDR (Transmission Line Reflection) is based on transmission line theory and the law of electromagnetic wave reflection. A cable in a uniform state has a constant characteristic impedance. When a low-voltage pulse propagates through the cable, if it encounters a location where the wave impedance changes, such as a fault point, intermediate joint, or termination, reflection will occur. The magnitude and polarity of the reflection are determined by the reflection coefficient, which is calculated using the following formula: in, The equivalent impedance at the fault point. The cable has a constant characteristic impedance under uniform conditions.

[0026] This invention completes the raw data acquisition of the cable condition by sending a first pulse signal to the faulty cable line and obtaining its reflected wave information. However, the raw reflected wave information is a continuous analog signal or unprocessed digital waveform data, which contains both effective reflected pulses characterizing the fault point and is mixed with noise, interference, and non-fault reflections generated by the cable's own structure (such as joints and branches). If this raw data is not processed and used directly for fault location, the true fault point cannot be accurately identified.

[0027] Further, in step S2, firstly, based on the Fourier transform algorithm, waveform features are extracted from the first pulse reflected wave information to obtain voltage waveform data. Secondly, based on the bandpass filtering algorithm, the voltage waveform data is denoised to remove power frequency interference and high-frequency random noise, resulting in smooth and clean voltage waveform data to be detected. Next, a digital peak detection algorithm is used to process the voltage waveform data to be detected. Specifically, when the waveform amplitude changes from a continuous increase to a decrease, it is identified as a local maximum; when the waveform amplitude changes from a continuous decrease to an increase, it is identified as a local minimum. This process traverses the entire waveform data, generating an initial peak sequence containing all local extreme points. Finally, the initial peak sequence is subjected to amplitude threshold filtering. Specifically, a dynamic threshold proportional to the amplitude of the transmitted pulse (e.g., 10%) is set, retaining only extreme points whose amplitude exceeds this threshold. Simultaneously, combined with the polarity characteristics of the reflected pulse, adjacent extreme points are paired and verified to ensure that the identified extreme points truly correspond to valid reflection events. Finally, the time location and amplitude polarity of the selected extreme points are output as the first pulse reflection extreme value information.

[0028] Further, in step S3, the faulty cable section is determined based on the topology information of the faulty cable line and the extreme value information of the first pulse reflection. This step is the key bridge connecting "preliminary distance measurement" and "precise location determination." Specifically, firstly, topological features are extracted from the topology information and quantitatively analyzed to obtain a topological structure feature vector. Preferably, the topology information includes the physical structure data of the cable, such as the total length of the line, the coordinates of each intermediate joint, the precise distance of branch points, cable turning points, and terminal locations. Secondly, the first fault distance analysis is performed by combining the extreme value information of the first pulse reflection and the topological structure feature vector to obtain the line fault distance information. Preferably, the pulse propagation speed, time difference, and topological feature vector from the first pulse reflection extreme value information are input into the trained AI algorithm model for distance calculation to obtain the line fault distance information. Finally, considering that the first pulse reflection extreme value information may have errors, and that the cable may be bent or reserved during actual laying, resulting in a discrepancy between the path length and the calculated fault distance, a second fault segment analysis is performed on the line fault distance information to obtain the fault cable segment of the faulty cable line. Specifically, the error interval is expanded, and the fault distance L is expanded into a confidence interval [L-Δ, L+Δ]. It is determined which segment has the largest intersection with this interval, thereby determining the faulty cable segment to which it belongs.

[0029] The AI ​​algorithm model, trained on massive amounts of data, can learn statistical regularities, feature relationships, and patterns within the data. In practical applications, the trained algorithm model can be used to obtain corresponding data results. The processing procedure of the AI ​​algorithm model is as follows: First, a sample dataset is constructed, including pulse propagation velocity samples, time difference samples, and topology feature vector samples. Corresponding data labels are assigned to each of these data sets to represent the line fault distance information corresponding to the selected pulse propagation velocity, time difference, and topology feature vector samples. Then, based on a learning algorithm, the AI ​​algorithm model is trained using this sample dataset. During training, existing techniques can be consulted to retrain or fine-tune the AI ​​algorithm model to improve its generalization ability. Finally, a second AI algorithm model is obtained after training. In practical applications, the pulse propagation velocity, time difference, and topology feature vector samples are input into the second AI algorithm model, which analyzes and processes the data, then outputs the relevant data results, i.e., the line fault distance information.

[0030] Although the cable section where the fault point is located has been identified, this section may still be tens to hundreds of meters long. Directly excavating and investigating this section would not only be time-consuming and labor-intensive, but could also damage roads, green spaces, or other underground infrastructure, causing significant economic losses and social impact. Therefore, it is essential to achieve precise fault location based on coarse positioning. However, precise location within a identified section faces the following technical challenges: first, a physical signal is needed that can penetrate media such as soil and cable sheaths and be effectively picked up by sensors; second, this signal must have a one-to-one correspondence with the fault point, meaning the signal source must originate from the fault point itself; and third, the signal acquisition process must be safe, reliable, and adaptable to complex field environments. The discharge acoustic method is an effective means to address these challenges.

[0031] Different types of faults (such as low-resistance grounding, high-resistance short circuits, and open circuits) have drastically different requirements for subsequent acoustic localization steps: for low-resistance faults, a small amount of discharge energy is sufficient to generate a detectable acoustic signal; while for high-resistance faults, a higher voltage amplitude is required to break down the fault point and generate a sufficiently strong discharge acoustic wave. Using uniform discharge parameters may lead to energy waste in low-resistance faults or insufficient energy to generate an effective acoustic wave in high-resistance faults, resulting in localization failure. Therefore, before executing the second discharge information transmission step, it is necessary to thoroughly analyze the extreme value information of the first pulse reflection, examine the impedance characteristics of the fault point, and then adaptively determine the subsequent discharge parameters to ensure the reliability and effectiveness of acoustic localization.

[0032] Further, before executing step S4, preferably, firstly, impedance analysis is performed on the extreme value information of the first pulse reflection to obtain the equivalent impedance characteristics. Specifically, the equivalent impedance of the fault point is deduced from the amplitude and polarity of the reflected pulse using the reflection coefficient formula in transmission line theory. Secondly, based on the equivalent impedance characteristics, the fault type of the faulty cable line is determined. Specifically, the equivalent impedance is compared with a preset impedance threshold to automatically identify the fault type. Preferably, two thresholds can be set: a low-resistance threshold and a high-resistance threshold. If the equivalent impedance is less than or equal to the low-resistance threshold, it is determined to be a low-resistance fault; if the equivalent impedance is greater than the low-resistance threshold but less than the high-resistance threshold, it is determined to be a medium-resistance fault; if the equivalent impedance is greater than or equal to the high-resistance threshold, it is determined to be a high-resistance fault. Finally, based on the fault type, the voltage amplitude of the second discharge information is determined. Specifically, if it is determined to be a low-resistance fault, since the impedance of the fault point is extremely low... Low voltage amplitude (e.g., 5kV to 10kV) can be selected to generate a large current and produce an arc discharge, even with relatively low discharge energy. This ensures effective sound wave excitation while avoiding energy waste. If the fault is determined to be medium resistance, the discharge energy needs to be increased appropriately to ensure breakdown. A medium voltage amplitude (e.g., 10kV to 20kV) can be selected. If the fault is determined to be high resistance, a higher voltage is required to break down the gap and form a discharge due to the extremely high impedance at the fault point. Therefore, a higher voltage amplitude (e.g., 20kV to 35kV, or even higher) should be selected to ensure that a sufficiently strong discharge sound wave signal is generated for the sensor to pick up.

[0033] Further, in step S4, based on the fault type analyzed from the first pulse reflection extreme value information, the optimal voltage amplitude of the second discharge information is adaptively determined. This voltage amplitude parameter is then used to control the high-voltage pulse generation circuit to charge the energy storage capacitor to a preset voltage level. When charging is complete and the external trigger signal is ready, a controllable discharge switch instantly releases the stored high-voltage energy to the faulty phase of the faulty cable line. This high-voltage pulse propagates along the cable to the fault point. Due to insulation damage or gaps at the fault point, breakdown occurs under high voltage, resulting in a violent arc discharge. When the sound waves generated by the discharge at the fault point propagate to the ground, the sensor picks up the weak vibration signal and converts it into an electrical signal. The high-speed data acquisition module continuously digitizes the sensor output signal at a high sampling rate (e.g., above 1MHz) and stores the acquired sound wave waveform data in a buffer, forming the second discharge sound wave information.

[0034] Although the original second discharge acoustic wave information completely records the acoustic signal picked up by the sensor, its form is a continuous time-domain waveform, which has the following inherent limitations: First, the features are not obvious, and the effective discharge signal is often submerged in background noise, making it difficult to identify directly; second, the parameters are not quantified, and the waveform itself cannot be directly used for mathematical calculations, requiring the extraction of quantitative indicators such as arrival time, amplitude, and dominant frequency distribution; third, there is ambiguity, as the waveforms generated by different fault point distances and different discharge energies vary, requiring the establishment of a unified feature space for comparison. Therefore, it is necessary to use signal processing methods to extract instantaneous features with clear physical meaning from the original waveform and quantify them into numerical positioning parameters.

[0035] Further, in step S5, firstly, the second discharge acoustic wave information is processed by instantaneous acoustic wave feature extraction to obtain instantaneous acoustic wave features. Preferably, the second discharge acoustic wave information is decomposed into several intrinsic mode function (IMF) components using an empirical mode decomposition algorithm. These IMF components represent oscillation modes at different time scales in the signal. As a typical impulse signal, the discharge acoustic wave is usually concentrated in the first few high-frequency IMF components. The selected effective IMF components are then subjected to Hilbert transform to obtain the instantaneous amplitude and instantaneous frequency, constituting the instantaneous acoustic wave features of the signal. Secondly, based on a convolutional neural network model, feature enhancement processing is performed on the instantaneous acoustic wave features to obtain the instantaneous acoustic wave features to be quantized. Preferably, the instantaneous acoustic wave features are input into a trained convolutional neural network model. The model extracts deep features from the input data layer by layer through forward propagation: the convolutional layer automatically detects local patterns related to the discharge acoustic wave by performing convolution operations with the input data through convolution kernels; the pooling layer downsamples the feature map output by the convolutional layer, retaining the most representative features and reducing the data dimensionality; after multiple layers of convolution and pooling, the fully connected layer combines and maps the learned high-dimensional features. Finally, the model outputs the enhanced instantaneous acoustic wave features. Finally, feature quantization analysis is performed on the instantaneous acoustic wave characteristics to be quantified to obtain acoustic positioning parameters. Preferably, the arrival time of the acoustic wave signal is accurately extracted from the instantaneous acoustic wave characteristics to be quantified. For the single-sensor moving detection mode along the cable path, the amplitude attenuation law or arrival time change trend of the acoustic wave corresponding to different measuring points is recorded, and the position point with the maximum amplitude or the minimum time delay is found by using an interpolation fitting algorithm. This point corresponds to the ground surface projection directly above the fault point. For the multi-sensor synchronous detection mode, the arrival time difference of the signals received by each sensor is calculated. Combined with the known geometric distance between the sensors and the pre-determined equivalent propagation speed of the acoustic wave in the soil-cable composite medium, the hyperbolic positioning equation is substituted into the equation for solution to obtain the accurate two-dimensional coordinates of the fault point. Finally, these solution results are integrated into structured acoustic positioning parameters.

[0036] Furthermore, in step S6, the fault point of the faulty cable line is determined based on acoustic positioning parameters. Specifically, firstly, the abstract positioning parameters are transformed into spatial coordinates with geographical attributes through spatial mapping processing. Then, the coordinates are output in a visual form through the display module of the tester: the fault point location is marked with a prominent icon on the electronic map, and dynamic navigation guidance is generated in combination with real-time positioning technology (such as differential GPS) to guide maintenance personnel to move along the cable path to the target line fault point with arrows and distance values.

[0037] Another embodiment of the present invention provides a cable fault location system; for details, please refer to [link to relevant documentation]. Figure 2 , Figure 2 The diagram shown illustrates the structure of a cable fault location system according to one embodiment of the present invention. The system includes: The first pulse signal transmitting module 11 is used to send a first pulse signal to the faulty cable line and to obtain the first pulse reflected wave information of the faulty cable line. The first pulse reflection wave information processing module 12 is used to extract voltage waveform data from the first pulse reflection wave information and perform peak detection processing on the voltage waveform data to obtain the first pulse reflection extreme value information. The faulty cable section determination module 13 is used to determine the faulty cable section of the faulty cable line based on the topology information of the faulty cable line and the first pulse reflection extreme value information. The second discharge signal transmitting module 14 is used to transmit a second discharge signal to the faulty cable line and acquire the second discharge acoustic wave information of the faulty cable section during the discharge process. The instantaneous acoustic wave feature analysis module 15 is used to extract instantaneous acoustic wave features from the second discharge acoustic wave information and perform feature quantization processing on the instantaneous acoustic wave features to obtain acoustic positioning parameters. The line fault location module 16 is used to determine the line fault point of the faulty cable line based on the acoustic location parameters.

[0038] Furthermore, in the above embodiments, the first pulse reflection wave information processing module includes: The waveform feature extraction unit is used to extract waveform features from the first pulse reflection wave information based on the Fourier transform algorithm to obtain the voltage waveform data; The data denoising unit is used to perform denoising processing on the voltage waveform data based on the bandpass filtering algorithm to obtain the voltage waveform data to be detected. A peak detection unit is used to perform peak detection processing on the voltage waveform data to be detected to obtain the first pulse reflection extreme value information.

[0039] Furthermore, in the above embodiments, the faulty cable section determination module includes: The feature quantization unit is used to extract topological features from the topological information and perform quantization analysis on the topological features to obtain a topological structure feature vector. The first fault distance analysis unit is used to perform a first fault distance analysis by combining the first pulse reflection extreme value information and the topology feature vector to obtain line fault distance information. The second fault section analysis unit is used to perform a second fault section analysis on the line fault distance information to obtain the fault cable section of the faulty cable line.

[0040] Furthermore, in the above embodiments, the instantaneous acoustic wave feature analysis module includes: The acoustic feature extraction unit is used to perform acoustic feature extraction processing on the second discharge acoustic wave information based on the empirical mode decomposition algorithm to obtain the instantaneous acoustic wave features. The feature enhancement unit is used to perform feature enhancement processing on the instantaneous acoustic wave features based on a convolutional neural network model to obtain the instantaneous acoustic wave features to be quantized. An acoustic positioning parameter determination unit is used to perform feature quantization processing on the instantaneous acoustic wave features to be quantized to obtain the acoustic positioning parameters.

[0041] Furthermore, in the above embodiments, before the second discharge signal transmitting module, the system further includes: Impedance analysis unit is used to perform impedance analysis on the extreme value information of the first pulse reflection to obtain the equivalent impedance characteristics; The fault type analysis unit is used to determine the fault type of the faulty cable line based on the equivalent impedance characteristics. A voltage amplitude determination unit is used to determine the voltage amplitude of the second discharge information based on the fault type.

[0042] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) By sending a first pulse signal and a second discharge signal to the faulty cable line and obtaining the corresponding first pulse reflection wave information and second discharge sound wave information, the present invention solves the technical problem in the prior art that the cable line nodes are scattered and it is time-consuming and labor-intensive to collect current data manually node by node. It can realize the rapid location of cable fault points. (2) By combining cable topology information and first pulse reflection extreme value information, the present invention determines the faulty cable section and limits subsequent acoustic wave acquisition to a small range of paths, thus avoiding blind large-scale search; (3) The present invention performs instantaneous acoustic feature extraction and quantitative analysis on discharge acoustic wave information, transforms subjective listening into objective acoustic parameters, reduces reliance on human experience, and improves the accuracy of fault location. (4) By integrating electrical and acoustic information and through feature quantization, this invention effectively distinguishes between real discharge signals and environmental interference, significantly reducing the probability of fault misjudgment or missed judgment.

[0043] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for fault location in a cable, characterized in that, include: Send a first pulse signal to the faulty cable line and obtain the first pulse reflected wave information of the faulty cable line; Extract the voltage waveform data from the first pulse reflection wave information, and perform peak detection processing on the voltage waveform data to obtain the first pulse reflection extreme value information; Based on the topology information of the faulty cable line and the extreme value information of the first pulse reflection, the faulty cable section of the faulty cable line is determined. Send a second discharge information to the faulty cable line and acquire the second discharge acoustic wave information of the faulty cable section during the discharge process; The instantaneous acoustic wave features are extracted from the second discharge acoustic wave information, and the instantaneous acoustic wave features are subjected to feature quantization processing to obtain acoustic positioning parameters. The fault point of the faulty cable line is determined based on the acoustic positioning parameters.

2. The cable fault location method as described in claim 1, characterized in that, The step of extracting voltage waveform data from the first pulse reflection wave information and performing peak detection processing on the voltage waveform data to obtain the first pulse reflection extreme value information includes: Based on the Fourier transform algorithm, waveform features are extracted from the first pulse reflection wave information to obtain the voltage waveform data; Based on the bandpass filtering algorithm, the voltage waveform data is denoised to obtain the voltage waveform data to be detected. The peak detection processing is performed on the voltage waveform data to be detected to obtain the first pulse reflection extreme value information.

3. The cable fault location method as described in claim 1, characterized in that, The step of determining the faulty cable section based on the topology information of the faulty cable line and the first pulse reflection extreme value information includes: Extract the topological features from the topological information and perform quantitative analysis on the topological features to obtain the topological structure feature vector; By combining the first pulse reflection extreme value information and the topology feature vector, a first fault distance analysis is performed to obtain the line fault distance information; The fault distance information of the line is analyzed in a second fault section to obtain the fault cable section of the faulty cable line.

4. The cable fault location method as described in claim 1, characterized in that, The step of extracting instantaneous acoustic features from the second discharge acoustic wave information and performing feature quantization on the instantaneous acoustic wave features to obtain acoustic positioning parameters includes: Based on the empirical mode decomposition algorithm, acoustic wave feature extraction processing is performed on the second discharge acoustic wave information to obtain the instantaneous acoustic wave features; Based on a convolutional neural network model, feature enhancement processing is performed on the instantaneous acoustic wave features to obtain the instantaneous acoustic wave features to be quantized; The instantaneous acoustic wave features to be quantized are subjected to feature quantization processing to obtain the acoustic positioning parameters.

5. The cable fault location method as described in claim 1, characterized in that, Before sending a second discharge message to the faulty cable line, the method further includes: Impedance analysis is performed on the first pulse reflection extreme value information to obtain the equivalent impedance characteristics; Based on the equivalent impedance characteristics, the fault type of the faulty cable line is determined; Based on the fault type, the voltage amplitude of the second discharge information is determined.

6. A cable fault location system, characterized in that, include: The first pulse signal transmitting module is used to send a first pulse signal to the faulty cable line and to obtain the first pulse reflected wave information of the faulty cable line. The first pulse reflection wave information processing module is used to extract voltage waveform data from the first pulse reflection wave information and perform peak detection processing on the voltage waveform data to obtain the first pulse reflection extreme value information. The faulty cable section determination module is used to determine the faulty cable section of the faulty cable line based on the topology information of the faulty cable line and the first pulse reflection extreme value information. The second discharge signal transmitting module is used to transmit a second discharge signal to the faulty cable line and acquire the second discharge acoustic wave information of the faulty cable section during the discharge process. The instantaneous acoustic wave feature analysis module is used to extract instantaneous acoustic wave features from the second discharge acoustic wave information and perform feature quantization processing on the instantaneous acoustic wave features to obtain acoustic positioning parameters. The line fault location module is used to determine the line fault point of the faulty cable line based on the acoustic location parameters.

7. A cable fault location system as described in claim 6, characterized in that, The first pulse reflection wave information processing module includes: The waveform feature extraction unit is used to extract waveform features from the first pulse reflection wave information based on the Fourier transform algorithm to obtain the voltage waveform data; The data denoising unit is used to perform denoising processing on the voltage waveform data based on the bandpass filtering algorithm to obtain the voltage waveform data to be detected. A peak detection unit is used to perform peak detection processing on the voltage waveform data to be detected to obtain the first pulse reflection extreme value information.

8. A cable fault location system as described in claim 6, characterized in that, The faulty cable section determination module includes: The feature quantization unit is used to extract topological features from the topological information and perform quantization analysis on the topological features to obtain a topological structure feature vector. The first fault distance analysis unit is used to perform a first fault distance analysis by combining the first pulse reflection extreme value information and the topology feature vector to obtain line fault distance information. The second fault section analysis unit is used to perform a second fault section analysis on the line fault distance information to obtain the fault cable section of the faulty cable line.

9. A cable fault location system as described in claim 6, characterized in that, The instantaneous acoustic wave feature analysis module includes: The acoustic feature extraction unit is used to perform acoustic feature extraction processing on the second discharge acoustic wave information based on the empirical mode decomposition algorithm to obtain the instantaneous acoustic wave features. The feature enhancement unit is used to perform feature enhancement processing on the instantaneous acoustic wave features based on a convolutional neural network model to obtain the instantaneous acoustic wave features to be quantized. An acoustic positioning parameter determination unit is used to perform feature quantization processing on the instantaneous acoustic wave features to be quantized to obtain the acoustic positioning parameters.

10. A cable fault location system as described in claim 6, characterized in that, Before the second discharge signal transmitting module, the system further includes: Impedance analysis unit is used to perform impedance analysis on the extreme value information of the first pulse reflection to obtain the equivalent impedance characteristics; The fault type analysis unit is used to determine the fault type of the faulty cable line based on the equivalent impedance characteristics. A voltage amplitude determination unit is used to determine the voltage amplitude of the second discharge information based on the fault type.

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