Digital distribution network fault traveling wave range finder and method

Abstract

The invention relates to the technical field of fault positioning, and particularly discloses a digital distribution network fault traveling wave range finder and method, and the range finder employs fault traveling wave collection devices disposed at all key nodes of a distribution network to collect fault traveling wave signals in real time, and obtains stable line mode traveling wave signals through signal filtering and line mode component extraction. Then introducing a signal processing algorithm based on deep learning to carry out time domain feature analysis on the line mode traveling wave signal of each key node so as to detect the wave head position of the signal, further determining a power distribution network section where a fault point is located by comparing the signal arrival time of each key node, and determining the fault location of the power distribution network based on a double-end traveling wave distance measurement algorithm. And calculating the distance between the fault point and the nodes at the two ends according to the signal arrival time difference of the nodes at the two ends of the section and the signal propagation speed. Thus, through deep time domain analysis of the fault traveling wave signal, the wave head position of the signal can be accurately identified, and the reliability of distribution network fault traveling wave distance measurement is improved.

Description

Technical Field

[0001] The present application relates to the technical field of fault location, and more specifically, to a digital distribution network fault traveling wave ranging instrument and method. Background Art

[0002] As a key link between the power system and the user side, the operation reliability of the distribution network directly affects the power supply quality and user satisfaction. With the continuous expansion of the power system, the distribution network has a complex topology (multiple branches, multiple nodes), strong heterogeneity of line parameters, and is often affected by factors such as distributed power supply access and nonlinear load shocks, resulting in frequent faults. Traditional fault location methods (such as impedance method, traveling wave method, etc.) are relatively mature in transmission networks, but face significant challenges when applied to distribution networks.

[0003] Specifically, the impedance method determines the fault location by measuring the impedance of the fault line. However, the distribution network has a complex structure and many branches, and the line parameters are easily affected by the operating environment, such as changes in temperature and humidity, which leads to large impedance calculation errors and makes it difficult to accurately locate the fault point. In addition, when there are multiple fault points or transition resistances in the distribution network, the measurement accuracy of the impedance method will be further reduced. The traveling wave ranging technology is based on the propagation characteristics of the traveling wave generated by the fault on the transmission line, and calculates the fault location by measuring the time difference between the traveling wave reaching different measurement points. Compared with traditional methods, the traveling wave ranging technology has higher accuracy and faster response speed, can quickly locate the fault point, shorten the power outage time, and improve the reliability of power supply.

[0004] However, due to the short lines and multiple branches of the distribution network, the fault traveling wave is easily affected by factors such as line impedance discontinuity and equipment reflection during the propagation process, resulting in rapid signal amplitude attenuation and severe waveform distortion. This makes it difficult for the traditional traveling wave detection method based on setting a threshold to accurately identify the signal wave head, which in turn causes time measurement errors and affects the accuracy of fault location.

[0005] Therefore, an optimized digital distribution network fault traveling wave ranging instrument and method are expected. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a digital distribution network fault traveling wave ranging instrument and method, which utilizes the fault traveling wave acquisition device deployed at each key node of the distribution network to collect the fault traveling wave signal in real time, and obtains the stable line mode traveling wave signal through signal filtering and line mode component extraction, and then introduces the signal processing algorithm based on deep learning to perform time domain feature analysis on the line mode traveling wave signal of each key node to detect the wave head position of the signal, and marks the time when the fault traveling wave signal arrives at the acquisition device accordingly, and then, by comparing the signal arrival time of each key node, the distribution network section where the fault point is located is determined, and based on the two-end traveling wave ranging algorithm, according to the signal arrival time difference of the nodes at both ends of the section, the distance between the fault point and the nodes at both ends is calculated in combination with the signal propagation speed. This method can accurately identify the wave head position of the signal by performing in-depth time domain analysis on the fault traveling wave signal, thereby improving the reliability of the distribution network fault traveling wave ranging.

[0007] According to one aspect of the present application, a digital distribution network fault traveling wave ranging method is provided, which includes: The fault traveling wave acquisition device deployed at each key node of the power distribution network is used to collect the fault traveling wave signal in real time, wherein the key nodes include all branch nodes and both ends of the trunk line of the power distribution network; Filtering the fault traveling wave signals of each key node and extracting the line mode components to obtain a set of line mode traveling wave signals; Performing wave head detection based on deep learning on each line mode traveling wave signal in the set of line mode traveling wave signals and determining the wave head arrival time to obtain a set of critical node fault traveling wave arrival times; Processing the set of arrival times of the key node fault traveling waves based on a section location algorithm to determine the distribution network section where the fault point is located between two adjacent key nodes; The arrival time of the fault traveling waves of the two adjacent key nodes is processed based on a double-terminal traveling wave ranging algorithm to determine the distance between the fault point and the two adjacent key nodes.

[0008] According to another aspect of the present application, a digital distribution network fault traveling wave ranging instrument is provided, which includes: A fault traveling wave signal acquisition module is used to collect fault traveling wave signals in real time using fault traveling wave acquisition devices deployed at each key node of the power distribution network, wherein the key nodes include all branch nodes and both ends of the trunk line of the power distribution network; A fault traveling wave signal preprocessing module, used for filtering the fault traveling wave signals of each key node and extracting the line mode components to obtain a set of line mode traveling wave signals; A wave head detection module, used for performing wave head detection based on deep learning on each line mode traveling wave signal in the set of line mode traveling wave signals and determining the wave head arrival time to obtain a set of critical node fault traveling wave arrival times; A distribution network section determination module, used for processing the set of arrival times of the key node fault traveling waves based on a section location algorithm to determine the distribution network section where the fault point is located between two adjacent key nodes; The double-end traveling wave ranging module is used to process the fault traveling wave arrival time of the two adjacent key nodes based on the double-end traveling wave ranging algorithm to determine the distance between the fault point and the two adjacent key nodes.

[0009] Compared with the prior art, the digital distribution network fault traveling wave ranging instrument and method provided by the present application utilizes the fault traveling wave acquisition device deployed at each key node of the distribution network to collect the fault traveling wave signal in real time, and obtains the stable line mode traveling wave signal through signal filtering and line mode component extraction, and then introduces the signal processing algorithm based on deep learning to perform time domain feature analysis on the line mode traveling wave signal of each key node to detect the wave head position of the signal, and marks the time when the fault traveling wave signal arrives at the acquisition device accordingly, and then, by comparing the signal arrival time of each key node, the distribution network section where the fault point is located is determined, and based on the double-end traveling wave ranging algorithm, according to the signal arrival time difference of the nodes at both ends of the section, the distance between the fault point and the nodes at both ends is calculated in combination with the signal propagation speed. This method can accurately identify the wave head position of the signal by performing in-depth time domain analysis on the fault traveling wave signal, and improve the reliability of the distribution network fault traveling wave ranging. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0011] Figure 1 The present invention is a flowchart of a traveling wave fault location method for a digital distribution network according to an embodiment of the present application.

[0012] Figure 2 Schematic diagram of data flow of a traveling wave fault location method for a digital distribution network according to an embodiment of the present application.

[0013] Figure 3 This is a flowchart of sub-step S3 of the digital distribution network fault traveling wave ranging method according to an embodiment of the present application.

[0014] Figure 4This is a flowchart of sub-step S32 of the digital distribution network fault traveling wave ranging method according to an embodiment of the present application.

[0015] Figure 5 This is a flowchart of sub-step S323 of the digital distribution network fault traveling wave ranging method according to an embodiment of the present application.

[0016] Figure 6 This is a flowchart of sub-step S3232 of the digital distribution network fault traveling wave ranging method according to an embodiment of the present application.

[0017] Figure 7 The block diagram of the digital distribution network fault traveling wave ranging instrument according to the embodiment of the present application. DETAILED DESCRIPTION

[0018] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0019] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0020] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0021] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0022] It is worth noting that in this application, all actions to obtain data are carried out in compliance with the relevant data protection laws and policies of the country where the data is located, and with the authorization given by the owner of the corresponding device.

[0023] In the power system, when a transmission line fails (such as a short circuit or a broken line), the fault point will produce sudden changes in voltage and current. These sudden changes will form a transient electromagnetic wave that propagates along the transmission line, the so-called fault traveling wave. The fault traveling wave propagates along the line to both ends at a speed close to the speed of light and can be captured by sensors installed at both ends of the line. The "wave head" refers to the starting part of the fault traveling wave, that is, the steep voltage or current change front generated at the moment of the fault, which marks the beginning of the fault traveling wave and is the exact time point when the fault occurs. For fault location, accurately detecting the wave head of the traveling wave can calculate the location of the fault point by comparing the time difference between the traveling wave reaching the two ends of the line.

[0024] However, due to the short lines and many branches of the distribution network, the fault traveling wave is easily affected by factors such as line impedance discontinuity and equipment reflection during the propagation process, resulting in rapid signal amplitude attenuation and severe waveform distortion, making it difficult for the traditional traveling wave detection method based on setting a threshold to accurately identify the signal wave head, thereby generating time measurement errors and affecting the accuracy of fault location. In this regard, the present application proposes a digital distribution network fault traveling wave ranging method, which uses a fault traveling wave acquisition device deployed at each key node of the distribution network to collect fault traveling wave signals in real time, and obtains stable line mode traveling wave signals through signal filtering and line mode component extraction, and then introduces a signal processing algorithm based on deep learning to perform time domain feature analysis on the line mode traveling wave signals of each key node to detect the wave head position of the signal, and accordingly marks the time when the fault traveling wave signal arrives at the acquisition device, and then, by comparing the signal arrival time of each key node, the distribution network section where the fault point is located is determined, and based on the double-end traveling wave ranging algorithm, the distance between the fault point and the two end nodes is calculated according to the signal arrival time difference of the two end nodes of the section combined with the signal propagation speed. This method can accurately identify the wave head position of the signal by performing in-depth time domain analysis on the fault traveling wave signal, thereby improving the reliability of distribution network fault traveling wave ranging.

[0025] Figure 1 The present invention is a flowchart of a traveling wave fault location method for a digital distribution network according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the traveling wave fault location method for digital distribution network according to an embodiment of the present application. Figure 1 and Figure 2As shown, the digital distribution network fault traveling wave ranging method includes the following steps: S1, using a fault traveling wave acquisition device deployed at each key node of the distribution network to collect fault traveling wave signals in real time, wherein the key nodes include all branch nodes and both ends of the trunk line of the distribution network; S2, filtering the fault traveling wave signals of each key node and extracting the line mode components to obtain a set of line mode traveling wave signals; S3, performing deep learning-based wave head detection on each line mode traveling wave signal in the set of line mode traveling wave signals and determining the wave head arrival time to obtain a set of key node fault traveling wave arrival times; S4, processing the set of key node fault traveling wave arrival times based on a section positioning algorithm to determine the distribution network section where the fault point is located between two adjacent key nodes; S5, processing the fault traveling wave arrival times of the two adjacent key nodes based on a double-end traveling wave ranging algorithm to determine the distance between the fault point and the two adjacent key nodes.

[0026] In the above-mentioned digital distribution network fault traveling wave ranging method, the step S1 uses the fault traveling wave acquisition device deployed at each key node of the distribution network to collect the fault traveling wave signal in real time, wherein the key nodes include all branch nodes and both ends of the trunk line of the distribution network. It should be understood that the distribution network has the topological characteristics of multiple branches and short lines. During the propagation process, the fault traveling wave will be reflected and transmitted due to the sudden change of line impedance (such as branch points, transformers), resulting in rapid attenuation of signal energy and complexity of the path. In order to ensure that the characteristics of the fault traveling wave on the key propagation path can be captured, the present application deploys a fault traveling wave acquisition device at all branch nodes and both ends of the trunk line of the distribution network to cover the main channels that the fault traveling wave may reach, and provide basic data support for subsequent section positioning. Specifically, the fault traveling wave acquisition device converts the electromagnetic energy carried by the traveling wave signal propagating in the line into an electrical signal for collection by means of the principle of electromagnetic induction, and discretizes the continuous analog electrical signal through analog-to-digital conversion technology, and converts it into a digital signal that is easy to store, transmit and process, so as to realize the transformation of the traveling wave signal from a physical quantity to a digital quantity.

[0027] Specifically, any fault occurring on the power transmission line will generate a specific form of traveling waves, which carry a wealth of information, including important parameters such as the location of the fault. When the traveling wave propagates along the transmission line, it will be reflected or transmitted when encountering impedance mutation points such as branch points and transformers, causing the signal energy to attenuate and the path to become more complicated. Therefore, in order to accurately capture and analyze these complex signals, it is necessary to deploy fault traveling wave acquisition devices at all branch nodes of the distribution network and at both ends of the trunk line. These locations are regarded as key nodes because they cover the main channels where the fault traveling waves may reach, providing a solid basic data support for subsequent data processing and fault location.

[0028] The core function of the fault traveling wave acquisition device is to convert the electromagnetic energy carried by the traveling wave signal propagating in the line into an electrical signal. In the specific implementation process, precisely designed sensors are required, which can efficiently detect weak traveling wave signals without affecting the normal operation of the power system. After the traveling wave signal is captured, the next step is to convert it from an analog signal to a digital signal. In this way, the originally continuously changing physical quantity can be represented by a discrete numerical value, which is convenient for storage, transmission and further calculation and processing. The technology to achieve this transformation is called analog-to-digital conversion technology. Through this technology, the original traveling wave signal can not only be accurately recorded, but also can be quickly transmitted to the background system for in-depth analysis.

[0029] Considering the complexity of the topological structure of the distribution network, especially the characteristics of multiple branches and short lines, the real-time acquisition of fault traveling wave signals is particularly important. Since the fault traveling wave may experience multiple reflections and transmissions when passing through multiple branch nodes and different types of equipment, the signal strength will be rapidly weakened, and the waveform may be severely distorted. Therefore, it is often impossible to fully understand the true situation of the fault by relying solely on monitoring at a single location. Installing fault traveling wave acquisition devices at all key nodes can effectively solve this problem. Each acquisition point can work independently and cooperate with each other to form a complete monitoring network. In this way, no matter where the fault occurs, the specific location of the fault can be determined by comparing the arrival time difference of the traveling wave signals from different directions.

[0030] In addition, considering the impact of environmental factors such as temperature and humidity on line parameters, the fault traveling wave acquisition device also needs to have a certain degree of adaptability. This means that in hardware design, high-precision and high-stability components must be used to ensure the quality of signal acquisition even in harsh environments. At the same time, software algorithms also need to be continuously optimized to more accurately identify and compensate for errors caused by external conditions. In this way, not only can the accuracy of fault location be improved, but the robustness of the entire system can also be enhanced, enabling it to operate stably under various complex conditions.

[0031] In the above-mentioned digital distribution network fault traveling wave ranging method, the step S2 filters the fault traveling wave signals of each key node and extracts the line mode components to obtain a set of line mode traveling wave signals. Specifically, due to the switching noise of power electronic equipment, arc high-frequency interference and power frequency harmonic superposition in the distribution network, the signal-to-noise ratio (SNR) of the original traveling wave signal is low, which seriously affects the accurate analysis of the fault traveling wave signal. At the same time, the fault traveling wave contains line mode components (line-to-line propagation) and ground mode components (line-to-ground propagation), among which the line mode components are less affected by factors such as line parameter changes and electromagnetic interference during propagation, and have higher stability and reliability, while the ground mode components are easily affected by ground impedance and distorted. Therefore, in order to extract signal features with higher stability, the present application adopts signal filtering and line mode component extraction technology to pre-process the fault traveling wave signals of each key node. Specifically, the wavelet threshold denoising technology is first used to eliminate the baseline noise to improve the signal-to-noise ratio of the signal. Wavelet threshold denoising is an effective signal denoising method. It uses the multi-scale analysis characteristics of wavelet transform to decompose the signal into wavelet coefficients of different frequency components, and then by setting a threshold, the wavelet coefficients below the threshold are regarded as noise and removed, and then the denoised signal is reconstructed by inverse wavelet transform, thereby retaining the main characteristics of the signal. The line mode component extraction is based on the multi-phase transmission line theory. The line mode component is separated from other components by performing a specific matrix transformation on the collected three-phase traveling wave signal. In the embodiment of the present application, the Db4 wavelet basis is selected to perform a 6-layer decomposition of the signal, and the high-frequency coefficients are adaptively shrunk by improving the threshold function (such as SUREShrink), retaining the steep wavefront characteristics of the traveling wave while filtering out the baseline noise. Then, based on the KLM (Karenbauer) transformation, the filtered three-phase voltage / current signal is converted into a line mode component (α mode) and a ground mode component (β mode, γ mode), and the α mode signal is extracted by matrix operation. The propagation speed of the line mode traveling wave signal is stable and is less affected by line parameters, and is suitable as a ranging reference.

[0032] In the specific implementation process, it is a very effective means to use wavelet threshold denoising technology to process the baseline noise in the original traveling wave signal. The multi-scale analysis characteristics of wavelet transform enable the signal to be decomposed into wavelet coefficients of different frequency components. By setting a reasonable threshold, the wavelet coefficients below the threshold are regarded as noise and removed, and then the denoised signal is reconstructed by inverse wavelet transform. This method can not only effectively eliminate noise, but also retain the main features of the signal to the maximum extent. Taking the Db4 wavelet basis as an example, it is selected to decompose the signal into 6 layers, and the high-frequency coefficients are adaptively shrunk in combination with the SUREShrink improved threshold function, which can filter out the baseline noise while retaining the steep wavefront characteristics of the traveling wave. In this way, the quality of the processed signal is significantly improved, providing clear data support for further analysis.

[0033] At the same time, considering that the fault traveling wave contains both line mode components and ground mode components, and the ground mode components are easily distorted due to the influence of ground impedance, it is more inclined to use the line mode components with higher stability as the main analysis object. Based on the multiphase transmission line theory, the line mode components can be effectively separated from other components by performing specific matrix transformation on the collected three-phase traveling wave signals. Specifically, KLM (Karenbauer) transformation, as a commonly used transformation method, can convert the filtered three-phase voltage or current signal into line mode components (α mode) and ground mode components (β mode, γ mode). Through a series of precise matrix operations, the α mode signal, that is, the line mode component, is extracted. This line mode component is less affected by line parameter changes and electromagnetic interference during propagation, has high stability and reliability, and is very suitable for use as a ranging reference.

[0034] At the same time, it is also crucial to ensure that the fault traveling wave acquisition devices at all key nodes can accurately capture the original signal. These devices must not only have high sensitivity, but also have good anti-interference capabilities so that high-quality data can be obtained even in complex environments. Next, the signals obtained at each acquisition point must go through the above-mentioned wavelet threshold denoising technology and line mode component extraction steps. In this process, the selection of appropriate parameters such as wavelet basis type, number of decomposition layers, and threshold function plays a decisive role in ensuring the processing effect. For example, using the Db4 wavelet basis for 6-layer decomposition and applying SUREShrink to improve the threshold function can achieve a better denoising effect while avoiding excessive smoothing that causes loss of signal features.

[0035] In the above-mentioned digital distribution network fault traveling wave ranging method, the step S3 performs deep learning-based wave head detection on each line mode traveling wave signal in the set of line mode traveling wave signals and determines the wave head arrival time to obtain a set of key node fault traveling wave arrival times. It should be understood that the complex operating environment of the distribution network, such as the discontinuity of line impedance, reflection and refraction of electrical equipment, etc., will cause the fault traveling wave signal to be distorted. Traditional wave head detection methods (such as wavelet transform modulus maximum method) rely on manually set thresholds, which are prone to misjudgment in low signal-to-noise ratio or complex waveform (such as multiple reflection superposition) scenarios. Therefore, in order to improve the accuracy and robustness of wave head detection, the present application introduces a signal processing algorithm based on deep learning, and with the help of the powerful feature extraction and pattern recognition capabilities of the deep learning model, the time domain feature analysis of the line mode traveling wave signals of each key node is performed to adaptively identify the wave head position under different noise levels and distortion forms, thereby improving the time marking accuracy. Among them, Figure 3 FIG. 4 is a flowchart of sub-step S3 of the traveling wave fault distance measurement method for a digital distribution network according to an embodiment of the present application. Figure 3As shown, the step S3 includes the steps of: S31, performing local time domain coding on the line mode traveling wave signal to obtain a set of local time domain feature coding vectors of the line mode traveling wave signal; S32, performing time domain-semantic dual constraint optimization on the set of local time domain feature coding vectors of the line mode traveling wave signal to obtain a set of local time domain feature coding vectors of enhanced line mode traveling wave signals; S33, performing wave head detection on the set of local time domain feature coding vectors of the enhanced line mode traveling wave signal to determine the wave head position, and marking the wave head arrival time.

[0036] Specifically, in a specific example of the present application, the step S31 includes: extracting local waveform features of the line mode traveling wave signal based on one-dimensional convolution coding to obtain a set of local time domain feature coding vectors of the line mode traveling wave signal. It should be understood that the wave head of the distribution network fault traveling wave is manifested as a steep mutation point in the time domain signal (such as a microsecond rising edge), but its morphology is easily affected by line parameters, noise interference and multiple reflection superposition, showing local nonlinear characteristics. Traditional manual features (such as gradients and curvatures) are difficult to fully characterize the difference between the wave head and the background noise, while the one-dimensional convolutional neural network (1D-CNN) can use a one-dimensional convolution kernel to automatically learn the abstract representation of the local time domain pattern of the signal, extract more discriminative feature coding, and capture the microscopic fluctuation characteristics near the wave head, thereby laying the foundation for subsequent refined wave head detection. In an embodiment of the present application, a one-dimensional convolution layer with a sliding step size of 1 is designed to perform a convolution operation on the line mode traveling wave signal, and the one-dimensional convolution layer has a plurality of convolution kernels of different sizes, which are used to capture local features of different scales, such as high-frequency mutation details, low-frequency trend components, etc., so as to enhance the sensitivity and generalization ability of the model to the wave head features. In this way, at each time step of the line mode traveling wave signal, a convolution operation is performed on the local neighborhood of the time step by stacking a plurality of one-dimensional convolution kernels of different scales to extract local waveform features of different dimensions, and the outputs of each convolution kernel are spliced ​​to obtain the local time domain feature coding vector at the time step. In addition, since the sliding step size of each convolution kernel is 1, it is ensured that the extracted local time domain feature coding vectors of each line mode traveling wave signal are strictly aligned with the time resolution of the original signal (that is, each time step corresponds to a local time domain feature coding vector of a line mode traveling wave signal), thereby avoiding the loss of feature information.

[0037] Specifically, the step S32 performs a time domain-semantic dual constraint optimization on the set of local time domain feature coding vectors of the line mode traveling wave signal to obtain a set of enhanced local time domain feature coding vectors of the line mode traveling wave signal. It should be understood that in the distribution network fault traveling wave ranging scenario, due to the short line, many branches, and frequent impedance mutations, the fault traveling wave signal is susceptible to reflection superposition and noise interference during the propagation process, resulting in distortion or blurring of local time domain features (such as wave head steepness, phase jump). Therefore, in order to distinguish between real wave head features and noise, the present application further performs a time domain-semantic dual constraint optimization on each local time domain feature representation of the line mode traveling wave signal, so as to improve the feature representation discrimination of each local time domain feature coding vector of the line mode traveling wave signal by jointly modeling the physical time domain laws of signal propagation (such as wave speed consistency, path topology dependence) and high-order semantic discriminability (such as wave head mutation mode, fault type association), thereby laying the foundation for subsequent wave head detection. Among them, Figure 4 FIG. 4 is a flowchart of sub-step S32 of the traveling wave fault distance measurement method for a digital distribution network according to an embodiment of the present application. Figure 4 As shown, the step S32 includes the steps of: S321, extracting the local time domain feature coding vector of the line mode traveling wave signal to be enhanced from the set of local time domain feature coding vectors of the line mode traveling wave signal; S322, performing n random sampling on the set of local time domain feature coding vectors of the line mode traveling wave signal to obtain n local time domain feature coding vectors of the line mode traveling wave signal as a sparse set of local time domain reference feature coding vectors of the line mode traveling wave signal; S323, based on the sparse set of local time domain reference feature coding vectors of the line mode traveling wave signal, performing time domain-semantic dual-domain information compensation optimization on the local time domain feature coding vector of the line mode traveling wave signal to be enhanced to obtain the enhanced local time domain feature coding vector of the line mode traveling wave signal.

[0038] More specifically, the step S321 is expressed by the formula: in, represents the set of local time domain feature coding vectors of line mode traveling wave signals, The first one in the set of local time domain feature coding vectors of line mode traveling wave signal The local time domain feature encoding vector of the line mode traveling wave signal at the position, Represents the local time domain feature coding vector of the line mode traveling wave signal to be enhanced.

[0039] Here, the local time domain feature coding vector of the line mode traveling wave signal to be enhanced can be any line mode traveling wave signal local time domain feature coding vector in the set of line mode traveling wave signal local time domain feature coding vectors. By traversing the entire set, each line mode traveling wave signal local time domain feature coding vector is enhanced one by one, so that it can more accurately characterize the real fluctuation characteristics of the distribution network fault traveling wave signal at each time step.

[0040] More specifically, the step S322 is expressed by the formula: in, , , and They represent the first, second, and third sparse sets of local time domain reference feature coding vectors of line mode traveling wave signals. and The local time domain reference feature coding vector of the line mode traveling wave signal, Represents a sparse set of local time-domain reference feature encoding vectors of line-mode traveling wave signals.

[0041] That is, by introducing a random scanning strategy, the selective attention mechanism of biological vision can be simulated, and the local signal fluctuation pattern can be dynamically selected from the set of local time domain feature coding vectors of line mode traveling wave signals to form a sparse set of local time domain reference feature coding vectors of line mode traveling wave signals covering different time domain features. This not only retains the key signal fluctuation characteristics, but also suppresses redundant noise interference through sparse sampling. At the same time, through the complementarity of contextual information of multiple sampling points in the sparse set, the time domain feature distribution of the fault traveling wave can be more comprehensively characterized, and the computational efficiency advantage of sparse sampling can be used to balance feature diversity and real-time requirements.

[0042] Figure 5 FIG. 4 is a flowchart of sub-step S323 of the traveling wave fault distance measurement method for a digital distribution network according to an embodiment of the present application. Figure 5As shown, the step S323 includes the steps of: S3231, calculating the information compensation coding vector between each line mode traveling wave signal local time domain reference feature coding vector in the sparse set of the line mode traveling wave signal local time domain reference feature coding vector and the line mode traveling wave signal local time domain feature coding vector to be enhanced to obtain a set of line mode traveling wave signal local time domain feature information compensation coding vectors to be enhanced; S3232, based on each line mode traveling wave signal local time domain reference feature coding vector in the sparse set of the line mode traveling wave signal local time domain reference feature coding vector The time domain-semantic dual-domain association between the coding vector and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced is performed, and the set of local time domain feature information compensation coding vectors of the line mode traveling wave signal to be enhanced is explicitly modulated and aggregated to obtain the implicit coding vector of the local time domain feature enhancement component of the line mode traveling wave signal to be enhanced; S3233, the implicit coding vector of the local time domain feature enhancement component of the line mode traveling wave signal to be enhanced and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced are merged to obtain the local time domain feature coding vector of the enhanced line mode traveling wave signal.

[0043] In a specific example of the present application, the step S3231 is expressed by the formula: in, express Relative to The local time domain characteristic information compensation coding vector of the line mode traveling wave signal to be enhanced.

[0044] That is, by introducing the information interaction mechanism between multiple local time domain reference feature coding vectors of line mode traveling wave signals and the local time domain feature coding vectors of the line mode traveling wave signals to be enhanced, the complementary information between different local features can be dynamically captured, the feature loss of a single perspective can be compensated, and the one-sidedness of local features can be solved.

[0045] Figure 6 FIG. 4 is a flowchart of sub-step S3232 of the traveling wave fault distance measurement method for a digital distribution network according to an embodiment of the present application. Figure 6As shown, the step S3232 includes the steps of: S32321, calculating the Poincare distance between the local time domain feature coding vector of the line mode traveling wave signal to be enhanced and each local time domain reference feature coding vector of the line mode traveling wave signal in the sparse set of the local time domain reference feature coding vector of the line mode traveling wave signal to obtain a local time domain feature time domain modulation matrix of the line mode traveling wave signal to be enhanced composed of multiple Poincare distances; S32322, calculating the sparse local time domain feature coding vector of the line mode traveling wave signal to be enhanced and the local time domain reference feature coding vector of the line mode traveling wave signal The implicit semantic association between the local time domain reference feature coding vectors of each line mode traveling wave signal in the set is used to obtain a set of semantic association coding matrices of local time domain features of the line mode traveling wave signal to be enhanced; S32323, based on the set of time domain modulation matrix of the local time domain features of the line mode traveling wave signal to be enhanced and the semantic association coding matrix of the local time domain features of the line mode traveling wave signal to be enhanced, the set of local time domain feature information compensation coding vectors of the line mode traveling wave signal to be enhanced is explicitly modulated and aggregated encoded to obtain an implicit coding vector of the local time domain feature enhancement component of the line mode traveling wave signal to be enhanced.

[0046] In a specific example of the present application, the step S32321 is expressed by the formula: in, represents the square of the norm of a vector, represents the inverse hyperbolic cosine function, express and The Poincare distance between express and The Poincare distance between Represents the time domain modulation matrix of the local time domain characteristics of the line mode traveling wave signal to be enhanced.

[0047] It should be understood that through the above compensation information extraction, the complementary information of each reference feature relative to the local time domain feature coding vector of the line mode traveling wave signal to be enhanced is captured, but not all of this complementary information is beneficial to the enhancement of the local time domain feature coding vector of the line mode traveling wave signal to be enhanced. Therefore, in order to more accurately guide each local time domain feature information compensation coding vector of the line mode traveling wave signal to be enhanced to participate in the final feature enhancement process, the present application further uses the time domain correlation and semantic correlation between each reference feature and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced to screen and optimize the set of local time domain feature information compensation coding vectors of the line mode traveling wave signal to be enhanced.

[0048] Here, firstly, by introducing the Poincare distance metric, an attention mechanism based on hyperbolic geometry is constructed. By quantifying the relative position relationship between the local time domain feature coding vector of the line mode traveling wave signal to be enhanced and the local time domain reference feature coding vector of the line mode traveling wave signal to be enhanced in the hyperbolic space, the key reference pattern with time domain correlation with the local time domain feature coding vector of the line mode traveling wave signal to be enhanced is dynamically identified. In this way, through the nonlinear distribution modeling capability of the signal characteristics of the Poincare distance, the local time domain feature time domain modulation matrix of the line mode traveling wave signal to be enhanced is generated, which can enhance the adaptability of the model to time-varying traveling wave signals under complex distribution network topologies.

[0049] In a specific example of the present application, the step S32322 is expressed by the formula: in, represents the weight matrix, represents the transpose of a vector, represents vector multiplication, represents the normalized exponential function, express and The semantic association coding matrix of the local time domain features of the line mode traveling wave signal to be enhanced.

[0050] Here, by introducing an implicit semantic association mechanism, we can explore the deep semantic connection between the local time domain feature coding vector of the line mode traveling wave signal to be enhanced and the local time domain reference feature coding vector of each line mode traveling wave signal, and construct a feature enhancement framework based on the semantic attention graph. In this way, by quantifying the similarity or complementarity between the local time domain feature coding vector of the line mode traveling wave signal to be enhanced and the local time domain reference feature coding vector of each line mode traveling wave signal in the semantic space, dynamically screening reference patterns with strong semantic associations, and suppressing the interference of irrelevant noise or conflicting features, we can decouple the deep semantics of complex fault modes of the distribution network through implicit semantic associations, and enhance the model's ability to understand the semantics of traveling wave signals in multi-branch, high-impedance heterogeneous scenarios.

[0051] In particular, in a preferred example of the present application, the step S32323 includes: first, based on the set of the time domain modulation matrix of the local time domain feature of the line mode traveling wave signal to be enhanced and the semantic association coding matrix of the local time domain feature of the line mode traveling wave signal to be enhanced, each local time domain feature information compensation coding vector of the line mode traveling wave signal to be enhanced in the set of the local time domain feature information compensation coding vector of the line mode traveling wave signal to be enhanced is subjected to dual-domain nested constraint optimization to obtain a set of optimized local time domain feature information compensation coding vectors of the line mode traveling wave signal to be enhanced, which is expressed by the formula: in, express The corresponding semantic nested optimization is used to compensate the coding vector of the local time domain characteristic information of the line mode traveling wave signal to be enhanced. express The corresponding optimized local time domain characteristic information compensation coding vector of the line mode traveling wave signal to be enhanced.

[0052] That is, the structural stability of semantic association is maintained by canonical transformation invariance constraints, while the dynamic geometric changes of signals in curved space are adapted by covariant transformation invariance, and the interaction strength of double-layer mask and information compensation is enhanced by means of the coupling mechanism of partial derivative form in field theory, thereby improving the robustness and representation accuracy of the local time domain feature information compensation coding vector of the line mode traveling wave signal to be enhanced in a complex distribution network environment. In this way, the multi-scale features of the fault traveling wave signal are collaboratively enhanced through dual-domain nested constraints, and the local time domain feature information compensation coding vector of the line mode traveling wave signal to be enhanced is generated to optimize the feature distortion caused by line impedance mutation and equipment reflection, and improve the positioning reliability of the dual-end traveling wave ranging algorithm in multi-branch and high-noise scenarios.

[0053] Then, each of the local time domain feature semantic association coding matrices of the line mode traveling wave signal to be enhanced in the set of the local time domain feature semantic association coding matrices of the line mode traveling wave signal to be enhanced is used as a primary mask modulation unit, and the local time domain feature time domain modulation degree matrix of the line mode traveling wave signal to be enhanced is used as a secondary mask modulation unit, and the set of the optimized local time domain feature information compensation coding vectors of the line mode traveling wave signal to be enhanced is explicitly modulated and aggregated to obtain the implicit coding vector of the local time domain feature enhancement component of the line mode traveling wave signal to be enhanced, which is expressed by the formula: in, Represents the implicit coding vector of the enhanced component of the local time domain characteristics of the line mode traveling wave signal to be enhanced.

[0054] Here, an explicit modulation mechanism is further introduced. Based on the control weights of the semantic association coding matrix of the local time domain features of the line mode traveling wave signal to be enhanced and the time domain modulation matrix of the local time domain features of the line mode traveling wave signal to be enhanced on the complementary information of the reference features, the semantic mask is first used to screen the complementary information that is semantically consistent with the local time domain features of the line mode traveling wave signal to be enhanced, and then the time domain mask is used to further screen the complementary information that is temporally correlated with the local time domain features of the line mode traveling wave signal to be enhanced. Through a hierarchical feature enhancement framework, the implicit coding vector of the enhanced component of the local time domain features of the line mode traveling wave signal to be enhanced is generated, so that the information compensation process focuses on the key difference components that are semantically consistent and time-domain adaptive.

[0055] In a specific example of the present application, the step S3233 includes: calculating the position-weighted sum between the implicit coding vector of the local time domain feature enhancement component of the line mode traveling wave signal to be enhanced and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced to obtain the enhanced line mode traveling wave signal local time domain feature coding vector, which is expressed by the formula: in, and Represents different fusion weight parameters, Represents the local time domain feature coding vector of the enhanced line mode traveling wave signal.

[0056] That is, by introducing a position-based weighting mechanism, the fusion ratio of the implicit coding vector of the enhanced component of the local time domain feature of the line mode traveling wave signal to be enhanced and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced is dynamically adjusted to solve the problem of local key information being submerged by global noise due to the static feature fusion weight. In this way, the expressive ability of the fault traveling wave signal characteristics is significantly improved through dynamic weighted fusion, and the enhanced line mode traveling wave signal local time domain feature coding vector generated in this way can more accurately characterize the real fluctuation characteristics of the distribution network fault traveling wave signal at each time step, thereby improving the positioning accuracy and anti-interference ability of the dual-end traveling wave ranging algorithm in complex topology and high impedance heterogeneous scenarios.

[0057] Specifically, the step S33 performs wave head detection on the set of local time domain feature coding vectors of the enhanced line mode traveling wave signal to determine the wave head position and mark the wave head arrival time. In a specific example of the present application, the step S33 includes: inputting the set of local time domain feature coding vectors of the enhanced line mode traveling wave signal into a wave head detection module based on a bidirectional LSTM model to determine the wave head position. It should be understood that when performing wave head detection, it is necessary to comprehensively consider the before and after timing correlation of the signal: for example, the signal before the wave head is usually relatively stable, while a significant mutation will occur when the wave head arrives, and the reflected wave is manifested as multiple small fluctuations after the main wave head. As a special recurrent neural network (RNN), the bidirectional LSTM (Long Short-Term Memory) model can simultaneously capture the forward and backward timing dependencies of the signal, and perform bidirectional modeling on the set of local time domain feature coding vectors of the enhanced line mode traveling wave signal to avoid misjudgment caused by the unidirectional LSTM due to "invisible future information" (such as mistaking the reflected secondary peak for the main wave head). Specifically, the bidirectional LSTM model receives the set of local time domain feature encoding vectors of the enhanced line mode traveling wave signal as input, and uses the forward LSTM layer and the backward LSTM layer to traverse the entire signal sequence from front to back and from back to front respectively, so as to capture the dependency between the local features of the signal at each time step and the features of the previous and next time steps, and integrates the context information of the previous and next time steps into the current time step to obtain the hidden state of each time step, and maps the hidden state of each time step to 2 dimensions through full connection encoding at the output layer, and calculates the probability distribution of binary classification (wave head and non-wave head) through the Softmax function to obtain the prediction result of whether it is a wave head at each time step. Then, by extracting the time step with the largest wave head probability in the prediction result as the wave head position, and marking the corresponding wave head arrival time, the accurate detection of the arrival time of the fault traveling wave head is achieved.

[0058] In the above-mentioned digital distribution network fault traveling wave ranging method, the step S4 processes the set of key node fault traveling wave arrival times based on the section positioning algorithm to determine the distribution network section where the fault point is located between two adjacent key nodes. It should be understood that the time when the distribution network fault traveling wave arrives at different nodes is directly related to its propagation path. The section positioning algorithm is based on the propagation characteristics of the fault traveling wave in the distribution network, that is, the fault traveling wave will arrive at different key nodes in a certain order. By comparing the order of arrival time of the fault traveling wave of each key node, and combining the topological structure and connection relationship of the distribution network, a corresponding logical judgment model is established, so that the section where the fault point is located can be determined. In this way, the scope of fault location can be quickly narrowed, and the originally complex full-network search problem can be converted into a positioning problem within a specific section, which greatly improves the efficiency of fault location and saves a lot of time for the subsequent accurate calculation of the fault point location.

[0059] In the above-mentioned digital distribution network fault traveling wave ranging method, the step S5 processes the arrival time of the fault traveling waves of the two adjacent key nodes based on the double-end traveling wave ranging algorithm to determine the distance between the fault point and the two adjacent key nodes. It should be understood that after determining the section where the fault point is located, in order to accurately locate the fault point, it is necessary to accurately calculate the distance between the fault point and the key nodes at both ends of the section. Specifically, the double-end traveling wave ranging algorithm is based on the physical principle that traveling waves propagate at a constant speed on a uniform transmission line, and based on the fact that the traveling wave propagation speed is , and the time difference between the fault wave reaching the key nodes at both ends of the section , through the formula ,in, is the distance from the fault point to a certain end node, The total length of the distribution network section between two adjacent key nodes can be used to calculate the relative distance between the fault point and the key nodes at both ends of the section, and then determine the precise location of the fault point, providing an accurate basis for power maintenance personnel to quickly find the fault point and carry out fault repair work.

[0060] In summary, a digital distribution network fault traveling wave ranging method based on the embodiment of the present application is explained, which utilizes a fault traveling wave acquisition device deployed at each key node of the distribution network to collect fault traveling wave signals in real time, and obtains a stable line mode traveling wave signal through signal filtering and line mode component extraction, and then introduces a signal processing algorithm based on deep learning to perform time domain feature analysis on the line mode traveling wave signal of each key node to detect the wave head position of the signal, and marks the time when the fault traveling wave signal arrives at the acquisition device accordingly, and then, by comparing the signal arrival time of each key node, the distribution network section where the fault point is located is determined, and based on the two-end traveling wave ranging algorithm, the distance between the fault point and the two end nodes is calculated according to the signal arrival time difference of the nodes at both ends of the section, combined with the signal propagation speed. This method can accurately identify the wave head position of the signal by performing in-depth time domain analysis on the fault traveling wave signal, thereby improving the reliability of distribution network fault traveling wave ranging.

[0061] Furthermore, a digital distribution network fault traveling wave distance meter is also provided.

[0062] Figure 7 FIG. 1 is a block diagram of a digital distribution network fault traveling wave ranging instrument according to an embodiment of the present application. Figure 7As shown, according to the embodiment of the present application, the digital distribution network fault traveling wave range finder 100 includes: a fault traveling wave signal acquisition module 110, which is used to collect fault traveling wave signals in real time using fault traveling wave acquisition devices deployed at each key node of the distribution network, wherein the key nodes include all branch nodes and both ends of the trunk line of the distribution network; a fault traveling wave signal preprocessing module 120, which is used to filter the fault traveling wave signals of each key node and extract the line mode components to obtain a collection of line mode traveling wave signals; a wave head detection module 130, which is used to filter the collection of line mode traveling wave signals The wave head detection based on deep learning is performed on each line mode traveling wave signal and the wave head arrival time is determined to obtain a set of key node fault traveling wave arrival times; a distribution network section determination module 140 is used to process the set of key node fault traveling wave arrival times based on a section positioning algorithm to determine the distribution network section where the fault point is located between two adjacent key nodes; a double-end traveling wave ranging module 150 is used to process the fault traveling wave arrival times of the two adjacent key nodes based on a double-end traveling wave ranging algorithm to determine the distance between the fault point and the two adjacent key nodes.

[0063] Here, those skilled in the art can understand that the specific operations of each module in the above-mentioned digital distribution network fault traveling wave range finder have been referred to above. Figures 1 to 6 The method for measuring faults in a digital distribution network has been described in detail in the description of the traveling wave fault location method of the present invention, and therefore, its repeated description will be omitted.

[0064] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0065] In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0066] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0067] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units stated in the system claims can also be implemented by one unit through software or hardware.

[0068] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A digital distribution network fault traveling wave ranging method, characterized in that: include: The fault traveling wave acquisition device deployed at each key node of the power distribution network is used to collect the fault traveling wave signal in real time, wherein the key nodes include all branch nodes and both ends of the trunk line of the power distribution network; Filtering the fault traveling wave signals of each key node and extracting the line mode components to obtain a set of line mode traveling wave signals; Performing wave head detection based on deep learning on each line mode traveling wave signal in the set of line mode traveling wave signals and determining the wave head arrival time to obtain a set of critical node fault traveling wave arrival times; Processing the set of arrival times of the key node fault traveling waves based on a section location algorithm to determine the distribution network section where the fault point is located between two adjacent key nodes; The arrival time of the fault traveling waves of the two adjacent key nodes is processed based on a double-terminal traveling wave ranging algorithm to determine the distance between the fault point and the two adjacent key nodes.

2. The digital distribution network fault traveling wave ranging method according to claim 1 is characterized in that: Performing deep learning-based wave head detection on each line mode traveling wave signal in the set of line mode traveling wave signals and determining the wave head arrival time to obtain a set of critical node fault traveling wave arrival times, including: Performing local time-domain coding on the line-mode traveling wave signal to obtain a set of local time-domain feature coding vectors of the line-mode traveling wave signal; Performing time domain-semantic dual constraint optimization on the set of local time domain feature coding vectors of the line mode traveling wave signal to obtain a set of enhanced local time domain feature coding vectors of the line mode traveling wave signal; The wave head detection is performed on the set of local time domain feature coding vectors of the enhanced line mode traveling wave signal to determine the wave head position, and the wave head arrival time is marked.

3. The digital distribution network fault traveling wave ranging method according to claim 2 is characterized in that: Performing local time domain coding on the line mode traveling wave signal to obtain a set of local time domain feature coding vectors of the line mode traveling wave signal includes: A local waveform feature extraction based on one-dimensional convolution coding is performed on the line mode traveling wave signal to obtain a set of local time domain feature coding vectors of the line mode traveling wave signal.

4. The digital distribution network fault traveling wave distance measurement method according to claim 3 is characterized in that: The set of local time domain feature coding vectors of the line mode traveling wave signal is subjected to time domain-semantic dual constraint optimization to obtain a set of local time domain feature coding vectors of the enhanced line mode traveling wave signal, including: Extracting the local time domain feature coding vector of the line mode traveling wave signal to be enhanced from the set of local time domain feature coding vectors of the line mode traveling wave signal; Performing n random sampling on the set of local time domain feature coding vectors of the line mode traveling wave signal to obtain n local time domain feature coding vectors of the line mode traveling wave signal as a sparse set of local time domain reference feature coding vectors of the line mode traveling wave signal; Based on the sparse set of local time domain reference feature coding vectors of the line mode traveling wave signal, the local time domain feature coding vectors of the line mode traveling wave signal to be enhanced are optimized by time domain-semantic dual domain information compensation to obtain the enhanced local time domain feature coding vectors of the line mode traveling wave signal.

5. The digital distribution network fault traveling wave distance measurement method according to claim 4, characterized in that: Based on the sparse set of local time domain reference feature coding vectors of the line mode traveling wave signal, the local time domain feature coding vectors of the line mode traveling wave signal to be enhanced are subjected to time domain-semantic dual-domain information compensation optimization to obtain the local time domain feature coding vectors of the enhanced line mode traveling wave signal, including: Calculating the information compensation coding vector between each local time domain reference feature coding vector of the line mode traveling wave signal in the sparse set of the local time domain reference feature coding vector of the line mode traveling wave signal and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced to obtain a set of local time domain feature information compensation coding vectors of the line mode traveling wave signal to be enhanced; Based on the time domain-semantic dual-domain association between each local time domain reference feature coding vector of the line mode traveling wave signal in the sparse set of local time domain reference feature coding vectors of the line mode traveling wave signal and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced, the set of local time domain feature information compensation coding vectors of the line mode traveling wave signal to be enhanced is explicitly modulated and aggregated to obtain an implicit coding vector of the local time domain feature enhancement component of the line mode traveling wave signal to be enhanced; The implicit coding vector of the enhanced component of the local time domain feature of the line mode traveling wave signal to be enhanced and the coding vector of the local time domain feature of the line mode traveling wave signal to be enhanced are fused to obtain the enhanced local time domain feature coding vector of the line mode traveling wave signal.

6. The digital distribution network fault traveling wave distance measurement method according to claim 5, characterized in that: Based on the time domain-semantic dual-domain association between each local time domain reference feature coding vector of the line mode traveling wave signal in the sparse set of the local time domain reference feature coding vector of the line mode traveling wave signal and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced, the set of the local time domain feature information compensation coding vector of the line mode traveling wave signal to be enhanced is explicitly modulated and aggregated to obtain an implicit coding vector of the local time domain feature enhancement component of the line mode traveling wave signal to be enhanced, including: Calculating the Poincare distance between the local time domain feature coding vector of the line mode traveling wave signal to be enhanced and each local time domain reference feature coding vector of the line mode traveling wave signal in the sparse set of the local time domain reference feature coding vector of the line mode traveling wave signal to obtain a local time domain feature time domain modulation matrix of the line mode traveling wave signal to be enhanced composed of multiple Poincare distances; Calculating the implicit semantic association between the local time domain feature coding vector of the line mode traveling wave signal to be enhanced and each line mode traveling wave signal local time domain reference feature coding vector in the sparse set of the line mode traveling wave signal local time domain reference feature coding vector to obtain a set of semantic association coding matrices of the local time domain features of the line mode traveling wave signal to be enhanced; Based on the set of the time domain modulation matrix of the local time domain characteristics of the line mode traveling wave signal to be enhanced and the set of the semantic association coding matrix of the local time domain characteristics of the line mode traveling wave signal to be enhanced, the set of local time domain characteristic information compensation coding vectors of the line mode traveling wave signal to be enhanced is explicitly modulated and aggregated coded to obtain the implicit coding vector of the local time domain characteristic enhancement component of the line mode traveling wave signal to be enhanced.

7. The digital distribution network fault traveling wave distance measurement method according to claim 6, characterized in that: Based on the set of the time domain modulation matrix of the local time domain feature of the line mode traveling wave signal to be enhanced and the set of the semantic association coding matrix of the local time domain feature of the line mode traveling wave signal to be enhanced, the set of the local time domain feature information compensation coding vector of the line mode traveling wave signal to be enhanced is subjected to explicit modulation aggregation coding to obtain the implicit coding vector of the local time domain feature enhancement component of the line mode traveling wave signal to be enhanced, including: Based on the set of the time domain modulation matrix of the local time domain characteristics of the line mode traveling wave signal to be enhanced and the set of the semantic association coding matrix of the local time domain characteristics of the line mode traveling wave signal to be enhanced, performing double-domain nested constraint optimization on each local time domain characteristic information compensation coding vector of the line mode traveling wave signal to be enhanced in the set of the local time domain characteristic information compensation coding vector of the line mode traveling wave signal to be enhanced to obtain a set of optimized local time domain characteristic information compensation coding vectors of the line mode traveling wave signal to be enhanced; Using each of the semantic association coding matrices of the local time domain characteristics of the line mode traveling wave signal to be enhanced in the set of the semantic association coding matrices of the local time domain characteristics of the line mode traveling wave signal to be enhanced as the first-level mask modulation unit, and using the time domain modulation degree matrix of the local time domain characteristics of the line mode traveling wave signal to be enhanced as the second-level mask modulation unit, the set of the optimized local time domain characteristic information compensation coding vectors of the line mode traveling wave signal to be enhanced is explicitly modulated and aggregated encoded to obtain the implicit coding vector of the local time domain characteristic enhancement component of the line mode traveling wave signal to be enhanced.

8. The digital distribution network fault traveling wave distance measurement method according to claim 7, characterized in that: The method of fusing the implicit coding vector of the enhanced component of the local time domain feature of the line mode traveling wave signal to be enhanced and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced to obtain the enhanced local time domain feature coding vector of the line mode traveling wave signal comprises: The position-weighted sum between the implicit coding vector of the local time domain feature enhancement component of the line mode traveling wave signal to be enhanced and the local time domain feature coding vector of the line mode traveling wave signal to be enhanced is calculated to obtain the enhanced local time domain feature coding vector of the line mode traveling wave signal.

9. The digital distribution network fault traveling wave distance measurement method according to claim 8, characterized in that: Performing wave head detection on the set of local time domain feature coding vectors of the enhanced line mode traveling wave signal to determine the wave head position and marking the wave head arrival time, including: The set of local time domain feature coding vectors of the enhanced line mode traveling wave signal is input into a wave head detection module based on a bidirectional LSTM model to determine the wave head position.

10. A digital distribution network fault traveling wave ranging instrument, characterized in that: include: A fault traveling wave signal acquisition module is used to collect fault traveling wave signals in real time using fault traveling wave acquisition devices deployed at each key node of the power distribution network, wherein the key nodes include all branch nodes and both ends of the trunk line of the power distribution network; A fault traveling wave signal preprocessing module, used for filtering the fault traveling wave signals of each key node and extracting the line mode components to obtain a set of line mode traveling wave signals; A wave head detection module, used for performing wave head detection based on deep learning on each line mode traveling wave signal in the set of line mode traveling wave signals and determining the wave head arrival time to obtain a set of critical node fault traveling wave arrival times; A distribution network section determination module, used for processing the set of arrival times of the key node fault traveling waves based on a section location algorithm to determine the distribution network section where the fault point is located between two adjacent key nodes; The double-terminal traveling wave ranging module is used to process the fault traveling wave arrival time of the two adjacent key nodes based on the double-terminal traveling wave ranging algorithm to determine the distance between the fault point and the two adjacent key nodes.

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