Ground fault line selection method and system for power distribution network

Abstract

The embodiment of the invention provides a ground fault line selection method and system for a power distribution network, and belongs to the field of ground fault line selection of the power distribution network. The method comprises the following steps: collecting earth fault related data, and preprocessing the earth fault related data; constructing an arc equivalent circuit model by using the preprocessed earth fault related data; according to the output of the arc equivalent circuit model and the preprocessed earth fault related data, a multi-source dynamic verification feature cluster is established, and the multi-source dynamic verification feature cluster outputs credible dynamic feature fingerprints; and based on the credible dynamic feature fingerprints, carrying out ground fault line selection through cross-dimension fusion and decision optimization. According to the invention, through dynamic noise reduction, adaptive arc modeling and multi-source feature fusion, the accuracy and anti-interference capability of ground fault detection are improved, and reliable line selection under complex working conditions is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of grounding fault line selection for distribution networks, and specifically relates to a grounding fault line selection method and system for distribution networks. Background Art

[0002] As an important part of the power system, the rapid and accurate detection of grounding faults in distribution networks is crucial for ensuring power supply reliability. Due to the complex topology of distribution networks, diverse neutral grounding methods (such as arc suppression coil grounding, small resistance grounding, etc.), and the influence of factors such as the non-linear characteristics of arcs, environmental noise, and line parameter fluctuations on fault characteristics, grounding fault line selection has always been a technical difficulty in the industry.

[0003] Traditional line selection methods mainly rely on steady-state zero-sequence current amplitude, phase, or harmonic characteristics for judgment. However, in scenarios such as high-resistance grounding and intermittent arcs, the fault signals are weak and the characteristics are fuzzy, and are easily interfered by line distributed capacitance and load fluctuations, resulting in a relatively high misjudgment rate. In recent years, methods based on high-frequency component analysis and traveling wave detection of transient signals have been gradually applied. However, transient characteristics are easily affected by arc randomness and sensor frequency response characteristics, and there is a lack of the ability to model the dynamic evolution process of faults and the spatial topology correlation. In addition, existing arc models mostly adopt fixed resistors or simplified piecewise linearized models, which are difficult to reflect the time-varying non-linear characteristics and energy interaction process of arcs, resulting in insufficient credibility of feature extraction. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a grounding fault line selection method and system for distribution networks, which improve the accuracy and anti-interference ability of grounding fault detection through dynamic noise reduction, adaptive arc modeling, and multi-source feature fusion, and achieve reliable line selection under complex working conditions.

[0005] In a first aspect, to achieve the above object, the embodiments of the present invention provide a grounding fault line selection method for distribution networks, including: collecting grounding fault-related data and preprocessing the grounding fault-related data; using the preprocessed grounding fault-related data to construct an arc equivalent circuit model; based on the output of the arc equivalent circuit model and the preprocessed grounding fault-related data, establishing a multi-source dynamic verification feature cluster, and the multi-source dynamic verification feature cluster outputs a credible dynamic feature fingerprint; based on the credible dynamic feature fingerprint, performing grounding fault line selection through cross-dimensional fusion and decision optimization.

[0006] Optionally, the constructing of the arc equivalent circuit model includes: defining the change of arc resistance over time and constructing a time-varying arc resistance model; based on the time-varying arc resistance model, establishing an arc equivalent circuit model including an inductor, a capacitor, and a non-linear resistor; and updating the initial arc resistance and line equivalent inductive reactance in the arc equivalent circuit model in real time through the RLS algorithm.

[0007] Optionally, the establishment of the multi-source dynamic verification feature cluster includes: establishing a dynamic behavior feature group, a spatial topology feature group, and a high-frequency fingerprint feature group. The dynamic behavior feature group includes an arc evolution trajectory and an energy interaction feature. The spatial topology feature group includes a network structure fingerprint and a fault propagation path. The high-frequency fingerprint feature group includes the spatio-temporal distribution law of high-frequency pulses and the evolution of the phase relationship between zero-sequence voltage and current.

[0008] Optionally, the establishment of the multi-source dynamic verification feature cluster further includes: calculating a time-varying box size, calculating a fractal dimension based on the time-varying box size; calculating a change rate of dimensions of adjacent windows based on the fractal dimension; constructing a fractal dimension decay trajectory based on the change rate of dimensions of adjacent windows; verifying the credibility of data in the high-frequency fingerprint feature group according to the fractal dimension decay trajectory.

[0009] Optionally, verifying the credibility of data in the high-frequency fingerprint feature group includes: during a first time period after an arc is triggered, not restricting the change rate of the fractal dimension to completely capture the complexity mutation feature at the moment of fault breakdown; during a second time period after the arc is triggered, restricting the change rate of the fractal dimension within a first interval, and determining it as abnormal if it exceeds the first interval; after the second time period after the arc is triggered, restricting the change rate of the fractal dimension within a second interval, and determining it as abnormal if it exceeds the second interval.

[0010] Optionally, the second interval is dynamically adjusted according to environmental noise, line length, and sensor accuracy.

[0011] Optionally, the ground fault line selection through cross-dimensional fusion and decision optimization includes: multi-source feature time synchronization, spatial topology mapping, physical constraint verification, dynamic weight allocation, three-dimensional decision coordinate system construction, and field strength aggregation.

[0012] Optionally, the multi-source feature time synchronization resamples multi-source signals by interpolation based on the fault trigger moment; the spatial topology mapping abstracts the distribution network as a weighted graph structure to quantify the spatial attributes of line nodes; the physical constraint verification is used to judge the physical rationality between features; the time axis of the three-dimensional decision coordinate system is the continuous evidence of the fault development stage, the frequency domain axis is the credibility accumulation of high-frequency transient features, and the spatial axis is the logical consistency verification of topological association; the field strength aggregation converts the evidence of each dimension into the energy density distribution in the field space, and locates the field strength extreme point through gradient tracking to determine the fault line.

[0013] Optionally, the judgment of the physical rationality between features includes: judging whether the high-frequency energy distribution conforms to the attenuation law determined by the line length, judging whether the arc dynamic parameters are within the allowable value range of the topological structure, and judging whether the phase distortion direction has spatial consistency with the fault location.

[0014] On the other hand, the present invention provides a grounding fault line selection system for a distribution network, which is used to implement a grounding fault line selection method for a distribution network. The system includes a control module, and the control module includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the grounding fault line selection method for the distribution network.

[0015] Through the construction of a time-varying arc equivalent circuit model and the introduction of the RLS algorithm to update parameters in real time, the above technical solution breaks through the linear simplification limitation of the traditional arc model, accurately characterizes the dynamic coupling characteristics of arc resistance, inductance, and capacitance, and solves the problem of feature distortion in the scenarios of high-resistance grounding and intermittent arcs. The multi-source dynamic verification feature cluster integrates the dynamic behavior feature group (arc evolution trajectory, energy interaction), the spatial topology feature group (network structure fingerprint, fault propagation path), and the high-frequency fingerprint feature group (spatiotemporal distribution, phase evolution), and combines the dynamic credibility verification mechanism of the fractal dimension decay trajectory to realize the adaptive screening and credibility quantification of high-frequency transient features in a noisy environment, improving the anti-interference ability. Through multi-source feature time synchronization, spatial topology mapping, three-dimensional decision coordinate system construction, and field strength aggregation technology, the deep integration of time-domain continuity evidence, frequency-domain credibility accumulation, and spatial logic consistency verification is achieved, solving the limitation of the traditional single-dimensional threshold criterion. In a complex distribution network with distributed power sources and a cable-overhead line hybrid connection, the line selection is more accurate and the positioning time is shorter, providing high-reliability and strong adaptability technical support for the grounding fault detection of the distribution network.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. Description of the Drawings

[0017] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. They are used to explain the embodiments of the present invention together with the following specific implementation manners, but do not constitute a limitation to the embodiments of the present invention. In the drawings: Figure 1 is a flowchart of the grounding fault line selection method for a distribution network.

[0018] Figure 2 is a flowchart of the high-frequency fingerprint feature verification. Specific Embodiment

[0019] The following combines the attached Figure 1 - attached Figure 2 The specific implementation manners of the embodiments of the present invention will be described in detail. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0020] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, some existing solutions in the industry such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0021] In the process of implementing the present invention, the inventors of this application found that the traditional methods have the following problems: when using fixed-threshold wavelet denoising for signal preprocessing in the traditional method, it is easy to cause the loss of high-frequency characteristics of transient arcs; using a static arc model cannot accurately describe the time-varying characteristics of non-linear impedance; relying solely on high-frequency fingerprints is vulnerable to electromagnetic interference and causes misjudgment; and the lack of multi-dimensional feature fusion leads to insufficient accuracy in fault line selection.

[0022] Embodiment 1 Referring to Figures 1 - 2 , which is the first embodiment of the present invention. This embodiment provides a grounding fault line selection method for a distribution network, including: S100: Collect data related to grounding faults and preprocess the data related to grounding faults.

[0023] Specifically, collect neutral point voltage, power oscillation of each feeder, environmental data, insulation aging data, etc.; and install high-frequency current sensors (bandwidth 2.5 MHz) and voltage sensors on each branch line of the distribution network to collect high-frequency transient characteristics, which is beneficial to detecting weak arc discharge signals.

[0024] Preferably, an 8th-order Butterworth low-pass filter with a cut-off frequency of 1.2 MHz is added before sampling to suppress high-frequency noise, avoid high-frequency components from aliasing into low frequencies, and ensure signal purity.

[0025] Furthermore, set a trigger mechanism. The trigger mechanism is that when the sudden change of zero-sequence voltage exceeds 5% of the rated voltage, trigger fault recording and record 4 cycle data before and after the fault (sampling rate 2.5 MHz).

[0026] Preferably, preprocess the data related to grounding faults collected to suppress noise and enhance fault characteristics, laying a data foundation for high-precision modeling and recognition.

[0027] Preferably, traditional wavelet threshold denoising (such as Donoho threshold) will over-smooth the transient arc signal, and it is necessary to dynamically adjust the threshold to retain high-frequency characteristics. The preprocessing of this solution includes performing 6-layer decomposition using the db4 wavelet basis. The determination of the wavelet decomposition layer number is based on the balance between signal band coverage requirements and calculation efficiency.

[0028] Further, based on the amplitude distribution of the detail coefficients of each layer (max / median ratio), the wavelet dynamic threshold is adjusted, and the wavelet dynamic threshold adjustment formula is as follows:

[0029] Wherein, represents the noise reduction threshold of the wavelet coefficients of the j-th layer, represents the standard deviation of the wavelet coefficients of the j-th layer, N represents the signal length, represents the k-th wavelet coefficient of the j-th layer, is the wavelet coefficient with the largest absolute value, representing the mutability; is the median of the absolute values of the coefficients of the j-th layer, used to measure the degree of concentration.

[0030] Preferably, this formula takes into account the discreteness and non-Gaussianity of the wavelet coefficients, and can adapt to the frequency domain distribution characteristics under different fault types. The signal-to-noise ratio after noise reduction is increased by no less than 12 dB, providing a clear input for subsequent arc modeling and nonlinear feature extraction.

[0031] Preferably, through the combined design of a high-frequency sensor (bandwidth 2.5 MHz) and a low-pass filter (cutoff frequency 1.2 MHz), the high-frequency noise aliasing is effectively suppressed, ensuring the purity of the signal, and at the same time accurately capturing the transient characteristics of the weak arc; the wavelet noise reduction method with dynamically adjusted threshold (db4 wavelet basis + 6-layer decomposition) significantly improves the signal-to-noise ratio (≥12 dB) on the premise of retaining the high-frequency mutation signal (such as arc pulse), solving the problem of excessive smoothing of the transient characteristics by the traditional threshold noise reduction; combined with the fault recording mechanism triggered by the sudden change of zero-sequence voltage (recording 4 cycle data before and after the fault), it provides a high-fidelity and high-timeliness data basis for subsequent modeling and feature extraction.

[0032] S200: Use the preprocessed grounding fault-related data to construct an arc equivalent circuit model.

[0033] Specifically, define the change of the arc resistance over time, and construct a time-varying arc resistance model. The expression of the time-varying arc resistance model is as follows:

[0034] Wherein, is the arc resistance at time t, is the initial arc resistance, is the arc current.

[0035] Preferably, this formula reflects the characteristic that the arc impedance is affected by the current change rate, which is beneficial to distinguish between stable arcs and transient intermittent arcs.

[0036] Furthermore, considering the distributed parameters and the arc's non-linear impedance characteristics in the distribution line, an arc equivalent circuit model including inductance, capacitance, and non-linear resistance is established. The formula of the arc equivalent circuit model is as follows:

[0037] Wherein, is the arc current; is the arc voltage; is the arc resistance at time t, which varies with time; is the equivalent inductive reactance of the line; is the capacitance of the line to the ground; is the integration variable used to calculate the current integral, and t here is the current time.

[0038] Preferably, the arc equivalent circuit model combines the energy storage effects of inductance and capacitance, reflects the response dynamics after the arc is triggered, and can accurately capture the current change trends during the initial ignition, stable arcing, and extinguishing processes of the arc.

[0039] Furthermore, the recursive least squares estimation (RLS) is performed every 5 ms to update and in the arc model, realizing the adaptive modeling of non-stationary arcs.

[0040] Preferably, non-linear tools are used to characterize the complex behaviors of the arc process, enhancing the understanding of the essence of the fault.

[0041] Preferably, the arc equivalent circuit model can accurately characterize the dynamic characteristics (such as non-linear impedance mutation and periodic fluctuation) of the entire process of the arc from breakdown, arcing to extinguishing, solving the defect of insufficient description of the transient process by the traditional linear model; by updating the model parameters and in real time through the recursive least squares method (RLS) every 5 ms, the adaptability to non-stationary arc conditions is enhanced, providing a physically interpretable modeling support for distinguishing stable arcs from transient intermittent arcs.

[0042] S300: According to the output of the arc equivalent circuit model and the preprocessed ground fault-related data, a multi-source dynamic verification feature cluster is established, and the multi-source dynamic verification feature cluster outputs a credible dynamic feature fingerprint.

[0043] Specifically, the time-varying box size is calculated, and the formula for the time-varying box size is as follows:

[0044] Wherein, is the time-varying box size in the calculation of dynamic fractal dimension, which is used to quantify the complexity change of the arc fault signal on the time axis; here, \(t\) is the arc duration, starting from the fault triggering moment; is the initial box size; is the box size attenuation coefficient, which is used to control the rate of decrease of the box size with time; is the segmentation threshold time (10 ms); is the minimum box size.

[0045] Preferably, the initial box size and the box size attenuation coefficient are obtained according to the current mutation trend output by the arc equivalent circuit model, and the minimum box size is adapted to the line length, noise level and steady-state signal characteristics collected.

[0046] Preferably, in the initial stage (\(t\leq10\) ms), the time-varying box size decreases linearly, and at this time , which can improve the resolution at the instant of arc initiation. In the steady state stage (\(t\gt10\) ms), the time-varying box size is fixed at , which can avoid the dimension drift caused by long-time calculation.

[0047] Preferably, the smaller the time-varying box size , the higher the time resolution, and more subtle transient changes can be captured, such as sudden faults like arc re-ignition and short-circuit breakdown, optimizing the time-frequency focusing; the larger the time-varying box size , the larger the time window, and the long-term energy distribution and trend changes of the signal can be effectively integrated, such as detecting slow-changing processes (time scale \(\gt1\) second) like the line temperature rise and insulation aging caused by arc faults.

[0048] Preferably, the transient process of arc faults (such as re-ignition and extinction) has time-varying characteristics, and the traditional method using a fixed box size cannot adapt to the time-varying characteristics, while the selection of the box size directly affects the quantization accuracy of signal complexity. This scheme significantly improves the resolution accuracy of complex transient characteristics and the working condition adaptability of arc fault detection by adaptively adjusting the box size.

[0049] Furthermore, divide the time domain grid with as the side length, count the number of grids containing signal points, and calculate the fractal dimension. The calculation formula of the fractal dimension is as follows:

[0050] where, is the fractal dimension, is the time-varying box size, is the number of non-empty boxes.

[0051] Further, slide the window along the time axis to obtain the fractal dimension sequence ; Calculate the rate of change of the dimension between adjacent windows:

[0052] where represents the rate of change of the dimension, is the fractal dimension, where t represents the starting time of the current analysis window, is the step size of the sliding window.

[0053] Preferably, construct a fractal dimension decay trajectory according to the rate of change of the dimension between adjacent windows. The fractal dimension decay trajectory here is used to subsequently verify the credibility of the high-frequency fingerprint.

[0054] Further, based on the preprocessed data and the output of the arc equivalent circuit model, establish a multi-source dynamic verification feature cluster of "dynamic behavior - spatial topology - high-frequency fingerprint", and obtain a fault criterion system with strong interpretability through the physical association and information complementarity between features.

[0055] Specifically, the multi-source dynamic verification feature cluster includes a dynamic behavior feature group, a spatial topology feature group, and a high-frequency fingerprint feature group.

[0056] (1) The dynamic behavior feature group includes an arc evolution trajectory and an energy interaction feature.

[0057] The arc evolution trajectory captures the whole process dynamics of the arc from breakdown to stability, including the non-linear impedance mutation in the initial breakdown stage, the periodic fluctuation in the stable arcing period, and the transient impact mode of the re-ignition / extinction event.

[0058] The energy interaction characteristic analyzes the energy exchange law between the fault point and the system, including the release path of high-frequency transient energy, the coupling relationship between power frequency energy and high-frequency energy, and the attenuation gradient of energy propagation.

[0059] (2) The spatial topology feature group includes a network structure fingerprint and a fault propagation path.

[0060] The network structure fingerprint is the functional positioning of the line in the distribution network, including the branch level (main / branch / end), the electrical distance (equivalent impedance to the power source point), and the coupling strength of adjacent lines.

[0061] The fault propagation path derives the fault influence range based on electromagnetic coupling and topological connection, including the set of directly disturbed lines, the propagation boundary of secondary inductive interference, and the chain reaction path of protection actions.

[0062] (3) The high-frequency fingerprint feature group includes the spatio-temporal distribution law of high-frequency pulses and the evolution of the phase relationship between zero-sequence voltage and current.

[0063] The spatio-temporal distribution law of high-frequency pulses includes the amplitude / polarity characteristics of the leading pulse, the resonant frequency components of the oscillatory decay, and the statistical characteristics of the time intervals of pulse clusters.

[0064] The evolution of the phase relationship between zero-sequence voltage and current includes the phase jump direction in the initial stage of the fault, the phase difference drift rate in the steady state stage, and the phase oscillation mode caused by the arc reignition.

[0065] Furthermore, high-frequency fingerprints (such as pulse amplitude, phase jump) are vulnerable to electromagnetic interference pollution, and directly relying on them for fault location may lead to misjudgment.

[0066] Preferably, the fractal dimension decay trajectory is used to verify the credibility of high-frequency fingerprints. After the arc is triggered, within the first time period (within the first 5 milliseconds), the change rate of the fractal dimension is not restricted , to fully capture the complexity mutation characteristics at the moment of fault breakdown; within the second time period after the arc is triggered (from 5 to 15 milliseconds), it is restricted within the first interval (-1.5 ≤ ≤ +2.0), and if it exceeds the first interval, it is determined as abnormal; after the second time period after the arc is triggered, that is, (after 15 milliseconds), it is strictly restricted within the second interval (-0.3 ≤ ≤ +0.3), and if it exceeds the second interval, it is determined as abnormal. If the on-site noise is large (such as near a substation), the second interval can be relaxed by 1.5 times (-0.45 ≤ ≤ +0.45); if the line length exceeds 10 kilometers or the sensor accuracy is high, the second interval is tightened to 0.8 times (-0.24 ≤ ≤ +0.24). Preferably, the transient process (breakdown, reignition) of the arc fault will cause a mutation in the signal complexity. At this time suddenly rises, while the complexity change of random noise is irregular, and will frequently exceed the limit; through dynamic interval adjustment, more than 90% of the interference signals (such as false high-frequency fingerprints caused by equipment switching) can be eliminated, and the false alarm rate can be reduced to less than 5%.

[0067] Furthermore, through the multi-source dynamic verification feature cluster, the fractal dimension decay trajectory is used to dynamically verify the credibility of high-frequency fingerprints, and cross-verification of multi-source features is realized through physical constraint verification, and finally a credible dynamic feature fingerprint with strong anti-interference ability and physical interpretability is output.

[0068] Preferably, based on the calculation of the fractal dimension of the time-varying box size, the time-frequency focusing of transient details (such as arc restrike) and long-term trends (such as insulation aging) is optimized, significantly improving the quantization accuracy of complex fault characteristics; the credibility of high-frequency fingerprints is verified through the fractal dimension decay trajectory, and combined with the physical relevance cross-verification of multi-source feature groups, false features caused by electromagnetic interference are effectively eliminated, the false alarm rate is reduced, and the pain point that traditional single features are vulnerable to noise interference is solved.

[0069] S400: Based on the credible dynamic feature fingerprints, ground fault line selection is carried out through cross-dimensional fusion and decision optimization.

[0070] Specifically, time-axis synchronization is performed. Taking the fault trigger moment as the reference, a time scale system with millisecond-level accuracy is established, and multi-source signals are interpolated and resampled to eliminate the acquisition asynchronization error.

[0071] Furthermore, spatial topology mapping is carried out. The distribution network is abstracted into a weighted graph structure to quantify the spatial attributes of line nodes; an electrical correlation matrix between lines is constructed to describe the energy propagation path.

[0072] Furthermore, physical constraint verification is carried out to verify the physical rationality between features, including whether the high-frequency energy distribution conforms to the attenuation law determined by the line length, whether the arc dynamic parameters are within the allowable value range of the topological structure, and whether there is spatial consistency between the phase distortion direction and the fault location, etc.

[0073] Preferably, a dynamic evaluation model of feature importance is constructed to dynamically adjust the weight distribution. In a low-noise environment, focus on the detail resolution of high-frequency fingerprints; in a complex topology, strengthen the correlation analysis of the spatial propagation path; in a developing fault, track the evolution trend of dynamic behavior parameters.

[0074] Furthermore, a three-dimensional decision coordinate system is constructed. The time axis is the continuous evidence of the fault development stage; the frequency domain axis is the credibility accumulation of high-frequency transient features; the spatial axis is the logical consistency verification of topological correlation. The time, frequency domain, and space are cross-dimensionally fused and quantified into a unified criterion.

[0075] Furthermore, the field strength aggregation algorithm is used to convert the evidence of each dimension into the energy density distribution in the field space, and the extreme point of the field strength is located by gradient tracking to determine the fault line.

[0076] Preferably, decision optimization is carried out through dynamic weight distribution, physical rule verification, and field strength aggregation algorithm, and the most reliable fault criterion is selected from multi-source features, and finally high-precision line selection is achieved.

[0077] Preferably, based on the cross-dimensional fusion mechanism of the three-dimensional decision coordinate system (time-frequency domain-space), through millisecond-level time scale synchronization, spatial topology mapping, and physical constraint verification, the spatio-temporal consistency verification of fault characteristics is achieved; combined with the field strength aggregation algorithm, multi-dimensional evidence is transformed into energy density distribution, and the extreme point of the field strength is located by gradient tracking, significantly improving the line selection accuracy in complex noise environments and long line scenarios; the dynamic feature importance evaluation mechanism (focusing on high-frequency fingerprints, propagation paths, or dynamic parameter evolution) further enhances the robustness and adaptability of the scheme to high-noise and multi-branch distribution networks.

[0078] The present invention also provides a grounding fault line selection system for a distribution network, which is used to implement the grounding fault line selection method for a distribution network. The system includes a control module, and the control module includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the grounding fault line selection method for a distribution network.

[0079] An embodiment of the present invention provides a storage medium, on which a program is stored, and when the program is executed by a processor, the grounding fault line selection method for a distribution network is implemented.

[0080] An embodiment of the present invention provides a processor, which is used to run a program, and when the program runs, the grounding fault line selection method for a distribution network is executed.

[0081] An embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, the grounding fault line selection method for a distribution network is implemented. The device herein can be a server, a PC, a PAD, a mobile phone, etc.

[0082] The present application also provides a computer program product, which is suitable for executing the grounding fault line selection method for a distribution network when executed on a data processing device.

[0083] Those skilled in the art should understand that the embodiments of the present application can provide methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0084] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0085] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0087] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0088] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flashRAM). The memory is an example of computer-readable media.

[0089] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined in this article, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0090] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0091] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A ground fault line selection method for a distribution network, characterized in that: include: Collecting ground fault related data and preprocessing the ground fault related data; Using the pre-processed ground fault related data, constructing an arc equivalent circuit model; According to the output of the arc equivalent circuit model and the preprocessed ground fault related data, a multi-source dynamic verification feature cluster is established, and the multi-source dynamic verification feature cluster outputs a credible dynamic feature fingerprint; Based on the trusted dynamic feature fingerprint, grounding fault line selection is performed through cross-dimensional fusion and decision optimization.

2. The ground fault line selection method for distribution network according to claim 1, characterized in that: The constructing of the arc equivalent circuit model comprises: Define the change of arc resistance with time and build a time-varying arc resistance model; According to the time-varying arc resistance model, an arc equivalent circuit model including inductance, capacitance and nonlinear resistance is established; The initial arc resistance and line equivalent inductive reactance in the arc equivalent circuit model are updated in real time by the RLS algorithm.

3. The ground fault line selection method for distribution network according to claim 1, characterized in that: The establishment of a multi-source dynamic verification feature cluster comprises: Establish dynamic behavior feature groups, spatial topology feature groups and high-frequency fingerprint feature groups, The dynamic behavior feature group includes arc evolution trajectory and energy interaction characteristics, The spatial topology feature group includes network structure fingerprint and fault propagation path, The high-frequency fingerprint feature group includes the temporal and spatial distribution law of high-frequency pulses and the phase relationship evolution of zero-sequence voltage and current.

4. The ground fault line selection method for distribution network according to claim 3, characterized in that: The establishing of a multi-source dynamic verification feature cluster further comprises: calculating a time-varying box size, and calculating a fractal dimension according to the time-varying box size; Calculating the dimension change rate of adjacent windows according to the fractal dimension; Based on the dimensionality change rate of the adjacent windows, constructing a fractal dimension attenuation trajectory; The credibility of the data in the high-frequency fingerprint feature group is verified according to the fractal dimension attenuation trajectory.

5. The ground fault line selection method for distribution network according to claim 4, characterized in that: Verifying the credibility of the data in the high-frequency fingerprint feature group includes: In the first time period of arc triggering, the fractal dimension change rate is not restricted, so as to completely capture the complexity mutation characteristics at the moment of fault breakdown; In the second time period of arc triggering, the fractal dimension change rate is limited to the first interval, and if it exceeds the first interval, it is determined to be abnormal; After the arc is triggered in the second time period, the fractal dimension change rate is limited to the second interval, and if it exceeds the second interval, it is determined to be abnormal.

6. The ground fault line selection method for distribution network according to claim 5, characterized in that: The second interval is dynamically adjusted according to environmental noise, line length and sensor accuracy.

7. The ground fault line selection method for distribution network according to claim 1, characterized in that: The ground fault line selection through cross-dimensional fusion and decision optimization includes: multi-source feature time synchronization, spatial topology mapping, physical constraint verification, dynamic weight allocation, three-dimensional decision coordinate system construction and field strength aggregation.

8. The ground fault line selection method for distribution network according to claim 7, characterized in that: The multi-source characteristic time synchronization is based on the fault triggering time, and interpolation resampling is performed on the multi-source signals; The spatial topological mapping abstracts the distribution network into a weighted graph structure and quantifies the spatial attributes of line nodes; The physical constraint check is used to determine the physical rationality between features; The time axis of the three-dimensional decision coordinate system is the continuity evidence of the fault development stage, the frequency domain axis is the credibility accumulation of high-frequency transient characteristics, and the space axis is the logical consistency verification of topological association; The field strength aggregation is to convert the evidence of each dimension into the energy density distribution in the field space, locate the field strength extreme point through gradient tracking, and thus determine the fault line.

9. The ground fault line selection method for distribution network according to claim 8, characterized in that: The physical rationality between the features is judged as follows: whether the high-frequency energy distribution conforms to the attenuation law determined by the line length, whether the arc dynamic parameters are within the value range allowed by the topological structure, and whether the phase distortion direction is spatially consistent with the fault location.

10. A ground fault line selection system for a distribution network, characterized in that: The system includes a control module, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the ground fault line selection method for a distribution network according to any one of claims 1 to 9.

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