Ground fault section accurate positioning and isolation method and system based on multi-edge terminal data association

Abstract

This invention relates to the field of power system fault diagnosis technology, specifically a method for accurate location and isolation of ground fault sections based on multi-edge terminal data association. It includes collecting transient current and voltage signals from multiple edge terminals in the distribution network, extracting high-frequency transient feature values, and combining them with a distributed computing model to generate a transient feature dataset, a candidate set of fault branches, and fault section location results. Finally, it generates isolation commands to achieve closed-loop control. This invention, by fusing transient signal frequency domain features, zero-sequence current distribution patterns, and fault phase abrupt change information, constructs a dynamic fault discrimination model, significantly improving the identification accuracy of intermittent arc grounding faults and high-resistance grounding faults, enhancing the self-healing capability of the distribution network, and ensuring power supply reliability.

Description

Technical Field

[0001] This invention relates to the field of power system fault diagnosis technology, and in particular to a method for accurate location and isolation of ground fault sections based on multi-edge terminal data association. Background Technology

[0002] In modern power systems, the distribution network, as a crucial link in power transmission, directly impacts power quality through its safety and reliability. However, with the rapid development of new power systems, the structure and operational characteristics of distribution networks have undergone significant changes, exhibiting features such as "complex structure, numerous branches, and short lines." Particularly in mixed cable and overhead line power supply lines, the location and isolation of grounding faults have become long-standing technical challenges. Traditional methods for diagnosing grounding faults in distribution networks primarily rely on analyzing changes in electrical quantities such as zero-sequence current and fault phase voltage. However, in complex scenarios such as distributed generation grid connection, planned islanded microgrid operation, and high-resistance grounding faults, the fault signals are weak and diverse, making it difficult for existing technologies to achieve accurate location and rapid isolation. Furthermore, intermittent arcing grounding faults, due to multiple instantaneous initiation and recovery cycles, often fail to trigger protection devices in a timely manner, further increasing the difficulty of fault handling.

[0003] Current technologies for ground fault diagnosis suffer from the following shortcomings: First, traditional methods have limited capabilities in extracting and analyzing transient signals, making them ill-suited to the diverse power flow variations and fault types prevalent in modern power systems. Second, existing diagnostic equipment typically relies on centralized processing, resulting in high computational resource consumption, slow response times, and difficulty meeting real-time requirements. Third, for specific fault types (such as high-resistance ground faults or intermittent arcing ground faults), traditional algorithms and criteria exhibit low accuracy and lack effective feature extraction and classification methods. These issues severely impact the self-healing capabilities and power supply reliability of distribution networks.

[0004] To address the aforementioned issues, a precise location and isolation method for ground fault sections based on multi-edge terminal data association is urgently needed. This method should consider the characteristics of new power systems, utilizing edge computing technology for on-site diagnosis and rapid response. It should also integrate an artificial intelligence feature library, frequency domain feature protection of fault transient electrical quantities, and high-precision waveform recording information to construct a comprehensive weak-feature fault diagnosis framework. By analyzing the characteristics of ground faults under different aggregation scenarios, fault locations, fault types, and transition resistances, a precise location algorithm suitable for complex power supply backgrounds is proposed, and distributed intelligent terminals are used to achieve data linkage and comprehensive judgment. Furthermore, an embedded distributed diagnostic module needs to be developed to support seamless integration with distribution network automation systems, thereby enabling visualized presentation, rapid isolation, and self-healing recovery of fault sections.

[0005] In summary, this invention aims to improve the accuracy and efficiency of grounding fault diagnosis in power distribution networks through digital and intelligent means, overcome the limitations of traditional technologies in complex scenarios, and provide strong technical support for the stable operation of new power systems. Summary of the Invention

[0006] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for accurate location and isolation of grounding fault sections based on multi-edge terminal data association.

[0007] According to the technical solution provided by this invention, on the one hand, this invention provides a method for accurate location and isolation of ground fault sections based on multi-edge terminal data association, including the following steps: S1: Obtain the three-phase current and zero-sequence voltage signals of multiple edge terminals in the distribution network line, extract high-frequency transient feature values, identify potential fault branches based on the change in transient current asymmetry, and generate a transient feature dataset. S2: Based on the high-frequency transient feature values ​​in the transient feature dataset, combined with the spatial location information of each terminal, calculate the transient synchronization difference between adjacent terminals, identify areas with inconsistent transient changes, and generate a candidate set of fault branches. S3: Based on the transient characteristic values ​​of each branch in the candidate set of fault branches, call the distributed computing model to analyze the zero-sequence current distribution law, combine the fault phase change information, correct the fault branch range, and generate the fault section location result. S4: Based on the fault section location results and the topology of the distribution network automation system, a corresponding isolation command is generated and sent to the corresponding switching equipment. After the command is sent, the action status and execution feedback information of the switching equipment are collected, and the distribution network operation status data after the isolation operation is recorded synchronously.

[0008] As a further aspect of the present invention, the transient feature dataset includes high-frequency transient feature values, transient current asymmetry change, and terminal spatial location information; the fault branch candidate set includes transient synchronization difference coefficient, branch number, and neighboring terminal association information; the fault segment location result includes fault segment number, fault type identifier, and isolation priority parameter; and the isolation instruction includes switching action sequence, isolation time window, and recovery strategy.

[0009] As a further aspect of the present invention, the step of obtaining S1 is as follows: S101: Collect the three-phase current and zero-sequence voltage signals of multiple edge terminals in the distribution network line, extract high-frequency transient feature values ​​for each terminal, calculate the asymmetry change of the three-phase current, mark the terminals with asymmetry change exceeding the set threshold as potential fault branches, and generate a set of transient current asymmetry change values. S102: Based on the set of transient current asymmetry changes, calculate the frequency domain distribution characteristics of high-frequency transient characteristic values ​​for each terminal, classify and judge according to the frequency domain distribution characteristics and the set transient characteristic threshold range, select the set of terminals that meet the fault characteristics, and generate a set of high-frequency transient characteristic values. S103: Call the high-frequency transient feature value set, combine it with the spatial location information of each terminal, detect the transient synchronization difference between terminals, extract potential fault branches based on the synchronization difference threshold, integrate the relevant terminal numbers and transient feature information, and generate a transient feature dataset.

[0010] As a further aspect of the present invention, the step of obtaining S2 is as follows: S201: Extract high-frequency transient feature values ​​from the transient feature dataset, obtain the spatial location information of each terminal, match this information with the topology of the distribution network line, calculate the transient synchronization difference coefficient between adjacent terminals, correct the terminal association relationship, and generate a terminal synchronization difference parameter set. S202: Based on the terminal synchronization difference parameter set, sort the transient synchronization difference coefficients between adjacent terminals, measure the consistency of transient changes between terminals, and mark terminals with consistency below a set threshold as candidates for fault branches to obtain the transient synchronization difference coefficient set. S203: Based on the transient synchronization difference coefficient set, and combined with the spatial location of neighboring terminals and the distribution law of transient characteristic values, calculate the transient change rate and synchronization deviation ratio of each neighborhood, compare the values ​​with the set fault branch threshold, dynamically adjust the fault branch candidate range, and generate a fault branch candidate set. The neighboring terminals are the adjacent terminals corresponding to all the marked fault branch candidate terminal pairs extracted from the transient synchronization difference coefficient set. The transient change rate is the average of the main frequency difference and time difference ratio between terminals in the same area. The synchronization deviation ratio is defined as the deviation ratio between the trigger time delay of different terminals in the neighborhood and the average value.

[0011] As a further aspect of the present invention, the step of obtaining S3 is as follows: S301: Based on the transient characteristic values ​​of each branch in the candidate set of fault branches, obtain the zero-sequence current distribution law of the corresponding branch, extract the amplitude and phase information of the zero-sequence current, match the zero-sequence current distribution law with the transient characteristic values, obtain the zero-sequence current difference between branches, and thus generate a set of zero-sequence current difference values. S302: Based on the zero-sequence current difference value set, compare the zero-sequence current differences between adjacent branches, mark the branches whose differences exceed the set threshold, correct the fault type of the marked branches, and generate a fault type correction dataset. S303: Based on the fault type correction dataset, compare the zero-sequence current distribution pattern of the corrected branch with that of the adjacent branch, identify the accuracy of the correction result, and combine the corrected fault type with the branch number information to generate the fault section location result.

[0012] As a further aspect of the present invention, the step of obtaining S4 is as follows: S401: Based on the fault section number in the fault section location result, detect the status of the switching equipment corresponding to each fault section, generate a switching action sequence according to the action priority of the switching equipment, determine the isolation time window, and generate a set of switching action sequences. S402: Call the set of switch action sequences and the topology information within the same distribution network line, calculate the impact of each switch action on the non-faulty section, filter out the switch action combination with the least impact, and generate an optimized switch action sequence; S403: Based on the optimized sequence of switch actions and the recovery strategy of the distribution network automation system, a corresponding isolation command is generated and sent to the corresponding switch equipment. The action status and execution feedback information of the switch equipment are recorded, and the distribution network operation status data after the isolation operation is collected synchronously.

[0013] As a further aspect of the present invention, the method further includes: S5: Based on the distribution network operation status data after the isolation operation, call the distributed computing model to analyze the power supply restoration status of non-faulty sections, combine real-time monitoring information to evaluate the execution effect of the isolation command, and generate a distribution network operation status evaluation report accordingly. The power distribution network operation status assessment report includes the isolation command execution time, power supply restoration efficiency, and fault section isolation success rate.

[0014] As a further aspect of the present invention, the step of obtaining S5 is as follows: S501: Based on the distribution network operation status data after the isolation operation, detect the power supply recovery time of each non-faulty section, compare each section item by item according to the set power supply recovery time threshold, calculate the difference between the power supply recovery time and the threshold, and judge the power supply recovery status of each section based on the difference, and generate a power supply recovery time difference set. S502: Call the numerical data in the power restoration time difference set, filter out the segments with power restoration time less than or equal to the minimum threshold based on the minimum threshold and the difference range, and set them as high-efficiency restoration segments. Define the segments with power restoration time difference between the threshold and the minimum threshold as medium-efficiency restoration segments, and generate a restoration efficiency division interval value group. S503: Based on the remaining data in the interval value group and power restoration time difference set according to the restoration efficiency, determine the segment where the power restoration time is greater than or equal to the set threshold, merge the restoration efficiency value of each segment with the corresponding spatial coordinates and segment number, integrate the restoration value distribution according to the efficiency interval, and generate a distribution network operation status assessment report.

[0015] As a further aspect of the present invention, after obtaining the fault segment location result, the zero-sequence current distribution patterns of different fault segments are classified and identified. Segments with similar zero-sequence current amplitude change trends, phase offset amplitudes and fault types are grouped into the same category and assigned corresponding fault mode labels. In the subsequent isolation command generation process, the isolation priority and recovery strategy are adjusted according to the fault mode labels.

[0016] As a further aspect of the present invention, after obtaining the distribution network operation status assessment report, the power supply restoration efficiency of different non-faulty sections is classified and identified. Sections with similar restoration time, load characteristics and topology are grouped into the same category and assigned corresponding restoration efficiency labels. In subsequent operation status assessments, differentiated power supply restoration optimization measures are implemented based on the restoration efficiency labels.

[0017] On the other hand, the present invention also provides a system for precise location and isolation of ground fault sections based on multi-edge terminal data association, the system comprising: The transient feature dataset construction module is used to acquire the three-phase current and zero-sequence voltage signals of multiple edge terminals in the distribution network line, extract high-frequency transient feature values, identify potential fault branches based on the change in transient current asymmetry, and generate a transient feature dataset. The fault branch candidate set generation module is used to calculate the transient synchronicity difference between adjacent terminals based on the high-frequency transient feature values ​​in the transient feature dataset and the spatial location information of each terminal, identify areas with inconsistent transient changes, and generate a fault branch candidate set. The fault section location module is used to analyze the zero-sequence current distribution law by calling a distributed computing model based on the transient characteristic values ​​of each branch in the candidate set of fault branches, and to correct the fault branch range by combining the fault phase change information and generating the fault section location result. The isolation command determination module is used to generate corresponding isolation commands based on the fault section location results and the topology of the distribution network automation system, and to send the commands to the corresponding switching equipment. After the commands are sent, the module collects the action status and execution feedback information of the switching equipment, and synchronously records the distribution network operation status data after the isolation operation.

[0018] Advantages of this invention: This invention achieves sensitive identification of branch-level anomalies through multi-edge terminal signal collaborative acquisition and transient high-frequency feature extraction. It identifies areas of inconsistent transient behavior by leveraging the synchronicity differences between terminals, clarifies local disturbance sources, and effectively locates abnormal sections. It combines zero-sequence current distribution and phase change characteristics for error correction, improving the accuracy of fault range determination. It triggers automated isolation commands based on the distribution network structure and provides feedback on switch action status, achieving closed-loop control of isolation execution. It integrates post-isolation operation data with real-time monitoring information for dynamic evaluation, forming a comprehensive judgment on the power supply recovery capability of non-faulty areas, enhancing the accuracy of fault handling, the timeliness of response, and the ability to track operational status. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the method described in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the transient feature dataset as described in an embodiment of the present invention. Figure 3 This is a flowchart illustrating the process of obtaining the candidate set of fault branches according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the process of obtaining the fault location result according to an embodiment of the present invention. Figure 5 This is a flowchart illustrating the process of obtaining distribution network operation status data after isolation operation according to an embodiment of the present invention. Figure 6 This is a flowchart illustrating the process of obtaining the distribution network operation status assessment report according to an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0021] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0022] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0023] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0024] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0025] Please see Figure 1 This invention provides a technical solution: a method for accurate location and isolation of ground fault sections based on multi-edge terminal data association, comprising the following steps: S1: Obtain the three-phase current and zero-sequence voltage signals of multiple edge terminals in the distribution network line, extract high-frequency transient feature values, identify potential fault branches based on the change in transient current asymmetry, and generate a transient feature dataset. Please see Figure 2 The steps to obtain S1 are as follows: S101: Collect the three-phase current and zero-sequence voltage signals of multiple edge terminals in the distribution network line, extract high-frequency transient feature values ​​for each terminal, calculate the asymmetry change of the three-phase current, mark the terminals with asymmetry change exceeding the set threshold as potential fault branches, and generate a set of transient current asymmetry change values. First, three-phase current transformers and zero-sequence voltage sensors are configured on each terminal. The corresponding measured values ​​are acquired as voltage signals by a sampling circuit, converted from analog to digital, and buffered for storage. Each terminal records the transient signal sequence within a 500ms range before and after the event trigger at a high sampling frequency of 10kHz. The three-phase current signals include Ia, Ib, and Ic, and the zero-sequence voltage signal is represented as Uz. Then, high-frequency transient feature values ​​are extracted from the signals of each terminal individually. These feature values ​​are obtained by high-pass filtering the Ia, Ib, and Ic signals to extract components with frequencies greater than 2kHz, and their peak values, durations, and frequencies are statistically analyzed to form a feature vector set indexed by the terminal number. For the three-phase current signals, the asymmetry is further calculated using the ratio of the root mean square value of the phase current deviation to the average value at each time point as the asymmetry index. For example, in the sampling data of a certain terminal, the peak value of Ia is 45A, the peak value of Ib is 30A, and the peak value of Ic is 28A. The calculated average three-phase current for the corresponding time period is 34.3A, and the asymmetry is... The root mean square value is 7.36A, so the asymmetry is 0.214. Subsequently, the difference between this value and the baseline asymmetry value before the fault is calculated. If the difference exceeds the set threshold, the terminal is marked as a potential fault branch. This threshold is set according to the 95% confidence interval of the asymmetry change in historical operating data, usually set to 0.1. The asymmetry changes of all marked terminals are then summarized to form a set of transient current asymmetry changes.

[0026] S102: Based on the set of transient current asymmetry changes, calculate the frequency domain distribution characteristics of high-frequency transient characteristic values ​​for each marked terminal, classify and judge according to the frequency domain distribution characteristics and the set transient characteristic threshold range, select the set of terminals that meet the fault characteristics, and generate a set of high-frequency transient characteristic values. Based on the aforementioned set of transient current asymmetry changes, frequency domain analysis is performed on each marked terminal. The specific process is as follows: First, a Fast Fourier Transform (FFT) is performed on the extracted high-frequency transient feature values ​​in the terminal to obtain a spectrum. The frequency domain components between 2kHz and 5kHz are selected as the analysis objects in the spectrum. Then, the energy density of this frequency band is statistically analyzed to obtain its energy peak, dominant frequency position, and bandwidth range. For example, in a certain terminal, the FFT analysis shows that the dominant frequency is 3.2kHz, the concentrated energy bandwidth is 2.8kHz to 3.5kHz, and the peak energy density is 15dB. Next, the terminal is classified and judged based on these statistical characteristics. The judgment criteria are whether the frequency domain energy density exceeds the set transient feature threshold range. For example, the dominant frequency threshold range is set to 2.5kHz to 3.5kHz, the energy density threshold is above 10dB, and the bandwidth range is above 0.5kHz. When the terminal meets the above three conditions, it is determined that its feature matches the fault characteristics. Such terminals are classified into the high-frequency transient feature terminal set, and their feature values ​​are further processed and numbered and stored in the high-frequency transient feature value set.

[0027] S103: Call the high-frequency transient feature value set, combine it with the spatial location information of each terminal, detect the transient synchronization difference between terminals, extract potential fault branches based on the synchronization difference threshold, integrate the relevant terminal numbers and transient feature information, and generate a transient feature dataset. After calling the high-frequency transient feature set, the physical installation location of each terminal is obtained. Spatial distance data between terminals is obtained through GIS coordinate system or line topology. Based on this, the synchronization difference between transient signals is detected. Specifically, the occurrence time of high-frequency transient signals of each terminal under the same event trigger is compared to determine whether there is a delay difference in the timestamp of the first occurrence of the signal waveform peak. The calculation method is to perform time difference statistics on the trigger point of the transient signal of each pair of terminals. If the time difference is greater than the set synchronization difference threshold, it indicates that there is a fault propagation path between the corresponding terminals. The synchronization difference threshold is set to 2ms. Based on the laboratory line model verification, the delay of the signal during normal propagation will not exceed 1.5ms. Therefore, 2ms is set as the effective judgment upper limit. If the delay between a pair of terminals is 2.3ms, it is included in the potential fault branch. By searching all terminal combinations that meet the synchronization difference requirements one by one, the relevant terminal numbers are extracted, and the main frequency, peak value, energy density and other contents in their transient features are recorded at the same time, and integrated into the final transient feature dataset. S2: Based on the high-frequency transient feature values ​​in the transient feature dataset, combined with the spatial location information of each terminal, calculate the transient synchronization difference between adjacent terminals, identify areas with inconsistent transient changes, and generate a candidate set of fault branches; Please see Figure 3 The steps to obtain S2 are as follows: S201: Extract high-frequency transient feature values ​​from the transient feature dataset, obtain the spatial location information of each terminal, match this information with the topology of the distribution network line, calculate the transient synchronization difference coefficient between adjacent terminals, correct the terminal correlation relationship, and generate a set of terminal synchronization difference parameters. In a preferred embodiment, the high-frequency feature records corresponding to each terminal in the transient feature dataset are read sequentially. The records include the peak value of the high-frequency signal, the main frequency position, and the bandwidth energy distribution when the terminal triggers an event. A data index is set for each feature to ensure that a specific terminal can be located in subsequent operations. For example, the high-frequency feature record for terminal T01 is: main frequency 3.1kHz, energy peak 16dB, signal start delay 1.2ms. Then, the spatial location information of each terminal in the actual distribution network is read. The spatial location is composed of the node number in the line topology and the actual physical coordinates. The distance between nodes is represented by the length value obtained from the line diagram. For example, the distance between T01 and T02 is 250 meters. Next, the spatial locations of the terminals are compared with the distribution network line... The topology is matched one by one, that is, the physical coordinates of the terminal are compared with the node numbers and connection relationships in the topology structure. For each line connected terminal, adjacent terminal pairs are formed. The transient synchronization difference coefficient is calculated by the signal start delay difference in the transient characteristics of each pair of terminals. The difference coefficient is defined as the difference between the signal response start time of the terminals divided by the spatial distance between them. For example, the signal start delay of T01 is 1.2ms and that of T02 is 1.5ms, then the difference is 0.3ms, the path distance is 250 meters, and the difference coefficient is 0.0012 / ms·m. This process is repeated for all terminal pairs with direct connection relationships to form a mapping relationship between terminal number pairs and corresponding difference coefficients. All terminal pairs and calculation results are recorded as a terminal synchronization difference parameter set.

[0028] S202: Based on the terminal synchronization difference parameter set, sort the transient synchronization difference coefficients between adjacent terminals, measure the consistency of transient changes between terminals, and mark terminals with consistency below a set threshold as candidates for fault branches to obtain the transient synchronization difference coefficient set. In a preferred embodiment, the difference coefficients of all terminal pairs in the terminal synchronization difference parameter set are sorted from smallest to largest. First, all records in the parameter set are traversed, and the terminal pair number and its difference coefficient are extracted. All difference coefficients are rounded to three decimal places to ensure consistent sorting precision. After sorting, each terminal pair is assigned a ranking number. For example, terminal pair T05-T06 has a difference coefficient of 0.0008 and ranks 1st, while T10-T11 has a difference coefficient of 0.0023 and ranks 20th. Then, the consistency of the transient changes measured by each terminal pair is assessed using an inverse proportionality rule, mapping the difference coefficient to the consistency. Consistency is calculated as 1 minus the normalized difference coefficient. Normalization is based on the range between the maximum and minimum values ​​of all difference coefficients. In the example, the difference coefficient ranges from 0.0008 to 0.0052, corresponding to a consistency range of 1 to 0.2. The consistency threshold is set to 0.5. Referring to the statistical difference between actual faulty branches and non-faulty branches, if the consistency of a terminal pair is lower than the set threshold, it is marked as a candidate for a faulty branch. The marking method is to add the mark "CAND" to the pair number in the original parameter set. Finally, all marked terminal pair numbers and corresponding difference coefficients are collected to form a transient synchronization difference coefficient set.

[0029] S203: Based on the transient synchronicity difference coefficient set, combined with the spatial location of neighboring terminals and the distribution law of transient characteristic values, calculate the transient change rate and synchronicity deviation ratio of each neighborhood, compare the values ​​with the set fault branch threshold, dynamically adjust the fault branch candidate range, and generate a fault branch candidate set. In a preferred embodiment, all marked fault branch candidate terminal pairs and their adjacent terminals are extracted from the transient synchronization difference coefficient set. The neighboring terminal set of each candidate terminal pair in the spatial topology map is obtained. For each set of neighboring terminals, the peak frequency and average energy density of the main frequency characteristic values ​​in the high-frequency transient feature values ​​are statistically analyzed. The transient change rate is calculated by combining the trigger time difference, i.e., the ratio of the main frequency difference to the time difference between terminals in the same area is averaged. For example, if T12-T13 is a candidate terminal pair, T12 has a main frequency of 3.2kHz, T13 has a main frequency of 3.0kHz, and the initial delay difference is 0.3ms, then the transient change rate is 0.2kHz / 0.3ms≈0.67kHz / ms. Then, the neighboring terminal sets are statistically analyzed. The mean and standard deviation of the rate of change of all terminal pairs within the domain are used to obtain the overall change intensity parameter. The synchronization deviation ratio is defined as the deviation ratio between the trigger time delay of different terminals in the neighborhood and the average value, calculated as the ratio of the standard deviation to the average value. For example, if the average delay is 1.4ms and the standard deviation is 0.35ms, then the synchronization deviation ratio is 0.25. The set fault branch threshold is that the rate of change is greater than 0.5kHz / ms and the synchronization deviation ratio is greater than 0.2. This setting value is determined with reference to historical line simulation data. All neighborhoods that meet both conditions are selected as the effective fault branch candidate range. Terminal pairs that do not meet the conditions are removed. Finally, the terminal number groups and their statistical parameters that meet the conditions are integrated to form the fault branch candidate set.

[0030] S3: Based on the transient characteristic values ​​of each branch in the candidate set of fault branches, call the distributed computing model to analyze the zero-sequence current distribution law, combine the fault phase change information, correct the fault branch range, and generate the fault section location result. Please see Figure 4 The steps to obtain S3 are as follows: S301: Based on the transient characteristic values ​​of each branch in the candidate set of fault branches, obtain the zero-sequence current distribution law of the corresponding branch, extract the amplitude and phase information of the zero-sequence current, match the zero-sequence current distribution law with the transient characteristic values, calculate the zero-sequence current difference between branches, and generate a set of zero-sequence current difference values. Specifically, in this embodiment, the transient main frequency, peak energy, trigger delay, and other records corresponding to each branch in the candidate set are read sequentially. These records are indexed according to the branch number. Then, the zero-sequence current sequence corresponding to the branch is read. The zero-sequence current is obtained by summing the three-phase current phasors collected by the terminal of each branch at the moment of the fault. During the reading, the zero-sequence current amplitude of the terminal corresponding to the branch before and after the trigger is extracted first. The amplitudes are arranged in the sampling order to form an amplitude sequence, and the maximum amplitude and average amplitude in the sequence are counted respectively. For example, if a branch B01 collects a zero-sequence current amplitude sequence of 6.2A, 7.1A, 8.0A, 7.4A, and 6.9A, then its maximum amplitude is 8.0A and its average amplitude is 7.12A. Subsequently, the phase information in the same sequence is read. The phase is obtained by summing the phase angles of the three-phase currents at the same sampling point using the terminal. The phase sequence is then expanded to avoid abrupt phase changes. For example, if the phase sequence corresponding to B01 is 22°, 25°, 28°, 29°, and 27°, the average phase is taken as 26.2°. Next, the acquired zero-sequence current amplitude and phase characteristics are matched sequentially with parameters such as the dominant frequency position, energy peak, and trigger delay in the transient characteristics. The matching process involves subtracting the zero-sequence amplitude from the transient peak value for the same branch, subtracting the mean phase value from the dominant frequency offset, and subtracting the trigger delay from the phase change rate. Each difference is calculated using absolute values ​​to obtain the specific difference amount. For example, if the transient peak value of a branch B02 is 15dB, corresponding to a zero-sequence amplitude of 7.8A, a reference ratio can be set to 1.5 for every 1A increase in amplitude corresponding to an energy increase. If dB, the theoretical energy is 11.7dB, and the actual difference from the theoretical value is 3.3dB. If the recorded main frequency of the same branch is 3.2kHz and the average zero-sequence phase is 30°, the reference ratio can be set as 0.1kHz for a main frequency offset of every 10° of phase. Then the theoretical offset main frequency is 3.3kHz, and the actual difference is 0.1kHz. By summarizing the above differences, the difference calculated for each branch is recorded as the zero-sequence current difference value. Finally, all the branch differences are sorted and numbered to generate a set of zero-sequence current difference values.

[0031] S302: Based on the zero-sequence current difference value set, compare the zero-sequence current difference between adjacent branches, mark the branches whose difference exceeds the set threshold, correct the fault type of the marked branches, and generate a fault type correction dataset. In a preferred embodiment, based on the zero-sequence current difference value set, the zero-sequence current difference between adjacent branches is compared. During the comparison, all branch difference records in the difference value set are first read, and then the difference values ​​of any two adjacent branches are compared in pairwise according to topological order. The comparison is completed by taking the absolute value of the difference and comparing it with a set threshold. The threshold is set based on the difference distribution range between normal and faulty branches in historical operating data. Generally, an amplitude difference greater than 3A, a phase difference greater than 20°, and an energy difference greater than 3dB are considered abnormal. Therefore, a unified threshold of 4 units for the comprehensive difference can be set. In actual calculations, for example, the difference value of branch B03 is 4.8, and the difference value of B04 is 1.9. If the difference is 2.9, it will not be marked if it does not exceed the threshold. However, if the difference of B05 is 7.2 and the difference with the adjacent branch B04 is 5.3, exceeding the threshold of 4, then B05 will be marked, and a type correction prompt will be added when marking it. By reading the main frequency and delay records corresponding to the branch in the transient characteristics, its fault type is corrected to single-phase grounding or phase-to-phase fault according to the zero-sequence characteristic offset direction. For example, when the zero-sequence amplitude increases significantly and the phase is concentrated in the 20° to 40° range, it is judged as single-phase grounding. The reason is that the zero-sequence current of the corresponding branch in this range changes a lot. The correction action is to rewrite the original fault type field to a new type field. Finally, all the corrected branch numbers and their correction types are formatted and recorded to generate a fault type correction dataset.

[0032] S303: Based on the fault type correction dataset, compare the zero-sequence current distribution pattern of the corrected branch with that of the adjacent branch to identify the accuracy of the correction result, and combine the corrected fault type with the branch number information to generate the fault section location result. In a preferred embodiment, based on the fault type correction dataset, the zero-sequence current distribution patterns of the corrected branch and adjacent branches are compared. During the comparison, the type and corresponding zero-sequence amplitude range of each branch in the correction dataset are first read. Then, the amplitude sequence and phase sequence of adjacent branches are extracted from the original zero-sequence current distribution record. The difference between the two is calculated using the same sampling points. The absolute value of the difference is taken and averaged over the sequence length. For example, the corrected amplitude range of branch B06 is 8A to 10A, and the zero-sequence amplitude of adjacent branch B07 is 5A to 6A. The average difference between the two is 3.2A. Then, the phase... The sequence is processed in the same way. If the average phase difference between branches is greater than 20°, the two are judged to be inconsistent and the result is recorded as low confidence level. If the difference between adjacent branches is kept within the set confidence range, such as amplitude difference less than 3A and phase difference less than 15°, it is recorded as high confidence level. Through this line-by-line comparison method, the accuracy of all corrected branch results is identified. The confidence level after identification is combined with the branch number and stored. Finally, the corrected fault type is output in the order of branch number, and the effective segment range is defined according to the confidence level to generate the fault segment location result.

[0033] S4: Based on the fault section location results and combined with the topology of the distribution network automation system, a corresponding isolation command is generated and sent to the corresponding switching equipment. After the command is sent, the action status and execution feedback information of the switching equipment are collected, and the distribution network operation status data after the isolation operation is recorded synchronously. Please see Figure 5 In this embodiment, the step of obtaining S4 is as follows: S401: Based on the fault section number in the fault section location result, detect the status of the switching equipment corresponding to each fault section, generate a switching action sequence according to the action priority of the switching equipment, determine the isolation time window, and generate a set of switching action sequences. In a preferred embodiment, the unique identification number of each fault segment is extracted from the fault segment location results. For each number, the corresponding list of switchgear is searched. The equipment information must include basic data such as switch number, location on the line, current status, and control permissions. Then, the operating status of the current switchgear is read, where the status item is divided into multiple labels such as "closed," "open," and "fault locked." For example, branch number B08 corresponds to switchgear SW21, whose status is "closed" and control permission is "automatic." Next, the priority of the involved switchgear is determined according to a preset action priority table. This priority is set based on the degree of association between the switch and the main line, load branch, and power supply. The priority levels are divided into 1 to 5, with lower levels indicating higher priority. If SW21 is associated with the main line, load branch, and power supply... The main line has no direct connection and is far from the power source, so its priority is set to 3. Conversely, if switch SW05 is close to the power source and connected to the main line, its priority is 1. Continue to sort all the switching devices in ascending order of priority, and sort the switches of the same priority in order of node distance to obtain a complete action sequence. Each step in this sequence represents the opening or closing operation of a switch. Set the isolation time window according to the status response delay of the switching device. The time window refers to the time from the first switch issuing the action command to the completion of the feedback of the last action. Assuming that the action delay is calculated at 0.5 seconds per switch, and there are 4 levels of action sequence, the window is set to 2 seconds. Record the start and end time of the action corresponding to each switch, and integrate them to form a set of switch action sequences with the switch number as the main key, the action status as the identifier, and the timestamp as the content.

[0034] S402: Call the switch action sequence set and the topology information within the same distribution network line, calculate the impact of each switch action on non-fault sections, filter out the switch action combination with the least impact, and generate an optimized switch action sequence; Specifically, the process involves reading the switch number and action status record of each switch in the action sequence set, reading the connection relationships between all nodes in the topology, mapping the switch equipment to nodes in the topology graph, identifying the boundary between faulty and non-faulty branches by constructing a branch graph, simulating the impact of each action on the non-faulty section, and calculating the impact level by weighting three data points: the area of ​​power outage caused by disconnection, the number of affected loads, and the status of power connection line blockage. Specifically, for each action executed, the number of users experiencing power outages, the length of the out-of-power cable, and the number of connecting switches in the non-faulty area are counted. For example, if disconnecting SW21 causes power outages for 5 transformers, a total out-of-power line length of 1.2km, and blockage of 2 connecting switches, the impact score for this combination is recorded as the sum of the three standardized indicators. The impact values ​​of all action combinations are compared sequentially, and the combination with the lowest total score is selected as the one with the smallest impact. If multiple combinations have the same impact value, the action combination with the lower priority is selected first. Finally, the action sequence with the lowest score is selected as the optimized sequence for switch actions, and an optimized sequence record is established with action order, equipment number, and action type as fields.

[0035] S403: Based on the optimized sequence of switch actions and the recovery strategy of the distribution network automation system, generate corresponding isolation instructions, send the isolation instructions to the corresponding switch equipment, record the action status and execution feedback information of the switch equipment, and synchronously collect the distribution network operation status data after the isolation operation; Specifically, based on the optimized sequence of switch actions and the recovery strategy of the distribution network automation system, the action command content and corresponding switch number in the optimized sequence are read one by one. Each action is converted into a command format, and the command content consists of the switch number, the target action status, and the execution time. For example, if SW21 needs to be disconnected in the action sequence, the generated command content is "SW21→Disconnect→Timestamp". According to the execution strategy set by the distribution network automation, the command is sent to the corresponding switch control port through the communication channel. After the command is sent, the switch status changes are monitored in real time, and the status feedback information after the switch execution is collected. The feedback information includes fields such as execution result (success / failure), actual action time, and abnormal information. The operating voltage and current data of the equipment that has performed the action are collected after the operation. If the zero-sequence current decreases or the load current stabilizes in the operating data, the data is recorded and added to the operating status dataset. All feedback information and collected data of all switch equipment are checked one by one, and abnormal actions are marked and registered. Finally, a complete isolated record containing command issuance, execution feedback, and status data is formed.

[0036] S5: Based on the distribution network operation status data after the isolation operation, the distributed computing model is called to analyze the power supply restoration status of non-faulty sections. Combined with real-time monitoring information, the execution effect of the isolation command is evaluated, and a distribution network operation status evaluation report is generated accordingly.

[0037] Specifically, the transient feature dataset includes high-frequency transient feature values, transient current asymmetry changes, and terminal spatial location information; the fault branch candidate set includes transient synchronization difference coefficients, branch numbers, and neighboring terminal association information; the fault section location results include fault section numbers, fault type identifiers, and isolation priority parameters; the isolation instructions include switch action sequences, isolation time windows, and recovery strategies; and the distribution network operation status assessment report includes isolation instruction execution time, power supply restoration efficiency, and fault section isolation success rate.

[0038] Please see Figure 6 The steps to obtain S5 are as follows: S501: Based on the distribution network operation status data after isolation operation, detect the power restoration time of each non-faulty section, compare each section item by item according to the set power restoration time threshold, calculate the difference between the power restoration time and the threshold, and judge the power restoration status of each section based on the difference, and generate a power restoration time difference set. Specifically, based on the distribution network operation status data after isolation operations, the voltage recovery time and current recovery time recorded for each non-faulty section are first read. The reading process is performed record by record, indexed by section number. The sampling point where the voltage reaches the steady-state threshold is set as the voltage recovery time, and the sampling point where the current reaches the steady-state threshold is set as the current recovery time. The later of the two times is then taken as the final power supply recovery time for that section. For example, if the voltage recovery time for section Z12 is 8.4 seconds and the current recovery time is 9.1 seconds, then the power supply recovery time is taken as 9.1 seconds. Based on this, a power restoration time threshold is set. This threshold is set according to line experience and historical operating data, and is usually between 6 and 10 seconds. It is set to 8 seconds. The restoration time of each section is compared with the threshold item by item. The comparison method is to subtract the threshold from the restoration time to obtain the difference. If the restoration time is 9.1 seconds, the difference is 1.1 seconds. If the restoration time is 7.5 seconds, the difference is -0.5 seconds. By judging the positive and negative of the difference, the difference is recorded as the restoration offset value and archived according to the section number, finally forming a power restoration time difference set.

[0039] S502: Call the numerical data in the power restoration time difference set, filter out the segments with power restoration time less than or equal to the minimum threshold based on the minimum threshold and the difference range, and set them as high-efficiency restoration segments. Define the segments with power restoration time difference between the threshold and the minimum threshold as medium-efficiency restoration segments, and generate a restoration efficiency division interval value group. Specifically, in this embodiment, the power restoration time difference values ​​are read one by one from the set and sorted by segment number. Then, a minimum threshold for power restoration time is set. This minimum threshold is determined based on the characteristics of the user-side sensitive load and is usually set to 5 seconds. Subsequently, a filtering action is performed to judge each difference value. The judgment rule is that if the power restoration time of a segment is less than or equal to the minimum threshold, its restoration difference must be negative and its absolute value must be greater than or equal to the difference between the threshold and the minimum threshold. For example, if the threshold is 8 seconds and the minimum threshold is 5 seconds, then a difference of -3 seconds meets the condition, and the segment is then designated as [missing information]. For high-efficiency recovery segments, add their numbers to the high-efficiency recovery segment set. Then, judge the recovery time between the minimum threshold and the set threshold. If the recovery time is 6.7 seconds, the difference is -1.3 seconds, which is in the range of -3 to 0. Record it in the medium-efficiency recovery segment set. According to the above judgment logic, group the recovery efficiency of all segments by interval, and record the interval boundary value as the recovery efficiency interval value group. The recorded content includes the high-efficiency interval (0 to -3 seconds) and the medium-efficiency interval (segments between 0 and -3 seconds). Finally, output the recovery efficiency interval value group.

[0040] S503: Based on the remaining data in the interval value group and the power supply recovery time difference set according to the recovery efficiency, determine the section where the power supply recovery time is greater than or equal to the set threshold, merge the recovery efficiency value of each section with the corresponding spatial coordinates and section number, integrate the recovery value distribution according to the efficiency interval, and generate a distribution network operation status assessment report. Sections not classified into the high-efficiency and medium-efficiency recovery zones within the difference set are identified. These sections must have a recovery time greater than or equal to a set threshold; for example, a recovery time of 9.1 seconds corresponds to a difference of 1.1 seconds, and these sections are classified as low-efficiency recovery sections. Subsequently, recovery efficiency values ​​are calculated for each low-efficiency, medium-efficiency, and high-efficiency section. The efficiency value is the absolute value of the difference and mapped according to the interval correspondence. For example, a difference of 1.1 seconds is mapped to low-efficiency level 3, a difference of -1.2 seconds is mapped to medium-efficiency level 2, and a difference of -3 seconds is mapped to high-efficiency level 1. Then, the spatial coordinate data corresponding to each section is read. The coordinates are based on the node XY values ​​recorded in the line GIS and merged with the section number and efficiency level to form a section recovery efficiency record table. Each record in the table consists of a section number, recovery efficiency level, and corresponding spatial point. Finally, the recovery efficiency of all sections is integrated according to the efficiency level, and the three types of efficiency data (high-efficiency, medium-efficiency, and low-efficiency) are output as numerical distribution results according to the overall spatial layout, generating a distribution network operation status assessment report.

[0041] On the one hand, the present invention also provides a system for precise location and isolation of ground fault sections based on multi-edge terminal data association, the system comprising: The transient feature dataset construction module is used to acquire the three-phase current and zero-sequence voltage signals of multiple edge terminals in the distribution network line, extract high-frequency transient feature values, identify potential fault branches based on the change in transient current asymmetry, and generate a transient feature dataset. The fault branch candidate set generation module is used to calculate the transient synchronicity difference between adjacent terminals based on the high-frequency transient feature values ​​in the transient feature dataset and the spatial location information of each terminal, identify areas with inconsistent transient changes, and generate a fault branch candidate set. The fault section location module is used to analyze the zero-sequence current distribution law by calling a distributed computing model based on the transient characteristic values ​​of each branch in the candidate set of fault branches, and to correct the fault branch range by combining the fault phase change information and generating the fault section location result. The isolation command determination module is used to generate corresponding isolation commands based on the fault section location results and the topology of the distribution network automation system, and to send the commands to the corresponding switching equipment. After the commands are sent, the module collects the action status and execution feedback information of the switching equipment, and synchronously records the distribution network operation status data after the isolation operation.

[0042] The other technical features of the ground fault section accurate location and isolation system based on multi-edge terminal data association described in this embodiment are similar to the corresponding ground fault section accurate location and isolation method based on multi-edge terminal data association, and will not be repeated here.

[0043] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this application.

[0044] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0045] The electronic device includes: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform a method for precise location and isolation of ground fault segments based on multi-edge terminal data association as provided in any one or more of the above embodiments. The electronic device includes: one or more central processing units (CPUs), and interfaces for connecting various components, such as displays, infrared sensors, and cameras. That is, the various components are interconnected using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processor can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory sets, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations. The components, their connections and relationships, and their functions shown in this embodiment are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0046] In a preferred embodiment of this invention, the electronic device may further include an input device and an output device. The processing unit, memory, input device, and output device may be connected via a bus or other means.

[0047] The input device can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. The output device may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The display device may include, but is not limited to, liquid crystal displays, light-emitting diode displays, and plasma displays. In some embodiments, the display device may be a touchscreen.

[0048] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device (e.g., a cathode ray tube or LCD monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback); and input from the user can be received in any form (e.g., voice input or tactile input).

[0049] In this embodiment, a computer-readable medium stores a computer program / instruction, which, when executed by a processor, implements the method for precise location and isolation of ground fault sections based on multi-edge terminal data association provided in any one or more of the above embodiments. This computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into that device. The aforementioned computer-readable medium carries one or more computer-readable instructions.

[0050] Memory can serve as a non-transitory computer-readable storage medium, used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The central processing unit executes various server functions and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the program instructions / modules corresponding to the methods provided in any one or more of the embodiments described above in this application.

[0051] The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device, etc. Furthermore, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, and these remote memories may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0052] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0053] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. 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, static random access memory, dynamic random access memory, other types of random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technologies, read-only optical discs, digital versatile optical discs or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0054] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0055] In the above embodiments, all or part of the implementation can be achieved through software, hardware, firmware, or any combination thereof. For example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the above steps or functions. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, and similar devices. In addition, some steps or functions of this application can be implemented in hardware, for example, as circuitry that cooperates with a processor to perform the various steps or functions.

[0056] The computer program product provided in this application includes one or more computer programs / instructions. When executed by a processor, these computer programs / instructions generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0057] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0058] The scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to encompass all variations falling within the meaning and scope of equivalents of the claims. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device in software or hardware. Terms such as "first," "second," etc., are used only for distinguishing descriptions and do not indicate any particular order, nor should they be construed as indicating or implying relative importance.

[0059] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A method for accurate location and isolation of ground fault sections based on multi-edge terminal data association, characterized in that, Includes the following steps: S1: Obtain the three-phase current and zero-sequence voltage signals of multiple edge terminals in the distribution network line, extract high-frequency transient feature values, identify potential fault branches based on the change in transient current asymmetry, and generate a transient feature dataset. S2: Based on the high-frequency transient feature values ​​in the transient feature dataset, combined with the spatial location information of each terminal, calculate the transient synchronization difference between adjacent terminals, identify areas with inconsistent transient changes, and generate a candidate set of fault branches. S3: Based on the transient characteristic values ​​of each branch in the candidate set of fault branches, call the distributed computing model to analyze the zero-sequence current distribution law, combine the fault phase change information, correct the fault branch range, and generate the fault section location result. S4: Based on the fault section location results and the topology of the distribution network automation system, a corresponding isolation command is generated and sent to the corresponding switching equipment. After the command is sent, the action status and execution feedback information of the switching equipment are collected, and the distribution network operation status data after the isolation operation is recorded synchronously.

2. The method for accurate location and isolation of ground fault sections based on multi-edge terminal data association according to claim 1, characterized in that, The steps for obtaining S1 are as follows: S101: Collect the three-phase current and zero-sequence voltage signals of multiple edge terminals in the distribution network line, extract high-frequency transient feature values ​​for each terminal, calculate the asymmetry change of the three-phase current, mark the terminals with asymmetry change exceeding the set threshold as potential fault branches, and generate a set of transient current asymmetry change values. S102: Based on the set of transient current asymmetry changes, calculate the frequency domain distribution characteristics of high-frequency transient characteristic values ​​for each terminal, classify and judge according to the frequency domain distribution characteristics and the set transient characteristic threshold range, select the set of terminals that meet the fault characteristics, and generate a set of high-frequency transient characteristic values. S103: Call the high-frequency transient feature value set, combine it with the spatial location information of each terminal, detect the transient synchronization difference between terminals, extract potential fault branches based on the synchronization difference threshold, integrate the relevant terminal numbers and transient feature information, and generate a transient feature dataset.

3. The method for precise location and isolation of ground fault sections based on multi-edge terminal data association according to claim 1, characterized in that, The steps for obtaining S2 are as follows: S201: Extract high-frequency transient feature values ​​from the transient feature dataset, obtain the spatial location information of each terminal, match this information with the topology of the distribution network line, calculate the transient synchronization difference coefficient between adjacent terminals, correct the terminal association relationship, and generate a terminal synchronization difference parameter set. S202: Based on the terminal synchronization difference parameter set, sort the transient synchronization difference coefficients between adjacent terminals, measure the consistency of transient changes between terminals, and mark terminals with consistency below a set threshold as candidates for fault branches to obtain the transient synchronization difference coefficient set. S203: Based on the transient synchronization difference coefficient set, and combined with the spatial location of neighboring terminals and the distribution law of transient characteristic values, calculate the transient change rate and synchronization deviation ratio of each neighborhood, compare the values ​​with the set fault branch threshold, dynamically adjust the fault branch candidate range, and generate a fault branch candidate set. The neighboring terminals are the adjacent terminals corresponding to all the marked fault branch candidate terminal pairs extracted from the transient synchronization difference coefficient set. The transient change rate is the average of the main frequency difference and time difference ratio between terminals in the same area. The synchronization deviation ratio is defined as the deviation ratio between the trigger time delay of different terminals in the neighborhood and the average value.

4. The method for precise location and isolation of ground fault sections based on multi-edge terminal data association according to claim 1, characterized in that, The steps for obtaining S3 are as follows: S301: Based on the transient characteristic values ​​of each branch in the candidate set of fault branches, obtain the zero-sequence current distribution law of the corresponding branch, extract the amplitude and phase information of the zero-sequence current, match the zero-sequence current distribution law with the transient characteristic values, obtain the zero-sequence current difference between branches, and thus generate a set of zero-sequence current difference values. S302: Based on the zero-sequence current difference value set, compare the zero-sequence current differences between adjacent branches, mark the branches whose differences exceed the set threshold, correct the fault type of the marked branches, and generate a fault type correction dataset. S303: Based on the fault type correction dataset, compare the zero-sequence current distribution pattern of the corrected branch with that of the adjacent branch, identify the accuracy of the correction result, and combine the corrected fault type with the branch number information to generate the fault section location result.

5. The method for precise location and isolation of ground fault sections based on multi-edge terminal data association according to claim 1, characterized in that, The steps for obtaining S4 are as follows: S401: Based on the fault section number in the fault section location result, detect the status of the switching equipment corresponding to each fault section, generate a switching action sequence according to the action priority of the switching equipment, determine the isolation time window, and generate a set of switching action sequences. S402: Call the set of switch action sequences and the topology information within the same distribution network line, calculate the impact of each switch action on the non-faulty section, filter out the switch action combination with the least impact, and generate an optimized switch action sequence; S403: Based on the optimized sequence of switch actions and the recovery strategy of the distribution network automation system, a corresponding isolation command is generated and sent to the corresponding switch equipment. The action status and execution feedback information of the switch equipment are recorded, and the distribution network operation status data after the isolation operation is collected synchronously.

6. The method for precise location and isolation of ground fault sections based on multi-edge terminal data association according to claim 1, characterized in that, The method further includes: S5: Based on the distribution network operation status data after the isolation operation, call the distributed computing model to analyze the power supply restoration status of non-faulty sections, combine real-time monitoring information to evaluate the execution effect of the isolation command, and generate a distribution network operation status evaluation report accordingly. The power distribution network operation status assessment report includes the isolation command execution time, power supply restoration efficiency, and fault section isolation success rate.

7. The method for precise location and isolation of ground fault sections based on multi-edge terminal data association according to claim 6, characterized in that, The steps for obtaining S5 are as follows: S501: Based on the distribution network operation status data after the isolation operation, detect the power supply recovery time of each non-faulty section, compare each section item by item according to the set power supply recovery time threshold, calculate the difference between the power supply recovery time and the threshold, and judge the power supply recovery status of each section based on the difference, and generate a power supply recovery time difference set. S502: Call the numerical data in the power restoration time difference set, filter out the segments with power restoration time less than or equal to the minimum threshold based on the minimum threshold and the difference range, and set them as high-efficiency restoration segments. Define the segments with power restoration time difference between the threshold and the minimum threshold as medium-efficiency restoration segments, and generate a restoration efficiency division interval value group. S503: Based on the remaining data in the interval value group and power restoration time difference set according to the restoration efficiency, determine the segment where the power restoration time is greater than or equal to the set threshold, merge the restoration efficiency value of each segment with the corresponding spatial coordinates and segment number, integrate the restoration value distribution according to the efficiency interval, and generate a distribution network operation status assessment report.

8. The method for precise location and isolation of ground fault sections based on multi-edge terminal data association according to claim 1, characterized in that, After obtaining the fault segment location result, the method further includes: classifying and identifying the zero-sequence current distribution patterns of different fault segments, grouping segments with similar zero-sequence current amplitude change trends, phase offset amplitudes and fault types into the same group, assigning corresponding fault mode labels to each, and adjusting the isolation priority and recovery strategy based on the fault mode labels during the subsequent isolation command generation process.

9. The method for precise location and isolation of ground fault sections based on multi-edge terminal data association according to claim 1, characterized in that, After obtaining the distribution network operation status assessment report, the process also includes: classifying and labeling the power supply restoration efficiency of different non-faulty sections, grouping sections with similar restoration time, load characteristics and topology into the same group, assigning corresponding restoration efficiency labels to each, and implementing differentiated power supply restoration optimization measures based on the restoration efficiency labels in subsequent operation status assessments.

10. A precise location and isolation system for ground fault sections based on multi-edge terminal data association, characterized in that, The system includes: The transient feature dataset construction module is used to acquire the three-phase current and zero-sequence voltage signals of multiple edge terminals in the distribution network line, extract high-frequency transient feature values, identify potential fault branches based on the change in transient current asymmetry, and generate a transient feature dataset. The fault branch candidate set generation module is used to calculate the transient synchronicity difference between adjacent terminals based on the high-frequency transient feature values ​​in the transient feature dataset and the spatial location information of each terminal, identify areas with inconsistent transient changes, and generate a fault branch candidate set. The fault section location module is used to analyze the zero-sequence current distribution law by calling a distributed computing model based on the transient characteristic values ​​of each branch in the candidate set of fault branches, and to correct the fault branch range by combining the fault phase change information and generating the fault section location result. The isolation command determination module is used to generate corresponding isolation commands based on the fault section location results and the topology of the distribution network automation system, and to send the commands to the corresponding switching equipment. After the commands are sent, the module collects the action status and execution feedback information of the switching equipment, and synchronously records the distribution network operation status data after the isolation operation.

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