Low-voltage distribution network power supply line electric leakage fault positioning method and device, and storage medium

Abstract

The invention discloses a low-voltage distribution network power supply line electric leakage fault positioning method and device and a storage medium, and relates to the technical field of power grid power supply safety. The positioning method comprises the following steps: acquiring historical electrical quantity data of a target station area in a normal operation state, further calculating equivalent mutual impedance among user nodes, and determining an anomaly judgment threshold value; acquiring actually measured electrical quantity data of the target station area when the electric leakage fault occurs, and further calculating current equivalent mutual impedance among the user nodes; comparing the current equivalent mutual impedance with a corresponding anomaly determination threshold to identify an abnormal mutual impedance; for each user node, counting the number of identified abnormal mutual impedance in the current equivalent mutual impedance between the user node and all other user nodes; and determining the user node with the maximum number of abnormal mutual impedance as a priority troubleshooting object of the electric leakage fault. According to the method, the problem of large calculation error of a virtual impedance method depending on strong hypothesis is solved, and the troubleshooting efficiency is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power grid power supply safety, and particularly relates to a low-voltage power distribution network power supply line leakage fault positioning method and device and a storage medium. BACKGROUND

[0002] The low-voltage power distribution network directly serves the daily life and production areas of users, and the electrical equipment and power supply lines thereof are widely distributed and have a large number of nodes. While providing power support for various users, the non-standard installation of equipment in some places, the complex operating environment, the lack of electrical safety awareness of personnel and other problems have brought about prominent safety hazards. In particular, the grounding faults (leakage faults) of the power supply lines occur frequently and have the characteristics of concealment and intermittence, which brings great difficulties to manual on-site troubleshooting and positioning. Such faults not only significantly increase the risk of electric shock and electrical fire, but also lead to an increase in line loss of the power distribution area, directly affecting the economy and power supply reliability of the power grid operation. Therefore, realizing rapid and accurate line leakage fault positioning is a key technical requirement for improving the level of user power safety and reducing the line loss of the low-voltage power distribution area.

[0003] The occurrence of a leakage fault changes the electrical connection relationship between users in the low-voltage power distribution network, and then affects the line parameters between users. At present, some researches have attempted to locate the fault by analyzing the electrical quantity changes on the user side, for example, based on the stability assumption of the user loop impedance, the virtual loop impedance of the user is calculated and the characteristic mutation thereof is captured to realize abnormal perception and positioning. However, such a method has obvious deficiencies in actual application:

[0004] On the one hand, it is difficult to accurately evaluate the actual distribution of the trunk current in the power supply line, and usually needs to rely on strong assumptions for approximate substitution, resulting in a large error in the calculation result; on the other hand, for leakage faults that exist continuously or intermittently and change slowly, the traditional method has limited ability to capture fault characteristics and is insufficient in adaptability, and it is difficult to realize long-term effective fault monitoring and positioning.

[0005] Therefore, how to make full use of the existing area and user side metering data (such as the voltage and current information collected by the intelligent electric meter) to break through the limitations of the traditional method and develop a technical solution capable of accurately and quickly positioning the leakage fault of the low-voltage power distribution network has become an important technical problem to be solved in the field of power system operation and maintenance. SUMMARY

[0006] In view of the above-mentioned defects in the prior art, the purpose of the present application is to provide a low-voltage power distribution network power supply line leakage fault positioning method and device and a storage medium to solve the problems of low efficiency of manual troubleshooting, large calculation error of the virtual impedance method relying on strong assumptions, and insufficient detection capability for continuous and intermittent leakage faults in the leakage fault positioning scenario of the low-voltage power distribution network.

[0007] The application solves the above technical problems by the following technical scheme: a low-voltage power distribution network power supply line electric leakage fault positioning method, comprising:

[0008] acquiring historical electrical quantity data of a target substation in a normal operation state;

[0009] based on the historical electrical quantity data, calculating equivalent mutual impedances between each user node, and determining abnormality determination thresholds of the equivalent mutual impedances between each user node according to calculation results of multiple normal time periods;

[0010] acquiring measured electrical quantity data of the target substation when an electric leakage fault occurs, and calculating current equivalent mutual impedances between each user node based on the measured electrical quantity data;

[0011] comparing each current equivalent mutual impedance with a corresponding abnormality determination threshold to identify abnormal mutual impedances;

[0012] for each user node, counting the number of current equivalent mutual impedances between the user node and all other user nodes that are identified as abnormal mutual impedances;

[0013] determining a user node with the largest number of abnormal mutual impedances as a priority object for electric leakage fault investigation.

[0014] The application directly calculates or fits to obtain equivalent mutual impedances between nodes based on collected electrical quantity data (voltage and load current data of each user node). The method does not need to know or assume the actual distribution of currents on the power distribution trunk line, thereby fundamentally avoiding systematic modeling errors caused by inaccurate current distribution assumptions, and improving the objectivity and reliability of parameter calculation.

[0015] The reliability of the application does not depend on the accuracy of the absolute value of the equivalent mutual impedance, but on the relative change of the same mutual impedance in normal and fault states, and the relative comparison of the number of abnormal mutual impedances between different user nodes. This relative analysis based on actual operation data reduces the dependence on the absolute accuracy of the calculation model, and improves the robustness and adaptability of the method under actual complex working conditions.

[0016] The application determines the abnormality determination threshold of mutual impedance by analyzing the historical electrical quantity data of multiple normal periods, and the threshold can truly reflect the natural fluctuation range of the system in the fault-free operation state. Therefore, the significant parameter deviation caused by the leakage fault and the inherent random fluctuation in the normal operation of the system can be effectively distinguished. As long as the mutual impedance change caused by the fault exceeds its historical normal fluctuation range, it can be identified as abnormal. Therefore, no matter whether the leakage fault is persistent, intermittent or slow development, as long as it has a sufficient impact on the line parameters during the occurrence period, it can be effectively captured by the method of the application. The detection capability of the method of the application is not limited by the fault duration or form, and has good recognition effect on hidden and intermittent faults.

[0017] The application automatically processes the data of the transformer area electric meter by algorithm, and outputs clear "priority investigation object", i.e. the user node with the largest number of abnormal mutual impedances. This completely changes the backward mode of relying on manual experience for "net-style" comprehensive inspection, and accurately narrows down the fault positioning range from the entire transformer area covering dozens to hundreds of users to a specific user node and its related power supply line, thereby greatly improving the fault investigation efficiency, significantly reducing the manual inspection cost and time consumption, and providing clear and operable technical guidance for field operation and maintenance.

[0018] Further, the historical electrical quantity data and the measured electrical quantity data each include voltage phasors and load current phasors of each user node.

[0019] The application analyzes the voltage phasors and load current phasors of each user node, and the data source is standardized and easy to obtain, without the need to add special sensors, directly using the complete electrical phasor information collected by the existing smart meter, which not only reduces the implementation cost, but also ensures the consistency and reliability of the data, and lays a real and unified data foundation for subsequent accurate calculation of equivalent mutual impedance.

[0020] Further, the equivalent mutual impedance between each user node is calculated, specifically including:

[0021] According to Kirchhoff's law, a multiple linear equation set is established with the voltage difference between user nodes as the dependent variable and the load current of each user as the independent variable;

[0022] Based on the historical electrical quantity data or the measured electrical quantity data, the least square method is used to solve the multiple linear equation set to obtain the equivalent mutual impedance between each user node.

[0023] The method of the application constructs a linear equation set describing network topology by Kirchhoff's law, and solves it by using the least square method, can robustly fit the equivalent mutual impedance between user nodes based on the measurement data with noise in actual operation, does not need to know the accurate network topology structure or perform complex high-order modeling, significantly reduces the dependence of the model on data quality and system priori knowledge while ensuring the calculation accuracy, and improves the applicability and reliability of the method in the actual distribution network.

[0024] Further, the root mean square error of the difference between the measured value and the calculated value of the voltage difference is minimized as the objective function when solving by using the least square method.

[0025] The application minimizes the root mean square error of the difference between the measured value and the calculated value of the voltage difference as the target, effectively balances the influence of the error of each measurement point, avoids excessive interference of individual abnormal data on the overall fitting result, thereby improving the overall accuracy and robustness of the equivalent mutual impedance solution, and makes the obtained parameters more truly reflect the average state of the operation of the distribution network, and lays a reliable foundation for accurate extraction of subsequent fault features.

[0026] Further, the determination of the abnormal determination threshold of the equivalent mutual impedance between each user node comprises:

[0027] The equivalent mutual impedance between each user node is calculated in a plurality of normal time periods;

[0028] For each specific pair of user nodes, the maximum value of the equivalent mutual impedance in all normal time periods is taken as the abnormal determination threshold of the equivalent mutual impedance between the specific pair of user nodes.

[0029] The method of the application takes the historical maximum value of each mutual impedance in a plurality of normal time periods as the abnormal determination threshold, fully considers the upper limit of the natural fluctuation when the system is normally operated, can effectively distinguish the significant parameter deviation caused by faults from normal random fluctuations, ensures the fault sensitivity while minimizing the false alarm rate, and improves the adaptability and scenario adaptability of the fault criterion.

[0030] Further, the calculation of the equivalent mutual impedance between each user node in a plurality of normal time periods comprises:

[0031] A plurality of equal-length normal time periods are sequentially intercepted from the historical electrical quantity data in a sliding time window manner, wherein adjacent normal time periods partially overlap or are connected end to end on the time axis;

[0032] The equivalent mutual impedance between each user node is calculated in each normal time period corresponding to each sliding time window.

[0033] The method can continuously intercept normal period data through a sliding time window, can dynamically and densely cover historical running states, can more comprehensively and meticulously capture natural fluctuation characteristics of the system in the fault-free case, can provide sufficient data basis for setting an adaptive and highly reliable abnormality judgment threshold, and enhances the adaptive capacity of the fault diagnosis system to the gradual change of the running environment and the change of the load mode.

[0034] Further, the abnormal mutual impedance is identified, and specifically, if the current equivalent mutual impedance is greater than the corresponding abnormality judgment threshold, the current equivalent mutual impedance is determined as the abnormal mutual impedance.

[0035] The judgment rule of the method is simple and clear, and has high calculation efficiency, can directly achieve rapid and batch abnormal state identification based on a preset threshold, avoids the calculation overhead required by complex pattern recognition or dynamic threshold adjustment, is beneficial to deployment in an embedded device or a real-time system, and ensures the timeliness and implementability of the fault positioning process.

[0036] Further, the number of the abnormal mutual impedances is counted, and specifically includes:

[0037] A mutual impedance set is constructed for each user node, and the set includes the current equivalent mutual impedances between the user node and all other user nodes in the transformer area;

[0038] The number of the abnormal mutual impedances in the mutual impedance set is counted as the number of the abnormal mutual impedances of the user node.

[0039] The method constructs and counts the abnormal number in the associated mutual impedance set of each user node, converts the complex whole-network topology association analysis into the calculation of a single node quantitative index, not only significantly reduces the analysis dimension and the calculation complexity, but also makes the "aggregation effect" of the fault influence intuitive, and provides clear and comparable judgment basis for accurately locking the user node most significantly affected by the fault.

[0040] Based on the same concept, the application further provides an electronic device, including a memory, a processor, and a computer program or instructions stored in the memory, and the processor executes the computer program or instructions to implement the low-voltage power distribution network power supply line leakage fault positioning method.

[0041] Based on the same concept, the application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program or instructions, and the computer program or instructions are executed by a processor to implement the low-voltage power distribution network power supply line leakage fault positioning method.

[0042] Compared with the prior art, the present application realizes the following main beneficial effects by calculating and comparing the relative changes of equivalent mutual impedance between user nodes in normal and fault states based on existing smart meter data:

[0043] The unique priority troubleshooting object is automatically output, the fault range is accurately narrowed from the entire transformer area to a single user node related line, the troubleshooting efficiency is greatly improved, and the labor cost is reduced;

[0044] The method is not dependent on the main line current assumption and the impedance absolute value accuracy, is not sensitive to data noise and model error by comparing the relative changes with the historical fluctuation threshold, and is suitable for various electric leakage faults such as continuous and intermittent electric leakage faults;

[0045] The method fully utilizes the existing smart meter data, does not need to modify the line or add special monitoring equipment, and is easy to deploy and popularize on a large scale;

[0046] The threshold is generated based on historical data statistics, the manual experience setting is avoided, the fault identification is more objective, the system running state can be adaptively updated, and the false alarm and missed alarm are reduced. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only one embodiment of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 is a flow chart of the electric leakage fault positioning method of the low-voltage power distribution network power supply line in the embodiment of the present application;

[0049] Figure 2 is a calculation result diagram of abnormal mutual impedance of each user node in the electric leakage state in the embodiment of the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the present application will be described clearly and completely in combination with the drawings in the embodiment of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the present application.

[0051] The technical solutions of the present application will be described in detail in combination with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0052] Embodiment one

[0053] As Figure 1 shown, the low-voltage power distribution network power supply line leakage fault positioning method provided by the embodiment of the application comprises the following steps:

[0054] Step S1: Obtain historical electrical quantity data of the target substation area under normal operating state.

[0055] In order to establish a benchmark for leakage fault positioning, the historical electrical quantity data of the target power distribution substation area under normal operating state without fault needs to be systematically collected and constructed first. The historical electrical quantity data refers to the electrical quantity time series data automatically collected and recorded by the user-side metering device in a period of continuous time confirmed to have no leakage or other grounding faults. The specific implementation is as follows:

[0056] The historical electrical quantity data is mainly derived from the existing metering and monitoring system of the target substation area, including but not limited to: collecting the voltage phasor (usually the phase voltage amplitude and phase) and load current phasor of each user access point.

[0057] In order to ensure the representativeness and effectiveness of the historical electrical quantity data, the following conditions need to be met during the collection process:

[0058] During the selected historical period, the residual current monitoring value of the substation should be continuously lower than the safety threshold (for example, less than 500 mA), and there should be no leakage alarm record, so as to ensure that the data represents the normal state; the data sampling interval should meet the needs of dynamic process capture, preferably seconds or minutes (such as 15 minutes / time), so as to obtain an electrical quantity change sequence with sufficient time resolution; data of continuous multiple periods (such as continuous 7-30 days) should be collected to cover different date types (weekdays, weekends) and typical load change modes, so as to ensure the comprehensiveness of the data set; the voltage and current data should be recorded in the form of phasor (i.e. containing amplitude and phase information) to support subsequent accurate calculation based on circuit principles.

[0059] The raw data collected needs to be preprocessed to form high-quality data that can be directly used for calculation. The preprocessing of this embodiment includes data cleaning, i.e. automatically or manually removing missing data, obviously out-of-limit or invalid abnormal records caused by communication interruption or metering device abnormalities.

[0060] The time series data after preprocessing is organized into structured samples in chronological order. Each sample point corresponds to a sampling time t, and contains a voltage phasor set and a current phasor set of all N users at the sampling time t, where represents the voltage phasor (measured value) of the i-th user at the sampling time t; represents the load current phasor (measured value) of the i-th user at the sampling time t. The complete historical electrical quantity data is composed of M sample points in chronological order.

[0061] To verify the effectiveness of the method of the present application under controlled conditions and optimize the parameters, data can be obtained through simulation or experimental platform:

[0062] In a physical simulation experiment platform or a high-fidelity digital simulation model of a distribution network, a substation topology containing a typical number of users (e.g., 139 households) is constructed. In the simulation environment, the substation is set to be in a normal operating state without faults, and runs for a continuous period of time (e.g., 10 days) at the same sampling frequency (e.g., 15 minutes / time) as the actual system, and collects the voltage and load current time series data of all simulated user meters. This process can generate pure, noise-free and topology-known historical electrical quantity data (i.e., a baseline data set).

[0063] In actual engineering deployment, step S1 automatically extracts the historical electrical quantity data of the target substation for a specified period of time through a standard data interface (such as a power consumption information collection system or a distribution automation system), and performs preprocessing.

[0064] Through step S1, high-quality, time-series consistent and physically meaningful normal state historical electrical quantity data can be obtained, providing a solid data foundation for establishing an accurate "normal state baseline" in subsequent steps.

[0065] Step S2: Based on the historical electrical quantity data, the equivalent mutual impedance between each user node is calculated, and the abnormal determination threshold of the equivalent mutual impedance between each user node is determined according to the calculation results of multiple normal time periods.

[0066] Step S2 is the core of establishing a leakage fault diagnosis baseline, aiming to extract parameters describing the electrical coupling relationship between users from normal operation historical electrical quantity data, and quantifying the normal fluctuation range. The specific implementation is as follows:

[0067] S2.1: Calculation principle and model establishment of equivalent mutual impedance.

[0068] Equivalent mutual impedance is an electrical parameter that describes the degree of mutual influence between two user nodes due to common paths or electromagnetic coupling. Based on Kirchhoff's voltage law (KVL) and the superposition principle, a network model is established.

[0069] For any two user nodes i and j (i≠j) in the substation, at a certain time t, the voltage difference can be expressed as the sum of the voltage drops caused by all user load currents in the loop. Based on this, the following system of linear equations can be established:

[0070] (1)

[0071] where N represents the number of user nodes in the substation; represents the equivalent mutual impedance to be solved, whose physical meaning is: the load current of user node p the voltage difference between user node i and user node j the impedance component contributed by user node p. represents the modeling error and measurement noise. For p = i or j, represents the main influence of user node p on its own voltage difference; for p ≠ i or j, represents the mutual influence generated by the coupling of other user load currents through the network.

[0072] Substitute all user voltage difference and load current data of M consecutive sampling time points (for example, a time period containing 160 sample points) into formula (1) to construct an overdetermined equation group:

[0073] (2)

[0074] Thus we have: where, is an M × (N(N-1) / 2) dimensional voltage difference matrix, each column corresponding to the voltage difference sequence of a pair of user nodes i, j at M sampling time points; is an M × N dimensional current matrix, each column corresponding to the load current sequence of a user node; is an N × (N(N-1) / 2) dimensional equivalent mutual impedance matrix to be solved; is an error matrix.

[0075] S2.2: Parameter solving based on least squares method.

[0076] To robustly estimate parameters from noisy measurement data, the least squares method is used to solve:

[0077] Solving objective: find a set of estimates that minimize the overall squared deviation sum between the measured and model calculated values of all user node pair voltage differences at all sampling time points.

[0078] Objective function: minimize the squared residual sum (or root mean square error RMSE):

[0079] (3)

[0080] where, represents the Frobenius norm of a matrix (the square root of the sum of the squares of all elements).

[0081] Use normal equations or QR decomposition to solve the least squares problem in formula (3) to obtain the estimate .Each element in is the estimated equivalent mutual impedance value in the selected time period. i.e. the equivalent mutual impedance value estimated in the selected time period.

[0082] S2.3: Multi-time period calculation and determination of abnormality threshold.

[0083] To obtain a robust normal state reference, the above calculation is repeated in multiple normal time periods, and the abnormality threshold is statistically determined:

[0084] A sliding time window approach is adopted to sequentially extract Q (e.g. Q=6) equal-length normal time periods from the complete historical electrical quantity data. The length M of each normal time period should be sufficient to guarantee the statistical significance of parameter estimation (e.g. containing 160 sample points). Adjacent normal time periods can partially overlap or be concatenated in the time axis.

[0085] For the q-th (q=1,2,...,Q) normal time period, perform step S2.1 and step S2.2 to calculate the equivalent mutual impedance matrix estimate .

[0086] For each specific user node pair relationship (i.e. for each fixed i,j,p index combination), extract its equivalent mutual impedance calculation values in all Q normal time periods , ,..., }; take the maximum value in this set of equivalent mutual impedance calculation values as the abnormality threshold for this specific mutual impedance . That is:

[0087] (4)

[0088] Organize all in the same structure to form an abnormality threshold matrix with the same dimension as . The abnormality threshold represents the upper limit value of this specific mutual impedance under all observed normal operating states in history. Any new measurement calculation value exceeding this threshold indicates that the mutual impedance relationship has gone beyond the historical normal fluctuation range, possibly caused by a fault.

[0089] Through step S2, the system not only learns the coupling relationship model between users in normal state (the average trend of ), but more importantly, quantifies the natural random fluctuation range of these relationships in the absence of faults (abnormality threshold matrix ), which provides an objective and adaptive criterion for accurately and reliably identifying the significant abnormality caused by the electric leakage fault in the subsequent steps.

[0090] Step S3: Obtain the measured electrical quantity data of the target substation area at the time of the electric leakage fault, and calculate the current equivalent mutual impedance between each user node based on the measured electrical quantity data.

[0091] Step S3 is the core of online diagnosis, which aims to collect data and calculate the current system state parameters in real time after a possible electric leakage fault is detected, so as to compare with the normal benchmark. The specific implementation is as follows:

[0092] S3.1: Electric leakage fault triggering and data collection.

[0093] The "time of electric leakage fault" can be determined and trigger step S3 to execute in at least one of the following ways:

[0094] Residual current operated protective device alarm: triggered when the total residual current monitoring value of the substation area continuously exceeds the preset safety threshold.

[0095] Zero sequence current / voltage overrun: triggered when persistent abnormal rise of zero sequence current or zero sequence voltage is detected.

[0096] Scheduled diagnosis triggering: executed regularly according to the operation and maintenance plan, or manually triggered for diagnosis.

[0097] Once triggered, the system immediately acquires the measured electrical quantity data of the target substation area in the current electric leakage (or suspected electric leakage) period through the power utilization information collection system or the power distribution automation system in real time or quasi-real time. The data content and format of the measured electrical quantity data should be consistent with the historical electrical quantity data defined in step S1, specifically including:

[0098] Voltage phasor of each user node , and load current phasor of each user node .

[0099] Synchronous data of a continuous period is collected, and the length M' of the period should be the same as or comparable to the length M of the historical normal period used for calculation in step S2 (for example, also containing 160 consecutive sampling points), to ensure consistency of calculation. The period should be immediately after the triggering time or cover the period with obvious fault characteristics.

[0100] S3.2: Calculation of current equivalent mutual impedance.

[0101] The equivalent mutual impedance between each user node in the current fault period (i.e. current equivalent mutual impedance) is calculated, which has exactly the same principle as the calculation model established in step S2.1, but uses the real-time collected fault period data.​

[0102] The fault period data is used to construct a set of linear equations or matrix equations as follows: M′ The collected voltage and load current data are used to construct a set of linear equations or matrix equations as follows:

[0103] (5)

[0104] The matrix form is as follows:

[0105] (6)

[0106] where, and are the voltage difference matrix and load current matrix constructed from the fault period data, is the to-be-solved current equivalent mutual impedance matrix, which reflects the coupling relationship between user nodes under the current (possibly faulted) system state.

[0107] The above equation is solved by using the same parameter estimation algorithm (i.e., least squares method, aiming to minimize the sum of squared residuals) as step S2.2. That is:

[0108] (7)

[0109] The solved is the estimated value of the equivalent mutual impedance matrix calculated based on the current fault period data.

[0110] The output of step S3 is the current equivalent mutual impedance matrix Each element of the matrix represents the estimated corresponding mutual impedance value under the current operating state. The current equivalent mutual impedance matrix will be input and transmitted to the subsequent step S4 for element-by-element comparison with the abnormality determination threshold matrix determined in step S2.

[0111] Through step S3, the system quantifies the current (possibly abnormal) system state into a set of comparable parameters , thereby providing a direct data basis for identifying which mutual impedance relationships have undergone abnormal changes beyond the normal range in the next step.

[0112] Step S4: Compare each current equivalent mutual impedance with the corresponding abnormality determination threshold to identify abnormal mutual impedances.

[0113] Step S4 is a crucial step in fault feature extraction. Its purpose is to map the numerous mutual impedance values ​​calculated under the current operating state into simple "normal" or "abnormal" binary state labels through element-by-element threshold comparison, thereby highlighting the electrical coupling paths most affected by leakage faults. The specific implementation method is as follows:

[0114] The current equivalent mutual impedance matrix calculated in step S3 Each element in (where i,j represent user node pairs (i≠j), and p represents the voltage difference) The contributing user load current index (p = 1, 2, ..., N) is compared with the anomaly detection threshold matrix determined in step S2. Threshold elements at exactly the same index position Compare them.

[0115] Each mutual impedance value Each has one and only one exclusive, predefined threshold. Correspondingly, this strict correspondence is based on historical normal data statistics, ensuring the relevance and fairness of the comparison.

[0116] For each pair of indices (i,j,p), perform the following judgment:

[0117] if > Then determine the current equivalent mutual impedance. It is an abnormal mutual impedance; otherwise (i.e.) ≤ Determine the current equivalent mutual impedance. This is normal mutual impedance.

[0118] This anomaly detection rule is based on the following assumption: Under normal operating conditions, each cross impedance value will reach its historical maximum value (i.e., the threshold). Fluctuations within a certain range. Leakage faults alter local network parameters, causing a significant increase in certain mutual impedance values, exceeding the upper limit of their historical normal fluctuation range. This "over-limit" phenomenon is marked as "abnormal".

[0119] To facilitate subsequent statistics, the system generates a [database name]. and A binary state matrix (or anomaly marker matrix) with identical dimensions. .matrix elements in Assign values ​​according to the following rules:

[0120] (8)

[0121] matrix The abnormal distribution diagram of the mutual impedance relationship between all user nodes in the current state of the transformer area network is intuitively depicted.

[0122] Through step S4, the system completes the conversion from the complex continuous parameters to the clear abnormal features. The output abnormal marking matrix Directly indicates which specific mutual impedance relationship in the network has an abnormal change beyond the normal range, and provides the most direct clue for finally locating the electric leakage fault point.

[0123] Step S5: For each user node, the number of abnormal mutual impedances identified in the current equivalent mutual impedance between it and all other user nodes is counted.

[0124] Step S5 aims to aggregate the abnormal distribution generated in step S4, and the abnormal clues scattered in the whole network are summarized and quantified according to the user nodes, so as to generate a quantitative index that can directly reflect the degree of influence of each user on the electric leakage fault. The specific implementation is as follows:

[0125] For each user node k (k = 1, 2,..., N), the total number of abnormal mutual impedances related to user node k is counted. The abnormal mutual impedance related to user node k refers to the mutual impedance relationship represented by the element with at least one index i, j, p equal to k in the mutual impedance matrix (or the abnormal marking matrix ). In the preferred embodiment of the present application, in order to focus on the abnormality representing the "connection condition" of user node k itself, the statistics are usually concentrated on the following two more direct associations:

[0126] The voltage difference loop with k as one end: that is, all mutual impedances of the form or (i ≠ k, j ≠ k), which describe the coupling relationship between user node k and other user nodes on the voltage difference loop.

[0127] Other user loops affected by the load current of k: that is, all mutual impedances of the form (i ≠ j, i ≠ k, j ≠ k), which describe the influence of the load current of user node k on the voltage difference of other user nodes.

[0128] For each user node, an equivalent mutual impedance association set can be defined, which contains the indexes of all elements related to the user node in the abnormal marking matrix .

[0129] The current equivalent mutual impedance matrix and the abnormal marking matrix Reorganize by row (or by column) so that the k-th row (or column) directly corresponds to the equivalent mutual impedance vector of a user node k. This vector contains all the mutual impedance values (or anomaly markers) that satisfy the condition of i=k or j=k or p=k, that is, the vector is the embodiment of the equivalent mutual impedance association set of user node k on the data structure.

[0130] For each user node k, traverse its equivalent mutual impedance association set, and for each element in the association set, if the corresponding anomaly marker =1 (indicating an anomaly), increase the number of abnormal mutual impedances of user node k by 1 (the initial value is 0).

[0131] After the traversal is completed, the number of abnormal mutual impedances of user node k can be obtained, which directly quantifies the degree of abnormality of user node k in the network mutual impedance relationship.

[0132] The statistics of abnormal mutual impedance is based on a core inference: an electric leakage fault point will significantly change the electrical parameters of the lines near it, causing the mutual impedance on the current path passing through or near the fault point to change more easily. Therefore, the number of anomalies in the mutual impedance associated with the user node closest to the fault point will be significantly higher than that of other user nodes far from the fault point. The greater the value of the number of abnormal mutual impedances, the more extensive and intense the "spread" or "impact" of the fault on the electrical relationship of the user node.

[0133] Through step S5, the system converts the complex network-level abnormal pattern into a single quantified index with strong comparability for each user node, thereby providing a direct basis for sorting for the final step of determining the priority of the object to be checked.

[0134] Step S6: Determine the user node with the most abnormal mutual impedances as the priority of the electric leakage fault to be checked.

[0135] Step S6 is the final decision-making link of the positioning method of the present application, and its purpose is to automatically and clearly identify the user node most likely to be adjacent to the electric leakage fault point based on the quantified index generated in step S5, and to provide clear and executable action guidance for the field operation personnel. The specific implementation is as follows:

[0136] According to the number of abnormal mutual impedances of each user node output by step S5, find the element with the largest value, and the corresponding user node is determined as the priority of the object to be checked.

[0137] ​Under normal circumstances, there exists a single user node that results in the maximum number of abnormal cross impedances. If multiple user nodes have the same maximum number of abnormal cross impedances, they can be handled according to preset auxiliary rules. For example, maintenance personnel can conduct combined investigations according to network topology (e.g., from the transformer side to the user side) or geographical proximity, or analyze the changing trends of the number of abnormal cross impedances of these user nodes in a recent historical period, and prioritize the user node with the fastest rising trend.

[0138] Through step S6, the method of the present invention completes a closed loop from data acquisition, feature calculation, anomaly identification to final decision-making. The results directly serve on-site operations and maintenance, realizing the transformation from "data-driven analysis" to "action-driven guidance," effectively solving the core problems of low efficiency and wide scope of manual investigation in the background technology.

[0139] Example 2

[0140] To verify the effectiveness of the method of the present invention, a simulated transformer area containing 139 simulated users is used as an example. Based on steps S1 and S2, historical electrical quantity data of the simulated transformer area under normal conditions for 10 consecutive days are obtained. Using a sliding window method, the historical electrical quantity data is divided into 6 equal-length normal time period subsets, each containing 160 sample points. For each normal time period, the equivalent mutual impedance matrix between each user node is calculated by constructing a system of multivariate linear equations and applying the least squares method. For each equivalent mutual impedance parameter between each pair of user nodes, the maximum value among the calculated values ​​of all 6 normal time periods is taken as the anomaly judgment threshold for that equivalent mutual impedance parameter, thus forming a complete threshold benchmark.

[0141] When a leakage fault is simulated and injected into the branch power circuit to which user node 68 belongs, the system enters an abnormal leakage state. According to steps S3 to S5, the measured electrical quantity data under the leakage state (e.g., lasting for 2 days) is obtained, the current equivalent mutual impedance is calculated, and it is compared element by element with the aforementioned abnormal judgment threshold to identify abnormal mutual impedance. Finally, the number of abnormal mutual impedances for each user node is counted.

[0142] The number of abnormal cross impedances of user nodes obtained from statistics is visualized, and the results are as follows: Figure 2 As shown. From Figure 2 It can be clearly observed that the number of abnormal mutual impedances of user node 68 shows a very significant peak, which is much higher than that of all other user nodes in the area.

[0143] This phenomenon indicates that, for user node 68, the equivalent mutual impedance relationship between it and almost all other user nodes in the transformer area is out of the range of historical normal fluctuations and is determined to be abnormal. This directly verifies the core inference of the method of the present application: the leakage fault point will "pollute" the electrical parameters of the line segment on which it is located, causing a systematic and large-scale abnormal deviation in the coupling relationship between the user node (user node 68 in this embodiment) located on the line segment and other user nodes in the entire network. Therefore, user node 68 is determined to be the location of the nearest leakage fault point.

[0144] In Figure 2 , it can be observed that the number of abnormal mutual impedances of some users (such as user nodes 67, 69, etc.) near user node 68 also increases to a certain extent, but is significantly lower than that of user node 68. As the electrical or topological distance from user node 68 increases, the number of abnormal mutual impedances of other user nodes rapidly decreases to near the baseline level. This intuitively reflects the physical law that the influence of the leakage fault decreases with the increase of the electrical distance, further confirming the rationality of the identification result of the method of the present application.

[0145] Based on the decision rule of step S6, the system automatically determines user node 68, which has the most number of abnormal mutual impedances, as the preferred object of leakage fault investigation. This positioning result instantly and accurately narrows down the investigation range of the maintenance personnel from a large transformer area containing 139 users to a single branch power circuit corresponding to user node 68 and its adjacent lines. Field personnel can directly check the access point of user node 68, the branch box to which it belongs, and the upstream power supply line, thereby quickly discovering and isolating the leakage fault point.

[0146] Embodiment Three

[0147] The embodiments of the present application also provide an electronic device, which comprises a memory, a processor and a computer program or instructions stored in the memory, and the processor executes the computer program or instructions to implement the low-voltage power distribution network power supply line leakage fault positioning method in the embodiments of the present application.

[0148] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) or loaded from a storage portion into random access memory (RAM). The processor can be a multi-core processor or may contain multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more specialized coprocessors, such as a central processing unit, graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Various programs and data required for device operation are also stored in RAM. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0149] The processor and memory described above are used together to execute programs / instructions stored in the memory. When the program / instructions are executed by the computer, they can implement the methods, steps, or functions described in the above embodiments.

[0150] Although not shown, embodiments of the present invention also provide a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implements the method for locating leakage faults in low-voltage power distribution lines according to embodiments of the present invention.

[0151] Readable storage media include both permanent and non-permanent, removable and non-removable media that 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 (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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0152] The above description only discloses specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or modifications that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for locating leakage faults in low-voltage power distribution lines, characterized in that, The positioning method includes: Acquire historical electrical quantity data of the target transformer area under normal operating conditions; Based on the historical electrical quantity data, the equivalent mutual impedance between each user node is calculated, and based on the calculation results of multiple normal time periods, the abnormal judgment threshold of the equivalent mutual impedance between each user node is determined. Acquire measured electrical quantity data of the target distribution area when a leakage fault occurs, and calculate the current equivalent mutual impedance between each user node based on the measured electrical quantity data; Each of the current equivalent mutual impedances is compared with its corresponding anomaly detection threshold to identify abnormal mutual impedances; For each user node, count the number of abnormal mutual impedances identified in the current equivalent mutual impedance between it and all other user nodes. The user node with the highest number of abnormal mutual impedances is identified as the priority target for investigating leakage faults.

2. The method for locating leakage faults in low-voltage power distribution lines according to claim 1, characterized in that, Both the historical electrical quantity data and the measured electrical quantity data include the voltage phasor and load current phasor of each user node.

3. The method for locating leakage faults in low-voltage power distribution lines according to claim 1, characterized in that, Calculating the equivalent mutual impedance between user nodes specifically includes: Based on Kirchhoff's laws, a system of multiple linear equations is established with the voltage difference between user nodes as the dependent variable and the load current of each user as the independent variable. Based on the historical electrical quantity data or the measured electrical quantity data, the least squares method is used to solve the multivariate linear equation system to obtain the equivalent mutual impedance between each user node.

4. The method for locating leakage faults in low-voltage power distribution lines according to claim 3, characterized in that, When using the least squares method, the objective function is to minimize the root mean square error of the difference between the measured and calculated voltage difference.

5. The method for locating leakage faults in low-voltage power distribution lines according to claim 1, characterized in that, The determination of the anomaly threshold for the equivalent mutual impedance between user nodes specifically includes: The equivalent mutual impedance between each user node was calculated in several normal time periods. For each specific pair of user nodes, the maximum value of their equivalent mutual impedance over all normal time periods is taken as the threshold for determining the abnormality of the equivalent mutual impedance between that specific pair of user nodes.

6. The method for locating leakage faults in low-voltage power distribution lines according to claim 5, characterized in that, The calculation of the equivalent mutual impedance between each user node during multiple normal time periods specifically includes: A sliding time window method is used to sequentially extract multiple normal time periods of equal length from the historical electrical quantity data; wherein adjacent normal time periods partially overlap or are connected end to end on the time axis; Calculate the equivalent mutual impedance between each user node within the normal time period corresponding to each sliding time window.

7. The method for locating leakage faults in low-voltage power distribution lines according to claim 1, characterized in that, The identification of abnormal mutual impedance specifically involves: if the current equivalent mutual impedance is greater than its corresponding abnormal determination threshold, then the current equivalent mutual impedance is determined to be an abnormal mutual impedance.

8. The method for locating leakage faults in low-voltage power distribution lines according to any one of claims 1 to 7, characterized in that, The number of abnormal mutual impedances identified among the current equivalent mutual impedances between the node and all other user nodes specifically includes: For each user node, a set of mutual impedances is constructed, which contains the current equivalent mutual impedances between that user node and all other user nodes in the transformer area. The number of abnormal mutual impedances identified in the set of mutual impedances is counted and taken as the number of abnormal mutual impedances of the user node.

9. An electronic device comprising a memory, a processor, and a computer program or instructions stored in the memory, characterized in that, The processor executes the computer program or instructions to implement the method for locating leakage faults in low-voltage power distribution lines as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they implement the method for locating leakage faults in low-voltage power distribution lines as described in any one of claims 1 to 8.

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