Method, device and storage medium for high-precision positioning of users with wiring errors and electric leakage

Abstract

This invention discloses a high-precision method, device, and storage medium for locating users with wiring errors and leakage current, relating to the field of leakage current detection technology. The method includes collecting time-series data of the residual current of the tested transformer area and the load current of each user within the tested area; calculating the time-series correlation strength between the load current and residual current of each user based on the time-series data; generating a differentiated penalty coefficient for each user based on the time-series correlation strength; constructing a regression model with the residual current as the dependent variable and the load current of each user as the independent variable; reconstructing the objective loss function of the regression model by using the penalty coefficient as the coefficient of the regularization term; estimating the weight coefficient corresponding to the load current of each user by minimizing the objective loss function, thereby identifying users with wiring errors and leakage current faults. This invention effectively solves the problem of misjudgment caused by interference from normal users in traditional leakage current user identification methods.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of leakage detection, and particularly relates to a wiring error leakage user high-precision positioning method, equipment and a storage medium. BACKGROUND

[0002] In a low-voltage distribution network, leakage faults caused by wiring errors are widespread due to improper construction, line aging, and private wiring on the user side. Such faults not only cause power loss but also may cause electrical fires, electric shock, and other safety accidents. Therefore, it is of great engineering significance to quickly and accurately identify and locate leakage users. With the widespread deployment of smart meters, the use of user load current time series data collected by smart meters, combined with data-driven methods such as attribution analysis, provides a new technical approach to accurately identifying wiring error leakage users.

[0003] In theory, in a transformer area where a wiring error leakage fault occurs, the leakage current of an abnormal user will directly constitute an important part of the residual current of the transformer area, and the time series fluctuation characteristics of the two are highly correlated. The load current of a normal user has weak correlation with the residual current of the transformer area. Therefore, by constructing a regression model between the residual current of the transformer area and the load current of each user, and solving the weight coefficient (i.e. the "contribution" of the residual current) corresponding to the load current of each user, it is theoretically possible to distinguish between abnormal users and normal users: the weight coefficient of an abnormal user should be significantly larger (close to 1), and the weight coefficient of a normal user should be close to 0.

[0004] However, in practical applications, it is found that the traditional attribution analysis method based on multiple linear regression has obvious limitations. The normal users in the transformer area have complex and diverse electricity behaviors, which are often affected by factors such as holidays, business trips, changes in family population, etc. For example, a user may turn off the main switch when they are away for a long time, resulting in zero electricity consumption; when they are away for a short time, they may only leave standby current devices such as refrigerators and routers, resulting in very small electricity consumption. According to the principle of current phasor synthesis, the current of such small or zero electricity consumption normal users should have no significant correlation with the large amplitude residual current that occurs during abnormal periods. However, in the regression calculation process, due to the weak and unobvious characteristics of the load current signals of these users, the traditional method cannot accurately evaluate their true contribution, and instead they are easily assigned a larger weight coefficient in the fitting process, resulting in the model mistakenly identifying them as "leakage users". This misjudgment is particularly prominent in transformer areas with a large number of users and complex electricity behaviors, seriously affecting the accuracy and reliability of fault identification.

[0005] To improve the sparsity and anti-interference ability of the regression model, a regularization method is introduced in the prior art, such as Lasso regression. It tends to compress some coefficients to zero by imposing an L1 norm penalty on all weight coefficients, thereby theoretically enhancing the discrimination between abnormal users and normal users. However, Lasso regression adopts a non-discriminatory penalty strategy, that is, the same penalty strength is imposed on all user variables. This uniform penalty may over-weak the coefficients of truly strong correlation between abnormal user variables and residual current while compressing irrelevant variables (normal users), resulting in false negatives or reduced recognition effect in multi-leakage-point complex scenarios.

[0006] Therefore, there is a lack of a leakage user positioning method in the prior art that can effectively distinguish and differentiate between different correlation characteristic user variables, especially when a large number of normal users are in a small power special state, the existing method is prone to misjudgment, and a more accurate and robust solution is urgently needed. SUMMARY

[0007] In view of the above defects in the prior art, the purpose of the present application is to provide a wiring error leakage user high-precision positioning method, device and storage medium, to solve the problem that the traditional attribution analysis method (such as multiple linear regression) cannot effectively distinguish the correlation strength between user current and residual current in the transformer area when there are a large number of small or zero power normal users, resulting in misjudgment of normal users as leakage users; and to overcome the technical problem that the non-discriminatory penalty of the existing regularization method (such as Lasso) may over-compress the real leakage user signal, thereby affecting the accuracy and reliability of leakage user identification in complex scenarios.

[0008] The present application solves the above technical problems by the following technical solutions: a wiring error leakage user high-precision positioning method, comprising:

[0009] Collecting time series data of residual current of the measured transformer area and load current of each user in the measured transformer area;

[0010] Based on the time series data, calculating the time series correlation strength between the load current of each user and the residual current;

[0011] Based on the time series correlation strength, generating a differentiated penalty coefficient for each user; wherein the weaker the time series correlation strength, the larger the generated penalty coefficient;

[0012] Constructing a regression model with the residual current as the dependent variable and the load current of each user as the independent variable, and reconstructing the objective loss function of the regression model by taking the differentiated penalty coefficient as the coefficient of the regularization term;

[0013] By minimizing the target loss function, the weight coefficients corresponding to the load currents of each user are estimated;

[0014] According to the weight coefficients of the load currents of each user, the user with the wiring error leakage fault is determined.

[0015] The present application can objectively quantify the fluctuation following relationship between the load current of each user and the residual current by calculating the time sequence correlation strength. For small electricity normal users, the time sequence correlation strength must be weak. The differentiated penalty coefficient generated based on this will "anti-correlation" increase (the weaker the correlation, the greater the penalty). In the reconstructed target loss function, these users are subjected to stronger regularization penalties, thereby mathematically forcing the corresponding weight coefficients to be compressed to near zero. This fundamentally suppresses the influence of these interference signals on the regression model, directly avoiding the misjudgment mentioned in the background art.

[0016] The "differentiation" mechanism of the present application is bidirectional. For real wiring error leakage users, there is a strong time sequence correlation between their load current and the residual current of the transformer area, and the calculated correlation strength is large, so the generated penalty coefficient is small. In the optimization process, the regularization penalty force on the corresponding weight coefficient is weak, so it can be relatively retained or even amplified. This enables the regression model to more clearly and prominently capture the contribution of real leakage users, enhancing the distinction between abnormal users and normal users.

[0017] The present application provides an adaptive penalty mechanism. It does not depend on fixed thresholds or empirical parameters, but dynamically adjusts based on the correlation strength of the data itself. No matter how complex the user's electricity behavior is in the transformer area, and no matter how many users are in the small electricity state, this penalty mechanism can automatically identify and differentiate, making the regression model solution more stable and the result more reliable. This greatly improves the practical value of the application method in large-scale and complex low-voltage distribution transformer areas.

[0018] The technical concept of the present application method deeply reflects the understanding of the physical nature of the leakage fault (leakage current is a component of the residual current, and the two are strongly correlated), and converts it into the calculable time sequence correlation strength, a data feature, and then embeds this physical insight into the data model optimization process of the differentiated penalty. This fusion makes the algorithm not only mathematically effective, but also reasonable in the physical sense of power system fault analysis, improving the interpretability and engineering credibility of the solution.

[0019] Further, the time sequence correlation strength is a Pearson correlation coefficient between the load current of the user and the residual current.

[0020] The time sequence correlation strength is embodied as a Pearson correlation coefficient, a correlation measurement benchmark with high efficiency, clear definition, strong interpretability and easy implementation in engineering is provided, and the differentiated punishment mechanism is effectively supported, so that the reliability and practicability of the algorithm are significantly improved on the premise of ensuring identification accuracy.

[0021] The Pearson correlation coefficient directly and intuitively measures the linear correlation degree of the amplitude fluctuation of the two current sequences, is highly consistent with the physical intuition that the leakage current causes strong correlation, and is convenient for understanding and verification. Due to the value range characteristics of the Pearson correlation coefficient, it can be converted into a differentiated punishment coefficient through simple operation, so that weakly correlated users (coefficient close to 0) are greatly punished, and strongly correlated users (coefficient close to 1) are weakly punished, the punishment gradient is natural and reasonable, and the weight coefficient difference between normal and abnormal users is effectively amplified.

[0022] Further, the punishment coefficient is calculated by the following formula:

[0023] ;

[0024] Wherein, The punishment coefficient corresponding to the jth user is represented by j; The Pearson correlation coefficient between the jth user load current and the residual current is represented by r j; , c all represent preset constants.

[0025] Further, the time sequence correlation strength is the maximum cross-correlation coefficient between the user load current and the residual current.

[0026] The time sequence correlation strength is embodied as the maximum cross-correlation coefficient, which can accurately capture and quantify the time lag synchronous fluctuation that may exist between the user load current and the residual current in the transformer area, thereby more completely revealing the essential correlation characteristics of the leakage fault, effectively avoiding the strong correlation signal with fixed delay that may be missed due to simple instantaneous correlation calculation (such as Pearson coefficient), and further improving the robustness and accuracy of abnormal user identification in complex leakage scenarios.

[0027] Further, the expression of the target loss function is:

[0028] ;

[0029] ;

[0030] Wherein, The target loss function to be minimized is represented by L; The weight coefficient vector to be estimated is represented by w; The i th sampling value of the residual current is represented by x i; a weight coefficient of a jth user; an ith sampling value of a jth user load current; a penalty coefficient corresponding to the jth user; M represents a sampling point number; and N represents a user number in a measured transformer area.

[0031] The application reconstructs the target loss function into a residual square sum form with a differential weighting L1 regularization term, provides an accurate and optimized mathematical model core for the entire positioning method, and directly realizes the differential penalty mechanism of the weighting L1 regularization term, that is, according to the size of the penalty coefficient, different degrees of sparse constraint are applied to the corresponding weight coefficient, so that the strong correlation user coefficient is guaranteed to be retained or highlighted, and the weak correlation user coefficient is strongly compressed, thereby solidifying the ability of the algorithm to distinguish normal and abnormal users from the loss function level, and ensuring the realizability and stability of the entire technical solution.

[0032] Further, the weight coefficient is estimated by minimizing the target loss function through a numerical optimization algorithm.

[0033] The application provides a general, efficient and stable calculation framework for solving the nonlinear optimization problem containing the complex weighting L1 regularization term by introducing the numerical optimization algorithm to minimize the target loss function, so that the mathematical model containing the differential penalty mechanism can be reliably solved in engineering practice, thereby converting the theoretical method innovation into an actual executable fault diagnosis process, and ensuring the accuracy of the weight coefficient estimation result and the practicability of the algorithm as a whole.

[0034] Further, the user with the wiring error leakage fault is determined according to the size of the weight coefficient of each user load current, and specifically:

[0035] If the weight coefficient amplitude of the jth user is greater than or equal to a first preset threshold, the user is determined as a wiring error leakage user;

[0036] If the weight coefficient amplitude of the jth user is less than or equal to a second preset threshold, the user is determined as a normal user.

[0037] The first preset threshold is greater than the second preset threshold.

[0038] The application sets the first and second preset thresholds and determines the user state according to the thresholds, provides a clear, objective and operable decision boundary for the weight coefficient output by the algorithm, maps the continuous coefficient amplitude to the discrete fault diagnosis conclusion, thereby realizing seamless connection from model calculation to engineering judgment, making the identification result of the wiring error leakage user clear and reliable, and being flexible to adapt to the sensitivity requirements of different transformer areas by adjusting the thresholds, and greatly improving the practicability and interpretability of the application method.

[0039] Further, the residual current and the time sequence data of each user load current are complex data containing amplitude and phase information.

[0040] The application adopts complex time sequence data containing amplitude and phase information, fully retains the complete phasor characteristics of current signals in an alternating power distribution system, enables the analysis based on time sequence correlation strength and subsequent complex weight coefficient estimation to truly reflect the amplitude and phase relationship between currents, thereby more accurately depicting the synthesis and decomposition physical process of currents under an electric leakage fault, and significantly improves the accuracy and physical reasonableness of identifying a wiring error electric leakage user under a complex three-phase imbalance and harmonic scenario.

[0041] Based on the same concept, the application also 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 realize the wiring error electric leakage user high-precision positioning method as described above.

[0042] Based on the same concept, the application also provides a computer readable storage medium having a computer program or instructions stored thereon, and the computer program or instructions are executed by a processor to realize the wiring error electric leakage user high-precision positioning method as described above.

[0043] Compared with the prior art, the application has the following beneficial effects:

[0044] The application effectively solves the misjudgment problem caused by normal users (especially small / zero electricity users) interference in the traditional electric leakage user identification method, and the problem that the non-discriminatory regularization method may weaken the real electric leakage signal, thereby significantly improving the accuracy and reliability of electric leakage user positioning. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the 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 application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0046] Figure 1 is the wiring error electric leakage user high-precision positioning method flowchart in the embodiment of the application;

[0047] Figure 2 is the complex weight coefficient amplitude column chart of each wiring error electric leakage user in the embodiment of the application. DETAILED DESCRIPTION

[0048] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0050] like Figure 1 As shown, the high-precision location method for wiring errors and leakage current in users provided in this embodiment of the invention includes the following steps:

[0051] Step S1: Collect time-series data of the residual current of the tested transformer area and the load current of each user in the tested transformer area.

[0052] Using the residual current transformer deployed at the main outgoing line of the low-voltage side of the transformer in the tested area, and the load current acquisition unit installed in each user's meter box, the residual current time sequence data of the tested area and the load current time sequence data of each user are collected synchronously within a set acquisition time period and at a uniform sampling interval.

[0053] The data collection period should cover one or more complete suspected leakage fault cycles or typical power consumption cycles, and its length can be set to several minutes, several hours or several days according to actual needs. The setting of the sampling interval needs to take into account the amount of data, communication load and fault characteristic fidelity, and can usually be selected in the range of seconds to minutes, such as 1 second, 15 seconds or 1 minute.

[0054] The time-series data of residual current and load current of each user are complex data (i.e., phasor data) containing amplitude and phase information. The amplitude unit is amperes, and the phase references the phase of the common voltage of the distribution area. After data acquisition, the data is uploaded to the data server of the distribution area management system for storage and subsequent processing via power line carrier communication, wireless private network, or fiber optic network.

[0055] To ensure data quality and the reliability of subsequent analysis, data verification can be performed simultaneously during the data collection process. This includes checking data continuity, removing obvious outliers, and interpolating or marking missing data.

[0056] Step S2: Based on the time series data, calculate the time series correlation strength between the load current and the remaining current of each user.

[0057] Based on the complex time-series data collected in step S1, the time-series correlation strength between the load current time-series data and the residual current time-series data of each user in the tested area is calculated to quantify the following or consistency of the fluctuation patterns of the two.

[0058] In a preferred embodiment of the present invention, the temporal correlation strength is specifically calculated using the Pearson correlation coefficient. The calculation formula is as follows:

[0059] (1)

[0060] in, represents the Pearson correlation coefficient between the load current and the residual current of the j-th user, and its value is a real number with a range of [0,1]; M represents the number of sampling points of the time series data, and M is greater than the number of users N; This represents the complex value of the residual current at the i-th sampling point (i.e., the sampled value, including amplitude and phase). This represents the complex value mean of the time-series data of the residual current in the transformer area. This represents the complex value of the load current of the j-th user at the i-th sampling point; This represents the complex value mean of the time-series load current data for the j-th user.

[0061] In another embodiment of the present invention, the timing correlation strength is the cross-correlation coefficient between the user load current timing data and the transformer area residual current timing data under different time delays. The maximum value (i.e. the maximum cross-correlation coefficient) is taken as the timing correlation strength, which is applicable to scenarios with fixed response delays.

[0062] The calculation results of the time-series correlation strength directly characterize the degree of synchronization between user load current fluctuations and transformer area residual current fluctuations: for users with wiring errors and leakage, their load current directly constitutes part of the residual current, and the fluctuations of the two are highly synchronized, hence the time-series correlation strength is relatively large (usually greater than 0.5 and close to 1); for normal users, especially those with low electricity consumption or in an idle state, their load current fluctuations are unrelated to the transformer area residual current, hence the time-series correlation strength is relatively small (usually less than 0.5 and close to 0). The time-series correlation strength will serve as the direct basis for generating the differentiated penalty coefficient in the next step.

[0063] Step S3: Generate a differentiated penalty coefficient for each user based on the temporal correlation strength.

[0064] Based on the temporal correlation strength of each user calculated in step S2, a corresponding differentiated penalty coefficient is generated for each user. The core principle of this generation process is that the weaker the temporal correlation strength of a user, the larger the penalty coefficient generated, thereby achieving strong compression of weakly correlated variables in subsequent model optimization.

[0065] In a preferred embodiment of the present invention, the penalty coefficient is directly calculated using the following formula:

[0066] (2)

[0067] in, This represents the penalty coefficient corresponding to the j-th user; This indicates the strength of the time-series correlation between the load current and the residual current of the j-th user; Both and 'c' represent preset constants used to adjust the sensitivity of the penalty or avoid division by zero errors. For example, 'c' can be set to 1. The value is 0.01.

[0068] For users with wiring errors and leakage, the timing correlation strength is relatively high (close to 1), the calculated penalty coefficient is relatively small, and the penalty effect is weak in subsequent optimization.

[0069] For normal users, especially those with low battery or idle battery, the temporal correlation strength is very small (close to 0), and the calculated penalty coefficient will be very large (approaching +∞). In subsequent optimization, a strong penalty will be applied to the corresponding weight coefficient.

[0070] The differential penalty coefficient generated in step S3 will be introduced as a key parameter into the target loss function of subsequent reconstruction, thereby achieving "differential treatment" of user variables with different correlation characteristics. This is the core mechanism by which the method of the present invention can overcome the misjudgment caused by normal user interference in traditional methods.

[0071] Step S4: Construct a regression model with residual current as the dependent variable and load current of each user as the independent variable. Reconstruct the target loss function of the regression model by using the differentiated penalty coefficient as the coefficient of the regularization term.

[0072] A multiple linear regression model is established, with the residual current of the transformer area as the dependent variable and the load current of all users as the independent variable. The mathematical expression of the model is:

[0073] (3)

[0074] in, This represents the estimated value of the residual current in the transformer area at the i-th sampling point; This represents the complex value (i.e., the sampled value) of the load current of the j-th user at the i-th sampling point. The complex weighting coefficient of the j-th user is represented, and its physical meaning characterizes the contribution of the user's load current to the residual current and the phase relationship; N represents the number of users in the transformer area. This indicates the background leakage current of the line.

[0075] The residual current timing data can be represented as follows: The weight coefficient vector to be estimated can be represented as: The time-series data of the load current of the j-th user can be represented as: The goal of a regression model is to find an optimal set of regression parameters. This makes the estimated residual current value As close as possible to the actual sampled value .

[0076] The initial target loss function is defined as the sum of squares of the estimation errors over all sampling points (least squares criterion):

[0077] (4)

[0078] To introduce a differentiated penalty mechanism, the differentiated penalty coefficients generated in step S3, corresponding one-to-one with each user, are used as weight coefficients for the regularization term to reconstruct the initial target loss function. The reconstructed target loss function is as follows:

[0079] (5)

[0080] In the reconstructed target loss function:

[0081] First item The second term is the fitting error term, which forces the model estimates to approximate the true values, ensuring the overall accuracy of the model; This is the weighted L1 regularization term. Wherein, This represents the penalty coefficient corresponding to the j-th user. It is a complex weighting coefficient The magnitude of the regularization term. This regularization term penalizes all weight coefficients, but the strength of the penalty varies. Differentiation based on differences:

[0082] for For users with large values ​​(i.e., normal users with weak sequential correlation), this item has a greater impact on them. Apply stronger penalties to force the optimization algorithm to assign its corresponding penalties. The amplitude is compressed to near 0;

[0083] for For users with low values ​​(i.e., users suspected of leakage with strong temporal correlation), this item applies to them. Apply weak penalties, allowing or even encouraging optimization algorithms to apply their corresponding penalties. The amplitude is preserved or amplified (approaching 1).

[0084] By applying this "user-specific" regularization penalty, the reconstructed target loss function, while pursuing overall fitting accuracy, automatically and significantly increases the difference between the final estimated weight coefficient magnitudes of normal and abnormal users, laying a crucial mathematical model foundation for subsequent accurate judgment.

[0085] Step S5: Estimate the weighting coefficients corresponding to the load current of each user by minimizing the target loss function.

[0086] Step S5 aims to solve the objective loss function reconstructed in step S4, which contains a weighted L1 regularization term, to estimate the complex weighting coefficients corresponding to the load currents of each user. This is a constrained convex optimization problem, and its standard form is as follows:

[0087] (6)

[0088] (7)

[0089] The core task is to find an optimal set of complex weight coefficients. This ensures that, under the above magnitude constraint (i.e., formula (7)), the value of the target loss function J is minimized.

[0090] In a preferred embodiment of the present invention, the primal dual interior-point method is used for solving the problem. The implementation process of this algorithm includes the following key steps:

[0091] Problem Transformation: First, the problem of the first term (L1 norm) in formula (6) and the complex field is solved by introducing auxiliary variables (such as...). By converting it into inequality constraints and separating the real and imaginary parts, it can be transformed into a standard quadratic programming or second-order cone programming problem defined on the real number field.

[0092] Constructing the Lagrangian function: A Lagrangian function is constructed for the transformed problem, while a logarithmic barrier function is introduced to handle inequality constraints. (and auxiliary variable constraints), transforming the constrained optimization problem into a series of unconstrained or simply constrained minimization problems.

[0093] Iterative solution: The primal-dual interior-point method is used for iterative solution. In each iteration:

[0094] Original variable update: along the target loss function with respect to the complex weight coefficients The gradient descent direction is determined, and the influence of Lagrange multipliers (dual variables) is considered when updating. The estimated value.

[0095] Dual variable update: Update the Lagrange multipliers associated with the constraints.

[0096] Central parameter adjustment: Adjust the obstacle parameters so that the iteration points gradually approach the optimal solution from inside the feasible region along the "central path".

[0097] Convergence criterion: Calculate the original residual, dual residual, and complementary gap. When all these values ​​are less than the preset tolerance threshold, the algorithm is considered to have converged and the iteration stops.

[0098] Output result: The converged result The value is used as the complex weighting coefficient corresponding to the final estimated load current of each user.

[0099] The primal dual interior-point method is an existing algorithm. In another specific embodiment of the present invention, other numerical optimization algorithms suitable for sparse regularized regression can also be used to solve the problem, such as the iterative weighted least squares method.

[0100] Regardless of the specific algorithm used, the ultimate goal is to satisfy... Under physical constraints, we can efficiently and stably obtain the weight coefficient estimates that minimize the objective loss function J. These complex weighting coefficients are the core criteria used in subsequent steps to distinguish user states.

[0101] Step S6: Based on the weighting coefficient of each user's load current, determine the users with wiring errors and leakage faults.

[0102] Step S6 aims to use the user complex weight coefficients estimated in step S5 as a basis. The system makes a final determination on the user's wiring status. Its core logic is based on the following physical principle: In the case of wiring error and leakage, the load current of the user with leakage will be almost entirely converted into residual current, and its contribution (i.e., the magnitude of the weighting coefficient) should be close to 1; while the contribution of normal users is negligible, and their weighting coefficient should be close to 0.

[0103] The specific determination method is as follows:

[0104] For each user, calculate their estimated complex weighting coefficients. The amplitude (modulus) .

[0105] Two preset thresholds are set: the first preset threshold (leakage current detection threshold) is used to identify users with leakage current, and is usually set in a value range close to 1; the second preset threshold (normal detection threshold) is used to confirm normal users, and is usually set in a value range close to 0, and satisfies that the first preset threshold is greater than the second preset threshold.

[0106] For each user j, based on the magnitude of its complex weighting coefficients Make a judgment:

[0107] User determination of wiring error and leakage: If If the current load is greater than or equal to the first preset threshold, then the j-th user is determined to be a user with wiring errors and leakage current. This determination indicates that the user's load current is highly correlated with the residual current in the transformer area and contributes significantly, which is consistent with the characteristics of a leakage current fault.

[0108] Normal user determination: If If the load current is less than or equal to the second preset threshold, then the j-th user is determined to be a normal user. This determination indicates that the user's load current contributes negligibly to the residual current.

[0109] Intermediate state handling: For a few Users whose current falls between the second and first preset thresholds can be marked as users to be observed or users suspected of having minor leakage. In practical applications, further analysis can be conducted by combining historical data, on-site investigation, or extending the monitoring time.

[0110] In this embodiment, the first preset threshold is 0.9, and the second preset threshold is 0.1. If If the value is ∈[0.9,1.0], it is determined to be a user with wiring errors and leakage; if If the value is ∈[0,0.1], it is determined to be a normal user.

[0111] The setting of the first and second preset thresholds has been verified in simulations to effectively distinguish between leakage current users and normal users (including users with low power consumption), and has a significant degree of differentiation.

[0112] Finally, the judgment results (such as user ID, weight coefficient amplitude, and judgment status) are summarized and output. For users with identified leakage current, alarm information is generated and reported to the transformer area management system, prompting maintenance personnel to conduct on-site verification and fault handling.

[0113] Through step S6, this invention ultimately achieves a complete and automated diagnostic process from raw current data to specific fault location for the user. Based on the weighting coefficients estimated by the differentiated penalty mechanism, combined with clear threshold judgment rules, it can identify users with wiring errors and leakage faults with high accuracy and high reliability, effectively overcoming the problem of misjudging normal users with small current charges by traditional methods.

[0114] To verify the effectiveness and superiority of the proposed high-precision location method for wiring error leakage users based on variable differentiation penalty, a simulation experiment was conducted on a full-scale simulated low-voltage distribution transformer area containing 204 users. This full-scale simulated transformer area includes 204 users of three phases (A, B, and C), which can realistically reflect the load characteristics and topology of the actual transformer area.

[0115] The simulation constructed a complex scenario involving three households experiencing wiring errors and leakage faults. Specifically, users numbered 16 and 22 (phase A) were designated as abnormal users with wiring errors and leakage faults. The remaining 202 households were all normal users, including some special users who were either idle or operating at low power levels, to simulate common differences in electricity consumption behavior in actual transformer substations.

[0116] Taking into account the current measurement performance of smart meters, the computational efficiency under a large user base, and the timeliness requirements for fault identification, the data acquisition parameters are set as follows: the sampling interval is 1 minute (min level), the sampling span is 24 hours (1 day), and the complex time-series data (including amplitude and phase) of the residual current of the transformer area and the load current of each user are collected simultaneously.

[0117] Based on the above parameters, a complete sample set containing M=1440 sampling points (24 hours × 60 minutes) is obtained for subsequent model training and evaluation.

[0118] To highlight the improvements of this invention, a comparative experiment was designed: traditional complex multiple linear regression and the proposed location method based on variable differentiation penalty were used to fit the same sample set, and the weight coefficients for each user were calculated. Figure 2 As shown.

[0119] After fitting the data using traditional complex multivariate linear regression (i.e., least squares method, without regularization or with uniform Lasso regularization), the magnitudes of the weight coefficients for some key users are as follows:

[0120] Abnormal users: User 16 has a weighting coefficient of 0.8993, and User 22 has a weighting coefficient of 0.8664. Both coefficients are significantly higher than those of most normal users, indicating a strong correlation with residual current.

[0121] Normal user (example): The weighting coefficient of user 110 with low power consumption is as high as 0.5882.

[0122] Although the coefficient for abnormal users is high, there are also cases where the coefficient is too high among normal users (e.g., user number 110 exceeds 0.5). If a simple threshold segmentation is used (e.g., using 0.5 as the boundary), these normal users are very likely to be misclassified as abnormal users. This indicates that traditional methods are insufficient in resisting interference from normal users with special electricity consumption behaviors and have limited distinguishing ability.

[0123] After fitting the data using the method of this invention (introducing a differential penalty coefficient based on the Pearson correlation coefficient and reconstructing the target loss function into a weighted L1 regularized form), the resulting weight coefficients exhibit a distinctly different distribution:

[0124] Abnormal users: The weighting coefficients of users No. 16 and No. 22 were both accurately estimated to be 1.0 (reaching the theoretical upper limit), indicating that their load current contributed almost entirely to the residual current.

[0125] Normal users: The weighting coefficients of all normal users (202 in total) were significantly compressed to near 0, with the maximum value being only 0.015.

[0126] Depend on Figure 2 As can be seen, the method of this invention achieves precise preservation (or even enhancement) of the coefficients of abnormal users and strong compression of the coefficients of normal users. A near-absolute gap (1.0 vs. <0.015) is formed between the coefficients of abnormal users and normal users, with extremely significant distinguishability.

[0127] Therefore, simulation results show that the method of this invention can accurately identify the designated leakage users (numbers 16 and 22), and their estimated weights reach the theoretical maximum value of 1.0. Compared with traditional methods, this invention completely overcomes the misjudgment problem caused by interference from normal users (especially those with low electricity consumption or those that are not currently active) through a differentiated penalty mechanism. The weight coefficients of normal users are generally suppressed to an extremely low level, thus fundamentally eliminating the risk of misjudgment at the algorithm level. In complex transformer substations containing more than 200 households, this invention can achieve high-precision positioning with only minute-level and day-spanning data, meeting the dual requirements of timeliness and reliability in practical engineering. This embodiment fully verifies the significant advantages of the method described in this invention in improving the accuracy and reliability of identifying leakage users with wiring errors.

[0128] Example 2

[0129] This invention also provides an electronic device, which includes a memory, a processor, and a computer program or instructions stored in the memory. The processor executes the computer program or instructions to implement the high-precision location method for wiring error leakage in this invention.

[0130] 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.

[0131] 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.

[0132] 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 high-precision location method for wiring error leakage users in embodiments of the present invention.

[0133] 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.

[0134] 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 high-precision method for locating users with wiring errors and leakage current, characterized in that, The positioning method includes: Collect time-series data of the residual current of the tested transformer area and the load current of each user in the tested transformer area; Based on the time-series data, calculate the time-series correlation strength between the load current and the residual current for each user; Based on the temporal correlation strength, a differentiated penalty coefficient is generated for each user; wherein, the weaker the temporal correlation strength, the larger the generated penalty coefficient. A regression model is constructed with the residual current as the dependent variable and the load current of each user as the independent variable. The objective loss function of the regression model is reconstructed by using the differential penalty coefficient as the coefficient of the regularization term. Its specific expression is as follows: ； ； in, This represents the objective loss function to be minimized. Let be the vector of weight coefficients to be estimated; This represents the i-th sampled value of the residual current; This represents the weight coefficient of the j-th user; This represents the i-th sampled value of the load current of the j-th user; represents the penalty coefficient corresponding to the j-th user; M represents the number of sampling points; N represents the number of users in the tested area. By minimizing the target loss function, the weighting coefficients corresponding to the load current of each user are estimated. Based on the weighting coefficient of each user's load current, users with wiring errors and leakage faults are identified.

2. The high-precision location method for wiring error leakage users according to claim 1, characterized in that, The time-series correlation strength is the Pearson correlation coefficient between the user load current and the residual current.

3. The high-precision positioning method for users with wiring errors and leakage current according to claim 2, characterized in that, The penalty coefficient is calculated using the following formula: ； in, This represents the penalty coefficient corresponding to the j-th user; This represents the Pearson correlation coefficient between the load current and the residual current of the j-th user. c and c both represent preset constants.

4. The high-precision positioning method for users with wiring errors and leakage current according to claim 1, characterized in that, The time-series correlation strength is the maximum cross-correlation coefficient between the user load current and the residual current.

5. The high-precision positioning method for users with wiring errors and leakage current according to claim 1, characterized in that, The weight coefficients are estimated by minimizing the target loss function using a numerical optimization algorithm.

6. The high-precision positioning method for users with wiring errors and leakage current according to claim 1, characterized in that, Based on the weighting coefficients of each user's load current, users with wiring errors and leakage faults are identified, specifically: If the magnitude of the weight coefficient of the j-th user is greater than or equal to the first preset threshold, then the user is determined to be a user with wiring error and leakage. If the magnitude of the weight coefficient of the j-th user is less than or equal to the second preset threshold, then the user is determined to be a normal user. Wherein, the first preset threshold is greater than the second preset threshold.

7. The high-precision method for locating users with wiring errors and leakage current according to any one of claims 1 to 6, characterized in that, The time-series data of the residual current and the current of each user load are complex data containing amplitude and phase information.

8. 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 high-precision location method for wiring error leakage of users as described in any one of claims 1 to 7.

9. 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 high-precision location method for wiring error leakage of users as described in any one of claims 1 to 7.

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