Method, system and equipment for monitoring and positioning double-terminal collaborative line loss checking device and medium
By using a dual-terminal collaborative line loss investigation method, terminal identifiers are generated based on the physical characteristics of the line. Identity verification and dynamic sampling are performed, and adaptive filtering is applied. This method improves the accuracy of line loss monitoring and the efficiency of operation and maintenance management under complex conditions, and solves the problems of accuracy and reliability of line loss monitoring in existing technologies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUIZHOU POWER GRID CO LTD
- Filing Date
- 2025-12-18
- Publication Date
- 2026-04-17
AI Technical Summary
Existing line loss monitoring and investigation technologies rely on single-end metering data, which makes it difficult to accurately reflect the true line loss situation under complex topology and dynamic load conditions. They lack verification of line physical attributes, are susceptible to terminal counterfeiting and insufficient data reliability, have fixed sampling parameters that lead to noise interference, have low anomaly location efficiency, and offer only one alarm method.
The dual-terminal collaborative line loss investigation method generates terminal identifiers based on the physical characteristics of the line, performs identity verification, dynamically adjusts sampling parameters, performs adaptive filtering, and combines collaborative relationships to locate anomalies and issue graded alarms.
It achieves accuracy in line loss monitoring and efficiency in operation and maintenance management under complex conditions, improves system security and data reliability, narrows the scope of anomaly location, and provides hierarchical alarm support.
Smart Images
Figure CN121878366A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of device monitoring technology, and in particular to a dual-terminal collaborative line loss investigation device monitoring and positioning method, system, equipment and medium. Background Technology
[0002] With the continuous expansion of power distribution networks and the increasing complexity of electricity consumption scenarios, line loss has become a significant factor affecting the operational efficiency, economy, and safety of power distribution systems. Existing line loss monitoring and investigation technologies typically rely on single-end metering data or simple comparative analysis between concentrators and user-side meters, limiting their perception of line operating status and making it difficult to accurately reflect the true line loss situation under complex topologies and dynamic load conditions. Furthermore, existing solutions often use logical addresses or software protocols for authentication during terminal identification and communication, lacking a verification mechanism that integrates with the physical attributes of the line, leading to issues such as terminal spoofing, unauthorized access, and insufficient data reliability. In terms of data acquisition and processing, traditional line loss monitoring systems often employ fixed sampling parameters and static filtering methods, failing to adaptively adjust according to load changes and operating status. This results in difficulty capturing key features in a timely manner during load fluctuations or the initial stages of anomalies, and the acquired data is susceptible to noise interference. Regarding anomaly analysis and location, existing technologies primarily focus on determining whether line loss exceeds the standard, lacking effective means of locating anomalies, resulting in a large investigation scope, low efficiency, and heavy reliance on manual experience. At the alarm and maintenance level, alarm methods are usually quite simple and fail to be managed hierarchically according to the severity of anomalies, making it difficult to provide maintenance personnel with clear and effective handling guidelines. Therefore, there is an urgent need for a line loss investigation method that can achieve dynamic sampling, adaptive data processing, precise anomaly location, and hierarchical alarms while ensuring terminal reliability, in order to improve the accuracy of line loss monitoring and the efficiency of maintenance management. Summary of the Invention
[0003] In view of the above-mentioned problems, the present invention provides a monitoring and positioning method, system, equipment and medium for a dual-terminal collaborative line loss investigation device.
[0004] Therefore, the problem that this invention aims to solve is that existing line loss monitoring and investigation technologies usually rely on single-end metering data or simple comparative analysis based on concentrators and user-side meters, which have limited dimensions of perception of line operating status and are difficult to accurately reflect the true line loss situation under complex topology and dynamic load conditions.
[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a monitoring and positioning method for a dual-terminal collaborative line loss investigation device, comprising: acquiring concentrator identification information from a terminal on the distribution area concentrator side and generating a terminal identifier for dual-terminal collaborative communication; establishing a collaborative relationship between a terminal on the field meter box side and the terminal on the distribution area concentrator side based on the physical characteristics of the line, and completing identity verification between the two terminals; dynamically adjusting sampling parameters according to the line operating status based on the collaborative relationship, and synchronously collecting power data from the incoming line side and the corresponding user side; performing adaptive filtering on the collected power data to obtain effective data for line loss analysis; calculating anomaly level indicators based on the effective data, and determining the abnormal location of line loss accordingly; and outputting line loss monitoring results and corresponding anomaly location information.
[0006] As a preferred embodiment of the monitoring and positioning method for a dual-terminal collaborative line loss investigation device according to the present invention, the step of generating a terminal identifier for dual-terminal collaborative communication includes: obtaining the long address information of the concentrator from the terminal on the concentrator side of the distribution area, and extracting an address field of a preset number of bits from the long address information; generating line feature verification information based on the address field and the physical feature parameters of the corresponding line; and performing a combination operation on the address field and the line feature verification information to obtain a terminal identifier for dual-terminal collaborative communication.
[0007] As a preferred embodiment of the monitoring and positioning method for a dual-terminal collaborative line loss investigation device according to the present invention, the step of completing the identity verification between the two terminals includes: calculating the physical characteristic consistency index of the corresponding lines of the two terminals based on the line physical characteristic data collected by the terminal on the transformer concentrator side and the terminal on the field meter box side respectively; comparing the physical characteristic consistency index with a preset verification threshold; when the consistency index meets the preset conditions, determining that the identity verification between the terminal on the transformer concentrator side and the terminal on the field meter box side has passed; and establishing a collaborative communication relationship between the two terminals when the identity verification has passed.
[0008] As a preferred embodiment of the monitoring and positioning method of the dual-terminal collaborative line loss investigation device described in this invention, the step of dynamically adjusting the sampling parameters according to the line operating status includes: determining the load change rate of the line at the current moment based on the line operating data obtained based on the collaborative communication relationship; and dynamically adjusting the sampling parameters according to the changing trend of the load change rate.
[0009] The beneficial effects of this preferred technical solution are as follows: by determining the load change rate based on line operation data and dynamically adjusting the sampling parameters according to the trend of the load change rate, the sampling process can adapt to changes in the line operation status, improve sampling sensitivity when the load fluctuates greatly, and reduce sampling redundancy when the load is relatively stable. Thus, while ensuring that key operating characteristics are effectively captured, invalid data collection is reduced, sampling efficiency is improved, and system resource consumption is reduced.
[0010] As a preferred embodiment of the monitoring and positioning method of the dual-terminal collaborative line loss investigation device described in this invention, the step of performing adaptive filtering on the collected power data includes: determining the corresponding filtering convergence characteristics of the collected power data based on the sampling parameters and line operating status corresponding to the current sampling time; performing adaptive filtering on the power data according to the filtering convergence characteristics; and outputting the power data after adaptive filtering for line loss analysis.
[0011] As a preferred embodiment of the monitoring and positioning method of the dual-terminal collaborative line loss investigation device described in this invention, the step of determining the abnormal line loss location includes: calculating the abnormal line loss index corresponding to the line at the current time based on the power data after adaptive filtering; comparing the abnormal line loss index with preset abnormal judgment conditions to identify line sections or nodes with abnormal characteristics; and performing correlation analysis on the line sections or nodes with abnormal characteristics in conjunction with the dual-terminal collaborative relationship to determine the abnormal line loss location.
[0012] As a preferred embodiment of the monitoring and positioning method of the dual-terminal collaborative line loss investigation device according to the present invention, the output of line loss monitoring results and corresponding abnormal positioning information includes: generating corresponding line loss monitoring results based on the determined abnormal line loss location, wherein the line loss monitoring results include at least the location information of the abnormal section or node and the corresponding abnormal state description; classifying the abnormal state according to the degree of abnormality reflected in the line loss monitoring results to form alarm information corresponding to different abnormal levels; and outputting the line loss monitoring results and graded alarm information.
[0013] The beneficial effects of this preferred technical solution are: by generating line loss monitoring results based on the abnormal location of line loss and classifying the abnormal status into alarms, the monitoring system can distinguish line loss anomalies of different severity and avoid over-responding to minor or short-term anomalies.
[0014] To address the aforementioned technical problems, this invention provides the following technical solution: a dual-terminal collaborative line loss investigation device monitoring and positioning system, comprising: a collaboration module, a data acquisition module, a data processing module, and an anomaly positioning module; the collaboration module controls the concentrator-side terminal in the control area to acquire concentrator identification information and generate a terminal identifier for dual-terminal collaborative communication, and the field meter box-side terminal establishes a collaborative relationship with the concentrator-side terminal in the control area based on the physical characteristics of the line, and completes identity verification between the two terminals; the data acquisition module, based on the collaborative relationship, dynamically adjusts sampling parameters according to the line operating status, and synchronously acquires power data from the incoming line side and the corresponding user side; the data processing module performs adaptive filtering processing on the acquired power data to obtain effective data for line loss analysis; the anomaly positioning module calculates anomaly level indicators based on the effective data, determines the abnormal location of line loss accordingly, and outputs line loss monitoring results and corresponding anomaly positioning information.
[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the monitoring and positioning method of the dual-terminal collaborative line loss investigation device as described above.
[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the monitoring and positioning method of a dual-terminal collaborative line loss investigation device as described above.
[0017] The beneficial effects of this invention are as follows: By constructing a dual-terminal collaborative line loss investigation device monitoring and positioning method, this invention introduces the physical characteristics of the line into the terminal identifier generation and identity verification process, realizing trusted collaborative communication based on physical attributes. This effectively improves system security while avoiding unauthorized terminal access and data distortion. By dynamically adjusting sampling parameters based on load change rate and cooperating with adaptive filtering, the data acquisition and processing process can change in real time with the line operating status, reducing noise interference and resource consumption while ensuring that key operating characteristics are fully captured. Furthermore, by combining the dual-terminal collaborative relationship to perform correlation analysis on line loss anomalies, it achieves precise location of anomalies and narrows the investigation scope. By comprehensively evaluating and classifying alarms for abnormal states, line loss anomalies of different severity receive differentiated prompts and handling support, thereby improving the overall accuracy of line loss monitoring, the targeting of positioning, and the efficiency and reliability of operation and maintenance management. Attached Figure Description
[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a flowchart of a monitoring and positioning method for a dual-terminal collaborative line loss investigation device in Example 1. Detailed Implementation
[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.
[0022] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a monitoring and positioning method for a dual-terminal collaborative line loss investigation device, including: S1: The terminal on the concentrator side of the distribution area obtains the concentrator identification information and generates a terminal identifier for dual-terminal collaborative communication.
[0023] S2: The field meter box side terminal establishes a collaborative relationship with the distribution area concentrator side terminal based on the physical characteristics of the line, and completes the identity verification between the two terminals.
[0024] S3: Based on the collaborative relationship, the sampling parameters are dynamically adjusted according to the line operation status, and the power data of the incoming side and the corresponding user side are collected synchronously.
[0025] S4: Perform adaptive filtering on the collected power data to obtain effective data for line loss analysis.
[0026] S5: Calculate the anomaly level index based on valid data, and determine the location of abnormal line loss accordingly.
[0027] S6: Outputs line loss monitoring results and corresponding anomaly location information.
[0028] It should be noted that existing line loss monitoring and investigation technologies, in terms of security, mostly rely on terminal identification and communication authentication based on software protocols or logical addresses, lacking verification mechanisms related to the physical attributes of the line. This makes them susceptible to terminal imitation, unauthorized access, or data tampering, making it difficult to guarantee the reliability of monitoring data. In terms of accuracy, sampling parameters are usually configured in a fixed manner and fail to be dynamically adjusted according to changes in line load and operating status. This makes it difficult to capture key features in a timely manner when load fluctuations are large or in the early stages of anomalies, and the collected data is easily affected by random noise. In terms of intelligence, anomaly judgment is mostly limited to simple threshold comparisons, lacking a comprehensive assessment of the degree, persistence, and trend of anomalies. It is difficult to distinguish between short-term fluctuations and real line loss anomalies, resulting in significant false alarms and missed alarms.
[0029] Therefore, in response to the above problems, such as Figure 1 As shown, through steps S1-S6, a collaborative relationship based on the physical characteristics of the line is first established between the concentrator side of the distribution area and the field meter box side. By binding the terminal identifier with the line attribute and verifying the consistency of physical characteristics, it is ensured that the two terminals participating in the monitoring are in a reliable state. On this basis, the system continuously acquires line operation data and adaptively adjusts the data acquisition strategy according to load changes to match the sampling process with the line operation status. Subsequently, the acquired power data is adaptively filtered to suppress random fluctuations while retaining effective features that reflect the true operating status. When abnormal features are detected, the abnormality is correlated with the collaborative relationship between the two terminals to determine the specific location of the line loss anomaly. Finally, the line loss monitoring results, anomaly location information, and corresponding anomaly levels are output to achieve hierarchical prompts and orderly handling of line loss anomalies, forming a complete monitoring and investigation process from reliable acquisition and dynamic processing to precise location and alarm output.
[0030] Example 2, the second embodiment of the present invention, differs from the first embodiment in that: a monitoring and positioning method for a dual-terminal collaborative line loss investigation device further includes, in step S1, generating a terminal identifier for dual-terminal collaborative communication, comprising the following steps A1-A3: A1: The long address information of the concentrator is obtained from the terminal on the concentrator side of the distribution area, and the address field with a preset number of bits is extracted from the long address information.
[0031] A2: Generate line feature verification information based on the address field and the corresponding line's physical characteristic parameters.
[0032] A3: Combine the address field with the line feature verification information to obtain the terminal identifier used for dual-terminal collaborative communication.
[0033] In this embodiment of the application, in A2, the method for generating line characteristic verification information adopts a method for generating line characteristic verification information based on line impedance spectrum characteristics, including the following steps A211-A213: A211: At multiple preset frequency points, impedance measurements are performed on the line between the concentrator side terminal and the field meter box side terminal. The complex impedance values of the line at each frequency point are obtained. The complex impedance values are used to characterize the resistance and reactance components of the line under different frequency conditions, thereby forming a set of spectral characteristics of the line impedance.
[0034] A212: The spectral characteristic set of line impedance is normalized, and a spectral characteristic vector is constructed based on the distribution relationship of impedance amplitude and phase angle at each frequency point to reflect the overall electrical characteristic stability and consistency of the line in the frequency domain.
[0035] A213: Output the spectral feature vector as line feature verification information, so that the line feature verification information can uniquely represent the physical characteristics of the corresponding line, and be used in conjunction with the address field to generate the terminal identifier for dual-terminal collaborative communication.
[0036] Specifically, the formula for the line impedance spectrum characteristics is as follows: in, It is expressed as frequency and is used to describe the characteristics of impedance as a function of frequency; Represented as in frequency The central complex impedance below, Represented as in frequency The resistance below, Represented as in frequency The reactance below, It is represented as an imaginary unit.
[0037] In an optional implementation, the method for generating line characteristic verification information can also employ a method for generating line characteristic verification information based on the rate of change of the line's equivalent impedance, including the following steps A221-A223: A221: At multiple consecutive sampling times, the equivalent impedance of the line between the terminal on the concentrator side of the distribution area and the terminal on the field meter box side is measured to obtain the equivalent impedance change data of the line between adjacent sampling times, so as to characterize the dynamic impedance change characteristics of the line during operation.
[0038] A222: Based on the equivalent impedance change data, calculate the rate of change parameter of the line's equivalent impedance over time, and perform statistical processing on the rate of change parameter to extract a set of rate of change features that can reflect the long-term operating status and stability of the line.
[0039] A223: Construct the set of change rate features as the output of line feature verification information, so that the line feature verification information can be used to characterize the physical characteristic change law of the line in the time dimension and participate in the generation process of terminal identifier.
[0040] In another optional implementation, the method for generating line feature verification information can also employ a method for generating line feature verification information based on the combination features of line equivalent parameters, including the following steps A231-A233: A231: Perform parameter measurements on the line between the concentrator side terminal and the field meter box side terminal to obtain the line's equivalent resistance parameters, equivalent reactance parameters, and equivalent parameter information related to the line's distribution characteristics, which are used to describe the overall electrical structure characteristics of the line.
[0041] A232: Equivalent parameter information is combined and processed to construct a set of parameter features to characterize the comprehensive characteristics of the line in terms of spatial structure and electrical performance, thereby forming a parameter feature description that can distinguish different lines.
[0042] A233: Output the parameter feature set as line feature verification information, so that the line feature verification information can be used to participate in the generation of terminal identifier, and complete the verification of line physical characteristics without relying on specific frequency sampling conditions.
[0043] It should be noted that by combining the concentrator address information with the physical characteristics of the corresponding line when generating the terminal identifier for dual-terminal collaborative communication, the terminal identifier is no longer a simple logical address or random number, but is bound to the physical attributes of a specific line. This effectively avoids the situation where different lines or illegal terminals impersonate the same identifier to access the system, improves the uniqueness and reliability of dual-terminal collaborative communication, and provides a stable and reliable foundation for subsequent identity verification and collaborative monitoring.
[0044] Furthermore, in step S2, completing the identity verification between the two terminals includes the following steps B1-B3: B1: Based on the line physical characteristic data collected by the concentrator side terminal and the field meter box side terminal respectively, calculate the physical characteristic consistency index of the corresponding line of the two terminals; the field meter box side terminal is deployed on the incoming line side of the meter box to be tested.
[0045] B2: Compare the physical characteristic consistency index with the preset verification threshold. When the consistency index meets the preset conditions, it is determined that the identity verification between the terminal on the concentrator side of the distribution area and the terminal on the field meter box side has passed.
[0046] B3: If identity verification is successful, establish a collaborative communication relationship between the two terminals.
[0047] In this embodiment of the application, step B1 uses a physical characteristic consistency calculation method based on line impedance spectral matching degree, including the following steps B111-B113: B111: Acquire the line impedance spectrum characteristic data collected by the concentrator side terminal and the field meter box side terminal respectively. The impedance spectrum characteristic data includes the impedance amplitude and phase information of the line at multiple preset frequency points, which is used to characterize the electrical characteristic distribution of the line in the frequency domain.
[0048] B112: Based on impedance spectrum characteristic data, construct the spectrum feature vector corresponding to the two terminals, and normalize the spectrum feature vector to eliminate the influence of different measurement conditions on the amplitude.
[0049] B113: By calculating the degree of matching between the spectral feature vectors of the two terminals, the line impedance spectrum matching degree is obtained, and the impedance spectrum matching degree is used as a physical feature consistency index to characterize the degree of consistency of the corresponding lines of the two terminals in the frequency domain physical features.
[0050] Specifically, the matching degree is calculated based on the impedance measurement data between terminals, and the formula is expressed as: in, Represented as matching degree, Represented as the maximum frequency value, Represented as the minimum frequency value, Represented as in frequency The impedance value of the terminal on the concentrator side of the substation. Represented as in frequency The impedance value of the field meter box side terminal; when Authentication is successful in time, and a terminal relationship graph is dynamically constructed.
[0051] In an optional implementation, the consistency index can also employ a physical characteristic consistency calculation method based on the similarity of the changing trends of the equivalent impedance of the lines, including the following steps B121-B123: B121: At multiple consecutive sampling times, the equivalent impedance change data of the corresponding lines of the concentrator side terminal and the field meter box side terminal are obtained respectively, which are used to reflect the dynamic characteristics of the line impedance changing with time.
[0052] B122: Based on the equivalent impedance change data, the trend characteristics of the line impedance change over time are extracted, and the trend characteristics are processed with a unified scale to obtain a description of the changing trend that can reflect the long-term operating status of the line.
[0053] B123: By calculating the similarity between the impedance change trends of the corresponding lines of the two terminals, a trend similarity index is obtained, and the trend similarity index is used as a physical characteristic consistency index to determine whether the two terminals correspond to the same line.
[0054] In another optional implementation, the consistency index can also employ a physical characteristic consistency calculation method based on the comprehensive consistency of multi-dimensional line physical characteristics, including the following steps B131-B133: B131: Acquire multi-dimensional line physical characteristic data collected by the concentrator side terminal and the field meter box side terminal respectively. The multi-dimensional line physical characteristic data includes at least the line impedance characteristics, stability characteristics and operating status related characteristics, which are used to describe the physical attributes of the line from multiple dimensions.
[0055] B132: Perform feature combination processing on multi-dimensional line physical characteristic data to construct a feature set that can comprehensively reflect the overall physical attributes of the line, and perform unified processing on features of different dimensions to eliminate dimensional differences.
[0056] B133: Calculate the comprehensive consistency result of the corresponding lines of the two terminals based on the feature set, and use the comprehensive consistency result as a physical feature consistency index to characterize the degree of consistency of the two terminals at the multi-dimensional physical feature level.
[0057] It should be noted that by completing the identity verification between the two terminals based on the consistency of the physical characteristics of the line, the identity verification process is directly based on the objective physical attributes of the line, rather than relying on software protocols or preset keys, thereby effectively reducing the security risks caused by terminal impersonation, unauthorized access, or data forgery. At the same time, after the verification is passed, a collaborative communication relationship between the two terminals is established, so that the subsequent sampling, analysis, and positioning processes are all limited to the scope of the verified trusted terminals, thereby improving the security and reliability of the overall monitoring system.
[0058] Furthermore, in step S3, dynamically adjusting the sampling parameters according to the line operating status includes the following steps C1-C2: C1: Based on the line operation data obtained from the cooperative communication relationship, determine the load change rate of the line at the current moment.
[0059] C2: Dynamically adjust sampling parameters based on the trend of load change rate.
[0060] Specifically, the dynamic sampling frequency is achieved through the following differential equation: in, Represented as time, Represented as dynamic sampling frequency, it means that at... The speed at which the system collects data; This is expressed as the rate of change of the dynamic sampling frequency over time. It is represented as a constant coefficient, usually set manually or derived from system training, and represents the system's sensitivity. =5~20; Expressed as real-time load power, representing the power at... Instantaneous power consumption detected by the time-of-use system; Expressed as the rate of change of real-time load power over time. It is expressed as the sliding window average power, representing the average load power over a period of time.
[0061] To further explain, if the line impedance suddenly changes drastically (this usually means that someone has illegally connected to the line to steal electricity, or that the line is short-circuited / open-circuited). Increasing the number of units increases the frequency of use. When the value is reduced, the frequency of use is also reduced.
[0062] Furthermore, in step S4, the adaptive filtering process performed on the collected power data includes the following steps D1-D3: D1: Based on the sampling parameters and line operating status at the current sampling time, determine the corresponding filtering convergence characteristics for the collected power data.
[0063] D2: Based on the filtering convergence characteristics, perform adaptive filtering on the power data.
[0064] D3: Outputs electrical power data after adaptive filtering for line loss analysis.
[0065] In this embodiment of the application, step E2, the adaptive filtering process employs a power data filtering method based on adaptive exponential weighting, including the following steps D211-D213: D211: Based on the sampling parameters and line operating status corresponding to the current sampling time, determine the filtering convergence characteristics corresponding to the power data so that the filtering convergence characteristics can reflect the speed of changes in the line operating status.
[0066] D212: Based on the convergence characteristics of filtering, different weighting ratios are assigned to the currently collected power data and the historical filtering results. When the line operating status changes rapidly, the weight of the currently sampled data is increased, and when the line operating status is relatively stable, the weight of the historical data is increased, thereby performing adaptive exponential weighted filtering processing.
[0067] D213: Outputs power data after adaptive exponential weighted filtering, which is used for subsequent line loss analysis and anomaly assessment.
[0068] Specifically, the adaptive filtering formula is expressed as follows: in, Indicated as in Filtered battery level at any time This is expressed as the rate of change of the filtered battery charge over time. Indicated as in The original sampled charge of the filter at any time. The adaptive convergence factor is denoted as follows: ; otherwise And satisfy in, This is represented as the sampling time interval. Represented as the fast convergence coefficient, This is represented as the fast convergence threshold. It is represented as the slow convergence coefficient.
[0069] Preferably, spatial domain filtering is added to the adaptive filtering, using the discrete Laplace operator to: effectively suppress high-frequency noise and abnormal pulse interference; maintain the overall trend characteristics of the signal; and improve the stability and reliability of line loss calculation. The formula is expressed as: in, It is represented as a constant used to control the strength or speed of the smoothing process. The larger the diffusion effect, the stronger the data "flow," and the faster the image or data distribution will quickly become blurred and smoothed; when The smaller the size, the weaker the smoothing effect, but more original details are preserved. Represented as The discrete Laplace operator.
[0070] In an optional implementation, the adaptive filtering process can also employ an adaptive filtering method for power data based on a segmented weight adjustment strategy, including the following steps D221-D223: D221: Based on the determined filtering convergence characteristics, the line operating state is divided into different state intervals, including at least rapidly changing states and stable operating states, to guide the selection of subsequent filtering strategies.
[0071] D222: When the power consumption data is in a rapidly changing state, the first set of weighting strategies is used to focus on weighting the current sampled data; when the power consumption data is in a stable operating state, the second set of weighting strategies is used to focus on weighting the historical data, so as to achieve adaptive filtering under different operating states.
[0072] D223: The filtering result obtained by adopting the segmented weight adjustment strategy will be used as the output power data after adaptive filtering.
[0073] In another alternative implementation, the adaptive filtering process can also employ an adaptive filtering method for power data based on time-series smoothing intensity self-adjustment, including the following steps D231-D233: D231: Based on the filtering convergence characteristics, determine the smoothing intensity adjustment rules for the power data in the time series, so that the smoothing intensity can change dynamically with the changes in the line operating status.
[0074] D232: Based on the smoothing intensity adjustment rule, adaptive smoothing processing is performed on the time series of power data. When the fluctuation of line operation status is detected to be enhanced, the smoothing intensity is reduced, and when the line operation status is detected to be stable, the smoothing intensity is increased, thereby suppressing random fluctuations and preserving effective change trends.
[0075] D233: Outputs power data after time-series smoothing intensity self-adjustment processing, which serves as input data for subsequent line loss monitoring and anomaly assessment.
[0076] It should be noted that this step involves performing adaptive filtering on the power data based on dynamic adjustment of sampling parameters. This allows the filtering process to automatically adjust its convergence characteristics according to changes in the line's operating status. When power data changes rapidly, it maintains the ability to track effective change features, and when power data is stable, it enhances the ability to suppress random fluctuations. This effectively reduces the impact of noise interference on line loss analysis results and improves the stability and reliability of subsequent anomaly identification and location results.
[0077] Furthermore, in step S5, determining the location of abnormal line loss includes the following steps E1-E3: E1: Based on the power consumption data after adaptive filtering, calculate the line loss anomaly index corresponding to the line at the current moment.
[0078] E2: Compare the abnormal line loss indicators with the preset abnormal judgment conditions to identify line sections or nodes with abnormal characteristics.
[0079] E3: By combining the collaborative relationship between the two terminals, correlation analysis is performed on line sections or nodes with abnormal characteristics to determine the location of abnormal line loss.
[0080] Specifically, the abnormal line loss index is calculated in real time based on the following differential equation, expressed as: in, Represented as The time anomaly level index is a dimensionless quantity, and its value range is automatically limited to the interval [0, 1]. Represented as Real-time line loss rate at any given moment; This is represented as the line loss threshold, which is dynamically adjusted based on historical data. Represented as The phase imbalance at any given time reflects the degree of imbalance in the three-phase load; , , These are represented as weighting coefficients, determined through optimization using machine learning algorithms.
[0081] To further explain, The saturated response term uses a hyperbolic tangent function instead of the traditional linear term. It has high sensitivity when the line loss deviation is small, and automatically saturates when the deviation is large to avoid oversensitivity.
[0082] The phase imbalance contribution term directly reflects the contribution of the three-phase imbalance to the anomaly index. It is independent of the line loss term and is additive.
[0083] As a self-restricted logical term, it ensures that the abnormal index is naturally limited to the interval [0, 1], when When the growth rate approaches 0 or 1, it automatically slows down, forming a stable dynamic equilibrium.
[0084] exist Continuously exceeding the abnormal threshold Reaching the preset duration If this occurs, it indicates that there are abnormal characteristics in the current line segment or node.
[0085] In this embodiment of the application, step E3, determining the abnormal location of line loss, adopts a method for determining the abnormal location of line loss based on the correlation of power differences between two terminals, including the following steps E311-E313: E311: After identifying line sections or nodes with abnormal characteristics, the power data collected by the concentrator side terminal and the field meter box side terminal in the corresponding time period are obtained respectively to characterize the metering differences at both ends of the same line.
[0086] E312: Based on the power consumption data of the two terminals, calculate the power consumption difference characteristics of abnormal sections or nodes between the two terminals, and perform correlation analysis between the power consumption difference characteristics and the expected line loss distribution to determine the concentrated location of abnormal differences.
[0087] E313: Based on the distribution results of power difference characteristics under the dual-terminal cooperative relationship, determine the line section or node where line loss anomaly is most likely to occur, and output it as the location of line loss anomaly.
[0088] In an optional implementation, the location of abnormal line loss can also be determined using a method for determining the location of abnormal line loss based on dual-terminal cooperative topology constraints, including the following steps E321-E323: E321: After identifying the line segments or node sets with abnormal characteristics, obtain the line topology association information corresponding to the dual-terminal collaborative relationship, which is used to describe the upstream and downstream relationship of each segment or node in the power supply path.
[0089] E322: Based on line topology association information, perform topology constraint analysis on abnormal sections or nodes to identify candidate locations that simultaneously meet the abnormal characteristics and are located on the same power supply path on the dual-terminal cooperative path.
[0090] E323: From the candidate locations, determine the abnormal line loss location based on their relative position in the power supply topology, so that the abnormal location is consistent with the dual-terminal collaborative path.
[0091] In another alternative implementation, the location of abnormal line loss can also be determined using a method for determining the location of abnormal line loss based on the priority of the abnormality level distribution, including the following steps E331-E333: E331: For multiple identified line sections or nodes with abnormal characteristics, obtain the corresponding line loss anomaly indicators or anomaly level results to reflect the degree of anomaly in each section or node.
[0092] E332: Based on the anomaly level results, the segments or nodes are sorted to form a priority sequence of anomaly level distribution, so as to highlight the positions with higher anomaly levels.
[0093] E333: Based on the dual-terminal collaboration relationship, select the segment or node with the highest correlation with the dual terminals in the priority sequence and determine it as the output of the abnormal line loss location.
[0094] It should be noted that this step, after identifying abnormal features, combines the collaborative relationship between the two terminals to perform correlation analysis on the abnormal sections or nodes and determine the abnormal location of line loss. This allows line loss investigation to go beyond simply judging whether an anomaly exists, and further clarify the specific location where the anomaly occurred. This effectively narrows the scope of investigation, improves the pertinence and accuracy of anomaly location, and reduces the difficulty of investigation and the workload of maintenance personnel on site.
[0095] Furthermore, in step S6, outputting the line loss monitoring results and corresponding anomaly location information includes the following steps F1-F3: F1: Based on the determined abnormal location of line loss, generate the corresponding line loss monitoring results. The line loss monitoring results shall include at least the location information of the abnormal section or node and the corresponding abnormal status description.
[0096] F2: Based on the degree of abnormality reflected in the line loss monitoring results, the abnormal status is classified and processed to generate alarm information corresponding to different abnormality levels.
[0097] F3: Outputs line loss monitoring results and graded alarm information.
[0098] Specifically, after determining the location of the abnormal line loss, the system further obtains the abnormal line loss index or abnormal level result corresponding to the abnormal location, and combines the abnormal duration, abnormal change trend and other status information to comprehensively evaluate the degree of abnormality; based on the comprehensive evaluation result, the abnormal state is mapped to multiple preset alarm levels, and different alarm levels are used to characterize the different development stages of the abnormal line loss from slight and suspicious to significant and serious.
[0099] For different alarm levels, corresponding alarm information content is generated. The alarm information includes at least an abnormal location identifier, an abnormal level identifier, and an abnormal state description. Among them, the lower-level alarm information is used to indicate potential abnormal risks, while the higher-level alarm information is used to indicate confirmed or highly credible line loss abnormal events, thereby achieving differentiation of alarm severity at the information level.
[0100] By outputting tiered alarm information as part of the line loss monitoring results, maintenance personnel can take differentiated handling strategies based on different alarm levels. Low-level alarms can be used for subsequent continuous monitoring or verification, while high-level alarms can be used to trigger key investigations or on-site handling. This allows for tiered management and orderly response to line loss anomalies without increasing system complexity.
[0101] To further explain, the comprehensive evaluation steps include: Anomaly severity assessment: After determining the location of the abnormal line loss, obtain the line loss anomaly index results corresponding to the abnormal location, and compare the abnormal index with the historical normal operation range to assess the degree of deviation of the current anomaly from the normal state. When the deviation is small and still within the acceptable fluctuation range, it is judged as low anomaly severity. When the deviation significantly exceeds the historical normal range, it is judged as high anomaly severity, thus obtaining the anomaly severity assessment result that reflects the severity of the anomaly.
[0102] Anomaly persistence and trend assessment: The changes in anomaly indicators corresponding to anomaly locations are tracked and analyzed over multiple consecutive sampling periods to determine whether the anomaly has persistent characteristics and whether the degree of anomaly shows an upward trend. When an anomaly only appears in a short period of time and does not continuously increase, its persistence is judged to be low. When an anomaly persists over multiple sampling periods or shows a gradual worsening trend, its persistence is judged to be high. Persistence and trend are used as important assessment criteria for the credibility of anomalies.
[0103] Comprehensive assessment and alarm classification determination: Based on the comprehensive assessment results of the degree of anomaly and the assessment results of the persistence and trend of anomalies, an overall judgment is made on the abnormal state of line loss, and the comprehensive assessment results are mapped to a preset alarm level system. Among them, the abnormal state with a low degree of anomaly and a weak persistence is classified as a low-level alarm, which is used to indicate potential risks and enter a continuous monitoring state. The abnormal state with a high degree of anomaly and a significant persistence is classified as a high-level alarm, which is used to indicate line loss anomalies that require key investigation or further handling, thereby completing the classification and determination of line loss anomalies.
[0104] Example 3, the third embodiment of the present invention, differs from the previous two embodiments in that it provides a dual-terminal collaborative line loss investigation device monitoring and positioning system, comprising a collaboration module, a data acquisition module, a data processing module, and an anomaly positioning module. The collaboration module controls the concentrator-side terminal in the control area to acquire concentrator identification information and generates a terminal identifier for dual-terminal collaborative communication. The field meter box-side terminal establishes a collaborative relationship with the concentrator-side terminal based on the physical characteristics of the line and completes identity verification between the two terminals. The data acquisition module dynamically adjusts sampling parameters according to the line operating status based on the collaborative relationship, synchronously acquiring power data from the incoming line side and the corresponding user side. The data processing module performs adaptive filtering on the acquired power data to obtain effective data for line loss analysis. The anomaly positioning module calculates anomaly level indicators based on the effective data and determines the abnormal line loss location accordingly, outputting the line loss monitoring results and corresponding anomaly positioning information.
[0105] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0106] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.
[0107] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.
[0108] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented in combination with any of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A monitoring and positioning method for a dual-terminal collaborative line loss investigation device, characterized in that: include, The concentrator identification information is obtained from the terminal on the concentrator side of the distribution area, and a terminal identification for dual-terminal collaborative communication is generated. The field meter box side terminal establishes a collaborative relationship with the distribution area concentrator side terminal based on the physical characteristics of the line, and completes the identity verification between the two terminals; Based on the aforementioned collaborative relationship, the sampling parameters are dynamically adjusted according to the line operation status, and the power data of the incoming side and the corresponding user side are collected synchronously. Adaptive filtering is performed on the collected power data to obtain effective data for line loss analysis. Based on the effective data, an anomaly level index is calculated, and the location of the abnormal line loss is determined accordingly. Output the line loss monitoring results and the corresponding anomaly location information.
2. The monitoring and positioning method of the dual-terminal collaborative line loss investigation device as described in claim 1, characterized in that: The generated terminal identifier for dual-terminal collaborative communication includes, The long address information of the concentrator is obtained from the terminal on the concentrator side of the distribution area, and an address field with a preset number of bits is extracted from the long address information; Based on the address field and the corresponding physical characteristic parameters of the line, line characteristic verification information is generated; The address field and the line feature verification information are combined and calculated to obtain the terminal identifier used for dual-terminal collaborative communication.
3. The monitoring and positioning method of the dual-terminal collaborative line loss investigation device as described in claim 2, characterized in that: The process of completing identity verification between the two terminals includes... Based on the line physical characteristic data collected by the concentrator side terminal and the field meter box side terminal respectively, the physical characteristic consistency index of the line corresponding to the dual terminals is calculated. The physical characteristic consistency index is compared with the preset verification threshold. When the consistency index meets the preset conditions, it is determined that the identity verification between the terminal on the concentrator side of the distribution area and the terminal on the field meter box side has passed. Once the identity verification is successful, a collaborative communication relationship is established between the two terminals.
4. The monitoring and positioning method of the dual-terminal collaborative line loss investigation device as described in claim 3, characterized in that: The dynamic adjustment of sampling parameters according to the line operating status includes... Based on the line operation data obtained from the cooperative communication relationship, determine the load change rate of the line at the current moment; The sampling parameters are dynamically adjusted based on the trend of the load change rate.
5. The monitoring and positioning method of the dual-terminal collaborative line loss investigation device as described in claim 4, characterized in that: The adaptive filtering process performed on the collected power data includes, Based on the sampling parameters and line operating status at the current sampling time, the corresponding filtering convergence characteristics are determined for the collected power data. Based on the aforementioned filtering convergence characteristics, adaptive filtering processing is performed on the power data. The output is electrical power data that has been adaptively filtered and used for line loss analysis.
6. The monitoring and positioning method of the dual-terminal collaborative line loss investigation device as described in claim 5, characterized in that: The determination of abnormal line loss locations includes, Based on the power data after adaptive filtering, calculate the line loss anomaly index corresponding to the line at the current moment. The abnormal line loss index is compared with preset abnormal judgment conditions to identify line sections or nodes with abnormal characteristics. By combining the collaborative relationship between the two terminals, correlation analysis is performed on the line sections or nodes with abnormal characteristics to determine the location of abnormal line loss.
7. The monitoring and positioning method of the dual-terminal collaborative line loss investigation device as described in claim 6, characterized in that: The output line loss monitoring results and corresponding anomaly location information include: Based on the determined abnormal location of line loss, a corresponding line loss monitoring result is generated. The line loss monitoring result includes at least the location information of the abnormal section or node and the corresponding abnormal state description. Based on the degree of abnormality reflected in the line loss monitoring results, the abnormal state is classified and processed to generate alarm information corresponding to different abnormality levels. Output the line loss monitoring results and graded alarm information.
8. A monitoring and positioning system for a dual-terminal collaborative line loss investigation device, using the monitoring and positioning method for a dual-terminal collaborative line loss investigation device as described in any one of claims 1 to 7, characterized in that: It includes a collaboration module, a data acquisition module, a data processing module, and an anomaly location module; The collaborative module controls the concentrator-side terminal in the control area to obtain concentrator identification information and generate a terminal identifier for dual-terminal collaborative communication. The field meter box-side terminal establishes a collaborative relationship with the concentrator-side terminal in the control area based on the physical characteristics of the line and completes the identity verification between the two terminals. The acquisition module, based on the cooperative relationship, dynamically adjusts the sampling parameters according to the line operation status and synchronously acquires power data from the incoming side and the corresponding user side. The data processing module performs adaptive filtering on the collected power data to obtain effective data for line loss analysis. The anomaly location module calculates anomaly level indicators based on the valid data, determines the location of abnormal line loss accordingly, and outputs line loss monitoring results and corresponding anomaly location information.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the monitoring and positioning method of the dual-terminal collaborative line loss investigation device according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the monitoring and positioning method of the dual-terminal collaborative line loss investigation device according to any one of claims 1 to 7.