A power distribution network line loss anomaly positioning method

Abstract

The application discloses a power distribution network line loss anomaly positioning method and belongs to the technical field of power distribution network line loss anomaly positioning. The method comprises the following steps: quantitatively judging the state of an electric meter according to the equipment state parameters of each electric meter in each monitoring time period in a power distribution network to obtain an electric meter state quantitative judgment result, and judging whether to perform dynamic correction of power sales based on the electric meter state quantitative judgment result; quantitatively judging the harmonic influence according to the harmonic influence parameters of each region in each monitoring time period in the power distribution network to obtain a harmonic influence quantitative judgment result, and judging whether to perform dynamic correction of power supply based on the harmonic influence quantitative judgment result; and positioning the line loss anomaly of the power distribution network according to the total power sales of each monitoring time period after the electric meter state quantitative judgment and the total power supply of each monitoring time period after the harmonic influence quantitative judgment. The application improves the accuracy of the obtained power supply and power sales, thereby improving the accuracy of line loss anomaly positioning.

Description

Technical Field

[0001] This invention relates to the field of distribution network line loss anomaly location technology, and in particular to a method for distribution network line loss anomaly location. Background Technology

[0002] With the widespread adoption of smart meters, SCADA (Supervisory Control And Data Acquisition System) systems, and advanced measurement systems, line loss analysis can rely on real-time monitored data such as voltage, current, and power, combined with big data analysis, artificial intelligence, and topology modeling, to quickly locate abnormal areas or users, thereby improving the economic efficiency of the power grid, reducing energy waste, and preventing illegal electricity use.

[0003] Existing distribution network line loss anomaly location systems collect real-time operational data from smart meters, SCADA systems, and advanced measurement systems within the distribution network. Combined with the power grid topology and historical line loss models, these systems utilize big data analytics and artificial intelligence algorithms to perform multi-source data fusion analysis, identifying areas or users with abnormal line losses. Simultaneously, by integrating electricity consumption behavior analysis, metering equipment status monitoring, and transformer area balancing comparisons, the systems accurately locate technical or non-technical losses. Furthermore, they assist maintenance personnel in rapid intervention through visualization platforms or alarm mechanisms, achieving refined line loss management and improved efficiency.

[0004] For example, patent application CN117805546A discloses a method and storage medium for rapid line loss location based on ant colony optimization (ACO). The method includes: determining the level of abnormal line loss using a distribution area line loss hierarchical calculation model based on Kirchhoff's laws; then, within the high-loss line range, locating high-loss points in the distribution area at the level of abnormal line loss based on the electrical parameter curve data and reported events from the electricity meters, and using an electricity meter theft analysis model, thus identifying the abnormal electricity meters. Each detection point is transformed into a node in a Transmission Suspension Problem (TSP), transforming the line loss location in the distribution network into finding the optimal path. Based on the ant colony optimization (ACO) algorithm, the optimal solution is iteratively searched according to the objective function to identify the abnormal electricity meters.

[0005] For example, the patent application with publication number CN117706263A discloses a method for identifying abnormal line losses in a distribution network, which includes: identifying and marking the actual route, structure, and external insulation layer of the distribution network within a region; analyzing the degree of influence of the external environment on the resistance of the distribution network in combination with the environmental characteristics around the distribution network; determining the ideal line loss of each cable segment in the distribution network under the current conditions by combining a database; analyzing the amount of power loss on the cable, and if the amount of power loss is greater than the ideal line loss, applying a test signal at one end of the cable along the power transmission direction; and inferring the cause of the abnormal state on the cable by analyzing the fluctuation of power parameters at the other end of the cable.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0007] In existing technologies, in the power distribution link from substation to user terminal in the power system, when judging the abnormality of distribution network line loss based on the line loss rate in the distribution network, the theoretical line loss calculation is usually based on the electricity sales and supply. However, load imbalance may generate harmonics, and harmonic currents generate additional power losses in the distribution network. These losses are not included in the power supply, resulting in low accuracy of line loss rate calculation and low accuracy of distribution network line loss anomaly location. Summary of the Invention

[0008] This invention provides a method for locating anomalies in distribution network lines, solving the problem in existing power systems where, when judging anomalies in the distribution network from substations to user terminals based on the line loss rate, theoretical line loss calculations are usually based on electricity sales and supply. However, load imbalances may generate harmonics, and harmonic currents produce additional energy losses in the distribution network that are not included in the electricity supply. This leads to low accuracy in calculating the line loss rate and thus low accuracy in locating anomalies in the distribution network. This invention improves the accuracy of locating anomalies in distribution network lines.

[0009] This invention provides a method for locating anomalies in distribution network line losses, comprising the following steps: Quantitatively determining the meter status based on the equipment status parameters of each meter in the distribution network during each monitoring time period, obtaining a quantitative determination result for the meter status; determining whether to dynamically correct the electricity sales volume based on the quantitative determination result for the meter status, whereby dynamic correction of the electricity sales volume means correcting the electricity sales volume according to the equipment status parameters to reduce the impact of abnormal electricity sales volume collection accuracy on the accuracy of locating anomalies in distribution network line losses; Quantitatively determining the harmonic influence based on the harmonic influence parameters of each area in the distribution network during each monitoring time period, obtaining a quantitative determination result for the harmonic influence; determining whether to dynamically correct the power supply volume based on the quantitative determination result for the harmonic influence, whereby dynamic correction of the power supply volume means correcting the power supply volume according to the harmonic influence parameters to reduce the impact of abnormal power supply volume collection accuracy on the accuracy of locating anomalies in distribution network line losses; and locating distribution network line losses anomalies based on the total electricity sales volume in each monitoring time period after the quantitative determination of meter status and the total power supply in each monitoring time period after the quantitative determination of harmonic influence.

[0010] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0011] 1. This invention provides a method for locating abnormal line losses in a distribution network. By dynamically correcting the impact of abnormal equipment status and harmonics on the accuracy of electricity sales and power supply data collection through quantitative judgment of meter status and harmonic influence, this invention improves the accuracy of distribution network line loss rate and abnormal line loss location.

[0012] 2. This invention accurately quantifies and determines the status of electricity meters, and then compares the results with the device status threshold. This allows for timely identification and reasonable correction of abnormal electricity meter status, thereby improving the accuracy of electricity sales data collection, reducing errors caused by abnormal device status, ensuring the normal operation and maintenance of electricity meters, and guaranteeing the stability and accuracy of the power distribution network system.

[0013] 3. This invention achieves improved accuracy in power supply data acquisition and reduced impact of harmonics on the accuracy of the power distribution network system by accurately quantifying and determining the harmonic influence parameters, and then comparing the harmonic influence thresholds based on the quantification results. Attached Figure Description

[0014] Figure 1 A flowchart of a method for locating abnormal line losses in a power distribution network, provided in an embodiment of this application.

[0015] Figure 2 Mind map for correcting the accuracy of electricity sales provided in the embodiments of this application.

[0016] Figure 3 Mind map for power supply accuracy correction provided in the embodiments of this application. Detailed Implementation

[0017] This application provides a method for locating anomalies in distribution network line losses. It addresses the problem in existing power systems where, when judging anomalies based on the line loss rate, theoretical line loss calculations are typically based on electricity sales and supply. However, load imbalances can generate harmonics, leading to additional energy losses in the distribution network that are not included in the electricity supply. This results in low accuracy in calculating the line loss rate and thus low accuracy in locating anomalies. The method quantifies the meter status based on the equipment status parameters of each meter during different monitoring periods, obtaining a quantitative determination result. Based on this result, it determines whether dynamic correction of electricity sales is necessary. Dynamic correction refers to adjusting electricity sales based on equipment status parameters to reduce the impact of abnormal electricity sales data collection accuracy on the accuracy of distribution network line loss anomaly location. It involves quantifying harmonic impact parameters for each region within each monitoring time period in the distribution network, obtaining quantified harmonic impact results, and determining whether to perform dynamic power supply correction based on these results. Dynamic power supply correction further reduces the impact of abnormal power supply data collection accuracy on the accuracy of distribution network line loss anomaly location. Finally, it uses the total electricity sales for each monitoring time period after meter status quantification and the total power supply for each monitoring time period after harmonic impact quantification to locate distribution network line loss anomalies, thereby improving the accuracy of line loss anomaly location.

[0018] The technical solution in this application aims to address the problem in the power distribution system from substation to user terminal. When judging abnormal line losses in the distribution network based on the line loss rate, theoretical line loss calculations are typically based on electricity sales and supply. However, load imbalances may generate harmonics, and harmonic currents produce additional energy losses in the distribution network. These losses are not included in the electricity supply, leading to low accuracy in line loss rate calculations and thus low accuracy in locating abnormal line losses in the distribution network. The overall approach is as follows:

[0019] By quantifying the equipment status parameters of each meter in the distribution network during each monitoring period, the results of the meter status quantification are obtained, thereby determining whether dynamic correction of electricity sales is needed. At the same time, based on the harmonic impact parameters of each area in each monitoring period, the harmonic impact is quantified to determine whether dynamic correction of power supply is needed. Finally, by combining the total electricity sales after the quantification of meter status and the total power supply after the quantification of harmonic impact, the distribution network line loss anomaly location is performed, improving the accuracy of distribution network line loss anomaly location.

[0020] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0021] like Figure 1 The diagram shows a flowchart of a distribution network line loss anomaly location method provided in this application embodiment. The method includes the following steps: Quantitatively determining the meter status based on the equipment status parameters of each meter in each monitoring time period in the distribution network, obtaining a quantitative determination result for the meter status; determining whether to dynamically correct the electricity sales volume based on the quantitative determination result for the meter status, whereby dynamic correction of the electricity sales volume means correcting the electricity sales volume according to the equipment status parameters to reduce the impact of abnormal electricity sales volume collection accuracy on the accuracy of distribution network line loss anomaly location; Quantitatively determining the harmonic influence based on the harmonic influence parameters of each area in each monitoring time period in the distribution network, obtaining a quantitative determination result for the harmonic influence; determining whether to dynamically correct the power supply volume based on the quantitative determination result for the harmonic influence, whereby dynamic correction of the power supply volume means correcting the power supply volume according to the harmonic influence parameters to reduce the impact of abnormal power supply volume collection accuracy on the accuracy of distribution network line loss anomaly location; and locating the distribution network line loss anomaly based on the total electricity sales volume in each monitoring time period after the meter status quantification determination and the total power supply in each monitoring time period after the harmonic influence quantification determination.

[0022] In this embodiment, the quantitative determination of meter status is achieved by analyzing the equipment status parameters of each meter during each monitoring period to determine whether the meter is within the standard operating range. By promptly detecting meter anomalies, the metering error in electricity sales caused by meter malfunctions can be reduced. The quantitative determination of harmonic impact is achieved by analyzing the harmonic impact parameters of each area in the distribution network to assess the impact of harmonics on the power supply of the distribution network and determine whether the power supply needs to be corrected. Controlling the harmonic impact can improve the accuracy of the power supply, thereby improving the accuracy of the line loss rate and reducing the power loss caused by harmonics. Distribution network line loss anomaly location is detected by comparing the total electricity sales volume after quantitative determination of meter status with the total electricity supply volume after quantitative determination of harmonic impact. If the difference between the total electricity supply and total electricity sales volume in the distribution network exceeds a preset range, a preliminary judgment can be made that a line loss anomaly exists. Based on multi-dimensional cross-analysis of distribution network operation status and meter data, the line loss anomaly can be accurately located: First, based on the grid topology, segmented line loss rates are calculated step by step, and the abnormal sections are identified by combining the voltage / current curves monitored in real time by the SCADA system. Second, the meter operation status and electricity consumption behavior patterns, including load curves and phase angles, are analyzed to identify metering faults or suspected electricity theft. At the same time, harmonic monitoring data is used to judge power quality interference, and infrared thermal imaging is used to detect hot spots on the line and locate aging nodes. Finally, big data tools such as cluster analysis and graph database topology tracing are used to classify the causes of the anomaly into metering errors, electricity theft, line loss, or harmonic interference, and trigger corresponding handling measures, such as meter calibration, anti-electricity theft inspection, or filter switching. By promptly detecting and repairing abnormal line losses, energy losses in the distribution network can be significantly reduced, thereby improving the overall efficiency of the distribution network.

[0023] In addition, the line loss anomaly database is used to store relevant data on distribution network line loss anomaly location methods, including: critical current imbalance rate, number of critical current abnormal changes, critical power factor, first threshold for harmonic influence and second threshold for harmonic influence, etc. The data in the line loss anomaly database can be directly queried from public databases such as the power marketing system database and the dispatch automation system database, or it can be obtained through cooperation with relevant departments such as power dispatching departments and equipment operation departments.

[0024] Furthermore, the steps for quantifying the meter status based on the equipment status parameters of each meter in the distribution network during each monitoring period include: obtaining reference data of equipment status parameters from a preset line loss anomaly database, specifically including: critical current imbalance rate, critical current abnormal change frequency, and critical power factor; performing a proportion convergence calculation on the current imbalance rate, critical current abnormal change frequency, and critical power factor with the critical current imbalance rate, critical current abnormal change frequency, and power factor, respectively; then weighting the proportion convergence calculation results using the weight ratio of equipment status parameters; and finally coupling the weighted results to obtain the observed equipment status values ​​of each meter in each monitoring period. The weight ratio of equipment status parameters includes the weight ratio of current imbalance rate, the weight ratio of current abnormal change frequency, and the weight ratio of power factor. The observed equipment status values ​​represent the combined effect of equipment status parameters on the meter's status. The impact of electricity sales data collection accuracy is quantified using equipment status parameters, including current imbalance rate, number of abnormal current changes, and power factor. A first and second threshold for equipment status are obtained from a pre-set line loss anomaly database. The observed equipment status values ​​of each meter in each monitoring period are compared with both the first and second thresholds. If the observed equipment status value of a meter in a given monitoring period is greater than or equal to the second threshold, the corresponding meter status quantification result is recorded as the first result. If the observed equipment status value of a meter in a given monitoring period is greater than or equal to the first threshold but less than the second threshold, the corresponding meter status quantification result is recorded as the second result. If the observed equipment status value of a meter in a given monitoring period is less than the first threshold, the corresponding meter status quantification result is recorded as the third result.

[0025] The methods for obtaining the equipment status observation values ​​of each meter for each monitoring period are as follows:

[0026] ;

[0027] In the formula, This represents the observed device status value of the j-th meter during the i-th monitoring time period. This indicates the weighting ratio of the current imbalance rate. This indicates the weighting ratio of the number of abnormal current changes. Indicates the power factor weighting ratio. This represents the current imbalance rate of the j-th meter during the i-th monitoring period. It is the ratio of the maximum deviation of the current difference of the meter to the average current during that monitoring period. It can be obtained through ammeter monitoring. The larger the current imbalance rate, the smaller the observed equipment status value. This represents the critical current imbalance rate. This indicates the number of abnormal current changes recorded by the j-th meter during the i-th monitoring period. A higher number of abnormal current changes indicates a lower observed value for the device status. It also represents the number of times the current value of the meter exceeded a preset current change threshold during the monitoring period, which can be obtained through ammeter monitoring. This indicates the number of abnormal changes in the critical current. This represents the power factor of the j-th meter during the i-th monitoring time period. The larger the power factor, the larger the observed equipment status. It is the ratio of active power to apparent power. Active power is the actual power done, which can be obtained directly using a wattmeter. Apparent power is the vector sum of active and reactive power, obtained by multiplying current and voltage. The critical power factor is represented by i, where i is the monitoring time period number, i=1,2,3,...,N, N is the total number of monitoring time periods, and j is the meter number, j=1,2,3,...,M, M is the total number of meters.

[0028] , and These are the weighted proportions for the current imbalance rate, the number of current anomalies, and the power factor, preset in the line loss anomaly database. These weighted proportions represent the numerical values ​​indicating the degree of influence of the current imbalance rate, the number of current anomalies, and the power factor on the observed equipment status values. They can be directly obtained from the line loss anomaly database during use. These relationships are organized into a "mapping set," i.e., a lookup table or corresponding rules. When a value of a certain equipment status parameter is actually monitored, this value can be found in the corresponding mapping set, and then the corresponding weighted proportion can be obtained. This weighted proportion is a number between 0 and 1, representing the assessment result of the degree of interference of the current equipment status parameter on the accuracy of electricity sales. For example, for the current imbalance rate, there exists a mapping set between the current imbalance rate and its corresponding weighted proportions. Inputting the current imbalance rate value yields the corresponding weighted proportion. Similarly, for the number of abnormal current changes, there exists a mapping set between the number of abnormal current changes and its corresponding weighted proportions. Inputting the number of abnormal current changes yields the corresponding weighted proportion. Likewise, for the power factor, there exists a mapping set between the power factor and its corresponding weighted proportions. Inputting the power factor value yields the corresponding weighted proportion. These mapping relationships can be many-to-one or one-to-one.

[0029] In this embodiment, the current imbalance rate, the number of abnormal current changes, and the power factor are interrelated. For example, current imbalance leads to a decrease in system efficiency, thus affecting the power factor; the higher the current imbalance rate, the lower the power factor. Conversely, a higher number of abnormal current changes may lead to current imbalance, resulting in a higher current imbalance rate and a lower power factor. Comprehensive analysis of the device status observations allows for a complete assessment of the meter's condition, helping to accurately diagnose potential faults or performance degradation that could affect the accuracy of electricity sales data. Adjusting the meter's electricity sales based on these observations can prevent inaccurate metering due to equipment malfunctions or performance degradation, ensuring more accurate electricity sales data and avoiding energy loss or user error.

[0030] like Figure 2The diagram shown is a mind map for correcting the accuracy of electricity sales provided in this application embodiment. It includes: real-time monitoring of the equipment status parameters of each meter in the distribution network during each monitoring time period to obtain the observed equipment status values ​​of each meter in each monitoring time period, and quantitative judgment based on the observed equipment status values ​​of each meter in each monitoring time period; if the observed equipment status value of a meter in a certain monitoring time period is greater than or equal to the second threshold of equipment status (i.e., threshold 2), then the corresponding quantitative judgment result of the meter status is recorded as the first result of equipment status, and a stable electricity sales command is issued without further processing; if the observed equipment status value of a meter in a certain monitoring time period is greater than or equal to the first threshold of equipment status (i.e., threshold 1) and less than the second threshold of equipment status, then the corresponding quantitative judgment result of the meter status is recorded as the second result of equipment status, and a qualified electricity sales command is issued to correct the electricity sales; if the observed equipment status value of a meter in a certain monitoring time period is less than the first threshold of equipment status, then the corresponding quantitative judgment result of the meter status is recorded as the third result of equipment status, and a qualified electricity sales command is issued to notify manual maintenance.

[0031] Specifically, the steps for determining whether to dynamically correct electricity sales volume based on the meter status quantification results include: if the meter status quantification result for a certain monitoring period is the first result of the equipment status, then a stable electricity sales volume instruction is issued for that meter. The stable electricity sales volume instruction is used to indicate that the electricity sales volume collected by the preset personnel has no impact on the location of abnormal line losses in the distribution network; if the meter status quantification result for a certain monitoring period is the second result of the equipment status, then a qualified electricity sales volume instruction is issued for that meter. The difference between the second threshold of the equipment status and the observed equipment status value of the meter for that monitoring period is recorded as the equipment status deviation value. The equipment status deviation value is matched with the electricity sales volume correction value corresponding to each preset equipment status deviation value in the line loss anomaly database. A one-to-one correspondence between each equipment status deviation value and the electricity sales volume correction value in the line loss anomaly database is formed. The mapping table records the status deviation value of each device and its corresponding electricity sales correction value. These relationships can be one-to-one or many-to-one. When obtaining the electricity sales correction value, simply input the device status deviation value into the mapping table, and the line loss anomaly database can quickly locate and return the corresponding electricity sales correction value. Summing the electricity sales correction value with the electricity sales yields the corrected electricity sales. The electricity sales qualified instruction is used to indicate that the electricity sales collected by the preset personnel has no impact on the location of distribution network line loss anomalies after correction. If the meter status quantification judgment result of a certain meter during a certain monitoring period is the third result of the device status, an electricity sales unqualified instruction is issued to notify the preset personnel to carry out maintenance. The electricity sales unqualified instruction is used to indicate that the electricity sales collected by the preset personnel has an impact on the location of distribution network line loss anomalies.

[0032] In this embodiment, the present invention, by quantitatively determining the operating status of each meter, can accurately identify deviations in electricity sales data caused by equipment failure, aging, or communication anomalies. By comparing thresholds based on equipment status observations, the system can identify the meter operating status in real time and trigger a tiered processing strategy, significantly improving the intelligence and accuracy of meter data processing. Dynamically correcting meter readings based on equipment status observations reduces electricity sales collection errors caused by meter anomalies, lowers the impact of measurement errors on line loss analysis, effectively improves the accuracy of distribution network electricity sales data, avoids line loss calculation distortion due to inaccurate meter readings, and thus improves the reliability of distribution network line loss anomaly location. This provides more reliable data for line loss management and refined power grid management, ultimately reducing the interference of abnormal electricity sales collection on distribution network line loss anomaly location.

[0033] Furthermore, the steps for quantifying the harmonic impact based on the harmonic impact parameters of each region in the distribution network during each monitoring period to obtain the quantified harmonic impact results include: obtaining reference data of harmonic impact parameters from a pre-set line loss anomaly database, specifically including: critical harmonic distortion rate, critical average harmonic content, and critical harmonic order; performing a proportion convergence calculation on the harmonic impact parameters and their corresponding reference data, then weighting the proportion convergence calculation results using the weight ratios of the harmonic impact parameters, and finally coupling the weighted results to obtain the observed harmonic impact values ​​for each region in each monitoring period. The weight ratios of the harmonic impact parameters include the weight ratio of harmonic distortion rate, the weight ratio of average harmonic content, and the weight ratio of harmonic order. The observed harmonic impact values ​​represent the quantified degree of influence of the harmonic impact parameters on the accuracy of power supply data acquisition. The data, including harmonic distortion rate, average harmonic content, and harmonic order, are used to determine the harmonic impact parameters. A first threshold and a second threshold for harmonic impact are retrieved from a pre-defined line loss anomaly database. The observed harmonic impact values ​​for each region during each monitoring time period are compared with both the first and second thresholds. If the observed harmonic impact value for a region during a monitoring time period is less than or equal to the first threshold, the corresponding harmonic impact quantification result is recorded as the first harmonic impact result. If the observed harmonic impact value for a region during a monitoring time period is greater than the first threshold and less than or equal to the second threshold, the corresponding harmonic impact quantification result is recorded as the second harmonic impact result. If the observed harmonic impact value for a region during a monitoring time period is greater than the second threshold, the corresponding harmonic impact quantification result is recorded as the third harmonic impact result.

[0034] The methods for obtaining the harmonic impact observations for each region during each monitoring period are as follows:

[0035] ;

[0036] In the formula, This represents the observed harmonic impact value of the k-th region during the i-th monitoring time period. This indicates the weighting ratio of harmonic distortion rate. This indicates the weighting ratio of average harmonic content. Indicates the weighting ratio of harmonic orders. This represents the harmonic distortion rate of the k-th region during the i-th monitoring time period. It is used to measure the impact of harmonic signals in the power grid. The larger the harmonic distortion rate, the greater the observed harmonic impact. The calculation formula is as follows: In the formula, The amplitude of the fundamental frequency. , ,…, It is the amplitude of the harmonic component, where n-1 is the number of harmonic components; Indicates the critical harmonic distortion rate. Let represent the average harmonic content of the k-th region during the i-th monitoring period, and let represent the sum of the average harmonic contents of each region during a certain monitoring period. The higher the average harmonic content, the greater the impact of harmonics on the observed values. This represents the critical average harmonic content. This represents the harmonic frequency in the k-th region during the i-th monitoring time period, indicating the number of times the harmonic frequency in that region exceeds a preset harmonic frequency threshold within a certain monitoring time period. Both can be directly obtained using equipment such as a power quality analyzer. The higher the harmonic frequency, the greater the harmonic impact on the observed value. This represents the critical harmonic order, where k is the region number, k=1,2,3,...W, and W is the total number of regions.

[0037] , and These are the weight ratios for harmonic distortion rate, average harmonic content, and harmonic order, preset in the line loss anomaly database. These weight ratios represent the numerical values ​​indicating the degree of influence of harmonic distortion rate, average harmonic content, and harmonic order on the observed harmonic impact values, and can be directly obtained from the line loss anomaly database. These relationships are organized into a "mapping set," i.e., a lookup table or corresponding rules. When a value of a harmonic impact parameter is actually detected, this value can be found in the corresponding mapping set, and then the corresponding weight ratio can be obtained. This weight ratio is a number between 0 and 1, representing the assessment result of the degree of interference of the current harmonic impact parameter on the accuracy of power supply. For example, for harmonic distortion rate, there exists a mapping set of harmonic distortion rates and their corresponding weight ratios. Inputting the value of the harmonic distortion rate yields the corresponding weight ratio. Similarly, for average harmonic content, there exists a mapping set of average harmonic content and its corresponding weight ratio. Inputting the value of the average harmonic content yields the corresponding weight ratio. Likewise, for harmonic order, there exists a mapping set of harmonic orders and their corresponding weight ratios. Inputting the value of the harmonic order yields the corresponding weight ratio. These mapping relationships can be many-to-one or one-to-one.

[0038] In this embodiment, harmonic distortion rate, average harmonic content, and harmonic order are interrelated. For example, a higher average harmonic content usually leads to a higher harmonic distortion rate because a larger proportion of harmonics in the system results in more severe signal distortion. High-frequency harmonic fluctuations may lead to more harmonic orders, indicating that frequent harmonic fluctuations may exacerbate system interference. Comprehensive analysis of harmonic impact observations provides a complete understanding of harmonic impacts in different time periods and regions, accurately assesses the operational quality of the power system, and allows for adjustments to power supply based on these observations. This avoids the impact of harmonics on the accuracy of power supply in the region, ensures power quality in the distribution network, and improves the accuracy of locating abnormal line losses in the distribution network.

[0039] like Figure 3The diagram shown is a mind map for power supply accuracy correction provided in this application embodiment. It includes: monitoring harmonic influence parameters for each region during each monitoring time period in the real-time distribution network; obtaining the observed harmonic influence values ​​for each region during each monitoring time period; and quantifying the harmonic influence values ​​for each region during each monitoring time period: if the observed harmonic influence value for a region during a certain monitoring time period is less than or equal to the first harmonic influence threshold (i.e., the first threshold), the corresponding harmonic influence quantification result is recorded as the first harmonic influence result, a power supply stabilization command is issued, and the time reference point for power supply and sales is corrected; if the observed harmonic influence value for a region during a certain monitoring time period is greater than the first harmonic influence threshold and less than or equal to the second harmonic influence threshold (i.e., the second threshold), the corresponding harmonic influence quantification result is recorded as the second harmonic influence result, a power supply qualification command is issued, and the power supply is corrected; if the observed harmonic influence value for a region during a certain monitoring time period is greater than the second harmonic influence threshold, the corresponding harmonic influence quantification result is recorded as the third harmonic influence result, a power supply non-qualification command is issued, and harmonic influence processing is performed.

[0040] Specifically, the steps for determining whether to perform dynamic correction of power supply based on the harmonic impact quantification results include: if the harmonic impact quantification result of a certain area during a certain monitoring period is the first result of harmonic impact, then a power supply stabilization command for that area is issued; the time reference points corresponding to the meter end and the grid end at the initial time point of the monitoring period are obtained respectively; the difference between the time reference point of the grid end and the time reference point corresponding to the meter end is marked as the deviation time; and the time reference points corresponding to the meter end and the grid end are corrected according to the deviation time corresponding to the meter end and the grid end respectively. The power supply stabilization command is used to indicate that the power supply collected by the preset personnel has no impact on the abnormal location of distribution network line loss.

[0041] The steps for correcting the time reference points corresponding to the meter and the grid, based on the deviation time at the meter and grid ends respectively, include: obtaining a deviation time threshold from a preset line loss anomaly database; comparing the deviation time between the meter and the grid with the deviation time threshold; if the deviation time between the meter and the grid is less than or equal to the deviation time threshold, no additional processing is performed; if the deviation time between the meter and the grid is greater than the deviation time threshold, it is determined whether the deviation between the time reference point corresponding to the meter and the grid and the reference time point exceeds the deviation threshold; if the deviation at the meter exceeds the deviation threshold and the deviation at the grid does not exceed the deviation threshold, the time reference point at the meter is aligned with the reference time point. A high-precision reference time signal, such as that periodically broadcast by a SCADA system, is used. The meter receives this signal through its built-in clock synchronization module, calculates the deviation at the meter, and when the deviation at the meter exceeds the deviation threshold, the internal RTC (Real-Time Loop) is automatically adjusted using digital phase-locked loop technology. The clock (real-time clock) chip's running frequency is used to gradually reduce time deviation. For historical data, the system adds time compensation markers to ensure that timestamps of frozen data and event records are consistent with the reference time, until the deviation at the meter end does not exceed the deviation threshold. The same applies to the grid end, and the meter end calibration interval is adjusted according to the deviation at the meter end. If the deviation at the meter end does not exceed the deviation threshold but the deviation at the grid end exceeds the deviation threshold, the time reference point at the grid end is aligned with the reference time point, and the grid end calibration interval is adjusted according to the deviation at the grid end. If the deviations at both the meter end and the grid end exceed the deviation threshold, the time reference points corresponding to the meter end and the grid end are aligned with the reference time point respectively, and the calibration intervals at the meter end and the grid end are adjusted respectively according to the deviations at the meter end and the grid end. If the deviations at both the meter end and the grid end do not exceed the deviation threshold, a time anomaly warning is issued, and designated personnel are notified to check and handle the issue.

[0042] If the quantitative determination result of harmonic impact in a certain area during a certain monitoring period is the second result of harmonic impact, then a power supply qualified instruction for that area is issued. The observed value of harmonic impact in that area during the monitoring period is matched with the preset power supply correction value in the line loss anomaly database to obtain the power supply correction value. The power supply is then corrected according to the power supply correction value. The power supply qualified instruction is used to indicate that the impact of the power supply collected by the preset personnel on the location of the distribution network line loss anomaly is within the correctable range.

[0043] The steps for correcting the current in each region based on the current fluctuation parameters of each line in each region include: First, quantifying the impact of the current balance in each region on the accuracy of power supply acquisition based on the current fluctuation parameters of each line in each region, and obtaining the current fluctuation observation values ​​of each region. The current fluctuation observation values ​​represent the quantitative data on the degree of influence of the current fluctuation parameters on the accuracy of power supply acquisition. The current fluctuation parameters include the number of abnormal current changes, the maximum current difference, and the current variance.

[0044] The steps to quantify the impact of current balance on the accuracy of power supply acquisition based on current fluctuation parameters of each region and each line, and to obtain the current fluctuation observation values ​​of each region, include: obtaining reference data of current fluctuation parameters from a pre-set line loss anomaly database, specifically including: the number of critical current anomaly changes, the maximum difference of critical current, and the variance of critical current; performing a proportion convergence calculation on the current fluctuation parameters of each region and each line with the corresponding current fluctuation parameter reference data, then weighting the proportion convergence calculation results using the current fluctuation parameter weight ratios, and finally coupling the weighted results to obtain the current fluctuation observation values ​​of each region. The current fluctuation parameter weight ratios include the weight ratio of the number of current anomaly changes, the weight ratio of the maximum difference of current, and the weight ratio of current variance.

[0045] The current fluctuation observations for each region were obtained as follows:

[0046] ;

[0047] In the formula, This represents the observed current fluctuation value in the k-th region. This indicates the weighting ratio of the number of abnormal current changes. This indicates the weighting ratio of the maximum current difference. This indicates the weighting ratio of the current variance. This represents the number of abnormal current changes in the y-th line within the k-th region. This indicates the number of abnormal changes in the critical current. This represents the maximum current difference of the y-th line in the k-th region, which is the difference between the maximum and minimum current values ​​within a certain monitoring period. This represents the maximum difference in critical current. The variance of the current in the y-th line of the k-th region can be directly obtained using the variance formula. The aforementioned current values ​​can be obtained directly from an ammeter. Let y represent the variance of the critical current, where y is the line number, y=1,2,3,...,Y, and Y is the total number of lines.

[0048] , and These are the weight ratios corresponding to the number of current anomaly changes, the maximum current difference, and the current variance, preset in the line loss anomaly database. These weight ratios represent the numerical values ​​indicating the degree of influence of the number of current anomaly changes, the maximum current difference, and the current variance on the observed current fluctuation value, and can be directly obtained from the line loss anomaly database. These relationships are organized into a "mapping set," i.e., a lookup table or corresponding rules. When a value of a certain current fluctuation parameter is actually monitored, this value can be found in the corresponding mapping set, and then the corresponding weight ratio can be obtained. This weight ratio is a number between 0 and 1, representing the assessment result of the degree of interference of the current fluctuation parameter on the accuracy of power supply. For example, for the number of abnormal current changes, there exists a mapping set between the number of abnormal current changes and their corresponding weight ratios. By inputting the value of the number of abnormal current changes, the weight ratios corresponding to those changes can be obtained. Similarly, for the maximum current difference, there exists a mapping set between the maximum current difference and its corresponding weight ratios. By inputting the value of the maximum current difference, the weight ratios corresponding to that maximum current difference can be obtained. Likewise, for the current variance, there exists a mapping set between the current variance and its corresponding weight ratios. By inputting the value of the current variance, the weight ratios corresponding to that variance can be obtained. These mapping relationships can be many-to-one or one-to-one.

[0049] It's important to note that the number of abnormal current changes, the maximum current difference, and the current variance are interconnected. The number of abnormal changes reflects the frequency and suddenness of fluctuations, the maximum difference quantifies the amplitude of each fluctuation, and the variance provides the overall stability of current fluctuations. A higher number of abnormal current changes likely leads to a larger current variance, and a larger maximum current difference may also result in a larger current variance. Comprehensive analysis of the observed current fluctuations allows for a complete assessment of the distribution network's current fluctuation situation. Accurate correction of the distribution network current based on these observations can enhance load balancing and optimize scheduling strategies, reducing fluctuations and thus minimizing the impact of harmonics generated by current fluctuations on the accuracy of total power supply data collection. This improves the accuracy of calculating the distribution network line loss rate, thereby enhancing the stability and accuracy of distribution network anomaly location.

[0050] Then, the current fluctuation threshold is obtained from the preset line loss anomaly database; the current fluctuation observation value of each region is compared with the current fluctuation threshold. If the current fluctuation observation value of a certain region is less than or equal to the current fluctuation threshold, the corresponding current balance quantification judgment result is recorded as the first result of current fluctuation; if the current fluctuation observation value of a certain region is greater than the current fluctuation threshold, the corresponding current balance quantification judgment result is recorded as the second result of current fluctuation; based on the current balance quantification judgment result, it is determined whether to perform dynamic current correction.

[0051] The steps for determining whether to perform dynamic current correction based on the quantitative judgment result of current balance include: if the quantitative judgment result of current balance in a certain area is the first result of current fluctuation, a current fluctuation stabilization command is issued for that area without additional processing. The current fluctuation stabilization command is used to inform the preset personnel that the current fluctuation in that area has no impact on the location of abnormal line loss in the distribution network; if the quantitative judgment result of current balance in a certain area is the second result of current fluctuation, a current fluctuation instability command is issued for that area, and dynamic current correction is performed based on the current fluctuation observation value of that area. The current fluctuation instability command is used to inform the preset personnel that the current fluctuation in that area has an impact on the location of abnormal line loss in the distribution network. Dynamic current correction means that the current is corrected according to the current fluctuation parameters to reduce the impact of harmonics generated by current fluctuations on the accuracy of power supply data acquisition.

[0052] The steps for dynamic current correction based on the observed current fluctuations in the region include: marking the difference between the observed current fluctuations in the region and the current fluctuation threshold as the current fluctuation deviation value; matching the current fluctuation deviation value with the line current range corresponding to each current fluctuation deviation value preset in the line loss anomaly database to obtain the line current range; if the current value of a line in the region exceeds the maximum value of the line current range, then reducing the current value of the line to the maximum value of the line current range; if the current value of a line in the region does not exceed the minimum value of the line current range, then increasing the current value of the line to the minimum value of the line current range.

[0053] Finally, if the harmonic impact quantification result for a certain area during a certain monitoring period is the third harmonic impact result, a power supply failure instruction for that area is issued. The system then checks if there is a monitoring period in the adjacent time period where the harmonic impact quantification result is the third harmonic impact result. If not, no further processing is performed. If so, it checks if harmonic suppression devices, such as harmonic isolation transformers, are activated in that area. If not, they are activated. If activated, the system checks if the harmonic impact index change value at the next monitoring point under the current device power exceeds the preset harmonic impact index change threshold. If so, no further processing is performed; otherwise, the device power is gradually increased until the harmonic impact index change value at the next monitoring point exceeds the threshold. If the harmonic suppression device is activated and the device power has reached its maximum value, but the harmonic impact index change value at the next monitoring point still does not exceed the threshold, the current in each area is corrected based on the current fluctuation parameters of each line in each area. The power supply failure instruction is used to indicate that the power supply collected by the preset personnel has an impact on the abnormal location of distribution network line losses.

[0054] In this embodiment, when adjusting the calibration interval time of the electricity meter based on the deviation at the meter end, the deviation at the meter end is matched with the calibration interval adjustment value corresponding to each preset deviation value in the line loss anomaly database. A mapping relationship table is formed in the line loss anomaly database, with each deviation value and calibration interval adjustment value corresponding to a one-to-one correspondence. The table records each deviation value and its corresponding calibration interval adjustment value. These relationships can be one-to-one or many-to-one. When obtaining the calibration interval adjustment value, it is only necessary to input the deviation at the meter end into the mapping relationship table, and the line loss anomaly database can quickly locate and return the calibration interval adjustment value corresponding to the deviation at the meter end. The calibration interval adjustment value is summed with the current grid end calibration interval time to obtain the adjusted grid end calibration interval time. The adjustment of the grid end calibration interval time is carried out in the same way. This invention, by quantitatively determining the harmonic impact of each region, can effectively identify power supply acquisition errors caused by harmonic interference, thereby dynamically correcting power supply data, reducing the interference of harmonic distortion on metering accuracy, significantly improving the reliability of power supply data, avoiding line loss rate calculation deviations caused by harmonic pollution, and thus enhancing the accuracy of distribution network line loss anomaly location. This provides more realistic data support for line loss management and grid optimization, and ultimately reduces the negative impact of power supply acquisition anomalies on line loss analysis.

[0055] In summary, this application embodiment quantifies and determines the equipment status parameters of each meter in the distribution network during each monitoring time period to obtain the meter status quantification result, thereby determining whether dynamic correction of electricity sales is needed. Simultaneously, it quantifies and determines the harmonic impact based on the harmonic impact parameters of each region during each monitoring time period to determine whether dynamic correction of power supply is needed. Finally, by combining the total electricity sales after quantifying the meter status and the total power supply after quantifying the harmonic impact, it performs distribution network line loss anomaly location, improving the accuracy of distribution network line loss anomaly location.

[0056] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0058] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0059] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0060] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0061] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for locating abnormal line losses in a distribution network, characterized in that, Includes the following steps: The meter status is quantitatively determined based on the equipment status parameters of each meter in the distribution network during each monitoring period. The result of the quantitative determination of meter status is obtained. Based on the result of the quantitative determination of meter status, it is determined whether to perform dynamic correction of electricity sales. The dynamic correction of electricity sales means that the electricity sales are corrected according to the equipment status parameters to reduce the impact of abnormal electricity sales collection accuracy on the accuracy of abnormal location of distribution network line loss. The harmonic impact is quantitatively determined based on the harmonic impact parameters of each area in each monitoring time period in the distribution network, and the harmonic impact quantitative determination result is obtained. Based on the harmonic impact quantitative determination result, it is determined whether to perform dynamic correction of power supply. The dynamic correction of power supply means correcting the power supply according to the harmonic impact parameters to reduce the impact of abnormal power supply acquisition accuracy on the accuracy of distribution network line loss anomaly location. The step of determining whether to perform dynamic power supply correction based on the harmonic influence quantification result includes: If the harmonic impact quantification result of a certain area during a certain monitoring period is the first result of harmonic impact, then a power supply stabilization command is issued for that area. The time reference points corresponding to the meter end and the power grid end at the initial time point of the monitoring period are obtained respectively. The difference between the time reference point of the power grid end and the time reference point corresponding to the meter end is marked as the deviation time. Based on the deviation time corresponding to the meter end and the power grid end respectively, the time reference points corresponding to the meter end and the power grid end are corrected. The power supply stabilization command is used to indicate that the power supply collected by the preset personnel has no impact on the abnormal location of the distribution network line loss. If the harmonic impact quantification result of a certain area during a certain monitoring period is the second result of harmonic impact, then a power supply qualified instruction for that area is issued. The observed value of harmonic impact in that area during the monitoring period is matched with the preset power supply correction value in the line loss anomaly database to obtain the power supply correction value. The power supply is then corrected according to the power supply correction value. The power supply qualified instruction is used to indicate that the impact of the power supply collected by the preset personnel on the location of the distribution network line loss anomaly is within the correctable range. If the harmonic impact quantification result of a certain area during a certain monitoring period is the third result of harmonic impact, then a power supply failure instruction for that area will be issued. It will be determined whether there is a monitoring period in the adjacent time period where the harmonic impact quantification result is the third result of harmonic impact. If not, no additional processing will be performed. If so, it will be determined whether the harmonic suppression device in that area has been turned on. Based on the quantitative determination of meter status, the total electricity sales volume for each monitoring period and the quantitative determination of harmonic impact, the total power supply for each monitoring period are used to locate abnormal line losses in the distribution network.

2. The method for locating abnormal line losses in a distribution network as described in claim 1, characterized in that: The step of quantifying the meter status based on the equipment status parameters of each meter in the distribution network during each monitoring time period to obtain the meter status quantification result includes: The equipment status parameter reference data is obtained from the preset line loss anomaly database, specifically including: critical current imbalance rate, number of critical current abnormal changes and critical power factor; The current imbalance rate, the number of abnormal current changes, and the critical power factor are respectively compared with the critical current imbalance rate, the number of abnormal current changes, and the power factor to calculate the approximation degree. Then, the approximation degree calculation results are weighted using the weight ratio of equipment status parameters. Finally, the weighted results are coupled to obtain the equipment status observation values ​​of each meter in each monitoring time period. The weight ratio of equipment status parameters includes the weight ratio of current imbalance rate, the weight ratio of the number of abnormal current changes, and the weight ratio of power factor. The equipment status observation values ​​represent the quantitative data of the degree of influence of equipment status parameters on the accuracy of electricity sales data collection. The equipment status parameters include current imbalance rate, the number of abnormal current changes, and power factor. Obtain the first threshold and the second threshold of equipment status from the preset line loss anomaly database; The observed device status values ​​of each meter in each monitoring period are compared with the first threshold and the second threshold of device status. If the observed device status value of a meter in a certain monitoring period is greater than or equal to the second threshold of device status, the corresponding meter status quantification result is recorded as the first result of device status. If the observed value of the device status of a certain meter is greater than or equal to the first threshold of device status and less than the second threshold of device status during a certain monitoring period, the corresponding quantitative judgment result of the meter status is recorded as the second result of device status. If the observed value of a meter's equipment status is less than the first threshold of equipment status during a certain monitoring period, the corresponding quantitative judgment result of the meter's status will be recorded as the third result of equipment status.

3. The method for locating abnormal line losses in a distribution network as described in claim 2, characterized in that: The step of determining whether to dynamically adjust the electricity sales volume based on the quantitative determination result of the electricity meter status includes: If the quantitative determination result of the status of a certain meter during a certain monitoring period is the first result of the equipment status, then a power sales volume stabilization instruction is issued for that meter. The power sales volume stabilization instruction is used to indicate that the power sales volume collected by the preset personnel has no impact on the abnormal location of the distribution network line loss. If the quantitative determination result of the meter status of a certain meter during a certain monitoring period is the second result of the equipment status, then a qualified sales volume instruction for the meter is issued. The difference between the second threshold of the equipment status and the observed value of the equipment status of the meter during the monitoring period is recorded as the equipment status deviation value. The equipment status deviation value is matched with the sales volume correction value corresponding to each preset equipment status deviation value in the line loss anomaly database to obtain the sales volume correction value. The sales volume of the meter is corrected according to the sales volume correction value. The qualified sales volume instruction is used to remind the preset personnel that the sales volume collected after correction has no impact on the location of the distribution network line loss anomaly. If the quantitative determination result of the status of a certain meter during a certain monitoring period is the third result of the equipment status, an instruction for the meter's sales volume is issued, and the preset personnel are notified to carry out maintenance. The instruction for the sales volume is used to remind the preset personnel that the sales volume collected has an impact on the location of abnormal line loss in the distribution network.

4. The method for locating abnormal line losses in a distribution network as described in claim 1, characterized in that: The steps for quantifying the harmonic impact based on the harmonic impact parameters of each area in the power distribution network during each monitoring time period, and obtaining the quantification result of the harmonic impact, include: Reference data on harmonic influence parameters are obtained from a pre-set line loss anomaly database, specifically including: critical harmonic distortion rate, critical average harmonic content, and critical harmonic order. The harmonic influence parameters are compared with their corresponding harmonic influence parameter reference data to calculate the approximation of their proportions. Then, the approximation results are weighted using the weight ratios of the harmonic influence parameters. Finally, the weighted results are coupled to obtain the observed values ​​of harmonic influence in each region during each monitoring period. The weight ratios of the harmonic influence parameters include the weight ratio of harmonic distortion rate, the weight ratio of average harmonic content, and the weight ratio of harmonic order. The observed values ​​of harmonic influence represent the quantitative data on the degree of influence of the harmonic influence parameters on the accuracy of power supply acquisition. The harmonic influence parameters include harmonic distortion rate, average harmonic content, and harmonic order. Obtain the first threshold and the second threshold of harmonic influence from the preset line loss anomaly database; The observed values ​​of harmonic impact in each region during each monitoring period are compared with the first threshold and the second threshold of harmonic impact. If the observed value of harmonic impact in a certain region during a certain monitoring period is less than or equal to the first threshold of harmonic impact, the corresponding harmonic impact quantification result is recorded as the first result of harmonic impact. If the observed value of harmonic influence in a certain area during a certain monitoring period is greater than the first threshold of harmonic influence and less than or equal to the second threshold of harmonic influence, then the corresponding harmonic influence quantification result is recorded as the second result of harmonic influence. If the observed value of harmonic influence in a certain area during a certain monitoring period is greater than the second threshold of harmonic influence, then the corresponding quantitative judgment result of harmonic influence is recorded as the third result of harmonic influence.

5. The method for locating abnormal line losses in a distribution network as described in claim 4, characterized in that: The step of determining whether to perform dynamic power supply correction based on the harmonic influence quantification result also includes: If the harmonic suppression device is not turned on, then turn on the harmonic suppression device; If the harmonic suppression device has been activated, determine whether the change value of the harmonic influence index at the next time monitoring point under the current device power exceeds the preset harmonic influence index change threshold. If so, no additional processing is performed; otherwise, gradually increase the device power until the change value of the harmonic influence index at the next time monitoring point exceeds the harmonic influence index change threshold. If the harmonic suppression device has been activated and the device power has reached its maximum value, but the change value of the harmonic influence index at the next time monitoring point still does not exceed the harmonic influence index change threshold, then the current in each area will be corrected according to the current fluctuation parameters of each line in each area. The power supply failure instruction is used to indicate that the power supply collected by the preset personnel has an impact on the location of abnormal line losses in the distribution network.

6. The method for locating abnormal line losses in a distribution network as described in claim 5, characterized in that: The step of correcting the time reference points corresponding to the meter terminal and the power grid terminal based on the deviation time corresponding to the meter terminal and the power grid terminal respectively includes: Obtain the deviation time threshold from the preset line loss anomaly database; The deviation time between the meter and the grid is compared with the deviation time threshold. If the deviation time between the meter and the grid is less than or equal to the deviation time threshold, no additional processing is performed. If the time deviation between the meter terminal and the grid terminal is greater than the deviation time threshold, then determine whether the deviation between the time reference point corresponding to the meter terminal and the reference time point exceeds the deviation threshold respectively: If the deviation at the meter exceeds the deviation threshold while the deviation at the grid does not exceed the deviation threshold, the time reference point at the meter will be aligned with the reference time point, and the meter calibration interval will be adjusted according to the deviation at the meter. If the deviation at the meter does not exceed the deviation threshold but the deviation at the grid exceeds the deviation threshold, then the time reference point at the grid is aligned with the reference time point, and the calibration interval at the grid is adjusted according to the deviation at the grid. If the deviations at both the meter end and the grid end exceed the deviation threshold, the time reference points corresponding to the meter end and the grid end will be aligned with the reference time point, and the calibration interval time of the meter end and the grid end will be adjusted according to the deviations at the meter end and the grid end respectively. If the deviations at both the meter and the grid do not exceed the deviation threshold, a time anomaly alert will be issued, notifying designated personnel to conduct an inspection and handle the issue.

7. The method for locating abnormal line losses in a distribution network as described in claim 5, characterized in that: The step of correcting the current in each region based on the current fluctuation parameters of each line in each region includes: The impact of current balance in each region on the accuracy of power supply data collection is quantified based on the current fluctuation parameters of each region and each line. The current fluctuation observation values ​​of each region are obtained. The current fluctuation observation values ​​represent the quantitative data on the degree of influence of the current fluctuation parameters on the accuracy of power supply data collection. The current fluctuation parameters include the number of abnormal current changes, the maximum current difference, and the current variance. Obtain the current fluctuation threshold from the preset line loss anomaly database; The observed current fluctuation values ​​of each region are compared with the current fluctuation threshold. If the observed current fluctuation value of a certain region is less than or equal to the current fluctuation threshold, the corresponding current balance quantification judgment result is recorded as the first result of current fluctuation. If the observed current fluctuation value in a certain area is greater than the current fluctuation threshold, the corresponding current balance quantification judgment result is recorded as the second result of current fluctuation. Whether to perform dynamic current correction is determined based on the quantitative judgment result of current balance.

8. The method for locating abnormal line losses in a distribution network as described in claim 7, characterized in that: The steps for quantifying the impact of current balance in each region on the accuracy of power supply acquisition based on current fluctuation parameters of each line in each region, and obtaining the current fluctuation observation values ​​for each region, include: Reference data on current fluctuation parameters are obtained from a pre-set database of abnormal line losses, including: the number of abnormal changes in critical current, the maximum difference in critical current, and the variance of critical current. The current fluctuation parameters of each region and each line are compared with the corresponding current fluctuation parameter reference data to calculate the degree of convergence. Then, the results of the degree of convergence calculation are weighted according to the weight ratio of the current fluctuation parameters. Finally, the weighted results are coupled to obtain the current fluctuation observation values ​​of each region. The weight ratio of the current fluctuation parameters includes the weight ratio of the number of abnormal current changes, the weight ratio of the maximum current difference, and the weight ratio of the current variance.

9. The method for locating abnormal line losses in a distribution network as described in claim 7, characterized in that: The step of determining whether to perform dynamic current correction based on the current balance quantization result includes: If the quantitative determination result of current balance in a certain area is the first result of current fluctuation, then a current fluctuation stabilization command is issued for that area without any additional processing. The current fluctuation stabilization command is used to remind the preset personnel that the current fluctuation in that area has no impact on the abnormal location of distribution network line loss. If the quantitative determination result of current balance in a certain area is the second result of current fluctuation, an unstable current fluctuation command is issued for that area. The current is dynamically corrected based on the observed current fluctuation value of that area. The unstable current fluctuation command is used to prompt the preset personnel that the current fluctuation in that area has an impact on the abnormal location of distribution network line loss. The dynamic current correction means that the current is corrected according to the current fluctuation parameters to reduce the impact of harmonics generated by current fluctuation on the accuracy of power supply acquisition.

10. The method for locating abnormal line losses in a distribution network as described in claim 9, characterized in that: The step of dynamically correcting the current based on the observed current fluctuations in the region includes: The difference between the observed current fluctuation value and the current fluctuation threshold in this area is marked as the current fluctuation deviation value; The current fluctuation deviation value is matched with the line current range corresponding to each current fluctuation deviation value preset in the line loss anomaly database to obtain the line current range. If the current value of a line in the area exceeds the maximum value of the line current range, then the current value of that line will be reduced to the maximum value of the line current range. If the current value of a line in the area does not exceed the minimum value of the line current range, then the current value of that line will be increased to the minimum value of the line current range.

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