Distribution network line loss monitoring method and system based on data value degree

Abstract

The invention discloses a data value degree-based distribution network line loss monitoring method and system, and belongs to the technical field of intelligent power grid monitoring, and the method comprises the following steps: S1, obtaining the power time sequence data of a data collection point, and obtaining the state data of a distribution network switch; s2, obtaining a real-time topological structure connection relation of the distribution network; s3, performing data value evaluation on the power time sequence data to obtain a data value score of each piece of power time sequence data; s4, performing section line loss value calculation on the distribution network, weighting the power time sequence data of the upper level and / or the lower level of each section by adopting the data value score, and performing section line loss value calculation by adopting the weighted power time sequence data; and S5, outputting a line loss value calculation result and a value degree rating of each section. According to the invention, the data acquired by the data acquisition equipment is subjected to value analysis and is applied to abnormal line loss judgment, so that the reliability of an abnormal line loss analysis result can be effectively guaranteed.

Description

Technical Field

[0001] This invention relates to the field of smart grid monitoring technology, and in particular to a method and system for monitoring distribution network line losses based on data value. Background Technology

[0002] With the development of smart grid technology, refined line loss monitoring solutions based on dense data collection points have replaced traditional line loss detection systems. Compared to traditional technologies, these solutions can not only effectively and accurately locate abnormal line losses, facilitating subsequent investigations, but also identify abnormal line losses more quickly, providing conditions for rapid handling of abnormal line losses. Specifically, traditional methods use the difference between the total power supply at the front end and the sum of the power supply at the back end of the line to obtain the line loss value. When applied to line loss analysis systems such as those based on monthly summaries, this not only fails to achieve timely identification and handling of abnormal line losses, but also, for the large number of end users at the back end, the limited location and number of data collection points in the power grid topology generally make it difficult to accurately locate abnormal line losses down to specific line segments or end users. To address the issues of delayed identification of abnormal line losses in traditional methods, and the large workload and time-consuming nature of the subsequent step-by-step investigation, a new approach is adopted. This involves using high-density, high-frequency data acquisition within the distribution network topology. Monitoring terminals (TTUs or FTUs) are installed at distribution network branch line nodes, line sections, and line branch boxes to achieve high-density data acquisition of key nodes in the distribution network. Real-time voltage, current, and power data are collected at minute-level or even second-level after an anomaly is detected. Real-time alarms are issued immediately upon detecting abnormal line losses, pinpointing the specific line section, thus enabling timely alerts and accurate location of abnormal line losses.

[0003] The existing technology specifically involves a patented technical solution entitled "Intelligent Diagnosis and Visualization Method for Power Grid Line Loss Based on Multi-Source Data Acquisition" (patent application number: CN202511099532.X). This solution involves technical means including: analyzing the power input and output differences between adjacent feeder nodes, extracting abnormal nodes from the feeder line loss mutation marker results, and generating abnormal path map results of line loss structure, etc. As someone skilled in the art, this technical solution achieves line loss fault diagnosis and location visualization by using identified abnormal data. The abnormal data is obtained based on power difference comparison results and node number change trend comparison results. It is a method that, from the collected data, filters out abnormal data indicating line loss and then uses this abnormal data to achieve line loss fault diagnosis.

[0004] Line loss monitoring is an important guarantee for maintaining the healthy and reliable operation of the power grid. Improving the accuracy and reliability of the line loss monitoring system is undoubtedly of great significance for promoting the development of smart grid technology, improving the level of power grid management, and ensuring the normal operation of the power grid.

[0005] In existing technologies, distribution network line loss monitoring includes technical means of comparing power time-series data collected from data acquisition points at various levels in the distribution network topology. The specific logic is as follows: for all collected power time-series data, no data value judgment is made; these data are directly used as real data that accurately reflects the distribution network status, and further used as a reference for abnormal line loss in certain sections, thereby obtaining the line loss value of each section or locating abnormal line loss sections. However, factors affecting the acquisition results of power time-series data may include data gaps (the power time-series data uploaded by the data acquisition point at the set acquisition time was not collected; the conventional method is to use interpolation to fill in the data, or to exclude the missing data in the line loss analysis), or significant distortion of the power time-series data. For example, the power value in the power time-series data (which can also be voltage, current, etc.) significantly exceeds the limit of the power value that the data acquisition point may have. Using such power time-series data for distribution network line loss monitoring will lead to significant distortion of the monitoring results. Alternatively, the power time-series data itself may be within a reasonable range, but in terms of timing, two adjacent data points may come from the same source. The data collection results from the data acquisition points are significantly distorted and do not conform to the general continuous pattern of power grid load changes. Such data may also be questionable data that affects the reliability of line loss monitoring results. At the same time, there is also the following possibility: For the power time series data uploaded by the data acquisition points, the power time series data itself is accurate. However, if the distribution automation terminal fails to accurately report the opening and closing actions or status of switches (such as phase switching switches) in the topology, or if the system fails to obtain the switch actions or status under operations such as line maintenance, fault isolation, and manual load switching, it will lead to a mismatch between the logic of the distribution network line loss monitoring data and the connection relationship of the real-time distribution network topology, resulting in meaningless line loss monitoring results for certain sections. Summary of the Invention

[0006] To address the aforementioned technical issues concerning improving the accuracy and reliability of line loss monitoring systems, this invention provides a distribution network line loss monitoring method and system based on data value. This invention analyzes the value of data collected by data acquisition equipment and applies the value analysis results to the determination of abnormal line losses, effectively ensuring the reliability of the abnormal line loss analysis results.

[0007] The technical solution of this invention is: a method for monitoring distribution network line loss based on data value, comprising the following steps: S1. Obtain the power time sequence data of each level data acquisition point in the distribution network topology at the same time, and obtain the switching status data of the switches in the distribution network at that time. S2. Based on the switch status data, obtain the real-time topology connection relationship of the distribution network at the time; S3. Perform a data value assessment on the power time series data to obtain a data value score for each power time series data, wherein: the value score includes a quality assessment score for the reliability of the power time series data itself, and a data consistency score for the power time series data under the connection relationship of the real-time topology. S4. Based on the real-time topology connection relationship, calculate the segment line loss value of the distribution network. When calculating the segment line loss value, the power time series data of the upper and / or lower levels of each segment are weighted by the data value score, and the segment line loss value is calculated by using the weighted power time series data. S5. Output the line loss calculation results for each section and the value rating of the line loss calculation results for each section.

[0008] A further technical solution to the data-value-based distribution network line loss monitoring method is as follows: The quality assessment score is the sum of the data integrity score, the data reasonableness score, and the data stability score; The data consistency score is a score for the consistency of the power time series data between the upper and lower levels of each segment under the real-time topology connection relationship, based on the principle of energy conservation. The data value score is a comprehensive score of the data value of the power time series data collected from each data collection point, obtained by integrating the quality assessment score and the data consistency score.

[0009] For any data acquisition point, acquire the power time-series data of that data acquisition point, as well as the power time-series data of the data acquisition points adjacent to that data acquisition point in the real-time topology connection relationship, and compare the difference between the two power time-series data; compare the absolute value of the difference with a preset power deviation threshold, and generate a data consistency score for the power time-series data based on the comparison result.

[0010] The data integrity score is determined based on the power time-series data missing rate; the data rationality score is determined based on whether the power time-series data exceeds the range of the equipment / line rated parameters; and the data stability score is determined based on the mutation rate of power time-series data that are adjacent in time at the same data acquisition point.

[0011] For line loss calculation of a segment with multiple parallel data acquisition points at the lower level, the line loss value of the segment is calculated using the following formula: ; Among them, P l This refers to the segment's line loss value; P u This refers to the power timing data of the higher level of the segment; P di This refers to the power timing data of the i-th data acquisition point in the lower level of the segment; V i Score the data value of the i-th data collection point at the next lower level of the segment; n represents the number of data collection points in the lower level of the segment.

[0012] It also includes step S6, which is: based on the real-time topology connection relationship, traverse and compare the value rating of adjacent segments at the level, obtain the abnormal segments of the line loss value calculation result, and generate a data value alarm for the abnormal segments of the line loss value. The abnormal line loss segment is defined as follows: for two adjacent segments at the hierarchical level, when the difference in the value rating of the two adjacent segments exceeds a set difference threshold, the segment with the relatively lower value rating is designated as the abnormal line loss segment.

[0013] It also includes step S6, which is: based on the real-time topology connection relationship, traverse the value rating of each segment, determine the segment with the value rating below the set threshold as the abnormal line loss segment, and generate a data value alarm for the abnormal line loss segment.

[0014] The line loss calculation results and value rating results of each section are visualized in the distribution network topology information system diagram; among them, the line loss calculation results of sections with different value ratings and / or different sections are distinguished by any one or more of the following visualization effects: different color display effect, different display shape display effect, and different flashing frequency display effect.

[0015] The method for rating the value of the line loss calculation results for each segment is as follows: For any given segment, a comprehensive data value score is calculated based on the data value scores of all data collection points involved in the line loss calculation for that segment. The data value score is compared with a preset comprehensive score threshold range, and the value rating of that range is determined. The comprehensive score for the value of the data in this section can be calculated using any of the following methods: The arithmetic mean of the data value scores of the data collection points participating in the calculation of this section is calculated, and the obtained arithmetic mean is used as the comprehensive data value score of this section. The data value scores of the data collection points participating in the calculation of this section are weighted and a weighted value is obtained. The comprehensive value score is the average value of the weighted values. The weights used in the weighting calculation are determined based on the position of each data collection point in the hierarchy of the real-time topology connection relationship. The lowest score of the data collection point participating in the calculation of this section will be used as the comprehensive score of the data value of this section.

[0016] This invention also relates to a distribution network line loss monitoring system based on data value, comprising: The data acquisition module is used to acquire the power timing data and the switch status data; The topology management module is used to generate the real-time topology connection relationship based on the switch status data; The value assessment module is used to obtain the quality assessment score and data consistency score of each power time series data, and output the data value score of each power time series data. The weighted calculation module is used to perform weighted calculations based on data value scores and output the calculated line loss value of the segment with a value rating. The analysis and alarm module is used to identify abnormal data value segments based on value rating and output alarms.

[0017] In its operation, this invention applies data value scoring to the calculation of line loss values ​​for a given section. Compared to existing technologies that assume that the power time-series data collected by the data acquisition points is accurate, this invention effectively reduces the impact of erroneous and unreliable data on line loss monitoring results and improves the reliability of line loss monitoring results through data value scoring steps and dynamic weighted calculation. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to the embodiments, but the present invention is not limited to the following embodiments: Example 1: like Figure 1 As shown, a method for monitoring distribution network line losses based on data value includes the following steps: S1. Obtain the power time sequence data of each level data acquisition point in the distribution network topology at the same time, and obtain the switching status data of the switches in the distribution network at that time. S2. Based on the switch status data, obtain the real-time topology connection relationship of the distribution network at the time; S3. Perform a data value assessment on the power time series data to obtain a data value score for each power time series data, wherein: the value score includes a quality assessment score for the reliability of the power time series data itself, and a data consistency score for the power time series data under the connection relationship of the real-time topology. S4. Based on the real-time topology connection relationship, calculate the segment line loss value of the distribution network. When calculating the segment line loss value, the power time series data of the upper and / or lower levels of each segment are weighted by the data value score, and the segment line loss value is calculated by using the weighted power time series data. S5. Output the line loss calculation results for each section and the value rating of the line loss calculation results for each section.

[0020] This invention provides a method for evaluating the value of power time-series data, using the resulting value score for weighted processing of the power time-series data, and obtaining the calculated line loss values ​​for each segment, along with a value rating for these calculated line loss values. The aim is to effectively ensure the reliability of abnormal line loss analysis results by performing reliability analysis on the data uploaded from data collection points and then applying the value analysis to the determination of abnormal line losses. Specifically: The power time-series data used in this invention refers to the power data obtained by data collection points at different levels of the distribution network at the same time. This power data can be power, voltage, current, or other data. The switch status data can be obtained based on the results automatically uploaded after the distribution automation terminal operates, or it can be the results manually entered after the topology is adjusted. The real-time topology connection relationship refers to the topology connection relationship of the distribution network at the above power data collection time. The quality assessment score is the average score for the reliability of the power time-series data itself. The data consistency score is the score obtained by mutual verification of the power time-series data uploaded by each data collection point under the current topology. Steps S4 and S5 are to apply the above two scores to the calculation of the line loss value of the distribution network section and obtain the calculation results of the line loss value of each section and the value rating of the calculation results of the line loss value of each section.

[0021] Specifically, in this invention: By synchronously collecting power time-series data from various data collection points (transformers, branch boxes, smart meters, etc.) in the distribution network, and receiving real-time pointer switch change information, a dynamic topology structure reflecting the connection relationship of the distribution network data acquisition time map is constructed. The quality assessment score is used to self-verify the power time-series data collected by each data collection point, and to address the impact of missing data (completeness), exceeding reasonable range (reasonableness), and drastic jumps (stability) on line loss monitoring results. The data consistency score compares the upstream and downstream power time-series data of each segment in the dynamic topology diagram, based on Kirchhoff's current law (the power flowing into a node should be approximately equal to...). The outflowing power (considering line loss) is used to verify the matching degree between the power time-series data obtained from the data collection points and the dynamic topology map. If the verification result shows a poor matching degree, the data consistency score decreases, and vice versa. Based on the fusion of the quality assessment score and the data consistency score, a data value score is obtained for the power time-series data obtained by each data collection point. This score is used to quantify the reliability of each power time-series data in the current line loss calculation, eliminating or weakening the interference caused by the real-time topology connection relationship not being the actual distribution network topology connection relationship at the time of data acquisition on the line loss monitoring results. Furthermore, based on the data value score, the segment line loss value is calculated to obtain the calculation results and value rating associated with the data value. In summary, compared with the prior art, this invention applies the data value score to the segment line loss value calculation. Compared with the prior art, which assumes that the power time-series data collected by the data collection points is accurate data, the data value score step and dynamic weighted calculation can effectively reduce the impact of erroneous and unreliable data on the line loss monitoring results and improve the reliability of the line loss monitoring results.

[0022] Example 2: This embodiment is a further refinement of embodiment 1: The quality assessment score is the sum of the data integrity score, the data reasonableness score, and the data stability score; The data consistency score is a score for the consistency of the power time series data between the upper and lower levels of each segment under the real-time topology connection relationship, based on the principle of energy conservation. The data value score is a comprehensive score of the data value of the power time series data collected from each data collection point, obtained by integrating the quality assessment score and the data consistency score.

[0023] The above provides a specific data value scoring method. Specifically, the data integrity score, data rationality score, and data stability score together constitute a multi-dimensional scoring system for the credibility of the data itself. Integrity addresses data missing issues, rationality prevents interference from excessive data, and stability identifies abnormal mutations. The three work together to effectively ensure the data quality of the quality assessment score. The data consistency score is a verification of the logical relationship of the data based on the law of conservation of energy under the dynamic topology structure. It is used to identify the unusable or low-value results of line loss monitoring caused by power flow mismatch due to switch file errors, distribution network equipment failures, etc. By integrating the quality assessment score and the topology consistency assessment score, two different dimensions of scores, a comprehensive data value score that can fully reflect the value of power time series data is generated, providing an accurate basis for subsequent weighted calculations.

[0024] Example 3: This embodiment is a further refinement of embodiment 2: For any data acquisition point, acquire the power time-series data of that data acquisition point, as well as the power time-series data of the data acquisition points adjacent to that data acquisition point in the real-time topology connection relationship, and compare the difference between the two power time-series data; compare the absolute value of the difference with a preset power deviation threshold, and generate a data consistency score for the power time-series data based on the comparison result.

[0025] The above provides a specific implementation method for data consistency scoring. Specifically, it involves acquiring power data from any data collection point and its adjacent nodes in the real-time topology, calculating the difference between them, and comparing these values ​​to determine whether the power time-series data obtained from different data collection points conforms to the topological logic. For example, if the difference significantly exceeds the normal line loss range (i.e., exceeds the power deviation threshold), it indicates that the power time-series data associated with this data collection point does not match the actual topological structure connection relationship, and its data consistency score will be positively reduced according to the degree to which it exceeds the normal line loss range. This method directly utilizes the basic physical laws of distribution networks, is computationally simple, and can effectively avoid or mitigate the impact of erroneous data caused by topology file errors, distribution network equipment failures, etc., on the value of line loss monitoring results.

[0026] Example 4: This embodiment is a further refinement of embodiment 2: The data integrity score is determined based on the power time-series data missing rate; the data rationality score is determined based on whether the power time-series data exceeds the range of the equipment / line rated parameters; and the data stability score is determined based on the mutation rate of power time-series data that are adjacent in time at the same data acquisition point.

[0027] The above provides a more specific method for obtaining quality assessment scores, specifically focusing on the core dimensions of the quality assessment scores. For easy understanding, the data integrity score is determined based on the data missing rate, directly reflecting the reliability of data acquisition and communication links; specifically, a high missing rate correlates with a high score. The data rationality score is determined by judging whether the power time-series data exceeds the rated parameter range of the equipment or line, effectively filtering out obvious data errors that are theoretically impossible due to sensor failures or transmission errors. The data stability score is calculated based on the mutation rate between power time-series data at the same data acquisition point. For abnormal situations such as data spikes, dead data with no change in multiple acquisition results, and abnormal data fluctuations, this invention provides a method for obtaining specific quality assessment scores based on a combination of these three scores, which can effectively achieve automatic diagnosis of the health status of the data itself.

[0028] Example 5: This embodiment is a further refinement of embodiment 1: For line loss calculation of a segment with multiple parallel data acquisition points at the lower level, the line loss value of the segment is calculated using the following formula: ; Among them, P l This refers to the segment's line loss value; P u This refers to the power timing data of the higher level of the segment; P di This refers to the power timing data of the i-th data acquisition point in the lower level of the segment; V i Score the data value of the i-th data collection point at the next lower level of the segment; n represents the number of data collection points in the lower level of the segment.

[0029] The above provides a specific method for obtaining the segment line loss value after weighting power time-series data, specifically a normalized weighted average algorithm. In this invention, when calculating the total power at the lower level (downstream), the power time-series data of each downstream data acquisition point is weighted according to its data value score, and normalized by summing the data value scores. This method effectively avoids the problem of severely underestimating the total power due to a large amount of low-value data downstream (in the power grid topology, power time-series data collected by upper-level data acquisition points is generally more reliable than that collected by lower-level data acquisition points), ensuring that the calculation results are always within a reasonable range. Compared to the existing technology that simply sums the lower-level data and then subtracts the upper-level data from the lower-level data, this invention provides a smoother calculation result, effectively suppresses noise interference from low-quality data, and thus yields a line loss value closer to reality, improving the accuracy and robustness of the calculation results.

[0030] Example 6: This embodiment is a further refinement of embodiment 1: It also includes step S6, which is: based on the real-time topology connection relationship, traverse and compare the value rating of adjacent segments at the level, obtain the abnormal segments of the line loss value calculation result, and generate a data value alarm for the abnormal segments of the line loss value. The abnormal line loss segment is defined as follows: for two adjacent segments at the hierarchical level, when the difference in the value rating of the two adjacent segments exceeds a set difference threshold, the segment with the relatively lower value rating is designated as the abnormal line loss segment.

[0031] The above provides a specific method for locating abnormal data value. The abnormal line loss segment should be understood as a segment with low line loss value that does not well match the actual line loss value of the segment. Specifically, this method traverses and compares the value ratings of directly adjacent segments at the same level. Based on a difference threshold, it identifies abrupt changes in the value rating on the distribution network topology to locate abnormal line loss segments (segments where the line loss value itself is incorrect). Specifically, when the rating difference between two adjacent segments exceeds the difference threshold, the downstream segment is considered an abnormal segment causing a break in the value chain and is marked as an abnormal line loss segment. The characteristic of this method is that it provides accurate location capabilities for abnormal data value segments. By clearly indicating which segment's data or equipment first shows an anomaly, it guides maintenance personnel to prioritize checking the data collection points in that segment, thereby improving the targeting and efficiency of maintenance work.

[0032] Example 7: This embodiment is a further refinement of embodiment 1: It also includes step S6, which is: based on the real-time topology connection relationship, traverse the value rating of each segment, determine the segment with the value rating below the set threshold as the abnormal line loss segment, and generate a data value alarm for the abnormal line loss segment.

[0033] The above scheme is a parallel technical solution for the specific method of locating abnormal data value in Example 6. Specifically, it is a global screening method based on a set threshold. In particular, the method traverses the value rating of all segments and directly judges the segments with the rating below the set threshold as abnormal data segments. This invention focuses on quickly identifying abnormal segments of line loss value from the global perspective and is suitable for rapid data risk elimination.

[0034] Example 8: This embodiment is a further refinement of embodiment 1: The line loss calculation results and value rating results of each section are visualized in the distribution network topology information system diagram; among them, the line loss calculation results of sections with different value ratings and / or different sections are distinguished by any one or more of the following visualization effects: different color display effect, different display shape display effect, and different flashing frequency display effect.

[0035] The above provides an intuitive method for displaying loss value calculation results and their value rating results. Specifically, it is visualized in the distribution network topology information system diagram, and different colors, shapes, or flashing effects are used to distinguish different value ratings or line loss values. Compared with providing data reports that include relevant information, operation and maintenance personnel can quickly identify the reliability distribution of the entire network's line loss calculation results and abnormal data areas simply by looking at the graphical interface display style, which can effectively improve the efficiency of subsequent distribution network operation and maintenance.

[0036] Example 9: This embodiment is a further refinement of embodiment 1: The method for rating the value of the line loss calculation results for each segment is as follows: For any given segment, a comprehensive data value score is calculated based on the data value scores of all data collection points involved in the line loss calculation for that segment. The data value score is compared with a preset comprehensive score threshold range, and the value rating of that range is determined. The comprehensive score for the value of the data in this section can be calculated using any of the following methods: The arithmetic mean of the data value scores of the data collection points participating in the calculation of this section is calculated, and the obtained arithmetic mean is used as the comprehensive data value score of this section. The data value scores of the data collection points participating in the calculation of this section are weighted and a weighted value is obtained. The comprehensive value score is the average value of the weighted values. The weights used in the weighting calculation are determined based on the position of each data collection point in the hierarchy of the real-time topology connection relationship. The lowest score of the data collection point participating in the calculation of this section will be used as the comprehensive score of the data value of this section.

[0037] The above provides a specific value rating method. This method clarifies the mapping relationship between value rating and data value score, and obtains a specific segment value rating by comparing the comprehensive data value score with a comprehensive score threshold range. This invention provides three specific comprehensive score calculation methods: one based on arithmetic average; another based on considering the importance of the data collection point location within the hierarchy, making the rating structure more consistent with actual power grid management; and the third based on the lowest score principle, representing a most conservative data value risk control strategy. This invention compares the calculated comprehensive data value score with a preset comprehensive score threshold range to ultimately determine intuitive value rating levels such as high, medium, and low, making the final output value rating a clear and quantifiable indicator, thereby avoiding subjectivity introduced by relying on subjective factors.

[0038] Example 10: This embodiment, based on Embodiment 1, provides a distribution network line loss monitoring system based on data value, including: The data acquisition module is used to acquire the power timing data and the switch status data; The topology management module is used to generate the real-time topology connection relationship based on the switch status data; The value assessment module is used to obtain the quality assessment score and data consistency score of each power time series data, and output the data value score of each power time series data. The weighted calculation module is used to perform weighted calculations based on data value scores and output the calculated line loss value of the segment with a value rating. The analysis and alarm module is used to identify abnormal data value segments based on value rating and output alarms.

[0039] As is easily understood, the above distribution network line loss monitoring system is the physical structure for implementing the aforementioned distribution network line loss monitoring method.

[0040] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, other embodiments derived without departing from the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring distribution network line losses based on data value, characterized in that, Includes the following steps: S1. Obtain the power time sequence data of each level data acquisition point in the distribution network topology at the same time, and obtain the switching status data of the switches in the distribution network at that time. S2. Based on the switch status data, obtain the real-time topology connection relationship of the distribution network at the time; S3. Perform a data value assessment on the power time series data to obtain a data value score for each power time series data, wherein: the value score includes a quality assessment score for the reliability of the power time series data itself, and a data consistency score for the power time series data under the connection relationship of the real-time topology. S4. Based on the real-time topology connection relationship, calculate the segment line loss value of the distribution network. When calculating the segment line loss value, the power time series data of the upper and / or lower levels of each segment are weighted by the data value score, and the segment line loss value is calculated by using the weighted power time series data. S5. Output the line loss calculation results for each section and the value rating of the line loss calculation results for each section.

2. The method for monitoring distribution network line loss based on data value as described in claim 1, characterized in that, The quality assessment score is the sum of the data integrity score, the data reasonableness score, and the data stability score; The data consistency score is a score for the consistency of the power time series data between the upper and lower levels of each segment under the real-time topology connection relationship, based on the principle of energy conservation. The data value score is a comprehensive score of the data value of the power time series data collected from each data collection point, obtained by integrating the quality assessment score and the data consistency score.

3. The method for monitoring distribution network line loss based on data value as described in claim 2, characterized in that, For any data acquisition point, acquire the power time-series data of that data acquisition point, as well as the power time-series data of the data acquisition points adjacent to that data acquisition point in the real-time topology connection relationship, and compare the difference between the two power time-series data; compare the absolute value of the difference with a preset power deviation threshold, and generate a data consistency score for the power time-series data based on the comparison result.

4. The method for monitoring distribution network line loss based on data value as described in claim 2, characterized in that, The data integrity score is determined based on the power time-series data missing rate; the data rationality score is determined based on whether the power time-series data exceeds the range of the equipment / line rated parameters; and the data stability score is determined based on the mutation rate of power time-series data that are adjacent in time at the same data acquisition point.

5. The method for monitoring distribution network line loss based on data value as described in claim 1, characterized in that, For line loss calculation of a segment with multiple parallel data acquisition points at the lower level, the line loss value of the segment is calculated using the following formula: ； Among them, P l This refers to the segment's line loss value. P u This refers to the power timing data of the higher level of the segment; P di This refers to the power timing data of the i-th data acquisition point in the lower level of the segment; V i Score the data value of the i-th data collection point at the next lower level of the segment; n represents the number of data collection points in the lower level of the segment.

6. A method for monitoring distribution network line losses based on data value according to any one of claims 1 to 5, characterized in that, It also includes step S6, which is: based on the real-time topology connection relationship, traverse and compare the value rating of adjacent segments at the level, obtain the abnormal segments of the line loss value calculation result, and generate a data value alarm for the abnormal segments of the line loss value. The abnormal line loss segment is defined as follows: for two adjacent segments at the hierarchical level, when the difference in the value rating of the two adjacent segments exceeds a set difference threshold, the segment with the relatively lower value rating is designated as the abnormal line loss segment.

7. A method for monitoring distribution network line losses based on data value according to any one of claims 1 to 5, characterized in that, It also includes step S6, which is: based on the real-time topology connection relationship, traverse the value rating of each segment, determine the segment with the value rating below the set threshold as the abnormal line loss segment, and generate a data value alarm for the abnormal line loss segment.

8. A method for monitoring distribution network line loss based on data value according to any one of claims 1 to 5, characterized in that, The line loss calculation results and value rating results of each section are visualized in the distribution network topology information system diagram; among them, the line loss calculation results of sections with different value ratings and / or different sections are distinguished by any one or more of the following visualization effects: different color display effect, different display shape display effect, and different flashing frequency display effect.

9. A method for monitoring distribution network line losses based on data value according to any one of claims 1 to 5, characterized in that, The method for rating the value of the line loss calculation results for each segment is as follows: For any given segment, a comprehensive data value score is calculated based on the data value scores of all data collection points involved in the line loss calculation for that segment. The data value score is compared with a preset comprehensive score threshold range, and the value rating of that range is determined. The comprehensive score for the value of the data in this section can be calculated using any of the following methods: The arithmetic mean of the data value scores of the data collection points participating in the calculation of this section is calculated, and the obtained arithmetic mean is used as the comprehensive data value score of this section. The data value scores of the data collection points participating in the calculation of this section are weighted and a weighted value is obtained. The comprehensive value score is the average value of the weighted values. The weights used in the weighting calculation are determined based on the position of each data collection point in the hierarchy of the real-time topology connection relationship. The lowest score of the data collection point participating in the calculation of this section will be used as the comprehensive score of the data value of this section.

10. A distribution network line loss monitoring system based on data value, characterized in that, The distribution network line loss monitoring system is used to implement the distribution network line loss monitoring method according to any one of claims 1 to 9, and the distribution network line loss monitoring system includes: The data acquisition module is used to acquire the power timing data and the switch status data; The topology management module is used to generate the real-time topology connection relationship based on the switch status data; The value assessment module is used to obtain the quality assessment score and data consistency score of each power time series data, and output the data value score of each power time series data. The weighted calculation module is used to perform weighted calculations based on data value scores and output the calculated line loss value of the segment with a value rating. The analysis and alarm module is used to identify abnormal data value segments based on value rating and output alarms.

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