Self-adaptive transformer area line loss diagnosis and monitoring method and system and electronic equipment

Abstract

The invention relates to the technical field of transformer area line loss rate monitoring, in particular to a self-adaptive transformer area line loss diagnosis and monitoring method and system and electronic equipment. According to the method, the initial monitoring parameters are determined through comprehensive analysis by obtaining the transformer area operation information, the equipment state and the monitoring layout information; differential monitoring parameters are calculated for different areas in combination with the spatial distribution characteristics of the monitoring points, so that the monitoring strategy is matched with the actual demand; formulating a monitoring scheme and issuing a data acquisition instruction; operation feedback information is obtained in real time and compared with a standard value, and when the deviation exceeds a threshold value, self-adaptive adjustment is triggered; and recalculating the monitoring parameters and updating the acquisition instruction. Through a parameter dynamic adjustment mechanism, monitoring blind areas are avoided, the resource utilization efficiency is improved, and the accuracy and real-time performance of line loss monitoring are ensured.

Description

Technical Field

[0001] This application relates to the technical field of transformer area line loss rate monitoring, and in particular to an adaptive transformer area line loss diagnosis and monitoring method, system and electronic equipment. Background Technology

[0002] With the continuous expansion of the power grid and the sustained growth of electricity demand, the management of distribution network line losses faces greater challenges. Distribution network line losses not only directly affect power supply efficiency and economic benefits, but also relate to power grid operation safety and power quality. Therefore, establishing an accurate and effective distribution network line loss monitoring system is of great significance for improving the operational level of the distribution network.

[0003] Currently, line loss monitoring in transformer substations mainly employs a fixed-period sampling and unified threshold judgment method. This involves deploying monitoring terminals at key nodes to periodically collect operating parameters such as voltage and current, and then performing data analysis and line loss calculations based on preset standards. This monitoring method has already achieved certain results in practical applications.

[0004] However, existing technologies suffer from problems such as fixed monitoring parameters, a single sampling strategy, and a lack of flexibility in monitoring schemes. Because the electricity consumption characteristics and load variation patterns differ across monitoring areas, uniform monitoring parameters are difficult to adapt to the actual conditions of each area, easily leading to monitoring blind spots or wasted resources, and affecting the accuracy and timeliness of line loss monitoring; this situation needs further improvement. Summary of the Invention

[0005] To address the problems of existing transformer substation line loss monitoring methods being ill-suited to the actual conditions of different areas, easily leading to monitoring blind spots or resource waste, and affecting the accuracy and timeliness of line loss monitoring, this application provides an adaptive transformer substation line loss diagnosis and monitoring method, system, and electronic equipment, employing the following technical solution: In a first aspect, this application provides an adaptive method for diagnosing and monitoring line loss in transformer substations, comprising the following steps: Acquire the operating information of the transformer substation, the status information of the equipment, and the layout information of the monitoring equipment, and determine the initial monitoring parameters based on the operating information of the transformer substation, the status information of the equipment, and the layout information of the monitoring equipment; Based on the spatial distribution of each monitoring point in the transformer area and the initial monitoring parameters, differentiated monitoring parameters are calculated for each monitoring area. Based on the differentiated monitoring parameters and the initial monitoring parameters, a monitoring scheme for the transformer area is determined, and based on the monitoring scheme for the transformer area, data acquisition instructions are sent to the corresponding monitoring devices. Obtain operational feedback information from each monitoring area, compare the operational feedback information with the standard values ​​in the initial monitoring parameters, and calculate the operational deviation value; if the operational deviation value exceeds the preset change threshold range, trigger an adaptive adjustment command; Based on the adaptive adjustment command, the corresponding monitoring and adjustment parameters are calculated and the adjustment and acquisition command is sent to the corresponding monitoring area.

[0006] By adopting the above technical solution, the traditional transformer substation line loss monitoring uses a uniform sampling period and fixed alarm threshold, which cannot perform differentiated monitoring based on the electricity consumption characteristics of different areas. For example, using the same monitoring frequency in commercial and residential areas leads to monitoring blind spots in commercial areas during peak electricity consumption periods, while residential areas waste resources during off-peak electricity consumption periods. This application first obtains transformer substation operation information, equipment status information, and monitoring equipment layout information, and determines the initial monitoring parameters through comprehensive analysis of multi-dimensional information. Then, combined with the spatial distribution characteristics of each monitoring point, differentiated monitoring parameters are calculated for different monitoring areas, so that the monitoring strategy can match the actual needs of each area. Based on this, the system formulates a complete monitoring plan for the transformer area according to the differentiated monitoring parameters and the initial monitoring parameters, and issues data acquisition instructions to the corresponding monitoring equipment. During the monitoring process, the system acquires the operational feedback information of each area in real time, compares and analyzes it with the standard values, and immediately triggers an adaptive adjustment instruction once the operational deviation value exceeds the preset threshold range. Finally, the system recalculates the monitoring adjustment parameters based on the adjustment instructions and sends updated acquisition instructions to the relevant areas. This enables the monitoring parameters to be dynamically adjusted according to the regional characteristics, which not only avoids the generation of monitoring blind spots, but also improves the utilization efficiency of monitoring resources, while ensuring the accuracy and real-time performance of line loss monitoring.

[0007] Optionally, the system acquires transformer area operation information, equipment status information, and monitoring equipment layout information, and determines initial monitoring parameters based on the transformer area operation information, equipment status information, and monitoring equipment layout information, specifically including the following steps: Extract standard line loss rate and load curve characteristics from the transformer area operation information; Based on the transformer load rate, three-phase imbalance and power factor in the equipment status information, and combined with the coverage of each monitoring point in the monitoring equipment layout information, the data acquisition frequency for each monitoring area is determined. Based on the standard line loss rate, load curve characteristics, and equipment status information, determine the line loss calculation method and the data acquisition strategy for key monitoring equipment; The initial monitoring parameters are obtained by associating the data acquisition frequency, line loss calculation method, and acquisition strategy of key monitoring equipment.

[0008] By adopting the above technical solution, traditional methods often use empirical fixed values ​​or simple statistical averages to determine initial monitoring parameters, lacking in-depth analysis of the actual operating status of the transformer substation. For example, in some substations, although the average line loss rate is within the normal range, the equipment load rate and three-phase imbalance are high. If conventional monitoring parameter configuration methods are still used, potential operational risks cannot be detected in time. This application first extracts standard line loss rate and load curve characteristics from the substation operating information to establish a basic monitoring benchmark. Then, it combines key indicators such as transformer load rate, three-phase imbalance, and power factor from the equipment status information, and considers the actual layout and coverage of the monitoring equipment to customize the data acquisition frequency for each monitoring area. Then, based on the extracted standard line loss rate and load curve characteristics, combined with the real-time equipment status information, the system selects the most suitable line loss calculation method and formulates special acquisition strategies for key monitoring equipment. Finally, through correlation analysis, the differentiated data acquisition frequency, targeted line loss calculation method, and key equipment acquisition strategy are deeply integrated to form a complete set of initial monitoring parameters. This enables the initial monitoring parameters to accurately reflect the operating characteristics and equipment status of the substation, improving the targeting and effectiveness of monitoring.

[0009] Optionally, based on the spatial distribution of each monitoring point in the distribution area and the initial monitoring parameters, differentiated monitoring parameters are calculated for each monitoring area, specifically including the following steps: Calculate the sampling time interval for each monitoring area based on the electricity consumption characteristics and load density of each monitoring area; Based on the electricity consumption characteristics and load density, and the line loss calculation method, determine the line loss alarm threshold for each monitoring area; Based on the electricity consumption characteristics and load density, as well as the data acquisition strategy of key monitoring equipment, and combined with the peak and valley load variation patterns of the distribution area, the data upload cycle and measurement accuracy requirements are determined. The differential monitoring parameters are obtained by associating the sampling time interval, line loss alarm threshold, data upload cycle, and measurement accuracy requirements.

[0010] By adopting the above technical solutions, traditional transformer substation line loss monitoring uses a uniform sampling interval and alarm threshold, ignoring the significant differences in electricity consumption characteristics and load density among different areas. This application first analyzes the electricity consumption characteristics and load density of each monitoring area, and determines a unique sampling time interval for each area based on the degree of load change and electricity consumption patterns. Then, it combines regional characteristics with line loss calculation methods, establishes regional electricity consumption models, and sets matching line loss alarm thresholds for different areas. Next, it analyzes regional electricity consumption characteristics in depth, matches the data acquisition strategy of key monitoring equipment with the peak-valley load change patterns of the area, scientifically determines the data upload cycle, and sets corresponding measurement accuracy requirements based on load sensitivity. Finally, through correlation analysis, it systematically integrates the sampling time interval, line loss alarm threshold, data upload cycle, and measurement accuracy requirements to form a complete differentiated monitoring parameter system. By establishing a mapping relationship between regional characteristics and monitoring parameters, it achieves precise allocation of monitoring resources and improves the targeting and accuracy of line loss monitoring.

[0011] Optionally, operational feedback information from each monitoring area is acquired, and the operational feedback information is compared with the standard values ​​in the initial monitoring parameters to calculate the operational deviation value. If the operational deviation value exceeds a preset change threshold range, an adaptive adjustment command is triggered, specifically including the following steps: According to the data acquisition instructions, the voltage, current and power data of each monitoring area are obtained to obtain the partition operation data; Obtain load stability information, and trigger a line loss calculation command based on the load stability information; Obtain line loss calculation results information, and based on the line loss calculation results information and the partition operation data, obtain the real-time line loss level of the specified area; Based on the real-time line loss level and the standard line loss rate in the initial monitoring parameters, and combined with the load change characteristics of the transformer area, the line loss deviation value is calculated. The line loss deviation value is compared with a preset change threshold range; if the line loss deviation value exceeds the preset change threshold range, an adaptive adjustment command is triggered to the corresponding monitoring device.

[0012] By adopting the above technical solution, traditional line loss monitoring systems lack a dynamic analysis mechanism for operational data, often only discovering problems after line loss anomalies occur. Furthermore, they do not consider the impact of load conditions on line loss calculations. For example, in a certain transformer area, rapid load fluctuations can lead to improper calculation timing, causing a significant deviation between the calculated line loss rate and the actual value, delaying the detection and handling of anomalies. This application first acquires voltage, current, and power data for each monitoring area in real time based on data acquisition commands, forming a complete zonal operational dataset. Then, it obtains load stability information through real-time analysis, ensuring that line loss calculation commands are triggered during periods of relatively stable load, avoiding sudden load fluctuations. The calculation errors caused by severe fluctuations are then addressed. Next, the obtained line loss calculation results are combined with zoned operation data to assess the real-time line loss level of a specified area. Then, the real-time line loss level is compared with the standard line loss rate in the initial monitoring parameters, while considering the load variation characteristics of the distribution area, to calculate the line loss deviation value. Finally, the calculated line loss deviation value is compared in real-time with the preset change threshold range. Once an excessive deviation is detected, an adaptive adjustment command is immediately triggered to the corresponding monitoring equipment. By establishing a real-time data analysis and adaptive adjustment mechanism, rapid identification and response to line loss anomalies are achieved, improving the real-time performance and accuracy of monitoring, while avoiding interference from load fluctuations in line loss calculation.

[0013] Optionally, based on the adaptive adjustment command, the corresponding monitoring and adjustment parameters are calculated and the adjustment acquisition command is sent to the corresponding monitoring area, specifically including the following steps: According to the adaptive adjustment instruction, the monitoring and adjustment parameter information is obtained, wherein the monitoring and adjustment parameter information includes the line loss calculation cycle adjustment value, power factor alarm value adjustment amount and load rate warning value adjustment amount for each monitoring area; The line loss calculation cycle adjustment value is compared with the standard monitoring cycle of the transformer area. If the line loss calculation cycle adjustment value is within a reasonable range, a regular monitoring command is triggered based on the monitoring adjustment parameter information and the power consumption characteristics of the transformer area. If the line loss calculation cycle adjustment value exceeds a reasonable range, a refined monitoring instruction is triggered. Based on the refined monitoring instruction, time-segmented line loss calculation parameters, area-segmented load monitoring parameters, and equipment status assessment parameters are obtained. Based on the refined monitoring parameters, update the monitoring plan and issue adjustment instructions.

[0014] By adopting the above technical solution, traditional monitoring systems often use a fixed adjustment scheme after detecting anomalies, failing to provide graded responses based on the severity of the anomaly. For example, they might initiate the highest frequency data acquisition when a slight fluctuation in line loss occurs in a certain transformer area, or maintain the regular monitoring frequency even during severe anomalies, resulting in unreasonable allocation of monitoring resources and poor performance. This application first obtains complete monitoring and adjustment parameter information, including the line loss calculation cycle adjustment value, power factor alarm value adjustment amount, and load rate warning value adjustment amount, based on adaptive adjustment instructions. Then, it intelligently compares the line loss calculation cycle adjustment value with the standard monitoring cycle of the transformer area. When the adjustment value is within the range of the standard monitoring cycle of the transformer area, the system will adjust accordingly. Within a reasonable range, the system makes appropriate adjustments using conventional monitoring commands, taking into account the actual power consumption characteristics of the distribution area. If the adjustment value exceeds the reasonable range, it automatically upgrades to a refined monitoring mode. The system then acquires more detailed time-segmented line loss calculation parameters, conducts in-depth monitoring of regional load conditions, and comprehensively evaluates equipment operating status. Finally, based on these refined monitoring parameters, the system automatically updates the monitoring plan and issues adjustment commands to relevant areas. By establishing a tiered response mechanism, monitoring resources are allocated on demand, avoiding the mismatch between monitoring intensity and anomaly severity, while improving the accuracy and efficiency of anomaly handling.

[0015] Optionally, before acquiring the transformer area operation information, equipment status information, and monitoring equipment layout information, the method further includes the following steps: Obtain basic configuration information for the distribution area; wherein, the basic configuration information for the distribution area includes transformer capacity and parameters, line layout and impedance, user distribution characteristics and metering device type; Based on the transformer capacity and parameters, the line layout and impedance, determine the layout scheme of key monitoring points; Based on the user distribution characteristics, a regional monitoring priority table is established; Obtain the judgment criteria indicating abnormal line loss, including line loss rate exceeding the limit, load rate warning value, voltage exceeding the limit standard, and three-phase imbalance limit value; based on the judgment criteria and the regional monitoring priority table, generate a transformer area monitoring plan, which is used to guide the deployment of monitoring equipment and the formulation of operation strategies.

[0016] By adopting the above technical solution, traditional transformer substation monitoring schemes neglect the systematic analysis of the basic characteristics of the substation and the scientific planning of the monitoring layout. For example, when deploying monitoring equipment in a certain substation, the differences in transformer capacity and line impedance distribution were not fully considered, resulting in both blind spots in the monitoring of important nodes and wasted monitoring resources, which affected the overall monitoring effect. This application first comprehensively obtains the basic configuration information of the substation, including key information such as transformer capacity and parameters, line layout and impedance, user distribution characteristics, and metering device type; then, based on the transformer capacity and parameters and line layout and impedance data, through electrical topology analysis and load flow calculation, the layout of key monitoring points is scientifically determined. The plan optimizes monitoring coverage; it then analyzes user distribution characteristics, considering user electricity consumption patterns, load characteristics, and importance, to establish a hierarchical regional monitoring priority table; finally, it develops a complete system of line loss anomaly judgment criteria, including line loss rate exceeding limits, load rate warning values, voltage exceeding limits, and three-phase imbalance limits, and combines these judgment criteria with the regional monitoring priority table to generate a transformer area monitoring plan, providing a scientific basis for the subsequent deployment and operation strategy of monitoring equipment; this achieves optimized allocation of monitoring resources, avoids the inefficiency caused by blind deployment, and improves the systematicness and pertinence of line loss monitoring.

[0017] Optionally, while acquiring the partition's runtime data, the method further includes the following steps: Acquire user electricity consumption data and transformer area meter data, wherein the user electricity consumption data includes time-of-use electricity consumption, power factor and load curve, and the transformer area meter data includes total power supply, phase data and harmonic data; Based on the user electricity consumption data, the transformer area master meter data, and the zone operation data, calculate the user electricity consumption characteristics and the transformer area power supply and consumption balance. Based on the user's electricity consumption characteristics, the power supply and consumption balance of the transformer area, and the load stability information, an abnormal electricity consumption identification model is established. Based on the abnormal power consumption identification model, the monitoring data is analyzed to generate abnormal power consumption judgment results.

[0018] By adopting the above technical solution, traditional abnormal electricity consumption identification methods often rely on only a single data source for judgment, ignoring the complexity and diversity of user electricity consumption behavior. For example, if a transformer area is analyzed solely based on the total meter data, it will be unable to identify electricity theft through load shifting while maintaining a stable total supply, severely impacting the efficiency of detecting abnormal electricity consumption. This application, firstly, acquires regional operation data while comprehensively collecting user electricity consumption data and transformer area total meter data, including time-of-use electricity consumption, power factor, load curves, and key information such as total power supply, phase data, and harmonic data. The system first gathers information; then it deeply integrates and analyzes user electricity consumption data, transformer area master meter data, and zone operation data to establish an electricity consumption characteristic model and a power supply and consumption balance assessment system, thereby achieving a comprehensive characterization of electricity consumption behavior; next, it combines user electricity consumption characteristics, transformer area power supply and consumption balance, and load stability information to construct an abnormal electricity consumption identification model; finally, it systematically analyzes the monitoring data based on this model to generate reliable abnormal electricity consumption judgment results; this improves the accuracy of identifying abnormal electricity consumption behavior, reduces the judgment error caused by a single data source, and improves the accuracy and timeliness of abnormal electricity consumption identification.

[0019] Optionally, the method further includes the following steps: Obtain electricity theft feature database information, wherein the electricity theft feature database information includes abnormal feature data of metering equipment, abnormal power load patterns, current and voltage distortion features, and abnormal power consumption change patterns; Based on the information in the electricity theft feature database, the abnormal electricity consumption judgment results are analyzed in depth to extract suspicious electricity theft features; Based on the aforementioned suspicious electricity theft characteristics, and combined with the historical electricity theft case database of the transformer area, an electricity theft probability score is calculated; If the probability score of electricity theft exceeds a preset threshold, an on-site verification instruction is triggered. The on-site verification instruction includes the location information of the verification location, abnormal electricity consumption data records, on-site image acquisition requirements, and a verification result feedback mechanism. Based on the results of on-site verification, the database of electricity theft characteristics and the model for identifying abnormal electricity use were updated.

[0020] By adopting the above technical solution, traditional electricity theft detection methods mainly rely on manual experience and lack a systematic feature analysis and quantitative evaluation mechanism. For example, when a transformer substation discovers abnormal electricity usage, it directly sends people to the site for inspection. Due to the lack of scientific judgment basis, this not only wastes human resources but also easily misses highly concealed electricity theft. This application first establishes a complete electricity theft feature database, systematically collecting multi-dimensional feature information such as abnormal feature data of metering equipment, abnormal power load patterns, current and voltage distortion characteristics, and abnormal power consumption change patterns. Then, it uses the standard features in the feature database to conduct in-depth analysis of the abnormal electricity usage judgment results, through pattern analysis. The system uses matching and feature extraction to identify suspicious behaviors with characteristics of electricity theft. Then, it compares and analyzes the extracted suspicious features with a historical electricity theft case database for the transformer area, using a weighted scoring method to calculate an electricity theft probability score. When the score exceeds a preset threshold, the system automatically generates an on-site verification instruction that includes the location information of the verification site, abnormal electricity usage data records, on-site image acquisition requirements, and a verification result feedback mechanism. Finally, based on the on-site verification results, the system dynamically updates the electricity theft feature database and the abnormal electricity usage identification model, achieving continuous model optimization. This enables accurate identification and efficient processing of electricity theft, while improving the accuracy and efficiency of electricity theft detection.

[0021] Secondly, this application provides an adaptive transformer area line loss diagnosis and monitoring system, comprising: The initial monitoring parameter determination module is used to acquire the operating information of the transformer substation, the status information of the equipment, and the layout information of the monitoring equipment, and to determine the initial monitoring parameters based on the operating information of the transformer substation, the status information of the equipment, and the layout information of the monitoring equipment. The differential monitoring parameter calculation module is used to calculate differential monitoring parameters for each monitoring area based on the spatial distribution of each monitoring point in the transformer substation and the initial monitoring parameters. The data acquisition instruction sending module is used to determine the monitoring scheme for the transformer area based on the differentiated monitoring parameters and the initial monitoring parameters, and to send data acquisition instructions to the corresponding monitoring devices based on the monitoring scheme for the transformer area. The adaptive adjustment module is used to acquire operational feedback information from various monitoring areas, compare the operational feedback information with the standard values ​​in the initial monitoring parameters, and calculate the operational deviation value; if the operational deviation value exceeds the preset change threshold range, an adaptive adjustment command is triggered. The monitoring and adjustment parameter calculation module is used to calculate the corresponding monitoring and adjustment parameters based on the adaptive adjustment command and send the adjustment acquisition command to the corresponding monitoring area.

[0022] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described adaptive transformer area line loss diagnosis and monitoring method.

[0023] In summary, this application includes at least one of the following beneficial technical effects: This application obtains and comprehensively analyzes information on transformer substation operation, equipment status, and monitoring layout to determine initial monitoring parameters. Based on the spatial distribution characteristics of monitoring points, it calculates differentiated monitoring parameters for different areas, ensuring the monitoring strategy matches actual needs. A monitoring plan is then developed and data acquisition instructions are issued. Real-time operational feedback information is acquired and compared with standard values; adaptive adjustments are triggered when deviations exceed thresholds. Monitoring parameters are recalculated and acquisition instructions are updated. Through a dynamic parameter adjustment mechanism, monitoring blind spots are avoided, resource utilization efficiency is improved, and the accuracy and real-time performance of line loss monitoring are guaranteed. This application analyzes the electricity consumption characteristics and load density of each region, and determines unique sampling intervals based on load changes; it establishes an electricity consumption model by combining line loss calculation methods and sets matching alarm thresholds; it matches the data acquisition strategy of key monitoring equipment with peak and valley load patterns to determine the data upload cycle and measurement accuracy requirements; it integrates various parameters through correlation analysis to form a differentiated monitoring parameter system; and it establishes a mapping relationship between regional characteristics and monitoring parameters to achieve precise allocation of monitoring resources and improve the targeting and accuracy of monitoring. Traditional methods for identifying abnormal electricity consumption rely on a single data source, making it difficult to detect complex electricity theft. This application comprehensively collects user electricity consumption data and transformer substation data, including time-of-use electricity consumption, power factor, load curves, total power supply, phase and harmonic data. It deeply integrates and analyzes various data types to establish an electricity consumption characteristic model and a power supply-consumption balance assessment system. Combining user characteristics and load status, it constructs an identification model incorporating criteria such as load abrupt changes, power factor deviations, and abnormal electricity consumption patterns. The system analyzes and generates reliable judgment results, improving the accuracy of abnormal electricity consumption identification, reducing judgment errors, and enhancing identification precision. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating an adaptive transformer area line loss diagnosis and monitoring method according to an embodiment of this application; Figure 2 This is a flowchart illustrating step S100 in the adaptive transformer area line loss diagnosis and monitoring method of this application embodiment; Figure 3 This is a flowchart illustrating step S200 in the adaptive transformer area line loss diagnosis and monitoring method of this application embodiment; Figure 4 This is a flowchart illustrating step S400 in the adaptive transformer area line loss diagnosis and monitoring method of this application embodiment; Figure 5 This is a flowchart illustrating step S500 in the adaptive transformer area line loss diagnosis and monitoring method of this application embodiment; Figure 6 This is a schematic diagram of the process for generating a monitoring plan for a transformer area in the adaptive transformer area line loss diagnosis and monitoring method of this application embodiment; Figure 7 This is a flowchart illustrating the abnormal power consumption assessment process in the adaptive transformer area line loss diagnosis and monitoring method of this application embodiment; Figure 8 This is a flowchart illustrating the electricity theft probability assessment process in the adaptive transformer area line loss diagnosis and monitoring method of this application embodiment; Figure 9 This is a schematic diagram of the modules of the adaptive transformer area line loss diagnosis and monitoring system according to an embodiment of this application; Figure 10 This is an internal structural diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0025] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0026] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0027] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0028] Firstly, this application provides an adaptive method for diagnosing and monitoring line losses in transformer substations, referring to... Figure 1 It includes the following steps: S100: Obtain the operating information of the transformer substation, the status information of the equipment, and the layout information of the monitoring equipment, and determine the initial monitoring parameters based on the operating information of the transformer substation, the status information of the equipment, and the layout information of the monitoring equipment.

[0029] In this embodiment, the transformer substation operation information refers to basic data reflecting the operating status of the power system in the substation area, including power supply load, power factor, voltage quality indicators, and line impedance parameters; equipment status information includes transformer operating parameters, switchgear status, metering device operating status, and line equipment integrity rate; monitoring equipment layout information includes the installation location of monitoring devices, coverage area, data acquisition capabilities, and communication method type. Initial monitoring parameters include sampling time interval, data upload cycle, measurement accuracy requirements, and line loss alarm threshold.

[0030] Specifically, firstly, a basic information database for the transformer substation is established, storing substation operation information according to power supply areas and creating a mapping table between monitoring equipment locations and power supply areas. Then, a hierarchical storage structure is used to record equipment operating status, and status assessment tables are created for different types of equipment, prioritizing monitoring based on equipment importance. Next, based on the coverage area of ​​the monitoring equipment, the transformer substation is divided into multiple sub-areas, and the power consumption density and load fluctuation index for each sub-area are calculated. Finally, initial monitoring parameters are determined through table lookup, where the sampling time interval is selected based on the load fluctuation index, the data upload cycle corresponds to the equipment monitoring priority, the measurement accuracy requirement is related to the power consumption density, and the line loss alarm threshold is obtained based on historical operating data statistics.

[0031] S200: Based on the spatial distribution of each monitoring point in the substation area and the initial monitoring parameters, calculate the differentiated monitoring parameters for each monitoring area.

[0032] In this embodiment, spatial distribution refers to the geographical distribution characteristics of monitoring points within the transformer area, including the distance between monitoring points, coverage density, the influence of topography, and the distribution of electricity load; differentiated monitoring parameters include regional sampling frequency, measurement accuracy coefficient, data upload time, and alarm criterion parameters. The monitoring area refers to the power supply range centered on a single monitoring point.

[0033] Specifically, firstly, a spatial distribution feature database of monitoring points is established, recording the geographical coordinates, coverage radius, and user type distribution of each monitoring point; then, the distance matrix between adjacent monitoring points is calculated to identify weak and key monitoring areas; next, a parameter adjustment rule table is established based on the regional electricity consumption characteristics, quantifying user type, load density, and distance factors into adjustment coefficients; finally, differentiated parameters for each region are calculated by looking up the table, where the regional sampling frequency is determined by the load change rate, the measurement accuracy coefficient increases with distance, the data upload time is determined based on communication quality, and the alarm criterion parameters are related to the importance of the user.

[0034] S300: Based on the differentiated monitoring parameters and the initial monitoring parameters, determine the monitoring plan for the transformer area, and based on the monitoring plan, send data acquisition instructions to the corresponding monitoring devices.

[0035] In this embodiment, the transformer area monitoring scheme refers to a complete monitoring strategy formulated for a specific transformer area, including monitoring point layout planning, data acquisition rules, monitoring parameter configuration, and alarm triggering conditions; the data acquisition instructions include sampling time setting, measurement parameter selection, data upload rules, and storage space allocation. The monitoring equipment includes smart meters, distribution transformer monitoring terminals, line measurement and control devices, and branch monitoring units.

[0036] Specifically, firstly, a monitoring scheme template library is established, and a basic monitoring scheme framework is designed for the characteristics of different power supply areas; then, differentiated monitoring parameters are integrated with initial monitoring parameters to generate a monitoring equipment configuration table, which includes the specific parameter settings for each monitoring point; next, a data acquisition task list is formulated, arranging the monitoring tasks in chronological order and determining the start and end times of acquisition, data item selection, and storage methods; finally, acquisition instructions are sent to each monitoring device through the communication network, and the instructions are sent in batches and by region, and an instruction execution status tracking mechanism is established.

[0037] S400: Obtain operational feedback information from each monitoring area, compare the operational feedback information with the standard values ​​in the initial monitoring parameters, and calculate the operational deviation value; if the operational deviation value exceeds the preset change threshold range, trigger an adaptive adjustment command.

[0038] In this embodiment, the operation feedback information refers to the real-time operation data collected by the monitoring equipment, including voltage level, current magnitude, active power and reactive power; the standard value refers to the reference value determined based on historical operation experience, including normal operating range, fluctuation limit and change trend; the operation deviation value refers to the degree of difference between the measured value and the standard value; the change threshold range refers to the allowable deviation change range.

[0039] Specifically, firstly, a data receiving buffer is established to store the received operational data according to the monitoring device number; then, the corresponding standard values ​​are extracted from the initial monitoring parameters, a real-time comparison table is established, and the standard value and measured value of each monitoring point are recorded; next, the operating deviation value is calculated using the sliding window method, and data analysis is performed by setting a fixed time window to obtain the deviation change curve; finally, the calculated deviation value is compared with a preset threshold, and an adjustment command is triggered when the deviation value of multiple consecutive time windows exceeds the threshold.

[0040] S500 calculates the corresponding monitoring and adjustment parameters based on adaptive adjustment commands and sends adjustment and acquisition commands to the corresponding monitoring area.

[0041] In this embodiment, the monitoring and adjustment parameters refer to the control quantities used to correct the monitoring strategy, including the sampling interval adjustment amount, accuracy correction coefficient, upload cycle change value, and alarm threshold offset; the adjustment acquisition command refers to the parameter modification command sent to the monitoring device.

[0042] Specifically, firstly, a parameter adjustment rule base is established, defining adjustment strategies corresponding to different degrees of deviation. Then, based on adaptive adjustment instructions, an appropriate adjustment scheme is selected, and the adjustment amounts for each parameter are calculated. Next, an adjustment instruction sequence is generated, and the parameter adjustment information is sorted according to execution priority to determine the order in which instructions are issued. Finally, adjustment instructions are sent to monitoring equipment in designated areas via a communication network, establishing an instruction execution confirmation mechanism to ensure that parameter adjustments are in place. By dynamically adjusting monitoring parameters, the monitoring strategy remains adaptable to the operational status.

[0043] In one embodiment, refer to Figure 2 In step S100, the operating information of the distribution area, the equipment status information, and the layout information of the monitoring equipment are obtained. Based on the operating information of the distribution area, the equipment status information, and the layout information of the monitoring equipment, the initial monitoring parameters are determined. Specifically, this includes the following steps: S110. Extract standard line loss rate and load curve characteristics from the transformer area operation information.

[0044] In this embodiment, the standard line loss rate refers to the ratio of power loss to power supply in a transformer substation under normal operating conditions, reflecting the power supply efficiency level of the substation. The load curve features include daily load factor, peak-valley difference rate, load duration, and load change rate, used to describe the temporal distribution pattern of electricity load. The substation operation information is stored in the substation basic database, and historical operation data is recorded in time series.

[0045] Specifically, firstly, a table for querying operating parameters of distribution transformer areas is established, classifying them according to power supply capacity, user type, and power supply radius, with each type of distribution transformer area corresponding to a standard line loss rate reference value; then, a load feature extraction algorithm is used to statistically analyze historical load data, calculate typical daily load curves, and identify load fluctuation patterns; next, the extracted load feature parameters are stored in a feature database, and a table of correspondence between load features and monitoring requirements is established; finally, the standard line loss rate is obtained by looking up the table, and the preliminary monitoring frequency requirements are determined in combination with the load features.

[0046] S120. Based on the transformer load rate, three-phase imbalance and power factor in the equipment status information, and combined with the coverage of each monitoring point in the monitoring equipment layout information, determine the data acquisition frequency for each monitoring area.

[0047] In this embodiment, the transformer load factor refers to the ratio of the actual load of the transformer to its rated capacity; the three-phase unbalance is used to characterize the distribution of the three-phase load; the power factor reflects the relationship between active power and apparent power; and the monitoring point coverage area refers to the spatial range in which a single monitoring device can effectively collect data, which is determined by communication capabilities and installation location.

[0048] Specifically, firstly, an equipment status assessment table is established, dividing the transformer load rate into three intervals: light load, normal, and heavy load. Three-phase imbalance and power factor are each divided into two intervals. Then, the spatial coverage matrix of the monitoring equipment is calculated, recording the monitoring blind spots and overlapping areas. Next, a data acquisition frequency setting table is established, mapping different status combinations to the corresponding acquisition frequency levels. Finally, the basic acquisition frequency is determined by looking up the table based on the equipment status assessment results, and then adjusted according to the coverage area, appropriately reducing the frequency in overlapping areas and appropriately increasing the frequency around blind spots.

[0049] S130. Based on the standard line loss rate, load curve characteristics, and equipment status information, determine the line loss calculation method and the data acquisition strategy for key monitoring equipment.

[0050] In this embodiment, the line loss calculation methods include the theoretical line loss method, the statistical line loss method, and the segmented accumulation method; the key monitoring equipment refers to the critical equipment that has a significant impact on the line loss calculation results, including the main transformer, trunk line equipment, and branch line equipment; the data acquisition strategy includes sampling time, sampling interval, data item selection, and storage method.

[0051] Specifically, firstly, a selection table for line loss calculation methods is established, and the applicable calculation methods are determined based on the standard line loss rate, load characteristics, and comprehensive equipment status score; then, rules for identifying key monitoring equipment are established, and monitoring priorities are determined through equipment importance evaluation; next, differentiated data acquisition strategies are formulated, using high-frequency sampling and multiple data acquisition strategies for key equipment, and conventional data acquisition strategies for ordinary equipment; finally, a monitoring task configuration table is generated, clarifying the specific data acquisition requirements for various types of equipment.

[0052] S140. Associate the data acquisition frequency, line loss calculation method, and acquisition strategy of key monitoring equipment to obtain initial monitoring parameters.

[0053] In this embodiment, correlation refers to integrating various monitoring parameters into a complete monitoring scheme, ensuring that the parameters are coordinated and logically consistent. Initial monitoring parameters refer to the initial configuration parameters of the monitoring scheme, serving as the benchmark for subsequent adaptive adjustments. Parameter correlation needs to consider limitations in data acquisition capabilities, storage space, and communication bandwidth.

[0054] Specifically, firstly, a parameter constraint rule base is established to define the logical relationships between parameters and ensure that the acquisition frequency matches the calculation method; then, a monitoring parameter configuration template is established to organize the data acquisition frequency, calculation method selection, and equipment acquisition strategy in a unified format; next, parameter conflict checks are performed to adjust parameter combinations that do not meet the constraints; finally, an initial monitoring parameter configuration file is generated to record the complete parameter setting scheme and provide a basis for the execution of subsequent monitoring tasks.

[0055] In one embodiment, refer to Figure 3In step S200, based on the spatial distribution of each monitoring point in the transformer area and the initial monitoring parameters, differentiated monitoring parameters are calculated for each monitoring area, specifically including the following steps: S210. Calculate the sampling time interval for each monitoring area based on the electricity consumption characteristics and load density of each monitoring area.

[0056] In this embodiment, electricity consumption characteristics refer to the classification of electricity consumption features of users within the monitoring area, including industrial electricity consumption, commercial electricity consumption, residential electricity consumption, and agricultural electricity consumption; load density refers to the level of electricity load per unit area, reflecting the degree of concentration of electricity consumption in the area; sampling time interval refers to the time interval between two data collections, which directly affects the real-time performance and accuracy of data collection.

[0057] Specifically, firstly, an electricity consumption classification table is established to classify users according to their electricity consumption type and scale, forming an electricity consumption characteristic scoring system; then, load density calculation rules are established to divide the monitoring area into grid units and calculate the capacity of electrical equipment in each grid; next, a sampling interval setting table is formulated to map different combinations of electricity consumption and load density to corresponding sampling interval levels; finally, the basic sampling interval for each area is determined by looking up the table and adjusted according to the area area to ensure that the sampling efficiency matches the monitoring requirements.

[0058] S220. Based on the nature of electricity consumption and load density, as well as the line loss calculation method, determine the line loss alarm threshold for each monitoring area.

[0059] In this embodiment, the line loss alarm threshold includes the upper limit of instantaneous line loss rate, the upper limit of cumulative line loss rate, and the limit of line loss change rate, which are used to determine whether the line loss is abnormal; the nature of electricity use and load density determine the normal line loss level of the area; the line loss calculation method includes three methods: theoretical calculation, statistical analysis, and segmented accumulation, and different methods correspond to different error characteristics.

[0060] Specifically, firstly, a baseline database of line loss values ​​is established to record typical line loss levels under different combinations of electricity consumption and load density; then, an error correction table for calculation methods is established, setting correction coefficients for different line loss calculation methods; next, threshold calculation rules are formulated, multiplying the baseline line loss value by the correction coefficient and adding a safety margin to obtain the alarm threshold; finally, a partitioned alarm threshold configuration table is generated to record the specific threshold settings for each monitoring area, facilitating subsequent alarm judgment.

[0061] S230. Based on the nature of electricity consumption and load density, as well as the data acquisition strategy of key monitoring equipment, and combined with the peak and valley load variation patterns of the distribution area, determine the data upload cycle and measurement accuracy requirements.

[0062] In this embodiment, the peak-valley load variation pattern refers to the periodic characteristics of load changes over time, including the time period, duration, and rate of change of peaks and valleys; the data upload cycle refers to the time interval between the transmission of monitoring data to the main station; and the measurement accuracy requirements include the measurement error limits for voltage, current, and power.

[0063] Specifically, firstly, a load characteristic cycle table is established to record typical load change patterns for different types of electricity consumption; then, upload cycle configuration rules are formulated to establish a correspondence between load change rate and upload frequency; next, a measurement accuracy grading table is established to determine the accuracy level based on load density and equipment importance; finally, the upload cycle is adjusted in conjunction with load change patterns, with increased upload frequency during periods of drastic load changes and appropriately relaxed during stable periods, and corresponding measurement accuracy requirements are determined.

[0064] S240. By associating the sampling time interval, line loss alarm threshold, data upload cycle, and measurement accuracy requirements, differentiated monitoring parameters are obtained.

[0065] In this embodiment, differentiated monitoring parameters refer to personalized monitoring schemes formulated for the characteristics of different monitoring areas to ensure the reasonable allocation of monitoring resources; parameter association needs to consider the processing power, storage capacity, and communication bandwidth of the monitoring equipment to ensure that the parameter settings are executable.

[0066] Specifically, firstly, a parameter optimization rule base is established to define the constraints between various monitoring parameters; then, a monitoring scheme template is created, integrating sampling intervals, alarm thresholds, upload cycles, and measurement accuracy in a unified format; next, parameter matching is checked to ensure that the settings of various parameters are coordinated; finally, a differentiated monitoring parameter configuration file is generated to fully record the specific parameter settings for each monitoring area, serving as the basis for the execution of monitoring tasks.

[0067] In one embodiment, refer to Figure 4 In step S400, operational feedback information from each monitoring area is obtained, and the operational feedback information is compared with the standard values ​​in the initial monitoring parameters to calculate the operational deviation value. If the operational deviation value exceeds the preset change threshold range, an adaptive adjustment command is triggered, specifically including the following steps: S410. According to the data acquisition command, acquire the voltage, current and power data of each monitoring area to obtain the partition operation data.

[0068] In this embodiment, the zoned operation data refers to the real-time electrical parameter measurements of each monitoring area. Voltage data includes phase voltage, line voltage, and voltage deviation; current data includes phase current, zero-sequence current, and current imbalance; and power data includes active power, reactive power, and apparent power. The data acquisition instructions specify the acquisition time, acquisition items, and data format.

[0069] Specifically, first, a data acquisition task queue is established to poll the monitoring equipment according to the acquisition instructions; then, a data buffer is established to store the acquired raw data according to the monitoring area; next, a data preprocessing process is executed, including data validity verification, outlier filtering, and format standardization; finally, a partitioned operation data report is generated to record the measured values ​​of various electrical parameters and mark the data acquisition timestamp.

[0070] S420: Obtain load stability information and trigger line loss calculation command based on the load stability information.

[0071] In this embodiment, load stability information refers to characteristic indicators describing the stability of load operation, including load change rate, power fluctuation amplitude, and voltage stability; the line loss calculation instruction includes the selection of calculation period, setting of calculation method, and parameter configuration requirements. Load stability directly affects the accuracy of line loss calculation.

[0072] Specifically, firstly, a load stability assessment table is established, setting limits for load change rate, power fluctuation, and voltage stability; then, a data window analysis mechanism is established, using a sliding window method to assess the load operating status; next, triggering criteria rules are formulated, determining a stable state when multiple consecutive data windows meet the stability requirements; finally, a line loss calculation instruction is generated, specifying the calculation start and end times, data selection range, and calculation parameter settings.

[0073] S430. Obtain line loss calculation results. Based on the line loss calculation results and combined with the partition operation data, obtain the real-time line loss level of the specified area.

[0074] In this embodiment, the line loss calculation results include theoretical line loss value, measured line loss value, and line loss composition analysis; real-time line loss level refers to the line loss rate value at a specific moment, reflecting the current power supply efficiency; zone operation data is used to verify the rationality of the calculation results.

[0075] Specifically, firstly, a line loss calculation model library is established, including theoretical calculation models, statistical analysis models, and measurement models; then, the calculation process is executed to obtain various line loss values; next, the results are verified by using partitioned running data to verify the accuracy of the calculation results; finally, a line loss analysis report is generated, recording the real-time line loss level and providing the analysis results of the line loss composition.

[0076] S440. Calculate the line loss deviation value based on the real-time line loss level and the standard line loss rate in the initial monitoring parameters, combined with the load change characteristics of the transformer area.

[0077] In this embodiment, the standard line loss rate refers to the reference line loss level under normal operating conditions; the load change characteristics include the load growth rate, peak-valley difference rate, and load duration; the line loss deviation value is used to quantify the degree of difference between the actual line loss and the standard value.

[0078] Specifically, first, a load characteristic analysis table is established to record the load variation patterns at different times; then, a line loss correction coefficient table is established to determine the correction value based on the load characteristics; next, the corrected standard line loss rate is calculated by multiplying the benchmark value by the correction coefficient; finally, the line loss deviation value is calculated and expressed as a percentage of the difference between the real-time value and the standard value.

[0079] S450: Compare the line loss deviation value with the preset change threshold range; if the line loss deviation value exceeds the preset change threshold range, trigger an adaptive adjustment command to the corresponding monitoring device.

[0080] In this embodiment, the range of change thresholds includes an upper threshold, a lower threshold, and a rate of change threshold; the adaptive adjustment instruction includes the adjustment object, adjustment direction, adjustment magnitude, and execution time. The preset threshold takes into account the effects of measurement error, load fluctuation, and seasonal changes.

[0081] Specifically, first, a threshold judgment rule table is established, and threshold criteria are set for different monitoring areas; then, threshold comparison is performed to determine whether the deviation value exceeds the limit; next, adjustment instruction generation rules are formulated, and adjustment strategies are determined based on the degree of exceeding the limit; finally, adjustment instructions are sent to the monitoring equipment, and an instruction execution confirmation mechanism is established to ensure that the adjustment measures are implemented effectively.

[0082] In one embodiment, refer to Figure 5 In step S500, based on the adaptive adjustment command, the corresponding monitoring and adjustment parameters are calculated and the adjustment acquisition command is sent to the corresponding monitoring area, specifically including the following steps: S510. Obtain monitoring and adjustment parameter information according to the adaptive adjustment command.

[0083] The monitoring and adjustment parameter information includes the line loss calculation cycle adjustment value, power factor alarm value adjustment amount, and load rate warning value adjustment amount for each monitoring area.

[0084] In this embodiment, the monitoring and adjustment parameter information refers to the set of control quantities used to correct the monitoring strategy; the line loss calculation cycle adjustment value is used to change the time interval of line loss calculation; the power factor alarm value adjustment amount is used to correct the power factor judgment standard; and the load rate warning value adjustment amount is used to update the equipment load monitoring threshold.

[0085] Specifically, firstly, an adjustment parameter parsing table is established to convert adaptive adjustment instructions into specific parameter adjustment requirements; then, parameter adjustment calculation rules are established to determine the adjustment range of each parameter based on the degree of operational deviation; next, parameter validity is verified to ensure that the adjusted parameters meet the constraints of the monitoring system; finally, a monitoring adjustment parameter configuration sheet is generated, which records in detail the specific parameter values ​​that need to be adjusted for each monitoring area.

[0086] S520. Compare the line loss calculation cycle adjustment value with the standard monitoring cycle of the transformer area. If the line loss calculation cycle adjustment value is within a reasonable range, trigger a regular monitoring command based on the monitoring and adjustment parameter information and the power consumption characteristics of the transformer area.

[0087] In this embodiment, the standard monitoring cycle of the transformer area refers to the benchmark monitoring time interval under normal operating conditions; the reasonable range refers to the periodic variation range determined by considering equipment capabilities and monitoring needs; and the routine monitoring instructions include basic data collection requirements, routine analysis procedures, and standard report generation rules.

[0088] S530. If the line loss calculation cycle adjustment value exceeds the reasonable range, a refined monitoring command is triggered. Based on the refined monitoring command, time-period line loss calculation parameters, area-period load monitoring parameters, and equipment status assessment parameters are obtained.

[0089] In this embodiment, the refined monitoring instruction refers to the requirement for in-depth analysis of abnormal situations; the time-segmented line loss calculation parameters include time period division rules, calculation method selection and parameter configuration requirements; the regional load monitoring parameters include load distribution characteristics, fluctuation patterns and measurement accuracy; and the equipment status assessment parameters include operating status indicators, performance evaluation standards and alarm criteria.

[0090] Specifically, first, a refined monitoring strategy library is established, and specialized analysis schemes are designed for different abnormal situations; then, a parameter acquisition rule table is established to determine the source and acquisition method of various monitoring parameters; next, the parameter extraction process is executed to obtain the required monitoring parameters from historical data; finally, a refined monitoring configuration scheme is generated, and all monitoring parameters that need to be adjusted are fully recorded.

[0091] S540. Update the monitoring plan and issue adjustment instructions based on the refined monitoring parameters.

[0092] In one embodiment, refer to Figure 6 Before step S100, which involves obtaining the operating information of the distribution area, the equipment status information, and the layout information of the monitoring equipment, the method further includes the following steps: S610: Obtain basic configuration information for the transformer area.

[0093] The basic configuration information of the distribution area includes transformer capacity and parameters, line layout and impedance, user distribution characteristics and metering device type.

[0094] S620. Based on the transformer capacity and parameters, line layout and impedance, determine the layout scheme of key monitoring points.

[0095] S630. Establish a regional monitoring priority table based on user distribution characteristics.

[0096] In this embodiment, the regional monitoring priority table is used to guide the rational allocation of monitoring resources; the priority division takes into account the importance of users, the scale of electricity consumption and the scope of fault impact; the monitoring priority determines the monitoring frequency and accuracy requirements of the region.

[0097] Specifically, first, a user classification and rating table is established to classify users according to their electricity consumption characteristics and capacity scale; then, regional division rules are established to group users with similar characteristics into the same monitoring area; next, priority evaluation criteria are formulated to determine regional priorities by comprehensively considering user level and load density; finally, a monitoring priority configuration table is generated to record the priority level and corresponding monitoring requirements of each region.

[0098] S640. Obtain the judgment criteria indicating abnormal line loss, including line loss rate exceeding the limit, load rate warning value, voltage exceeding the limit standard, and three-phase imbalance limit value; generate a transformer area monitoring plan based on the judgment criteria and the regional monitoring priority table. The transformer area monitoring plan is used to guide the deployment of monitoring equipment and the formulation of operation strategies.

[0099] In one embodiment, refer to Figure 7 In addition to obtaining partition runtime data, the method also includes the following steps: S710: Obtain user electricity consumption data and transformer area master meter data.

[0100] The user electricity consumption data includes time-of-use electricity consumption, power factor, and load curve, while the transformer area meter data includes total power supply, phase data, and harmonic data.

[0101] In this embodiment, time-of-use electricity consumption refers to the amount of electricity consumed in different time periods, reflecting the distribution of electricity load; the power factor reflects the relationship between active power and apparent power; the load curve describes the change pattern of electricity load over time; the total power supply refers to the sum of electricity consumption of all users in the transformer area; the phase data includes voltage phase angle and current phase angle; the harmonic data includes voltage harmonics and current harmonics.

[0102] S720. Calculate user electricity consumption characteristics and power supply-consumption balance of the distribution area based on user electricity consumption data, distribution area master meter data, and zone operation data.

[0103] In this embodiment, user electricity consumption characteristics include electricity consumption regularity, load change characteristics, and electricity consumption behavior patterns; the power supply and consumption balance of the transformer area is used to evaluate the degree of matching between power supply and power consumption; the calculation process needs to consider the impact of line loss and metering error.

[0104] Specifically, firstly, an electricity consumption characteristic extraction rule table is established, defining the calculation method for characteristic parameters; then, a balance assessment model is established to calculate the power supply and consumption deviation; next, data correlation analysis is performed to identify abnormal electricity consumption characteristics; finally, a characteristic analysis report is generated, recording the electricity consumption characteristic parameters and balance indicators.

[0105] S730. Based on the user's electricity consumption characteristics, the power supply and consumption balance degree of the distribution area, and the load stability state information, an abnormal electricity consumption identification model is established.

[0106] Among them, the abnormal electricity consumption identification model includes the following criteria: the load mutation coefficient exceeds the preset threshold, the power factor deviates from the range of 0.9 - 1.0, the electricity consumption deviates from the historical同期 value by more than 30%, and the change in electricity consumption between adjacent periods exceeds the preset ratio.

[0107] In this embodiment, the abnormal electricity consumption identification model is constructed by using a multi-dimensional feature criterion and weight fusion method. First, the load mutation coefficient LSC is calculated by the ratio of the load change amount within a unit time to the rated load. Considering the electricity consumption characteristics of different user types, the preset thresholds of the load mutation coefficient are set to 0.3 for industrial users, 0.4 for commercial users, and 0.5 for residential users. The power factor PF is calculated by the ratio of the active power to the apparent power, and a three-level determination rule is set according to the degree of deviation of the power factor: when PF < 0.85 or PF > 1.05, it is a serious deviation, and the weight coefficient is taken as 1.0; when 0.85 ≤ PF < 0.9 or 1.0 < PF ≤ 1.05, it is a moderate deviation, and the weight coefficient is taken as 0.6; when 0.9 ≤ PF ≤ 1.0, it is a mild deviation, and the weight coefficient is taken as 0.3. The electricity consumption deviation degree PD is calculated by comparing the current electricity consumption with the historical同期 electricity consumption, that is, PD = |E(t) - E(h)| / E(h) × 100%, where E(t) is the electricity consumption in the current period (kWh), and E(h) is the electricity consumption in the historical同期 (kWh). The model conducts comparison and analysis on three time scales of day, week, and month respectively, and the corresponding deviation degree thresholds are set as: 40% for the day scale, 35% for the week scale, and 30% for the month scale. The comparison at the day scale uses the data at the same time period every day, and the week scale and month scale use the data corresponding to the natural cycle dates. The adjacent period change rate CR is calculated by the ratio of the difference in electricity consumption between the previous and the current billing periods to the electricity consumption in the previous period, that is, CR = |E(t) - E(t - 1)| / E(t - 1) × 100%, where E(t) is the electricity consumption in the current period, and E(t - 1) is the electricity consumption in the previous period. Considering the electricity consumption law characteristics of different users, the preset ratio thresholds are respectively: 25% for industrial users, 30% for commercial users, and 35% for residential users.

[0108] The model uses a weighted summation method for comprehensive evaluation. The anomaly score is calculated using the following formula: Score = W1 × LSC / LSC threshold + W2 × PF deviation weight + W3 × PD / PD threshold + W4 × CR / CR threshold. The weight coefficients for each feature criterion are: W1 = 0.3 (load mutation), W2 = 0.2 (power factor), W3 = 0.3 (historical deviation), and W4 = 0.2 (periodic variation). Four levels of judgment criteria are set based on the comprehensive score: a score greater than 1.2 indicates high anomaly; a score between 0.8 and 1.2 indicates moderate anomaly; a score between 0.5 and 0.8 indicates slight anomaly; and a score not exceeding 0.5 indicates normal power consumption. It should be noted that the specific values ​​of the above weight coefficients can be appropriately adjusted according to actual application scenarios and historical operating experience, and the grading thresholds of the judgment criteria can also be optimized according to the actual situation of the transformer area.

[0109] S740. Based on the abnormal power consumption identification model, analyze the monitoring data and generate abnormal power consumption judgment results.

[0110] In one embodiment, refer to Figure 8 The method also includes the following steps: S810, Obtain information from the electricity theft feature database.

[0111] The electricity theft feature database includes abnormal feature data of metering equipment, abnormal patterns of electricity load, current and voltage distortion features, and abnormal change patterns of electricity consumption.

[0112] In this embodiment, the abnormal characteristic data of the metering equipment includes the working status of the metering device, abnormal data acquisition, and communication failure mode; the abnormal power load mode includes sudden load changes, periodic anomalies, and abnormal fluctuations; the current and voltage distortion characteristics include waveform distortion rate, phase shift, and harmonic content; and the abnormal power consumption change mode includes step changes, periodic decreases, and irregular fluctuations.

[0113] Specifically, first, a feature classification database is established to classify and store various electricity theft features according to their manifestations; then, feature description rules are established, and feature parameters are recorded using a standardized format; next, a feature update mechanism is formulated to promptly supplement the feature database based on newly discovered electricity theft methods; finally, a feature retrieval table is generated to facilitate quick querying and matching of specific types of electricity theft features.

[0114] S820. Based on the information in the electricity theft feature database, conduct in-depth analysis of the abnormal electricity consumption judgment results and extract suspicious electricity theft features.

[0115] In this embodiment, in-depth analysis refers to a detailed analysis of abnormal electricity consumption behavior; suspicious electricity theft features refer to abnormal behaviors that are similar to known electricity theft behaviors; the analysis process needs to consider the combined effects and temporal relationships of multiple features.

[0116] Specifically, the process begins by establishing a feature analysis template and defining the steps and methods for in-depth analysis. Then, a feature matching process is executed, comparing abnormal electricity consumption patterns with the electricity theft feature database. Next, feature extraction is performed to identify abnormal patterns that match the characteristics of electricity theft. Finally, a feature analysis report is generated, which records in detail the specific manifestations of suspicious electricity theft features.

[0117] S830. Based on suspicious electricity theft characteristics and combined with the historical electricity theft case database of the transformer area, calculate the probability score of electricity theft.

[0118] In this embodiment, the historical electricity theft case database contains records of confirmed electricity theft events; the electricity theft probability score is used to quantify the degree of suspicion of electricity theft; the score calculation needs to consider feature matching degree, abnormal duration and impact degree.

[0119] S840. If the probability score of electricity theft exceeds the preset threshold, an on-site verification instruction will be triggered.

[0120] The on-site verification instructions include verification location information, abnormal power consumption data records, on-site image acquisition requirements, and verification result feedback mechanism.

[0121] S850. Update the electricity theft feature database and abnormal electricity use identification model based on the results of on-site verification.

[0122] In this embodiment, when the system triggers an on-site verification command, staff need to complete the on-site verification according to the command requirements and record the verification results. Based on the verification results, the electricity theft feature database is updated using a combination of classification extraction and dynamic adjustment. For meter-twisting theft, three main feature parameters are extracted: current distortion rate exceeding 15%, power factor fluctuation amplitude exceeding 0.3, and electricity consumption reduction ratio exceeding 50%. For magnetic interference theft, the focus is on: current fluctuation coefficient exceeding 0.25, phase offset angle exceeding 30 degrees, and zero-sequence current ratio exceeding 20%. For data tampering theft, the main monitoring parameters are: CRC check error rate exceeding 1%, data packet loss rate exceeding 5%, and communication interruption frequency exceeding 3 times / day. The thresholds for the feature parameters are updated using an adaptive mechanism. The new feature parameter thresholds are calculated using a weighted fusion method: threshold = original threshold × (1-α) + new threshold × α, where α is the update coefficient, α = weight coefficient × credibility coefficient. The weight coefficient equals the proportion of this type of case to the total number of cases, and the credibility coefficient equals the ratio of confirmed cases to discovered cases. When the false positive rate exceeds 10%, the threshold is increased by 10%; when the false negative rate exceeds 10%, the threshold is decreased by 10%, thus maintaining a dynamic balance of feature parameters. The criterion weights of the identification model are also updated dynamically. The new weight calculation formula is: New weight = Original weight × (1-β) + (Hit rate × Weight coefficient) × β, where β is the learning rate, with a value of 0.3; the hit rate is equal to the ratio of correctly identified cases to the total number of identified cases; and the weight coefficient is equal to the ratio of the number of cases of that type to the total number of cases. After calculation, all weights are normalized to ensure that the sum of the weights is 1. In this way, the identification model can continuously learn and optimize, improving the accuracy of abnormal electricity consumption identification.

[0123] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0124] Secondly, this application provides an adaptive transformer area line loss diagnosis and monitoring system. The adaptive transformer area line loss diagnosis and monitoring system of this application will be described below in conjunction with the above-mentioned adaptive transformer area line loss diagnosis and monitoring method.

[0125] Reference Figure 9 An adaptive transformer area line loss diagnosis and monitoring system, comprising: The initial monitoring parameter determination module is used to acquire the operating information of the transformer area, the status information of the equipment, and the layout information of the monitoring equipment, and to determine the initial monitoring parameters based on the operating information of the transformer area, the status information of the equipment, and the layout information of the monitoring equipment. The differential monitoring parameter calculation module is used to calculate differential monitoring parameters for each monitoring area based on the spatial distribution of each monitoring point in the substation and the initial monitoring parameters. The data acquisition command sending module is used to determine the monitoring plan for the transformer area based on the differentiated monitoring parameters and the initial monitoring parameters, and to send data acquisition commands to the corresponding monitoring devices based on the monitoring plan for the transformer area. The adaptive adjustment module is used to acquire operational feedback information from various monitoring areas, compare the operational feedback information with the standard values ​​in the initial monitoring parameters, and calculate the operational deviation value; if the operational deviation value exceeds the preset change threshold range, an adaptive adjustment command is triggered. The monitoring and adjustment parameter calculation module is used to calculate the corresponding monitoring and adjustment parameters based on the adaptive adjustment command and send the adjustment acquisition command to the corresponding monitoring area.

[0126] In one embodiment, this application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 10 As shown, this electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements an adaptive method for diagnosing and monitoring line losses in transformer substations.

[0127] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0128] In one embodiment, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0129] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0130] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. An adaptive method for diagnosing and monitoring line losses in transformer substations, characterized in that, Includes the following steps: Acquire the operating information of the transformer substation, the status information of the equipment, and the layout information of the monitoring equipment, and determine the initial monitoring parameters based on the operating information of the transformer substation, the status information of the equipment, and the layout information of the monitoring equipment; Based on the spatial distribution of each monitoring point in the transformer area and the initial monitoring parameters, differentiated monitoring parameters are calculated for each monitoring area. Based on the differentiated monitoring parameters and the initial monitoring parameters, a monitoring scheme for the transformer area is determined, and based on the monitoring scheme for the transformer area, data acquisition instructions are sent to the corresponding monitoring devices. Obtain operational feedback information from each monitoring area, compare the operational feedback information with the standard values ​​in the initial monitoring parameters, and calculate the operational deviation value; If the operational deviation value exceeds the preset change threshold range, an adaptive adjustment command is triggered; Based on the adaptive adjustment command, the corresponding monitoring and adjustment parameters are calculated and the adjustment and acquisition command is sent to the corresponding monitoring area.

2. The adaptive transformer area line loss diagnosis and monitoring method according to claim 1, characterized in that, The process involves acquiring transformer area operation information, equipment status information, and monitoring equipment layout information, and determining initial monitoring parameters based on these information. Specifically, this includes the following steps: Extract standard line loss rate and load curve characteristics from the transformer area operation information; Based on the transformer load rate, three-phase imbalance and power factor in the equipment status information, and combined with the coverage of each monitoring point in the monitoring equipment layout information, the data acquisition frequency for each monitoring area is determined. Based on the standard line loss rate, load curve characteristics, and equipment status information, determine the line loss calculation method and the data acquisition strategy for key monitoring equipment; The initial monitoring parameters are obtained by associating the data acquisition frequency, line loss calculation method, and acquisition strategy of key monitoring equipment.

3. The adaptive transformer area line loss diagnosis and monitoring method according to claim 2, characterized in that, Based on the spatial distribution of each monitoring point in the distribution area and the initial monitoring parameters, differentiated monitoring parameters are calculated for each monitoring area, specifically including the following steps: Calculate the sampling time interval for each monitoring area based on the electricity consumption characteristics and load density of each monitoring area; Based on the electricity consumption characteristics and load density, and the line loss calculation method, determine the line loss alarm threshold for each monitoring area; Based on the electricity consumption characteristics and load density, as well as the data acquisition strategy of key monitoring equipment, and combined with the peak and valley load variation patterns of the distribution area, the data upload cycle and measurement accuracy requirements are determined. The differential monitoring parameters are obtained by associating the sampling time interval, line loss alarm threshold, data upload cycle, and measurement accuracy requirements.

4. The adaptive transformer area line loss diagnosis and monitoring method according to claim 1, characterized in that, Obtain operational feedback information from various monitoring areas, compare the operational feedback information with the standard values ​​in the initial monitoring parameters, and calculate the operational deviation value; if the operational deviation value exceeds a preset change threshold range, trigger an adaptive adjustment command, specifically including the following steps: According to the data acquisition instructions, the voltage, current and power data of each monitoring area are obtained to obtain the partition operation data; Obtain load stability information, and trigger a line loss calculation command based on the load stability information; Obtain line loss calculation results information, and based on the line loss calculation results information and the partition operation data, obtain the real-time line loss level of the specified area; Based on the real-time line loss level and the standard line loss rate in the initial monitoring parameters, and combined with the load change characteristics of the transformer area, the line loss deviation value is calculated. The line loss deviation value is compared with a preset change threshold range; If the line loss deviation value exceeds the preset change threshold range, an adaptive adjustment command is triggered to the corresponding monitoring device.

5. The adaptive transformer area line loss diagnosis and monitoring method according to claim 1, characterized in that, Based on the adaptive adjustment command, the corresponding monitoring and adjustment parameters are calculated and the adjustment acquisition command is sent to the corresponding monitoring area, specifically including the following steps: According to the adaptive adjustment instruction, the monitoring and adjustment parameter information is obtained, wherein the monitoring and adjustment parameter information includes the line loss calculation cycle adjustment value, power factor alarm value adjustment amount and load rate warning value adjustment amount for each monitoring area; The line loss calculation cycle adjustment value is compared with the standard monitoring cycle of the transformer area. If the line loss calculation cycle adjustment value is within a reasonable range, a regular monitoring command is triggered based on the monitoring adjustment parameter information and the power consumption characteristics of the transformer area. If the line loss calculation cycle adjustment value exceeds a reasonable range, a refined monitoring instruction is triggered. Based on the refined monitoring instruction, time-segmented line loss calculation parameters, area-segmented load monitoring parameters, and equipment status assessment parameters are obtained. Based on the time-segmented line loss calculation parameters, regional load monitoring parameters, and equipment status assessment parameters, the monitoring scheme is updated and adjustment instructions are issued.

6. The adaptive transformer area line loss diagnosis and monitoring method according to claim 1, characterized in that, Before acquiring the transformer area operation information, equipment status information, and monitoring equipment layout information, the method further includes the following steps: Obtain basic configuration information for the distribution area; wherein, the basic configuration information for the distribution area includes transformer capacity and parameters, line layout and impedance, user distribution characteristics and metering device type; Based on the transformer capacity and parameters, the line layout and impedance, determine the layout scheme of key monitoring points; Based on the user distribution characteristics, a regional monitoring priority table is established; Obtain the judgment criteria indicating abnormal line loss, including line loss rate exceeding the limit, load rate warning value, voltage exceeding the limit standard, and three-phase imbalance limit value; based on the judgment criteria and the regional monitoring priority table, generate a transformer area monitoring plan, which is used to guide the deployment of monitoring equipment and the formulation of operation strategies.

7. The adaptive transformer area line loss diagnosis and monitoring method according to claim 4, characterized in that, While acquiring the partition's runtime data, the method also includes the following steps: Acquire user electricity consumption data and transformer area meter data, wherein the user electricity consumption data includes time-of-use electricity consumption, power factor and load curve, and the transformer area meter data includes total power supply, phase data and harmonic data; Based on the user electricity consumption data, the transformer area master meter data, and the zone operation data, calculate the user electricity consumption characteristics and the transformer area power supply and consumption balance. Based on the user's electricity consumption characteristics, the power supply and consumption balance of the transformer area, and the load stability information, an abnormal electricity consumption identification model is established. Based on the abnormal power consumption identification model, the monitoring data is analyzed to generate abnormal power consumption judgment results.

8. The adaptive transformer area line loss diagnosis and monitoring method according to claim 7, characterized in that, The method further includes the following steps: Obtain electricity theft feature database information, wherein the electricity theft feature database information includes abnormal feature data of metering equipment, abnormal power load patterns, current and voltage distortion features, and abnormal power consumption change patterns; Based on the information in the electricity theft feature database, the abnormal electricity consumption judgment results are analyzed in depth to extract suspicious electricity theft features; Based on the aforementioned suspicious electricity theft characteristics, and combined with the historical electricity theft case database of the transformer area, an electricity theft probability score is calculated; If the probability score of electricity theft exceeds a preset threshold, an on-site verification instruction is triggered. The on-site verification instruction includes the location information of the verification location, abnormal electricity consumption data records, on-site image acquisition requirements, and a verification result feedback mechanism. Based on the results of on-site verification, the database of electricity theft characteristics and the model for identifying abnormal electricity use were updated.

9. An adaptive transformer area line loss diagnosis and monitoring system, characterized in that, include: The initial monitoring parameter determination module is used to acquire the operating information of the transformer area, the equipment status information, and the layout information of the monitoring equipment, and to determine the initial monitoring parameters based on the operating information of the transformer area, the equipment status information, and the layout information of the monitoring equipment. The differential monitoring parameter calculation module is used to calculate differential monitoring parameters for each monitoring area based on the spatial distribution of each monitoring point in the transformer substation and the initial monitoring parameters. The data acquisition instruction sending module is used to determine the monitoring scheme for the transformer area based on the differentiated monitoring parameters and the initial monitoring parameters, and to send data acquisition instructions to the corresponding monitoring devices based on the monitoring scheme for the transformer area. An adaptive adjustment module is used to acquire operational feedback information from various monitoring areas, compare the operational feedback information with the standard values ​​in the initial monitoring parameters, and calculate the operational deviation value. If the operational deviation value exceeds the preset change threshold range, an adaptive adjustment command is triggered; The monitoring and adjustment parameter calculation module is used to calculate the corresponding monitoring and adjustment parameters based on the adaptive adjustment command and send the adjustment acquisition command to the corresponding monitoring area.

10. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the adaptive transformer area line loss diagnosis and monitoring method according to any one of claims 1-7.

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