Setting calculation quality evaluation method and device for power distribution network, terminal equipment and storage medium

Abstract

The invention discloses a setting calculation quality evaluation method and device for a power distribution network, terminal equipment and a storage medium, and belongs to the field of power systems, and the method comprises the steps: obtaining topological structure data, operation parameters and setting configuration data of a plurality of setting objects in the power distribution network; calculating an evaluation index of each setting object based on the topological structure data, the operation parameters and the setting configuration data; for each setting object, based on the corresponding evaluation index, determining a setting evaluation result; determining a setting coordination deviation value of the power distribution network based on the topological structure data and the setting configuration data of the plurality of setting objects; determining a setting quality evaluation report of each setting object of the power distribution network according to a setting coordination deviation value and the setting evaluation result of each setting object; therefore, by implementing the method, the problem of low accuracy of setting calculation quality evaluation of the power distribution network in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to a method, apparatus, terminal equipment, and storage medium for setting calculation quality assessment in distribution networks. Background Technology

[0002] In modern power systems, online calculation of protection setting parameters involves collecting various operational data from the distribution network (such as short-circuit capacity, feeder length, and load current) and combining this data with established rules or formulas to calculate setting parameters such as protection operating current and time. This process digitizes and automates the setting calculation process. However, the following problems exist in the setting calculation evaluation process: Using static threshold judgments as the basis for quality assessment, and evaluating the quality of setting calculation results for a single setting object, typically relies on a fixed set of threshold ranges or error limits. This lacks comprehensive quantitative analysis of protection performance, fails to fully reflect the quality of setting calculations, and cannot demonstrate the actual impact of setting strategies on system operating performance. Therefore, existing technologies suffer from low accuracy in evaluating the quality of setting calculations in distribution networks. Summary of the Invention

[0003] This invention provides a method, apparatus, terminal equipment, and storage medium for evaluating the setting calculation quality of distribution networks, which can solve the problem of low accuracy in the setting calculation quality evaluation of distribution networks in the prior art.

[0004] The method for evaluating the setting calculation quality of a distribution network provided by this invention includes: Acquire topology data, operating parameters, and setting configuration data of several setting objects in the power distribution network; Based on the topology data, the operating parameters, and the tuning configuration data, an evaluation index is calculated for each tuning object. For each of the aforementioned tuning objects, the tuning calculation and evaluation results are determined based on the corresponding evaluation indicators; Based on the topology data and setting configuration data of several of the aforementioned setting objects, the setting coordination deviation value of the distribution network is determined; Based on the setting coordination deviation value and the setting evaluation results of each setting object, a setting calculation quality evaluation report for each setting object in the distribution network is determined.

[0005] Furthermore, the acquisition of the power grid topology, operating parameters, and setting configuration data of several setting objects in the distribution network includes: Obtain the power grid topology of the distribution network, determine the topology data of each setting object based on the power grid topology, and obtain the setting configuration data of each setting object. Collect several types of observation values ​​for each setting object in the power distribution network; Preprocessing operations are performed on several types of observations for each tuned object to obtain several types of operational data for each tuned object; In each tuned object, for each type of running data, it is determined whether the number of data sources to which the running data belongs is greater than the number threshold; if so, the average value of the observations is calculated based on the running data of each data source, the numerical weight of each running data is calculated based on the average value of the observations and the preset smoothing term, and the running parameters are obtained by weighted fusion based on each running data and the numerical weight corresponding to each running data; if not, the running data is directly used as the running parameters.

[0006] Furthermore, the preprocessing operation includes: Obtain the upper confidence limit and lower confidence limit of the observed values; In each tuning object, for each type of observation, it is determined whether there is missing data. If so, the adjustment parameter is determined based on the trend of the observation, and the imputed data is obtained by estimating based on the two observations adjacent to the missing data, the adjustment parameter, the upper confidence value of the observation, and the lower confidence value of the observation. The imputed data is then added to the observation to obtain the running data. If not, the observation is directly used as the running data.

[0007] Furthermore, the evaluation metrics include: accuracy metrics, reliability metrics, sensitivity metrics, selectivity metrics, and efficiency metrics; the calculation of the evaluation metrics for each tuning object based on the topology data, the operating parameters, and the tuning configuration data includes: Acquire historical fault data for several setting objects in the power distribution network; For each tuning object, the degree of adaptation is calculated based on the operating parameters and tuning configuration data, and the accuracy index is determined based on the degree of adaptation. For each tuning object, a reliability index is determined based on the tuning configuration data and fault history data; For each calibrated object, a sensitivity index is determined based on the operating parameters and the historical fault data; For each tuning object, a selective index is determined based on the historical fault data; For each tuning object, efficiency indicators are determined based on the operating parameters, the tuning configuration data, and the fault history data.

[0008] Furthermore, determining the tuning calculation evaluation result based on the corresponding evaluation indicators includes: Based on the region type to which the tuning object belongs, determine the weights of the accuracy index, reliability index, sensitivity index, selectivity index, and efficiency index. The tuning calculation and evaluation results are calculated based on the accuracy index, reliability index, sensitivity index, selectivity index, efficiency index, weight of accuracy index, weight of reliability index, weight of sensitivity index, weight of selectivity index, and weight of efficiency index.

[0009] Furthermore, determining the setting coordination deviation value of the distribution network based on the topology data and setting configuration data of several of the setting objects includes: Based on the topology data of each of the aforementioned tuning objects, each tuning object is treated as a node, and a graph model of the distribution network is constructed. In the graph model, for the tuning configuration data of the two nodes corresponding to each side, the coordination deviation value corresponding to each side is calculated; The coordination deviation values ​​of all sides are superimposed to obtain the setting coordination deviation value of the distribution network.

[0010] Further, the step of determining the setting calculation quality assessment report for each setting object of the distribution network based on the setting assessment results and setting coordination deviation values ​​of each setting object includes: Based on the tuning evaluation results and tuning coordination deviation values ​​of each tuning object, control optimization suggestions are generated for each tuning object; Based on the setting evaluation results and control optimization suggestions for each setting object, a setting calculation quality evaluation report for each setting object of the distribution network is generated.

[0011] Another embodiment of the present invention provides a setting calculation quality assessment device for a distribution network, comprising: a data acquisition module, a data calculation module, a data assessment module, a coordination deviation module, and a result generation module; The data acquisition module is used to acquire topology data, operating parameters and setting configuration data of several setting objects in the distribution network; The data calculation module is used to calculate the evaluation index of each of the tuning objects based on the topology data, the operating parameters, and the tuning configuration data. The data evaluation module is used to determine the adjustment evaluation result for each of the adjustment objects based on the corresponding evaluation indicators. The coordination deviation module is used to determine the setting coordination deviation value of the distribution network based on the topology data and setting configuration data of several of the setting objects. The result generation module is used to determine the setting quality assessment report for each setting object in the distribution network based on the setting coordination deviation value and the setting assessment result of each setting object.

[0012] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the setting calculation quality assessment method for distribution networks provided by the present invention.

[0013] Another embodiment of the present invention also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the setting calculation quality assessment method for distribution networks provided by the present invention.

[0014] The following benefits can be obtained by implementing the present invention: This invention acquires topology data, operating parameters, and setting configuration data of several setting objects in a distribution network; calculates evaluation indicators for each setting object based on the topology data, operating parameters, and setting configuration data; for each setting object, determines the setting calculation evaluation result based on the corresponding evaluation indicators; determines the setting coordination deviation value of the distribution network based on the topology data and setting configuration data of the several setting objects; and determines the setting calculation quality evaluation report for each setting object in the distribution network based on the setting coordination deviation value and the setting evaluation result of each setting object. This invention evaluates the setting calculation quality of the distribution network by calculating the setting calculation evaluation result for each setting object and by calculating the setting coordination deviation value of the distribution network, thus considering both the global setting coordination deviation value of the distribution network and the setting calculation evaluation result of individual setting objects. Compared with existing technologies that evaluate the quality of setting calculation results for a single setting object, this invention improves the accuracy of setting calculation quality evaluation. Attached Figure Description

[0015] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a method for evaluating the setting calculation quality of a power distribution network according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a setting calculation quality assessment device for power distribution networks provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0019] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0022] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0023] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0024] See Figure 1 To address the problem of low accuracy in the quality assessment of setting calculations for distribution networks in existing technologies, an embodiment of the present invention provides a method for quality assessment of setting calculations for distribution networks, comprising: 101. Obtain the topology data, operating parameters, and setting configuration data of several setting objects in the distribution network.

[0025] In a specific embodiment, the setting operation involves adjusting parameters such as the operating current, operating time, and sensitivity of the setting object to ensure accurate operation during a fault. The setting object represents the protection device in the distribution network. For example, the setting object can be substation equipment, including transformers, busbars, and outgoing lines within the substation. Transformer protection requires setting parameters of relevant protection devices through setting calculations to monitor their health status and prevent faults such as overload and short circuits. Busbar protection and line protection are also important setting objects to ensure the stable operation of the substation. The setting object can also be feeder switches in a switching station. The setting of these switches involves overcurrent protection, zero-sequence protection, etc., to ensure that the fault current can be quickly interrupted and the equipment safe during a fault. The setting object can also be long-distance branch line circuit breakers and load switches. The setting of these switches needs to consider the line length, load distribution, and the magnitude of the fault current to ensure the selectivity and sensitivity of the protection. The setting object can also be other critical power equipment, such as automatic transfer switch (ATS) and automatic reclosing device.

[0026] In a specific embodiment, the power grid topology is key information describing the connection relationships and layout of various devices (such as lines, transformers, switches, buses, etc.) in the distribution network. Topology data for each device (which can be selected as a setting object) can be obtained from the power grid topology. Operating parameters are data reflecting the real-time operating status of the power grid, equipment performance, and power quality, such as current, voltage, active power, reactive power, and frequency. Setting configuration data includes operating current settings (such as instantaneous overcurrent protection settings, time-limited overcurrent protection settings, timed overcurrent protection settings, etc.), operating time, and tripping delay. Fault history data includes fault occurrence time, faulty equipment, fault type (such as short-circuit fault (single-phase grounding, two-phase short circuit, three-phase short circuit), open-circuit fault, grounding fault, overload fault, etc.), operating parameters before the fault, electrical quantities at the time of the fault (voltage and current amplitudes and trends at the moment of the fault (such as sudden changes, harmonic content)), fault duration, fault location results, fault protection actions, and affected area.

[0027] In a specific embodiment, the topology data, operating parameters, and setting configuration data are all in a standardized format structure, carrying a unified timestamp (i.e., observation time), data source number, measurement type, object level (such as feeder, user, substation), and geographical region.

[0028] In this embodiment, obtaining the power grid topology, operating parameters, and setting configuration data of several setting objects in the distribution network includes: Obtain the power grid topology of the distribution network, determine the topology data of each setting object based on the power grid topology, and obtain the setting configuration data of each setting object. Collect several types of observation values ​​for each setting object in the power distribution network; Preprocessing operations are performed on several types of observations for each tuned object to obtain several types of operational data for each tuned object; In each tuned object, for each type of running data, it is determined whether the number of data sources to which the running data belongs is greater than a threshold (e.g., 3). If so, the average value of the observed values ​​is calculated based on the running data of each data source. Based on the average value of the observed values ​​and a preset smoothing term, the numerical weight of each running data is calculated. Then, a weighted fusion is performed based on each running data and its corresponding numerical weight to obtain the running parameters. If not, the running data is directly used as the running parameters.

[0029] In one specific embodiment, when there are observations of the same type from multiple data sources (such as redundant data collected from the main station and field collection), a bias-weighted fusion method is used, that is, the observation is obtained from multiple data sources and the average value is calculated. For example, suppose... For n data sources observing the same parameter at time t, the average value is calculated using the following formula: ;in, This represents the average value. This represents the i-th observation.

[0030] Then, the numerical weight of each observation is calculated using the following formula: ;in, This represents the numerical weight of the i-th observation. This indicates a smoothing term to avoid division by zero.

[0031] Then, a weighted calculation is performed using the following formula: ;in, This indicates the missing power grid data.

[0032] In one specific embodiment, the data source includes a substation system, a feeder intelligent terminal, a protection device, a reclosing control system, a communication gateway, and a master station database.

[0033] In this embodiment, the preprocessing operation includes: Obtain the upper confidence limit and lower confidence limit of the observed values; In each tuning object, for each type of observation, it is determined whether there is missing data. If so, the adjustment parameter is determined based on the trend of the observation, and the imputed data is obtained by estimating based on the two observations adjacent to the missing data, the adjustment parameter, the upper confidence value of the observation, and the lower confidence value of the observation. The imputed data is then added to the observation to obtain the running data. If not, the observation is directly used as the running data.

[0034] It is understood that a corresponding data confidence interval is constructed for the observation time corresponding to the observation value. The data confidence interval includes the upper confidence value and the lower confidence value of the observed value of the power grid data at the observation time. When a missing observation is detected at any observation time, adjustment parameters are determined based on the contextual trend data of the missing observation, and the missing observation is estimated based on the data confidence interval, the adjustment parameters, and the two observations adjacent to the missing data. The contextual trend data is determined by multiple power grid data that are in time before the missing data and multiple power grids that are after the missing data.

[0035] In one specific embodiment, a data confidence interval is constructed for each time series (i.e., the observation time). The upper and lower confidence limits of the observed values ​​in this data confidence interval can be set according to the historical fluctuation range and the equipment accuracy. Optionally, when an observation is missing (or distorted and needs to be removed) at a certain time, the missing observation can be calculated using the following formula: ;in, Indicates the missing observations. This represents the preceding observation that is temporally adjacent to the missing data. This represents the next observation that is temporally adjacent to the missing data. This indicates parameter adjustment. It should be noted that the adjusted parameters adaptively adjust based on contextual trends to make the data more consistent with the actual operational trajectory and avoid boundary offsets.

[0036] 102. Based on the topology data, the operating parameters, and the tuning configuration data, calculate the evaluation index for each tuning object.

[0037] In this embodiment, the evaluation metrics include: accuracy metrics, reliability metrics, sensitivity metrics, selectivity metrics, and efficiency metrics; the calculation of the evaluation metrics for each tuning object based on the topology data, the operating parameters, and the tuning configuration data includes: Acquire historical fault data for several setting objects in the power distribution network; For each tuning object, the degree of adaptation is calculated based on the operating parameters and tuning configuration data, and the accuracy index is determined based on the degree of adaptation. For each tuning object, a reliability index is determined based on the tuning configuration data and fault history data; For each calibrated object, a sensitivity index is determined based on the operating parameters and the historical fault data; For each tuning object, a selective index is determined based on the historical fault data; For each tuning object, efficiency indicators are determined based on the operating parameters, the tuning configuration data, and the fault history data.

[0038] In one specific embodiment, the accuracy index is used to measure the degree of agreement between the setting configuration data and the expected action behavior under operating conditions. Specifically, based on the setting configuration data and operating parameters, the degree of fit between the setting configuration data and the operating conditions is determined. The degree of fit can be the proportional deviation between the operating current setting value and the short-circuit calculated current, the time error between the operating time setting and the allowable range, etc.

[0039] Specifically, the accuracy index reflects the degree of matching between the setting configuration and the operating conditions, focusing on the rationality of the setting of the operating current and operating time. For example, the calculation method for the value of this accuracy index is as follows: 1) Input data: The setting configuration data includes: operating current setting value. Action time Operating parameters (such as running simulations or historical data) include the short-circuit calculated current. Standard action time range 2) Calculation formula: Construct two deviation terms and normalize them to calculate the accuracy index value (i.e., accuracy score). The formula is Among them, the time deviation penalty factor Defined as: , 3) Output data: accuracy score (for the allowed time interval median) .

[0040] In one specific embodiment, the reliability index is used to evaluate the consistency of response and the ability to control the false trip rate of the setting configuration data under various fault scenarios (i.e., fault types), such as the stability of the setting action in various fault types such as overload, single-phase grounding, and short circuit. Specifically, the index value of the reliability index is determined by judging whether the operating current setting value and operating time in the setting configuration data are consistent with the response action under various fault types.

[0041] Specifically, the reliability index evaluates the response consistency and false trip rate of the setting configuration under various fault scenarios. For example, the calculation method for the reliability index value is as follows: 1) Input data: The setting configuration data includes the operating current setting value. Action time The fault history data includes several typical fault event sets. The response action for each fault event includes whether to take action. Expected action result 2) Calculation formula: Calculate the reliability index value (i.e., reliability score) using the consistency score. The formula is , This indicates the number of response actions; that is, all expected actions are compared with actual actions. Mistakes and failures to act are penalized. A higher score indicates a more stable tuning strategy. 3) Output data: Reliability score. .

[0042] Sensitivity indicators are used to evaluate the ability of the setting configuration data to quickly identify and respond to faults, such as whether the action time meets the fault protection requirements and whether the device has a delayed action phenomenon.

[0043] Specifically, the sensitivity index quantifies action response speed and fault identification capability, primarily based on a comparison of action time and fault response threshold. For example, the calculation method for the sensitivity index value is as follows: 1) Input data: The tuning configuration data includes action time. Historical fault data includes the maximum recommended action time under fault scenarios. (i.e., the fault response threshold, set by standards or experts); 2) Calculation formula: Calculate the sensitivity index value (i.e., sensitivity score) based on the response deviation. The formula is If the action time exceeds the upper limit, the sensitivity score is set to 0; 3) Output data: the index value of the sensitivity index. .

[0044] In one specific embodiment, the selectivity index is used to determine whether the setting object can disconnect only the faulty equipment when a fault occurs, so as to avoid accidentally disconnecting normal equipment and ensure power supply continuity.

[0045] Specifically, the selectivity index is used to assess whether the protection action is limited to the target device when a fault occurs, preventing false tripping. For example, the calculation method for the value of this selectivity index is as follows: 1) Input data: Fault history data includes the target device number recorded for each fault event. Actual resection equipment set and the target removal set in fault simulation or operation and maintenance records ; 2) Calculation formula: The index value of the selective indicator (i.e., the selective score) The accuracy rate of fault clearance is given by the formula: If there is a miscut, the intersection ratio will decrease and the score will be lower. 3) Output data: Selective scores .

[0046] In one specific embodiment, efficiency metrics are used to evaluate the impact of setting configuration data on system operation while ensuring protection functions, such as an expansion of the protection zone, increased power loss, or insufficient equipment load due to overly conservative settings.

[0047] Specifically, efficiency indicators can assess the impact of tuning strategies on operational economy and system resource utilization. For example, the calculation method for the efficiency indicator value is as follows: 1) Input data: Tuning configuration data includes the protection zone size A corresponding to the tuning configuration; fault history data includes post-fault recovery energy consumption data. Operating parameters include protection effect reference parameters (regional tolerance). Permissible loss ); 2) Calculation formula: The numerical value of the efficiency index (i.e., the efficiency score) Scoring was conducted separately for regional expansion and energy consumption, and a weighted average was taken, using the following formula: ; in, This represents the weighting factor for data on changes in protected areas. This represents the weighting factor for data on changes in electricity loss. The default value is 0.5, which can be dynamically adjusted by the regional differences module; 3) Output data: Efficiency score .

[0048] Subsequently, this embodiment standardizes the original technical parameters corresponding to each evaluation indicator and maps them uniformly to a scoring range of 0 to 100, forming a single quantitative score (i.e., indicator value). All scores are automatically analyzed and calculated based on collected real-time operational data and configured parameters, ensuring the evaluation process is traceable, repeatable, and real-time. Furthermore, this embodiment can generate various types of structured data, including indicator values, indicator comparison charts, evaluation trend charts, and scoring anomaly alerts, to support subsequent collaborative analysis and intelligent optimization processes. This embodiment also includes a historical scoring version management mechanism, supporting the comparison of score change trends for the same configured object under different time periods or different configuration strategies, providing data support for strategy iteration and optimization effect verification.

[0049] Therefore, this embodiment introduces accuracy, reliability, sensitivity, selectivity, and efficiency indicators to evaluate the tuning configuration data from the perspectives of the degree of consistency between the tuning results and the operating conditions, fault response capability, non-fault isolation, tuning effect, and maintainability. It also combines standardized mapping processing to achieve automated scoring, thereby enhancing the accuracy and coverage of the tuning evaluation and avoiding misjudgments of important strategies due to a single evaluation perspective.

[0050] 103. For each of the aforementioned tuning objects, the tuning calculation evaluation result is determined based on the corresponding evaluation index.

[0051] In this embodiment, determining the tuning calculation evaluation result based on the corresponding evaluation index includes: Based on the region type to which the tuning object belongs, determine the weights of the accuracy index, reliability index, sensitivity index, selectivity index, and efficiency index. The tuning calculation and evaluation results are calculated based on the accuracy index, reliability index, sensitivity index, selectivity index, efficiency index, weight of accuracy index, weight of reliability index, weight of sensitivity index, weight of selectivity index, and weight of efficiency index.

[0052] In a specific embodiment, it should be noted that the distribution network varies in terms of network structure, equipment density, load size, power supply layout, power supply reliability requirements, and fault occurrence characteristics in urban, suburban, rural, and mountainous areas. For example, urban core area power grids typically feature concentrated power loads, dense equipment, and rapid operation and maintenance response, and their setting assessment focuses more on selectivity and reliability. In contrast, in rural and remote areas, due to dispersed lines, weaker protection equipment configuration, and higher fault frequency, the assessment should focus more on sensitivity and efficiency. Therefore, using fixed assessment index weights will lead to a disconnect between the assessment results and actual operational needs, failing to address the differences in structure, power supply density, load characteristics, and fault risks in different regional distribution networks. This results in assessment results deviating from actual operational needs and significantly impacts the accuracy of the assessment.

[0053] In this embodiment, the assessment areas are classified and modeled, and each assessment indicator is assigned a corresponding weight according to the area. Specifically, the assessment areas are classified according to the regional power grid structure parameters (such as power supply radius and equipment density), load characteristic parameters (such as load concentration and peak-valley ratio), historical fault data (such as annual average fault frequency and single-line tripping rate), and power grid level identification. For example, the area type is determined by classifying the area into urban central areas, urban fringe areas, industrial parks, rural distribution networks, and mountainous weak network areas.

[0054] Subsequently, weights are assigned to each evaluation indicator for each region type, with larger weights assigned to relevant evaluation indicators and smaller weights assigned to other evaluation indicators. These weights are derived from expert experience, historical data regression analysis, and policy priority settings, and can be manually set or automatically adjusted. For example, urban core area power grids typically have characteristics such as concentrated power load, dense equipment, and rapid operation and maintenance response. Therefore, for regions classified as urban core areas, selectivity and reliability indicators are used as relevant evaluation indicators.

[0055] For example, taking the urban center as an example: the "urban center" refers to the power distribution network located in the densely loaded areas of large and medium-sized cities, which usually has the following typical characteristics: large power supply load per unit area, with a load density of not less than 3MW / km²; dense equipment layout, including multiple feeder outgoing lines and a high-reliability ring network structure; high requirements for power supply continuity, with an allowable fault recovery time of less than 5 minutes; sufficient operation and maintenance resources, and rapid remote monitoring and fault response.

[0056] Acquire power grid data, which includes: 1) Load operation data: collected by the master station system or feeder terminal, including maximum load. Volatility 1) Historical annual average load curve; 2) Equipment operation data: including protection device setting configuration data (operating current setting value) Action time 1) Equipment deployment spacing and equipment reliability level; 2) Fault history data: exported from the dispatch system, including fault type, fault location point, actual action equipment number and equipment removal range; 3) Regional feature labels: automatically extracted by the GIS system and assigned to the urban center area.

[0057] Further, the evaluation process and indicator calculation example: 1) Location of the evaluation object: In this example, the feeder protection equipment is located in the city center, serving more than 1200 users, and is a key load power supply line. 2) Configuration of regional weight vector: For the city center, the selectivity indicator and the reliability indicator are used as associated evaluation indicators. The indicator weight of this associated evaluation indicator is higher than the indicator weight of other indicators. The following indicator weight vector (five dimensions) is assigned: in, The weights of the accuracy indicators, The weights of reliability indicators, The weights of the sensitivity index, The indicator weights are for the selective indicators. The weights of efficiency indicators.

[0058] Furthermore, based on the calculation method given in the above embodiments, the index values ​​of each indicator are calculated: 1) The numerical value of the accuracy index: the short-circuit calculated current in the system simulation. The operating current setting value is The ratio deviation was 6.15%, the action time was set within the allowable range, and the overall accuracy score was 94 points. 2) Reliability index values: 17 relevant events were extracted from the failure data of the past 12 months. The consistency rate between the actual action and the expected action was 94.1%, and the reliability score was 94. 3) Sensitivity index values: The set action time is 120ms, the upper limit of the area is 150ms, and the score is 80 points; 4) Selectivity index value: Among the two concurrent failures, one of the normal equipment was mistakenly disconnected, and the score is 70 points; 5) Efficiency index values: The analysis shows that the redundant protection range caused by the setting accounts for 12%, the estimated annual power loss increases by 2.4%, and the efficiency score is 75 points.

[0059] Furthermore, based on the indicator weights and values ​​of each indicator, the final weighted score calculation formula is as follows: The tuning evaluation result is 0.15*94+0.30*94+0.10*80+0.35*70+0.10*75=83.1.

[0060] Therefore, this embodiment classifies and models the regions where the setting objects are located by identifying regional attributes and configuring differentiated index weights. This allows for increased weights of selectivity and reliability indicators in densely populated urban areas, and enhanced weights of sensitivity and economic indicators in long-distance rural areas. This results in targeted and accurate scoring, improving the regional adaptability and strategic reference value of the assessment. It can guide maintenance personnel to optimize setting configurations according to scenarios, greatly improving the accuracy of setting assessments and enhancing the stability and security of regional power grid operation.

[0061] Optionally, a simplified alternative can be made by using a pre-defined static regional classification table. For example, regions can be fixedly divided into four categories: "city center, industrial zone, suburbs, and rural areas". Each category has a set of fixed indicator weights. There is no need for automatic identification and updating. The structure is clear and easy to deploy, making it suitable for environments where data collection is not yet complete or where rapid deployment is possible.

[0062] 104. Based on the topology data and setting configuration data of several of the aforementioned setting objects, determine the setting coordination deviation value of the distribution network.

[0063] In this embodiment, determining the setting coordination deviation value of the distribution network based on the topology data and setting configuration data of several of the setting objects includes: Based on the topology data of each of the aforementioned tuning objects, each tuning object is treated as a node, and a graph model of the distribution network is constructed. In the graph model, for the tuning configuration data of the two nodes corresponding to each side, the coordination deviation value corresponding to each side is calculated; The coordination deviation values ​​of all sides are superimposed to obtain the setting coordination deviation value of the distribution network.

[0064] In one specific embodiment, a graph model for the tuning object is constructed based on the power grid topology data, wherein a node in the graph model represents a tuning object, and an edge in the graph model represents the tuning coordination relationship between two connected tuning objects; Based on the operating current setting value and operating time of the object being set, determine the setting parameter vector of the object being set; Based on the preset target coordination interval vector and the tuning parameter vectors of any two adjacent target tuning objects, the tuning coordination deviation value is determined. By traversing the graph model, the tuning coordination deviation values ​​of any two adjacent target tuning objects are superimposed to obtain the overall tuning coordination deviation value of the distribution network.

[0065] It should be noted that most existing technologies employ single-point calculations and equipment independence evaluations, failing to consider the coherence and matching of protection logic across levels. This can easily lead to problems such as overstepping of protection levels, failure to operate, or protection blind spots. This embodiment calculates the setting coordination deviation of setting path consistency to achieve overall coordination modeling and optimization measurement among multi-level protection strategies, thereby identifying and quantifying the consistency and coordination of setting configurations among various protection levels in the distribution network.

[0066] In this embodiment, the setting coordination relationship of the setting objects in the physical topology of the distribution network is abstracted into a graph model. Each node of the graph model represents a setting object with protection function, and the edge of the graph model represents that the two connected setting objects have direct setting coordination requirements, such as the coordinated action relationship between the upper-level protection and the lower-level protection.

[0067] For example, define a vector of setting parameters for the setting object (such as a protection device) i. , This indicates the operating current setting value. Indicates the duration of the action.

[0068] For any tuning pair (i,j) consisting of tuning objects i and j with a coordination relationship, let its target coordination interval vector be: ;in, This represents the target coordination interval vector, which can be set by operating specifications or expert experience, and represents the minimum set interval between the desired operating current and operating time. This represents the target operating current setting vector. This represents the target action time vector.

[0069] Furthermore, the tuning coordination deviation value is determined using the following formula: ;in, The tuning coordination deviation value of tuning pair (i,j) is represented by the sum of squares of the deviations between the actual tuning interval and the ideal interval. This function has good convexity, which facilitates subsequent error aggregation under the global path. This represents the tuning parameter vector of the tuning object j. This represents the tuning parameter vector for tuning object i. Furthermore, by traversing all edges in the entire directed graph, the overall tuning coordination deviation value of the distribution network is obtained.

[0070] Therefore, this embodiment uses vector form to handle the coordination logic between multiple parameters such as time and current, applicable to any scale, and beneficial to improving the accuracy of the setting parameter vector. The deviation function is in quadratic form, suitable for embedding into the optimization model, improving computational convergence. Furthermore, a target coordination interval vector is introduced to form a learning-based coordination model. In this regard, this embodiment models the setting configuration relationship as a directed graph, calculates the target deviation between all upper and lower level setting points, constructs a quantifiable coordination consistency index, and considers the action coordination relationship between different levels of protection devices, thus improving the accuracy of setting evaluation.

[0071] 105. Based on the setting coordination deviation value and the setting evaluation result of each setting object, determine the setting calculation quality evaluation report for each setting object in the distribution network.

[0072] In this embodiment, determining the setting calculation quality assessment report for each setting object of the distribution network based on the setting assessment results and setting coordination deviation values ​​of each setting object includes: Based on the tuning evaluation results and tuning coordination deviation values ​​of each tuning object, control optimization suggestions are generated for each tuning object; Based on the setting evaluation results and control optimization suggestions for each setting object, a setting calculation quality evaluation report for each setting object of the distribution network is generated.

[0073] In one specific embodiment, after the tuning assessment is completed, based on multi-dimensional information such as the tuning assessment results, tuning coordination deviation values, and regional strategy requirements, targeted, implementable, and operational control optimization suggestions are automatically generated, thereby realizing intelligent recommendation of tuning parameter adjustments and closed-loop feedback of operation and maintenance strategies.

[0074] The data processed mainly comes from: 1. Multi-dimensional evaluation: the output of various tuning evaluation results, used to identify weak points in the tuning effect; 2. Tuning coordination deviation value: the provided area labels and evaluation sensitivity directions, used to guide the optimization target to focus on specific indicators; 3. Collaborative analysis: the output of tuning coordination deviation and over-level alarm information, used to locate specific tuning conflict sections and key affected equipment.

[0075] First, based on the current equipment setting assessment results, determine the dimension to which its main shortcomings belong (such as low sensitivity or poor economy). Then, combine the regional weights to determine whether the indicator has adjustment priority in the current region. Subsequently, the system enters the rule invocation phase, generating candidate control optimization suggestions based on typical rule sets (such as "if the feeder protection action time is 200ms greater than the superior action time, the setting time should be moved forward by 50ms" and "if the user-side overcurrent action value is less than the feeder action value, it is recommended to upgrade it by one level").

[0076] For tuning objects with multi-path coordination conflicts, this module will prioritize calling the strategy of minimizing the tuning coordination deviation value in the cooperating path. Under the premise of satisfying the coordination constraints, the current tuning coordination deviation value will be guided and corrected by the target vector, that is, the current tuning coordination deviation value will be adjusted to minimize the deviation function, the deviation function will be adjusted to be near the optimal value, and the recommended value will be output.

[0077] The final quality assessment report, which includes recommendations for regulation and optimization, includes: 1. Current evaluation indicator scores and suggested areas for improvement; 2. The content of the regulation and optimization suggestions includes: the recommended adjustment range of the setpoint, the suggested value, the reason for the adjustment, and the explanation of the related paths, etc.

[0078] When the direction of the inter-layer tuning parameter vector and the target coordination interval vector is opposite, the tuning object corresponding to the inter-layer tuning parameter vector is identified as the first level-avoidance risk point, and the corresponding level-avoidance risk data is generated. The inter-layer tuning parameter vector is determined by the tuning parameter vectors of any two adjacent target tuning objects, and the level-avoidance risk data includes the equipment identifier, equipment location and corresponding tuning coordination deviation value of the tuning object corresponding to the level-avoidance risk point. When any tuning coordination deviation value is detected to be greater than the preset deviation threshold, the path between the corresponding tuning objects is located and marked as the tuning problem segment.

[0079] In this embodiment, the smaller the overall network tuning coordination deviation value, the better the overall consistency of the network tuning coordination. If there are some critical path tuning intervals that are reversed (i.e. and (If the direction is opposite), it is identified as the first level of risk point, and the equipment number, location, and corresponding deviation are output, thereby generating a list of level-avoiding or reverse coordination alarms. In addition, local high-deviation paths are extracted by adjusting the coordination deviation value to locate the problem segment.

[0080] Therefore, this embodiment introduces protection setting coordination analysis into the whole network topology perspective by realizing the structured identification of over-level risks, coordination anomalies, and path tensions, providing a precise positioning basis for subsequent optimization and improving the accuracy of the whole network setting assessment.

[0081] Optionally, this application can adjust the assessment sensitivity based on regional characteristics and setting coordination deviation values. For example, the upper limit of allowable deviation can be reduced in densely populated urban areas, and the time coordination interval requirements can be appropriately relaxed for long-distance rural lines, thereby forming a differentiated, closed-loop distribution protection setting assessment model.

[0082] In some embodiments, the difference in action time between any two adjacent target tuning objects is determined based on the action time of the tuning object. When the difference in the action time is detected to be less than a preset difference threshold, the corresponding adjustment object is identified as a second level risk point.

[0083] In this embodiment, the time difference between any two adjacent target tuning objects is used as the criterion for judging the coordination between upper and lower level tuning. If the time difference is less than a set threshold (e.g., 100ms), it is considered to have a risk of exceeding the level, and is marked as a second risk point exceeding the level. Therefore, this embodiment can simplify calculations and adapt to resource-constrained scenarios, meet the judgment requirements in a large number of practical projects, and is suitable for remote rural power distribution networks or simplified deployment scenarios, while ensuring the accuracy of risk judgment.

[0084] In some embodiments, a shortcoming assessment index is determined based on the index value of the tuning assessment result. The shortcoming assessment index is used to indicate the assessment index corresponding to when the index value is lower than a preset value threshold. When the correlation between the short-board assessment index and the area type corresponding to the current power distribution area is detected to be greater than the preset adjustment priority threshold, the setting configuration data involved in the short-board assessment index is adjusted according to the setting assessment result and the setting coordination deviation value of the whole network. When the setting coordination deviation value detects that any setting object involves multiple setting problem segments, the operating current setting value and operating time of the detected setting object are adjusted to minimize the setting coordination deviation value between the detected setting object and its adjacent setting objects.

[0085] In this embodiment, based on the numerical values ​​of the evaluation indicators, the weakest evaluation indicators (such as low sensitivity or low efficiency) are determined. Then, considering the region type, it is determined whether the weakest evaluation indicator has adjustment priority within the current region (i.e., the weakest evaluation indicator has a high correlation with the region). Subsequently, candidate correction suggestions for the setting configuration data can be generated based on typical rule sets (such as "if the feeder protection action time is 200ms greater than the superior action time, the setting time should be moved forward by 50ms", "if the user-side overcurrent action value is less than the feeder action value, it is recommended to upgrade it by one level", etc.).

[0086] In addition, for tuning objects with multi-path coordination conflicts, the strategy of minimizing tuning deviation in the coordination path will be called first. Under the premise of satisfying the coordination constraints, the target vector will be used to guide and correct the current tuning value, so as to minimize the tuning coordination deviation between the tuning object and its adjacent tuning objects.

[0087] Therefore, this embodiment automatically recommends setting value correction schemes based on the setting evaluation results and setting coordination deviation values, outputs an optimization suggestion report, and clearly indicates the suggested operating current, delay time, adjustment priority, and impact on coordination relationships. This not only improves the automation level of setting decisions but also realizes intelligent closed-loop processing from diagnosis to correction, reducing manual workload and improving the efficiency and reliability of setting configuration.

[0088] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a setting calculation quality assessment device for a distribution network, comprising: a data acquisition module 201, a data calculation module 202, a data assessment module 203, a coordination deviation module 204, and a result generation module 205; The data acquisition module is used to acquire topology data, operating parameters and setting configuration data of several setting objects in the distribution network; The data calculation module is used to calculate the evaluation index of each of the tuning objects based on the topology data, the operating parameters, and the tuning configuration data. The data evaluation module is used to determine the adjustment evaluation result for each of the adjustment objects based on the corresponding evaluation indicators. The coordination deviation module is used to determine the setting coordination deviation value of the distribution network based on the topology data and setting configuration data of several of the setting objects. The result generation module is used to determine the setting quality assessment report for each setting object in the distribution network based on the setting coordination deviation value and the setting assessment result of each setting object.

[0089] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the method for setting calculation quality assessment of distribution networks provided by any of the above-described method embodiments of the present invention.

[0090] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0091] Based on the above embodiments of the setting calculation quality assessment method for distribution networks, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the setting calculation quality assessment method for distribution networks according to any embodiment of the present invention.

[0092] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0093] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0094] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0095] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the setting calculation quality assessment method for distribution networks described in any of the above-described method embodiments of the present invention.

[0096] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0097] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the quality of setting calculations in a distribution network, characterized in that, include: Acquire topology data, operating parameters, and setting configuration data of several setting objects in the power distribution network; Based on the topology data, the operating parameters, and the tuning configuration data, an evaluation index is calculated for each tuning object. For each of the aforementioned tuning objects, the tuning calculation and evaluation results are determined based on the corresponding evaluation indicators; Based on the topology data and setting configuration data of several of the aforementioned setting objects, the setting coordination deviation value of the distribution network is determined; Based on the setting coordination deviation value and the setting evaluation results of each setting object, a setting calculation quality evaluation report for each setting object in the distribution network is determined.

2. The method for evaluating the setting calculation quality of a distribution network as described in claim 1, characterized in that, The acquisition of the power grid topology, operating parameters, and setting configuration data of several setting objects in the distribution network includes: Obtain the power grid topology of the distribution network, determine the topology data of each setting object based on the power grid topology, and obtain the setting configuration data of each setting object. Collect several types of observation values ​​for each setting object in the power distribution network; Preprocessing operations are performed on several types of observations for each tuned object to obtain several types of operational data for each tuned object; In each tuned object, for each type of running data, it is determined whether the number of data sources to which the running data belongs is greater than the number threshold; if so, the average value of the observations is calculated based on the running data of each data source, the numerical weight of each running data is calculated based on the average value of the observations and the preset smoothing term, and the running parameters are obtained by weighted fusion based on each running data and the numerical weight corresponding to each running data; if not, the running data is directly used as the running parameters.

3. The method for evaluating the setting calculation quality of a distribution network as described in claim 2, characterized in that, The preprocessing operation includes: Obtain the upper confidence limit and lower confidence limit of the observed values; In each tuning object, for each type of observation, it is determined whether there is missing data. If so, the adjustment parameter is determined based on the trend of the observation, and the imputed data is obtained by estimating based on the two observations adjacent to the missing data, the adjustment parameter, the upper confidence value of the observation, and the lower confidence value of the observation. The imputed data is then added to the observation to obtain the running data. If not, the observation is directly used as the running data.

4. The method for evaluating the setting calculation quality of a distribution network as described in claim 3, characterized in that, The evaluation metrics include: accuracy metrics, reliability metrics, sensitivity metrics, selectivity metrics, and efficiency metrics; the calculation of the evaluation metrics for each tuning object based on the topology data, the operating parameters, and the tuning configuration data includes: Acquire historical fault data for several setting objects in the power distribution network; For each tuning object, the degree of adaptation is calculated based on the operating parameters and tuning configuration data, and the accuracy index is determined based on the degree of adaptation. For each tuning object, a reliability index is determined based on the tuning configuration data and fault history data; For each calibrated object, a sensitivity index is determined based on the operating parameters and the historical fault data; For each tuning object, a selective index is determined based on the historical fault data; For each tuning object, efficiency indicators are determined based on the operating parameters, the tuning configuration data, and the fault history data.

5. The method for evaluating the setting calculation quality of a distribution network as described in claim 4, characterized in that, The determination of the tuning calculation evaluation result based on the corresponding evaluation indicators includes: Based on the region type to which the tuning object belongs, determine the weights of the accuracy index, reliability index, sensitivity index, selectivity index, and efficiency index. The tuning calculation and evaluation results are calculated based on the accuracy index, reliability index, sensitivity index, selectivity index, efficiency index, weight of accuracy index, weight of reliability index, weight of sensitivity index, weight of selectivity index, and weight of efficiency index.

6. The method for evaluating the setting calculation quality of a distribution network as described in claim 5, characterized in that, The determination of the distribution network's setting coordination deviation value based on the topology data and setting configuration data of several of the aforementioned setting objects includes: Based on the topology data of each of the aforementioned tuning objects, each tuning object is treated as a node, and a graph model of the distribution network is constructed. In the graph model, for the tuning configuration data of the two nodes corresponding to each side, the coordination deviation value corresponding to each side is calculated; The coordination deviation values ​​of all sides are superimposed to obtain the setting coordination deviation value of the distribution network.

7. The method for evaluating the setting calculation quality of a distribution network as described in claim 6, characterized in that, The step of determining a setting calculation quality assessment report for each setting object of the distribution network based on the setting assessment results and setting coordination deviation values ​​of each setting object includes: Based on the tuning evaluation results and tuning coordination deviation values ​​of each tuning object, control optimization suggestions are generated for each tuning object; Based on the setting evaluation results and control optimization suggestions for each setting object, a setting calculation quality evaluation report for each setting object of the distribution network is generated.

8. A device for evaluating the quality of setting calculations in a power distribution network, characterized in that, include: The system includes a data acquisition module, a data calculation module, a data evaluation module, a deviation reconciliation module, and a result generation module. The data acquisition module is used to acquire topology data, operating parameters and setting configuration data of several setting objects in the distribution network; The data calculation module is used to calculate the evaluation index of each of the tuning objects based on the topology data, the operating parameters, and the tuning configuration data. The data evaluation module is used to determine the adjustment evaluation result for each of the adjustment objects based on the corresponding evaluation indicators. The coordination deviation module is used to determine the setting coordination deviation value of the distribution network based on the topology data and setting configuration data of several of the setting objects. The result generation module is used to determine the setting quality assessment report for each setting object in the distribution network based on the setting coordination deviation value and the setting assessment result of each setting object.

9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the method for setting calculation quality assessment for a distribution network as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the setting calculation quality assessment method for a distribution network as described in any one of claims 1-7.

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