Power flow data convergence improvement method and system based on self-locking check

Abstract

The application discloses a power flow data convergence improvement method and system based on self-locking checking, and the method comprises the following steps: obtaining power flow data of a power system at multiple time sequence sections; determining convergent power flow data corresponding to non-convergent power flow data according to power flow operation modes corresponding to the power flow data, and calculating operation deviations of the non-convergent power flow data relative to the corresponding convergent power flow data; classifying and aggregating the operation deviations to determine abnormal types corresponding to the non-convergent power flow data; determining checking objects in the power system according to the abnormal types, and performing self-locking checking on the checking objects to obtain self-locking checking results; correcting power flow operation modes corresponding to the non-convergent power flow data according to the self-locking checking results, and performing power flow calculation again to obtain power flow data with improved convergence. The application can improve the pertinence of non-convergent cause positioning and power flow operation mode correction, thereby improving the convergence of massive power flow data generation.

Description

Technical Field

[0001] This application relates to the field of power transmission system technology, and in particular to a method and system for improving power flow data convergence based on self-locking checks. Background Technology

[0002] With the continuous expansion of power system scale and the sustained increase in renewable energy penetration, the complexity of power grid operation has increased significantly. Traditional AC power flow calculation methods face poor convergence when dealing with massive scenarios. After obtaining different start-up combinations from production simulations, it is necessary to generate corresponding massive AC power flow operation modes. Therefore, improving the convergence of massive AC power flow generation has become a key research topic.

[0003] Production simulation is a crucial support for power system planning and operational decision-making. Its core is to analyze the safety, economy, and reliability of the power grid by simulating unit start-up combinations under different operating conditions. Current technologies, when processing non-convergent data in massive AC power flow data, typically rely on manual experience or standardized parameter adjustments for troubleshooting. They fail to perform cluster analysis based on the operational deviations between non-convergent and convergent data, and do not conduct self-locking checks based on the clustering results. This results in a lack of targeted approaches to improving the convergence of power flow calculations, making it difficult to improve the convergence of massive AC power flow data. Summary of the Invention

[0004] This application provides a method and system for improving the convergence of power flow data based on self-locking checks. The aim is to improve the accuracy of locating the causes of non-convergence and the convergence of power flow calculation in massive AC power flow data by aggregating and classifying the operational deviations between non-converging and convergent power flow data, and performing self-locking checks and correction recalculations based on the classification results.

[0005] To achieve the above objectives, the first aspect of this application provides a method for improving the convergence of power flow data based on self-locking checks, comprising: The power flow data of the power system under multiple time-series sections are acquired, wherein each power flow data corresponds to a power flow operation mode under one time-series section, and the multiple power flow data include convergent power flow data and non-convergent power flow data. Based on the power flow operation mode corresponding to each of the power flow data, determine the convergent power flow data corresponding to the non-convergent power flow data, and calculate the operation deviation of each of the non-convergent power flow data relative to the corresponding convergent power flow data. The operational deviations are aggregated and classified to determine the anomaly type corresponding to each non-converging power flow data. Based on the anomaly type, multiple inspection objects in the power system are identified, and a self-locking check is performed on each inspection object to obtain the self-locking check result. Based on the self-locking check results, the power flow operation mode corresponding to the non-convergent power flow data is corrected, and the power flow is recalculated to obtain power flow data with improved convergence.

[0006] By aggregating and classifying the operational deviations between non-convergent and convergent power flow data, and performing self-locking checks and corrective recalculations based on the anomaly type, this application can improve the accuracy of locating the cause of non-convergence and enhance the convergence of power flow calculations compared to existing technologies that rely on manual experience or uniform parameter adjustment methods to process non-convergent power flow data.

[0007] Furthermore, based on the power flow operation mode corresponding to each of the aforementioned power flow data, the convergent power flow data corresponding to the non-convergent power flow data is determined, including: Based on the power flow operation mode corresponding to the non-convergent power flow data, determine the operation characteristics corresponding to the non-convergent power flow data; Based on the operational characteristics, convergent power flow data that meets preset matching conditions with the non-convergent power flow data is determined from the convergent power flow data and used as the convergent power flow data corresponding to the non-convergent power flow data.

[0008] By determining the operating characteristics based on the power flow operation mode corresponding to the non-convergent power flow data, and matching the corresponding convergent power flow data according to the operating characteristics, this application can improve the matching accuracy of the operating deviation calculation object compared with the existing technology of directly selecting reference data or data comparison method lacking corresponding relationship, making the subsequent deviation analysis results more reliable.

[0009] Further, the operational deviation of each of the non-convergent power flow data points relative to the corresponding convergent power flow data is calculated, including: The node voltage of the target node in the non-converging power flow data is calculated with the node voltage of the same target node in the corresponding convergent power flow data, and the node voltage deviation is determined based on the difference calculation result. The difference between the active power of the target line in the non-convergent power flow data and the active power of the same target line in the corresponding convergent power flow data is calculated, and the line power deviation is determined based on the difference calculation result. The relative difference between the load of the target bus in the non-converging power flow data and the load of the same target bus in the corresponding convergent power flow data is calculated, and the bus load deviation is determined based on the relative difference calculation result. The operating deviation corresponding to the non-converging power flow data is determined based on the node voltage deviation, the line power deviation, and the bus load deviation.

[0010] By calculating node voltage deviation, line power deviation, and bus load deviation separately, compared to the existing technology that judges the cause of non-convergence based on only a single error message, this application can characterize the abnormal features of non-convergent power flow data from multiple dimensions of voltage, power, and load, thereby improving the comprehensiveness of anomaly identification.

[0011] Furthermore, the operational deviations are aggregated and classified to determine the anomaly types corresponding to each non-convergent power flow data, including: Based on the node voltage deviation, line power deviation, and bus load deviation in each of the aforementioned operational deviations, construct the deviation characteristics corresponding to each of the non-converging power flow data. Cluster analysis is performed on each of the aforementioned deviation features to obtain the deviation clustering results; Based on the deviation clustering results, the anomaly type corresponding to each of the non-converging power flow data is determined, wherein the anomaly type includes high voltage deviation type, high power deviation type and comprehensive deviation type.

[0012] By constructing deviation features based on node voltage deviation, line power deviation, and bus load deviation, and performing cluster analysis on the deviation features, this application can classify non-convergent power flow data into high-voltage deviation type, high-power deviation type, and comprehensive deviation type, compared with the existing technology that does not distinguish between non-convergent anomaly types and conducts unified investigation. This provides a targeted basis for subsequent self-locking checks.

[0013] Furthermore, a self-locking check is performed on each of the aforementioned inspection objects to obtain the self-locking check results, including: Determine the object type corresponding to each of the aforementioned inspection objects; According to the object type, one or more of the following methods are performed on each of the inspection objects: line parameter inspection method, transformer parameter inspection method, node injection quantity inspection method, or branch power rationality inspection method based on DC power flow, to obtain the self-locking inspection result.

[0014] By selecting line parameter checks, transformer parameter checks, node injection quantity checks, or branch power rationality checks based on the type of the object being checked, this application can narrow the scope of inspection and improve the targeting and processing efficiency of self-locking checks compared to the indiscriminate inspection of equipment parameters and operating data in the prior art.

[0015] Furthermore, the methods for checking line parameters include: Obtain the line parameters corresponding to the object under inspection, wherein the line parameters include line resistance, line reactance, line length and line voltage level; Based on the line parameters and the line length, the unit length line parameters of the inspection object are determined, and the unit length line parameters are compared with a preset line parameter range to obtain the line unit length parameter inspection result; Based on the line resistance and the line reactance, the resistance-to-reactance ratio of the object under inspection is determined, and the resistance-to-reactance ratio is compared with a preset resistance-to-reactance ratio range to obtain the line resistance-to-reactance ratio inspection result; Based on the line reactance and the voltage level corresponding to the inspection object, the estimated line length corresponding to the inspection object is determined, and the estimated line length inspection result is obtained based on the estimated line length and the preset length judgment condition. The line parameter inspection result is obtained based on one or more of the line unit length parameter inspection result, the line resistance-inductance ratio inspection result, and the estimated line length inspection result.

[0016] By checking the line parameters per unit length, the resistance-to-reaction ratio, and the calculated line length, this application can more accurately identify line parameter anomalies and reduce the impact of line parameter errors on the convergence of power flow calculations, compared to existing technologies that struggle to detect errors in line parameter input or per-unit value conversion in a timely manner.

[0017] Furthermore, the methods for checking transformer parameters include: Obtain the transformer parameters corresponding to the object under inspection, wherein the transformer parameters include transformer model, transformer turns ratio and voltage level; Based on the transformer model, determine the standard transformer parameters corresponding to the inspection object, and compare the transformer parameters with the standard transformer parameters to obtain the transformer standard parameter inspection results; Based on the transformer turns ratio, determine the per-unit turns ratio corresponding to the inspection object, and obtain the transformer per-unit turns ratio inspection result based on the comparison result between the per-unit turns ratio and the preset per-unit turns ratio range; The short-circuit voltage percentage corresponding to the inspection object is calculated based on the transformer parameters, and the typical short-circuit voltage percentage range corresponding to the inspection object is determined based on the voltage level. The short-circuit voltage percentage is compared with the typical short-circuit voltage percentage range to obtain the converted short-circuit voltage inspection result. The transformer parameter inspection results are obtained based on one or more of the transformer standard parameter inspection results, the transformer per-unit turns ratio inspection results, and the converted short-circuit voltage inspection results.

[0018] By performing standard parameter checks, per-unit turns ratio checks, and short-circuit voltage percentage checks on the transformer, compared to the existing technology that relies solely on manual verification of transformer parameters, this application can improve the automation and accuracy of transformer parameter anomaly identification and reduce power flow non-convergence problems caused by transformer parameter errors.

[0019] Furthermore, the node injection volume checking methods include: Obtain node injection data corresponding to multiple inspection objects, wherein the node injection data includes node load data or node power generation data; Based on the node load data or the node power generation data, determine the node injection amount corresponding to each of the inspected objects; The node injection amount corresponding to each of the inspection objects is compared with the preset injection amount range to determine the target node whose node injection amount exceeds the preset injection amount range; Based on the target node, the result of the node injection amount check is obtained.

[0020] By determining the node injection amount based on node load data or node power generation data, and screening target nodes whose node injection amount exceeds the preset injection amount range, this application can locate nodes with abnormal load or power generation injection amounts, improving the efficiency of node injection amount inspection, compared to existing technologies that are difficult to quickly detect abnormal node injection amounts.

[0021] Furthermore, the method for checking the rationality of branch power based on DC power flow includes: Based on the power flow operation mode corresponding to the non-converging power flow data, the node load and load correction coefficient of the power system are obtained; The node load is corrected according to the load correction factor to obtain the corrected node load; A DC power flow model for the power system is established based on the corrected node load. Based on the DC power flow model, calculate the branch load rate of the branch corresponding to each of the inspected objects; The branch load rate of each branch corresponding to the inspection object is compared with a preset load rate threshold, and the target branches whose branch load rate exceeds the preset load rate threshold are selected. Based on the target branch, the results of the branch power rationality check are obtained.

[0022] By introducing a load correction factor to correct the node load and calculating the branch load rate based on the DC power flow model to screen overload target branches, compared with the existing technology where it is difficult to quickly determine the rationality of branch power distribution after AC power flow does not converge, this application can quickly identify branches that may have power anomalies or overload risks, thus improving the efficiency of branch power rationality checking.

[0023] Secondly, this application provides a power flow data convergence improvement system based on self-locking check, including: a data acquisition module, a deviation calculation module, an anomaly classification module, a self-locking check module, and a correction calculation module; The data acquisition module is used to acquire power flow data of the power system under multiple time-series sections, wherein each power flow data corresponds to a power flow operation mode under one time-series section, and the multiple power flow data include convergent power flow data and non-convergent power flow data. The deviation calculation module is used to determine the convergent power flow data corresponding to the non-convergent power flow data according to the power flow operation mode corresponding to each power flow data, and to calculate the operation deviation of each non-convergent power flow data relative to the corresponding convergent power flow data. The anomaly classification module is used to aggregate and classify each of the operational deviations to determine the anomaly type corresponding to each of the non-converging power flow data. The self-locking check module is used to determine multiple check objects in the power system according to the anomaly type, perform self-locking checks on each check object, and obtain self-locking check results. The correction calculation module is used to correct the power flow operation mode corresponding to the non-converged power flow data according to the self-locking check result, and recalculate the power flow to obtain power flow data with improved convergence. Attached Figure Description

[0024] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. 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.

[0025] Figure 1 This is a flowchart illustrating a method for improving the convergence of power flow data based on self-locking checks, provided in an embodiment of this application. Figure 2 This is a schematic diagram of an equivalent DC power flow model provided in an embodiment of this application; Figure 3 This is a schematic diagram of the core parameter optimization process based on self-locking check provided in an embodiment of this application; Figure 4 This is a schematic diagram of a power flow data convergence improvement system based on self-locking checks, provided in another embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of this application more thorough and comprehensive. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0027] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0028] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting this application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0029] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in the specification of this application is for the purpose of describing specific embodiments only and is not intended to limit the application. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0030] To address the non-convergence scenarios in existing technologies involving massive time-series power flow data, the main approach relies on manual experience or standardized parameter adjustments. This makes it difficult to pinpoint the root cause of anomalies based on operational deviations between converged and non-converged power flow data. This results in low efficiency in troubleshooting non-convergence, insufficiently targeted correction of power flow operation methods, and low convergence rates in batch power flow data generation. Figure 1As shown in the figure, this application provides a detailed flowchart of a power flow data convergence improvement method based on self-locking checks. The power flow data convergence improvement method based on self-locking checks in this embodiment includes steps S101 to S105, which are described in detail below: Step S101: Obtain power flow data of the power system under multiple time-series sections, wherein each power flow data corresponds to a power flow operation mode under one time-series section, and the multiple power flow data include convergent power flow data and non-convergent power flow data.

[0031] Specifically, during the generation of massive time-series power flow data, the corresponding BPA power flow data and production simulation calculation result files of the power system are read in. The BPA power flow data provides the basic network data required for power flow data generation. This basic network data may include node data, line data, transformer data, generator data, load data, and section control data. The section control data may include the line components and control power values ​​for each section.

[0032] Furthermore, in this embodiment, the production simulation calculation result file is a PPS file, which is used to provide operating boundary data under multiple time-series sections. The operating boundary data may include one or more of the following obtained from time-series production simulation calculations: unit start-up combination, unit output, new energy output, and load level.

[0033] After obtaining the BPA power flow data and the production simulation calculation result file, the unit start-up combination, unit output, renewable energy output, load level, and section control data under the same time series section can be written or configured into the corresponding BPA power flow data to generate the power flow operation mode corresponding to that time series section. In other words, the BPA power flow data provides the grid topology, equipment parameters, and section constraints, while the production simulation calculation result file provides the source-load operating status under each time series section. The combination of these two provides power flow data corresponding to multiple time series sections. Specifically, based on the start-up unit combination calculated and solved through 8760 hours of time series production simulation, power flow data corresponding to 8760 time series sections are generated.

[0034] Furthermore, AC power flow calculations can be performed on the power flow data generated at each time series section, and the convergence status of each power flow data can be determined based on the AC power flow calculation results. If the power flow data corresponding to the target time series section meets the preset convergence conditions, then the power flow data corresponding to the target time series section is determined as converged power flow data; if the power flow data corresponding to the target time series section does not meet the preset convergence conditions, or the power flow calculation program outputs a non-convergence error message, then the power flow data corresponding to the target time series section is determined as non-converged power flow data.

[0035] Therefore, basic network data and section control data of the power system are obtained from the BPA power flow data. The unit start-up combination and source load operation status under multiple time sections are obtained from the production simulation calculation result file. Power flow data corresponding to multiple time sections are formed during the power flow data generation process. The convergence status of each power flow data is obtained during the AC power flow calculation process. Thus, the multiple power flow data are divided into convergent power flow data and non-convergent power flow data, providing a data basis for subsequently determining the convergent power flow data corresponding to the non-convergent power flow data and calculating the operation deviation.

[0036] Step S102: Based on the power flow operation mode corresponding to each power flow data, determine the convergent power flow data corresponding to the non-convergent power flow data, and calculate the operation deviation of each non-convergent power flow data relative to the corresponding convergent power flow data.

[0037] In some preferred embodiments, based on the power flow operation mode corresponding to each of the power flow data, the convergent power flow data corresponding to the non-convergent power flow data is determined, including: Based on the power flow operation mode corresponding to the non-convergent power flow data, determine the operation characteristics corresponding to the non-convergent power flow data; Based on the operational characteristics, convergent power flow data that meets preset matching conditions with the non-convergent power flow data is determined from the convergent power flow data and used as the convergent power flow data corresponding to the non-convergent power flow data.

[0038] Specifically, after obtaining power flow data from multiple time-series sections, for each non-convergent power flow data point, the corresponding power flow operation mode can be determined, and operation features can be extracted from the power flow operation mode. After determining the operation features corresponding to the non-convergent power flow data, based on the operation features, convergent power flow data that meets preset matching conditions with the non-convergent power flow data can be determined from multiple convergent power flow data points, and this convergent power flow data can be used as the convergent power flow data corresponding to the non-convergent power flow data.

[0039] By implementing the above preferred scheme, the operating characteristics are determined based on the power flow operation mode corresponding to the non-convergent power flow data, and the corresponding convergent power flow data is matched according to the operating characteristics. Compared with the existing technology of directly selecting reference data or comparing data without corresponding relationships, this application can improve the matching accuracy of the operating deviation calculation object and make the subsequent deviation analysis results more reliable.

[0040] In some preferred embodiments, calculating the operational deviation of each of the non-convergent power flow data relative to the corresponding convergent power flow data includes: The node voltage of the target node in the non-converging power flow data is calculated with the node voltage of the same target node in the corresponding convergent power flow data, and the node voltage deviation is determined based on the difference calculation result. The difference between the active power of the target line in the non-convergent power flow data and the active power of the same target line in the corresponding convergent power flow data is calculated, and the line power deviation is determined based on the difference calculation result. The relative difference between the load of the target bus in the non-converging power flow data and the load of the same target bus in the corresponding convergent power flow data is calculated, and the bus load deviation is determined based on the relative difference calculation result. The operating deviation corresponding to the non-converging power flow data is determined based on the node voltage deviation, the line power deviation, and the bus load deviation.

[0041] Specifically, after determining the corresponding convergent power flow data, the operational deviation of the non-convergent power flow data relative to the corresponding convergent power flow data can be calculated. This operational deviation is used to quantify the difference between the non-convergent power flow data and the corresponding convergent power flow data, and includes node voltage deviation, line power deviation, and bus load deviation.

[0042] Specifically, the node voltage of the target node in the non-convergent power flow data is calculated by comparing it with the node voltage of the corresponding target node in the convergent power flow data, resulting in a node voltage deviation; wherein, the node voltage deviation can be expressed as: For nodes in non-convergent power flow data Voltage, For the nodes in the corresponding convergent power flow data Voltage.

[0043] Further, the difference between the active power of the target line in the non-convergent power flow data and the active power of the corresponding target line in the corresponding convergent power flow data is calculated to obtain the line power deviation; wherein, the line power deviation can be expressed as: and These are the lines in the non-convergent power flow data and the corresponding convergent power flow data, respectively. Active power.

[0044] Further, the relative difference between the load of the target bus in the non-convergent power flow data and the load of the corresponding target bus in the corresponding convergent power flow data is calculated to obtain the bus load deviation; wherein, the bus load deviation can be expressed as: Indicating non-convergent power flow data, the bus... The corresponding load value, This indicates the bus in the corresponding convergent power flow data. The corresponding load value, the bus load deviation is used to reflect the relative deviation of the load.

[0045] Specifically, when calculating operational deviations, target nodes, target lines, or target buses can be determined based on the error reports from non-converging power flows in the BPA software. These operational error reports can provide the voltage exceedance values ​​for voltage-over-limit nodes and the load rate for heavily overloaded nodes. For voltage-over-limit nodes, their node voltage deviation can be calculated; for heavily overloaded nodes or related branches, their line power deviation or load rate-related deviation can be calculated. Voltage-over-limit nodes and heavily overloaded nodes can be processed in parallel at the same level.

[0046] Therefore, the operational deviations corresponding to the non-convergent power flow data can be determined based on the node voltage deviation, line power deviation, and bus load deviation, thereby quantifying the differences in key electrical quantities between the convergent and non-convergent power flow data and providing a data basis for subsequent aggregation and classification of operational deviations.

[0047] Step S103: Aggregate and classify the operational deviations to determine the anomaly type corresponding to each non-converging power flow data.

[0048] In some preferred embodiments, the operational deviations are aggregated and classified to determine the anomaly type corresponding to each of the non-converging power flow data, including: Based on the node voltage deviation, line power deviation, and bus load deviation in each of the aforementioned operational deviations, construct the deviation characteristics corresponding to each of the non-converging power flow data. Cluster analysis is performed on each of the aforementioned deviation features to obtain the deviation clustering results; Based on the deviation clustering results, the anomaly type corresponding to each of the non-converging power flow data is determined, wherein the anomaly type includes high voltage deviation type, high power deviation type and comprehensive deviation type.

[0049] Specifically, after obtaining the operating deviations corresponding to each non-convergent power flow data, deviation features can be constructed based on the node voltage deviation, line power deviation, and bus load deviation within the operating deviations. These deviation features characterize the deviations of the non-convergent power flow data relative to the corresponding convergent power flow data in terms of key electrical quantities, including node voltage, line power, and bus load.

[0050] During data processing, the abnormal deviations in the non-convergent power flow data can be identified based on the error reports from the BPA software's non-convergent power flow operation results. Specifically, the BPA software's non-convergent power flow operation results can provide precise voltage limit exceedance values ​​for nodes and the load rate of heavily overloaded nodes. Therefore, by combining the operational deviations and the BPA software error reports, deviation clustering statistics can be performed on the non-convergent power flow data.

[0051] In one specific implementation, the anomaly types corresponding to non-converging power flow data can be classified according to the correspondence between cluster categories, deviation features and typical scenarios shown in Table 1.

[0052] Table 1 Classification of Non-convergent Power Flow Data Anomalies In some preferred embodiments, cluster analysis can be performed on the deviation characteristics corresponding to each non-converging power flow data to obtain deviation clustering results, and the anomaly type corresponding to each non-converging power flow data can be determined based on the deviation clustering results. The anomaly type may include high-voltage deviation type, high-power deviation type, and combined deviation type.

[0053] By using the above clustering bias statistics, the bias locking results of non-convergent power flow data relative to well-convergent power flow data can be obtained. The well-convergent power flow data can be those with a small number of power flow calculation iterations. Based on the bias locking results, self-locking check objects for various types of node devices corresponding to the power flow operation mode of the non-convergent power flow data can be extracted as the basis for subsequent preliminary convergence improvement strategies. These self-locking checks for various types of node devices can include one or more of the following: branch parameter checks, transformer parameter checks, node injection quantity checks, and branch power rationality checks based on DC power flow.

[0054] Step S104: Based on the anomaly type, determine multiple inspection objects in the power system, and perform self-locking checks on each inspection object to obtain self-locking check results.

[0055] In some preferred embodiments, a self-locking check is performed on each of the inspection objects to obtain a self-locking check result, including: Determine the object type corresponding to each of the aforementioned inspection objects; According to the object type, one or more of the following methods are performed on each of the inspection objects: line parameter inspection method, transformer parameter inspection method, node injection quantity inspection method, or branch power rationality inspection method based on DC power flow, to obtain the self-locking inspection result.

[0056] Therefore, by selecting line parameter checks, transformer parameter checks, node injection quantity checks, or branch power rationality checks based on the type of the object being checked, this application can narrow the scope of checks and improve the targeting and processing efficiency of self-locking checks compared to the indiscriminate inspection of equipment parameters and operating data in the prior art.

[0057] In some preferred embodiments, the line parameter checking method includes: Obtain the line parameters corresponding to the object under inspection, wherein the line parameters include line resistance, line reactance, line length and line voltage level; Based on the line parameters and the line length, the unit length line parameters of the inspection object are determined, and the unit length line parameters are compared with a preset line parameter range to obtain the line unit length parameter inspection result; Based on the line resistance and the line reactance, the resistance-to-reactance ratio of the object under inspection is determined, and the resistance-to-reactance ratio is compared with a preset resistance-to-reactance ratio range to obtain the line resistance-to-reactance ratio inspection result; Based on the line reactance and the voltage level corresponding to the inspection object, the estimated line length corresponding to the inspection object is determined, and the estimated line length inspection result is obtained based on the estimated line length and the preset length judgment condition. The line parameter inspection result is obtained based on one or more of the line unit length parameter inspection result, the line resistance-inductance ratio inspection result, and the estimated line length inspection result.

[0058] It should be noted that line parameters are crucial in power flow calculations. In the initial production simulation analysis, the impact of these core line parameters on power flow convergence was not considered. Incorrectly entered line parameters can easily lead to non-convergence. In power flow data, line parameters include line resistance, line reactance, line conductance, and line susceptance. Although the parameters differ between different line models, certain patterns exist that can be used to check and verify these parameters.

[0059] Specifically, in this embodiment, the line parameter checking method includes line unit length parameter checking, line resistance-to-reactance ratio checking, and line length estimation checking.

[0060] For checking line unit length parameters, the line length information corresponding to the object being checked can be obtained, and the resistance, reactance, conductance, and susceptance per unit length of the line can be calculated based on this information. When the line model is known, the calculated line unit length parameters can be compared with the standard parameters of the same model; when the line model is unknown, the calculated line unit length parameters can be compared with typical line parameters. If the comparison results indicate that the line parameters are incomplete or significantly exceed reasonable ranges, the line can be output as a parameter anomaly line to prompt for parameter verification. This method can check line parameters relatively accurately, but it requires parameters other than those used in power flow calculations, such as line length and line model.

[0061] For checking the line resistance-to-reactance ratio, the resistance-to-reactance ratio can be calculated based on the line resistance and reactance of the object under inspection, and then compared with a preset resistance-to-reactance ratio range or a preset threshold value. When the resistance-to-reactance ratio does not meet the preset range or threshold value, the corresponding line can be filtered out as a line with potentially incorrect line parameters. This method does not require parameters other than line length and line type for power flow calculation, making it convenient to use in conventional power flow calculation data.

[0062] For estimating line length, line data can be read from the power flow data, and typical line reactance parameters corresponding to different voltage levels can be read from the interface file. Since line reactance parameters are key parameters in the power flow data and significantly impact power flow calculations, the line length corresponding to the object under inspection can be estimated based on these parameters and the typical line reactance parameters for the corresponding voltage level. If the actual line length exists in the power flow data, the estimated line length can be compared with the actual line length, and lines with significantly different lengths can be listed. If the actual line length does not exist in the power flow data, lines whose estimated length exceeds a preset length threshold can be listed to prompt verification of the line parameters.

[0063] By checking the line unit length parameter, line resistance-to-reactance ratio, and calculated line length as described above, the line parameter check results can be obtained. Since line parameters differ between different models, these check results can be used to approximate whether the line parameters are reasonable. They are particularly helpful in identifying abnormal line parameter magnitudes caused by per-unit parameter conversion errors or data copying errors, thus providing a basis for subsequent correction of erroneous parameters and improving the convergence of power flow calculations.

[0064] In some preferred embodiments, the transformer parameter checking method includes: Obtain the transformer parameters corresponding to the object under inspection, wherein the transformer parameters include transformer model, transformer turns ratio and voltage level; Based on the transformer model, determine the standard transformer parameters corresponding to the inspection object, and compare the transformer parameters with the standard transformer parameters to obtain the transformer standard parameter inspection results; Based on the transformer turns ratio, determine the per-unit turns ratio corresponding to the inspection object, and obtain the transformer per-unit turns ratio inspection result based on the comparison result between the per-unit turns ratio and the preset per-unit turns ratio range; The short-circuit voltage percentage corresponding to the inspection object is calculated based on the transformer parameters, and the typical short-circuit voltage percentage range corresponding to the inspection object is determined based on the voltage level. The short-circuit voltage percentage is compared with the typical short-circuit voltage percentage range to obtain the converted short-circuit voltage inspection result. The transformer parameter inspection results are obtained based on one or more of the transformer standard parameter inspection results, the transformer per-unit turns ratio inspection results, and the converted short-circuit voltage inspection results.

[0065] It should be noted that when the object being inspected is a transformer, a transformer parameter inspection method can be performed on the object to obtain the transformer parameter inspection results. Transformer parameters may include the transformer's equivalent resistance, equivalent reactance, equivalent conductance, equivalent susceptance, and turns ratio. Incorrectly entered transformer parameters may affect the convergence of power flow calculations. Therefore, the transformer parameters can be checked and verified based on the patterns existing between them.

[0066] Specifically, the transformer parameter checking method includes checking the transformer standard parameters, checking the transformer per-unit turns ratio, and checking the converted short-circuit voltage.

[0067] For checking transformer standard parameters, the transformer model information corresponding to the object being checked can be obtained, and the standard parameters of the same model transformer can be determined based on the transformer model information. The transformer parameters corresponding to the object being checked are compared with the standard parameters. If the comparison results show that the transformer parameters are incomplete or significantly exceed the reasonable range, the transformer can be output as a transformer with abnormal parameters to prompt the verification of the transformer parameters.

[0068] For checking the per-unit turns ratio of transformers, the per-unit turns ratio of the transformer can be calculated based on the turns ratio of the transformer corresponding to the object being checked, and then compared with a preset per-unit turns ratio range. Since the per-unit turns ratio value of a transformer usually does not deviate too much from 1, if the per-unit turns ratio significantly deviates from the preset per-unit turns ratio range, the corresponding transformer can be screened out as a transformer that may have an incorrectly entered turns ratio. This method does not require parameters other than transformer model for power flow calculation, making it convenient to use in conventional power flow data.

[0069] For short-circuit voltage verification, transformer data can be read from the power flow data, and the typical short-circuit voltage percentage range for different voltage levels can be retrieved from the interface file. Since transformer parameters in power flow calculations are typically entered as per-unit values, errors in parameter entry mainly include incorrect per-unit parameter conversion and data copying errors. Therefore, the short-circuit voltage percentage of the transformer can be calculated from the transformer parameters in the power flow data. Then, based on the voltage level of the transformer, the calculated short-circuit voltage percentage can be compared with the typical short-circuit voltage percentage range for that voltage level. If the comparison results show a significant difference, the transformer can be listed to prompt verification of its parameters.

[0070] The transformer parameter check results can be obtained by checking the transformer standard parameters, the transformer per-unit turns ratio, and the converted short-circuit voltage as described above.

[0071] In some preferred embodiments, the node injection amount checking method includes: Obtain node injection data corresponding to multiple inspection objects, wherein the node injection data includes node load data or node power generation data; Based on the node load data or the node power generation data, determine the node injection amount corresponding to each of the inspected objects; The node injection amount corresponding to each of the inspection objects is compared with the preset injection amount range to determine the target node whose node injection amount exceeds the preset injection amount range; Based on the target node, the result of the node injection amount check is obtained.

[0072] It should be noted that when the object type of the object being checked is a node, the node injection quantity check method can be performed on the object to obtain the node injection quantity check result. The node injection quantity check is used to check the load and power injection quantities of nodes in the power flow data. Unlike equipment parameters such as line parameters and transformer parameters, node injection quantities are usually variables entered by the calculator and do not have fixed standard values, making it difficult to determine their correctness through direct comparison. Therefore, the reasonableness of the node injection quantity can be approximated based on its magnitude and power factor.

[0073] Specifically, the node injection quantity checking method may include load power factor checking and node injection quantity size checking.

[0074] For load power factor checks, load data corresponding to the object under inspection can be read from the power flow data, and the load power factor range can be read from the interface file. The load power factor corresponding to the object under inspection is calculated based on the load data, and then compared with the calculated load power factor range. If the comparison result indicates that the load power factor exceeds the load power factor range, the corresponding node can be listed as an abnormal load power factor node, prompting a check of the load data for that node.

[0075] For checking the size of node injection volume, the generation and load data corresponding to the object to be checked can be read from the power flow data, and the injection volume check range can be read from the interface file. The node injection volume corresponding to the object to be checked is determined based on the generation and load data, and then compared with the injection volume check range. If the comparison result shows that the node injection volume exceeds the injection volume check range, the corresponding node can be listed as a node with an excessively large injection volume, prompting a check of the node's generation or load data.

[0076] The node injection quantity check results can be obtained through the above-mentioned load power factor check and node injection quantity size check. These results can include nodes whose load power factor exceeds a preset range, nodes whose node injection quantity exceeds a preset range, nodes with abnormal power generation injection quantity, or nodes with abnormal load injection quantity. It should be noted that the node injection quantity check results are used to approximately determine whether the node injection quantity is reasonable. The listed nodes may not necessarily be incorrect; rather, they are used to identify nodes where the node injection quantity may have been entered incorrectly. This assists calculation personnel in further verifying the relevant node data and provides a basis for subsequently correcting the power flow operation mode corresponding to non-convergent power flow data.

[0077] In some preferred embodiments, the branch power rationality check method based on DC power flow includes: Based on the power flow operation mode corresponding to the non-converging power flow data, the node load and load correction coefficient of the power system are obtained; The node load is corrected according to the load correction factor to obtain the corrected node load; A DC power flow model for the power system is established based on the corrected node load. Based on the DC power flow model, calculate the branch load rate of the branch corresponding to each of the inspected objects; The branch load rate of each branch corresponding to the inspection object is compared with a preset load rate threshold, and the target branches whose branch load rate exceeds the preset load rate threshold are selected. Based on the target branch, the results of the branch power rationality check are obtained.

[0078] In some preferred embodiments, when the object type of the object to be inspected is a branch, a branch power rationality inspection method based on DC power flow can be performed on the object to be inspected to obtain the branch power rationality inspection result.

[0079] It should be noted that power flow calculations typically employ nonlinear AC power flow models and iterative methods such as Newton's method and PQ decomposition. When the power system network is large, AC power flow calculations are slow and prone to convergence issues; even after convergence, it is difficult to directly identify the cause. DC power flow, by simplifying the nonlinear power flow problem into a linear circuit problem, is more convenient to calculate, requires no iteration, and avoids the convergence problems inherent in AC power flow iterative calculations.

[0080] For a normally operating power system, node voltages are typically near rated voltages, and the phase angle difference between the two ends of a branch is small. For a transmission network, branch resistance is usually much smaller than branch reactance. Based on these characteristics, DC power flow calculation results can be used to approximately reflect the active power distribution of branches in a power system. In some implementations, when AC power flow does not converge, DC power flow can be used to quickly calculate the active power of each branch, obtaining an approximate active power distribution. For heavily loaded or overloaded branches, the power flow distribution can be optimized by improving the power flow injection amount and reactive power configuration to promote power flow re-convergence.

[0081] Specifically, a DC power flow model corresponding to the power system is established. See [link / reference] Figure 2 , Figure 2 This is a schematic diagram of the DC power flow equivalent model provided in the embodiments of this application. The DC power flow model can be simplified from the AC power flow model.

[0082] It should be noted that in the equivalent model of transmission lines, nodes The active power equation can be expressed as: in, For nodes The active power injected; For nodes The voltage amplitude; For nodes Connected nodes The voltage amplitude; Represents a node For nodes Adjacent nodes that have branch connections; , The first node in the admittance matrix is ​​the... Line number The real and imaginary parts of column elements; For nodes With nodes The voltage phase angle difference between them. Any branch The active power can be expressed as: in, branch road Non-standard transformer turns ratio; branch road The phase angle difference of the node voltages at both ends; , Let be the real and imaginary parts of the nodal admittance matrix elements. Among them, branches... The admittance can be expressed as: In the formula, in the formula and branch road The resistance and reactance. When i=j, the elements of the nodal admittance matrix can be expressed as: In practical high-voltage power grids, branch conductance can be considered relatively small compared to branch susceptance, voltage phase angle difference is small, and node voltage amplitude is close to 1.0. Simultaneously, line resistance and all branches to ground can be neglected. Based on these simplified conditions, we can obtain... In contrast It can be considered The voltage phase angle difference is very small and can be considered as... , Node voltage amplitude Meanwhile, the line resistance and all branches to ground can be neglected, thus yielding the DC power flow equations: in ,here and They are nodes Generator output and load.

[0083] Furthermore, the DC power flow equations can be written in matrix form: In the formula, P is the node injected power vector, and θ is the node voltage phase angle vector. After determining the node voltage phase angle vector, the DC branch power can be calculated. The DC branch power can be expressed as: Furthermore, its matrix form can be expressed as: in: This is the vector formed by the active power flow of each branch. It is a diagonal array composed of the admittances of each branch ( a branch road for (square matrix), This represents the phase angle difference vector between the two ends of each branch. Given the system operating mode and wiring configuration, we obtain the following... The equation can be obtained through trigonometric decomposition or matrix inversion. This leads to the contribution of each branch line.

[0084] As can be seen from the above model, the DC power flow method simplifies the power flow problem of a nonlinear power system into a linear circuit problem. It has the characteristics of simple and fast calculation and is convenient for real-time calculation. It can be used to approximate the active power of branches when the AC power flow does not converge.

[0085] It should be noted that DC power flow has a certain error compared to AC power flow. The error in DC power flow, compared to AC power flow, is related to network structure, operating status, and other factors. The error in DC power flow is related to the following factors: It is closely related to the degree of deviation of the entire network voltage from 1; generally, the error increases with the increase of the voltage standard deviation. The error in DC power flow increases with the increase of network resistance. The error gradually decreases with the increase of the X / R value. Typically, when X / R is greater than 4, the error is basically constant. The error in both DC and AC power flow increases with the increase of network losses, showing a basically linear growth trend. However, there is no discernible pattern between the error and the proportion of reactive power flow in the network. The error in DC power flow is the result of the combined effect of the above-mentioned indicators; the quality of a single indicator cannot provide an accurate assessment of the final error result.

[0086] For large-scale transmission networks, the overall network voltage is generally around 1.0 pu, and the branch reactance is much greater than the branch resistance. Therefore, network losses are a major factor affecting DC power flow error. In AC power flow, the system's allocated power generation is usually greater than the load due to network losses; however, in DC power flow, this difference is typically borne by the balancing machine. When the system is large, network losses can be significant, easily leading to increased errors in branches surrounding the balancing machine. Therefore, a load correction factor can be introduced into conventional DC power flow to reduce errors caused by network losses.

[0087] Specifically, network loss coefficients can be preset according to zones. Adjust the node load: in, This represents the corrected nodal load used in DC power flow model calculations; This represents the original node load in the power flow operation mode corresponding to the non-convergent power flow data. Through the above load correction, network losses can be distributed to load nodes, avoiding the problem of large local errors caused by the balancing machine bearing all network losses. After establishing the DC power flow model and calculating the DC power flow results, a branch power rationality check can be further performed. Specifically, a DC power flow model can be formed, the DC power flow can be calculated, and the branch power of each branch can be obtained. For line branches, the rated current of the line is usually given, and the rated transmission capacity of the line can be calculated based on the line voltage level and the rated current. in: For line voltage level, This refers to the rated current of the line. For transformer branches, the rated capacity is usually given, so the rated capacity of the transformer can be directly used as the rated capacity of the branch.

[0088] Furthermore, the branch load factor can be calculated based on the branch power and rated capacity obtained from the DC power flow calculation, and then compared with a preset load factor threshold. Considering the inherent errors in DC power flow calculations and the fact that DC power flow does not include reactive power, the preset load factor threshold can be appropriately lowered while retaining a certain margin when selecting branches. When the load factor of a branch exceeds the preset load factor threshold, that branch can be identified as a target branch and output for further analysis.

[0089] By conducting the above-mentioned branch power rationality check based on DC power flow, an approximate branch active power distribution can be quickly obtained when AC power flow does not converge, and branches with high risk of heavy load or overload can be screened out. For non-convergence problems caused by equipment parameters, more reasonable power flow mode data can be obtained based on the check results. After correcting relevant parameters or adjusting power flow injection and reactive power configuration, AC power flow calculation can be carried out again to observe the convergence improvement effect.

[0090] Step S105: Based on the self-locking check result, the power flow operation mode corresponding to the non-converged power flow data is corrected, and the power flow is recalculated to obtain power flow data with improved convergence.

[0091] Specifically, after obtaining the self-locking check result, the abnormal cause corresponding to the non-converging power flow data can be determined based on the self-locking check result, and the power flow operation mode corresponding to the non-converging power flow data can be corrected based on the abnormal cause.

[0092] After completing the above corrections, the AC power flow calculation can be re-performed based on the corrected power flow operation mode to obtain the corresponding power flow calculation results. These results are then output as the power flow data with improved convergence. By re-performing the power flow calculation on the corrected operation mode, the effectiveness of the correction measures in improving power flow convergence can be verified, and more reasonable power flow calculation results can be obtained.

[0093] Therefore, by utilizing the results of the checks on the rationality of line parameters, transformer parameters, node injection quantities, and branch power, we can generate more reasonable power flow operation data to address non-convergence issues caused by abnormal equipment static parameters, abnormal node injection quantities, or unreasonable branch power distribution. By re-performing power flow calculations and observing the convergence improvement effect, we can obtain power flow data with improved convergence.

[0094] For a clearer illustration of this embodiment, see [link to example]. Figure 3 , Figure 3 This is a schematic diagram illustrating the core parameter optimization process based on self-locking checks. For example... Figure 3 As shown, after obtaining the timing start-up combination and completing the clustering of convergence deviation and node partitioning, line equipment and transformer equipment can be distinguished according to the object type of the inspection object. For line equipment, the rated current of the branch is obtained and the rated power of the line is calculated; for transformer equipment, the rated capacity of the transformer branch is directly obtained. Subsequently, a self-locking preliminary check is performed on the corresponding line or transformer, and the core parameters are optimized to a reasonable range based on the check results, so as to re-perform power flow calculation and improve the convergence of power flow data.

[0095] By implementing the embodiments of this application, the operational deviations between non-convergent power flow data and convergent power flow data are aggregated and classified, and self-locking checks and correction recalculations are performed according to the anomaly type. Compared with the prior art's reliance on manual experience or uniform parameter adjustment methods to process non-convergent power flow data, this application can improve the accuracy of non-convergence cause location and enhance the convergence of power flow calculation.

[0096] Based on the above method embodiments, this application provides corresponding system embodiments.

[0097] like Figure 4 As shown, another embodiment of this application provides a power flow data convergence improvement system based on self-locking check, including: a data acquisition module 100, a deviation calculation module 200, an anomaly classification module 300, a self-locking check module 400, and a correction calculation module 500; The data acquisition module 100 is used to acquire power flow data of the power system under multiple time sections, wherein each power flow data corresponds to a power flow operation mode under one time section, and the multiple power flow data include convergent power flow data and non-convergent power flow data. The deviation calculation module 200 is used to determine the convergent power flow data corresponding to the non-convergent power flow data according to the power flow operation mode corresponding to each power flow data, and to calculate the operation deviation of each non-convergent power flow data relative to the corresponding convergent power flow data. The anomaly classification module 300 is used to aggregate and classify each of the operational deviations to determine the anomaly type corresponding to each of the non-converging power flow data. The self-locking check module 400 is used to determine multiple check objects in the power system according to the anomaly type, perform self-locking checks on each check object, and obtain self-locking check results. The correction calculation module 500 is used to correct the power flow operation mode corresponding to the non-converged power flow data according to the self-locking check result, and recalculate the power flow to obtain power flow data with improved convergence.

[0098] It is understood that the above system embodiments correspond to the method embodiments of this application, and can implement any of the above method embodiments of this application to provide a method for improving the convergence of power flow data based on self-locking checks.

[0099] 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 in this application, 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.

[0100] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for improving the convergence of power flow data based on self-locking checks, characterized in that, include: The power flow data of the power system under multiple time-series sections are acquired, wherein each power flow data corresponds to a power flow operation mode under one time-series section, and the multiple power flow data include convergent power flow data and non-convergent power flow data. Based on the power flow operation mode corresponding to each of the power flow data, determine the convergent power flow data corresponding to the non-convergent power flow data, and calculate the operation deviation of each of the non-convergent power flow data relative to the corresponding convergent power flow data. The operational deviations are aggregated and classified to determine the anomaly type corresponding to each non-converging power flow data. Based on the anomaly type, multiple inspection objects in the power system are identified, and a self-locking check is performed on each inspection object to obtain the self-locking check result. Based on the self-locking check results, the power flow operation mode corresponding to the non-convergent power flow data is corrected, and the power flow is recalculated to obtain power flow data with improved convergence.

2. The power flow data convergence improvement method based on self-locking check according to claim 1, characterized in that, The step of determining the convergent power flow data corresponding to the non-convergent power flow data based on the power flow operation mode corresponding to each of the power flow data includes: Based on the power flow operation mode corresponding to the non-convergent power flow data, determine the operation characteristics corresponding to the non-convergent power flow data; Based on the operational characteristics, convergent power flow data that meets preset matching conditions with the non-convergent power flow data is determined from the convergent power flow data and used as the convergent power flow data corresponding to the non-convergent power flow data.

3. The power flow data convergence improvement method based on self-locking check according to claim 1, characterized in that, The calculation of the operational deviation of each of the non-convergent power flow data relative to the corresponding convergent power flow data includes: The node voltage of the target node in the non-converging power flow data is calculated with the node voltage of the same target node in the corresponding convergent power flow data, and the node voltage deviation is determined based on the difference calculation result. The difference between the active power of the target line in the non-convergent power flow data and the active power of the same target line in the corresponding convergent power flow data is calculated, and the line power deviation is determined based on the difference calculation result. The relative difference between the load of the target bus in the non-converging power flow data and the load of the same target bus in the corresponding convergent power flow data is calculated, and the bus load deviation is determined based on the relative difference calculation result. The operating deviation corresponding to the non-converging power flow data is determined based on the node voltage deviation, the line power deviation, and the bus load deviation.

4. The power flow data convergence improvement method based on self-locking check according to claim 3, characterized in that, The aggregation and classification of the aforementioned operational deviations to determine the anomaly type corresponding to each of the non-converging power flow data includes: Based on the node voltage deviation, line power deviation, and bus load deviation in each of the aforementioned operational deviations, construct the deviation characteristics corresponding to each of the non-converging power flow data. Cluster analysis is performed on each of the aforementioned deviation features to obtain the deviation clustering results; Based on the deviation clustering results, the anomaly type corresponding to each of the non-converging power flow data is determined, wherein the anomaly type includes high voltage deviation type, high power deviation type and comprehensive deviation type.

5. The power flow data convergence improvement method based on self-locking check according to claim 1, characterized in that, The step of performing a self-locking check on each of the aforementioned objects to obtain the self-locking check result includes: Determine the object type corresponding to each of the aforementioned inspection objects; According to the object type, one or more of the following methods are performed on each of the inspection objects: line parameter inspection method, transformer parameter inspection method, node injection quantity inspection method, or branch power rationality inspection method based on DC power flow, to obtain the self-locking inspection result.

6. The power flow data convergence improvement method based on self-locking check according to claim 5, characterized in that, The method for checking line parameters includes: Obtain the line parameters corresponding to the object under inspection, wherein the line parameters include line resistance, line reactance, line length and line voltage level; Based on the line parameters and the line length, the unit length line parameters of the inspection object are determined, and the unit length line parameters are compared with a preset line parameter range to obtain the line unit length parameter inspection result; Based on the line resistance and the line reactance, the resistance-to-reactance ratio of the object under inspection is determined, and the resistance-to-reactance ratio is compared with a preset resistance-to-reactance ratio range to obtain the line resistance-to-reactance ratio inspection result; Based on the line reactance and the voltage level corresponding to the inspection object, the estimated line length corresponding to the inspection object is determined, and the estimated line length inspection result is obtained based on the estimated line length and the preset length judgment condition. The line parameter inspection result is obtained based on one or more of the line unit length parameter inspection result, the line resistance-inductance ratio inspection result, and the estimated line length inspection result.

7. The power flow data convergence improvement method based on self-locking check according to claim 5, characterized in that, The transformer parameter checking method includes: Obtain the transformer parameters corresponding to the object under inspection, wherein the transformer parameters include transformer model, transformer turns ratio and voltage level; Based on the transformer model, determine the standard transformer parameters corresponding to the inspection object, and compare the transformer parameters with the standard transformer parameters to obtain the transformer standard parameter inspection results; Based on the transformer turns ratio, determine the per-unit turns ratio corresponding to the inspection object, and obtain the transformer per-unit turns ratio inspection result based on the comparison result between the per-unit turns ratio and the preset per-unit turns ratio range; The short-circuit voltage percentage corresponding to the inspection object is calculated based on the transformer parameters, and the typical short-circuit voltage percentage range corresponding to the inspection object is determined based on the voltage level. The short-circuit voltage percentage is compared with the typical short-circuit voltage percentage range to obtain the converted short-circuit voltage inspection result. The transformer parameter inspection results are obtained based on one or more of the transformer standard parameter inspection results, the transformer per-unit turns ratio inspection results, and the converted short-circuit voltage inspection results.

8. The power flow data convergence improvement method based on self-locking check according to claim 5, characterized in that, The node injection volume checking method includes: Obtain node injection data corresponding to multiple inspection objects, wherein the node injection data includes node load data or node power generation data; Based on the node load data or the node power generation data, determine the node injection amount corresponding to each of the inspected objects; The node injection amount corresponding to each of the inspection objects is compared with the preset injection amount range to determine the target node whose node injection amount exceeds the preset injection amount range; Based on the target node, the result of the node injection amount check is obtained.

9. The power flow data convergence improvement method based on self-locking check according to claim 5, characterized in that, The method for checking the rationality of branch power based on DC power flow includes: Based on the power flow operation mode corresponding to the non-converging power flow data, the node load and load correction coefficient of the power system are obtained; The node load is corrected according to the load correction factor to obtain the corrected node load; A DC power flow model for the power system is established based on the corrected node load. Based on the DC power flow model, calculate the branch load rate of the branch corresponding to each of the inspected objects; The branch load rate of each branch corresponding to the inspection object is compared with a preset load rate threshold, and the target branches whose branch load rate exceeds the preset load rate threshold are selected. Based on the target branch, the results of the branch power rationality check are obtained.

10. A power flow data convergence improvement system based on self-locking checks, characterized in that, include: Data acquisition module, deviation calculation module, anomaly classification module, self-locking check module, and correction calculation module; The data acquisition module is used to acquire power flow data of the power system under multiple time-series sections, wherein each power flow data corresponds to a power flow operation mode under one time-series section, and the multiple power flow data include convergent power flow data and non-convergent power flow data. The deviation calculation module is used to determine the convergent power flow data corresponding to the non-convergent power flow data according to the power flow operation mode corresponding to each power flow data, and to calculate the operation deviation of each non-convergent power flow data relative to the corresponding convergent power flow data. The anomaly classification module is used to aggregate and classify each of the operational deviations to determine the anomaly type corresponding to each of the non-converging power flow data. The self-locking check module is used to determine multiple check objects in the power system according to the anomaly type, perform self-locking checks on each check object, and obtain self-locking check results. The correction calculation module is used to correct the power flow operation mode corresponding to the non-converged power flow data according to the self-locking check result, and recalculate the power flow to obtain power flow data with improved convergence.

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