A grid support capacity evaluation method and system based on electrical digital model

Abstract

The present application relates to a method and system for evaluating the support capacity of a grid based on an electrical digital model. The method includes obtaining the line connection relationship of electrical digital data and performing topological modeling of the electrical digital data to obtain an electrical digital data model based on the line connection relationship, calculating the topological criticality of each topological connection point in the electrical digital data model, selecting a number of topological key points according to the topological criticality and performing adaptive regional division to obtain topological sub-regional data centered on the topological key points, classifying and counting the electrical digital data lines of each topological sub-region according to the topological sub-regional data, and calculating the line connection structure index score of each topological sub-region, calculating the line connection structure score and electrical equipment score of each topological sub-region according to the line connection structure index score, performing data aggregation on the calculation results, and obtaining an electrical digital data scoring system for evaluating the support capacity of electrical digital data. The present application has the effect of improving the reliability of electrical digital data power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data management, and in particular to a grid support capacity evaluation method and system based on an electrical digital model. Background Art

[0002] At present, with the continuous advancement of the construction of new power systems, the operating characteristics and line connection structures of new power systems have undergone major changes compared to traditional power systems. The power supply reliability of the power system is directly related to the people's livelihood power supply guarantee capability, so it is necessary to ensure the operating reliability of the power system.

[0003] The existing power system reliability assessment is usually carried out by analyzing and organizing basic data and derived data, and based on the experience analysis of grassroots business personnel and related simplified models, the weak points in the electric digital data are checked for risks. However, with the continuous growth of the scale of electric digital data, the data calculation workload of evaluating electric digital data is also increasing. The traditional assessment method not only requires a lot of manpower and material resources, but also the assessment results depend on the analyst's knowledge reserve and personal experience of the distribution network. The single-angle and single-level assessment method is difficult to accurately discover the potential problems of electric digital data in the massive electric digital data, and there is a defect of lack of a standard assessment system for the reliability of electric digital data. Summary of the invention

[0004] In order to build a standardized evaluation system for electrical digital data and improve the reliability of electrical digital data power supply, the present application provides a grid support capacity evaluation method and system based on an electrical digital model.

[0005] In the first aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0006] A grid support capacity assessment method based on an electrical digital model, comprising:

[0007] Acquiring a line connection relationship of the electrical digital data, performing a topological modeling process on the electrical digital data according to the line connection relationship, and obtaining an electrical digital data model based on the line connection relationship;

[0008] Calculating the topological criticality of each topological connection point in the electrical digital data model, selecting a number of topological key points according to the topological criticality and performing adaptive regional division to obtain topological sub-regional data centered on the topological key points;

[0009] According to the topological sub-region data, classify and count the electrical digital data lines in each topological sub-region, and calculate the line connection structure index score of each topological sub-region;

[0010] According to the line connection structure indicator score, the line connection structure score and electrical equipment score of each topological sub-area are calculated, and the calculation results are aggregated to obtain an electrical digital data scoring system for evaluating the electrical digital data support capability.

[0011] In a preferred example, the present application can be further configured as follows: the topological criticality of each topological connection point in the electrical digital data model is calculated, a number of topological key points are selected according to the topological criticality, and adaptive regional division is performed to obtain topological regional data centered on the topological key points, specifically including:

[0012] Obtain the historical fault data and line adjacency of each topological connection point, and calculate the topological criticality of each topological connection point using formula (1). Formula (1) is as follows:

[0013] (1);

[0014] in, Represents the first The topological criticality of the topological connection points, Indicates The number of line segments with topological connection points, Indicates The historical monthly average number of faults for each line section is: Indicates The number of primary adjacent points of a topological connection point in the electrical digital data model, Indicates The number of secondary neighbors of a topological connection point in the electrical digital data model;

[0015] According to the topological criticality calculation result, several topological connection points with the best topological criticality are selected as topological critical points, and adaptive region division is performed with each topological critical point as the center to obtain topological region data with the topological critical point as the center.

[0016] In a preferred example, the present application can be further configured as follows: the electrical digital data lines of each topological sub-region are classified and counted according to the topological sub-region data, and the line connection structure index score of each topological sub-region is calculated, specifically including:

[0017] Classify and count the electrical digital data lines in each topological sub-area according to a preset line classification standard, and obtain classified data of a number of electrical digital data lines in the topological sub-area that meet the topological line classification standard;

[0018] Combined with the classification index coefficient preset in each line classification standard, the electrical digital data line classification data is subjected to structural index aggregation processing to obtain the line connection structure index score of each topological sub-area, wherein the line connection structure index score is expressed by formula (2), which is as follows:

[0019] (2);

[0020] in, Indicates The regional line connection structure index score for each topological region, , , , Respectively represent Line connection structure indicators in each topological sub-area, , , , Respectively represent the indicator weight corresponding to each line connection structure indicator, where, .

[0021] In a preferred example, the present application can be further configured as follows: according to the line connection structure index score, the line connection structure score and the electrical equipment score of each topological sub-area are calculated, and the calculation results are aggregated to obtain an electrical digital data scoring system for evaluating the electrical digital data support capability. The calculation of the line connection structure score and the electrical equipment score specifically includes:

[0022] The regional sparsity of each topological sub-region is calculated by formula (3), which is as follows:

[0023] (3);

[0024] in, Indicates The regional sparsity of the topological sub-regions, Indicates The total number of edges in the topological structure of the topological sub-regions, Indicates The total number of topological nodes in each topological sub-area;

[0025] According to the calculation result of the regional sparsity, the line connection structure score of the electrical digital data topology structure is calculated, and the line connection structure score is expressed by formula (4), which is as follows:

[0026] (4);

[0027] in, A line connection structure score representing the electrical digital data topology, Indicates the current digital data has Topological sub-regions, Indicates the current topological sub-area. Indicates The regional sparsity of the topological sub-regions, represents the sum of the sparsity of all topological regions, Indicates The regional line connection structure index score for each topological sub-region;

[0028] The electrical equipment score of each topology sub-area is calculated by formula (5), which is as follows:

[0029] (5);

[0030] in, Electrical equipment ratings representing electrical digital data topology, Represents the aging equipment rate in the digital data rack topology, Indicates the high loss distribution rate in the electrical digital data rack topology, , Indicates the insulation rate of medium and low voltage lines in the topology of the digital data rack. .

[0031] In a preferred example, the present application can be further configured as follows: according to the line connection structure indicator score, the line connection structure score and the electrical equipment score of each topology sub-area are calculated, and the calculation results are aggregated to obtain an electrical digital data scoring system for evaluating the electrical digital data support capability, wherein the calculation results are aggregated to obtain an electrical digital data scoring system for evaluating the electrical digital data support capability, specifically including:

[0032] The data aggregation results of the line connection structure score and the electrical equipment score are expressed by formula (6), and the electrical digital data scoring system for evaluating the electrical digital data support capability is obtained, where formula (6) is as follows:

[0033] (6);

[0034] in, It represents the data aggregation result of line connection structure score and electrical equipment score. , They represent the scoring weights corresponding to the line connection structure score and the electrical equipment score, .

[0035] In a preferred example, the present application may be further configured as follows: according to the line connection structure index score, the line connection structure score and the electrical equipment score of each topology sub-area are calculated, and the calculation results are aggregated to obtain the electrical digital data scoring system for evaluating the electrical digital data support capability, and further includes:

[0036] According to the topological connection relationship in the electrical digital data model, analyzing the state of the tie switches and the opposite feeder lines between the topological connection relationships, and classifying all electrical digital data lines to obtain a line classification list;

[0037] Acquire the operation data of each electric digital data line, combine the line classification list, perform feeder traversal processing on the list of directly transferable lines, and analyze whether the load rate of the opposite side transformer after being transferred to the directly transferable route exceeds a preset load rate threshold;

[0038] According to the load rate threshold analysis results, determine whether the current traversed feeder in the list of directly transferable lines can be restored after indirect transfer;

[0039] If possible, the current traversal feeder is classified into the indirect transfer list;

[0040] If not possible, the current traversal feeder is classified into the non-transferable list.

[0041] In a preferred example, the present application may be further configured as follows: after judging whether the currently traversed feeder in the list of directly transferable lines can be restored to power after being indirectly transferred out according to the load rate threshold analysis result, the application may further include:

[0042] Traverse the feeders in the indirect transfer list, and analyze whether the load rate of the opposite transformer exceeds the preset load rate threshold after it is transferred to the indirect transfer list and the load of the direct transfer line list is superimposed;

[0043] If so, the currently traversed feeder of the indirectly transferable list is classified into the non-transferable list;

[0044] According to the feeder traversal results, the risk level of the safe power supply plan of each substation's electrical digital data in the full stop or full transfer state is analyzed to obtain the risk identification data of each substation.

[0045] In the second aspect, the above invention objective of the present application is achieved through the following technical solutions:

[0046] A grid support capacity assessment system based on an electrical digital model is applied to the above-mentioned grid support capacity assessment method based on an electrical digital model, and the system comprises:

[0047] A model building module, used to obtain the line connection relationship of the electrical digital data, perform topological modeling processing of the electrical digital data according to the line connection relationship, and obtain an electrical digital data model based on the line connection relationship;

[0048] A region division module is used to calculate the topological criticality of each topological connection point in the electrical digital data model, select a number of topological key points according to the topological criticality and perform adaptive region division to obtain topological sub-region data centered on the topological key points;

[0049] An index scoring module is used to classify and count the electrical digital data lines of each topological sub-region according to the topological sub-region data, and calculate the line connection structure index score of each topological sub-region;

[0050] The system construction module is used to calculate the line connection structure score and electrical equipment score of each topological sub-area according to the line connection structure indicator score, aggregate the calculation results, and obtain an electrical digital data scoring system for evaluating the electrical digital data support capability.

[0051] On the third aspect, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0052] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for evaluating the support capacity of a grid based on an electrical digital model are implemented.

[0053] Fourthly, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0054] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for evaluating the support capacity of a grid based on an electrical digital model.

[0055] In summary, the present application includes at least one of the following beneficial technical effects:

[0056] This application constructs an electric digital data power supply safety capability assessment system from the dual dimensions of line connection structure and electrical equipment, which is convenient for grid reliability scoring. At the same time, from the dimension of power supply capacity and in combination with historical operation data sections, the possibility of full shutdown and full operation of substations is analyzed to form a grid support capability risk assessment system, which helps to improve the reliability score of electric digital data. This application divides regions based on the sparsity of the network topology structure, and configures corresponding evaluation indicators for each topological region, so as to evaluate whether the line connection structure is reasonable in different regions, and discover the weak points of the electric digital data structure through multi-level classification, forming a list of risk issues, combining the grid reliability score and the line connection structure score, and jointly building a unified and standardized multi-dimensional electric digital data support capability assessment system, which helps to improve the reliability of the grid support capability assessment.

[0057] Based on digital technology and with the goal of business digitalization and intelligence, a power supply safety capability assessment system is established to facilitate managers to quickly perceive the current operating characteristics and risks of electric digital data, provide intelligent improvement solutions, achieve accurate investment in distribution networks, improve the reliability of electric digital data power supply, and further empower the entire business field of distribution networks. The focus of improving quality and efficiency will be expanded from benefit growth to efficiency improvement and performance improvement, accelerating the digital transformation and intelligent upgrading of grid support capability assessment.

[0058] 3. This application adopts technologies such as data fusion, weak point identification, multi-dimensional data model and multi-level classification algorithm to conduct comprehensive and intelligent analysis of electric digital data from multiple dimensions. By building a digital evaluation system for electric digital data, it conducts intelligent analysis of electric digital data from multiple dimensions such as grid structure, power supply capacity, electrical equipment and transparent perception ability, so as to provide guidance for targeted reinforcement and improvement of electric digital data in combination with the evaluation results of grid support capacity. It not only helps to promote the optimization and improvement of grid architecture and enhance power supply guarantee capacity, but also provides decision support for precise investment in distribution network. It not only demonstrates the application value of big data technology in modern equipment management, but also reflects the forward-looking and practical ability of digital and intelligent development of electric digital data analysis, which can better understand the operation status and potential problems of electric digital data, formulate more accurate investment strategies and construction plans, improve the accuracy of electric digital data construction projects, and improve the reliability of electric digital data analysis and customer satisfaction with electricity consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of a generalization of a grid support capacity evaluation method based on an electrical digital model in this embodiment.

[0060] Figure 2 This is a flowchart for implementing the multi-dimensional electrical digital data support capability evaluation system of this embodiment.

[0061] Figure 3It is a schematic diagram of the implementation of step S20 of the multi-dimensional electrical digital data support capability assessment method of this embodiment.

[0062] Figure 4 It is a schematic diagram of the implementation of step S30 of the multi-dimensional electrical digital data support capability assessment method of this embodiment.

[0063] Figure 5 It is a schematic diagram of realizing line classification according to the multi-dimensional electrical digital data support capability evaluation method of this embodiment.

[0064] Figure 6 This is a structural block diagram of a grid support capacity evaluation system based on an electrical digital model in this embodiment.

[0065] Figure 7 The present invention is a schematic diagram of the internal structure of a computer device for implementing a multi-dimensional electrical digital data support capability evaluation method. DETAILED DESCRIPTION

[0066] The present application is further described in detail below in conjunction with the accompanying drawings.

[0067] In one embodiment, if Figure 1 As shown, the present application discloses a grid support capacity assessment method based on an electrical digital model, the assessment method includes topological modeling, three-dimensional percentage calculation, and full-stop and full-turn analysis. Among them, topological modeling includes regional zoning based on hierarchical association clustering, calculation of 11 line connection structure indicators and corresponding weight determination, regional indicator aggregation to form line connection structure dimension evaluation score, and combined with the electrical equipment dimension score obtained by the three-dimensional percentage calculation to construct a multi-dimensional evaluation result aggregation to form a final electrical digital data scoring system. Through the risk identification report obtained by the full-stop and full-turn analysis, a regional adaptive multi-dimensional electrical digital data support capacity assessment system is collaboratively built. Figure 2 As shown, the specific steps include:

[0068] S10: Acquire the line connection relationship of the electrical digital data, perform topological modeling processing on the electrical digital data according to the line connection relationship, and obtain an electrical digital data model based on the line connection relationship.

[0069] Specifically, the line connection relationship of the electrical digital data is obtained according to the connection relationship between the cable input and output in the electrical digital data, and each connection line is used as a topological basic unit for topological modeling, that is, the connection relationship between the lines is used as the topological association line between the points in the topology, thereby forming an electrical digital data model based on the line connection relationship.

[0070] S20: Calculate the topological criticality of each topological connection point in the electrical digital data model, select a number of topological key points according to the topological criticality, and perform adaptive regional division to obtain topological regional data centered on the topological key points.

[0071] Specifically, Figure 3 As shown, step S20 specifically includes:

[0072] S201: Obtain historical fault data and line adjacency of each topological connection point, and calculate the topological criticality of each topological connection point by formula (1). Formula (1) is as follows:

[0073] (1);

[0074] in, Represents the first The topological criticality of the topological connection points, Indicates The number of line segments with topological connection points, Indicates The historical monthly average number of faults for each line section is: Indicates The number of primary adjacent points of a topological connection point in the electrical digital data model, Indicates The number of secondary neighbors of a topological connection point in the electrical digital data model.

[0075] S202: According to the topological criticality calculation result, several topological connection points with the best topological criticality are selected as topological key points, and adaptive region division is performed with each topological key point as the center to obtain topological region data with the topological key point as the center.

[0076] Specifically, according to the topological criticality calculation results of each topological connection point, select The top 10 topological connection points with the largest values ​​are regarded as topological key points. In this embodiment, the topological key degree is the best. The value is the largest, and the corresponding number of topological key points is selected according to actual needs. Each selected topological key point is taken as the center, and the remaining topological connection points except the topological key points are taken as topological adjacent nodes. According to the adjacent relationship with the topological key point in the center, each topological key point adds an adjacent node and repeats multiple times until all topological adjacent nodes are assigned to the corresponding topological key points and there are no topological connection points that have not been assigned, thereby obtaining multiple topological sub-regional data centered on each topological key point.

[0077] S30: Classify and count the electrical digital data lines of each topological sub-region according to the topological sub-region data, and calculate the line connection structure index score of each topological sub-region.

[0078] Specifically, Figure 4 As shown, step S30 includes:

[0079] S301: Classify and count the electrical digital data lines in each topological sub-area according to a preset line classification standard, and obtain classified data of a number of electrical digital data lines in the topological sub-area that meet the topological line classification standard.

[0080] Specifically, the digital data lines in each topological sub-area are classified and counted according to the preset line classification and statistical standards, such as the number of trunk lines of 10 kV overhead or cable lines fed by substations or switch stations and responsible for the main power transmission and distribution functions is the number of main lines; the number of lines whose power supply radius exceeds the preset standard value; the 10 kV lines derived from the 10 kV main line and other than the main line are branch lines, and the number of branch lines is counted; the number of lines that are not segmented or the number of segmented users is greater than the preset standard value is the number of unreasonable power supply segmentation; the number of straight lines of cables or overhead lines is the number of unreasonable power supply segmentation. The number of unreasonable wire diameters whose diameters are greater than the preset standard values; the number of power supply restriction lines in the power grid due to sufficient power supply capacity in the power grid but small cross-sectional area of ​​the power supply line, large power consumption on the load side, resulting in low voltage; the number of special transformers without boundaries, which are composed of switch bodies and distribution automation terminals and are mainly used to cut off phase-to-phase short circuit faults and grounding faults on the user side during operation, and can be used to operate and pull load current; the first switch after the branch line branches off from the main line and before all the loads is the branch line main switch, and the number of lines without branch line main switches is counted; the number of section switches without loads in the section is the number of invalid switches.

[0081] Among them, the classification criteria for unreasonable power supply segmentation lines include: based on the electrical digital data model, determining the line and the section of the line where the transformer is located in the substation connection line through a sequential search algorithm to determine the number of transformers and the number of users in each section of the line, comparing the number of transformers in each section of the line with the corresponding preset transformer number threshold, and comparing the number of users in each section with the corresponding preset user number threshold. When the number of transformers in any section of the line exceeds the preset user number threshold, the corresponding line is marked as an unreasonable line.

[0082] In this embodiment, 11 line connection structure indicators are set, including the number of main lines, the number of lines with too long power supply radius, the number of branch lines, the number of unreasonable power supply segments, the number of unreasonable line diameters, the number of power supply restricted lines, the number of special transformers without boundary switches, the number of branch lines without main switches, the number of invalid switches, the line communication rate, and the inter-station communication rate.

[0083] Among them, line connection rate = (number of connected feeders / total number of feeders)*100%; inter-station connection rate = (number of feeders with inter-station connection / total number of feeders)*100%.

[0084] S302: Combine the classification index coefficients preset in each line classification standard, perform structural index aggregation processing on the electrical digital data line classification data, and obtain the line connection structure index score of each topology sub-area, wherein the line connection structure index score is expressed by formula (2), which is as follows:

[0085] (2);

[0086] in, Indicates The regional line connection structure index score for each topological region, , , , Respectively represent Line connection structure indicators in each topological sub-area, , , , Respectively represent the indicator weight corresponding to each line connection structure indicator, where, .

[0087] S40: Calculate the line connection structure score and electrical equipment score for each topology sub-area based on the line connection structure indicator score, perform data aggregation on the calculation results, and obtain an electrical digital data scoring system for evaluating the electrical digital data support capability.

[0088] Specifically, step S40 includes:

[0089] S401: Calculate the regional sparsity of each topological sub-region by formula (3), which is as follows:

[0090] (3);

[0091] in, Indicates The regional sparsity of the topological sub-regions, Indicates The total number of edges in the topological structure of the topological sub-regions, Indicates The total number of nodes in the topological structure of a topological sub-area.

[0092] S402: Calculate the line connection structure score of the electrical digital data topology structure according to the calculation result of the regional sparsity. The line connection structure score is expressed by formula (4). Formula (4) is as follows:

[0093] (4);

[0094] in, A line connection structure score representing the electrical digital data topology, Indicates the current digital data has Topological sub-regions, Indicates the current topological sub-region. Indicates The regional sparsity of the topological sub-regions, represents the sum of the sparsity of all topological regions, Indicates The regional line connection structure index score is calculated for each topological region.

[0095] S403: Calculate the electrical equipment score of each topology sub-area using formula (5), which is as follows:

[0096] (5);

[0097] in, Electrical equipment ratings representing electrical digital data topology, Represents the aging equipment rate in the digital data rack topology, Indicates the high loss distribution rate in the electrical digital data rack topology, , Indicates the insulation rate of medium and low voltage lines in the topology of the digital data rack. .

[0098] Specifically, step S40 also includes:

[0099] S404: The data aggregation result of the line connection structure score and the electrical equipment score is expressed by formula (6), and an electrical digital data scoring system for evaluating the electrical digital data support capability is obtained, wherein formula (6) is as follows:

[0100] (6);

[0101] in, It represents the data aggregation result of line connection structure score and electrical equipment score. , They represent the scoring weights corresponding to the line connection structure score and the electrical equipment score, .

[0102] In this embodiment, Figure 5 As shown, after step S40, the following steps are also included:

[0103] S501: According to the topological connection relationship in the electrical digital data model, the states of the tie switches and the opposite feeders between the topological connection relationships are analyzed, and all electrical digital data lines are classified to obtain a line classification list.

[0104] Specifically, according to the topological connection relationship in the electrical digital data model, the interconnection switch position and the opposite feeder status in the topological connection relationship are obtained, so as to determine the direct-transferable lines in the topological model according to the interconnection switch position and the opposite feeder status, classify the lines and construct the corresponding line classification list, forming a direct-transferable route list L1, an indirect-transferable list L2, and a non-transferable list L3, wherein the indirect-transferable line indicates a line that is transferred twice to achieve power restoration, and the non-transferable line indicates a power-off line.

[0105] S502: Acquire the operation data of each electrical digital data line, combine the line classification list, perform feeder traversal processing on the direct-convertible line list, and analyze whether the load rate of the opposite side transformer after being transferred to the direct-convertible route exceeds a preset load rate threshold.

[0106] Specifically, the operation data of each electrical digital data line is obtained, and the data is superimposed in combination with time and space information to obtain the superimposed comprehensive operation data. Each feeder in the list of direct-convertible lines is traversed according to the comprehensive operation data, and it is analyzed whether the load rate of the opposite-side transformer after being transferred to the direct-convertible route exceeds a preset load rate threshold. The preset load rate threshold in this embodiment is not more than 90%.

[0107] S503: According to the load rate threshold analysis result, it is determined whether the currently traversed feeder in the list of directly transferable lines can be restored to power after being indirectly transferred out.

[0108] Specifically, according to the load rate threshold analysis result, it is determined whether the current traversed feeder in the list of directly transferable lines can realize power restoration after indirect transfer, that is, whether the power restoration function can be realized after transfer twice.

[0109] S504: If possible, classify the current traversal feeder into the indirect transferable list.

[0110] Specifically, if the power restoration function can be realized after transferring twice, the currently traversed feeder is classified into the indirect transfer-out list.

[0111] S505: If not possible, the currently traversed feeder is classified into a non-transferable list.

[0112] Specifically, if not possible, the current traversal feeder is classified into a non-transferable list.

[0113] In this embodiment, after step S503, the following steps are further included:

[0114] S601: Traverse the feeders in the indirect transfer list, and analyze whether the load rate of the opposite transformer after being transferred into the indirect transfer list and superimposed with the load of the direct transfer line list exceeds a preset load rate threshold.

[0115] Specifically, each feeder in the indirect transfer-out list is traversed to analyze whether the load rate of the opposite-side transformer after being transferred into the indirect transfer-out list and superimposed with the load of the direct transfer line list exceeds the preset load rate threshold. The preset load rate threshold in this embodiment is not more than 90%, wherein the superimposed direct transfer line list load means that when the feeder in the direct transfer line list has been transferred out, the load rate of the opposite-side transformer of the feeder that meets the indirect transfer-out list is calculated.

[0116] S602: If yes, the currently traversed feeder in the indirect transferable list is classified into the non-transferable list.

[0117] Specifically, if the load rate of the indirect transfer-out list exceeds a preset load rate threshold, the currently traversed feeder in the indirect transfer-out list is classified into the non-transfer-out list.

[0118] S603: According to the feeder traversal results, the risk level of the safe power supply transfer scheme of each substation's electric digital data in the full stop or full transfer state is analyzed to obtain risk identification data of each substation.

[0119] Specifically, based on the feeder traversal results, combined with the superimposed comprehensive operation data and historical operation data, each substation's safe power transfer plan in the case of full stop or full transfer is checked and approved one by one to see if there are risks, so as to timely evaluate and deal with high-risk lines, realize risk identification of electrical digital data, and divide them into high risk, medium risk and low risk according to the degree of risk. Among them, the lines in the non-transferable list are evaluated as high-risk lines, the lines in the indirect transfer list are evaluated as medium-risk lines, and the lines in the direct transfer list are evaluated as low-risk lines.

[0120] The above-mentioned multi-dimensional electrical digital data support capability evaluation system can intelligently generate quantitative comprehensive evaluation results of electrical digital data support capabilities with one click. Users only need to trigger a button or command to automatically collect relevant data of electrical digital data, and use built-in evaluation algorithms and models to comprehensively quantify the support capabilities of electrical digital data.

[0121] Based on the high-quality development report on electric digital data compiled in advance by managers, a comprehensive quantitative evaluation system for electric digital data with rich levels, multiple perspectives and flexible rules is constructed using a variable weight indicator classification method. Intelligent quantitative evaluation of electric digital data is carried out in a "regional" and "step-by-step" manner, providing important reference for the optimization and upgrading of electric digital data.

[0122] Through the custom configuration function of grid risk judgment rules and calculation weights, the judgment rules and calculation weights are adaptively adjusted according to the electrical digital data structure characteristics of different regions. The risk judgment rules and weights are flexibly configured according to actual conditions to establish a comprehensive power supply reliability assessment system to meet various complex power supply needs and expand the scope of application.

[0123] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0124] In one embodiment, a grid support capacity evaluation system based on an electrical digital model is provided, and the grid support capacity evaluation system based on an electrical digital model corresponds one-to-one to the grid support capacity evaluation method based on an electrical digital model in the above embodiment. Figure 6 As shown, the grid support capacity evaluation system based on the electrical digital model includes a model building module, a regional division module, an index scoring module and a system building module. The detailed description of each functional module is as follows:

[0125] The model building module is used to obtain the line connection relationship of the electrical digital data, perform topological modeling processing on the electrical digital data according to the line connection relationship, and obtain an electrical digital data model based on the line connection relationship.

[0126] The region division module is used to calculate the topological criticality of each topological connection point in the electrical digital data model, select several topological key points according to the topological criticality and perform adaptive region division to obtain topological region data centered on the topological key points.

[0127] The index scoring module is used to classify and count the electrical digital data lines of each topological sub-region according to the topological sub-region data, and calculate the line connection structure index score of each topological sub-region.

[0128] The system construction module is used to calculate the line connection structure score and electrical equipment score of each topological sub-area according to the line connection structure indicator score, aggregate the calculation results, and obtain the electrical digital data scoring system for evaluating the electrical digital data support capability.

[0129] Preferably, the area division module specifically includes:

[0130] The criticality calculation submodule is used to obtain the historical fault data and line adjacency relationship of each topological connection point, and calculate the topological criticality of each topological connection point through formula (1). Formula (1) is as follows:

[0131] (1);

[0132] in, Represents the first The topological criticality of the topological connection points, Indicates The number of line segments with topological connection points, Indicates The historical monthly average number of faults for each line section is: Indicates The number of primary adjacent points of a topological connection point in the electrical digital data model, Indicates The number of secondary neighbors of a topological connection point in the electrical digital data model.

[0133] The region division submodule is used to select several topological connection points with the best topological criticality as topological key points according to the topological criticality calculation results, and to perform adaptive region division with each topological key point as the center to obtain topological sub-region data centered on the topological key point.

[0134] Preferably, the indicator scoring module specifically includes:

[0135] The line classification submodule is used to classify and count the electrical digital data lines in each topological sub-area according to a preset line classification standard, and obtain a number of electrical digital data line classification data in the topological sub-area that meet the topological line classification standard.

[0136] The index scoring submodule is used to combine the classification index coefficients preset in each line classification standard, perform structural index aggregation processing on the electrical digital data line classification data, and obtain the line connection structure index score of each topological sub-area, wherein the line connection structure index score is expressed by formula (2), which is as follows:

[0137] (2);

[0138] in, Indicates The regional line connection structure index score for each topological region, , , , Respectively represent Line connection structure indicators in each topological sub-area, , , , Respectively represent the indicator weight corresponding to each line connection structure indicator, where, .

[0139] Preferably, the calculation of the line connection structure score and the electrical equipment score in the system construction module specifically includes:

[0140] The sparsity calculation submodule is used to calculate the regional sparsity of each topological sub-region through formula (3). Formula (3) is as follows:

[0141] (3);

[0142] in, Indicates The regional sparsity of the topological sub-regions, Indicates The total number of edges in the topological structure of the topological sub-regions, Indicates The total number of nodes in the topological structure of a topological sub-area.

[0143] The structure score calculation submodule is used to calculate the line connection structure score of the electrical digital data topology structure according to the calculation result of the regional sparsity. The line connection structure score is expressed by formula (4), which is as follows:

[0144] (4);

[0145] in, A line connection structure score representing the electrical digital data topology, Indicates the current digital data has Topological sub-regions, Indicates the current topological sub-area. Indicates The regional sparsity of the topological sub-regions, represents the sum of the sparsity of all topological regions, Indicates The regional line connection structure index score is calculated for each topological region.

[0146] The equipment score calculation submodule is used to calculate the electrical equipment score of each topology sub-area through formula (5), which is as follows:

[0147] (5);

[0148] in, Electrical equipment ratings representing electrical digital data topology, Represents the aging equipment rate in the digital data rack topology, Indicates the high loss distribution rate in the electrical digital data rack topology, , Indicates the insulation rate of medium and low voltage lines in the topology of the digital data rack. .

[0149] Preferably, the system construction module aggregates the calculation results to obtain an electrical digital data scoring system for evaluating the electrical digital data support capability, which specifically includes:

[0150] The scoring system aggregation submodule is used to express the data aggregation results of the line connection structure score and the electrical equipment score through formula (6), and obtain the electrical digital data scoring system for evaluating the electrical digital data support capability, wherein formula (6) is as follows:

[0151] (6);

[0152] in, It represents the data aggregation result of line connection structure score and electrical equipment score. , They represent the scoring weights corresponding to the line connection structure score and the electrical equipment score, .

[0153] Preferably, after the system building module, it also includes:

[0154] The route classification submodule is used to analyze the connection switches and opposite feeder states between the topological connection relationships according to the topological connection relationships in the electrical digital data model, and classify all electrical digital data lines to obtain a line classification list.

[0155] The direct transfer load analysis submodule is used to obtain the operating data of each electrical digital data line, combine the line classification list, perform feeder traversal processing on the direct transfer line list, and analyze whether the load rate of the opposite side transformer after being transferred to the direct transfer route exceeds the preset load rate threshold.

[0156] The power restoration analysis submodule is used to determine whether the current traversed feeder in the list of directly transferable lines can be restored after indirect transfer based on the load rate threshold analysis results.

[0157] The indirect transfer classification submodule is used to classify the current traversal feeder into the indirect transfer list if possible.

[0158] The non-transferable classification submodule is used to classify the current traversal feeder into the non-transferable list if it is not possible.

[0159] Preferably, after the complex electrical analysis submodule, it also includes:

[0160] The indirect transfer load analysis submodule is used to traverse the feeders in the indirect transfer list and analyze whether the load rate of the opposite side transformer exceeds the preset load rate threshold after being transferred into the indirect transfer list and superimposed with the load of the direct transfer line list.

[0161] The non-transferable classification submodule is used to classify the current traversal feeder of the indirect transferable list into the non-transferable list if so.

[0162] The risk identification and analysis submodule is used to analyze the risk level of the safe power supply plan of each substation's electric digital data in the full stop or full transfer state according to the feeder traversal results, and obtain the risk identification data of each substation.

[0163] For the specific definition of the grid support capacity assessment system based on the electrical digital model, please refer to the definition of the grid support capacity assessment method based on the electrical digital model above, which will not be repeated here. Each module in the above-mentioned grid support capacity assessment system based on the electrical digital model can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0164] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store regional adaptive multi-dimensional electrical digital data support capability assessment data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a grid support capability assessment method based on an electrical digital model is implemented.

[0165] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for evaluating the support capacity of a grid based on an electrical digital model are implemented.

[0166] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0167] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0168] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for evaluating grid support capacity based on an electrical digital model, characterized in that: include: Acquire the line connection relationship corresponding to the electrical digital data, perform topological modeling processing on the electrical digital data according to the line connection relationship, and obtain an electrical digital data model based on the line connection relationship; Calculating the topological criticality of each topological connection point in the electrical digital data model, selecting a number of topological key points according to the topological criticality and performing adaptive regional division to obtain topological sub-regional data centered on the topological key points; According to the topological sub-region data, classify and count the electrical digital data lines in each topological sub-region, and calculate the line connection structure index score of each topological sub-region; According to the line connection structure index score, the line connection structure score and the electrical equipment score of each topological sub-area are calculated, and the calculation results are aggregated to obtain an electrical digital data scoring system for evaluating the electrical digital data support capability; The process of classifying and counting the electrical digital data lines of each topological sub-region according to the topological sub-region data and calculating the line connection structure index score of each topological sub-region specifically includes: Classify and count the electrical digital data lines in each topological sub-area according to a preset line classification standard, and obtain classified data of a number of electrical digital data lines in the topological sub-area that meet the topological line classification standard; Combined with the classification index coefficient preset in each line classification standard, the electrical digital data line classification data is subjected to structural index aggregation processing to obtain the line connection structure index score of each topological sub-area, wherein the line connection structure index score is expressed by formula (2), which is as follows: （2）； in, Indicates The regional line connection structure index score for each topological region, , , , Respectively represent Line connection structure indicators in each topological sub-area, , , , Respectively represent the indicator weight corresponding to each line connection structure indicator, where, ; Among them, according to the line connection structure indicator score, the line connection structure score and the electrical equipment score of each topology sub-area are calculated, and the calculation results are aggregated to obtain the electrical digital data scoring system for evaluating the electrical digital data support capability. The calculation of the line connection structure score and the electrical equipment score specifically includes: The regional sparsity of each topological sub-region is calculated by formula (3), which is as follows: （3）； in, Indicates The regional sparsity of the topological sub-regions, Indicates The total number of edges in the topological structure of the topological sub-regions, Indicates The total number of topological nodes in each topological sub-area; According to the calculation result of the regional sparsity, the line connection structure score of the electrical digital data topology structure is calculated, and the line connection structure score is expressed by formula (4), which is as follows: （4）； in, A line connection structure score representing the electrical digital data topology, Indicates the current digital data has Topological sub-regions, Indicates the current topological sub-region. Indicates The regional sparsity of the topological sub-regions, represents the sum of the sparsity of all topological regions, Indicates The regional line connection structure index score for each topological sub-region; The electrical equipment score of each topology sub-area is calculated by formula (5), which is as follows: （5）； in, Electrical equipment ratings representing electrical digital data topology, Represents the aging equipment rate in the digital data rack topology, Indicates the high loss distribution rate in the electrical digital data rack topology, , Indicates the insulation rate of medium and low voltage lines in the topology of the digital data rack. .

2. The grid support capacity evaluation method based on the electrical digital model according to claim 1 is characterized in that: The calculating of the topological criticality of each topological connection point in the electrical digital data model, selecting a number of topological key points according to the topological criticality and performing adaptive regional division to obtain topological regional data centered on the topological key points, specifically includes: Obtain the historical fault data and line adjacency of each topological connection point, and calculate the topological criticality of each topological connection point using formula (1). Formula (1) is as follows: （1）； in, Represents the first The topological criticality of the topological connection points, Indicates The number of line segments with topological connection points, Indicates The historical monthly average number of faults for each line section is: Indicates The number of primary adjacent points of a topological connection point in the electrical digital data model, Indicates The number of secondary neighbors of a topological connection point in the electrical digital data model; According to the calculation result of the topological criticality, several topological connection points with the best topological criticality are selected as topological critical points, and adaptive region division is performed with each topological critical point as the center to obtain topological region data with the topological critical point as the center.

3. The grid support capacity evaluation method based on the electrical digital model according to claim 1 is characterized in that: According to the line connection structure index score, the line connection structure score and the electrical equipment score of each topology sub-area are calculated, and the calculation results are aggregated to obtain an electrical digital data scoring system for evaluating the electrical digital data support capability, wherein the calculation results are aggregated to obtain an electrical digital data scoring system for evaluating the electrical digital data support capability specifically includes: The data aggregation results of the line connection structure score and the electrical equipment score are expressed by formula (6), and the electrical digital data scoring system for evaluating the electrical digital data support capability is obtained, where formula (6) is as follows: （6）； in, It represents the data aggregation result of line connection structure score and electrical equipment score. , They represent the scoring weights corresponding to the line connection structure score and the electrical equipment score, .

4. The grid support capacity evaluation method based on the electrical digital model according to claim 1 is characterized in that: After calculating the line connection structure score and the electrical equipment score of each topology sub-area according to the line connection structure indicator score, and aggregating the calculation results to obtain the electrical digital data scoring system for evaluating the electrical digital data support capability, the method further includes: According to the topological connection relationship in the electrical digital data model, analyzing the state of the tie switches and the opposite feeder lines between the topological connection relationships, and classifying all electrical digital data lines to obtain a line classification list; Acquire the operation data of each electric digital data line, combine the line classification list, perform feeder traversal processing on the list of directly transferable lines, and analyze whether the load rate of the opposite side transformer after being transferred to the directly transferable line exceeds a preset load rate threshold; According to the load rate threshold analysis results, determine whether the current traversed feeder in the list of directly transferable lines can be restored after indirect transfer; If possible, the current traversal feeder is classified into the indirect transfer list; If not possible, the current traversal feeder is classified into the non-transferable list.

5. The grid support capacity evaluation method based on the electrical digital model according to claim 4 is characterized in that: After judging whether the currently traversed feeder in the list of directly transferable lines can be restored to power after being indirectly transferred out according to the load rate threshold analysis result, the method further includes: Traverse the feeders in the indirect transfer list, and analyze whether the load rate of the opposite transformer exceeds the preset load rate threshold after it is transferred to the indirect transfer list and the load of the direct transfer line list is superimposed; If so, the currently traversed feeder of the indirectly transferable list is classified into the non-transferable list; According to the feeder traversal results, the risk level of the safe power supply transfer plan of each substation's electrical digital data in the full stop or full transfer state is analyzed to obtain the risk identification data of each substation.

6. A grid support capacity assessment system based on an electrical digital model, characterized in that: The grid support capacity assessment method based on the electrical digital model applied to any one of claims 1 to 5 above, the system comprising: A model building module, used to obtain the line connection relationship of the electrical digital data, perform topological modeling processing of the electrical digital data according to the line connection relationship, and obtain an electrical digital data model based on the line connection relationship; A region division module is used to calculate the topological criticality of each topological connection point in the electrical digital data model, select a number of topological key points according to the topological criticality and perform adaptive region division to obtain topological sub-region data centered on the topological key points; An index scoring module is used to classify and count the electrical digital data lines of each topological sub-region according to the topological sub-region data, and calculate the line connection structure index score of each topological sub-region; A system construction module, used to calculate the line connection structure score and electrical equipment score of each topological sub-area according to the line connection structure indicator score, perform data aggregation on the calculation results, and obtain an electrical digital data scoring system for evaluating the electrical digital data support capability; The indicator scoring module specifically includes: The line classification submodule is used to classify and count the electrical digital data lines in each topological sub-area according to a preset line classification standard, and obtain a number of electrical digital data line classification data in the topological sub-area that meet the topological line classification standard; The index scoring submodule is used to combine the classification index coefficients preset in each line classification standard, perform structural index aggregation processing on the electrical digital data line classification data, and obtain the line connection structure index score of each topology sub-area, wherein the line connection structure index score is expressed by formula (2), which is as follows: （2）； in, Indicates The regional line connection structure index score for each topological region, , , , Respectively represent Line connection structure indicators in each topological sub-area, , , , Respectively represent the indicator weight corresponding to each line connection structure indicator, where, ; Among them, according to the line connection structure indicator score, the line connection structure score and the electrical equipment score of each topology sub-area are calculated, and the calculation results are aggregated to obtain the electrical digital data scoring system for evaluating the electrical digital data support capability. The calculation of the line connection structure score and the electrical equipment score specifically includes: The sparsity calculation submodule is used to calculate the regional sparsity of each topological sub-region through formula (3). Formula (3) is as follows: （3）； in, Indicates The regional sparsity of the topological sub-regions, Indicates The total number of edges in the topological structure of the topological sub-regions, Indicates The total number of topological nodes in each topological sub-area; The structure score calculation submodule is used to calculate the line connection structure score of the electrical digital data topology structure according to the calculation result of the regional sparsity. The line connection structure score is expressed by formula (4), which is as follows: （4）； in, A line connection structure score representing the electrical digital data topology, Indicates the current digital data has Topological sub-regions, Indicates the current topological sub-region. Indicates The regional sparsity of the topological sub-regions, represents the sum of the sparsity of all topological regions, Indicates The regional line connection structure index score for each topological sub-region; The equipment score calculation submodule is used to calculate the electrical equipment score of each topology sub-area through formula (5), which is as follows: （5）； in, Electrical equipment ratings representing electrical digital data topology, Represents the aging equipment rate in the digital data rack topology, Indicates the high loss distribution rate in the electrical digital data rack topology, , Indicates the insulation rate of medium and low voltage lines in the topology of the digital data rack. .

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the grid support capacity assessment method based on the electrical digital model as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the grid support capacity assessment method based on an electrical digital model as described in any one of claims 1 to 5 are implemented.

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