Power grid equipment reliability evaluation method and device, computer equipment and storage medium

Abstract

The invention relates to a power grid equipment reliability evaluation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring equipment parameters and operation data of a plurality of power grid equipment in a power grid; by taking the equipment parameters and the operation data as input, calling a constructed power grid state simulation evaluation model, simulating the operation state of the power grid when different power grid equipment breaks down under different time dimensions, and obtaining the stable operation duration of the power grid under the condition that different power grid equipment breaks down under different time dimensions; based on the plurality of stable operation durations, determining the influence degree of the power grid equipment relative to the power grid under the condition that different power grid equipment breaks down under different time dimensions; wherein the power grid state simulation evaluation model is constructed based on the equipment parameters, the historical operation data and the historical fault data of the plurality of power grid equipment. The method is beneficial to improving the accuracy of reliability evaluation of the power grid equipment.

Description

Technical Field

[0001] The present application relates to the technical field of power system planning and operation, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for evaluating the reliability of power grid equipment. Background Art

[0002] As the scale and complexity of power systems increase, the safety and reliability of power grids have become the top priority in power system planning and operation. As an important criterion for evaluating the safety of power systems, the N-1 criterion requires that the system can maintain stable operation when any component fails or stops operating.

[0003] In the planning and operation of existing power systems, the N-1 criterion is widely used to evaluate the stability and reliability of power grids and power grid equipment. However, with the development of smart grid technology, the requirements for power grid stability and equipment reliability are becoming increasingly higher. The accuracy of the current inspection and evaluation methods for power grid equipment reliability is not high. Summary of the invention

[0004] Based on this, it is necessary to provide a power grid equipment reliability assessment method, device, computer equipment, computer-readable storage medium and computer program product that can improve the accuracy of power grid equipment reliability assessment in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for evaluating the reliability of power grid equipment, comprising:

[0006] Obtain equipment parameters and operating data of multiple power grid equipment in the power grid;

[0007] With equipment parameters and operation data as input, the constructed power grid status simulation and evaluation model is called to simulate the operation status of the power grid when different power grid equipment fails in different time dimensions, and the stable operation time of the power grid is obtained when different power grid equipment fails in different time dimensions;

[0008] Based on multiple stable operation durations, determine the impact of grid equipment on the grid when different grid equipment fails in different time dimensions;

[0009] Among them, the power grid status simulation evaluation model is constructed based on the equipment parameters, historical operation data and historical fault data of multiple power grid equipment.

[0010] In one embodiment, the power grid equipment reliability assessment method further includes:

[0011] Preprocess equipment parameters and operating data;

[0012] Taking the pre-processed equipment parameters and operation data as input, the constructed power grid state simulation evaluation model is called to simulate the operation state of the power grid when different power grid equipment fails in different time dimensions, and the stable operation time of the power grid is obtained;

[0013] Preprocessing includes at least one of data cleaning, format conversion and data verification.

[0014] In one embodiment, the power grid equipment reliability assessment method further includes:

[0015] The reliability of the power grid equipment is determined based on the stable operation time of the power grid when different power grid equipment fails.

[0016] In one embodiment, the power grid equipment reliability assessment method further includes:

[0017] Obtain the complementary relationship between different power grid areas and the transfer data of lower-level equipment;

[0018] Taking equipment parameters, operating data, complementary relationships and lower-level equipment transfer data as input, the constructed power grid status simulation and evaluation model is called to simulate the operating status of the power grid when different power grid equipment fails in different time dimensions, and the stable operation time of the power grid is obtained.

[0019] In one embodiment, evaluating the impact of the power grid equipment based on the fault passage time includes:

[0020] The impact degree of the grid equipment on the grid is determined based on the stable operation time and the preset impact degree rating standards.

[0021] In one embodiment, the power grid equipment reliability assessment method further includes:

[0022] Integrate the corresponding impact levels of different power grid equipment when failures occur, and generate a rating list of equipment impact levels.

[0023] In a second aspect, the present application also provides a power grid equipment reliability assessment device, comprising:

[0024] A data acquisition module, used to acquire equipment parameters and operation data of multiple power grid equipment in the power grid;

[0025] A stable operation duration determination module is used to use device parameters and operation data as inputs, call the constructed power grid state simulation evaluation model, simulate the operation state of the power grid when different power grid devices fail in different time dimensions, and obtain the stable operation duration of the power grid when different power grid devices fail in different time dimensions, wherein the power grid state simulation evaluation model is constructed based on device parameters, historical operation data, and historical failure data of multiple power grid devices;

[0026] The impact degree determination evaluation module is used to determine the impact degree of the power grid equipment on the power grid when different power grid equipment fails in different time dimensions based on multiple stable operation time periods.

[0027] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in any one of the above-mentioned power grid equipment reliability assessment method embodiments are implemented.

[0028] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in any one of the above-mentioned embodiments of the method for evaluating the reliability of power grid equipment are implemented.

[0029] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps in any one of the above-mentioned power grid equipment reliability assessment method embodiments.

[0030] The above-mentioned power grid equipment reliability assessment method, device, computer equipment, computer-readable storage medium and computer program product automatically simulate and evaluate the duration of stable operation of the power grid when different power grid equipment fails under different time dimensions by acquiring equipment parameters and operation data of multiple power grid equipment in the power grid, combining equipment parameters and operation data with the constructed power grid state simulation assessment model. On the one hand, the stable operation duration of the power grid under N-1 conditions of different equipment is simulated by the multi-time dimension simulation assessment method, which is conducive to improving the accuracy of the stability assessment of the power grid and providing richer data support for power grid planning and equipment maintenance; on the other hand, the complexity and possibility of manual operation are reduced and the calculation efficiency is improved by analyzing the model automatically. Based on the stable operation time of the power grid corresponding to multiple different equipment, the influence of different equipment on the operation of the power grid under N-1 conditions is evaluated. By quantifying the influence, it is helpful to capture the potential safety risks of power grid equipment, and then it is helpful for power grid operators to quickly identify and respond to potential safety risks, prevent and deal with them, and ensure the stability and reliability of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 An application environment diagram of a power grid equipment reliability assessment method in one embodiment;

[0033] Figure 2 A schematic diagram of a flow chart of a method for evaluating reliability of power grid equipment in one embodiment;

[0034] Figure 3 It is a flowchart of a method for evaluating reliability of power grid equipment in another embodiment;

[0035] Figure 4 A schematic diagram of a flow chart of a method for evaluating reliability of power grid equipment in another embodiment;

[0036] Figure 5 A schematic diagram of a flow chart of a method for evaluating reliability of power grid equipment in a detailed embodiment;

[0037] Figure 6 is a structural block diagram of a power grid equipment reliability assessment device in one embodiment;

[0038] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0040] N-1 criterion: This is a basic principle in the design and operation of power systems, which requires that when any component (such as lines, generators, transformers, etc.) fails or is shut down in a planned manner, the system can still maintain stable operation and normal power supply, other components are not overloaded, and the voltage and frequency are within the allowable range.

[0041] The power grid equipment reliability assessment method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0042] Specifically, the operator may upload the collected equipment parameters and operating data of multiple power grid devices in the power grid to the server 104 through the terminal 102, and then send the power grid equipment reliability assessment message to the server 104 through the terminal 102. The server 104 obtains the equipment parameters and operating data of the multiple power grid devices. Secondly, with the equipment parameters and operating data as input, the constructed power grid state simulation assessment model is called to simulate the operating state of the power grid when different power grid devices fail in different time dimensions, and the stable operation time of the power grid when different power grid devices fail in different time dimensions is obtained. Finally, based on multiple stable operation time periods, the degree of influence of the power grid equipment on the power grid is determined when different power grid devices fail in different time dimensions.

[0043] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0044] In an exemplary embodiment, Figure 2 As shown, a method for evaluating the reliability of power grid equipment is provided. Figure 1 The server 104 in FIG. 1 is used as an example to illustrate the process, including the following S200 to S600. Among them:

[0045] S200, obtaining equipment parameters and operation data of multiple power grid equipment in the power grid.

[0046] Among them, the grid equipment in the power grid may include transformers, circuit breakers, transmission lines, distribution lines, relay protection equipment and distribution boxes, etc.

[0047] Equipment parameters may include equipment rated parameters and equipment geographic location. It is understandable that different power grid equipment has different equipment rated parameters. Exemplarily, the equipment rated parameters of the transformer include rated capacity, rated voltage, rated current and rated frequency, etc.; the equipment rated parameters of the circuit breaker include the model of the circuit breaker, rated voltage, rated current, short-circuit capacity, etc.; the equipment rated parameters of the transmission line include the model of the cable, the type of conductor, the cross-sectional area of ​​the conductor, the line length, the wiring method, etc. The equipment parameters can be obtained by collecting data through manual on-site surveys, digital management systems, etc.

[0048] Operation data may include real-time operation data, operation status and maintenance logs of power grid equipment. Real-time operation data may include load data, power generation data, line flow and voltage level. Maintenance logs may include equipment failure records. Equipment failure records may include failure time, location, type, handling process and results. Operation data of power grid equipment may be collected through data acquisition terminals and smart meters.

[0049] In practical applications, the equipment parameters and operating data of multiple different power grid equipment in the power system can be obtained through a digital management system.

[0050] S400, taking equipment parameters and operation data as input, calls the constructed power grid status simulation and evaluation model, simulates the operation status of the power grid when different power grid equipment fails in different time dimensions, and obtains the stable operation time of the power grid when different power grid equipment fails in different time dimensions, wherein the power grid status simulation and evaluation model is constructed based on equipment parameters, historical operation data and historical fault data of multiple power grid equipment.

[0051] In practical applications, first, different power grid state simulation and evaluation sub-models can be constructed for multiple different time dimensions. For example, based on the equipment parameters, historical operation data and historical fault data of the power grid equipment, the time series model and the power grid model, a daily-scale power grid state simulation and evaluation sub-model is constructed to calculate the stable operation time of the power grid when different devices are out of operation due to faults. Specifically, the power grid state simulation and evaluation models of different time dimensions can include a monthly-scale power grid state simulation and evaluation sub-model, a monthly-scale power grid state simulation and evaluation model and an annual-scale power grid state simulation and evaluation model.

[0052] It is understandable that the power grid state simulation and evaluation sub-models at different time scales can provide different levels of information to assist power system planners and operators in planning and adjusting according to the power grid demand at different times. For example, the power grid state simulation and evaluation sub-model at the daily scale can be based on hours, focusing on the detailed operation within a day, which helps to assist and optimize short-term power dispatching decisions based on the stable operation time at the daily scale to ensure the balance of supply and demand of the power grid; the power grid state simulation and evaluation sub-model at the monthly scale is based on days, focusing on the operation within a month, which helps to cope with seasonal load changes and fluctuations in renewable energy, and optimize resource allocation; the power grid state simulation and evaluation sub-model at the annual scale can be based on weeks or months, focusing on the operation within a year, which helps long-term power grid planning and investment decisions.

[0053] After building the grid state simulation and evaluation model, the built grid state simulation and evaluation model is called, and the equipment parameters and operation data are respectively input into each grid state simulation and evaluation sub-model. Each sub-model obtains the predicted value of the grid operation data (such as load prediction value and power generation prediction value) according to the time series model. Based on the grid model simulation, the scenarios of different equipment failing and exiting operation at different times are simulated. Dynamic simulation is performed according to the predicted value of the operation data of the time series model. The stability of the grid operation is evaluated in combination with the performance indicators of the grid operation (such as overload index and voltage deviation index, etc.), and the stable operation time of the grid under each fault scenario is calculated.

[0054] S600, based on multiple stable operation time periods, determining the impact of the power grid equipment on the power grid when different power grid equipment fails in different time dimensions.

[0055] In practical applications, after obtaining multiple stable operation time periods, the historical fault records of different power grid devices can be combined to determine the impact of the power grid equipment relative to the power grid when different power grid equipment fails in different time dimensions. Specifically, the criticality of the power grid equipment relative to the power grid can be obtained based on the number of faults and fault levels in the historical fault records of the power grid equipment. For example, a scoring mechanism is set for the number of faults and fault levels and weights are assigned, and the criticality score of the power grid equipment relative to the power grid is obtained based on the score and weight. It can be understood that the higher the criticality score, the higher the criticality of the power grid equipment in the power grid.

[0056] Score the stable operation time corresponding to the power grid equipment to obtain a stability score. The scoring method can be to set a stable operation time threshold range corresponding to multiple scoring levels for each time dimension, and determine the stability score of the power grid equipment when a fault occurs in different time dimensions according to the stable operation time threshold range satisfied by the stable operation time corresponding to the power grid equipment. Assign an impact assessment weight to the stability score and criticality score of the power grid equipment, and for each time dimension, calculate the impact score of different power grid equipment relative to the power grid according to the impact assessment weight, thereby quantifying the impact of the power grid equipment relative to the power grid. It can be understood that the higher the impact score, the greater the impact of the power grid equipment relative to the power grid.

[0057] In the above-mentioned power grid equipment reliability assessment method, by obtaining the equipment parameters and operation data of multiple power grid equipment in the power grid, combining the equipment parameters and operation data with the constructed power grid state simulation assessment model, the time for the power grid to maintain stable operation when different power grid equipment fails in different time dimensions is automatically simulated and evaluated. On the one hand, the stable operation time of the power grid under N-1 conditions of different equipment is simulated by the multi-time dimension simulation assessment method, which is conducive to improving the accuracy of the stability assessment of the power grid and providing richer data support for power grid planning and equipment maintenance; on the other hand, through the model automation analysis, the complexity and possibility of manual operation are reduced, and the calculation efficiency is improved. Based on the stable operation time of the power grid corresponding to multiple different equipment, the influence of different power grid equipment on the operation of the power grid under N-1 conditions is evaluated. By quantifying the influence, it is helpful to capture the potential safety risks of power grid equipment, which in turn helps power grid operators to quickly identify and respond to potential safety risks, prevent and deal with them, and ensure the stability and reliability of power grid operation.

[0058] In order to improve the accuracy of stability assessment, in an exemplary embodiment, the method for assessing the stability of power grid equipment further includes S320 to S340.

[0059] S320, preprocessing the equipment parameters and operation data, wherein the preprocessing includes at least one of data cleaning, format conversion, and data verification.

[0060] In practical applications, after obtaining the equipment parameters and operation data of the power grid equipment, at least one of data cleaning, data conversion and data verification can be performed on the equipment parameters and operation data. Specifically, data cleaning includes removing duplicate data, processing missing values ​​and processing abnormal values, etc.; data conversion includes date and time format conversion, numerical format conversion, unit conversion and data normalization, etc.; data verification includes integrity verification and data consistency verification, etc.

[0061] S340, using the pre-processed equipment parameters and operation data as input, calls the constructed power grid state simulation evaluation model to simulate the operation state of the power grid when different power grid equipment fails in different time dimensions, and obtains the stable operation time of the power grid.

[0062] In practical applications, after obtaining the equipment parameters and operation data of the preprocessed power grid equipment, the preprocessed equipment parameters and operation data are used as input to call the constructed power grid state simulation evaluation model to determine the stable operation time of the power grid when different power grid equipment fails in different time dimensions. In this embodiment, the method for determining the stable operation time of the power grid when different power grid equipment fails in different time dimensions can refer to the above-mentioned method for obtaining the stable operation time of the power grid when the power grid equipment fails in different time dimensions. The specific process refers to the implementation step of S400, which will not be repeated here.

[0063] In this embodiment, before the simulation evaluation and analysis, the accuracy of the simulation analysis of the stable operation time of the power grid for different power grid equipment is improved by preprocessing the equipment parameters and operation data, which is beneficial to improving the accuracy of the assessment of the impact of the equipment on the power grid based on the stable operation time.

[0064] In an exemplary embodiment, the method for evaluating the reliability of power grid equipment further includes S360.

[0065] S360, determining the reliability of the power grid equipment based on the stable operation time of the power grid when different power grid equipment fails.

[0066] In practical applications, after obtaining the stable operation time of multiple power grids, the stable operation time of different time dimensions corresponding to each power grid device can be analyzed to determine the reliability of the power grid device. Specifically, data standardization can be performed on the stable operation time corresponding to the power grid device under different time dimensions, and the stable operation time of different time dimensions can be combined by weighted average to form a comprehensive scoring mechanism, such as assigning weights according to the importance of each time scale, and then calculating the weighted average, and evaluating the stability of each device according to the comprehensive score: high stability: the comprehensive score is above the first threshold (such as above 0.8), medium stability: the comprehensive score is between the first threshold and the second threshold (such as 0.5 to 0.8), and low stability: the comprehensive score is below the second threshold (such as below 0.5).

[0067] In this embodiment, the reliability of the power grid equipment is analyzed based on the stable operation time of the power grid when different power grid equipment fails, which helps to conduct a comprehensive safety assessment of the power grid equipment, so as to make timely adjustments and maintenance to ensure the stability and reliability of the power grid operation.

[0068] In an exemplary embodiment, Figure 3 As shown, the power grid equipment reliability assessment method further includes S520 to S540. Among them:

[0069] S520, obtaining complementary relationships between different power grid areas and lower-level equipment transfer data.

[0070] Among them, the complementary relationship indicates that in the power grid, a certain power grid device is compensated by the operation of another power grid device after it stops operating. The complementary relationship may include the association relationship of power grid devices with complementary relationships. The lower-level device transfer data indicates that in the power grid, when a certain power grid device fails or stops operating, its load can be taken over by the lower-level power grid device. The lower-level device transfer data may include the association relationship of power grid devices with the lower-level device transfer relationship.

[0071] In practical applications, the network topology file or CIM (Common Information Model) model of the power grid can be used to identify power grid equipment with complementary relationships in different power grid areas, as well as equipment with subordinate equipment transfer relationships, and obtain complementary relationships and subordinate equipment transfer data.

[0072] S540, taking equipment parameters, operation data, complementary relationships and lower-level equipment transfer data as input, calls the constructed power grid status simulation and evaluation model to simulate the operation status of the power grid when different power grid equipment fails in different time dimensions, and obtains the stable operation time of the power grid.

[0073] In practical applications, different power grid state simulation evaluation sub-models may be constructed for multiple different time dimensions. For the specific model construction process, refer to the implementation steps of constructing each power grid state simulation evaluation sub-model in S400, which will not be repeated here.

[0074] After constructing the grid state simulation and evaluation model, the constructed grid state simulation and evaluation model is called, and the equipment parameters, operation data, complementary relationship and lower-level equipment transfer data are respectively input into each grid state simulation and evaluation sub-model. Each sub-model obtains the predicted value of the grid operation data (such as load prediction value and power generation prediction value) according to the time series model, and simulates the scenarios of different equipment failing and exiting operation at different times through the grid model, complementary relationship and lower-level equipment transfer data. Dynamic simulation is performed according to the predicted value of the operation data of the time series model, and the stability of the grid operation is evaluated in combination with the performance indicators of the grid operation (such as overload index and voltage deviation index, etc.), and the stable operation time of the grid under each fault scenario is calculated.

[0075] In this embodiment, in the process of calculating the stable operation time of the power grid when different power grid equipment is in N-1 conditions, the complementary relationship between the equipment and the transfer of the lower-level equipment of the equipment are combined for analysis and calculation, which improves the accuracy of determining the stable operation time of the power grid, thereby facilitating improving the accuracy of the stability assessment of the power grid equipment.

[0076] In an exemplary embodiment, Figure 4 As shown, S600 includes S620. Among them:

[0077] S620: Determine the impact degree of the power grid equipment based on the stable operation time and a preset impact degree rating standard.

[0078] Among them, the preset impact rating standards include stable operation time rating standards, voltage deviation rating standards and grid load rating standards. The impact level includes the impact rating. Among them, the stable operation time rating standards can include: Class A: stable operation time ≤ 3 hours; Class B: 3 hours < stable operation time ≤ 6 hours; Class C: 6 hours < stable operation time ≤ 12 hours; Class D: 12 hours < stable operation time ≤ 24 hours; Class E: stable operation time > 24 hours.

[0079] Among them, the voltage deviation rating standards may include: Grade A: voltage deviation <±3%; Grade B: ±3%≤voltage deviation <±5%; Grade C: ±5%≤voltage deviation <±7%; Grade D: ±7%≤voltage deviation <±10%; Grade E: voltage deviation ≥±10%.

[0080] Among them, the load rating standards of the power grid may include Grade A: no overload or slight overload (<5%); Grade B: moderate overload (5%-10%); Grade C: heavy overload (10%-20%); Grade D: severe overload (20%-30%); Grade E: extreme overload (>30%).

[0081] Among them, each rating can correspond to the following rating standards: Class A: total score>90; Class B: 80<total score≤90; Class C: 70<total score≤80; Class D: 60<total score≤70; Class E: total score≤60. The stable operation time rating standard, voltage deviation rating standard and grid load rating standard are used to determine the stable operation time score, voltage deviation score and grid load score of the grid in the event of a fault in the grid equipment.

[0082] To determine the impact of the power grid equipment, it is possible to assign weights to the scores of each rating standard, perform weighted scoring, and obtain the impact score and rating. Specifically, the impact score = w1*stable operation time score of the power grid + w2*voltage deviation score + w3*load condition score of the power grid. Among them, w1, w2, and w7 are the weights of each score, which can be adjusted according to actual conditions. For example, the stable operation time corresponding to a certain power grid equipment is 18 hours → Class B, and the stable operation time score is 85 points; the overload ratio is 8% → Class B, and the load condition score is 85 points; the voltage deviation is ±4% → Class B, and the voltage level score is 85 points. Assume that the weight distribution is as follows: stable operation time: 0.6, voltage level: 0.2, frequency stability: 0.2. Then the impact score of the power grid equipment is 85 points and the rating is Class B.

[0083] In practical applications, when simulating the operating status of the power grid when power grid equipment fails in different time dimensions, the stable operation time of the power grid when power grid equipment fails in different time dimensions can be obtained. In addition, the voltage deviation and load conditions of the power grid when power grid equipment fails can be obtained. According to the preset impact rating standard, the impact of power grid equipment on power grid operation is quantified to obtain an impact rating, which helps to capture potential safety risks of power grid equipment based on the impact rating, thereby helping power grid operators to quickly identify and respond to potential safety risks, prevent and deal with them, and ensure the stability and reliability of power grid operation.

[0084] In an exemplary embodiment, the method for evaluating the stability of power grid equipment further includes S640. In which:

[0085] S640, integrating the corresponding impact levels of the power grid equipment when different power grid equipment fails, and generating a rating list of equipment impact levels.

[0086] In actual applications, after obtaining the impact ratings corresponding to different power grid equipment, they are sorted according to the impact level for each time dimension, and the equipment parameters, operating data, impact rating and ranking of each power grid equipment are recorded to generate a detailed impact rating list.

[0087] In other implementations, after obtaining the impact levels corresponding to different grid devices, all impact levels are comprehensively evaluated to generate an impact level rating list and optimization measures. Among them, the optimization measures may include at least one of the following: increasing spare capacity: adding spare generator sets or energy storage systems to increase the spare capacity of the system; network reconstruction: adjusting the grid topology, optimizing the flow distribution, and reducing the risk of overload; strengthening protection configuration: improving the configuration and action time of the protection device to improve the self-healing ability of the system.

[0088] In this embodiment, by integrating the impact levels corresponding to different power grid equipment and generating a device impact level rating list, the impact level of each device on the power grid can be comprehensively evaluated. On the one hand, the rating list helps power grid operators to quickly identify and respond to potential safety risks, take preventive measures and handle them, and ensure the stability and reliability of power grid operation. On the other hand, the integrated rating list provides rich data support for subsequent power grid planning and equipment maintenance.

[0089] In order to make a clearer description of the power grid equipment reliability assessment method provided by the present application, the following is a specific embodiment and the attached Figure 5 To illustrate, this specific embodiment includes the following steps:

[0090] S1, obtaining equipment parameters and operation data of multiple power grid equipment in the power grid, and preprocessing the equipment parameters and operation data, wherein the preprocessing includes at least one of data cleaning, format conversion and data verification.

[0091] S2, obtains the complementary relationship between different power grid areas and the transfer data of lower-level equipment.

[0092] S3, taking equipment parameters, operating data, complementary relationships and lower-level equipment transfer data as input, calls the constructed power grid status simulation and evaluation model to simulate the operating status of the power grid when different power grid equipment fails in different time dimensions, and obtains the stable operating time of the power grid. The power grid status simulation and evaluation model is constructed based on the equipment parameters, historical operating data and historical fault data of multiple power grid equipment.

[0093] S4, based on the stable operation time and the preset impact rating standard, determine the impact degree of the grid equipment relative to the grid.

[0094] S5, integrates the corresponding impact levels of different power grid equipment when failures occur in different power grid equipment, and generates a rating list of equipment impact levels.

[0095] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0096] In an exemplary embodiment, Figure 6 As shown, a power grid equipment reliability assessment device 600 is provided, comprising: a data acquisition module 610, a stable operation time determination module 620 and an impact degree determination assessment module 630, wherein:

[0097] The data acquisition module 610 is used to acquire device parameters and operation data of multiple power grid devices in the power grid.

[0098] The stable operation time determination module 620 is used to call the constructed power grid state simulation evaluation model with device parameters and operation data as input, simulate the operation state of the power grid when different power grid devices fail in different time dimensions, and obtain the stable operation time of the power grid when different power grid devices fail in different time dimensions, wherein the power grid state simulation evaluation model is constructed based on device parameters, historical operation data and historical fault data of multiple power grid devices.

[0099] The impact degree determination evaluation module 630 is used to determine the impact degree of the power grid equipment on the power grid when different power grid equipment fails in different time dimensions based on multiple stable operation time periods.

[0100] In an exemplary embodiment, the data acquisition module 610 is also used to pre-process the equipment parameters and operation data, and the pre-processing includes at least one of data cleaning, format conversion and data verification.

[0101] The stable operation duration determination module 620 is also used to call the constructed power grid state simulation evaluation model with pre-processed equipment parameters and operation data as input, simulate the operation state of the power grid when different power grid equipment fails in different time dimensions, and obtain the stable operation duration of the power grid.

[0102] In an exemplary embodiment, the power grid equipment reliability assessment apparatus 600 further includes a reliability determination module 640 for determining the reliability of the power grid equipment based on the stable operation time of the power grid when failures occur in different power grid equipment.

[0103] In an exemplary embodiment, the data acquisition module 610 is also used to obtain complementary relationships between different power grid areas and the transfer data of lower-level equipment.

[0104] The stable operation duration determination module 620 is also used to call the constructed power grid status simulation evaluation model with equipment parameters, operation data, complementary relationships and lower-level equipment transfer data as inputs, simulate the operation status of the power grid when different power grid equipment fails in different time dimensions, and obtain the stable operation duration of the power grid.

[0105] In an exemplary embodiment, the impact level determination and evaluation module 630 is further configured to determine the impact level of the power grid device relative to the power grid based on the stable operation time and a preset impact level rating standard.

[0106] In an exemplary embodiment, the power grid equipment stability assessment apparatus 600 further includes a rating list generation module 640 for integrating the impact levels of the power grid equipment when different power grid equipment fails, and generating a device impact level rating list.

[0107] Each module in the above-mentioned power grid equipment reliability assessment device 600 can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0108] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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 data such as equipment parameters and operating data of power grid equipment. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication 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 method for evaluating the reliability of power grid equipment is implemented.

[0109] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0110] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps in any one of the above-mentioned power grid equipment reliability assessment method embodiments are implemented.

[0111] 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 in any one of the above-mentioned power grid equipment reliability assessment method embodiments are implemented.

[0112] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps in any one of the above-mentioned power grid equipment reliability assessment method embodiments.

[0113] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0114] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0115] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0116] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for evaluating the reliability of power grid equipment, characterized in that: The method comprises: Obtain equipment parameters and operating data of multiple power grid equipment in the power grid; Taking the equipment parameters and the operation data as input, calling the constructed power grid state simulation evaluation model, simulating the operation state of the power grid when different power grid equipment fails in different time dimensions, and obtaining the stable operation time of the power grid when different power grid equipment fails in different time dimensions; Based on the plurality of stable operation durations, determining the degree of influence of the power grid equipment on the power grid when different power grid equipment fails in different time dimensions; The power grid status simulation evaluation model is constructed based on equipment parameters, historical operation data and historical fault data of multiple power grid equipment.

2. The method according to claim 1, characterized in that The method further comprises: Preprocessing the equipment parameters and the operation data; Taking the preprocessed equipment parameters and the operating data as input, calling the constructed power grid state simulation evaluation model, simulating the operating state of the power grid when different power grid equipment fails in different time dimensions, and obtaining the stable operating time of the power grid; The preprocessing includes at least one of data cleaning, format conversion and data verification.

3. The method according to claim 2, characterized in that The method further includes: determining the reliability of the power grid equipment based on the stable operation time of the power grid when different power grid equipment fails.

4. The method according to claim 1, characterized in that The method further comprises: Obtain complementary relationships between different power grid areas and data on lower-level equipment transfer; Taking the equipment parameters, the operating data, the complementary relationship and the lower-level equipment transfer data as input, the constructed power grid status simulation and evaluation model is called to simulate the operating status of the power grid when different power grid equipment fails in different time dimensions, and obtain the stable operation time of the power grid.

5. The method according to any one of claims 1 to 4, characterized in that: The determining, based on a plurality of stable operation time periods, the degree of influence of the power grid equipment on the power grid when different power grid equipment fails in different time dimensions comprises: Based on the stable operation time and a preset impact rating standard, the impact degree of the power grid device on the power grid is determined.

6. The method according to claim 5, characterized in that The method further comprises: Integrate the impact levels of the different power grid devices when they fail, and generate a rating list of the impact levels of the devices.

7. A power grid equipment reliability assessment device, characterized in that: The device comprises: A data acquisition module, used to acquire equipment parameters and operation data of multiple power grid equipment in the power grid; A stable operation duration determination module is used to use the device parameters and the operation data as inputs, call the constructed power grid state simulation evaluation model, simulate the operation state of the power grid when different power grid devices fail in different time dimensions, and obtain the stable operation duration of the power grid when different power grid devices fail in different time dimensions, wherein the power grid state simulation evaluation model is constructed based on the device parameters, historical operation data and historical fault data of multiple power grid devices; The impact degree determination and evaluation module is used to determine the impact degree of the power grid equipment on the power grid when different power grid equipment fails in different time dimensions based on the multiple stable operation time periods.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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