Coordinated testing method and system for substation multi-compartment protection devices

Abstract

The application discloses a kind of substation multi-interval protection device's cooperative testing method and system.The method comprises: collecting the entity operation data of the multiple protection devices of substation entity, constructs the digital twin model of substation, generates the cooperative testing scene of multiple protection devices, determines the electrical communication path between multiple protection devices under cooperative testing scene, determines the associated protection setting value of protection device under cooperative testing scene by data layer model and electrical communication path, determines the test excitation signal sequence corresponding to associated protection setting value according to business layer model, tests multiple protection devices in substation entity to obtain the cooperative testing result corresponding to cooperative testing scene, adaptively optimizes and adjusts the protection setting value parameters of multiple protection devices, and is updated to the data layer model of digital twin model, improve the response speed and detection accuracy of protection device, ensure the safety and reliability of substation operation, realize continuous optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of substation detection, and in particular to a method and system for cooperative testing of substation multi-interval protection devices. BACKGROUND

[0002] With the continuous expansion of the scale of the power system and the improvement of the intelligent level, the safe and stable operation of the substation as an important part of the power grid is crucial to the reliability of the entire power system.

[0003] In related technologies, substation protection device testing relies on manual operation or simple automation tools, which cannot comprehensively cover all possible working conditions and cannot effectively simulate complex fault conditions and the mutual influence between devices, resulting in difficulty in efficiently and accurately completing the cooperative testing between numerous protection devices in the substation and optimizing their configurations accordingly. SUMMARY

[0004] The present application provides a method and system for cooperative testing of substation multi-interval protection devices to solve the problem of difficulty in efficiently and accurately completing the cooperative testing between numerous protection devices in the substation and optimizing their configurations accordingly in related technologies.

[0005] According to an aspect of the present application, a method for cooperative testing of substation multi-interval protection devices is provided, which includes:

[0006] Collecting entity operation data of a plurality of protection devices of a substation entity, and constructing a digital twin model of the substation based on the entity operation data; wherein the digital twin model includes a physical layer model, a data layer model, and a business layer model; the physical layer model stores the topology of the primary equipment of the substation, the data layer model stores a mapping relationship table between the primary equipment operation parameters and the secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and the transition conditions between adjacent states in the state transition graph;

[0007] Generating a cooperative testing scenario for a plurality of protection devices based on the digital twin model, determining the electrical communication path between a plurality of protection devices in the cooperative testing scenario through the physical layer model of the digital twin model, determining the associated protection setting of the protection devices in the cooperative testing scenario through the data layer model and the electrical communication path, and determining the test excitation signal sequence corresponding to the associated protection setting according to the business layer model;

[0008] The test excitation signal sequence is used to test a plurality of protection devices in the transformer substation entity to obtain a cooperative test result corresponding to the cooperative test scene, protection setting parameters of the plurality of protection devices are adaptively optimized and adjusted according to the cooperative test result, and the optimized protection setting parameters are updated into the data layer model of the digital twin model.

[0009] According to another aspect of the present application, a cooperative testing system for transformer substation multi-interval protection devices is provided, which comprises:

[0010] A digital model construction module is configured to collect entity operation data of a plurality of protection devices of a transformer substation entity, and construct a digital twin model of the transformer substation based on the entity operation data; wherein the digital twin model comprises a physical layer model, a data layer model and a business layer model; the physical layer model stores a topology structure of primary equipment of the transformer substation, the data layer model stores a mapping relationship table between primary equipment operation parameters and secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and a transition condition between adjacent states in the state transition graph;

[0011] A test signal determination module is configured to generate a cooperative test scene for a plurality of protection devices based on the digital twin model, determine an electrical connection path between the plurality of protection devices in the cooperative test scene through the physical layer model of the digital twin model, determine associated protection settings of the protection devices in the cooperative test scene through the data layer model and the electrical connection path, and determine a test excitation signal sequence corresponding to the associated protection settings according to the business layer model;

[0012] A model parameter update module is configured to test a plurality of protection devices in the transformer substation entity by using the test excitation signal sequence to obtain a cooperative test result corresponding to the cooperative test scene, adaptively optimize and adjust protection setting parameters of the plurality of protection devices according to the cooperative test result, and update the optimized protection setting parameters into the data layer model of the digital twin model.

[0013] According to another aspect of the present application, an electronic device is provided, which comprises:

[0014] at least one processor; and

[0015] a memory connected in communication with the at least one processor; wherein

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for cooperative testing of a multi-bay protection device of a substation according to any one of the embodiments of the application.

[0017] According to another aspect of the application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the method for cooperative testing of a multi-bay protection device of a substation according to any one of the embodiments of the application when executed by the processor.

[0018] The technical scheme of the embodiment of the application first acquires entity operation data of a plurality of protection devices of a substation entity, and constructs a digital twin model of the substation based on the entity operation data. Since the digital twin model includes a physical layer model, a data layer model and a business layer model, the physical layer model stores a topology structure of primary equipment of the substation, the data layer model stores a mapping relationship table between primary equipment operation parameters and secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and conversion conditions between adjacent states in the state transition graph. The digital twin model can comprehensively cover the physical structure, operation parameters and state transition of the substation to achieve accurate simulation and optimization. Then, a cooperative testing scenario of a plurality of protection devices is generated based on the digital twin model. The electrical communication path between the plurality of protection devices in the cooperative testing scenario is determined by the physical layer model of the digital twin model. The associated protection setting of the protection devices in the cooperative testing scenario is determined by the data layer model and the electrical communication path. The test excitation signal sequence corresponding to the associated protection setting is determined according to the business layer model. The cooperative testing scenario, the electrical communication path and the protection setting are used to accurately generate the test excitation signal sequence, ensuring the comprehensiveness and accuracy of the test and optimizing the cooperative working performance of the protection devices. Finally, the plurality of protection devices in the substation entity are tested by using the test excitation signal sequence to obtain a cooperative testing result corresponding to the cooperative testing scenario. The protection setting parameters of the plurality of protection devices are adaptively optimized and adjusted according to the cooperative testing result, and the optimized protection setting parameters are updated to the data layer model of the digital twin model. The actual test and optimization adjustment can improve the response speed and detection accuracy of the protection devices, ensure the safety and reliability of the substation operation, and update the digital twin model to achieve continuous optimization. The method solves the problem in the related art that the test method cannot efficiently and accurately complete the cooperative testing between a plurality of protection devices in a substation and optimize the configuration accordingly, improves the response speed and detection accuracy of the protection devices, ensures the safety and reliability of the substation operation, and achieves continuous optimization.

[0019] It should be understood that the matters described in this detailed description are intended to be illustrative only and are not intended to limit the scope of the present application. Other features of the present application will be apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0021] Figure 1 is a flow chart of a method for cooperative testing of a multi-bay protection device of a substation according to an embodiment of the present application;

[0022] Figure 2 is a flow chart of a method for cooperative testing of a multi-bay protection device of a substation according to an embodiment of the present application;

[0023] Figure 3 is a structural schematic diagram of a cooperative testing device for a multi-bay protection device of a substation according to an embodiment of the present application;

[0024] Figure 4 is a structural schematic diagram of an electronic device for implementing the method for cooperative testing of a multi-bay protection device of a substation according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the technical personnel in the art better understand the present application scheme, the following will combine the drawings in the embodiments of the present application, and the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", and the like in the description and in the claims of the present application and the above-described drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] It should be noted that the modification of "one" or "multiple" mentioned in the present disclosure is illustrative but not limiting, and those skilled in the art should understand that unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0028] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0029] It can be understood that before using the technical solutions disclosed in the embodiments of the present disclosure, the type, use range, use scenario, etc. of the personal information involved in the present disclosure should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0030] For example, in response to receiving the active request of the user, the user is sent prompt information to explicitly prompt the user that the operation requested to be performed will require obtaining and using the personal information of the user. Thus, the user can voluntarily choose whether to provide personal information to the software or hardware such as electronic devices, application programs, servers or storage media that perform the operation of the technical solutions of the present disclosure according to the prompt information.

[0031] As an optional but non-limiting implementation manner, in response to receiving the active request of the user, the manner of sending prompt information to the user may, for example, be a pop-up window manner, and the prompt information may, for example, be presented in the form of text in the pop-up window. In addition, the pop-up window may also carry selection controls for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0032] It can be understood that the above notification and user authorization process is only illustrative and does not limit the implementation manner of the present disclosure, and other manners meeting the relevant laws and regulations can also be applied to the implementation manner of the present disclosure.

[0033] It can be understood that the data involved in the technical solution (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the corresponding laws, regulations and relevant provisions.

[0034] Embodiment one

[0035] Figure 1 A flowchart of a method for cooperative testing of a multi-interval protection device of a substation is provided for the first embodiment of the application. The embodiment can be applicable to the case of automatically adjusting the protection setting parameters of the protection device according to the cooperative testing results. The method can be executed by a cooperative testing system of a multi-interval protection device of a substation. The cooperative testing system of the multi-interval protection device of the substation can be realized in the form of hardware and / or software. Optionally, the cooperative testing system of the multi-interval protection device of the substation is realized by an electronic device, which can be a mobile terminal, a PC terminal or a server, etc.

[0036] As shown in Figure 1 , the method can specifically include:

[0037] S110, collecting entity operation data of a plurality of protection devices of a substation entity, and constructing a digital twin model of the substation based on the entity operation data; wherein the digital twin model includes a physical layer model, a data layer model and a business layer model; the physical layer model stores a topology structure of primary equipment of the substation, the data layer model stores a mapping relationship table between primary equipment operation parameters and secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and a transition condition between adjacent states in the state transition graph.

[0038] The plurality of protection devices can be understood as different types of protection devices existing in the substation, which are used to detect the state of the power system and can take action (such as cutting off power) to prevent damage from spreading when a fault or anomaly is detected. The entity operation data can be understood as data collected from the plurality of protection devices in actual operation, including but not limited to at least one of temperature, voltage, current and switch state, etc., for reflecting the real-time working condition of the protection device. The digital twin model can be understood as a virtual model corresponding to the actual physical system, which can be used to simulate the running state and behavior of the substation, etc. The digital twin model at least includes a physical layer model, a data layer model and a business layer model, etc. The physical layer model can be understood as a model describing the structure and composition of the physical system, which can represent the connection relationship of various primary equipment (such as circuit breakers, disconnectors and transformers, etc.) in the substation in a graphical manner. For example, nodes can be used to represent devices, and lines can be used to represent the electrical connection between primary equipment. The primary equipment can be understood as the main equipment directly participating in the generation, transmission and distribution of electric energy in the power system, such as transformers or circuit breakers, etc.

[0039] The data layer model can be understood as a model responsible for storing and processing data from the physical layer, and can store a mapping relationship table between primary equipment operating parameters and secondary protection device setting parameters, etc. The secondary protection device can be understood as a small auxiliary device for detecting the health status of the primary equipment and performing protection actions, such as a relay protection system. The mapping relationship table can be understood as a data table representing the relationship between the operating parameters of the primary equipment and the setting values of the secondary protection device. For example, for a certain circuit breaker, its primary equipment operating parameters include rated current, rated voltage, etc., and the corresponding secondary protection device setting parameters include at least one of overcurrent protection setting value, low voltage protection setting value, etc. The mapping relationship can be stored in the form of a relational database.

[0040] The business layer model can be understood as a model storing the protection device cooperative control logic corresponding to the control strategy parameters. The business layer model can store a protection device state transition diagram and a transition condition between adjacent states in the state transition diagram. The protection device state transition diagram can be understood as a visualization tool showing the conditions for a state of the protection device to transition to another state. The transition condition can be understood as a condition triggering the transition between adjacent states.

[0041] On the basis of the above scheme, optionally, the digital twin model of the substation is constructed based on the entity operation data, including: inputting the entity operation data into a preset graph database, and constructing a physical layer model based on the graph database and the entity operation data, wherein the physical layer model takes circuit breakers, disconnectors, and transformers as graph nodes, takes electrical connection relationships between the graph nodes as connection edges, and records graph node information corresponding to the graph nodes; inputting the graph node information in the physical layer model into a relational database, establishing a mapping relationship table of the mapping relationship between the primary equipment operating parameters and the secondary protection device setting parameters based on the relational database, and calculating coupling coefficients between different secondary protection device setting parameters based on the mapping relationship table to generate a setting parameter correlation matrix, to obtain a data layer model; inputting the setting parameter correlation matrix in the data layer model into a preset state machine, constructing a protection device state transition diagram for single-phase ground fault and two-phase short-circuit fault based on the state machine, and setting a transition condition between adjacent states in the state transition diagram based on the setting parameter correlation matrix, to obtain a business layer model.

[0042] The graph database can be understood as a database type for storing and querying complex relational data, and is suitable for modeling data sets with many-to-many relationships, such as the topology of a power system. The graph node can be understood as a protection device representing a substation entity in the graph database. For example, the graph node can be a primary equipment (such as a circuit breaker, disconnector, and transformer, etc.) in a substation. Each graph node can include graph node information of the primary equipment. The graph node information can be understood as detailed information of the graph node. The graph node information can include at least one of type information, rated parameter information, installation location information, current operating state information, and real-time measurement value information, etc. The connection edge can be understood as a connection line representing the relationship between two graph nodes in the graph database, that is, the electrical connection relationship between primary equipments.

[0043] In an optional embodiment, for a 110kV substation, the physical layer model can include the following graph nodes: a main transformer node (type: oil-immersed transformer, rated capacity: 50MVA, installation location: main transformer room, operating state: normal operation, active power: 30MW, reactive power: 10MVar, etc.), a 110kV circuit breaker node (type: SF6 circuit breaker, rated current: 2000A, installation location: 110kV distribution device, operating state: closed, passing current: 500A, etc.), and a current transformer node (type: oil-immersed CT, transformation ratio: 600 / 5, accuracy level: 0.2S, installation location: 110kV bus, operating state: normal, primary current: 500A, etc.). The electrical connection relationship between these nodes is represented by the connection edge.

[0044] The relational database can be understood as a database type that organizes data in the form of tables, and is usually used to store structured data for complex query operations. The mapping relationship table can be understood as a data table for describing the correspondence between the operating parameters of the primary equipment and the setting parameters of the secondary protection device. Specifically, a mapping relationship table is established in the data layer model, which records the setting parameters of the secondary protection device corresponding to each operating parameter of the primary equipment. The coupling coefficient can be understood as an index for measuring the degree of mutual influence between two or more protection device setting parameters. The larger the value, the stronger the mutual influence. The elements in the setting parameter association matrix represent the coupling coefficients between different protection device setting parameters, which are used to quantify the mutual coupling relationship between protection devices.

[0045] An optional embodiment, for 110 kV line protection, its mapping table can contain the following mappings: line rated current (primary equipment parameter) maps to over-current protection starting current setting (secondary protection setting), line impedance (primary equipment parameter) maps to distance protection impedance setting (secondary protection setting), etc. Based on the mapping table, the coupling coefficients between different secondary protection device setting parameters can be calculated, and the setting parameter correlation matrix is generated. The correlation matrix reflects the degree of correlation between different protection settings. For example, for distance protection and over-current protection, there is a strong correlation between their starting settings, and a higher coupling coefficient can be assigned; while for distance protection and low voltage blocking protection, the correlation between their settings is weak, and a lower coupling coefficient can be assigned. In this way, the mutual influence between different protection functions can be quantified.

[0046] The protection device state transition diagram can be understood as a diagram built by state mechanisms, which shows the state changes of the protection device when facing different fault types (such as single-phase ground fault or two-phase short circuit fault, etc.) and the transition conditions thereof. The single-phase ground fault can be understood as an abnormal electrical connection between a phase and the ground. The two-phase short circuit fault can be understood as an abnormal low resistance connection between any two phases. Each of the protection device state transition diagrams can include at least one of a starting state, a protection starting state, a fault judgment state, and a tripping action state. The transition conditions between adjacent states in the protection device state transition diagram can be set according to the setting parameter correlation matrix. The transition condition can be understood as a condition for triggering state transition. For example, for distance protection, the transition condition from the protection starting state to the fault judgment state can be set according to the coupling relationship between the impedance setting and the current setting reflected in the setting parameter correlation matrix.

[0047] An optional embodiment, taking 110 kV line distance protection as an example, its state transition diagram can contain the following state transitions: 1) the transition condition from the starting state to the protection starting state: the measured impedance is less than the starting setting and the current is greater than the starting current setting; 2) the transition condition from the protection starting state to the fault judgment state: the measured impedance enters the action region and the duration exceeds the time setting; 3) the transition condition from the fault judgment state to the tripping action state: the fault persists and no blocking signal is received.

[0048] The above scheme can intuitively display the layout of primary equipment in the substation and its electrical connection relationship by constructing a physical layer model, providing a basis for subsequent analysis; then, the mapping relationship between the operation parameters of the primary equipment and the setting value parameters of the secondary protection device is established using a relational database, and the setting value parameter correlation matrix is calculated, effectively quantifying the mutual influence between each protection device and improving the accuracy of system analysis; finally, the state transition diagram of the protection device is constructed combined with the state machine technology, which not only can simulate the behavior mode of the protection device when different types of faults occur, but also can dynamically adjust the protection setting value parameters according to the actual operation data, thereby realizing intelligent optimization of the protection system, improving the safety and reliability of the substation operation, reducing the maintenance cost, and enhancing the overall performance of the power system.

[0049] On the basis of the above scheme, after the digital twin model of the substation is constructed based on the entity operation data, the method further includes: determining simulation operation data of the substation entity based on the physical layer model, the data layer model and the business layer model, and determining a data difference amount between the simulation operation data and the entity operation data; in a case where the data difference amount is greater than a preset difference amount threshold, triggering a preset model parameter self-adaptive adjustment process to update the graph node information in the physical layer model, the mapping relationship table in the data layer model and the state transition diagram in the business layer model.

[0050] The simulation operation data can be understood as data obtained by simulating the operation of the substation based on the physical layer model, the data layer model and the business layer model, reflecting the expected working state of the substation under certain conditions. The data difference amount can be understood as the difference between the simulation operation data and the entity operation data. In a case where the data difference amount exceeds the preset difference amount threshold, it indicates that the model may need to be updated to more accurately reflect the actual situation.

[0051] An optional embodiment assumes that a certain factory has a device on its production line, and the simulation value of its temperature sensor in the digital twin model is 85℃, while the actual collected temperature data is 90℃. The system will calculate a difference of 5℃ and compare it with a preset temperature difference threshold (such as 3℃). In the case of detecting that the difference exceeds the preset threshold, a preset model parameter adaptive adjustment process will be automatically triggered, which can include the following steps: first, the cause of the difference will be analyzed to determine whether the physical layer, data layer, or business layer model needs to be adjusted. For the above temperature difference, it will be determined that the heat conduction parameter in the physical layer model needs to be updated; then, the relevant graph node information in the physical layer model will be updated, adjusting the heat conduction coefficient of the environment around the device or updating the heat generation model of the device itself. The updated graph node information will more accurately reflect the characteristics of the actual physical environment; next, the mapping relationship table in the data layer model will be checked and updated, which may need to adjust the sampling frequency or data processing algorithm of the temperature data to better capture the dynamic characteristics of temperature changes; finally, the state transition graph in the business layer model will be updated. Temperature changes may affect the working state or maintenance plan of the device, so it may be necessary to trigger an early warning when the temperature reaches 88℃, rather than the set 90℃.

[0052] The above scheme determines the difference between the simulation running data and the actual running data, triggers an adaptive adjustment process when the difference exceeds a preset difference threshold, ensures that the digital twin model can simulate the actual running state of the substation, not only improves the prediction accuracy and reliability of the model, but also timely discovers and corrects potential configuration errors or performance bottlenecks, thereby enhancing the safety and efficiency of the substation operation, reducing maintenance costs, and prolonging the service life of the equipment.

[0053] S120, based on the digital twin model, generate a cooperative test scene of multiple protection devices, determine an electrical communication path between multiple protection devices in the cooperative test scene through the physical layer model of the digital twin model, determine the associated protection setting value of the protection device in the cooperative test scene through the data layer model and the electrical communication path, and determine the test excitation signal sequence corresponding to the associated protection setting value according to the business layer model.

[0054] The cooperative test scenario can be understood as a simulated environment designed to detect the specific conditions between multiple protection devices. The electrical communication path can be understood as a specific path in the power system from one protection device to another. The associated protection setting value can be understood as a protection setting value determined according to the electrical communication path between protection devices, to ensure that the protection function is activated correctly under specific conditions. The test excitation signal sequence can be understood as a predefined input signal for triggering the protection device to test whether its response meets the expectation. The test excitation signal sequence can include at least one of voltage amplitude, current amplitude, phase angle, and timing relationship.

[0055] Based on the above scheme, optionally, the cooperative test scenario of the multiple protection devices based on the digital twin model includes: generating an initial population corresponding to the cooperative test scenario of the multiple protection devices in the digital twin model by using a Monte Carlo algorithm, inputting the initial population into a genetic algorithm, and iteratively calculating the scene coverage, the number of test working conditions, and the number of operation steps as optimization objectives to screen out the optimal test scenario combination; testing the cooperative test scenarios in the test scenario combination, respectively collecting the test results corresponding to each cooperative test scenario during the test execution, increasing the sampling density of the cooperative test scenario when the dispersion degree of the test result is greater than a preset dispersion threshold, and reducing the sampling density of the cooperative test scenario when the dispersion degree of the test result is less than the preset dispersion threshold.

[0056] The Monte Carlo algorithm is a statistical method for solving problems by random sampling or random numbers, and is used to generate an initial population, i.e., multiple possible collaborative test scenarios. The initial population can be understood as a set of randomly generated solutions in a genetic algorithm, serving as the starting point for iterative calculations. The scenario coverage can be understood as the percentage of parameter space covered by the collaborative test scenario, and a high scenario coverage means that more fault modes and operating conditions are considered. The number of test conditions can be understood as the number of different test conditions included in a test scenario, such as normal conditions, fault conditions, etc., and a larger number of conditions means more comprehensive testing. The number of operation steps can be understood as the number of steps required to complete a test scenario, and fewer operation steps can simplify the testing process. The optimal test scenario combination can be understood as a set of test scenarios selected after optimization by a genetic algorithm, which best meet the optimization objectives (such as scenario coverage, number of test conditions, and number of operation steps, etc.). The test results can be understood as the data obtained after executing the test scenarios, reflecting the performance of the protection device under specific conditions. The dispersion degree can be understood as a statistical indicator for quantifying the dispersion degree of data, and the higher the dispersion degree, the greater the difference between data points. The preset dispersion threshold can be understood as a pre-set standard value for determining whether the dispersion degree of the test results needs to adjust the sampling density. The sampling density can be understood as the frequency or number of data points collected during the testing process.

[0057] In an alternative embodiment, a Monte Carlo algorithm is used to generate an initial population corresponding to multiple protection device collaborative test scenarios. Specifically, the number of protection devices can be set to N, each protection device has M adjustable parameters, and K initial test scenarios are generated by randomly sampling within the value range of each parameter using the Monte Carlo method. For example, assuming there are 3 protection devices and each device has 5 adjustable parameters, 100 initial test scenarios can be generated, each containing 15 parameter values.

[0058] Next, the generated initial population is input into a genetic algorithm. The genetic algorithm iteratively calculates with the scenario coverage, number of test conditions, and number of operation steps as optimization objectives; during the iterative process of the genetic algorithm, the population is continuously optimized through selection, crossover, and mutation operations. The selection operation can use the roulette method to select parent individuals according to their fitness. The crossover operation can use the single-point crossover method to randomly select a crossover point and exchange part of the genes of the parent individuals. The mutation operation randomly changes some gene values in the individual. Through multiple iterations, the optimal test scenario combination is finally selected.

[0059] The scheme can automatically screen out an optimal test scene combination with high coverage, various working conditions and simple operation, thereby improving the accuracy and reliability of the test, and dynamically adjusting the sampling density according to the dispersion degree of the test result, so as to ensure that enough samples are obtained to identify potential problems when the data fluctuates greatly, and to reduce unnecessary test times when the data is stable, save resources, and improve the effect and efficiency of the protection device cooperative test of the transformer substation.

[0060] On the basis of the above scheme, optionally, the physical layer model of the digital twin model is used to determine the electrical connection path between the protection devices in the cooperative test scene, the data layer model and the electrical connection path are used to determine the associated protection setting value of the protection devices in the cooperative test scene, the business layer model is used to determine the test excitation signal sequence corresponding to the associated protection setting value, and the method comprises the following steps: performing a depth-first search on the physical layer model of the digital twin model according to the path weight corresponding to the on-position state and the off-position state of the circuit breaker, to search for the electrical connection path between the protection devices in the cooperative test scene and extract the primary equipment operating parameters on the searched electrical connection path; the mapping relationship between the primary equipment operating parameters on the electrical connection path and the secondary protection device setting value parameters in the data layer model is queried based on the primary equipment operating parameters, to obtain the multiple protection parameters corresponding to the protection device nodes, the multiple protection parameters are input into the setting value association matrix of the data layer model, and the associated protection setting value in the cooperative test scene is obtained, wherein the associated protection setting value comprises a distance protection action section parameter, a distance protection time delay parameter and a current protection starting current parameter; a fault point is set in the overlapping protection range of the distance protection action section parameter and the current protection starting current parameter, and a short-circuit calculation model corresponding to the fault point is constructed, the target power parameter of the fault point is determined according to the primary equipment operating parameters and the short-circuit calculation model, and the test excitation signal sequence corresponding to the associated protection setting value is determined according to the target power parameter and the distance protection time delay parameter.

[0061] The closed position of the circuit breaker can be understood as a state in which the circuit breaker allows current to pass through. The open position of the circuit breaker can be understood as a state in which the circuit breaker prevents current from passing through. The path weight can be understood as a numerical value in a search algorithm for evaluating the importance of a path from one node to another node, for reflecting the influence of the circuit breaker being in the closed position or the open position on the electrically connected path. The depth-first search is a graph traversal algorithm that starts from the root node, searches as deeply as possible along each branch, and then backtracks to the last node to continue the unexplored branch. The distance protection action section parameter can be understood as a set value representing the division of the area in which the protection device reacts to faults in different distance ranges. The distance protection time delay parameter can be understood as a set value representing the time delay of the protection device after detecting a fault before starting the protection action. The current protection starting current parameter can be understood as a minimum current value required for the protection device to start the protection action. The overlapping protection range can be understood as the protection range when the distance protection action section parameter and the current protection starting current parameter jointly act on the same area of the power system. The fault point can be understood as a pre-set fault occurrence position, for simulating the system response under fault conditions. The short-circuit calculation model can be understood as a mathematical model for calculating parameters such as current and voltage in the system when a short-circuit fault occurs. The target power parameter can be understood as an expected current or voltage parameter at the fault point obtained according to the short-circuit calculation model. The target power parameter can include at least one of parameters such as voltage signal amplitude and current signal amplitude.

[0062] An optional embodiment uses a preset graph database to perform a depth-first search, starting from the target protection device node. During the search, the path weight of the breaker combined position state is set to 1, and the path weight of the breaker split position state is set to infinity, ensuring that the searched path is actually electrically connected. Assuming that the target protection device is a transformer differential protection, adjacent protection devices such as line distance protection and bus differential protection connected to it can be found through the depth-first search. During the search process, various primary equipment parameters on the electrically connected path are recorded, including line impedance parameters, transformer ratio parameters, and current transformer ratio parameters. The data layer model is used to extract the associated protection settings of the adjacent protection devices. Based on the electrically connected path obtained through the search, the mapping relationship table is queried to extract the protection parameters corresponding to the adjacent protection device nodes. For example, for line distance protection, the action distances of one-, two-, and three-section protections and the corresponding time delays can be extracted. For transformer differential protection, the ratio restraint characteristic curve parameters and threshold values can be extracted. These protection parameters are then input into the setting parameter association matrix output by the data layer model to determine the associated protection settings that need to be tested. The fault point is set within the overlapping protection range of the distance protection action section parameters and the current protection starting current parameters. Assuming that the two-section distance protection of a 110kV line is set to 120% of the line length, and the current protection starting current is 1.2 times the rated current, the fault point can be set at 110% of the line length. Based on the fault point position, the short-circuit calculation model can calculate the corresponding voltage signal amplitude and current signal amplitude. According to the distance protection time delay parameters, the test excitation signal sequence of the voltage signal amplitude and current signal amplitude is set. For example, if the two-section time delay of the line distance protection is 0.5s, the fault voltage and current signals can be set to last for 0.6s to verify whether the protection can correctly act within the set time.

[0063] The above scheme determines the electrically connected path between protection devices through depth-first search and path weight analysis, extracts primary equipment operating parameters, calculates the associated protection settings in the cooperative testing scenario based on the mapping relationship of the data layer model, sets the fault point within the overlapping protection range, constructs the short-circuit calculation model, generates the test excitation signal sequence, improves the accuracy and reliability of the cooperative testing, optimizes the protection device settings, ensures the safety and stability of the substation operation, and reduces the testing time and resource consumption.

[0064] S130, test the plurality of protection devices in the substation entity by using the test excitation signal sequence to obtain a cooperative test result corresponding to the cooperative test scene, and adaptively optimize and adjust the protection setting value parameters of the plurality of protection devices according to the cooperative test result, and update the optimized protection setting value parameters to the data layer model of the digital twin model.

[0065] The cooperative test result can be understood as a result obtained after the test excitation signal sequence is implemented, and is used to reflect the actual performance of the protection device under a given test scene.

[0066] In an optional implementation, the protection setting value parameters of the plurality of protection devices are adaptively optimized and adjusted according to the cooperative test result. For example, in the case where it is found that the action time of a protection device does not meet the requirements, the time setting value of the protection device can be adjusted appropriately. In the case where there is a problem in the cooperation between adjacent protection devices, the setting values of the plurality of protection devices need to be adjusted comprehensively. The optimization and adjustment can be performed by using an intelligent optimization method such as a genetic algorithm to meet the coordination requirement between the plurality of protection devices.

[0067] On the basis of the above scheme, the test of the plurality of protection devices in the substation entity by using the test excitation signal sequence to obtain the cooperative test result corresponding to the cooperative test scene comprises: inputting the test excitation signal sequence to the plurality of protection devices in the substation entity, collecting the action response signals of the plurality of protection devices, and comparing the action response signals with the standard response signals defined in the digital twin model to obtain the cooperative test result corresponding to the cooperative test scene.

[0068] The action response signal can include an output signal generated by the protection device after receiving the test excitation signal. The action response signal can include, but is not limited to, a protection starting signal, a tripping signal, and a reclosing signal. The action response signal can be collected by using a digital quantity input port of a protection test instrument to realize the on-off quantity output signal of the protection device. The standard response signal can be understood as a response signal of the protection device defined in the digital twin model, and is used to compare with the action response signal.

[0069] The technical scheme of the embodiment of the present application first acquires entity operation data of multiple protection devices of a power transformation station entity, constructs a digital twin model of the power transformation station based on the entity operation data, and since the digital twin model comprises a physical layer model, a data layer model and a business layer model, the physical layer model stores a topology structure of primary equipment of the power transformation station, the data layer model stores a mapping relationship table between primary equipment operation parameters and secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and conversion conditions between adjacent states in the state transition graph. The digital twin model can comprehensively cover the physical structure, operation parameters and state transition of the power transformation station to realize accurate simulation and optimization. Then, a cooperative test scene of multiple protection devices is generated based on the digital twin model, an electrical connection path between the multiple protection devices under the cooperative test scene is determined through the physical layer model of the digital twin model, the associated protection setting of the protection devices under the cooperative test scene is determined through the data layer model and the electrical connection path, and a test excitation signal sequence corresponding to the associated protection setting is determined according to the business layer model. The cooperative test scene, the electrical connection path and the protection setting are used to accurately generate the test excitation signal sequence to ensure the comprehensiveness and accuracy of the test and optimize the cooperative working performance of the protection devices. Finally, the multiple protection devices in the power transformation station entity are tested by using the test excitation signal sequence to obtain a cooperative test result corresponding to the cooperative test scene, the protection setting parameters of the multiple protection devices are adaptively optimized and adjusted according to the cooperative test result, and the optimized protection setting parameters are updated to the data layer model of the digital twin model. The actual test and optimization adjustment can improve the response speed and detection accuracy of the protection devices, ensure the safety and reliability of the power transformation station operation, and update the digital twin model to realize continuous optimization. The method solves the problem that the test method in the related art cannot efficiently and accurately complete the cooperative test between the multiple protection devices in the power transformation station and optimize the configuration accordingly, improves the response speed and detection accuracy of the protection devices, ensures the safety and reliability of the power transformation station operation, and realizes continuous optimization.

[0070] Embodiment two

[0071] Figure 2 A flowchart of a power transformation station multi-interval protection device cooperative test method provided by the second embodiment of the present application, the embodiment is further refined on the basis of the above-mentioned embodiment, that is, the protection setting parameters of the multiple protection devices are adaptively optimized and adjusted according to the cooperative test result, and the optimized protection setting parameters are updated to the data layer model of the digital twin model. The specific implementation can be referred to the description of the embodiment. The same or similar technical features as the foregoing embodiments are not described herein.

[0072] As Figure 2 shown, the method can specifically include:

[0073] S210, collect entity operation data of a plurality of protection devices of a power transformation station entity, and construct a digital twin model of the power transformation station based on the entity operation data; wherein the digital twin model includes a physical layer model, a data layer model, and a business layer model; the physical layer model stores a topology structure of primary equipment of the power transformation station, the data layer model stores a mapping relationship table between primary equipment operation parameters and secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and conversion conditions between adjacent states in the state transition graph;

[0074] S220, generate a cooperative test scene of a plurality of protection devices based on the digital twin model, determine an electrical communication path between the plurality of protection devices in the cooperative test scene through the physical layer model of the digital twin model, determine associated protection settings of the protection devices in the cooperative test scene through the data layer model and the electrical communication path, and determine a test excitation signal sequence corresponding to the associated protection settings according to the business layer model;

[0075] S230, test a plurality of protection devices in a power transformation station entity using the test excitation signal sequence to obtain a cooperative test result corresponding to the cooperative test scene, determine a time response deviation, a current response deviation, and an impedance response deviation of the protection devices according to the cooperative test result, construct a judgment matrix using an analytic hierarchy process, calculate a maximum eigenvalue and a corresponding eigenvector of the judgment matrix, and obtain a weight coefficient through eigenvector normalization.

[0076] The time response deviation can be understood as the difference between the action time of the actual protection device and the preset standard time, and is used to evaluate the accuracy of the protection device in response speed. The current response deviation can be understood as the difference between the current value detected by the actual protection device and the preset standard current value, and is used to evaluate the accuracy of the protection device in current detection. The impedance response deviation can be understood as the difference between the impedance value detected by the actual protection device and the preset standard impedance value, and is used to evaluate the accuracy of the protection device in impedance detection. The analytic hierarchy process is a multi-criteria decision analysis method, which is used to decompose a complex decision problem into multiple levels of sub-problems, and determine the final decision scheme by comparing the importance of each sub-problem. The analytic hierarchy process is used to evaluate and optimize the response deviation of the protection device. The judgment matrix can be understood as a matrix used to compare the importance of different factors in the analytic hierarchy process, and each element in the matrix represents the relative importance ratio between two factors. The maximum eigenvalue can be understood as the largest one of the eigenvalues of the judgment matrix, which is used to evaluate the consistency of the judgment matrix. The eigenvector can be understood as a vector corresponding to the maximum eigenvalue, which represents the main direction of matrix transformation, and is used to determine the weight coefficient.

[0077] In an alternative embodiment, the time response deviation, the current response deviation and the impedance response deviation are first calculated according to the cooperative test results. A hierarchical model is established, and the optimization target, the optimization criteria and the parameters to be optimized are divided into different levels. Then, each factor in each level is compared with the corresponding factor in the previous level, and a pair-wise comparison judgment matrix is constructed. For example, for a certain protection device, the time response, the current response and the impedance response can be taken as the criteria layer, and a 3x3 judgment matrix is constructed. Then, the maximum eigenvalue of the judgment matrix is calculated, and the corresponding eigenvector is solved. Finally, the eigenvector is normalized to obtain the weight coefficients of each criterion, such as the time response weight of 0.5, the current response weight of 0.3 and the impedance response weight of 0.2.

[0078] Based on the above scheme, the cooperative test results can include the actual action time from starting to tripping output of the protection device, the starting current and the measured impedance. The determination of the time response deviation, the current response deviation and the impedance response deviation of the protection device according to the cooperative test results can include: determining the root mean square error between the actual action time from starting to tripping output of the protection device and the theoretical action time, and determining the time response deviation of the protection device according to the root mean square error; determining the current relative deviation between the starting current of the protection device and the theoretical setting value, and determining the current response deviation of the protection device according to the current relative deviation; determining the Euclidean distance between the measured impedance and the actual impedance, and determining the impedance response deviation of the protection device according to the Euclidean distance.

[0079] The actual action time can be understood as the actual time from the start to the trip output of the protection device, and is used to reflect the response speed of the protection device in actual operation. The starting current can be understood as the current value detected when the protection device starts, and is used to reflect the sensitivity of the protection device in detecting fault current. The measured impedance can be understood as the impedance value measured by the protection device when a fault is detected, and is used to evaluate the accuracy of the protection device in fault positioning and identification. The theoretical action time can be understood as the action time that the protection device should theoretically reach, and can be used as a benchmark for evaluating the actual action time. The time response deviation can be understood as the deviation of the protection device in response time determined according to the root mean square error, and is used to evaluate the accuracy of the protection device in response speed. The theoretical setting value can be understood as the starting current value set by the protection device, and can be pre-set according to system design and protection requirements. The current relative deviation can be understood as the relative difference between the actual starting current and the theoretical setting value, and can be expressed in percentage, and is used to quantify the difference between the actual starting current and the theoretical setting value. The current response deviation can be understood as the deviation of the protection device in current detection determined according to the current relative deviation, and is used to evaluate the accuracy of the protection device in current detection. The impedance response deviation can be understood as the deviation of the protection device in impedance detection determined according to the Euclidean distance, and is used to evaluate the accuracy of the protection device in impedance detection.

[0080] An optional embodiment, for a certain protection device, the actual measured 5 times action time is 100ms, 102ms, 98ms, 101ms, 99ms, the theoretical action time is 100ms, the root mean square error can be calculated to be about 1.41ms, that is, the time response deviation is 1.41ms. The current response deviation is determined by calculating the relative deviation between the starting current of the protection device and the theoretical setting value. The theoretical starting current setting value of a certain protection device is 5A, and the actual measured starting current is 5.2A, so the relative deviation is 4%, that is, the current response deviation is 4%. The impedance response deviation is determined by calculating the Euclidean distance between the measured impedance and the actual impedance. The actual impedance of a certain distance protection is 3+j4Ω, and the measured impedance is 3.1+j4.2Ω, so the Euclidean distance can be calculated to be about 0.224Ω, that is, the impedance response deviation is 0.224Ω.

[0081] The above scheme quantifies the performance of the protection device in time response, current detection and impedance detection by calculating the deviation between the actual action time, starting current and measured impedance of the protection device and the theoretical value, not only improves the accuracy and reliability of the test results, but also provides a scientific basis for optimizing the setting parameters of the protection device, ensures the safety and stability of the substation operation, and reduces unnecessary maintenance costs.

[0082] S240, constructing a local optimization sub-objective function according to the weight coefficient, the time response deviation, the current response deviation, and the impedance response deviation, and taking the load current multiple, the protection action time, and the distance measurement error as constraint conditions of the local optimization sub-objective function.

[0083] The local optimization sub-objective function can be understood as a mathematical function for optimizing the performance of a specific protection device, and contains at least one of indicators such as the time response deviation, the current response deviation, and the impedance response deviation, and is used to locally optimize the setting parameters of the protection device and improve the performance thereof. The load current multiple can be understood as the ratio of the actual load current to the rated current, and is used to ensure that the optimized protection device can still work normally under different load conditions. The protection action time can be understood as the time required for the protection device to detect and execute a protection action, and ensures that the protection device can take a protection action after detection.

[0084] S250, optimizing and solving the local optimization sub-objective function, constructing a regional optimization sub-objective function according to the first function value of the local optimization sub-objective function corresponding to the protection device and the time response deviation, and taking the backup protection delay margin and the reclosing time difference as constraint conditions of the regional optimization sub-objective function.

[0085] The first function value can be understood as the optimal value obtained in the process of optimizing and solving the local optimization sub-objective function, and is used to evaluate the performance of the optimized protection device and serves as the basis for constructing the regional optimization sub-objective function. The backup protection delay margin can be understood as the time margin of the backup protection device starting to delay when the main protection device fails to act in time, and can ensure that the backup protection device can start in time when the main protection device fails, thereby preventing the fault from expanding. The reclosing time difference can be understood as the time interval of the reclosing device after detecting a fault trip, and is used to ensure that power supply can be restored quickly after a short-term fault, while avoiding repeated reclosing of the device in the case of a permanent fault. The regional optimization sub-objective function can be understood as a mathematical function for optimizing the cooperative work of multiple protection devices in a specific region, and is used to optimize the setting parameters of the protection devices in the region and improve the overall performance of the system in the region.

[0086] In an alternative embodiment, for two adjacent distance protections, a regional optimization sub-objective function can be constructed as follows: F = α × (f1 + f2) + β × (|T1-T2|-ΔT)2, where f1 and f2 are the local optimization sub-objective function values of the two protections, T1 and T2 are the action times thereof, ΔT is the expected time difference, and α and β are weight coefficients. The constraint conditions can be set as: T2-T1≥200 ms (backup protection delay margin), |Tr1-Tr2|≥1 s (reclosing time difference), where Tr1 and Tr2 are the reclosing times of the two protections.

[0087] Optionally, based on the above scheme, the optimization and solving of the local optimization sub-objective function comprises: optimization and solving of the local optimization sub-objective function based on a particle swarm algorithm with adaptive inertia weight, wherein the particle swarm algorithm adopts adaptive inertia weight, and the adaptive inertia weight changes according to a quadratic function rule with iteration number.

[0088] The adaptive inertia weight can be understood as a parameter for controlling the moving speed of particles in the search space in the particle swarm algorithm. By dynamically adjusting the inertia weight, a greater exploration ability can be maintained in the early search stage, and the convergence speed can be improved in the later search stage, thereby improving the overall performance of the algorithm.

[0089] In an optional embodiment, a group of particles is first randomly initialized in position and speed, and the position of each particle represents a set of candidate optimization parameters. Then in each iteration, the inertia weight w is calculated according to the current iteration number k, w = w_max-(w_max-w_min)×(k / k_max)^2, where w_max and w_min are the maximum and minimum inertia weights, respectively, and k_max is the maximum iteration number. Then the speed and position of each particle are updated, and the fitness value of each particle is calculated. Finally, the individual optimal position and global optimal position are updated until the maximum iteration number is reached or the convergence condition is met.

[0090] S260, determining a system coordination index according to the protection setting parameters of the plurality of protection devices, and constructing a global optimization objective function according to the second function value of the regional optimization sub-objective function of the plurality of regions and the system coordination index.

[0091] The system coordination index can be understood as an index for evaluating the collaborative working ability of the plurality of protection devices in the entire system, to ensure the stability and reliability of the power system under different fault conditions. The global optimization objective function can be understood as a mathematical function for optimizing the performance of the entire system, to globally optimize the setting parameters of the protection devices and improve the overall performance of the system.

[0092] S270, sequentially performing local optimization, regional optimization and global optimization through the local optimization sub-objective function, the regional optimization sub-objective function and the global optimization objective function, respectively, and verifying the functional integrity and performance index of the optimization result of each stage through the digital twin model.

[0093] The function integrity verification can be understood as checking whether the optimized protection device can correctly perform its predetermined functions to ensure that the optimization result is effective in actual application, such as whether fault selection, action logic and the like are correct.

[0094] S280, in the case where the verification results of the function integrity verification and the performance index verification reach the preset verification standard, updating the optimized protection setting value parameter to the data layer model of the digital twin model.

[0095] The preset verification standard can be understood as a pre-set benchmark for evaluating function integrity and performance index to ensure that the verification result reaches the predetermined performance and reliability standard.

[0096] The technical scheme of the embodiment of the application determines the time response deviation, the current response deviation and the impedance response deviation through the cooperative test result, calculates the weight coefficient by using the analytic hierarchy process, and ensures the balance of each performance index; then, a local optimization sub-objective function is constructed and a constraint condition is introduced to ensure the feasibility of local optimization; then, the local optimization sub-objective function is optimized and solved by using the particle swarm algorithm with adaptive inertia weight, a regional optimization sub-objective function is generated, and a new constraint condition is added to further optimize the cooperative work of multiple protection devices in the region; subsequently, a global optimization objective function is constructed to realize the overall optimization of the system, and the function integrity verification and the performance index verification of each stage optimization result are performed by using the digital twin model to ensure the effectiveness of the optimization result; finally, the optimized protection setting value parameter is updated to the data layer model of the digital twin model to ensure that the model always reflects the latest optimization result, not only improves the response speed and detection accuracy of the protection device, but also optimizes the overall performance of the system, significantly improves the safety and reliability of the substation operation, and reduces the maintenance cost and fault risk.

[0097] Embodiment three

[0098] Figure 3 A structural schematic diagram of a substation multi-interval protection device cooperative test system provided by the embodiment three of the application. As shown in the figure, the device comprises a digital model construction module 310, a test signal determination module 320 and a model parameter updating module 330. Figure 3

[0099] ​The digital model construction module 310 is configured to collect entity operation data of a plurality of protection devices of a power transformation station entity, and construct a digital twin model of the power transformation station based on the entity operation data; wherein the digital twin model comprises a physical layer model, a data layer model and a business layer model; the physical layer model stores a topology structure of primary equipment of the power transformation station, the data layer model stores a mapping relationship table between primary equipment operation parameters and secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and a transition condition between adjacent states in the state transition graph; the test signal determination module 320 is configured to generate a cooperative test scene of a plurality of protection devices based on the digital twin model, determine an electrical connection path between the plurality of protection devices in the cooperative test scene through the physical layer model of the digital twin model, determine associated protection settings of the protection devices in the cooperative test scene through the data layer model and the electrical connection path, and determine a test excitation signal sequence corresponding to the associated protection settings according to the business layer model; and the model parameter updating module 330 is configured to test the plurality of protection devices in the power transformation station entity by using the test excitation signal sequence, obtain a cooperative test result corresponding to the cooperative test scene, adaptively optimize and adjust protection setting parameters of the plurality of protection devices according to the cooperative test result, and update the optimized protection setting parameters to the data layer model of the digital twin model.

[0100] The technical scheme of the embodiment of the present application first acquires entity operation data of a plurality of protection devices of a substation entity through a digital model construction module 310, constructs a digital twin model of the substation based on the entity operation data, and since the digital twin model comprises a physical layer model, a data layer model and a business layer model; the physical layer model stores a topology structure of primary equipment of the substation, the data layer model stores a mapping relationship table between primary equipment operation parameters and secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and conversion conditions between adjacent states in the state transition graph. The digital twin model can be constructed to comprehensively cover the physical structure, operation parameters and state transition of the substation, to realize accurate simulation and optimization. Then, a test signal determination module 320 generates a cooperative test scene of a plurality of protection devices based on the digital twin model, determines an electrical communication path between a plurality of the protection devices under the cooperative test scene through the physical layer model of the digital twin model, determines associated protection settings of the protection devices under the cooperative test scene through the data layer model and the electrical communication path, and determines a test excitation signal sequence corresponding to the associated protection settings according to the business layer model. The cooperative test scene, the electrical communication path and the protection settings can be determined to accurately generate the test excitation signal sequence, to ensure the comprehensiveness and accuracy of the test, and to optimize the cooperative working performance of the protection devices. Finally, a model parameter updating module 330 tests a plurality of the protection devices in the substation entity by using the test excitation signal sequence, to obtain a cooperative test result corresponding to the cooperative test scene, adaptively optimizes and adjusts protection setting parameters of a plurality of the protection devices according to the cooperative test result, and updates the optimized protection setting parameters to the data layer model of the digital twin model. The response speed and detection accuracy of the protection devices can be improved through actual testing and optimization adjustment, to ensure the safety and reliability of the operation of the substation, and to update the digital twin model to realize continuous optimization. The above solves the problem in the related art that a test method cannot efficiently and accurately complete cooperative testing between a plurality of protection devices in a substation and optimize configuration based on the test, to improve the response speed and detection accuracy of the protection devices, to ensure the safety and reliability of the operation of the substation, and to realize continuous optimization.

[0101] On the basis of the above scheme, optionally, the digital model construction module can comprise a physical layer model construction submodule, a data layer model obtaining submodule and a business layer model obtaining submodule.

[0102] The physical layer model construction submodule is configured to input the entity operation data into a preset graph database, and construct a physical layer model based on the graph database and the entity operation data, wherein the physical layer model takes circuit breakers, disconnectors and transformers as graph nodes, takes electrical connection relationships between the graph nodes as connection edges, and records graph node information corresponding to the graph nodes; the data layer model obtaining submodule is configured to input the graph node information in the physical layer model into a relational database, establish a mapping relationship table of a mapping relationship between the primary equipment operation parameters and the secondary protection device setting parameters based on the relational database, and calculate coupling coefficients between different secondary protection device setting parameters according to the mapping relationship table to generate a setting parameter correlation matrix, so as to obtain a data layer model; and the business layer model obtaining submodule is configured to input the setting parameter correlation matrix in the data layer model into a preset state machine, construct protection device state transition graphs for single-phase ground faults and two-phase short-circuit faults based on the state machine, and set transition conditions between adjacent states in the state transition graphs according to the setting parameter correlation matrix, so as to obtain a business layer model.

[0103] On the basis of the above scheme, the test signal determination module can optionally include a primary equipment operation parameter extraction submodule, an associated protection setting determination submodule, and a test excitation signal sequence determination submodule.

[0104] The primary equipment operation parameter extraction submodule is configured to perform a depth-first search on the physical layer model of the digital twin model according to path weights corresponding to breaker closed states and breaker open states, to search for an electrical communication path between the protection devices in the collaborative test scenario, and extract primary equipment operation parameters on the searched electrical communication path; the associated protection setting determination submodule is configured to query the mapping relationship between the primary equipment operation parameters and the secondary protection device setting parameters in the data layer model based on the primary equipment operation parameters on the electrical communication path, to obtain multiple protection parameters corresponding to the protection device nodes, input the multiple protection parameters into the setting parameter correlation matrix of the data layer model, and obtain associated protection settings in the collaborative test scenario, wherein the associated protection settings include distance protection action section parameters, distance protection time delay parameters, and current protection starting current parameters; and the test excitation signal sequence determination submodule is configured to set a fault point in an overlapping protection range of the distance protection action section parameters and the current protection starting current parameters, construct a short-circuit calculation model corresponding to the fault point, determine target power parameters of the fault point according to the primary equipment operation parameters and the short-circuit calculation model, and determine a test excitation signal sequence corresponding to the associated protection settings according to the target power parameters and the distance protection time delay parameters.

[0105] Optionally, the model parameter updating module comprises a cooperative test result obtaining submodule.

[0106] The cooperative test result obtaining submodule is configured to input the test excitation signal sequence into the multiple protection devices in the substation entity, collect action response signals of the multiple protection devices, and generate the action response signals and a preset standard response signal in the digital twin model to obtain a cooperative test result corresponding to the cooperative test scene.

[0107] Optionally, the model parameter updating module comprises a weight coefficient obtaining submodule, a first constraint condition determining submodule, a second constraint condition determining submodule, a global optimization objective function constructing submodule, a verifying submodule, and a model parameter updating submodule.

[0108] The weight coefficient obtaining submodule is configured to determine time response deviations, current response deviations, and impedance response deviations of the protection devices according to the cooperative test result, construct a judgment matrix by using an analytic hierarchy process, calculate a maximum eigenvalue and a corresponding eigenvector of the judgment matrix, and obtain weight coefficients by normalizing the eigenvector. The first constraint condition determining submodule is configured to construct a local optimization sub-objective function according to the weight coefficients, the time response deviations, the current response deviations, and the impedance response deviations, and take a load current multiple, a protection action time, and a distance measurement error as constraint conditions of the local optimization sub-objective function. The second constraint condition determining submodule is configured to optimize and solve the local optimization sub-objective function, construct a regional optimization sub-objective function according to a first function value of the local optimization sub-objective function corresponding to the protection device and the time response deviation, and take a backup protection delay margin and a reclosing time difference as constraint conditions of the regional optimization sub-objective function. The global optimization objective function constructing submodule is configured to determine a system coordination index according to the protection setting parameters of the multiple protection devices, and construct a global optimization objective function according to a second function value of the regional optimization sub-objective function of multiple regions and the system coordination index. The verifying submodule is configured to sequentially perform local optimization, regional optimization, and global optimization by using the local optimization sub-objective function, the regional optimization sub-objective function, and the global optimization objective function respectively, and perform functional integrity verification and performance index verification on an optimization result of each stage by using the digital twin model. The model parameter updating submodule is configured to update the optimized protection setting parameters to a data layer model of the digital twin model in a case where verification results of the functional integrity verification and the performance index verification reach a preset verification standard.

[0109] Based on the above scheme, optionally, the cooperative test result includes an actual action time of the protection device from starting to tripping output, a starting current and a measured impedance, and the weight coefficient obtaining sub-module can include: a time response deviation determination unit, a current response deviation determination unit and an impedance response deviation determination unit.

[0110] The time response deviation determination unit is configured to determine a root mean square error of an actual action time of the protection device from starting to tripping output and a theoretical action time, and determine a time response deviation of the protection device according to the root mean square error; the current response deviation determination unit is configured to determine a current relative deviation of a starting current of the protection device and a theoretical setting value, and determine a current response deviation of the protection device according to the current relative deviation; and the impedance response deviation determination unit is configured to determine a Euclidean distance of a measured impedance and an actual impedance, and determine an impedance response deviation of the protection device according to the Euclidean distance.

[0111] Based on the above scheme, optionally, the second constraint condition determination sub-module can include an optimization solving unit.

[0112] The optimization solving unit is configured to perform optimization solving on the local optimization sub-objective function based on a particle swarm algorithm with adaptive inertia weight, wherein the particle swarm algorithm adopts adaptive inertia weight, and the adaptive inertia weight changes according to a quadratic function rule with iteration number.

[0113] Based on the above scheme, optionally, the cooperative test system of the multi-interval protection device of the substation further includes a data difference amount determination module and a second model parameter updating module.

[0114] The data difference amount determination module is configured to, after the digital twin model of the substation is constructed based on the entity running data, determine simulation running data of the substation entity based on the physical layer model, the data layer model and the business layer model, and determine a data difference amount of the simulation running data and the entity running data; and the second model parameter updating module is configured to, in a case where the data difference amount is greater than a preset difference amount threshold, trigger a preset model parameter adaptive adjustment process, and update graph node information in the physical layer model, a mapping relationship table in the data layer model and a state transition graph in the business layer model.

[0115] Based on the above scheme, optionally, the test signal determination module can include a test scene combination screening sub-module and a cooperative test scene test sub-module.

[0116] The test scene combination screening submodule is configured for generating an initial population corresponding to the cooperative test scenes of the plurality of protection devices in the digital twin model by using a Monte Carlo algorithm, inputting the initial population into a genetic algorithm, and iteratively calculating scene coverage, test working condition quantity, and operation step quantity as optimization targets to screen out an optimal test scene combination.

[0117] The substation multi-interval protection device cooperative test system provided by the embodiment of the present application can perform the substation multi-interval protection device cooperative test method provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0118] Embodiment four

[0119] Figure 4 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0120] As shown in Figure 4 The electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which are communicatively connected to the at least one processor 11. The memory stores computer programs that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0121] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0122] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as a method for coordinated testing of substation multi-bay protection devices.

[0123] In some embodiments, a method for coordinated testing of substation multi-bay protection devices can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of a method for coordinated testing of substation multi-bay protection devices described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform a method for coordinated testing of substation multi-bay protection devices by any other appropriate means, such as by means of firmware.

[0124] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0125] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, enables the functions / acts specified in the flowcharts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.

[0126] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0127] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0128] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0129] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0130] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0131] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of coordinated testing of substation multi-bay protection devices, characterized in that, The method comprises: collecting entity operation data of a plurality of protection devices of a substation entity, and constructing a digital twin model of the substation based on the entity operation data; wherein the digital twin model comprises a physical layer model, a data layer model and a business layer model; the physical layer model stores a topological structure of primary equipment of the substation, the data layer model stores a mapping relationship table between primary equipment operation parameters and secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and conversion conditions between adjacent states in the state transition graph; generating a cooperative test scenario of a plurality of protection devices based on the digital twin model, determining an electrical connection path between a plurality of the protection devices in the cooperative test scenario through the physical layer model of the digital twin model, determining associated protection settings of the protection devices in the cooperative test scenario through the data layer model and the electrical connection path, and determining a test excitation signal sequence corresponding to the associated protection settings according to the business layer model; testing a plurality of the protection devices in the substation entity by using the test excitation signal sequence to obtain a cooperative test result corresponding to the cooperative test scenario, adaptively optimizing and adjusting protection setting parameters of a plurality of the protection devices according to the cooperative test result, and updating the optimized protection setting parameters to the data layer model of the digital twin model.

2. The method according to claim 1, characterized in that The method of constructing the digital twin model of the substation based on the entity operation data comprises: inputting the entity operation data into a preset graph database, and constructing a physical layer model based on the graph database and the entity operation data, wherein the physical layer model takes circuit breakers, disconnectors and transformers as graph nodes, takes electrical connection relationships between the graph nodes as connection edges, and records graph node information corresponding to the graph nodes; inputting the graph node information in the physical layer model into a relational database, establishing a mapping relationship table of a mapping relationship between the primary equipment operation parameters and the secondary protection device setting parameters based on the relational database, calculating coupling coefficients between different secondary protection device setting parameters according to the mapping relationship table, generating a setting parameter association matrix to obtain a data layer model; inputting the setting parameter association matrix in the data layer model into a preset state machine, constructing protection device state transition graphs for single-phase ground faults and two-phase short-circuit faults based on the state machine, and setting conversion conditions between adjacent states in the state transition graphs according to the setting parameter association matrix to obtain a business layer model.

3. The method of claim 1, wherein, The method of determining an electrical connection path between a plurality of the protection devices in the cooperative test scenario through the physical layer model of the digital twin model, determining associated protection settings of the protection devices in the cooperative test scenario through the data layer model and the electrical connection path, and determining a test excitation signal sequence corresponding to the associated protection settings according to the business layer model comprises: Performing a depth-first search in the physical layer model of the digital twin model based on the path weights corresponding to the circuit breaker closed state and the circuit breaker open state to search for electrical connectivity paths between the plurality of protection devices in the collaborative test scenario, and extracting primary equipment operating parameters on the searched electrical connectivity paths; Based on the primary equipment operating parameters on the electrical connection path, query the mapping relationship between the primary equipment operating parameters and the secondary protection device set value parameters in the data layer model to obtain a plurality of protection parameters corresponding to the protection device node, input the plurality of protection parameters into the set value parameter association matrix of the data layer model, and obtain the associated protection set values ​​in the collaborative test scenario, wherein the associated protection set values ​​include distance protection action section parameters, distance protection time delay parameters, and current protection starting current parameters; A fault point is set within the overlapping protection range of the distance protection action section parameters and the current protection starting current parameters, and a short-circuit calculation model corresponding to the fault point is constructed. The target power parameters of the fault point are determined according to the primary equipment operating parameters and the short-circuit calculation model. The test excitation signal sequence corresponding to the associated protection constant is determined according to the target power parameters and the distance protection time delay parameters.

4. The method according to claim 1, wherein The step of testing the plurality of protection devices in the substation entity using the test excitation signal sequence to obtain a collaborative test result corresponding to the collaborative test scenario includes: The test excitation signal sequence is input into the multiple protection devices in the substation entity, the action response signals of the multiple protection devices are collected, and the action response signals are generated with the standard response signals preset in the digital twin model to obtain the collaborative test results corresponding to the collaborative test scenario.

5. The method of claim 1, wherein, Adaptively optimizing and adjusting the protection setting parameters of the plurality of protection devices according to the collaborative test results, and updating the optimized protection setting parameters to the data layer model of the digital twin model, includes: Determining the time response deviation, current response deviation, and impedance response deviation of the protection device according to the collaborative test results, and constructing a judgment matrix using a hierarchical analysis method, calculating the maximum eigenvalue and corresponding eigenvector of the judgment matrix, and obtaining a weight coefficient by normalizing the eigenvector; Constructing a local optimization sub-objective function according to the weight coefficient, the time response deviation, the current response deviation, and the impedance response deviation, and using the load current multiple, the protection action time, and the ranging error as constraints of the local optimization sub-objective function; Optimizing and solving the local optimization sub-objective function, constructing a regional optimization sub-objective function based on the first function value of the local optimization sub-objective function corresponding to the protection device and the time response deviation, and using the backup protection delay margin and the reclosing time difference as constraints of the regional optimization sub-objective function; Determining a system coordination index according to the protection setting parameters of the plurality of protection devices, and constructing a global optimization objective function according to the second function values ​​of the regional optimization sub-objective functions of the plurality of regions and the system coordination index; Perform local optimization, regional optimization, and global optimization in sequence through the local optimization sub-objective function, the regional optimization sub-objective function, and the global optimization objective function, and perform functional integrity verification and performance index verification on the optimization results of each stage through the digital twin model; When the verification results of the functional integrity verification and the performance index verification meet the preset verification standards, the optimized protection constant parameters will be updated to the data layer model of the digital twin model.

6. The method of claim 5, wherein, The coordinated test results include the actual operation time, starting current, and measured impedance of the protection device from startup to trip output; and determining the time response deviation, current response deviation, and impedance response deviation of the protection device based on the coordinated test results includes: Determining a root mean square error between an actual operating time of the protection device from startup to trip output and a theoretical operating time, and determining a time response deviation of the protection device based on the root mean square error; Determining a current relative deviation between a starting current of a protection device and a theoretical setting value, and determining a current response deviation of the protection device according to the current relative deviation; A Euclidean distance between the measured impedance and the actual impedance is determined, and an impedance response deviation of the protection device is determined based on the Euclidean distance.

7. The method of claim 5, wherein, Optimizing and solving the local optimization sub-objective function includes: The local optimization sub-objective function is optimized and solved based on a particle swarm algorithm using an adaptive inertia weight, wherein the particle swarm algorithm uses an adaptive inertia weight, and the adaptive inertia weight changes according to a quadratic function law with the number of iterations.

8. The method of claim 1, wherein, After constructing the digital twin model of the substation based on the entity operation data, the method further includes: Determining simulated operation data of the substation entity based on the physical layer model, the data layer model, and the business layer model, and determining a data difference between the simulated operation data and the entity operation data; When the data difference is greater than the preset difference threshold, the preset model parameter adaptive adjustment process is triggered to update the graph node information in the physical layer model, the mapping relationship table in the data layer model, and the state transition diagram in the business layer model.

9. The method of claim 1, wherein, Generating a collaborative test scenario for multiple protection devices based on the digital twin model includes: A Monte Carlo algorithm is used to generate an initial population corresponding to the collaborative test scenarios of multiple protection devices in the digital twin model. The initial population is input into a genetic algorithm, and iterative calculations are performed with scenario coverage, the number of test conditions, and the number of operation steps as optimization objectives to screen out the optimal test scenario combination. Test the cooperative test scenes in the test scene combination, respectively collect the test results corresponding to each cooperative test scene during test execution, increase the sampling density of the cooperative test scene when the dispersion degree of the test results is greater than a preset dispersion threshold, and decrease the sampling density of the cooperative test scene when the dispersion degree of the test results is less than the preset dispersion threshold.

10. A coordinated testing system for substation multi-bay protection devices, characterized by, Comprise: A digital model construction module is configured to collect entity operation data of a plurality of protection devices of a substation entity, and construct a digital twin model of the substation based on the entity operation data; wherein the digital twin model comprises a physical layer model, a data layer model, and a business layer model; the physical layer model stores a topology structure of primary equipment of the substation, the data layer model stores a mapping relationship table between primary equipment operation parameters and secondary protection device setting parameters, and the business layer model stores a protection device state transition graph and conversion conditions between adjacent states in the state transition graph; A test signal determination module is configured to generate cooperative test scenes of a plurality of protection devices based on the digital twin model, determine an electrical connection path between the plurality of protection devices under the cooperative test scenes through the physical layer model of the digital twin model, determine associated protection settings of the protection devices under the cooperative test scenes through the data layer model and the electrical connection path, and determine a test excitation signal sequence corresponding to the associated protection settings according to the business layer model; A model parameter update module is configured to test the plurality of protection devices in the substation entity using the test excitation signal sequence to obtain cooperative test results corresponding to the cooperative test scenes, adaptively optimize and adjust the protection settings of the plurality of protection devices according to the cooperative test results, and update the optimized protection settings to the data layer model of the digital twin model.

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