Collaborative test method and system for multi-interval protection device of transformer substation

Abstract

The invention discloses a collaborative testing method and system for a multi-interval protection device of a transformer substation. The method comprises the following steps: collecting entity operation data of a plurality of protection devices of a substation entity, constructing a digital twin model of the substation, generating a collaborative test scene of the plurality of protection devices, determining an electrical communication path among the plurality of protection devices in the collaborative test scene, determining an associated protection constant value of the protection device in the collaborative test scene through the data layer model and the electrical connection path, determining a test excitation signal sequence corresponding to the associated protection constant value according to the business layer model, and testing the plurality of protection devices in the substation entity to obtain a collaborative test result corresponding to the collaborative test scene, the protection constant value parameters of the plurality of protection devices are adaptively optimized and adjusted and updated to the data layer model of the digital twinborn model, so that the response speed and the detection accuracy of the protection devices are improved, the safety and the reliability of the operation of the transformer substation are ensured, and continuous optimization is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of substation detection, and in particular to a coordinated testing method and system for a multi-compartment protection device of a substation. Background Art

[0002] With the continuous expansion of the scale of power systems and the improvement of their intelligence levels, substations, as an important part of the power grid, are crucial to ensuring the reliability of the entire power system through their safe and stable operation.

[0003] In the related technologies, the testing of substation protection devices mostly relies on manual operation or simple automated tools, which makes it difficult to fully cover all possible working conditions and effectively simulate complex fault conditions and the mutual influence between equipment, making it difficult to efficiently and accurately complete the coordinated testing of numerous protection devices in the substation and optimize their configuration accordingly. Summary of the invention

[0004] The present invention provides a coordinated testing method and system for multi-compartment protection devices in a substation, so as to solve the problem that the testing method in the related art is difficult to efficiently and accurately complete the coordinated testing between numerous protection devices in the substation and optimize the configuration accordingly.

[0005] According to one aspect of the present invention, a coordinated testing method for a multi-bay protection device in a substation is provided, the method comprising:

[0006] Collect entity operation data of multiple protection devices of the substation entity, and build 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 topological structure of the primary equipment of the substation, the data layer model stores the mapping relationship table between the primary equipment operation parameters and the secondary protection device setting parameters, and the business layer model stores the protection device state transition diagram and the conversion conditions between adjacent states in the state transition diagram;

[0007] Generate a collaborative test scenario for multiple protection devices based on the digital twin model, determine the electrical connection path between the multiple protection devices in the collaborative test scenario through the physical layer model of the digital twin model, determine the associated protection setting value of the protection device in the collaborative test scenario through the data layer model and the electrical connection path, and determine the test excitation signal sequence corresponding to the associated protection setting value according to the business layer model;

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

[0009] According to another aspect of the present invention, a coordinated testing system for a multi-bay protection device of a substation is provided, the system comprising:

[0010] A digital model building module, used to collect entity operation data of multiple protection devices of a substation entity, and build 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 topological structure of the primary equipment of the substation, the data layer model stores a mapping relationship table between the operating parameters of the primary equipment and the set parameters of the secondary protection device, and the business layer model stores a state transition diagram of the protection device and the conversion conditions between adjacent states in the state transition diagram;

[0011] A test signal determination module is used to generate a collaborative test scenario for multiple protection devices based on the digital twin model, determine the electrical connection path between the multiple protection devices in the collaborative test scenario through the physical layer model of the digital twin model, determine the associated protection setting value of the protection device in the collaborative test scenario through the data layer model and the electrical connection path, and determine the test excitation signal sequence corresponding to the associated protection setting value according to the business layer model;

[0012] A model parameter updating module is used to test the multiple protection devices in the substation entity using the test excitation signal sequence to obtain a collaborative test result corresponding to the collaborative test scenario, adaptively optimize and adjust the protection setting parameters of the multiple protection devices according to the collaborative test result, and update the optimized protection setting parameters to the data layer model of the digital twin model.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to 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 so that the at least one processor can execute the coordinated testing method for a substation multi-compartment protection device according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the coordinated testing method of a substation multi-compartment protection device described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention is, first, by collecting the entity operation data of multiple protection devices of the substation entity, building a digital twin model of the substation based on the entity operation data, because the digital twin model includes a physical layer model, a data layer model and a business layer model; the physical layer model stores the topological structure of the primary equipment of the substation, the data layer model stores the mapping relationship table between the primary equipment operation parameters and the secondary protection device setting parameters, the business layer model stores the protection device state transition diagram and the conversion conditions between adjacent states in the state transition diagram, it is possible to comprehensively cover the physical structure, operation parameters and state transition of the substation by building a digital twin model, and realize accurate simulation and optimization; then, by generating a collaborative test scenario of multiple protection devices based on the digital twin model, determining the electrical connection path between the multiple protection devices in the collaborative test scenario through the physical layer model of the digital twin model, determining the associated protection setting of the protection device in the collaborative test scenario through the data layer model and the electrical connection path, according to the business layer model Determine the test excitation signal sequence corresponding to the associated protection setting. By generating a collaborative test scenario and determining the electrical connectivity path and the protection setting, the test excitation signal sequence can be accurately generated to ensure the comprehensiveness and accuracy of the test and optimize the collaborative working performance of the protection device. Finally, by using the test excitation signal sequence to test multiple protection devices in the substation entity to obtain a collaborative test result corresponding to the collaborative test scenario, the protection setting parameters of the multiple protection devices are adaptively optimized and adjusted according to the collaborative test results, and the optimized protection setting parameters are updated to the data layer model of the digital twin model. Through actual testing and optimization adjustment, the response speed and detection accuracy of the protection device can be improved, the safety and reliability of the substation operation can be ensured, and the digital twin model can be updated to achieve continuous optimization, which solves the problem that the testing method in the related technology is difficult to efficiently and accurately complete the collaborative test between many protection devices in the substation and optimize the configuration accordingly, improves the response speed and detection accuracy of the protection device, ensures the safety and reliability of the substation operation, and achieves continuous optimization.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flow chart of a coordinated testing method for a multi-bay protection device of a substation provided according to Embodiment 1 of the present invention;

[0022] Figure 2 is a flow chart of a coordinated testing method for a multi-bay protection device of a substation provided according to a second embodiment of the present invention;

[0023] Figure 3 2 is a schematic diagram of the structure of a coordinated testing device for a multi-compartment protection device of a substation provided according to a third embodiment of the present invention;

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

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned 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 where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

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

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

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

[0032] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet the relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0033] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0034] Embodiment 1

[0035] Figure 1 A flowchart of a collaborative testing method for a multi-compartment protection device of a substation is provided for the first embodiment of the present invention. This embodiment is applicable to the case where the protection setting parameters of the protection device are automatically adjusted according to the collaborative testing results. The method can be executed by a collaborative testing system for a multi-compartment protection device of a substation. The collaborative testing system for the multi-compartment protection device of a substation can be implemented in the form of hardware and / or software, and optionally, can be implemented by an electronic device, which can be a mobile terminal, a PC or a server, etc.

[0036] like Figure 1 As shown, the method may specifically include:

[0037] S110. Collect entity operation data of multiple protection devices of the substation entity, and build 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 topological structure of the primary equipment of the substation, the data layer model stores a mapping relationship table between the operating parameters of the primary equipment and the set parameters of the secondary protection device, and the business layer model stores a state transition diagram of the protection device and the transition conditions between adjacent states in the state transition diagram.

[0038] Among them, the multiple protection devices can be understood as different types of protection equipment existing inside the substation, which are used to detect the state of the power system and can take action (such as cutting off the power supply, etc.) when a fault or abnormality is detected to prevent the damage from expanding. The physical operation data can be understood as data collected from multiple protection devices in actual operation, including but not limited to at least one of temperature, voltage, current and switch status, which is used to reflect the real-time working status 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 operating status and behavior of the substation. The digital twin model includes at least a physical layer model, a data layer model and a business layer model. The physical layer model can be understood as a model that describes the structure and composition of the physical system, and can graphically represent the connection relationship of various primary devices (such as circuit breakers, disconnectors and mutual inductors, etc.) in the substation. Exemplarily, nodes can be used to represent devices, and lines can be used to represent the electrical connection between primary devices. The primary device can be understood as the main equipment directly involved in the generation, transmission and distribution of electric energy in the power system, such as transformers or circuit breakers.

[0039] The data layer model can be understood as a model responsible for storing and processing data from the physical layer, and can store, including but not limited to, a mapping relationship table between the operating parameters of the primary device and the set value parameters of the secondary protection device. The secondary protection device can be understood as a small auxiliary device used to detect the health status of the primary device and perform protection actions, such as a relay protection system. The mapping relationship table can be understood as a data table used to represent the relationship between the operating parameters of the primary device and the setting values ​​of the secondary protection device. Exemplarily, for example, for a certain circuit breaker, its primary device operating parameters include rated current, rated voltage, etc., and the corresponding secondary protection device set value parameters include but are not limited to at least one of overcurrent protection set value, low voltage protection set 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 of the cooperative control logic of the protection device corresponding to the storage control strategy parameters. The business layer model can store the state transition diagram of the protection device and the transition conditions between adjacent states in the state transition diagram. The state transition diagram of the protection device can be understood as a visualization tool that shows the conditions for the transfer of one state of the protection device to another state. The transition condition can be understood as the condition that triggers 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 uses circuit breakers, disconnectors, and transformers as graph nodes, uses the electrical connection relationship between the graph nodes as the connection edge, and records the graph node information corresponding to the graph nodes; inputting the graph node information in the physical layer model into a relational database, and establishing a mapping relationship table of the mapping relationship between the primary equipment operation parameters and the secondary protection device set parameters based on the relational database, and calculating the coupling coefficient between different secondary protection device set parameters according to the mapping relationship table, generating a set parameter association matrix, so as to obtain a data layer model; inputting the set parameter association matrix in the data layer model into a preset state machine, and constructing a protection device state transition diagram for single-phase grounding fault and two-phase short circuit fault respectively based on the state machine, and setting the transition conditions between adjacent states in the state transition diagram according to the set parameter association matrix, so as to obtain a business layer model.

[0042] Among them, the graph database can be understood as a database type for storing and querying complex relational data, which is suitable for modeling data sets with many-to-many relationships, such as the topological structure of the power system. The graph node can be understood as a protection device representing a substation entity in the graph database. Exemplarily, the graph node can be a primary device in the substation (such as a circuit breaker, a disconnector, and a transformer, etc.). Each graph node may contain graph node information of the primary device. The graph node information can be understood as detailed information of the graph node. The graph node information may include but is not limited to at least one of type information, rated parameter information, installation location information, current operating status information, and real-time measurement value information. The connecting edge can be understood as a connecting line representing the relationship between two graph nodes in the graph database, that is, the electrical connection relationship between the primary devices.

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

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

[0045] In an optional implementation, for 110kV line protection, the mapping relationship table may include the following mappings: line rated current (primary device parameter) is mapped to overcurrent protection start current setting (secondary protection setting), line impedance (primary device parameter) is mapped to distance protection impedance setting (secondary protection setting), etc. Based on the mapping relationship table, the coupling coefficient between the setting parameters of different secondary protection devices can be calculated to generate a setting parameter association matrix. The association matrix reflects the degree of association between different protection settings. For example, for distance protection and overcurrent protection, there is a strong correlation between their start settings, and a higher coupling coefficient can be assigned; while for distance protection and low-voltage lockout 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] Among them, the state transition diagram of the protection device can be understood as a diagram constructed using a state machine, which shows the state changes and conversion conditions of the protection device when facing different fault types (such as single-phase grounding fault or two-phase short circuit fault, etc.). The single-phase grounding fault can be understood as an abnormal electrical connection between a phase and the earth. The two-phase short circuit fault can be understood as occurring between any two phases, resulting in an abnormal low-resistance connection between the two phases. Each state transition diagram of the protection device may include at least one of the states of the initial state, the protection start state, the fault judgment state, and the tripping action state. The conversion conditions between adjacent states in the state transition diagram of the protection device can be set according to the fixed value parameter association matrix. The conversion conditions can be understood as the conditions for triggering state transfer. Exemplarily, for distance protection, the conversion conditions from the protection start state to the fault judgment state can be set according to the coupling relationship between the impedance fixed value and the current fixed value reflected in the fixed value parameter association matrix.

[0047] An optional implementation method, taking 110kV line distance protection as an example, its state transfer diagram may include the following state transitions: 1) The transition condition from the initial state to the protection start state: the measured impedance is less than the start setting and the current is greater than the start current setting; 2) The transition condition from the protection start state to the fault judgment state: the measured impedance enters the action area 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 lockout signal is received.

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

[0049] On the basis of the above scheme, optionally, after constructing the digital twin model of the substation based on the entity operation data, it also includes: determining the simulation operation data of the substation entity based on the physical layer model, the data layer model and the business layer model, and determining the data difference between the simulation operation data and the entity operation data; when the data difference is greater than a preset difference threshold, triggering a preset model parameter adaptive adjustment process, updating 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 simulated 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 specific conditions. The data difference can be understood as the degree of difference between the simulated operation data and the actual operation data. When the data difference exceeds the preset difference threshold, it indicates that the model may need to be updated to more accurately reflect the actual situation.

[0051] In an optional implementation, suppose there is a device on a factory production line whose temperature sensor simulates a value of 85°C in the digital twin model, while the actual collected temperature data is 90°C. The system calculates the 5°C difference and compares it with the preset temperature difference threshold (e.g., 3°C). When it is detected that the difference exceeds the preset threshold, the preset model parameter adaptive adjustment process will be automatically triggered, which may 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 parameters in the physical layer model need to be updated; then, the relevant graph node information in the physical layer model will be updated, and the thermal conductivity coefficient of the environment around the device will be adjusted, or the device's own thermal generation model will be updated. The updated graph node information will more accurately reflect the characteristics of the actual physical environment; then, the mapping relationship table in the data layer model will be checked and updated, and the sampling frequency of the temperature data or the data processing algorithm may need to be adjusted to better capture the dynamic characteristics of temperature changes; finally, the state transition diagram in the business layer model will be updated. Temperature changes may affect the working status or maintenance plan of the equipment, so it may be necessary to trigger an early warning when the temperature reaches 88°C instead of the set 90°C.

[0052] The above solution compares the simulated operation data with the actual operation data, determines the difference between the data, and triggers the adaptive adjustment process when the difference exceeds the preset difference threshold, ensuring that the digital twin model can simulate the actual operation status of the substation. It not only improves the prediction accuracy and reliability of the model, but also can promptly discover and correct potential configuration errors or performance bottlenecks, thereby enhancing the safety and efficiency of substation operation, reducing maintenance costs, and extending equipment service life.

[0053] S120. Generate a collaborative test scenario for multiple protection devices based on the digital twin model, determine the electrical connectivity path between the multiple protection devices in the collaborative test scenario through the physical layer model of the digital twin model, determine the associated protection constants of the protection devices in the collaborative test scenario through the data layer model and the electrical connectivity path, and determine a test excitation signal sequence corresponding to the associated protection constants according to the business layer model.

[0054] Among them, the collaborative test scenario can be understood as a simulation environment designed to detect multiple protection devices to deal with specific situations. The electrical connection path can be understood as a specific path between current from one protection device to another protection device in the power system. The associated protection setting can be understood as a protection setting value determined according to the electrical connection path between the protection devices to ensure that the protection function is correctly activated under specific conditions. The test excitation signal sequence can be understood as a predefined input signal used to trigger the protection device to test whether its response is as expected. The test excitation signal sequence may include but is not limited to at least one of voltage amplitude, current amplitude, phase angle and timing relationship.

[0055] On the basis of the above scheme, optionally, the collaborative test scenarios of multiple protection devices are generated based on the digital twin model, including: using a Monte Carlo algorithm to generate an initial population corresponding to the collaborative test scenarios of multiple protection devices in the digital twin model, inputting the initial population into a genetic algorithm, and performing iterative calculations with scenario coverage, number of test conditions, and number of operation steps as optimization goals to screen out the optimal test scenario combination; testing the collaborative test scenarios in the test scenario combination, and collecting test results corresponding to each of the collaborative test scenarios during the test execution process, and when the discrete degree of the test results is greater than a preset discrete threshold, increasing the sampling density of the collaborative test scenarios, and when the discrete degree of the test results is less than the preset discrete threshold, reducing the sampling density of the collaborative test scenarios.

[0056] Among them, the Monte Carlo algorithm is a statistical method for solving problems through random sampling or random numbers, which is used to generate an initial population, that is, a variety of possible collaborative test scenarios. The initial population can be understood as a set of randomly generated solutions in the genetic algorithm, which serves as the starting point for the algorithm iteration calculation. The scenario coverage can be understood as the percentage of the collaborative test scenario covering the parameter space. A high scenario coverage means that more fault modes and operating conditions are taken into account. 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. A larger number of conditions means a more comprehensive test. The number of operating steps can be understood as the number of steps required to complete a test scenario. Fewer operating steps can simplify the test process. The optimal test scenario combination can be understood as a set of test scenarios that can best meet the optimization objectives (such as scenario coverage, number of test conditions, number of operating steps, etc.) after optimization by the genetic algorithm. The test result can be understood as the data obtained after executing the test scenario, which reflects the performance of the protection device under specific conditions. The degree of dispersion can be understood as a statistical indicator used to quantify the degree of data dispersion. The higher the degree of dispersion, the greater the difference between data points. The preset discrete threshold value can be understood as a pre-set standard value used to determine whether the discrete degree of the test result requires adjustment of the sampling density. The sampling density can be understood as the frequency or number of data points collected during the test.

[0057] An optional implementation method uses a Monte Carlo algorithm 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 the Monte Carlo method is used to randomly sample within the value range of each parameter to generate K initial test scenarios. For example, assuming there are 3 protection devices, each with 5 adjustable parameters, 100 initial test scenarios can be generated, each containing 15 parameter values.

[0058] Next, the generated initial population is input into the genetic algorithm. The genetic algorithm performs iterative calculations with scenario coverage, the number of test conditions, and the number of operation steps as optimization targets; 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 the fitness of the individuals. The crossover operation can use the single-point crossover method to randomly select the crossover point and exchange some genes of the parent individuals. The mutation operation randomly changes the values ​​of certain genes in the individuals. Through multiple iterations, the optimal test scenario combination is finally selected.

[0059] The above scheme, by combining the Monte Carlo algorithm and the genetic algorithm to generate and optimize collaborative test scenarios, can automatically screen out the optimal test scenario combination with high coverage, diverse working conditions and easy operation, thereby improving the accuracy and reliability of the test, and dynamically adjust the sampling density according to the discreteness of the test results. It can not only ensure that enough samples are obtained to identify potential problems when the data fluctuates greatly, but also reduce the number of unnecessary tests when the data is stable, saving resources and improving the effect and efficiency of the collaborative testing of substation protection devices.

[0060] On the basis of the above scheme, optionally, the electrical connectivity path between the multiple protection devices in the collaborative test scenario is determined through the physical layer model of the digital twin model, the associated protection setting of the protection device in the collaborative test scenario is determined through the data layer model and the electrical connectivity path, and the test excitation signal sequence corresponding to the associated protection setting is determined according to the business layer model, including: performing a depth-first search in the physical layer model of the digital twin model according to the path weights corresponding to the circuit breaker closed state and the circuit breaker open state to search for the electrical connectivity path between the multiple protection devices in the collaborative test scenario, and extracting the primary equipment operating parameters on the searched electrical connectivity path; querying the data layer model based on the primary equipment operating parameters on the electrical connectivity path. A mapping relationship between the primary equipment operating parameters and the set parameters of the secondary protection device is used to obtain multiple protection parameters corresponding to the protection device node, and the multiple protection parameters are input into the set parameter association matrix of the data layer model to obtain the associated protection set values ​​under 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; and the test excitation signal sequence corresponding to the associated protection set values ​​is determined according to the target power parameters and the distance protection time delay parameters.

[0061] Among them, the circuit breaker closing state can be understood as the state in which the circuit breaker is closed to allow current to pass. The circuit breaker opening state can be understood as the state in which the circuit breaker is open to prevent current from passing. The path weight can be understood as a value for evaluating the importance of a path from one node to another in a search algorithm, which is used to reflect the impact of the circuit breaker being in the circuit breaker closing state or the circuit breaker opening state on the electrical connection path. The depth-first search is a graph traversal algorithm that starts from the root node and searches as deeply as possible along each branch until it cannot continue, then backtracks to the previous node and continues the unexplored branch. The distance protection action section parameter can be understood as a set value indicating the division of regions in which the protection device responds to faults within different distance ranges. The distance protection time delay parameter can be understood as a set value indicating the time for the protection device to delay a certain time before starting the protection action after detecting a fault. The current protection starting current parameter can be understood as the 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 act together on the power system in the same area. The fault point can be understood as a preset fault location, which is used to simulate 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 the expected current or voltage at the fault point obtained according to the short-circuit calculation model. The target power parameter may include but is not limited to at least one of the voltage signal amplitude and the current signal amplitude.

[0062] An optional implementation method uses a preset graph database to perform a depth-first search, with the target protection device node as the starting point for the search. During the search, the path weight of the circuit breaker closed state is set to 1, and the path weight of the circuit breaker open state is set to infinity to ensure that the searched path is actually electrically connected; assuming that the target protection device is a transformer differential protection, the adjacent protection devices such as the line distance protection and bus differential protection connected to it can be found through depth-first search. During the search, various primary equipment parameters on the electrically connected path will be recorded, including line impedance parameters, transformer ratio parameters, current transformer ratio parameters, etc.; the data layer model is used to extract the associated protection constants of adjacent protection devices. Based on the electrical connection path obtained from the previous search, the preset 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 the first, second, and third stage protections can be extracted. Distance and corresponding time delay; For transformer differential protection, its ratio braking characteristic curve parameters, threshold values, etc. can be extracted. These protection parameters are then input into the fixed value parameter association matrix output by the data layer model to determine the associated protection fixed values ​​that need to be tested in coordination; 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 distance protection section 2 of a 110kV line is set to 120% of the total length of the line, 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 position of the fault point, the short-circuit calculation model can calculate the corresponding voltage signal amplitude and current signal amplitude; The test excitation signal sequence of the voltage signal amplitude and the current signal amplitude is set according to the distance protection time delay parameters. For example, if the time delay of the second section of the distance protection of the line is 0.5s, the fault voltage and current signals can be set to last for 0.6s to verify whether the protection can act correctly within the set time.

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

[0064] S130. Use the test excitation signal sequence to test the multiple protection devices in the substation entity to obtain a collaborative test result corresponding to the collaborative test scenario, adaptively optimize and adjust the protection setting parameters of the multiple protection devices according to the collaborative test result, and update the optimized protection setting parameters to the data layer model of the digital twin model.

[0065] The collaborative test result may be understood as a result obtained after implementing a test excitation signal sequence, and is used to reflect the actual performance of the protection device in a given test scenario.

[0066] An optional implementation method is to adaptively optimize and adjust the protection setting parameters of multiple protection devices based on the results of the coordinated test. For example, if it is found that the action time of a certain protection device does not meet the requirements, its time setting can be appropriately adjusted. If it is found that there are problems with the coordination between adjacent protection devices, it is necessary to comprehensively consider adjusting the setting of multiple protection devices. The optimization adjustment can adopt intelligent optimization methods such as genetic algorithms to meet the coordination requirements between multiple protection devices.

[0067] On the basis of the above scheme, optionally, the test excitation signal sequence is used to test the multiple protection devices in the substation entity to obtain a collaborative test result corresponding to the collaborative test scenario, including: inputting the test excitation signal sequence into the multiple protection devices in the substation entity, collecting action response signals of the multiple protection devices, and generating the action response signals with the standard response signals preset in the digital twin model to obtain a collaborative test result corresponding to the collaborative test scenario.

[0068] Among them, the action response signal may include an output signal generated by the protection device after receiving the test excitation signal. The action response signal may include but is not limited to a protection start signal, a trip signal, and a reclosing signal. The action response signal can be realized by collecting the switch output signal of the protection device through the digital input port of the protection test instrument. The standard response signal can be understood as the response signal of the protection device defined in the digital twin model, which is used for comparison with the action response signal.

[0069] The technical solution of the embodiment of the present invention is, first, by collecting the entity operation data of multiple protection devices of the substation entity, building a digital twin model of the substation based on the entity operation data, because the digital twin model includes a physical layer model, a data layer model and a business layer model; the physical layer model stores the topological structure of the primary equipment of the substation, the data layer model stores the mapping relationship table between the primary equipment operation parameters and the secondary protection device setting parameters, the business layer model stores the protection device state transition diagram and the conversion conditions between adjacent states in the state transition diagram, it is possible to comprehensively cover the physical structure, operation parameters and state transition of the substation by building a digital twin model, and realize accurate simulation and optimization; then, by generating a collaborative test scenario of multiple protection devices based on the digital twin model, determining the electrical connection path between the multiple protection devices in the collaborative test scenario through the physical layer model of the digital twin model, determining the associated protection setting of the protection device in the collaborative test scenario through the data layer model and the electrical connection path, according to the business layer model Determine the test excitation signal sequence corresponding to the associated protection setting. By generating a collaborative test scenario and determining the electrical connectivity path and the protection setting, the test excitation signal sequence can be accurately generated to ensure the comprehensiveness and accuracy of the test and optimize the collaborative working performance of the protection device. Finally, by using the test excitation signal sequence to test multiple protection devices in the substation entity to obtain a collaborative test result corresponding to the collaborative test scenario, the protection setting parameters of the multiple protection devices are adaptively optimized and adjusted according to the collaborative test results, and the optimized protection setting parameters are updated to the data layer model of the digital twin model. Through actual testing and optimization adjustment, the response speed and detection accuracy of the protection device can be improved, the safety and reliability of the substation operation can be ensured, and the digital twin model can be updated to achieve continuous optimization, which solves the problem that the testing method in the related technology is difficult to efficiently and accurately complete the collaborative test between many protection devices in the substation and optimize the configuration accordingly, improves the response speed and detection accuracy of the protection device, ensures the safety and reliability of the substation operation, and achieves continuous optimization.

[0070] Embodiment 2

[0071] Figure 2 A flowchart of a collaborative testing method for a multi-compartment protection device of a substation provided in the second embodiment of the present invention. This embodiment is based on the above embodiment, and adaptively optimizes and adjusts the protection setting parameters of multiple protection devices according to the collaborative test results, and updates the optimized protection setting parameters to the further refinement of the data layer model of the digital twin model. For specific implementation methods, please refer to the description of this embodiment. Among them, the technical features that are the same or similar to the above embodiments are not repeated here.

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

[0073] S210, collecting entity operation data of multiple protection devices of the 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 topological structure 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 the protection device state transition diagram and the conversion conditions between adjacent states in the state transition diagram;

[0074] S220, generating a collaborative test scenario for multiple protection devices based on the digital twin model, determining an electrical connection path between the multiple protection devices in the collaborative test scenario through the physical layer model of the digital twin model, determining an associated protection setting value of the protection device in the collaborative test scenario through the data layer model and the electrical connection path, and determining a test excitation signal sequence corresponding to the associated protection setting value according to the business layer model;

[0075] S230. Use the test excitation signal sequence to test the multiple protection devices in the substation entity to obtain a collaborative test result corresponding to the collaborative test scenario, determine the time response deviation, current response deviation, and impedance response deviation of the protection device according to the collaborative test result, and use the hierarchical analysis method to construct a judgment matrix, calculate the maximum eigenvalue and corresponding eigenvector of the judgment matrix, and obtain the weight coefficient by normalizing the eigenvector.

[0076] Among them, the time response deviation can be understood as the difference between the action time of the actual protection device and the preset standard time, which 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, which 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, which is used to evaluate the accuracy of the protection device in impedance detection. The hierarchical analysis method is a multi-criteria decision analysis method, which is used to decompose complex decision problems into sub-problems at multiple levels and determine the final decision plan by comparing the importance of each sub-problem. The hierarchical analysis method 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 hierarchical analysis method, and each element in the matrix represents the relative importance ratio between two factors. The maximum eigenvalue can be understood as the largest one among 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 the matrix transformation and is used to determine the weight coefficient.

[0077] An optional implementation method is to first calculate the time response deviation, current response deviation and impedance response deviation based on the collaborative test results. A hierarchical model is established to divide the optimization objectives, optimization criteria and parameters to be optimized into different levels. Then, each factor of each layer is compared with the corresponding factors of the previous layer to construct a pairwise comparison judgment matrix. For example, for a certain protection device, the time response, current response and impedance response can be used as the criterion layer to construct a 3x3 judgment matrix. 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 coefficient of each criterion, such as the time response weight is 0.5, the current response weight is 0.3, and the impedance response weight is 0.2.

[0078] On the basis of the above scheme, optionally, the collaborative test results include the actual action time, starting current and measured impedance of the protection device from starting to tripping output; determining the time response deviation, current response deviation and impedance response deviation of the protection device based on the collaborative test results includes: determining the root mean square error between the actual action time and the theoretical action time of the protection device from starting to tripping output, and determining the time response deviation of the protection device based on the root mean square error; determining the relative current deviation between the starting current of the protection device and the theoretical setting value, and determining the current response deviation of the protection device based on the relative current deviation; determining the Euclidean distance between the measured impedance and the actual impedance, and determining the impedance response deviation of the protection device based on the Euclidean distance.

[0079] Among them, the actual action time can be understood as the actual time from the start-up to the trip output of the protection device, which 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 is started, which is used to reflect the sensitivity of the protection device when detecting the fault current. The measured impedance can be understood as the impedance value measured by the protection device when the fault is detected, which is used to evaluate the accuracy of the protection device in fault location and identification. The theoretical action time can be understood as the action time that the protection device should theoretically reach, which 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, which 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, which can be pre-set according to the 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, which can be expressed as a 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, which 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 implementation method, for a certain protection device, the actual measured 5 action times are 100ms, 102ms, 98ms, 101ms, and 99ms respectively, and the theoretical action time is 100ms, then 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, then 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, then 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. It 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, ensuring the safety and stability of substation operation, while reducing unnecessary maintenance costs.

[0082] S240. Construct 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 use the load current multiple, protection action time, and ranging error as constraints of the local optimization sub-objective function.

[0083] Among them, the local optimization sub-objective function can be understood as a mathematical function that optimizes the performance of a specific protection device, including but not limited to at least one of the indicators such as time response deviation, current response deviation, impedance response deviation, etc., which is used to locally optimize the fixed parameters of the protection device to improve its performance. The load current multiple can be understood as the ratio of the actual load current to the rated current, which 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 perform the protection action from detection, ensuring that the protection device can take protection action after detection.

[0084] S250. Optimize and solve the local optimization sub-objective function, construct 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 use the backup protection delay margin and the reclosing time difference as constraints of the regional optimization sub-objective function.

[0085] Among them, the first function value can be understood as the optimal value obtained by the local optimization sub-objective function in the optimization solution process, which is used to evaluate the performance of the optimized protection device and serve as the basis for constructing the regional optimization sub-objective function. The backup protection delay margin can be understood as the time margin for the delayed start of the backup protection device when the main protection device fails to act in time, which can ensure that the backup protection device can be started in time when the main protection device fails to act in time to prevent the fault from expanding. The reclosing time difference can be understood as the time interval for the reclosing device to reclose after detecting the fault tripping, which is used to ensure that power supply can be quickly restored after a short-term fault, while avoiding damage to the equipment caused by repeated reclosing during permanent faults. The regional optimization sub-objective function can be understood as a mathematical function used to optimize the coordinated operation of multiple protection devices in a specific area, which is used to optimize the fixed parameters of the regional optimization protection device and improve the overall performance of the system in the area.

[0086] An optional implementation method, for two adjacent distance protections, can construct the following regional optimization sub-objective function: 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 their action times, ΔT is the expected time difference, and α and β are weight coefficients. The constraint conditions can be set as: T2-T1 ≥ 200ms (backup protection delay margin), |Tr1-Tr2| ≥ 1s (reclosing time difference), where Tr1 and Tr2 are the reclosing times of the two protections.

[0087] On the basis of the above scheme, optionally, the optimizing and solving the local optimization sub-objective function includes: optimizing and solving the local optimization sub-objective function 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 with the number of iterations according to a quadratic function law.

[0088] The adaptive inertia weight can be understood as a parameter that controls the speed of particles moving in the search space in the particle swarm algorithm. By dynamically adjusting the inertia weight, a large exploration capability can be maintained in the early search stage, while the convergence speed can be increased in the later search stage, thereby improving the overall performance of the algorithm.

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

[0090] S260. Determine a system coordination index according to the protection setting parameters of the plurality of protection devices, and construct 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.

[0091] The system coordination index can be understood as an index used to evaluate the ability of multiple protection devices in the entire system to work together, so as 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 used to optimize the performance of the entire system, so as to globally optimize the fixed value parameters of the protection devices and improve the overall performance of the system.

[0092] S270. 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 respectively, and verify the functional integrity and performance indicators of the optimization results of each stage through the digital twin model.

[0093] The functional integrity verification can be understood as checking whether the optimized protection device can correctly perform its predetermined functions to ensure that the optimization results are effective in practical applications, such as whether the fault selection and action logic are correct. The performance index verification can be understood as evaluating the performance improvement of the optimized protection device, such as whether the action time, reliability, selectivity and other indicators meet the standards.

[0094] S280. When the verification results of the functional integrity verification and the performance index verification meet the preset verification standards, the optimized protection constant parameters are updated to the data layer model of the digital twin model.

[0095] The preset verification standard may be understood as a pre-set benchmark for evaluating functional integrity and performance indicators to ensure that the verification results meet predetermined performance and reliability standards.

[0096] The technical solution of the embodiment of the present invention determines the time response deviation, current response deviation and impedance response deviation through collaborative test results, and uses the hierarchical analysis method to calculate the weight coefficient to ensure the balance of various performance indicators; then, constructs a local optimization sub-objective function and introduces constraints to ensure the feasibility of local optimization; then, the local optimization sub-objective function is optimized and solved by the particle swarm algorithm with adaptive inertia weight to generate a regional optimization sub-objective function, and adds new constraints to further optimize the collaborative work of multiple protection devices in the region; then, a global optimization objective function is constructed to achieve overall optimization of the system, and the optimization results of each stage are verified for functional integrity and performance indicators through the digital twin model to ensure the effectiveness of the optimization results; finally, the optimized protection setting parameters are updated to the data layer model of the digital twin model to ensure that the model always reflects the latest optimization results, which 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 substation operation, and reduces maintenance costs and failure risks.

[0097] Embodiment 3

[0098] Figure 3 This is a schematic diagram of the structure of a coordinated test system for a substation multi-compartment protection device provided in the third embodiment of the present invention. Figure 3 As shown, the device includes: a digital model building module 310, a test signal determination module 320, and a model parameter updating module 330.

[0099] A digital model building module 310 is used to collect entity operation data of multiple protection devices of a substation entity, and build 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 topological structure 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 constant parameters, and the business layer model stores the protection device state transition diagram and the transition conditions between adjacent states in the state transition diagram; a test signal determination module 320 is used to generate a collaborative test scenario for multiple protection devices based on the digital twin model, and the digital twin model is used to determine the coordinated test scenario of the protection devices. The physical layer model determines the electrical connection path between the multiple protection devices in the collaborative test scenario, determines the associated protection setting of the protection device in the collaborative test scenario through the data layer model and the electrical connection path, and determines the test excitation signal sequence corresponding to the associated protection setting according to the business layer model; the model parameter updating module 330 is used to use the test excitation signal sequence to test the multiple protection devices in the substation entity to obtain the collaborative test results corresponding to the collaborative test scenario, adaptively optimize and adjust the protection setting parameters of the multiple protection devices according to the collaborative test results, and update the optimized protection setting parameters to the data layer model of the digital twin model.

[0100] The technical solution of the embodiment of the present invention is as follows: first, the entity operation data of multiple protection devices of the substation entity are collected through the digital model construction module 310, and the digital twin model of the substation is constructed 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 the topological structure of the primary equipment of the substation, the data layer model stores the mapping relationship table between the primary equipment operation parameters and the secondary protection device setting parameters, and the business layer model stores the protection device state transition diagram and the conversion conditions between adjacent states in the state transition diagram, the physical structure, operation parameters and state transition of the substation can be fully covered by constructing a digital twin model to achieve accurate simulation and optimization; then, a collaborative test scenario of multiple protection devices is generated based on the digital twin model through the test signal determination module 320, the electrical connection path between the multiple protection devices in the collaborative test scenario is determined through the physical layer model of the digital twin model, the associated protection setting of the protection device in the collaborative test scenario is determined through the data layer model and the electrical connection path, and the The business layer model determines the test excitation signal sequence corresponding to the associated protection setting. By generating a collaborative test scenario and determining the electrical connectivity path and the protection setting, the test excitation signal sequence can be accurately generated to ensure the comprehensiveness and accuracy of the test and optimize the collaborative working performance of the protection device. Finally, the model parameter updating module 330 uses the test excitation signal sequence to test multiple protection devices in the substation entity to obtain a collaborative test result corresponding to the collaborative test scenario. According to the collaborative test result, the protection setting parameters of the multiple protection devices are adaptively optimized and adjusted, and the optimized protection setting parameters are updated to the data layer model of the digital twin model. Through actual testing and optimization adjustment, the response speed and detection accuracy of the protection device can be improved, the safety and reliability of the substation operation can be ensured, and the digital twin model can be updated to achieve continuous optimization, which solves the problem that the testing method in the related technology is difficult to efficiently and accurately complete the collaborative test between many protection devices in the substation and optimize the configuration accordingly, improves the response speed and detection accuracy of the protection device, ensures the safety and reliability of the substation operation, and achieves continuous optimization.

[0101] On the basis of the above solution, optionally, the digital model construction module may include: a physical layer model construction submodule, a data layer model acquisition submodule, and a business layer model acquisition submodule.

[0102] Among them, the physical layer model construction submodule is used 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 uses circuit breakers, disconnectors, and mutual inductors as graph nodes, uses the electrical connection relationship between the graph nodes as the connection edge, and records the graph node information corresponding to the graph nodes; the data layer model acquisition submodule is used to input the graph node information in the physical layer model into a relational database, establish a mapping relationship table of the mapping relationship between the primary equipment operation parameters and the secondary protection device set parameters based on the relational database, and calculate the coupling coefficient between different secondary protection device set parameters according to the mapping relationship table, generate a set parameter association matrix, so as to obtain the data layer model; the business layer model acquisition submodule is used to input the set parameter association matrix in the data layer model into a preset state machine, and construct the protection device state transition diagram for single-phase grounding fault and two-phase short circuit fault respectively based on the state machine, and set the conversion conditions between adjacent states in the state transition diagram according to the set parameter association matrix, so as to obtain the business layer model.

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

[0104] Among them, the primary equipment operating parameter extraction submodule is used to perform a depth-first search in the physical layer model of the digital twin model according to the path weights corresponding to the circuit breaker closed state and the circuit breaker open state, so as to search for the electrical connection path between the multiple protection devices in the collaborative test scenario, and extract the primary equipment operating parameters on the searched electrical connection path; the associated protection setting determination submodule is used to query the mapping relationship between the primary equipment operating parameters and the secondary protection device setting parameters in the data layer model based on the primary equipment operating parameters on the electrical connection path, so as to obtain multiple protection parameters corresponding to the protection device node, and input the multiple protection parameters into In the constant parameter association matrix of the data layer model, the associated protection constants under the collaborative test scenario are obtained, wherein the associated protection constants include distance protection action section parameters, distance protection time delay parameters, and current protection starting current parameters; the test excitation signal sequence determination submodule is used to set a fault point within the overlapping protection range of the distance protection action section parameters and the current protection starting current parameters, and construct a short-circuit calculation model corresponding to the fault point, determine the target power parameters of the fault point according to the primary equipment operating parameters and the short-circuit calculation model, and determine the test excitation signal sequence corresponding to the associated protection constants according to the target power parameters and the distance protection time delay parameters.

[0105] Based on the above solution, optionally, the model parameter updating module may include: a collaborative test result obtaining submodule.

[0106] Among them, the collaborative test result acquisition submodule is used to input the test excitation signal sequence into the multiple protection devices in the substation entity, collect the action response signals of the multiple protection devices, and generate the action response signals with the standard response signals preset in the digital twin model to obtain the collaborative test results corresponding to the collaborative test scenario.

[0107] Based on the above scheme, optionally, the model parameter updating module may include: 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 verification submodule, and a model parameter updating submodule.

[0108] Among them, the weight coefficient acquisition submodule is used to determine the time response deviation, current response deviation, and impedance response deviation of the protection device according to the collaborative test results, and to construct a judgment matrix using the hierarchical analysis method, calculate the maximum eigenvalue and corresponding eigenvector of the judgment matrix, and obtain the weight coefficient by normalizing the eigenvector; the first constraint condition determination submodule is used to construct 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 use the load current multiple, protection action time, and ranging error as constraints of the local optimization sub-objective function; the second constraint condition determination submodule is used to optimize and solve the local optimization sub-objective function, and construct 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 use the backup The protection delay margin and the reclosing time difference are used as constraints of the regional optimization sub-objective function; the global optimization objective function construction sub-module is used to determine the system coordination index according to the protection setting parameters of multiple protection devices, and to construct a global optimization objective function according to the second function values ​​of the regional optimization sub-objective functions of multiple regions and the system coordination index; the verification sub-module is used to 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 to perform functional integrity verification and performance index verification on the optimization results of each stage through the digital twin model; the model parameter updating sub-module is used to update the optimized protection setting parameters to the data layer model of the digital twin model when the verification results of the functional integrity verification and the performance index verification meet the preset verification standards.

[0109] Based on the above scheme, optionally, the collaborative test results include the actual action time, starting current and measured impedance of the protection device from starting to tripping output, and the weight coefficient acquisition submodule may include: a time response deviation determination unit, a current response deviation determination unit, and an impedance response deviation determination unit.

[0110] Among them, the time response deviation determination unit is used to determine the root mean square error between the actual action time and the theoretical action time of the protection device from starting to tripping output, and determine the time response deviation of the protection device based on the root mean square error; the current response deviation determination unit is used to determine the relative current deviation between the starting current of the protection device and the theoretical setting value, and determine the current response deviation of the protection device based on the current relative deviation; the impedance response deviation determination unit is used to determine the Euclidean distance between the measured impedance and the actual impedance, and determine the impedance response deviation of the protection device based on the Euclidean distance.

[0111] On the basis of the above solution, optionally, the second constraint condition determination submodule may include: an optimization solution unit.

[0112] Wherein, the optimization solving unit is used to optimize and solve the local optimization sub-objective function 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 with the number of iterations according to a quadratic function law.

[0113] On the basis of the above scheme, optionally, the collaborative testing system for the substation multi-compartment protection device further includes: a data difference amount determination module and a second model parameter updating module.

[0114] Among them, the data difference determination module is used to determine the simulated operation data of the substation entity based on the physical layer model, the data layer model and the business layer model after the digital twin model of the substation is constructed based on the entity operation data, and determine the data difference between the simulated operation data and the entity operation data; the second model parameter updating module is used to trigger the preset model parameter adaptive adjustment process when the data difference is greater than the preset difference threshold, and 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.

[0115] On the basis of the above solution, optionally, the test signal determination module may include: a test scenario combination screening submodule and a collaborative test scenario testing submodule.

[0116] Among them, the test scenario combination screening submodule is used to use the Monte Carlo algorithm to generate an initial population corresponding to the collaborative test scenarios of multiple protection devices in the digital twin model, input the initial population into the genetic algorithm, and perform iterative calculations with scenario coverage, number of test conditions and number of operation steps as optimization goals to screen out the optimal test scenario combination; the collaborative test scenario testing submodule is used to test the collaborative test scenarios in the test scenario combination, and collect the test results corresponding to each of the collaborative test scenarios during the test execution process. When the discrete degree of the test results is greater than a preset discrete threshold, the sampling density of the collaborative test scenarios is increased; when the discrete degree of the test results is less than the preset discrete threshold, the sampling density of the collaborative test scenarios is reduced.

[0117] The coordinated testing system for substation multi-compartment protection devices provided in the embodiment of the present invention can execute the coordinated testing method for substation multi-compartment protection devices provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0118] Embodiment 4

[0119] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0120] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to 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 number 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 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 may be a variety of general and / or special 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 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 executes the various methods and processes described above, such as a collaborative testing method for a multi-compartment protection device of a substation.

[0123] In some embodiments, a coordinated testing method for a multi-compartment protection device of a substation may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the coordinated testing method for a multi-compartment protection device of a substation described above may be executed. Alternatively, in other embodiments, the processor 11 may be configured to execute a coordinated testing method for a multi-compartment protection device of a substation in any other appropriate manner (e.g., by means of firmware).

[0124] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0126] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0127] To provide interaction with a user, the systems and techniques described herein may 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 trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0128] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may 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] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may 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 difficult management and weak business scalability in traditional physical hosts and VPS services.

[0130] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0131] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A coordinated testing method for a multi-compartment protection device of a substation, characterized in that: include: Collect entity operation data of multiple protection devices of the substation entity, and build 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 topological structure of the primary equipment of the substation, the data layer model stores the mapping relationship table between the primary equipment operation parameters and the secondary protection device setting parameters, and the business layer model stores the protection device state transition diagram and the conversion conditions between adjacent states in the state transition diagram; Generate a collaborative test scenario for multiple protection devices based on the digital twin model, determine the electrical connection path between the multiple protection devices in the collaborative test scenario through the physical layer model of the digital twin model, determine the associated protection setting value of the protection device in the collaborative test scenario through the data layer model and the electrical connection path, and determine the test excitation signal sequence corresponding to the associated protection setting value according to the business layer model; The test excitation signal sequence is used to test the multiple protection devices in the substation entity to obtain a collaborative test result corresponding to the collaborative test scenario, and the protection setting parameters of the multiple protection devices are adaptively optimized and adjusted according to the collaborative test result, and the optimized protection setting parameters are updated to the data layer model of the digital twin model.

2. The method according to claim 1, characterized in that The step of constructing the digital twin model of the substation based on the entity operation data includes: The entity operation data is input into a preset graph database, and a physical layer model is constructed based on the graph database and the entity operation data, wherein the physical layer model uses circuit breakers, disconnectors, and transformers as graph nodes, uses electrical connection relationships between the graph nodes as connection edges, and records graph node information corresponding to the graph nodes; Input the graph node information in the physical layer model into a relational database, establish a mapping relationship table of the mapping relationship between the primary equipment operating parameters and the secondary protection device set parameters based on the relational database, calculate the coupling coefficients between the set parameters of different secondary protection devices according to the mapping relationship table, generate a set parameter association matrix, and obtain a data layer model; The fixed parameter association matrix in the data layer model is input into a preset state machine, and a protection device state transition diagram is constructed based on the state machine for single-phase grounding fault and two-phase short circuit fault respectively, and the transition conditions between adjacent states in the state transition diagram are set according to the fixed parameter association matrix to obtain a business layer model.

3. The method according to claim 1, characterized in that The method further comprises: determining an electrical connection path between a plurality of protection devices in the collaborative test scenario through the physical layer model of the digital twin model, determining an associated protection setting value of the protection device in the collaborative test scenario through the data layer model and the electrical connection path, and determining a test excitation signal sequence corresponding to the associated protection setting value according to the business layer model, including: According to the path weights corresponding to the circuit breaker closed state and the circuit breaker open state, a depth-first search is performed in the physical layer model of the digital twin model to search for electrical connection paths between the plurality of protection devices in the collaborative test scenario, and primary equipment operating parameters on the searched electrical connection paths are extracted; Based on the primary equipment operating parameters on the electrical connection path, the mapping relationship between the primary equipment operating parameters and the secondary protection device set value parameters in the data layer model is queried to obtain a plurality of protection parameters corresponding to the protection device node, and the plurality of protection parameters are input into the set value parameter association matrix of the data layer model to obtain the associated protection set value in the collaborative test scenario, wherein the associated protection set value includes a distance protection action section parameter, a distance protection time delay parameter, and a current protection starting current parameter; 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; and 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, characterized in that The step of testing the plurality of protection devices in the substation entity by 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 according to claim 1, characterized in that The 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, comprises: Determine the time response deviation, current response deviation, and impedance response deviation of the protection device according to the collaborative test results, and construct a judgment matrix using a hierarchical analysis method, calculate the maximum eigenvalue and corresponding eigenvector of the judgment matrix, and obtain 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 taking the load current multiple, the protection action time, and the ranging error as constraint conditions of the local optimization sub-objective function; 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; 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; The local optimization sub-objective function, the regional optimization sub-objective function and the global optimization objective function are respectively used to sequentially perform local optimization, regional optimization and global optimization, and the functional integrity and performance index of the optimization results of each stage are verified by 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 setting parameters are updated to the data layer model of the digital twin model.

6. The method according to claim 5, characterized in that The coordinated test result includes the actual action time, starting current and measured impedance of the protection device from starting to tripping output; the time response deviation, current response deviation and impedance response deviation of the protection device are determined according to the coordinated test result, including: Determine the root mean square error between the actual action time of the protection device from startup to tripping output and the theoretical action time, and determine the time response deviation of the protection device according to the root mean square error; Determine a current relative deviation between a starting current of a protection device and a theoretical setting value, and determine 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 according to the Euclidean distance.

7. The method according to claim 5, characterized in that The 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 with the number of iterations according to a quadratic function law.

8. The method according to claim 1, characterized in that After the digital twin model of the substation is constructed based on the entity operation data, the method further includes: Determine the simulated operation data of the substation entity based on the physical layer model, the data layer model and the business layer model, and determine the 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 according to claim 1, characterized in that: The generating a collaborative test scenario of 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, and the initial population is input into a genetic algorithm to perform iterative calculations with scenario coverage, the number of test conditions, and the number of operation steps as optimization targets, so as to screen out the optimal test scenario combination; Test the collaborative test scenarios in the test scenario combination, and collect test results corresponding to each of the collaborative test scenarios during the test execution process. When the discrete degree of the test results is greater than a preset discrete threshold, increase the sampling density of the collaborative test scenarios; when the discrete degree of the test results is less than the preset discrete threshold, reduce the sampling density of the collaborative test scenarios.

10. A coordinated testing system for multi-compartment protection devices in a substation, characterized in that: include: A digital model building module, used to collect entity operation data of multiple protection devices of a substation entity, and build 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 topological structure of the primary equipment of the substation, the data layer model stores a mapping relationship table between the operating parameters of the primary equipment and the set parameters of the secondary protection device, and the business layer model stores a state transition diagram of the protection device and the conversion conditions between adjacent states in the state transition diagram; A test signal determination module is used to generate a collaborative test scenario for multiple protection devices based on the digital twin model, determine the electrical connection path between the multiple protection devices in the collaborative test scenario through the physical layer model of the digital twin model, determine the associated protection setting value of the protection device in the collaborative test scenario through the data layer model and the electrical connection path, and determine the test excitation signal sequence corresponding to the associated protection setting value according to the business layer model; A model parameter updating module is used to test the multiple protection devices in the substation entity using the test excitation signal sequence to obtain a collaborative test result corresponding to the collaborative test scenario, adaptively optimize and adjust the protection setting parameters of the multiple protection devices according to the collaborative test result, and update the optimized protection setting parameters to the data layer model of the digital twin model.

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