Power grid automatic control system test method based on multi-dimensional scene combination verification and related system

Abstract

The invention belongs to the technical field of power dispatching automation, and particularly relates to a multi-dimensional scene combination verification-based power grid automatic control system test method and a related system. Through presetting a multi-dimensional simulation model, the performance of a control system can be verified in different scenes; and therefore, normal work under various possible operation conditions can be ensured. According to the method, the real and complex disturbance data can be generated by inputting the real operation data into the simulation model, so that the test is closer to the actual operation condition, and the system is helped to better identify and cope with emergencies. According to the invention, through comparative analysis of the generated control instruction and the preset instruction, defects in the control strategy can be found in time, optimization is carried out, and effectiveness and accuracy of the control instruction are ensured. Through continuous simulation and analysis, data support can be provided for quality management of the system, iteration and optimization of a control algorithm are promoted, and adaptability and flexibility of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power dispatching automation, and in particular relates to a power grid automatic control system testing method based on multi-dimensional scenario combination verification and a related system. Background Art

[0002] As a crucial component of the power system, the electric power dispatch automation system undertakes core functions such as real-time monitoring and dispatch control. As the complexity of electric power dispatch systems increases, the automatic control software within them must not only process large amounts of data but also respond quickly and accurately to system changes. In recent years, with the rapid development of information technology, electric power dispatch automation systems have introduced new automatic control software and intelligent dispatching algorithms, placing increasingly stringent demands on software quality. Common issues include insufficient code robustness, irrational control strategies, and system failures during operation. Therefore, quality control of automatic control software has become a critical issue in ensuring system stability and security.

[0003] As power systems gradually evolve toward intelligence and digitalization, the importance of power dispatch automation systems is increasing. As the core of the dispatch system, automatic control software is directly related to the safety, stability, and efficiency of the power system. However, current quality control methods struggle to meet the high demands for software stability, security, and reliability. The main issues are: First, existing technologies rarely consider the impact of the automatic control software's operating environment and human factors, resulting in significant discrepancies between the software's actual performance and the experimental environment; second, automatic control software quality assessment methods are mostly based on static analysis, lacking dynamic integration and multi-dimensional testing; third, existing quality assessment systems are often cumbersome and fail to balance multi-dimensional characteristics such as code quality, control strategy rationality, and safety and error prevention mechanisms.

[0004] Existing methods are often limited to static analysis and some dynamic testing, making it difficult to cover the quality management needs of the entire software lifecycle. Furthermore, the lack of quality feature verification methods under complex functional dependencies and comprehensive system-level quality evaluation standards makes it difficult to effectively guarantee the quality management of existing automatic control methods and hinder their long-term stable operation. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of automatic control methods that cannot effectively guarantee quality management and are difficult to operate stably in the long term, and to provide a power grid automatic control system testing method and related systems based on multi-dimensional scenario combination verification.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a power grid automatic control system testing method based on multi-dimensional scenario combination verification, comprising the following steps: Obtain simulation requirements and preset corresponding simulation models based on the simulation requirements; Obtaining operation data of the power grid automatic control system, inputting the operation data of the power grid automatic control system into the simulation model, and generating disturbance data; Obtaining control instructions generated by the power grid automatic control system after receiving disturbance data; Compare the generated control instructions with the preset control instruction results to obtain the final analysis results.

[0007] A further improvement of the present invention is that the method further comprises the following steps: After the corresponding simulation model is preset, the simulation model is stored, and when display is required, all stored simulation models are displayed.

[0008] A further improvement of the present invention is that the operation data of the power grid automatic control system is input into the simulation model, and the specific method of generating disturbance data is as follows: Preprocess the operating data of the power grid automatic control system to remove noise and outliers, and convert it into the required format; Input the required format of operating data into the simulation model and establish a mapping relationship between the operating data and the model variables; According to the disturbance data generation rules preset in the simulation model, disturbance data is generated according to the mapping relationship.

[0009] A further improvement of the present invention is that the specific method for obtaining the control instructions generated by the power grid automatic control system after receiving the disturbance data is as follows: After receiving the disturbance data, the power grid automatic control system converts the disturbance data into control information for decision-making; Send the control information into the preset control strategy library for traversal, and select the strategy information that is the same as the control information; Generate control instructions based on policy information.

[0010] A further improvement of the present invention is that the generated control instruction is compared with the preset control instruction result, and the specific method for obtaining the final analysis result is as follows: A preset control instruction result library includes standard control instructions generated based on historical data and simulation results; The generated control instructions are compared with the standard control instructions in the control instruction result library to obtain difference data, which is used as the final analysis result.

[0011] A further improvement of the present invention is that the method further comprises the following steps: Based on the final analysis results, control instructions that meet the requirements and corresponding update instructions are generated and sent to the power grid automatic control system.

[0012] In a second aspect, the present invention provides a power grid automatic control system test system based on multi-dimensional scenario combination verification, comprising: The model preset module is used to obtain simulation requirements and preset the corresponding simulation model according to the simulation requirements; A disturbance data generation module is used to obtain the operation data of the power grid automatic control system, input the operation data of the power grid automatic control system into the simulation model, and generate disturbance data; A control instruction acquisition module is used to obtain the control instructions generated by the power grid automatic control system after receiving the disturbance data; The data comparison module is used to compare the generated control instructions with the preset control instruction results to obtain the final analysis results.

[0013] A further improvement of the present invention is that the present invention also includes: The model library is used to store simulation models and display all stored simulation models when they need to be displayed.

[0014] A further improvement of the present invention is that the function of the disturbance data generation module is realized by the following method: Preprocess the operating data of the power grid automatic control system to remove noise and outliers, and convert it into the required format; Input the required format of operating data into the simulation model and establish a mapping relationship between the operating data and the model variables; According to the disturbance data generation rules preset in the simulation model, disturbance data is generated according to the mapping relationship.

[0015] A further improvement of the present invention is that the function of the control instruction acquisition module is realized by the following method: A preset control instruction result library includes standard control instructions generated based on historical data and simulation results; The generated control instructions are compared with the standard control instructions in the control instruction result library to obtain difference data, which is used as the final analysis result.

[0016] A further improvement of the present invention is that the present invention comprises: The feedback update module is used to generate control instructions that meet the requirements and corresponding update instructions based on the final analysis results, and send them to the power grid automatic control system.

[0017] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of a power grid automatic control system testing method based on multi-dimensional scenario combination verification.

[0018] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a power grid automatic control system testing method based on multi-dimensional scenario combination verification.

[0019] Compared with the prior art, the present invention has the following beneficial effects: By presetting a multidimensional simulation model, the present invention can verify the performance of the control system in different scenarios, thereby ensuring that it functions properly under various possible operating conditions. By inputting real-world operating data into the simulation model, the present invention can generate realistic and complex disturbance data. Such testing is more similar to actual operating conditions, helping the system better identify and respond to emergencies. By comparing and analyzing the generated control instructions with the preset instructions, the present invention can promptly identify defects in the control strategy and optimize them to ensure the effectiveness and accuracy of the control instructions. Through continuous simulation and analysis, the present invention can provide data support for system quality management, promote the iteration and optimization of control algorithms, and enhance the adaptability and flexibility of the system. The enhanced stability and reliability of the present invention help reduce risks that may arise in power grid operation and avoid failures or power outages caused by control errors. The present invention can flexibly adjust the simulation model according to changes in power grid operation, thereby adapting to different operating environments and requirements and ensuring the long-term stability of the system. In summary, the present invention can effectively overcome the shortcomings of traditional automatic control methods in quality management and long-term operation, and promote the development of power grid automatic control systems to a higher level. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of the present invention; Figure 2 is a system diagram of the present invention; Figure 3 This is a system diagram of Example 3 of the present invention; Figure 4 This is a system diagram of Example 7 of the present invention. DETAILED DESCRIPTION

[0021] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0022] Example 1: See also Figure 1 , a power grid automatic control system testing method based on multi-dimensional scenario combination verification includes the following steps: S1, obtain simulation requirements and preset the corresponding simulation model according to the simulation requirements.

[0023] S2, obtaining operation data of the power grid automatic control system, inputting the operation data of the power grid automatic control system into the simulation model, and generating disturbance data.

[0024] S3, obtaining the control instructions generated by the power grid automatic control system after receiving the disturbance data.

[0025] S4, comparing the generated control instruction with the preset control instruction result to obtain the final analysis result.

[0026] Example 2: See also Figure 2 , a power grid automatic control system test system based on multi-dimensional scenario combination verification, including: Model preset module, used to preset the corresponding simulation model according to simulation requirements; A disturbance data generation module is used to obtain the operation data of the power grid automatic control system, input the operation data of the power grid automatic control system into the simulation model, and generate disturbance data; A control instruction acquisition module is used to obtain the control instructions generated by the power grid automatic control system after receiving the disturbance data; The data comparison module is used to compare the generated control instructions with the preset control instruction results to obtain the final analysis results.

[0027] Example 3: See also Figure 3 According to the simulation requirements, the required simulation model is input into the model management module, and the model management module stores the input simulation model in the model library. When the operator selects "full model browsing" on the human-machine interface, the model management module extracts all models from the model library into the real-time library and displays all model categories in the real-time library through the human-machine interface for the operator to select the required simulation model. The model management module sends the full model and full model section corresponding to the simulation model selected by the operator to the simulation layer. The full model and full model section include scenario test model, test section, test graphics and test model.

[0028] The system continuously receives operational data from the power grid automation control system, stores it in a log library, and sends it to the simulation processing module. The simulation processing module sends the operational data to the simulation layer via the TCP / IP interface. The simulation layer inputs the operational data from the power grid automation control system into the simulation model selected by the operator, generates disturbance data equivalent to the external network, and sends it to the model management module. The model management module saves the disturbance data to the relationship library, which is used to store all disturbance data output by the tested simulation model. The disturbance data is then sent to the real-time library for display through the human-machine interface. The module management module sends the disturbance data along with the scenario test model, test section, test graphics, and test model to the power grid automation control system.

[0029] The power grid automation system generates corresponding control instructions based on the disturbance data. These control instructions are sent to the result analysis module. The result analysis module compares the control instructions generated by the power grid automation system with the control instructions generated based on the preset fault generation control instructions, quality verification control instructions, and pre-fault control instructions. The final analysis results are sent to the real-time database and displayed on the human-machine interface. If the final analysis results deviate from the preset control instructions, the feedback update module generates a control instruction that meets the requirements and sends it to the power grid automation system. The power grid automation system then updates the original control instructions based on the control instructions that meet the requirements.

[0030] When comparing control instructions, quality verification begins by collecting operational data from the power grid automation control system within the simulation processing module. The system then generates corresponding control instructions based on the disturbance data, comparing these instructions with pre-qualified cases and samples in the built-in case and sample libraries to generate analysis results. If the analysis results deviate from the preset control instructions, a control instruction that meets the requirements is generated and then subjected to scenario testing. Once the scenario test passes, the control instruction is sent to the power grid automation control system to update the original control instruction.

[0031] The present invention ensures the stability and security of software in different environments through a multi-dimensional and multi-level combined testing method. On the basis of conventional testing of functional "points", a test design and execution strategy for business "lines" and business scenario profiles is proposed. By sorting out the business key points, call relationships, data flows, reasonable and illegal input values, synchronization / mutual exclusion relationships of scheduling automation system software such as automatic control, a number of business lines that can realize a certain independent process are established, extending the test of test "points" to the test of business "lines". At the same time, taking into full consideration factors such as power grid operation mode, human operation, and environmental status, the software is designed to run various normal scenarios, abnormal scenarios, fault scenarios, and extreme scenarios, extending the test of business "lines" to the test of operation scenario profiles, truly restoring various operating conditions and various expected operating conditions of the power grid and control system, and exploring the weak links that may cause business anomalies based on quality characteristics such as function, performance, safety and reliability, and error prevention.

[0032] Example 4: The operating data obtained from the power grid automatic control system is preprocessed to remove noise and outliers to ensure data accuracy, and the preprocessed data is converted into the standard format required by the simulation model, which includes timestamps, operating parameters, load information, etc.

[0033] The running data after preprocessing and data conversion is pushed into the simulation model, data mapping is established in the simulation model, and the running data is associated with the model variables.

[0034] The disturbance generation rules are preset in the simulation model. The preset disturbance generation rules include: Random Perturbation: Randomly generates disturbances within a certain range based on the current running data.

[0035] Set disturbance: Generate a specific disturbance based on specific conditions (such as sudden load changes).

[0036] When the operating data after preprocessing and data conversion is received, the disturbance generation rule is triggered, and the corresponding disturbance data is automatically generated based on the changes in the input data. The generated disturbance data is recorded and stored together with the operating data.

[0037] This embodiment preprocesses operational data to remove noise and outliers, ensuring input data accuracy, reducing simulation deviations caused by data quality issues, and improving model reliability. This embodiment converts preprocessed data into the standard formats required by the simulation model (such as timestamps, operating parameters, and load information), facilitating model input and processing and improving overall system compatibility. This embodiment establishes a data mapping within the simulation model, associating operational data with model variables. This helps more accurately reflect the actual operational status of the power grid and enhances the credibility of simulation results. This embodiment uses preset disturbance generation rules (such as random disturbances and set disturbances) to dynamically generate appropriate disturbance data under different operating conditions, better simulating various situations in actual operating environments. Upon receiving preprocessed and converted operational data, this embodiment automatically triggers disturbance generation rules, enabling real-time response, reducing manual intervention, and improving the system's automation and response speed. This embodiment records the generated disturbance data and stores it alongside the operational data, providing a rich data foundation for subsequent analysis and model optimization, further enhancing the system's intelligent decision-making capabilities. This embodiment, through subsequent analysis of stored data, can identify the system's performance under specific conditions, thereby optimizing control strategies and improving system design, thereby enhancing the overall efficiency and reliability of the power grid. This embodiment combines flexible disturbance generation with accurate data input, enabling the system to better cope with complex and dynamic power grid environments, improving its stability and security. In summary, this embodiment can more comprehensively enhance the testing and simulation capabilities of power grid automation control systems, thereby better supporting the safe, stable, and efficient operation of the power grid.

[0038] Example 5: The specific method for obtaining the control instructions generated by the power grid automatic control system after receiving the disturbance data is as follows: Convert received disturbance data into information that can be used for decision-making, including disturbance type, amplitude, duration, etc. Preset anomaly detection thresholds, monitor disturbance data in real time, and identify anomalies (such as excessively large disturbances) based on the comparison results between the disturbance data and the anomaly detection thresholds. If an anomaly is detected, a corresponding alarm message is generated.

[0039] A pre-set control strategy library includes response strategies for different disturbance types. Based on the analyzed disturbance data, the appropriate strategy is selected from the control strategy library, dynamically adjusted based on the state of the power grid automatic control system (such as current load and equipment status), and strategy information is generated.

[0040] Generate corresponding control instructions (such as adjusting generator output, switching loads, enabling backup equipment, etc.) based on the generation strategy information, ensuring that the generated control instructions comply with the communication protocol and data format of the control instructions.

[0041] By converting disturbance data into decision-making information (such as disturbance type, amplitude, and duration), this embodiment enables the system to quickly and accurately assess the current operating status and provide a reliable basis for subsequent control decisions. By presetting anomaly detection thresholds and monitoring disturbance data in real time, the system can promptly identify abnormal situations (such as excessively large disturbances), thereby quickly responding and taking necessary protective measures to reduce the risk of accidents. This embodiment generates corresponding alarm information when an anomaly is detected, ensuring that relevant personnel can quickly learn about the grid status and facilitate timely processing and response to potential risks. Through the preset control strategy library, the system can select appropriate response strategies based on different types of disturbances, improving the flexibility and adaptability of the system, and effectively responding to various situations in a complex grid environment. In summary, this embodiment can significantly improve the response capability and stability of the grid automatic control system, thereby better meeting the operating requirements of modern power systems.

[0042] Example 6: The specific method of comparing the generated control instructions with the preset control instruction results to obtain the final analysis results is as follows: A preset control instruction result library contains standard control instructions generated based on historical data and simulation results. The format of the control instruction result library is consistent with the instructions generated in real time to facilitate subsequent comparison.

[0043] The generated control instructions are compared with the standard control instructions in the control instruction result library. The comparison criteria are parameter range, state change, time response, etc. Similarity calculation (such as Euclidean distance, cosine similarity, etc.) is used to quantify the difference between the two and obtain difference data.

[0044] The quality of the power grid automatic control system is evaluated based on the difference data. The quality evaluation indicators are as follows: a) Functionality: The ability to achieve the explicit or implicit requirements of the power grid automation control software technical specifications in terms of data interaction, policy logic, business processes, result output, interface display, etc. when operating in specified scenarios, including completeness, sufficiency, correctness and ease of use.

[0045] Functional evaluation indicators mainly cover the following aspects: Data interaction: Whether the control software can ensure data integrity, consistency, and timeliness during data exchange with external systems or internal modules. By simulating actual data flow in power grid dispatch, the data interaction capabilities under high load conditions are analyzed.

[0046] Strategy Logic: Evaluate the correctness of scheduling strategies and control logic, as well as their performance in different operational scenarios. Use model deduction to build a system logic model and verify the correctness of each logic path through simulation.

[0047] Business Process: Verify that the control software can execute smoothly according to the predetermined business process. By simulating typical power grid dispatch processes, including load distribution and line switching, the software's functional performance at every stage is ensured.

[0048] Output results: Whether the output results of the control software after strategy execution meet expectations. For example, in the load distribution process of power grid scheduling, whether the output load data meets actual needs and whether there is any unreasonable distribution.

[0049] Interface Presentation: From the perspective of a power grid dispatcher and drawing on actual operational dispatching experience, the user interface design and ease of use are evaluated, including the completeness of displayed information and the user-friendliness of the interaction. The quality assessment of the interface presentation includes indicators of human-computer interaction usability, such as page response time and the rationality of information layout.

[0050] b) Effectiveness: The ability to provide appropriate performance relative to the resources used in terms of task completion time, number of tasks processed per unit time, task usage, user and grid size, etc., including time characteristics, processing efficiency, resource utilization and capacity.

[0051] The effectiveness evaluation indicators mainly cover the following aspects: Time characteristics: The control software's response time to complete a task, including instruction response, data processing, and task execution time. Time characteristics are measured by setting a time threshold for task processing.

[0052] Processing efficiency: This measures the software's ability to process tasks per unit time, also known as system throughput. This measure measures the number of tasks processed per second by simulating task processing efficiency under different load conditions.

[0053] Resource utilization: Control the software's use of CPU, memory, and network resources under different task loads to ensure balanced and reasonable resource utilization.

[0054] Capacity: The processing capacity of the control software under different user scales and grid scales. By simulating user requests of different scales and grid topology changes, the software's carrying capacity under extreme conditions is tested. c) Reliability: The ability to tolerate and prevent errors, maintain continuous and stable operation, and handle and recover from abnormalities under factors such as inherent or human operations, external operating environment, or abnormal changes in the power grid. It includes fault tolerance, error prevention, recoverability, stability, and extreme handling capabilities.

[0055] d) Standardization: Conformity to standards in terms of services and data objects, the degree of standardization of various functions of the application platform, and the compatibility of software and hardware, including standard compliance, application standardization and compatibility.

[0056] e) Security: Whether the system meets the requirements of GB / T 36572 and the "Security Protection Plan for the Next-Generation Dispatch Technical Support System" in terms of functional implementation, business interaction, and software operation, and has the ability to prevent malicious attacks and security risks, including functional implementation security, business interaction security, and operational security. System security is assessed through a multi-layered protection mechanism, including data encryption, identity authentication, operation logging, and other security measures.

[0057] Example 7: See also Figure 4 As shown, the present invention also provides an electronic device 100 for a power grid automatic control system testing method based on multi-dimensional scenario combination verification; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and capable of running on the at least one processor 102, and at least one communication bus 104.

[0058] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the power grid automatic control system testing method based on multi-dimensional scenario combination verification described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data (such as audio data) created based on the use of the electronic device 100. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0059] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100 and connects various parts of the entire electronic device 100 using various interfaces and lines.

[0060] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a power grid automatic control system testing method based on multi-dimensional scenario combination verification, and the processor 102 can execute the plurality of instructions to implement: Preset the corresponding simulation model according to simulation requirements; Obtaining operation data of the power grid automatic control system, inputting the operation data of the power grid automatic control system into the simulation model, and generating disturbance data; Obtaining control instructions generated by the power grid automatic control system after receiving disturbance data; Compare the generated control instructions with the preset control instruction results to obtain the final analysis results.

[0061] Example 8: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0062] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0064] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A power grid automatic control system testing method based on multi-dimensional scenario combination verification, characterized in that: The following steps are involved: Obtain simulation requirements, and preset corresponding simulation models according to the simulation requirements; Acquire the operation data of the power grid automatic control system, input the operation data of the power grid automatic control system into the simulation model, and generate disturbance data; Obtaining control instructions generated by the power grid automatic control system after receiving disturbance data; Compare the generated control instructions with the preset control instruction results to obtain the final analysis results.

2. The power grid automatic control system testing method based on multi-dimensional scenario combination verification according to claim 1 is characterized in that: The method further comprises the following steps: After the corresponding simulation model is preset, the simulation model is stored; When display is required, all stored simulation models are displayed.

3. The power grid automatic control system testing method based on multi-dimensional scenario combination verification according to claim 1 is characterized in that: The specific method of inputting the operation data of the power grid automatic control system into the simulation model and generating disturbance data is as follows: Preprocess the operation data of the power grid automatic control system, remove noise and abnormal values, and convert it into the required format of operation data; Inputting the operating data in the required format into the simulation model and establishing a mapping relationship between the operating data and the model variables; According to the disturbance data generation rules preset in the simulation model, the disturbance data is generated according to the mapping relationship.

4. The power grid automatic control system testing method based on multi-dimensional scenario combination verification according to claim 1 is characterized in that: The specific method of obtaining the control instructions generated by the power grid automatic control system after receiving the disturbance data is as follows: After receiving the disturbance data, the power grid automatic control system converts the disturbance data into control information for decision-making; Send the control information to a preset control strategy library for traversal, and select strategy information that is the same as the control information; Generate control instructions based on the strategy information.

5. The power grid automatic control system testing method based on multi-dimensional scenario combination verification according to claim 1 is characterized in that: The specific method of comparing the generated control instructions with the preset control instruction results to obtain the final analysis results is as follows: A preset control instruction result library, which includes standard control instructions generated based on historical data and simulation results; The generated control instructions are compared with the standard control instructions in the control instruction result library to obtain difference data, which are used as the final analysis results.

6. The power grid automatic control system testing method based on multi-dimensional scenario combination verification according to claim 1 is characterized in that: The method further comprises the following steps: According to the final analysis results, control instructions that meet the requirements and corresponding update instructions are generated and sent to the power grid automatic control system.

7. A power grid automatic control system test system based on multi-dimensional scenario combination verification, characterized in that: include: The model preset module is used to obtain simulation requirements and preset the corresponding simulation model according to the simulation requirements; A disturbance data generation module is used to obtain the operation data of the power grid automatic control system, input the operation data of the power grid automatic control system into the simulation model, and generate disturbance data; A control instruction acquisition module is used to acquire the control instructions generated by the power grid automatic control system after receiving the disturbance data; The data comparison module is used to compare the generated control instructions with the preset control instruction results to obtain the final analysis results.

8. The power grid automatic control system test system based on multi-dimensional scenario combination verification according to claim 7 is characterized in that: The system further comprises: The model library is used to store simulation models and display all stored simulation models when they need to be displayed.

9. The power grid automatic control system test system based on multi-dimensional scenario combination verification according to claim 7, characterized in that: The function of the disturbance data generation module is realized by the following method: Preprocess the operation data of the power grid automatic control system, remove noise and abnormal values, and convert it into the required format of operation data; Inputting the operating data in the required format into the simulation model and establishing a mapping relationship between the operating data and the model variables; According to the disturbance data generation rules preset in the simulation model, the disturbance data is generated according to the mapping relationship.

10. The power grid automatic control system test system based on multi-dimensional scenario combination verification according to claim 7, characterized in that: The function of the control instruction acquisition module is realized by the following method: A preset control instruction result library, which includes standard control instructions generated based on historical data and simulation results; The generated control instructions are compared with the standard control instructions in the control instruction result library to obtain difference data, which are used as the final analysis results.

11. The power grid automatic control system test system based on multi-dimensional scenario combination verification according to claim 7, characterized in that: include: The feedback update module is used to generate control instructions that meet the requirements and corresponding update instructions based on the final analysis results, and send them to the power grid automatic control system.

12. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power grid automatic control system testing method based on multi-dimensional scenario combination verification described in any one of claims 1 to 6 are implemented.

13. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power grid automatic control system testing method based on multi-dimensional scenario combination verification described in any one of claims 1 to 6 are implemented.

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