Method and system for establishing simulation working condition library of pumped storage equipment

Abstract

The invention provides a method and a system for establishing a simulation working condition library of pumped storage equipment. The method comprises the following steps: acquiring a design specification, a historical operation record and an industrial standard of the pumped storage equipment, and analyzing operation characteristics by adopting an operation analysis rule to obtain the operation characteristics; initial working conditions are screened according to the operation characteristics, key parameters are defined, and a parameterized simulation working condition initial set is obtained; establishing indexes based on the initial set and the working condition library, and performing optimization screening by adopting a coverage rate optimization algorithm to obtain a final simulation working condition data set; carrying out structured storage by adopting a data format specification, and establishing a simulation working condition database with a version control and maintenance mechanism; and finally, based on the database, realizing query, calling, visualization and extension functions of the working condition data, and constructing a simulation working condition library application system. According to the method, a comprehensive and standardized pumped storage equipment simulation working condition library is constructed, and the problems of incomplete working condition coverage, insufficient typicality and low management efficiency in a traditional simulation method are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of power system simulation and testing technology, and more specifically, to a method and system for establishing a simulation operating condition library for pumped storage equipment, used to establish a comprehensive and standardized simulation operating condition library for pumped storage equipment. Background Technology

[0002] Pumped storage power stations are important regulating power sources and energy storage units in the power system. Their operating conditions are complex and changeable. Accurate simulation testing of them is crucial for ensuring the safe and stable operation of the power grid and optimizing equipment design and control strategies.

[0003] In the field of power system simulation, general-purpose simulation software (such as PSCAD, PSS / E, DIgSILENT / PowerFactory, etc.) is typically used to build system models, and specific operating scenarios are set up for simulation according to the research objectives. For example, for generator sets, the processes of startup, grid connection, and load shedding may be simulated; for transmission lines, short-circuit faults and line breaks may be simulated. These simulations are usually targeted and ad-hoc, lacking systematicity and standardization.

[0004] For the simulation of pumped storage equipment, existing technologies often rely on engineers manually configuring simulation parameters and scenarios according to specific needs. For example, when performing controller parameter tuning or fault analysis, engineers will select a few typical operating points or fault conditions for simulation based on experience. Although there are some simulation models and studies for specific equipment or specific processes (such as hydraulic transient processes of pump turbines), there is usually a lack of a systematic and comprehensive set of operating conditions that can standardize the description of the various operating states and transition processes that pumped storage equipment may encounter.

[0005] The existing simulation method has significant drawbacks: First, it lacks comprehensive coverage of operating conditions. Manually selected operating conditions are often based on experience or specific research purposes, making it difficult to fully cover all normal, transient, and fault operating modes that the equipment may encounter throughout its entire life cycle. In particular, extreme operating conditions and boundary conditions are easily overlooked. Second, it lacks typicality and representativeness. The selected operating conditions may lack statistical representativeness and cannot accurately reflect the probability and impact of each operating condition in actual operation. Finally, it suffers from low management efficiency. The lack of standardized storage and management mechanisms makes it difficult to reuse, share, and update simulated operating conditions, increasing repetitive work and reducing the efficiency of research and development, testing, and training.

[0006] Therefore, there is an urgent need for a method and system that can systematically and standardizedly establish a simulation operating condition library for pumped storage equipment in order to improve the comprehensiveness, representativeness and efficiency of simulation testing. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for establishing a simulation operating condition library for pumped storage equipment. This system can systematically and standardizedly establish a comprehensive and representative simulation operating condition library for pumped storage equipment, thereby improving the efficiency and quality of simulation testing.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for establishing a simulation operating condition database for pumped storage equipment includes: The design specifications, historical operation records and industry standards of the pumped storage equipment are obtained, and the operation characteristics of the pumped storage equipment are analyzed using operation analysis rules to obtain the operation characteristics of the pumped storage equipment. Based on the operating characteristics of the pumped storage equipment, the initial operating conditions are screened and key parameters of the operating conditions are defined to obtain a parameterized initial set of simulation operating conditions. Based on the parameterized initial set of simulation conditions and the establishment index of the condition library, the coverage optimization algorithm is used to optimize and filter the initial set of parameterized simulation conditions to obtain the final simulation condition dataset. The final simulation condition dataset is structured and stored using data format specifications, and a simulation condition database with version control and maintenance mechanisms is established. Based on the aforementioned simulation working condition database, a system development framework is used to realize the functions of querying, calling, visualizing and extending working condition data, and to build a simulation working condition database application system.

[0009] In one embodiment of the present invention, the step of analyzing the operating characteristics of the pumped storage equipment using operational analysis rules to obtain the operating features of the pumped storage equipment includes: Based on the design specifications, historical operation records and industry standards, the physical structure and key parameters of each component of the pumped storage equipment are analyzed to obtain a list of key parameters. Based on the list of key parameters, the standard operating procedures and control logic of pumped storage equipment at different operating stages are analyzed, and standard operating flowcharts and control logic block diagrams are generated. Based on the standard operation flowchart and control logic block diagram, analyze the transient disturbance types and fault modes of pumped storage equipment, and generate a fault mode list. The operational characteristics of the pumped storage equipment are formed by combining the list of key parameters, the standard operation flowchart, the control logic block diagram, and the fault mode list.

[0010] In one embodiment of the present invention, the key operating parameters include: Initial condition parameters, including water level, rotational speed, load, and voltage; Event triggering parameters, which include disturbance type, disturbance amount, fault time, fault location, and fault impedance; The model and control parameters include governor mode, excitation mode, valve characteristics, and protection setpoints.

[0011] In one embodiment of the present invention, based on the operating characteristics of the pumped storage equipment, initial operating conditions are screened and key parameters of the operating conditions are defined to obtain a parameterized initial set of simulation operating conditions, including: Based on the operating characteristics of the pumped storage equipment, an operating condition classification framework is established, and operating condition screening principles are obtained. Based on the aforementioned operating condition screening principles, standard operating conditions, dynamic process operating conditions, and fault disturbance operating conditions are defined to obtain an initial operating condition definition list. Based on the key parameters of the operating conditions and the initial list of operating condition definitions, parameter values ​​are assigned to each defined operating condition to obtain the parameterized initial set of simulation operating conditions.

[0012] In one embodiment of the present invention, the coverage optimization algorithm includes: Construct a working condition relationship graph model, treating each parameterized working condition as a node, and establish edge connections based on the logical relationships between working conditions; Calculate the feature coverage of each operating condition and evaluate it based on the number and importance of features that can be covered for each operating condition; Perform graph theory optimization operations to maximize the feature coverage of selected working conditions while satisfying the connectivity constraints, or minimize the number of selected working conditions while satisfying full feature coverage.

[0013] In one embodiment of the present invention, the step of optimizing and filtering the initial set of parameterized simulation conditions using a coverage optimization algorithm includes: Based on the parameterized initial set of simulation conditions, a condition relationship graph model is constructed, in which each condition is a node. Based on the working condition relationship graph model, the corresponding approximate optimization algorithm is selected and executed to obtain a subset of candidate working conditions. The approximate optimization algorithm includes the maximum connectivity coverage algorithm, the minimum connectivity set coverage algorithm, or the node weighted group Steiner tree algorithm. Based on the preset coverage index and optimization target, the candidate working condition subset is evaluated, and the optimal working condition subset is selected as the final simulation working condition dataset.

[0014] In one embodiment of the present invention, based on the working condition relationship graph model, a corresponding approximate optimization algorithm is selected and executed to obtain a subset of candidate working conditions, including: If the optimization objective is to maximize the number of features covered while ensuring connectivity between work cases, then the maximum connectivity coverage algorithm is executed to calculate the work case combination with the maximum number of features covered, and the first candidate work case subset is obtained. If the optimization objective is to minimize the number of working conditions while ensuring full feature coverage, then the minimum connected set coverage algorithm is executed to determine the minimum number of working conditions that satisfy the full coverage constraint, thus obtaining the second candidate subset of working conditions. If the optimization objective is to consider the connection of multiple key states and the weight of the operating conditions, then the Steiner tree algorithm with node weighted groups is executed to comprehensively consider the weight of the operating conditions and the connection requirements of the key states, and to obtain the third candidate subset of operating conditions.

[0015] In one embodiment of the present invention, the final simulation condition dataset is structured and stored using a data format specification, including: Based on the final simulation working condition dataset, a basic information data table containing working condition number, working condition name, detailed description and classification label is established; Based on the basic information data table, metadata information including the applicable equipment range, simulation software compatibility and version number is created to generate the working condition metadata table; Based on the aforementioned operating condition metadata table, a parameter data structure is constructed that includes parameter name, parameter value, parameter unit, and parameter description, forming a complete operating condition data storage format.

[0016] In one embodiment of the present invention, the version control and maintenance mechanism includes: Operating condition data version tracking records the history of operating condition modifications and supports version addition and rollback. Regular data backups are performed, including regular backups and recovery of the operational database. Data integrity checks: Regularly check the integrity and consistency of operating data.

[0017] This invention also provides a system for establishing a simulation operating condition database for pumped storage equipment, comprising: The acquisition module is used to acquire the design specifications, historical operation records and industry standards of the pumped storage equipment, and to analyze the operation characteristics of the pumped storage equipment using operation analysis rules to obtain the operation characteristics of the pumped storage equipment. The filtering module is used to filter the initial operating conditions and define key parameters of the operating conditions based on the operating characteristics of the pumped storage equipment, so as to obtain a parameterized initial set of simulation operating conditions. The optimization module is used to establish indicators based on the parameterized initial set of simulation conditions and the condition library, and to optimize and filter the parameterized initial set of simulation conditions using a coverage optimization algorithm to obtain the final simulation condition dataset. A module is established to structure and store the final simulation condition dataset in accordance with data format specifications, and to establish a simulation condition database with version control and maintenance mechanisms. The module is used to build a simulation working condition database application system based on the simulation working condition database and using a system development framework to realize the query, call, visualization and expansion functions of working condition data.

[0018] Through the above technical solution, the present invention has the following beneficial effects: This invention systematically analyzes the operating characteristics of pumped storage equipment, comprehensively covering various normal operation, transient processes and fault conditions, and establishes a complete simulation operating condition library, avoiding the omissions that may occur when manually selecting operating conditions in the traditional way, especially the neglect of extreme operating conditions and boundary conditions. This invention applies graph theory optimization algorithms to screen simulation conditions, minimizing the number of conditions while ensuring coverage, and ensuring that the selected conditions are statistically representative and typical, thereby improving the efficiency and accuracy of simulation testing. This invention adopts a structured data storage format and version control mechanism to achieve standardized management and efficient access to operating condition data, support the reuse, sharing and updating of operating conditions, and reduce repetitive work; The application interface and tools developed in this invention support multiple query, call and visualization methods, making it convenient for users to flexibly use the working condition library according to different needs, and supporting user-defined extensions to meet personalized needs; The method and system of this invention can be widely applied to various aspects such as design verification, control optimization, fault analysis, and personnel training of pumped storage equipment, and have significant practical value and promotion prospects. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a method for establishing a simulation operating condition database for pumped storage equipment according to the present invention. Figure 2 This is a detailed flowchart of the operational characteristics and mode analysis steps of the present invention; Figure 3 This is a detailed flowchart of the steps for defining and parameterizing simulation conditions in this invention; Figure 4 This is a detailed flowchart of the working condition library selection steps based on coverage optimization in this invention; Figure 5 This is a detailed flowchart of the structured storage and management steps of the present invention; Figure 6 This is a system structure diagram for establishing a simulation operating condition database for pumped storage equipment according to the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0022] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0023] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1 like Figure 1 As shown in the figure, this embodiment of the invention provides a method for establishing a simulation operating condition database for pumped storage equipment, which includes the following steps: Step S1: Obtain the design specifications, historical operation records and industry standards of the pumped storage equipment, and analyze the operation characteristics of the pumped storage equipment using operation analysis rules to obtain the operation characteristics of the pumped storage equipment.

[0026] Step S1 is the operational characteristics and mode analysis phase. This step begins by systematically collecting basic data such as design specifications, historical operating data, and industry standards for pumped storage equipment. Subsequently, based on this data, an in-depth analysis of the core components of the pumped storage unit is conducted, including the pumps and turbines, generators and motors, speed control systems, excitation systems, and valve systems, to determine their physical characteristics and key parameters. On this basis, the standard operating procedures and control logic of the pumped storage unit at different operating stages are outlined, clarifying the coordination and signal interaction relationships between the subsystems. Next, potential transient disturbances and fault modes are analyzed, including load changes, grid disturbances, equipment failures, and corresponding protection action logic. Finally, these analysis results are systematically compiled into a comprehensive operational characteristics analysis report and a list of potential operating modes for the pumped storage equipment, providing a basis for the next step of defining operating conditions.

[0027] Step S2: Based on the operating characteristics of the pumped storage equipment, the initial operating conditions are screened and key parameters of the operating conditions are defined to obtain a parameterized initial set of simulation operating conditions.

[0028] Step S2 is the simulation condition definition and parameterization stage. Based on the operational characteristic analysis report output in Step S1, this step first clarifies the core principles for selecting and defining simulation conditions, establishing a multi-dimensional condition classification framework. Then, according to this framework, a series of representative standard operating conditions are defined, such as stable operating points under different water levels and load levels. Next, key dynamic processes and condition transition scenarios are defined, including startup, shutdown, and condition transition processes. Simultaneously, typical fault and disturbance conditions are defined, such as grid faults, equipment faults, and extreme conditions under operational boundary conditions. For each defined condition, its key influencing parameters are identified and quantified, including initial condition parameters, event triggering parameters, and model control parameters, ensuring the completeness and accuracy of the parameters. Finally, all parameterized conditions are summarized and organized to form a comprehensive set of initial simulation conditions, preparing for subsequent optimization and selection.

[0029] Step S3: Based on the parameterized initial set of simulation conditions and the condition library, establish indicators, and use the coverage optimization algorithm to optimize and filter the parameterized initial set of simulation conditions to obtain the final simulation condition dataset.

[0030] Step S3 is the work case library selection stage based on coverage optimization. This step first quantifies and defines work case coverage and representativeness indicators based on the initial work case set, and clearly sets the optimization objective. Then, each work case is treated as a node in a graph model, and a work case relationship graph model is established based on the logical relationships between work cases, coverage characteristics, and representativeness weights. On this basis, according to the specific optimization objective and the characteristics of the graph model, appropriate approximate optimization algorithms are selected and applied, such as the maximum connectivity coverage algorithm, the minimum connectivity set coverage algorithm, or the node weighted group Steiner tree algorithm, to calculate a subset of candidate work cases that meet the optimization objective. These candidate subsets are comprehensively evaluated, considering their coverage performance, representativeness, total number of work cases, and connectivity, and a final selection is made based on expert experience. Finally, the selected work case subsets and their detailed parameter information are organized and formatted to form a final simulation work case dataset with high coverage and representativeness, preparing for the establishment of the database.

[0031] Step S4: Use data format specifications to structure and store the final simulation condition dataset, and establish a simulation condition database with version control and maintenance mechanisms.

[0032] Step S4 is the structured storage and management stage. Based on the final selected simulation operating condition dataset, this step first designs a suitable structured data storage format, such as XML Schema, JSON structure, or relational database table structure, and defines in detail the metadata fields describing each operating condition. Then, the logical and physical architecture of the database, or an appropriate file system structure, is designed, and an efficient indexing strategy is established to support fast querying and retrieval. The required data structure is created using the selected database management system or file system tool. Next, a data import script is written or an ETL tool is used to accurately import the operating condition data into the created database or file system, and data validation is performed. Finally, a version management mechanism and maintenance process for the operating condition data are designed and implemented, including modification history tracking, version control, data backup and recovery mechanisms, and data integrity checks, to ensure the long-term availability and security of the database, forming a structured and manageable pumped storage equipment simulation operating condition database.

[0033] Step S5: Based on the simulation working condition database, a system development framework is used to realize the query, call, visualization and expansion functions of the working condition data, and to build a simulation working condition database application system.

[0034] Step S5 is the application interface and tool development phase. Based on the established simulation operating condition database, this step first analyzes the usage scenarios and needs of potential users, clarifying the core functions that the application interface needs to provide, such as operating condition query, detailed browsing, visualization, and data export. Then, the overall architecture of the application system is designed, including the front-end interface, back-end business logic, and data access layer, and an appropriate technology stack is selected. The back-end service logic is implemented, including functions such as handling user requests, database queries, data processing, and format conversion. Simultaneously, the front-end user interface or API interface is developed to ensure smooth communication with the back-end services. Data visualization tools are integrated or developed to provide intuitive display of operating condition parameters and export functionality to commonly used simulation software. Interfaces that allow users to extend and update the operating condition database are also developed, supporting the addition of custom operating conditions and receiving updates. Finally, system integration testing and deployment are conducted to ensure that all parts work together to provide stable and reliable services, ultimately forming a fully functional simulation operating condition database application system.

[0035] Example 2 like Figure 2 As shown, this embodiment is a further explanation of step S1 in embodiment 1. The step of analyzing the operating characteristics of the pumped storage equipment using operational analysis rules to obtain the operating features of the pumped storage equipment includes: S1.1: Based on the design specifications, historical operation records and industry standards, analyze the physical structure and key parameters of each component of the pumped storage equipment to obtain a list of key parameters; In section S1.1, analyzing the physical structure and key parameters of each component of a pumped storage power plant refers to an in-depth study of the physical characteristics and technical parameters of the core components, such as pumps, turbines, generators, motors, speed control systems, excitation systems, and valve systems. The list of key parameters includes the rated parameters and characteristic curve data of each component.

[0036] S1.2: Based on the list of key parameters, analyze the standard operating procedures and control logic of the pumped storage equipment at different operating stages, and generate a standard operating flowchart and control logic block diagram. In section S1.2, analyzing the standard operating procedures and control logic of pumped storage equipment at different operational stages refers to outlining the operational steps and automatic control methods for each stage, including startup, operation, and shutdown. The standard operating flowchart illustrates the complete process of the equipment transitioning from one state to another. The control logic block diagram describes the signal interactions and coordination relationships between the various subsystems.

[0037] S1.3: Based on the standard operation flowchart and control logic block diagram, analyze the transient disturbance types and fault modes of the pumped storage equipment, and generate a fault mode list; In section S1.3, analyzing the transient disturbance types and failure modes of pumped storage equipment refers to identifying various abnormal operating conditions that the equipment may encounter, including load changes, grid disturbances, and internal faults. The failure mode list lists all possible failure types and their corresponding protection actions and equipment responses.

[0038] S1.4: Combining the key parameter list, standard operation flowchart, control logic block diagram, and fault mode list, the operating characteristics of the pumped storage equipment are formed.

[0039] In S1.4, the comprehensive formation of pumped storage equipment operating characteristics refers to the systematic organization of various information obtained from the previous analysis into a document that comprehensively describes the equipment's operating characteristics, providing a basis for subsequent operating condition definitions.

[0040] Example 3 This embodiment further explains the key operating parameters involved in step S2 of Embodiment 1. These key operating parameters include: 1. Initial condition parameters, including water level, rotational speed, load, and voltage; The initial condition parameters define the state of the equipment and system at the start of the simulation. Water level refers to the water level height of the upstream and downstream reservoirs, which directly affects the effective head of the turbine. Speed ​​refers to the rotational speed of the unit, usually expressed as a percentage of the rated speed. Load refers to the output power level of the unit, usually expressed as a percentage of the rated power. Voltage refers to the generator terminal voltage or system voltage, usually expressed as a percentage of the rated voltage.

[0041] 2. Event triggering parameters, including disturbance type, disturbance amount, fault time, fault location, and fault impedance; The event triggering parameters define the events or disturbances that occur during the simulation. The disturbance type can be load changes, frequency disturbances, voltage fluctuations, etc. The disturbance amount refers to the amplitude or intensity of the disturbance. The fault time refers to the point in time when the fault occurs. The fault location refers to the specific location where the fault occurs, such as inside the generator, transformer, transmission line, etc. The fault impedance refers to the electrical impedance value of the fault point, which affects the magnitude of the fault current.

[0042] 3. Model and control parameters, including governor mode, excitation mode, valve characteristics and protection settings.

[0043] The model and control parameters define the control method and response characteristics of the equipment. The speed governor mode can be speed control, power control, frequency control, etc.; the excitation mode can be constant excitation, automatic voltage regulation, etc.; valve characteristics include the switching characteristics and time constants of the main valve, guide vanes, etc.; protection settings refer to the action threshold and time delay settings of various protection devices.

[0044] Example 4 like Figure 3 As shown, this embodiment is a further explanation of step S2 in embodiment 1. Based on the operating characteristics of the pumped storage equipment, the initial operating conditions are screened and key parameters of the operating conditions are defined to obtain a parameterized initial set of simulation operating conditions, including: S2.1: Based on the operating characteristics of the pumped storage equipment, establish a working condition classification framework and obtain the working condition screening principles; In S2.1, establishing a working condition classification framework refers to constructing a multi-dimensional working condition classification system, such as classification by operating stage, disturbance type, or fault location. Working condition selection principles refer to determining the criteria for selecting working conditions, such as typicality, representativeness, extreme nature, and coverage.

[0045] S2.2: Based on the aforementioned operating condition screening principles, define standard operating conditions, dynamic process operating conditions, and fault disturbance operating conditions to obtain an initial operating condition definition list; In S2.2, defining a standard operating condition refers to identifying a representative normal operating point, such as a stable operating condition under different water level and load combinations. Defining a dynamic process condition refers to describing the process of equipment state changes, such as startup, shutdown, and condition transition. Defining a fault disturbance condition refers to simulating various abnormal situations, such as power grid faults and internal equipment faults. The initial operating condition definition list contains all the operating condition types that need to be considered and their basic descriptions.

[0046] S2.3: Based on the key parameters of the working conditions and the initial working condition definition list, assign parameter values ​​to each defined working condition to obtain the parameterized initial set of simulation working conditions.

[0047] In S2.3, assigning parameter values ​​to each defined working condition means determining the specific values ​​or ranges of all key parameters for each working condition in the working condition list. The parameterized initial set of simulation working conditions is a dataset containing all working conditions and their complete parameter settings, which can be directly used for simulation or further optimization.

[0048] Example 5 This embodiment further illustrates the coverage optimization algorithm involved in step S3 of Embodiment 1. The coverage optimization algorithm includes: S3.1 Construct a working condition relationship graph model, treating each parameterized working condition as a node, and establishing edge connections based on the logical relationships between working conditions; Constructing a work condition relationship graph model transforms an abstract set of work conditions into a mathematical graph structure, facilitating the application of graph theory algorithms. Each work condition is a node in the graph, and the edges between nodes represent the logical relationships between work conditions, such as time sequence and state transitions.

[0049] S3.2 Calculate the feature coverage of the operating condition and evaluate it based on the number and importance of features that can be covered for each operating condition; Calculating operating condition feature coverage assesses the value of each operating condition, i.e., how many key features it covers and the importance of those features. Features can be operating areas, fault types, control modes, etc.

[0050] S3.3 performs graph theory optimization operations to maximize the feature coverage of selected working conditions while satisfying the connectivity constraints, or to minimize the number of selected working conditions while satisfying full feature coverage.

[0051] Performing graph theory optimization operations involves applying specific graph theory algorithms to find the optimal or near-optimal subset of work cases under certain constraints. Connectivity constraints ensure that there are reasonable logical relationships between the selected work cases, feature coverage measures the comprehensiveness of the selected work cases, and the number of work cases relates to efficiency.

[0052] Specifically, in S3.1, constructing the working condition relationship graph model involves transforming the abstract set of working conditions into a mathematical graph structure G=(V,E), where V represents the set of nodes and E represents the set of edges. Each parameterized working condition is represented as a node v∈V in the graph, with node attributes including key parameter values, covered feature sets, and importance weights. Edges e(vi,vj)∈E between nodes represent logical relationships between working conditions. These relationships can be of various types: temporal relationships (such as the sequence of steps in a startup process), state transition relationships (such as switching from power generation mode to pumping mode), similarity relationships (such as working conditions with similar parameters), or causal relationships (such as subsequent states caused by a fault). Edges can be directed, representing unidirectional logical relationships, or undirected, representing bidirectional associations. Furthermore, edges can be assigned weights to represent the strength of the relationship or the cost of the transition. In this way, the set of working conditions is transformed into a structured graph model, enabling the complex working condition selection problem to be solved using mature graph theory algorithms.

[0053] In S3.2, calculating the feature coverage of operating conditions involves evaluating the value of each operating condition node v, i.e., how many key features it covers and the importance of these features. First, a feature set F = {f1, f2, ..., fm} is defined, where each feature fi represents a key aspect of pumped storage equipment operation, such as a specific type of fault, a specific operating area, or a specific control mode. Then, an importance weight wi is assigned to each feature fi, reflecting its relative importance in practical applications. For each operating condition node v, its covered feature subset Fv⊆F is determined, and its weighted feature coverage score(v) = ∑fi∈Fv wi is calculated. Furthermore, the correlation and redundancy between features can be considered, and a submodular function can be introduced to more accurately evaluate the marginal coverage gain brought by adding a new operating condition. This evaluation method allows the algorithm to prioritize operating conditions that cover important and complementary features, improving the representativeness and comprehensiveness of the final operating condition database.

[0054] In S3.3, performing graph theory optimization operations involves applying specific graph theory algorithms to the constructed graph model to find the optimal or near-optimal subset of work cases that satisfies the constraints. Depending on the specific requirements, different optimization objectives can be defined: one is to maximize the feature coverage ∑fi∈FS wi when the number of work cases is finite (e.g., |S|≤k, where S is the selected subset of work cases and k is a preset upper limit), where FS=∪v∈S and Fv are all features covered by the subset S; the other is to minimize the number of selected work cases |S| when all features are required to be covered (i.e., FS=F). Simultaneously, connectivity constraints must be satisfied, meaning the selected subset of work cases S forms a connected subgraph in graph G, ensuring reasonable logical transitions between work cases. Since these problems are typically NP-hard, approximation algorithms, such as greedy algorithms, local search, or rounding algorithms based on linear programming, are often used in practical applications to obtain near-optimal solutions in polynomial time. These algorithms usually provide a theoretical guarantee of approximation ratio, ensuring that the quality of the solution is not too poor.

[0055] Example 6 like Figure 4 As shown, this embodiment is a further explanation of step S3 in embodiment 1. The optimization and screening of the parameterized initial set of simulation conditions using a coverage optimization algorithm includes: S3.4: Based on the parameterized initial set of simulation conditions, construct a condition relationship graph model, in which each condition is a node; In S3.4, constructing a working condition relationship graph model involves representing each parameterized working condition as a node in the graph, and establishing edges or defining node attributes / weights between nodes based on the logical relationships between working conditions (such as time sequence, state transition), coverage characteristics (such as which type of fault it belongs to, which operating area it covers), and representative weights (such as historical occurrence frequency, importance score).

[0056] S3.5: Based on the working condition relationship graph model, select and execute the corresponding approximate optimization algorithm to obtain a subset of candidate working conditions. The approximate optimization algorithm includes the maximum connectivity coverage algorithm, the minimum connectivity set coverage algorithm, or the node weighted group Steiner tree algorithm. In S3.5, the selection and execution of the corresponding approximate optimization algorithm is based on the specific optimization objective and graph model structure, applying appropriate graph theory algorithms. The maximum connectivity coverage algorithm is suitable for scenarios that maximize the number of covered features while ensuring connectivity between work cases; the minimum connected set coverage algorithm is suitable for scenarios that minimize the number of work cases while covering all necessary features; the node weighted group Steiner tree algorithm is suitable for complex scenarios that require connecting multiple key state points and considering node weights.

[0057] S3.6: Based on the preset coverage index and optimization target, evaluate the candidate working condition subset and select the optimal working condition subset as the final simulation working condition dataset.

[0058] In S3.6, evaluating the candidate working condition subset means evaluating the working condition subset generated by the algorithm based on predefined coverage indicators (such as feature coverage and operating area coverage) and optimization objectives (such as minimizing the number of working conditions and maximizing coverage), and selecting the subset that best meets the requirements as the final result.

[0059] Specifically, in S3.4, constructing the working condition relationship graph model involves transforming the parameterized initial set of simulation working conditions C2 into a mathematical graph structure. In practice, an empty graph G=(V,E) is first created, where V represents the set of nodes and E represents the set of edges. For each working condition ci in the initial set C2, a corresponding node vi is created in the graph, and attributes are attached to this node, including the complete parameter set of the working condition, a working condition type label (e.g., standard operation, dynamic process, fault disturbance, etc.), a set of covered features Fi, and an importance weight wi (which can be determined based on historical occurrence frequency or expert rating). Next, the logical relationships between working conditions are analyzed to determine the node pairs for which edges need to be established. For example, for working conditions representing different stages of the startup process, directed edges are established according to time sequence; for working conditions representing different operating states of the same equipment, edges are established based on the probability of state transitions; for working conditions with similar parameters, weighted edges can be established based on parameter similarity. Furthermore, virtual nodes can be introduced to represent specific initial or target states, serving as the starting or ending point for subsequent optimization algorithms. Ultimately, a complete graphical model G is obtained, whose structure reflects the inherent relationships and characteristics of the set of working conditions, providing a foundation for subsequent optimization algorithms.

[0060] In S3.5, selecting and executing the corresponding approximate optimization algorithm involves applying a suitable graph theory algorithm to the constructed graph model G based on the specific optimization objective and constraints. The Connected Maximum Coverage algorithm is suitable for situations with a limited number of work cases (e.g., selecting at most k work cases) to find the subset of work cases that covers the most features while maintaining connectivity. This algorithm typically employs a modified greedy strategy, selecting a new work case each time that covers the most uncovered features and maintains connectivity with already selected work cases until the limit is reached or the coverage cannot be further improved. The Minimum Connected Set Cover algorithm is suitable for situations requiring coverage of all necessary features, finding the minimum number of connected work cases. This algorithm typically first uses a set covering algorithm to find the minimum set of work cases covering all features, then adds necessary connecting nodes to ensure connectivity, or directly applies an integer programming method considering connectivity constraints. The Node-Weighted Group Steiner Tree algorithm is suitable for more complex scenarios requiring connection of multiple key state groups (e.g., different initial and target states), while considering node weights or costs. This algorithm comprehensively considers node weights and connectivity requirements, using approximation algorithms (such as LP-based rounding or tree-based approximation) to find the connected subgraph with the minimum total weight. The execution result of these algorithms is one or more candidate work case subsets, each subset being a subset of the original work case set, satisfying specific coverage and connectivity requirements.

[0061] In S3.6, the evaluation of candidate work case subsets is based on predefined coverage metrics and optimization objectives. Each candidate work case subset generated by the algorithm is comprehensively evaluated, and the subset that best meets the requirements is selected as the final result. Evaluation metrics may include: feature coverage (the proportion of covered features to the total number of features), weighted feature coverage (considering feature importance weights), number of work cases (the total number of selected work cases), connectivity metrics (such as average path length, diameter, etc.), and representativeness metrics (such as the degree of matching with historical statistical distributions). For each candidate subset, the values ​​of these metrics are calculated and compared with preset target values ​​or thresholds. Multi-objective evaluation methods, such as weighted summation, Pareto optimality, or analytic hierarchy process (AHP), can be used to comprehensively consider multiple metrics and obtain a comprehensive score for each candidate subset. Furthermore, qualitative evaluation can be combined with expert experience, especially for aspects that are difficult to quantify, such as the practical operational significance of the work cases and their consistency with existing procedures. Finally, the candidate subset with the highest comprehensive score or that best meets the key requirements is selected as the final simulation work case dataset C3 for subsequent structured storage and application system development.

[0062] Example 7 This embodiment further explains step S3.5 in Embodiment 6. Based on the aforementioned working condition relationship diagram model, a corresponding approximate optimization algorithm is selected and executed to obtain a subset of candidate working conditions, including: S3.5.1 If the optimization objective is to maximize the number of features covered and ensure connectivity between working conditions, then execute the maximum connectivity coverage algorithm to calculate the working condition combination with the maximum number of features covered and obtain the first candidate working condition subset. The maximum coverage algorithm is suitable for situations with limited resources, where the goal is to cover as many features as possible with a limited number of work cases, while ensuring reasonable logical connections between the selected work cases. This algorithm typically employs a greedy strategy, selecting a new work case each time that covers the most uncovered features and is connected to the selected work cases.

[0063] S3.5.2 If the optimization objective is to minimize the number of working conditions while ensuring full feature coverage, then execute the minimum connected set coverage algorithm to determine the minimum number of working conditions that satisfy the full coverage constraint, and obtain the second candidate subset of working conditions. The minimum connected set coverage algorithm is suitable for situations where all features must be covered, and the goal is to use the fewest possible number of cases. This algorithm typically first finds the minimum set that covers all features, and then adds necessary connections to this set to ensure connectivity.

[0064] S3.5.3 If the optimization objective is to consider the connection of multiple key states and the weight of the operating conditions, then the node weighted group Steiner tree algorithm is executed to comprehensively consider the weight of the operating conditions and the connection requirements of the key states to obtain the third candidate subset of operating conditions.

[0065] The node-weighted group Steiner tree algorithm is suitable for more complex scenarios that require consideration of connections between multiple key state points, with each node having different weights or costs. This algorithm comprehensively considers node weights and connection requirements to find the connected subgraph with the minimum total weight.

[0066] Specifically, when the optimization objective is to maximize the number of features covered while ensuring connectivity between work cases, the maximum coverage algorithm is the most suitable choice. This algorithm aims to solve the following problem: given a limited number of work cases (e.g., a maximum of k work cases can be selected), select a connected subgraph S⊆V from the work case relationship graph G=(V,E) such that the number of features covered by S, |∪v∈S Fv|, is maximized. Since this problem is NP-hard, approximate algorithms are typically used to solve it. An efficient implementation is a modified greedy algorithm: first, select the work case with the most covered features as the initial node; then, in each iteration, consider all unselected nodes adjacent to the selected node set, and add the node that can cover the most uncovered features to the set, until the node number limit k is reached or no further features can be added. The time complexity of this algorithm is O(k|V||F|), where |V| is the total number of work cases and |F| is the total number of features. In practical applications, this algorithm can cover as many operational features as possible with a limited number of operating conditions while ensuring reasonable connections between operating conditions. It is particularly suitable for scenarios with limited resources but requiring broad coverage, such as preliminary testing or training systems.

[0067] When the optimization objective is to minimize the number of job cases while ensuring full feature coverage, the minimum connected set coverage algorithm is the optimal choice. This algorithm solves the problem of finding the smallest connected subgraph S⊆V in the job case graph G=(V,E) such that S covers all target features, i.e., ∪v∈S Fv = F. This problem combines the characteristics of the minimum set coverage problem and the minimum connected dominance set problem, and is also NP-hard. A practical solution is a two-stage algorithm: in the first stage, a standard set coverage approximation algorithm (such as a greedy algorithm) is used to find the minimum set of job cases S' that covers all features; in the second stage, the minimum Steiner tree connecting all nodes in S' is found in the original graph G, resulting in the final connected subgraph S. Another approach is to directly use an integer linear programming model considering connectivity constraints and apply techniques such as branch and bound or cutting planes to solve the problem. The time complexity of this algorithm depends on the specific implementation, but is generally higher than the maximum connected set coverage algorithm. In practical applications, this algorithm can minimize the number of operating conditions required while ensuring coverage of all necessary features. It is particularly suitable for scenarios that require comprehensive testing but are resource-constrained, such as equipment acceptance testing or comprehensive performance evaluation.

[0068] When the optimization objective considers the connectivity of multiple critical states and the weights of different work cases, the node-weighted group Steiner tree algorithm is the most suitable choice. This algorithm solves a more complex problem: in a work case graph G=(V,E), given multiple node groups {T1, T2, ..., Tl} (each group Ti⊆V represents a class of critical states, such as different initial states or target states), and the weight w(v) of each node v (representing the cost, importance, or complexity of the work case), find a connected subgraph S⊆V such that S contains at least one node from each group Ti, and the total weight ∑v∈S w(v) is minimized. This is an extended version of the classic Steiner tree problem, with higher computational complexity. Solution methods include approximate algorithms based on linear programming relaxation and rounding, approximate algorithms based on dynamic programming, or heuristic algorithms (such as genetic algorithms and simulated annealing). This algorithm typically has high time complexity but can handle more complex constraints. In practical applications, this algorithm can comprehensively consider multiple attributes of the working condition and the connection requirements between multiple key state points to generate a combination of working conditions that satisfies complex constraints and has the optimal weight distribution. It is particularly suitable for advanced application scenarios that require simulating complex system behavior or multi-objective optimization, such as system integration testing or complex fault chain analysis.

[0069] Example 8 like Figure 5 As shown, this embodiment is a further explanation of step S4 in embodiment 1, which uses data format specifications to perform structured storage of the final simulation condition dataset, including: S4.1: Based on the final simulation working condition dataset, establish a basic information data table containing working condition number, working condition name, detailed description and classification label; In S4.1, establishing a basic information data table involves creating basic descriptive information for each working condition. The working condition number is a unique identifier; the working condition name is a short descriptive title; the detailed description is a complete textual description of the working condition; and the category tags are a set of keywords used for categorization and retrieval.

[0070] In S4.1, establishing the basic information data table is the process of structuring the basic descriptive information of each operating condition in the final simulation operating condition dataset C3. The operating condition number is a unique identifier, usually using meaningful coding rules, such as "PSH-STD-001" representing the first operating condition under standard operating conditions, and "PSH-FLT-001" representing the first operating condition under fault conditions, etc. The operating condition name is a brief description of the operating condition, such as "rated load generation operating condition," "three-phase short-circuit fault operating condition," etc., usually limited to 20-30 characters for easy display in the interface and reports. The detailed description is a complete textual description of the operating condition, including information such as the background, purpose, and expected behavior, usually consisting of one or more paragraphs. The classification tags are a set of keywords used for multi-dimensional classification of the operating condition, such as "operating mode: generation," "load level: full load," "fault type: electrical fault," etc. Each operating condition can have multiple tags for easy subsequent retrieval and filtering. This basic information constitutes the "identity card" of the operating condition, which is the main basis for users to understand and select the operating condition.

[0071] S4.2: Based on the basic information data table, create metadata information including the applicable equipment range, simulation software compatibility and version number, and generate a working condition metadata table; Creating metadata information involves adding management and compatibility-related information to the operating condition. Applicable equipment scope specifies the equipment models or types to which the operating condition is applicable; simulation software compatibility indicates which simulation software platforms the operating condition can be used on; and the version number is used to track the operating condition's update history.

[0072] In S4.2, creating metadata information involves adding management and compatibility-related technical information to the operating condition. The applicable equipment scope specifies the applicable equipment models, capacity ranges, or technical parameter ranges, such as "applicable to 300MW and above mixed-flow pumped storage units," ensuring users select the appropriate operating condition for their equipment. Simulation software compatibility indicates which simulation software platforms and versions the operating condition can use, such as "compatible with PSCAD X4, PSS / E 33.0 and above," helping users understand the technical requirements and limitations of the operating condition. The version number uses standard version naming rules (e.g., "v1.0.2") to track the operating condition's update history and typically includes information such as creation date, last modification date, creator, and modifier. Furthermore, metadata may also include management information such as the operating condition's verification status (e.g., "verified," "pending verification"), data source (e.g., "measured data," "theoretical calculation"), and confidentiality level. This metadata is associated with the basic information data table to form the operating condition metadata table, providing necessary technical support for the management, maintenance, and use of operating condition data.

[0073] S4.3: Based on the aforementioned operating condition metadata table, construct a parameter data structure that includes parameter name, parameter value, parameter unit, and parameter description to form a complete operating condition data storage format.

[0074] Constructing a parameter data structure is a specific technical aspect of defining operating conditions. Parameter names are identifiers for each parameter; parameter values ​​are specific numerical values ​​or ranges of values; parameter units specify the unit of measurement for the values; and parameter descriptions provide an explanation of the parameter's meaning and purpose. A complete operating condition data storage format integrates basic information, metadata, and parameter data into a structured data object.

[0075] In S4.3, constructing the parameter data structure is the specific technical content for defining operating conditions, and this is the core part of the operating condition data. Parameter names are identifiers for each parameter, using standardized naming rules, such as "upper_reservoir_level" representing the upper reservoir water level, and "generator_terminal_voltage" representing the generator terminal voltage. Parameter values ​​are specific numerical values ​​or ranges, which can be single values ​​(e.g., "500.0"), ranges (e.g., "480.0-520.0"), or time-series data (e.g., load curves). Parameter units specify the unit of measurement for the values, such as "m" (meter), "MW" (megawatt), "Hz" (hertz), etc., ensuring accurate data understanding. Parameter descriptions provide explanations of the parameter's meaning and purpose, helping users understand the parameter's physical meaning and impact. Parameter data is typically organized according to functional modules, such as initial condition parameter groups, event-triggered parameter groups, model control parameter groups, etc., with each group containing multiple related parameters. For complex time-series data or large matrix data, external files may be used for storage and referenced within the parameter structure. A complete operating condition data storage format integrates basic information, metadata, and parameter data into a structured data object. It can be implemented using XML, JSON, a dedicated database table structure, or a custom binary format to ensure data integrity, consistency, and accessibility.

[0076] Example 9 This embodiment further illustrates the version control and maintenance mechanism involved in step S4 of Embodiment 1. The version control and maintenance mechanism includes: Operating condition data version tracking records the history of operating condition modifications and supports version addition and rollback. Regular data backups are performed, including regular backups and recovery of the operational database. Data integrity checks: Regularly check the integrity and consistency of operating data.

[0077] Operating condition data version tracking refers to maintaining a version history for each operating condition, recording the content, time, and modifier of each modification. When an operating condition is updated, the system creates a new version instead of directly overwriting the old one, allowing users to revert to previous versions when needed.

[0078] Regular data backup refers to automatically creating a complete backup of the operational database at preset time intervals (such as daily or weekly) and storing the backup files in a secure location. In the event of data loss or corruption, data can be recovered from the most recent backup.

[0079] Data integrity checks refer to periodically running inspection programs to verify the integrity and consistency of operational data, such as checking whether required fields have values, whether parameter values ​​are within reasonable ranges, and whether related data matches. When problems are found, warnings or error reports are generated to remind administrators to fix them.

[0080] Example 10 like Figure 6 As shown, this embodiment provides a system for establishing a simulation operating condition database for pumped storage equipment, including: The acquisition module is used to acquire the design specifications, historical operation records and industry standards of the pumped storage equipment, and to analyze the operation characteristics of the pumped storage equipment using operation analysis rules to obtain the operation characteristics of the pumped storage equipment. The filtering module is used to filter the initial operating conditions and define key parameters of the operating conditions based on the operating characteristics of the pumped storage equipment, so as to obtain a parameterized initial set of simulation operating conditions. The optimization module is used to establish indicators based on the parameterized initial set of simulation conditions and the condition library, and to optimize and filter the parameterized initial set of simulation conditions using a coverage optimization algorithm to obtain the final simulation condition dataset. A module is established to structure and store the final simulation condition dataset in accordance with data format specifications, and to establish a simulation condition database with version control and maintenance mechanisms. The module is used to build a simulation working condition database application system based on the simulation working condition database and using a system development framework to realize the query, call, visualization and expansion functions of working condition data.

[0081] The acquisition module is responsible for collecting and analyzing basic technical data on pumped storage equipment, including design specifications, historical operation records, and industry standards. This module enables systematic analysis of the equipment's physical structure, control logic, and failure modes, outputting a comprehensive description of its operational characteristics.

[0082] Based on the analysis results from the acquisition module, the screening module establishes a working condition classification framework, defines various working conditions, and determines their key parameters. This module transforms qualitative working condition descriptions into a quantifiable set of parameters, forming an initial simulation working condition library.

[0083] The optimization module employs advanced graph theory optimization algorithms to select a subset of work cases from the initial set that has high coverage, strong representativeness, and a suitable number. This module is the core innovation of the system, improving the efficiency and quality of the work case database through mathematical optimization methods.

[0084] The module responsible for the structured storage and management of operating condition data includes designing data format specifications and implementing database creation, population, and maintenance. This module ensures the integrity, consistency, and sustainable management of operating condition data.

[0085] The module is used to develop user-oriented application systems, providing functions such as operating condition query, retrieval, visualization, and extension. This module serves as the system's user interface, enabling the operating condition database to be easily applied in practical work.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0087] It should be noted that those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of this invention. If such modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include such modifications and variations.

[0088] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the method for establishing a simulation operating condition library for pumped storage equipment as described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0089] In addition, this disclosure also provides a computer program product, which stores a computer program. When the computer program is run by a processor, it executes the steps of the method for establishing a simulation working condition library for pumped storage equipment as described above. For details, please refer to the above method embodiments, which will not be repeated here.

[0090] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium, which can be a volatile or non-volatile computer-readable storage medium. In another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0091] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices and apparatuses described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0092] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0094] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for establishing a simulation operating condition database for pumped storage equipment, characterized in that, include: The design specifications, historical operation records and industry standards of the pumped storage equipment are obtained, and the operation characteristics of the pumped storage equipment are analyzed using operation analysis rules to obtain the operation characteristics of the pumped storage equipment. Based on the operating characteristics of the pumped storage equipment, the initial operating conditions are screened and key parameters of the operating conditions are defined to obtain a parameterized initial set of simulation operating conditions. Based on the parameterized initial set of simulation conditions and the establishment index of the condition library, the coverage optimization algorithm is used to optimize and filter the initial set of parameterized simulation conditions to obtain the final simulation condition dataset. The final simulation condition dataset is structured and stored using data format specifications, and a simulation condition database with version control and maintenance mechanisms is established. Based on the aforementioned simulation working condition database, a system development framework is used to realize the functions of querying, calling, visualizing and extending working condition data, and to build a simulation working condition database application system.

2. The method according to claim 1, characterized in that, The operational characteristics of the pumped storage equipment are analyzed using operational analysis rules to obtain the operational features of the pumped storage equipment, including: Based on the design specifications, historical operation records and industry standards, the physical structure and key parameters of each component of the pumped storage equipment are analyzed to obtain a list of key parameters. Based on the list of key parameters, the standard operating procedures and control logic of pumped storage equipment at different operating stages are analyzed, and standard operating flowcharts and control logic block diagrams are generated. Based on the standard operation flowchart and control logic block diagram, analyze the transient disturbance types and fault modes of pumped storage equipment, and generate a fault mode list. The operational characteristics of the pumped storage equipment are formed by combining the list of key parameters, the standard operation flowchart, the control logic block diagram, and the fault mode list.

3. The method according to claim 1, characterized in that, The key parameters for the operating conditions include: Initial condition parameters, including water level, rotational speed, load, and voltage; Event triggering parameters, which include disturbance type, disturbance amount, fault time, fault location, and fault impedance; The model and control parameters include governor mode, excitation mode, valve characteristics, and protection setpoints.

4. The method according to claim 2, characterized in that, Based on the operating characteristics of the pumped storage equipment, initial operating conditions are screened and key parameters are defined to obtain a parameterized initial set of simulation operating conditions, including: Based on the operating characteristics of the pumped storage equipment, an operating condition classification framework is established, and operating condition screening principles are obtained. Based on the aforementioned operating condition screening principles, standard operating conditions, dynamic process operating conditions, and fault disturbance operating conditions are defined to obtain an initial operating condition definition list. Based on the key parameters of the operating conditions and the initial list of operating condition definitions, parameter values ​​are assigned to each defined operating condition to obtain the parameterized initial set of simulation operating conditions.

5. The method according to claim 1, characterized in that, The coverage optimization algorithm includes: Construct a working condition relationship graph model, treating each parameterized working condition as a node, and establish edge connections based on the logical relationships between working conditions; Calculate the feature coverage of each operating condition and evaluate it based on the number and importance of features that can be covered for each operating condition; Perform graph theory optimization operations to maximize the feature coverage of selected working conditions while satisfying the connectivity constraints, or minimize the number of selected working conditions while satisfying full feature coverage.

6. The method according to claim 5, characterized in that, The optimization and filtering of the initial set of parameterized simulation conditions using a coverage optimization algorithm includes: Based on the parameterized initial set of simulation conditions, a condition relationship graph model is constructed, in which each condition is a node. Based on the working condition relationship graph model, the corresponding approximate optimization algorithm is selected and executed to obtain a subset of candidate working conditions. The approximate optimization algorithm includes the maximum connectivity coverage algorithm, the minimum connectivity set coverage algorithm, or the node weighted group Steiner tree algorithm. Based on the preset coverage index and optimization target, the candidate working condition subset is evaluated, and the optimal working condition subset is selected as the final simulation working condition dataset.

7. The method according to claim 6, characterized in that, Based on the aforementioned working condition relationship diagram model, a corresponding approximate optimization algorithm is selected and executed to obtain a subset of candidate working conditions, including: If the optimization objective is to maximize the number of features covered while ensuring connectivity between work cases, then the maximum connectivity coverage algorithm is executed to calculate the work case combination with the maximum number of features covered, and the first candidate work case subset is obtained. If the optimization objective is to minimize the number of working conditions while ensuring full feature coverage, then the minimum connected set coverage algorithm is executed to determine the minimum number of working conditions that satisfy the full coverage constraint, thus obtaining the second candidate subset of working conditions. If the optimization objective is to consider the connection of multiple key states and the weight of the operating conditions, then the Steiner tree algorithm with node weighted groups is executed to comprehensively consider the weight of the operating conditions and the connection requirements of the key states, and to obtain the third candidate subset of operating conditions.

8. The method according to claim 1, characterized in that, The final simulation condition dataset is structured and stored according to data format specifications, including: Based on the final simulation working condition dataset, a basic information data table containing working condition number, working condition name, detailed description and classification label is established; Based on the basic information data table, metadata information including the applicable equipment range, simulation software compatibility and version number is created to generate the working condition metadata table; Based on the aforementioned operating condition metadata table, a parameter data structure is constructed that includes parameter name, parameter value, parameter unit, and parameter description, forming a complete operating condition data storage format.

9. The method according to claim 1, characterized in that, The version control and maintenance mechanism includes: Operating condition data version tracking records the history of operating condition modifications and supports version addition and rollback. Regular data backups are performed, including regular backups and recovery of the operational database. Data integrity checks: Regularly check the integrity and consistency of operating data.

10. A system for establishing a simulation operating condition database for pumped storage equipment, characterized in that, include: The acquisition module is used to acquire the design specifications, historical operation records and industry standards of the pumped storage equipment, and to analyze the operation characteristics of the pumped storage equipment using operation analysis rules to obtain the operation characteristics of the pumped storage equipment. The filtering module is used to filter the initial operating conditions and define key parameters of the operating conditions based on the operating characteristics of the pumped storage equipment, so as to obtain a parameterized initial set of simulation operating conditions. The optimization module is used to establish indicators based on the parameterized initial set of simulation conditions and the condition library, and to optimize and filter the parameterized initial set of simulation conditions using a coverage optimization algorithm to obtain the final simulation condition dataset. A module is established to structure and store the final simulation condition dataset in accordance with data format specifications, and to establish a simulation condition database with version control and maintenance mechanisms. The module is used to build a simulation working condition database application system based on the simulation working condition database and using a system development framework to realize the query, call, visualization and expansion functions of working condition data.

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