Power station six indexes automatic calculation system and method adaptive to complex environment

Abstract

The application provides a power station six-character index automatic calculation system and method suitable for complex environment, which comprises a user interaction layer, a model layer, an environment adaptation and dynamic correction layer, a core calculation engine and a data layer. By obtaining the electrical parameters and environmental parameters of the power station project input by the user, the reference model template is obtained from the model template database of the data layer based on the electrical parameters and environmental parameters, and the reference model template is output to the user, and the corresponding components of the power station project are input in the reference model template to obtain the updated model template. The basic data of the components matched with the component list are called from the component failure rate database, the maintainability reference database and the support parameter database, and the basic data is stress-coupled and corrected to obtain the corrected basic data. Based on the corrected basic data, the six-character index calculation is carried out and the six-character index calculation result is output, and the actual six-character index calculation result can be obtained.

Description

Technical Field

[0001] This application relates to the field of power plant design and evaluation technology, and in particular to an automatic calculation system and method for six performance indicators of power plants that are adaptable to complex environments. Background Technology

[0002] A power plant is an energy conversion facility or equipment that converts other energy sources into alternating current. During the design and evaluation phases of a power plant, six performance indicators need to be calculated to assess its electrical performance and reliability. These six indicators include: reliability, maintainability, supportability, testability, safety, and environmental adaptability.

[0003] The calculation of the six performance indicators of existing power plants mainly relies on manual methods or military-grade six performance calculation software. Manual calculation is inefficient and prone to errors, while military-grade six performance calculation software is general-purpose software that lacks adaptation scenarios for power plants and cannot reflect the real changes of electrical equipment in power plants under scenarios such as high altitude, high temperature, high salt spray, and high humidity, resulting in calculation results that deviate from reality. Summary of the Invention

[0004] In view of this, embodiments of this application provide an automatic calculation system and method for the six performance indicators of power plants that are adapted to complex environments, so as to accurately calculate the six performance indicators of electrical equipment in power plants under complex scenarios.

[0005] In a first aspect, embodiments of this application provide an automatic calculation system for six performance indicators of power plants adapted to complex environments. The system includes: a user interaction layer, a model layer, an environmental adaptation and dynamic correction layer, a core calculation engine, and a data layer. The data layer integrates a component failure rate database, a maintainability parameter database, a supportability parameter database, a model template database, and an environmental stress coefficient database. The model layer integrates system models, reliability block diagram models, and power plant templates for various types of power plants. The user interaction layer is used to obtain the electrical and environmental parameters of the power plant project input by the user. The model layer is used to call a reference model template recorded in the model template database from the data layer based on the electrical parameters and environmental parameters, and output the reference model template to the user interaction layer so that the user can input the components corresponding to the power plant project in the reference model template displayed in the user interaction layer; the model layer is also used to update the reference model template based on the components input by the user to obtain an updated model template; The environmental adaptation and dynamic correction layer is used to generate a component list for the updated model template, retrieve the basic data of the components that match the component list from the component failure rate database, maintainability parameter database and support parameter database of the data layer, call the environmental stress coefficient database to perform stress coupling correction on the basic data, and output the corrected basic data to the core computing engine. The core computing engine is used to calculate the six indicators based on the corrected basic data and output the calculation results of the six indicators to the user interaction layer. Secondly, embodiments of this application provide an automatic calculation method for six power plant performance indicators adapted to complex environments, applied to the automatic calculation system for six power plant performance indicators adapted to complex environments provided in the first aspect, wherein the method includes: Obtain the electrical and environmental parameters of the power plant project input by the user; Based on the electrical and environmental parameters, the reference model template recorded in the model template database is called from the data layer, and after the reference model template is output, the components corresponding to the power plant project input by the user in the reference model template are obtained. The reference model template is updated based on the components input by the user to obtain the updated model template; A component list is generated for the updated model template, and the basic data of the components that match the component list are retrieved from the component failure rate database, maintainability parameter database and supportability parameter database of the data layer. The environmental stress coefficient database is then called to perform stress coupling correction on the basic data. The six indicators are calculated based on the corrected basic data, and the calculation results of the six indicators are output.

[0006] Thirdly, embodiments of this application provide an electronic device, wherein the electronic device includes: a processor; and a memory storing a program; wherein the program includes instructions, which, when executed by the processor, cause the processor to execute the automatic calculation method for the six power plant performance indicators adapted to complex environments described in the second aspect.

[0007] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the automatic calculation method for the six power plant performance indicators adapted to complex environments described in the second aspect.

[0008] The beneficial effects of this application are: This application provides an automatic calculation system and method for six performance indicators of power plants adapted to complex environments. The system includes a user interaction layer, a model layer, an environmental adaptation and dynamic correction layer, a core calculation engine, and a data layer. The user interaction layer obtains electrical and environmental parameters of the power plant project input by the user. The model layer retrieves a reference model template from the model template database in the data layer based on these parameters and outputs the template to the user interaction layer. The user inputs the corresponding components of the power plant project into the reference model template, and the template is updated accordingly. The environmental adaptation and dynamic correction layer then generates a corresponding component list based on the updated template and retrieves the basic data of the matching components from the component failure rate database, maintainability reference database, and supportability parameter database in the data layer. Next, the environmental stress coefficient database is used to perform stress coupling correction on the basic data, resulting in corrected basic data. Finally, the core calculation engine calculates the six performance indicators based on the corrected data and outputs the results.

[0009] By using the system provided in this application, the environmental adaptation and dynamic correction layer in the system can automatically correct the basic data of the components of the power station according to the actual environmental changes of the power station, thereby obtaining a more realistic calculation result of the six performance indicators that are closer to the actual changes of electrical equipment, so as to achieve the effect of accurate calculation of the six performance indicators of electrical equipment in power stations under complex scenarios. Attached Figure Description

[0010] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This paper shows a system block diagram of an automatic calculation system for six power plant performance indicators adapted to complex environments, provided in an embodiment of this application. Figure 2 This paper presents another system block diagram of the automatic calculation system for the six performance indicators of a power plant adapted to complex environments, as provided in an embodiment of this application. Figure 3 This paper illustrates a flowchart of an automatic calculation method for six power plant performance indicators adapted to complex environments, provided in an embodiment of this application. Figure 4 This paper illustrates another flowchart of the automatic calculation method for the six performance indicators of a power plant adapted to complex environments, provided in an embodiment of this application. Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of this application is shown. Detailed Implementation

[0011] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0012] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0013] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this application are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0014] It should be noted that the terms "a" and "a plurality of" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0015] Firstly, this application provides an automatic calculation system for the six performance indicators of a power plant that is adaptable to complex environments, such as... Figure 1 As shown, the system includes: a user interaction layer, a model layer, an environmental adaptation and dynamic correction layer, a core computing engine, and a data layer; among which, the data layer integrates: a model template database, a component failure rate database, a maintainability parameter database, a support parameter database, and an environmental stress coefficient database; The model layer integrates system models, reliability block diagram models, and power plant templates for various types of power plant systems. The user interaction layer is used to obtain the electrical and environmental parameters of the power plant project input by the user. The model layer is used to call reference model templates recorded in the model template database from the data layer based on electrical and environmental parameters, and output the reference model templates to the user interaction layer so that users can input the components corresponding to the power plant project in the reference model templates displayed in the user interaction layer. The model layer is also used to update the reference model template based on the components input by the user, so as to obtain the updated model template; The environmental adaptation and dynamic correction layer is used for: Generate a component list based on the updated model template; Retrieve basic data of components that match the component list from the component failure rate database, maintainability parameter database, and support parameter database at the data layer; The environmental stress coefficient database is called to perform stress coupling correction on the basic data, and the corrected basic data is output to the core calculation engine. The core computing engine is used to calculate the six indicators based on the corrected basic data and output the calculation results of the six indicators to the user interaction layer.

[0016] By using the system provided in this application, the environmental adaptation and dynamic correction layer in the system can automatically correct the basic data of the components of the power station according to the actual environmental changes of the power station, thereby obtaining a more realistic calculation result of the six performance indicators that are closer to the actual changes of electrical equipment, so as to achieve the effect of accurate calculation of the six performance indicators of electrical equipment in power stations under complex scenarios.

[0017] The following section will describe in detail each part of the automatic calculation system for the six power plant performance indicators adapted to complex environments provided in this application, with specific examples: The automatic calculation system for the six performance indicators of power plants adapted to complex environments provided in this application can be applied to various stages of research and development, testing, and experimentation before the power plant system is put into use, as well as to various testing stages after the power plant system is put into use. The type of power plant system can be determined according to actual needs; for example, the power plant system can be a photovoltaic power plant, a wind power plant, a diesel engine power plant, a hydroelectric power plant, etc.

[0018] It is understood that the user interaction layer of the automatic calculation system for the six performance indicators of power plants adapted to complex environments provided in this application is a user interface. Users can input, check, slide, and so on on the user interface according to the guidance information on the user interface. As one implementation method, the electrical parameters and environmental parameters of the power plant project are used to describe the electrical effects to be achieved by the power plant and the environmental conditions. For different power plant projects, the corresponding electrical parameters and environmental parameters will vary greatly.

[0019] In one implementation, when users input electrical and environmental parameters of a power plant project in the user interaction layer, they can input these parameters one by one based on known project information. Alternatively, the relevant electrical and environmental parameters of the power plant project may have been determined and compiled into electronic data during the project's initiation. In this case, users can input or select the power plant project they wish to analyze in designated input boxes. The system can then automatically retrieve the relevant electronic data for that power plant project based on its name and extract the electrical and environmental parameters from that data.

[0020] For example, if the power station project is a photovoltaic power station in a certain area in Northwest China, the corresponding electrical and environmental parameters of the photovoltaic power station have been determined at the beginning of its establishment. When it is necessary to conduct a six-indicator analysis of the photovoltaic power station, the system can automatically obtain the corresponding project information by inputting the name of the photovoltaic power station, and then obtain the electrical and environmental parameters of the power station project.

[0021] The electrical parameters vary depending on the type of power plant project. Specifically, electrical parameters refer to various types of electricity-related parameters involved in the power plant project, such as equipment composition, equipment type, equipment quantity, current, power, power generation, operating temperature, DC voltage, AC voltage, etc.

[0022] Environmental parameters depend on the specific geographical location of the power plant project. These parameters include altitude, temperature, humidity, salt spray concentration, etc. These environmental parameters can be statistical values, such as annual average temperature, quarterly average temperature, monthly average humidity, etc.

[0023] These environmental parameters can be generated electronically during the project initiation phase, and the system can automatically retrieve the corresponding environmental parameters from relevant project data based on the power plant project name. Alternatively, these environmental parameters can be real-time. The system can connect to sensors at the power plant project site via a pre-set data interface to acquire real-time environmental data. These sensors include, but are not limited to, temperature sensors, humidity sensors, atmospheric sensors, and salt spray concentration sensors.

[0024] It is understood that the user interaction layer in this application is the direct application layer that interacts with the user, and can be used as follows: Figure 2 As shown, this user interaction layer includes a model editing interface. Users can drag and drop to obtain the power plant system hierarchical model from the model layer, or input information such as the power, composition, and functions of the power plant project according to their own technical needs. In addition, this user interaction layer can also visualize the calculation results of the six performance indicators, displaying the final calculation results of the six performance indicators to users through tables, charts, highlights, and other methods.

[0025] It is understood that in this application, the data layer is the underlying data source for the six-performance index analysis, recording various types of data required for the analysis. After classifying these data types, this application can store data belonging to the same category in the same database. For example, data related to component failure can be stored in the component failure rate database, data related to component maintainability can be stored in the maintainability parameter database, data related to component supportability can be stored in the supportability parameter database, and data related to component environmental stress performance can be stored in the environmental stress coefficient database. Figure 2 As shown, this data layer includes a component failure rate database (GJB299D / ICE626380), a maintainability / supportability parameter database, an environmental stress coefficient database (altitude / temperature / humidity / salt spray correction factors), and a list of high-reliability components. The user interaction layer interacts with the data layer and can retrieve the list of high-reliability components from the data layer to obtain the environmental parameters of each component.

[0026] Thus, when performing the six-performance index analysis later, the required data can be directly retrieved from the database corresponding to the data layer to calculate the six performance indicators. In this application, the model template database stores different types of power plant model templates. Among them, the power plant model template refers to a standardized mathematical abstract model of a power plant system. This model pre-encapsulates the topology, equipment composition, parameter system, failure mode library, and six-performance analysis rules of a specific type of power plant, etc.

[0027] After the user inputs the electrical and environmental parameters of the corresponding power plant project in the user interaction layer, a power plant model template with a similarity greater than a preset similarity threshold can be matched from the model template database according to the electrical and environmental parameters as a reference model template and output to the user interaction layer. This allows the user to modify specific parameters of the project in the reference model template according to the specific parameters of the actual power plant, such as address, equipment model, operating conditions, etc.

[0028] For example, a user can input "100MW / 2000MWh lithium iron phosphate energy storage power station in XX city" in the user interaction layer. Then, a reference power station template of a similar lithium iron phosphate energy storage power station in XX city can be obtained from the model template database. The user only needs to modify the reference power station template according to the project differences to obtain the updated model template. In this way, the user does not need to build a system model from scratch, and can quickly generate a complete project analysis model of the power station project, which greatly improves the analysis efficiency and standardization efficiency.

[0029] It can be understood that the model layer is located at the logical center of the entire system. It is used to obtain underlying data from the underlying database and output the corresponding model template to the user interaction layer. In this process, the model layer converts the information obtained from the underlying layer into data entities that can be calculated, simulated and analyzed through the built-in system model, reliability block diagram model and power station template (such as diesel generator set, photovoltaic power station, etc.).

[0030] The power plant model template includes: basic metadata and template identification information, system topology and architecture information, a standardized equipment component library, full lifecycle parameters, a six-property analysis rule algorithm library, a failure mode and impact knowledge base, output report template requirements, verification and validation rules, etc. The basic metadata and template identification information may include: The model template contains basic information such as template ID, version number, release date, and applicable scope; Power plant type identification, power level range, standards followed, compiling unit and review information, etc.

[0031] The system topology and architecture information includes the physical structure and logical relationships of the power plant, specifically including: System-level topology: overall electrical connections of the power plant, energy flow diagram; Subsystem-level topology: describes the connection relationships between various functional subsystems within a power plant, such as the topology relationships between different subsystems in a photovoltaic system, including photovoltaic panels, photovoltaic cells, and AC circuits.

[0032] Device-level topology: describes the detailed circuit structure inside electronic devices, such as energy conversion circuits, boost circuits, inverter circuits, etc. in a photovoltaic system.

[0033] Topology configuration parameters: number of circuit branches, voltage level, capacity level, etc.

[0034] Interface definition: electrical interfaces, signal interfaces, external interfaces, etc. of the power station.

[0035] The standardized equipment component library includes: Equipment attribute information and information about the components involved in the equipment. The component information may include: Basic information about the components: name, model, manufacturer, rated power, rated voltage; Reliability parameters: MTBF, failure rate λ, lifespan, annual degradation rate, failure distribution type; Maintainability parameters: MTTR, maximum repair time, repair level, spare parts list, replacement labor hours; Environmental adaptability parameters: operating temperature range, storage temperature range, humidity range, salt spray rating, and shock resistance rating; Security parameters: security level, hazard type, protection level, and IP code.

[0036] The full lifecycle parameter system defines all project-specific parameters and their specifications that require user input. These may include: basic project parameters (project information: project name, location, construction time, design life, operation mode; geographical parameters: longitude, latitude, altitude, terrain slope, array orientation), environmental parameters (meteorological parameters: annual average temperature, maximum / minimum temperature, diurnal temperature range, annual average wind speed, maximum wind speed, annual rainfall, annual thunderstorm days; special environmental parameters: salt spray concentration, SO2 concentration, H2S concentration, dust concentration, ultraviolet radiation intensity), geological parameters (seismic intensity, soil resistivity, groundwater level), operation parameters (operation mode: peak-valley arbitrage / grid frequency regulation / backup power supply / off-grid power supply), charging and discharging strategies (daily charging and discharging times, charging and discharging depth, charging and discharging rate), load curves (typical daily load curves, seasonal load curves), parameter specification information (unit, data type, default value, and value range for each parameter), parameter verification rules (legality verification, rationality verification, relevance verification), parameter priority (manufacturer data > third-party testing data > industry-standard data), and other information.

[0037] Users can modify and fill in the reference model template as needed, including basic metadata and template identification information, system topology and architecture information, standardized equipment component library, full life cycle parameters, six-property analysis rule algorithm library, failure mode and impact knowledge base, output report template requirements, verification and validation rules, etc., to generate an updated model template that can truly reflect the electrical and environmental conditions of the power plant.

[0038] Subsequently, the environmental adaptation and dynamic correction layer generates a list of components to be analyzed based on the updated model template. This list includes all components involved in the entire power plant system. The environmental adaptation and dynamic correction layer then retrieves the basic data of the components matching the list from the component failure rate database, maintainability parameter database, and supportability parameter database of the data layer.

[0039] The basic data before component correction refers to the six fundamental properties of the component under standard laboratory conditions (25°C, sea level). The environmental adaptation and correction layer retrieves the basic data of the corresponding component based on its model by calling the component failure rate database, maintainability parameter database, and supportability parameter database through interfaces.

[0040] Furthermore, based on the actual environmental conditions of the power plant, the basic data of the component is corrected. Specifically, this environmental adaptation and dynamic correction layer converts the component's basic data into effective parameters that match the complex operating environment of the power plant, thus solving the problem of environmental distortion in traditional six-property analysis. Specifically, by calling the environmental stress coefficient database, stress coupling correction is performed on the basic data according to the real environment to obtain the corrected basic data.

[0041] The environmental adaptation and dynamic correction layer supports two independent triggering modes: static triggering mode and dynamic triggering mode. The static triggering mode is used during the design phase. After the user completes project initialization (entering technical requirements and static environmental parameters), model building, and clicks the "One-Click Calculation" button, environmental stress correction is automatically triggered. The dynamic triggering mode is used during the runtime phase. When the external interface detects an update in real-time environmental parameters, the environmental adaptation and dynamic correction layer is automatically triggered to perform incremental correction calculations.

[0042] As one implementation method, the environmental stress coefficient database stores correction coefficients and acceleration factor mathematical models for the failure rates of various components under different types of environmental stresses; the environmental adaptation and dynamic correction layer is specifically used for: The basic data and the correction coefficients for the failure rates of various components under different types of environmental stress are input into the acceleration factor mathematical model to obtain the correction coefficients and actual repair time of the components that match the component list calculated by the acceleration factor mathematical model. The acceleration factor mathematical model calculates the component list and the correction coefficient and actual repair time of the basic data of the components to obtain the corrected basic data.

[0043] In this application embodiment, the acceleration factor mathematical model describes the ratio of product failure rate under different environmental stress conditions to that under a standard reference environment. It can be understood that the failure rate under the standard environment (baseline operating conditions) is a baseline value. When stresses such as high temperature, high altitude, high humidity, and salt spray are applied, failure will accelerate. The acceleration factor = actual environmental failure rate ÷ standard environmental failure rate.

[0044] As one implementation method, this environmental adaptation and dynamic correction layer is specifically used for: From the component failure rate database, obtain the baseline failure rate of each component in the component list; Based on the correction coefficients calculated using the acceleration factor mathematical model, and combined with the baseline failure rate of each component in the component list, the actual failure rate of each component is calculated.

[0045] In this application, the correction factor for component failure rate is a dimensionless multiplier used to quantify the change in component failure rate between the actual operating environment of the power plant and the standard reference environment (25℃, 0m sea level, relative humidity 45%-65%, no salt spray corrosion). The actual component failure rate can be obtained through λ. 实际 This indicates that the fundamental failure rate of components can be expressed through λ. 基准 This indicates that the correction factor for the failure rate corresponds to the acceleration factor of the component, expressed by the symbol π. 综合 This means that, in this way, λ 实际 =λ 基准 ×π 综合 .

[0046] Among them, the correction coefficient π for the failure rate of this component 综合 It is based on failure physics and follows the constraints of physical models such as the Arrhenius equation (thermal activation failure), the Alling model (multi-stress coupled failure), and the corrosion kinetic equation (chemical failure). Therefore, in this embodiment, physical models such as the Arrhenius equation (thermal activation failure), the Alling model (multi-stress coupled failure), and the corrosion kinetic equation (chemical failure) are set as acceleration factor models in the environmental adaptation and dynamic correction layer. By inputting the environmental parameters of the power plant project (temperature, humidity, salt spray concentration, etc.) into the corresponding mathematical models, and then weighted summing, a comprehensive failure rate π is obtained. 综合 .

[0047] Reference failure rate λ under standard conditions 基准 It can be obtained by consulting GJB 299D "Reliability Prediction Manual for Electronic Equipment" and IEC 62380 "General Model for Reliability Prediction of Electronic Components, PCBs and Equipment".

[0048] It is understandable that the environmental stress coefficient database includes: mathematical models of correction coefficients for various components corresponding to different environmental stresses, and mathematical models of special correction coefficients for special equipment in special power plants. Special power plants may include: power plants composed of diesel generator sets.

[0049] Since the environment in which a power plant is located, including temperature, humidity, altitude, and salt spray concentration, affects the six properties of components within the power plant, it can be understood that the comprehensive failure rate correction factor is a combined expression of the temperature failure rate correction factor, humidity failure rate correction factor, altitude failure rate correction factor, and salt spray concentration correction factor. As one implementation method, in this embodiment, the relationship between the temperature failure rate correction factor, humidity failure rate correction factor, altitude failure rate correction factor, salt spray concentration correction factor, and comprehensive failure rate correction factor can be characterized by the following expression: π 综合 =π 温度 ×π湿度 ×π 海拔 ×π 盐雾浓度 It can be understood that the actual failure rate of each component is equal to the reference failure rate λ. 基准 Multiply by temperature correction factor π 温度 Humidity correction factor π 湿度 Altitude correction factor π 海拔 and salt spray concentration correction factor π 盐雾浓度 .in: The temperature correction factor π 温度 Calculated using a pre-defined continuous junction temperature-failure rate model; Humidity correction factor π 湿度 Calculated using a pre-set relative humidity-corrosion rate kinetic model; Salt spray concentration correction factor π 盐雾浓度 The results were obtained through a pre-defined quantitative model of salt spray concentration, corrosion depth, and failure rate.

[0050] Among them, the continuous junction temperature-failure rate model, the relative humidity-corrosion rate kinetic model, and the salt spray concentration-corrosion depth-failure rate quantitative model can be obtained by fitting the failure conditions of a large number of different types of components under different environmental conditions.

[0051] In one implementation method, atmospheric pressure and convective heat transfer coefficient can be calculated based on altitude. Then, based on this atmospheric pressure and convective heat transfer coefficient, the junction temperature rise and the heat dissipation altitude correction coefficient π under the altitude difference can be calculated. △ Furthermore, the corona discharge correction factor π was calculated based on the operating voltage of the components. △corona Then based on π 海拔 =π △ ×π △corona The altitude correction factor π was calculated. 海拔 .

[0052] As another embodiment, the maintenance time of the power station can be calculated using an equivalent wear life model. For example, the equivalent wear life of power station equipment at high altitudes can be calculated using the following algorithm: T 实际 = T 基准 ×(1+K×(H-H0) / 1000) Among them, T 实际 T represents the actual operating time of the power plant. 基准 This is the reference operating time for the power plant, which refers to the operating time under standard sea level H0 conditions that produces the same degree of wear.

[0053] H0 is the reference sea level height, and H is the actual operating altitude of the power station.

[0054] K is the altitude correction factor for the power plant generator, used to reflect the engine's sensitivity to altitude changes. Specifically, it represents the percentage increase in the engine's equivalent operating time for every 1% increase in altitude (relative to sea level).

[0055] For example, a conventional turbocharged diesel engine with K=0.0005 operates for 10,000 hours at a high-altitude power station at an altitude of 4,000 meters. Its equivalent operating time is: T 实际 =10000×(1+0.0005×4000 / 1000)=10000×1.2=12000 hours That is, the wear and tear of operating at high altitude for 10,000 hours is equivalent to operating at sea level for 12,000 hours.

[0056] Therefore, the above equivalent wear life algorithm can be understood as: for every 1000m increase in altitude, the engine's equivalent operating time is converted into the equivalent wear life time in the standard sea level environment. By substituting this equivalent wear life time (e.g., 12000 hours as mentioned above) into the subsequent calculation of the six performance indicators, a more accurate maintainability index can be obtained.

[0057] It is understandable that the component failure rate database records the failure rates of various components, such as... Figure 2 As shown, this component failure rate database is mainly based on national standards GJB2990 or ICE62380, recording the failure rates of various components or parts. However, this database records the baseline failure rate of components under standard conditions, which does not conform to the actual deployment environment of power plant projects. Therefore, in this embodiment, the acceleration factor π calculated according to the acceleration factor model in the above steps can be used instead. 综合 Combined with the baseline failure rate λ 基准 The actual failure rate of the component can be calculated, and then the actual failure rate can be used to replace the reference failure rate for calculation.

[0058] In the embodiments of this application, the basic data of components can be modified individually for a single component, in batches for all components in a device, or individually for the basic data of a single device. For example, the basic data of all components in a power plant project can be modified, or the basic data of the generator in the power plant project can be modified.

[0059] To obtain realistic environmental data for accurate correction of component failure rates, one can, for example... Figure 2As shown, the environmental adaptation and dynamic correction layer includes a real-time / near-real-time data access interface, through which externally input real-time or near-real-time data can be acquired. The environmental adaptation and dynamic correction layer also includes a pre-built dynamic correlation model between environmental stress and failure rate. This model (also known as an acceleration factor mathematical model) allows for the calculation of correction coefficients under various environmental stresses. Then, a multi-stress coupling correction algorithm is used to obtain the correction coefficients under various environmental stresses. Based on this, in some possible embodiments, the environmental adaptation and dynamic correction layer is further used for: The system connects to sensors at the power plant project site via a pre-set data interface to acquire real-time environmental data. The sensors include one or more of the following: temperature sensor, humidity sensor, atmospheric sensor, and salt spray concentration sensor. The environmental stress coefficient database is invoked, and the basic data is stress-coupled and corrected based on the real-time environmental data to obtain the corrected basic data.

[0060] In this embodiment, the environmental stress coefficient database stores mathematical models, correction coefficients, and calculation constants for acceleration factors corresponding to four types of environmental stresses: temperature, humidity, altitude, and salt spray, covering various commonly used components in power plants. The system can directly call the corresponding formulas and parameters to calculate the single-stress correction coefficient based on the component type and input environmental parameters.

[0061] For example, temperature correction factor It can be calculated using the following formula: ; in, The activation energy for component failure can be obtained by querying the environmental stress coefficient database, where k is the Boltzmann constant, T0 is the standard ambient temperature, and Tj is the actual temperature of the power plant's actual environment.

[0062] In this application, stress coupling correction refers to the correction coefficients π obtained through calculation. 温度 π 湿度 π 海拔 π 盐雾浓度 Substitute into the failure rate calculation formula λ 实际 =λ 基准 ×π 温度 ×π 湿度 ×π 海拔 ×π 盐雾浓度 In the process, the actual failure rate λ of the components is calculated. 实际 Then, using this actual failure rate λ 实际 Alternate baseline failure rate λ 基准 The corrected baseline data is obtained, and then the six indicators are calculated using the corrected baseline data.

[0063] Traditionally, the six performance indicators are calculated based on a baseline maintenance time, which refers to the time required for a skilled maintenance worker to complete a specified repair using standard tools in a standard laboratory environment. In this application, the six performance indicators are calculated based on the actual maintenance time in a real-world environment. This actual maintenance time refers to the time required for a skilled maintenance worker to complete a specified repair using standard tools in a real-world environment. As a calculation method, this actual maintenance time = baseline maintenance time × maintenance time comprehensive environmental correction coefficient. Based on this, the maintainability index = actual maintenance time of each unit × corresponding failure probability weighting coefficient.

[0064] The baseline maintenance time can be obtained from the maintainability parameter database and supportability parameter database pre-stored in the data layer. The comprehensive environmental correction factor for this maintenance time can be obtained based on the following table: Table 1. Schematic diagram of a maintenance time correction coefficient

[0065] By using the embodiments of this application, the system can acquire real-time environmental data by accessing field sensors. It can perform an incremental dynamic correction process to obtain the actual failure rate and actual maintenance test plan that conform to the real-time environment, thereby calculating accurate results of the six performance indicators.

[0066] In some possible embodiments, the core computing engine has a built-in reliability block diagram model, and the core computing engine is specifically used for: Based on the reliability block diagram model and the corrected basic data, the reliability index calculation algorithm, maintainability index calculation algorithm, testability index calculation algorithm and supportability index calculation algorithm are called respectively to calculate the reliability index, maintainability index, testability index and supportability index of each component in the component list; Based on the system model, and using the calculated reliability, maintainability, testability, and supportability indicators, the equipment-level six performance indicators and system-level six performance indicators of the power plant project are calculated.

[0067] Among them, the core computing engine is the core module that performs the calculation of the six performance indicators. Its calculation logic is the same as that of traditional six performance indicators, the difference being that the input is the actual failure rate and the actual repair time, and the calculation is based on the actual failure rate and the actual repair time.

[0068] As one implementation method, it can be as follows Figure 2As shown, the automatic data matching engine in the core computing engine can obtain data related to reliability indicators, and then distribute this data to the reliability calculation module. The same automatic data matching engine obtains data related to maintainability indicators, testability indicators, and supportability indicators, and distributes these data to the corresponding maintainability calculation module, testability calculation module, and supportability calculation module, respectively. Then, the reliability calculation module calculates reliability indicators based on the data related to reliability indicators, the maintainability calculation module calculates maintainability indicators based on the data related to maintainability indicators, the testability calculation module calculates testability indicators based on the data related to testability indicators, and the supportability calculation module calculates supportability indicators based on the data related to supportability indicators.

[0069] The reliability calculation module is the foundation for calculating the six performance indicators, and all other indicators rely on the results of the reliability calculation. For series-connected power plant systems, the reliability calculation module can obtain the total system failure rate by summing the failure rates of all units. For parallel-connected power plant systems, the module calculates the system reliability and equivalent failure rate based on the number of parallel units and the actual failure rate of each unit. For voting systems, the module calculates the system reliability within a given task time according to the voting logic. Furthermore, the reliability calculation module can calculate the mean time between failures (MTBF) for each level based on the failure rate, then calculate the inherent availability of the system based on the MTBF, and finally calculate the task reliability R(t) for a given task time. The MTBF, inherent availability, and task reliability R(t) are then output as reliability indicators. For diesel engines, the actual failure rate is not directly obtained through failure rate multiplier correction; instead, it is first converted using equivalent operating hours in the Weibull life model before participating in the system-level reliability calculation.

[0070] The maintainability calculation module calculates the mean repair time (MTTR) and maximum repair time (MaxTR) based on environmentally modified maintenance time data and the failure probability of each unit. The MTTR and MaxTR are then output as maintainability indicators.

[0071] The testability calculation module calculates the fault detection rate (FDR), fault isolation rate (FIR), and false alarm rate (FAR) by combining the actual occurrence probability of component failure modes with the test point coverage capability. The FDR, FIR, and FAR are then output as testability indicators.

[0072] The reliability calculation module estimates the reliability resource requirements of the power plant throughout its entire life cycle by using the actual failure rate of each unit. Based on the actual failure rate of each unit, combined with task time, maintenance activities, and reliability strategies, it calculates the spare parts requirement and the reliability equipment utilization rate, and outputs the spare parts requirement and the reliability equipment utilization rate as reliability indicators.

[0073] Among them, all six performance indicators are calculated according to the bottom-up cascading logic of component level - device level - subsystem level - system level. The indicators of each level are calculated from the indicators of the next level unit according to the topological relationship of the reliability block diagram.

[0074] As one implementation method, this core computing engine is also used for: Based on the calculation results of the six characteristics indicators, a structured six characteristics indicator report is generated and output to the user interaction layer; The user interaction layer is also used to display the calculation results of the six performance indicators of the power plant project and highlight the key devices or components in the power plant project whose correlation with environmental stress is greater than a preset correlation threshold.

[0075] Can Figure 2 As shown, the core computing engine outputs a structured six-indicator report, and an environmental impact analysis is performed based on this report. The analysis identifies which environmental parameters in the power plant system affect the six indicators and highlights key components related to environmental stress (i.e., those with a correlation to environmental stress greater than a preset correlation threshold, which depends on actual needs). The results are then output to the user interaction layer, which displays the analysis results to the user.

[0076] By using the embodiments of this application, a dynamic correlation model of "environmental stress-failure rate" is constructed, and a power plant-specific model template library and a list of high-reliability components are integrated. This achieves full-process automation from technical requirement input, model construction, environmental parameter matching to one-click calculation of six performance indicators, which significantly improves the efficiency and accuracy of power plant design iteration and prediction, especially solving the problem of prediction distortion under extreme environments.

[0077] Based on the automatic calculation system for the six power plant performance indicators adapted to complex environments provided in the first aspect, this application provides a second aspect: an automatic calculation method for the six power plant performance indicators adapted to complex environments, wherein, as... Figure 3 As shown, the method includes: S31. Obtain the electrical and environmental parameters of the power plant project input by the user; S32. Based on the electrical parameters and environmental parameters, call the reference model template recorded in the model template database from the data layer, and after outputting the reference model template, obtain the components corresponding to the power station project input by the user in the reference model template; S33. Update the reference model template based on the components input by the user to obtain the updated model template; S34. Generate a component list for the updated model template, and retrieve the basic data of the components that match the component list from the component failure rate database, maintainability parameter database and supportability parameter database of the data layer, and call the environmental stress coefficient database to perform stress coupling correction on the basic data. S35. Calculate the six indicators based on the corrected basic data, and output the calculation results of the six indicators.

[0078] As one implementation method, the system provided in the first aspect can be as follows: Figure 4 As shown, the automatic calculation method for the six power plant performance indicators adapted to complex environments provided in this application may further include the following steps: Steps 1a-1b: The user creates a new project in the user interface and enters the core technical requirements of the power plant (such as power, system composition, etc.) and inputs the actual operating environment parameters (altitude, temperature, humidity, salt spray level) in real time. Step 2: The system calls the recommended typical power plant model template from the model layer, or creates a system model; Step 3: The model layer obtains the reference model template from the reference model template library in the underlying six-character basic database of the data layer; Step 4: Return the recommended template to the user interaction layer; Step 6: User drag-and-drop editing of the model. Users perform drag-and-drop operations on the graphical interface to select and add specific components to the model from the system's built-in "High-Reliability Component List". This is a cyclical editing process until the model is complete.

[0079] Step 7: Update the model template; Step 8: After the user confirms that the model is correct, click the core function button "One-click Calculation".

[0080] Step 9: In response to the user's confirmation, the model layer automatically matches the engine to obtain a complete list of model components and environmental parameters, and inputs the environmental correction model after matching the basic failure rate.

[0081] Step 10: The model layer environment adaptation and dynamic correction layer sends the list and environmental parameters; Steps 11a and 11b: The data layer queries the component failure rate and other data by calling the six-characteristic basic database and returns the baseline parameter values.

[0082] Steps 12a, 12b, and 12c: The environmental adaptation and dynamic correction layer receives the reference parameters and environmental parameters sent by the data layer, calls the environmental stress coefficient library to perform multi-stress coupling correction, calculates the failure rate and repair time after environmental correction for each component, and returns the corrected failure rate and repair time to the data layer.

[0083] Step 13: The core computing engine distributes model data and corrected parameters to each computing module; Step 14: The various computing modules in the core computing engine perform cascade calculations of the six performance indicators and output the corrected MTBF, task reliability, mean time to repair, spare parts requirement, and other indicators.

[0084] Steps 15-17: The calculation results are generated into a structured report, which is then presented to the user in a clear and visual way (such as data tables, charts, etc.) on the user interface, highlighting the most critical components that are significantly affected by the environment.

[0085] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0086] Thirdly, exemplary embodiments of this application also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform a method according to an embodiment of this application.

[0087] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of this application.

[0088] An exemplary embodiment of this application also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a method according to an embodiment of this application.

[0089] refer to Figure 5The present invention describes a structural block diagram of an electronic device 500 that can serve as a server or client of this application, which is an example of a hardware device that can be applied to various aspects of this application. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0090] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM 502) or a computer program loaded from a storage unit 508 into a random access memory (RAM 503). The RAM 503 may also store various programs and data required for the operation of the electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output interface (I / O interface 505) is also connected to the bus 504.

[0091] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, output unit 507, storage unit 508, and communication unit 509. Input unit 506 can be any type of device capable of inputting information to electronic device 500. Input unit 506 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 507 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 508 may include, but is not limited to, disk and optical disk. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0092] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the aforementioned automatic calculation method for the six power plant performance indicators adapted to complex environments can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. In some embodiments, the computing unit 501 can be configured by any other suitable means (e.g., by means of firmware) to perform the aforementioned automatic calculation method for the six power plant performance indicators adapted to complex environments.

[0093] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0094] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0095] As used in this application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0096] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0097] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0098] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

Claims

1. An automatic calculation system for six performance indicators of power plants adapted to complex environments, characterized in that, The system comprises: a user interaction layer, a model layer, an environmental adaptation and dynamic correction layer, a core computing engine, and a data layer; wherein, the data layer integrates a component failure rate database, a maintainability parameter database, a supportability parameter database, a model template database, and an environmental stress coefficient database; the model layer integrates system models, reliability block diagram models, and power plant templates for various types of power plant systems; The user interaction layer is used to obtain the electrical and environmental parameters of the power plant project input by the user. The model layer is used to call a reference model template recorded in the model template database from the data layer based on the electrical parameters and environmental parameters, and output the reference model template to the user interaction layer so that the user can input the components corresponding to the power plant project in the reference model template displayed in the user interaction layer; the model layer is also used to update the reference model template based on the components input by the user to obtain an updated model template; The environmental adaptation and dynamic correction layer is used to generate a component list for the updated model template, retrieve the basic data of the components that match the component list from the component failure rate database, maintainability parameter database and support parameter database of the data layer, call the environmental stress coefficient database to perform stress coupling correction on the basic data, and output the corrected basic data to the core computing engine. The core computing engine is used to calculate the six indicators based on the corrected basic data and output the calculation results of the six indicators to the user interaction layer.

2. The system according to claim 1, characterized in that, The environmental stress coefficient database stores correction coefficients and acceleration factor mathematical models for the failure rates of various components under different types of environmental stress; the environmental adaptation and dynamic correction layer is specifically used for: The basic data and the correction coefficients for the failure rates of various components under different types of environmental stress are input into the acceleration factor mathematical model to obtain the correction coefficients and actual repair time of the components that match the component list calculated by the acceleration factor mathematical model. The acceleration factor mathematical model calculates the component list and the correction coefficient and actual repair time of the basic data of the components to obtain the corrected basic data.

3. The system according to claim 2, characterized in that, The environmental adaptation and dynamic correction layer is specifically used for: From the component failure rate database, obtain the baseline failure rate of each component in the component list; Based on the correction coefficients calculated using the acceleration factor mathematical model, and combined with the baseline failure rate of each component in the component list, the actual failure rate of each component is calculated.

4. The system according to claim 1, characterized in that, The environmental adaptation and dynamic correction layer is also used for: The system connects to sensors at the power plant project site via a pre-set data interface to acquire real-time environmental data. The sensors include one or more of the following: temperature sensor, humidity sensor, atmospheric sensor, and salt spray concentration sensor. The environmental stress coefficient database is invoked, and the basic data is stress-coupled and corrected based on the real-time environmental data to obtain the corrected basic data.

5. The system according to claim 1, characterized in that, The core computing engine incorporates a reliability block diagram model, and the core computing engine is specifically used for: Based on the reliability block diagram model and the corrected basic data, the reliability index calculation algorithm, maintainability index calculation algorithm, testability index calculation algorithm and supportability index calculation algorithm are called respectively to calculate the reliability index, maintainability index, testability index and supportability index of each component in the component list; Based on the system model, and using the calculated reliability, maintainability, testability, and supportability indicators, the equipment-level six performance indicators and system-level six performance indicators of the power plant project are calculated.

6. The system according to claim 1, characterized in that, The core computing engine is also used for: Based on the calculation results of the six characteristics indicators, a structured six characteristics indicator report is generated and output to the user interaction layer; The user interaction layer is also used to display the calculation results of the six performance indicators of the power plant project and highlight the key devices or components in the power plant project whose correlation with environmental stress is greater than a preset correlation threshold.

7. An automatic calculation method for six performance indicators of power plants adapted to complex environments, characterized in that, The method is applied to the system as described in any one of claims 1-6, and the method includes: Obtain the electrical and environmental parameters of the power plant project input by the user; Based on the electrical and environmental parameters, the reference model template recorded in the model template database is called from the data layer, and after the reference model template is output, the components corresponding to the power plant project input by the user in the reference model template are obtained. The reference model template is updated based on the components input by the user to obtain the updated model template; A component list is generated for the updated model template, and the basic data of the components that match the component list are retrieved from the component failure rate database, maintainability parameter database and supportability parameter database of the data layer. The environmental stress coefficient database is then called to perform stress coupling correction on the basic data. The six indicators are calculated based on the corrected basic data, and the calculation results of the six indicators are output.

8. The method according to claim 7, characterized in that, The environmental stress coefficient database stores correction coefficients and acceleration factor mathematical models for the failure rates of various components under different types of environmental stress. The method further includes: The basic data and the correction coefficients for the failure rates of various components under different types of environmental stress are input into the acceleration factor mathematical model to obtain the correction coefficients and actual repair time of the components that match the component list calculated by the acceleration factor mathematical model. The acceleration factor mathematical model calculates the component list and the correction coefficient and actual repair time of the basic data of the components to obtain the corrected basic data.

9. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing a program; wherein the program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 7-8.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 7-8.

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