A method for evaluating the electrical performance of an electrical appliance

Abstract

The present invention relates to the technical field of electrical performance of power electrical appliances, and particularly relates to an electrical performance evaluation method for power electrical appliances. The method includes the following steps: identifying data sources of power electrical appliance equipment and performing multi-dimensional data association to obtain a power electrical appliance association data set; constructing an electrical appliance static and dynamic information model based on the power electrical appliance association data set to obtain a multi-source information model of power electrical appliances, where the multi-source information model of power electrical appliances includes an electrical appliance static information model and an electrical appliance dynamic information model; performing component material attribute assignment processing on the electrical appliance static information model to obtain a power electrical appliance material mapping model; generating a power electrical appliance grid model according to the power electrical appliance material mapping model to obtain a power electrical appliance grid model. The present invention solves the problems of ignoring the multi-physical field coupling effect and the limitation of isolated analysis indicators in the traditional method, and overall realizes a more comprehensive and accurate electrical appliance performance evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of the electrical performance of power electrical appliances, and particularly to an electrical performance evaluation method for power electrical appliances. Background Art

[0002] Power electrical appliances are key devices indispensable in the power system, and their operating status is directly related to the safe and stable operation of the entire power system. Therefore, it is crucial to accurately and reliably evaluate the electrical performance of power electrical appliances. Early electrical performance evaluation methods mainly relied on off-line testing and empirical judgment, with problems such as strong subjectivity, low efficiency, and difficulty in reflecting the actual operating status of equipment. With the rapid development of sensor technology, computer technology, and simulation technology, the electrical performance evaluation methods for power electrical appliances have gradually developed towards on-line monitoring, data-driven, and intelligent directions. Although new methods are emerging continuously, traditional electrical performance evaluation methods for power electrical appliances are difficult to meet the requirements of modern power systems for the accuracy, reliability, and predictability of equipment status evaluation due to ignoring the multi-physical-field coupling effect and the interaction effect between evaluation indicators.

[0003] Insufficiency manifestation of the multi-physical-field coupling effect: During the actual operation of power electrical appliances, they are often affected by the coupling of multiple physical fields, such as electric fields, magnetic fields, temperature fields, fluid fields, etc. Traditional methods usually only focus on a single physical field, for example, only considering the electric field intensity or temperature change, while ignoring the mutual influence between different physical fields, resulting in a deviation between the evaluation result and the actual situation.

[0004] Insufficiency manifestation of the interaction effect between evaluation indicators: The performance of power electrical appliances is usually characterized by multiple indicators, such as insulation resistance, dielectric loss angle, partial discharge quantity, etc. Traditional methods usually analyze each indicator in isolation, ignoring the mutual influence between indicators, and it is difficult to reveal complex failure mechanisms, resulting in incomplete and inaccurate evaluation results. Summary of the Invention

[0005] Based on this, it is necessary to provide an electrical performance evaluation method for power electrical appliances to solve at least one of the above technical problems.

[0006] To achieve the above object, an electrical performance evaluation method for power electrical appliances includes the following steps:

[0007] Step S1: Identify data sources for power electrical appliance equipment and perform multi-dimensional data association to obtain a power electrical appliance association data set; construct an electrical appliance static and dynamic information model based on the power electrical appliance association data set to obtain a multi-source information model for power electrical appliances, where the multi-source information model for power electrical appliances includes an electrical appliance static information model and an electrical appliance dynamic information model;

[0008] Step S2: Perform component material property assignment processing on the electrical appliance static information model to obtain the power electrical appliance material mapping model; generate the power electrical appliance grid model according to the power electrical appliance material mapping model; apply boundary conditions to the electrical appliance dynamic information model to obtain the power electrical appliance boundary condition model; integrate the power electrical appliance grid model and the power electrical appliance boundary condition model to obtain the power electrical twin body;

[0009] Step S3: Define the coupled physical fields according to the power electrical twin body to obtain the coupled physical field list data; associate the aging variable model according to the power electrical appliance multi-source information model to obtain the integrated aging simulation model; perform multi-physical field and aging coupling processing and multi-physical field coupling aging simulation according to the integrated aging simulation model and the coupled physical field list data to obtain the multi-physical field coupling aging simulation result data set;

[0010] Step S4: Evaluate the electrical performance state according to the power electrical appliance multi-source information model and the multi-physical field coupling aging simulation result data set to obtain the electrical performance state evaluation report; analyze the factor interaction effect according to the electrical performance state evaluation report to obtain the interaction effect analysis data; generate the electrical performance interaction analysis report according to the interaction effect analysis data to obtain the electrical performance interaction analysis report;

[0011] Step S5: Construct an electrical performance prediction model according to the electrical performance interaction analysis report and the preset power electrical appliance historical operation database to obtain the electrical performance prediction model; evaluate the electrical performance of the power electrical appliance according to the electrical performance prediction model and the preset power electrical appliance operation safety standard data to obtain the power electrical appliance electrical performance evaluation report.

[0012] Through data source identification and multi-dimensional data association, the present invention can break information silos, establish a multi-source information model for power electrical appliances, integrate heterogeneous data from links such as design, manufacturing, and operation, and provide a comprehensive and real data basis for subsequent analysis. By constructing a power electrical twin, information such as the geometric structure, material properties, and boundary conditions of power electrical appliances is mapped to a simulation platform, providing a highly realistic digital model for multi-physics field coupling simulation and improving the accuracy and reliability of simulation results. Through multi-physics field and aging coupling simulation, the performance degradation process of power electrical appliances under the combined action of multiple physical fields and aging factors during long-term operation can be more realistically simulated, such as insulation aging and increased contact resistance, providing more accurate data support for electrical performance evaluation. Through multi-index evaluation, sensitivity analysis, and factor interaction effect analysis, the key factors affecting the electrical performance of power electrical appliances and their interaction relationships can be more comprehensively and deeply revealed, providing a scientific basis for formulating more targeted maintenance strategies. By constructing an electrical performance prediction model, the change trend of the electrical performance of power electrical appliances in the future period can be predicted and evaluated in combination with safety standards, providing decision support for the preventive maintenance of power electrical appliances and avoiding power outages and economic losses caused by equipment failures. Therefore, the present invention provides a method for evaluating the electrical performance of power electrical appliances. By constructing a power electrical twin and coupling multi-physics fields with aging factors for simulation, the problem of ignoring the multi-physics field coupling effect in traditional methods is solved. At the same time, through means such as sensitivity analysis and causal analysis, the interaction effects between various evaluation indexes are deeply analyzed, revealing the root causes and key factors affecting the performance of electrical appliances, overcoming the limitations of isolated analysis of indexes in traditional methods, and generally achieving a more comprehensive and accurate evaluation of the performance of electrical appliances. For example, the method for evaluating the electrical performance of power electrical appliances can be a method for evaluating the electrical performance of power devices.

[0013] Preferably, step S1 includes the following steps:

[0014] Step S11: Identify the data source of the power electrical appliance equipment to obtain the original data set of the power electrical appliance;

[0015] Step S12: Clean the original data set of the power electrical appliance to obtain the cleaned data set of the power electrical appliance;

[0016] Step S13: Extract the electrical structure and function data from the cleaned data set of the power electrical appliance to obtain the electrical structure and function data; perform multi-dimensional data association on the cleaned data set of the power electrical appliance according to the electrical structure and function data to obtain the associated data set of the power electrical appliance;

[0017] Step S14: Construct an electrical static and dynamic information model based on the associated data set of the power electrical appliance to obtain a multi-source information model of the power electrical appliance, where the multi-source information model of the power electrical appliance includes an electrical static information model and an electrical dynamic information model.

[0018] By identifying data sources for power electrical equipment, the present invention can comprehensively collect various data related to power electrical appliances, including design documents, operation logs, sensor data, etc., laying a foundation for subsequent data analysis and model construction, and avoiding inaccurate evaluation results caused by missing or insufficient data. By cleaning the original data set of power electrical appliances, problems such as errors, missing values, anomalies, and noise in the original data can be removed or corrected, improving the accuracy, integrity, and consistency of the data, and providing a high-quality data foundation for subsequent data analysis and model construction. By extracting electrical appliance structure and function data and performing multi-dimensional data association on the cleaned data set of power electrical appliances, data from different sources and of different types can be integrated and associated to form a more comprehensive and systematic associated data set of power electrical appliances, providing data support for constructing a more accurate and refined power electrical appliance model. By constructing an electrical appliance static and dynamic information model based on the associated data set of power electrical appliances, the static information (such as structure and materials) and dynamic information (such as operating status and environmental parameters) of power electrical appliances can be separated and integrated to form a multi-source information model of power electrical appliances containing complete information of power electrical appliances, providing a more accurate and comprehensive model foundation for subsequent multi-physics field simulation and performance evaluation.

[0019] Preferably, step S14 includes the following steps:

[0020] Step S141: Identify the data source types of the associated data set of power electrical appliances to obtain multi-source heterogeneous data; perform data unification and spatio-temporal alignment on the multi-source heterogeneous data to obtain a spatio-temporally consistent standardized data set;

[0021] Step S142: Mine multi-source fusion features from the spatio-temporally consistent standardized data set to obtain a multi-source fusion feature set;

[0022] Step S143: Construct an electrical appliance holographic information library according to the multi-source fusion feature set and a preset multi-source information fusion framework to obtain an electrical appliance holographic information library;

[0023] Step S144: Extract static information from the electrical appliance holographic information library and construct an electrical appliance static information model to obtain an electrical appliance static information model;

[0024] Step S145: Extract dynamic information from the electrical appliance holographic information library and construct an electrical appliance dynamic information model to obtain an electrical appliance dynamic information model;

[0025] Step S146: Perform electrical appliance static and dynamic information mapping atlas association on the electrical appliance static information model and the electrical appliance dynamic information model to obtain a multi-source information model of power electrical appliances.

[0026] By identifying the data source type and unifying and aligning multi-source heterogeneous data in terms of space and time, the present invention can eliminate differences in formats, units, time, etc. between different data sources, obtain a standardized data set with consistent space-time, lay a foundation for subsequent multi-source information fusion and feature mining, and avoid analysis errors caused by data differences. By performing multi-source fusion feature mining on the space-time consistent standardized data set, more comprehensive and essential operating characteristics of electrical appliances can be extracted from multiple data sources, such as temperature change trends, vibration frequency characteristics, etc., providing key information for constructing more accurate and reliable electrical appliance models. By using the multi-source fusion feature set and a preset multi-source information fusion framework to construct an electrical appliance holographic information library, multi-source heterogeneous data can be deeply fused to form a holographic information library containing complete information of electrical appliances, providing a rich data basis for subsequent static information extraction, dynamic information extraction, and mapping graph association of electrical appliance static and dynamic information. By performing static information extraction on the electrical appliance holographic information library and constructing an electrical appliance static information model, static information such as the structure and materials of electrical appliances that do not change with time can be modeled and expressed, providing a basic model for subsequent simulation analysis and performance evaluation. By performing dynamic information extraction on the electrical appliance holographic information library and constructing an electrical appliance dynamic information model, dynamic information such as the operating state and environmental parameters of electrical appliances that change with time can be modeled and expressed, providing dynamic change information for subsequent simulation analysis and performance evaluation. By performing mapping graph association of electrical appliance static and dynamic information on the electrical appliance static information model and the electrical appliance dynamic information model, the static information and dynamic information of electrical appliances can be associated to form a complete multi-source information model of electrical appliances, providing a more comprehensive and accurate model basis for subsequent multi-physics field simulation and performance evaluation.

[0027] Preferably, step S2 includes the following steps:

[0028] Step S21: Extract geometric information from the electrical appliance static information model to obtain electrical appliance geometric information; use 3D modeling software to perform 3D geometric modeling of the electrical appliance based on the electrical appliance geometric information to obtain a 3D geometric model of the electrical appliance;

[0029] Step S22: Extract material information from the electrical appliance static information model to obtain electrical appliance material information; perform material modeling based on the electrical appliance material information to obtain a material model; map the material model into the 3D geometric model of the electrical appliance to perform component material attribute assignment processing to obtain a material mapping model of the electrical appliance;

[0030] Step S23: Divide the structural regions of the power electrical material mapping model to obtain structural region division data, where the structural region division data includes simple structural region data and complex structural region data; perform regular hexahedron mesh division on the simple structural region data to obtain low-precision mesh data; perform unstructured tetrahedron mesh division on the complex structural region data and perform local mesh encryption processing to obtain high-precision mesh data; generate a power electrical mesh model based on the low-precision mesh data and the high-precision mesh data to obtain a power electrical mesh model;

[0031] Step S24: Extract the operating conditions and environmental parameters from the electrical dynamic information model to obtain the operating conditions and environmental parameters; apply boundary conditions to the electrical dynamic information model according to the operating conditions and environmental parameters to obtain a power electrical boundary condition model;

[0032] Step S25: Integrate the power electrical mesh model and the power electrical boundary condition model to obtain a power electrical twin.

[0033] Through extracting geometric information from the electrical static information model and performing three-dimensional geometric modeling, the present invention can intuitively and accurately express the structural characteristics of power electrical appliances, providing a realistic geometric model basis for subsequent simulation analysis and performance evaluation, and improving the accuracy and reliability of simulation results. By extracting material information and performing material modeling on the electrical static information model and mapping the material model into the three-dimensional geometric model, the material properties of each component of the power electrical appliances, such as conductivity, thermal conductivity, etc., can be accurately described, providing real material parameters for simulation analysis and improving the reliability of simulation results. By dividing the structural regions of the power electrical material mapping model and adopting different mesh division strategies according to the complexity of different regions, the calculation amount can be effectively reduced and the simulation efficiency can be improved on the premise of ensuring simulation accuracy. By extracting the operating conditions and environmental parameters from the electrical dynamic information model and applying corresponding boundary conditions, the stress, heat, etc. of the power electrical appliances in the actual operating environment can be simulated, making the simulation results closer to the actual situation. By integrating the power electrical mesh model and the boundary condition model to form a complete power electrical twin, a simulation model including a complete geometric structure, material properties, and boundary conditions can be provided for subsequent multi-physics field coupling simulation, providing a more comprehensive and accurate basis for simulation analysis.

[0034] Preferably, step S3 includes the following steps:

[0035] Step S31: Perform power electrical twin import processing based on the power electrical twin to obtain a simulation platform model; define the coupled physical fields of the simulation platform model to obtain coupled physical field list data;

[0036] Step S32: Select physical field interfaces based on the coupled physical field inventory data to obtain a physical field interface solution;

[0037] Step S33: Associate the aging variable model based on the multi-source information model of electrical power appliances and the simulation platform model to obtain an integrated aging simulation model; perform multi-physical field and aging coupling processing based on the physical field interface solution, the integrated aging simulation model, and the coupled physical field inventory data to obtain a multi-physical field - aging model coupling solution;

[0038] Step S34: Conduct multi-physical field coupled aging simulation based on the multi-physical field - aging model coupling solution to obtain a multi-physical field coupled aging simulation result dataset.

[0039] In the present invention, by importing the electrical power twin into the simulation platform and defining the coupled physical fields, the real electrical power appliances and their operating environments can be mapped into the simulation platform, and multiple physical fields that need to be considered and their interactions can be clarified, laying a foundation for subsequent multi-physical field coupled simulations. By selecting appropriate physical field interfaces according to the coupled physical field inventory data, the interaction relationships between different physical fields can be accurately described, such as the coupling of the electric field and the temperature field, the coupling of the magnetic field and structural mechanics, etc., thus more realistically simulating the operating state of electrical power appliances. By associating the aging variable model with the simulation platform model and incorporating aging factors into the multi-physical field simulation, the performance degradation of electrical power appliances caused by aging during long-term operation can be evaluated more comprehensively and accurately, such as insulation aging, increase in contact resistance, etc. By conducting multi-physical field coupled aging simulation, the multi-physical field distributions of electrical power appliances under different operating conditions and aging states can be obtained, such as temperature field distribution, electric field intensity distribution, mechanical stress distribution, etc., providing more detailed and accurate data support for subsequent performance evaluation.

[0040] Preferably, step S31 includes the following steps:

[0041] Step S311: Determine the simulation objectives and scope based on the electrical power twin to obtain a simulation objective solution;

[0042] Step S312: Perform electrical power twin import processing on the electrical power twin according to the simulation objective solution to obtain a simulation platform model;

[0043] Step S313: Set the environmental parameters for the simulation platform model according to the simulation objective solution to obtain an environmental parameter setting table;

[0044] Step S314: Apply boundary conditions and excitations to the simulation platform model according to the simulation objective solution to obtain a boundary condition and excitation setting table;

[0045] Step S315: Define the coupled physical fields based on the environmental parameter setting table, boundary conditions, and excitation setting table to obtain the coupled physical field list data.

[0046] By determining the simulation objectives and scope according to the power and electrical digital twins and actual requirements, the present invention can avoid blind modeling and simulation, improve simulation efficiency, and ensure that the simulation results can effectively serve the objective of electrical performance evaluation. Importing and performing necessary processing on the power and electrical digital twins according to the simulation objective plan can obtain a simulation platform model that meets the requirements of the simulation software and satisfies the simulation objectives, providing a basis for subsequent simulation analysis. Setting environmental parameters according to the simulation objective plan and forming an environmental parameter setting table can ensure the consistency between the simulation environment and the actual operating environment of the power appliances, improving the accuracy and reliability of the simulation results. Applying boundary conditions and excitations to the simulation platform model according to the simulation objective plan and forming the corresponding setting table can simulate various forces and excitations that the power appliances withstand during actual operation, such as voltage, current, mechanical load, etc., making the simulation results closer to the actual situation. Based on the environmental parameter setting table and the boundary condition and excitation setting table, the physical fields that need to be coupled in the simulation model, such as the electric field, magnetic field, temperature field, etc., can be more accurately defined, providing more accurate settings for subsequent multi-physical field coupling simulations.

[0047] Preferably, step S33 includes the following steps:

[0048] Step S331: Conduct aging mechanism analysis based on the multi-source information model of the power appliances to obtain aging mechanism analysis data; select aging model parameters based on the aging mechanism analysis data to obtain an aging model parameter table;

[0049] Step S332: Associate the aging variable model with the aging model parameter table and the simulation platform model to obtain an integrated aging simulation model;

[0050] Step S333: Construct a multi-physical field simulation model based on the integrated aging simulation model and the coupled physical field list data to obtain a multi-physical field simulation model;

[0051] Step S334: Embed the aging model parameter table into the multi-physical field simulation model, and perform multi-physical field and aging coupling processing according to the physical field interface plan to obtain a multi-physical field-aging model coupling plan.

[0052] By analyzing the multi-source information model of power electrical appliances, identifying the aging mechanism, and selecting appropriate models and parameters, the present invention can more accurately describe the aging process of power electrical appliances and its impact on performance, providing a more realistic model basis for subsequent simulation analysis. By correlating the aging model parameters with the simulation platform model, aging factors can be introduced into the simulation model. For example, the impact of insulation aging on insulation performance and the impact of temperature changes on material parameters can be reflected in the simulation calculation, making the simulation results closer to the actual performance changes of power electrical appliances during long-term operation. According to the integrated aging simulation model and the coupled physical field inventory data, a multi-physical field simulation model can be constructed to couple aging factors with multiple physical fields. For example, considering the impact of aging on material parameters, the impact of aging on the performance of power electrical appliances can be evaluated more comprehensively. By embedding the aging model parameters into the multi-physical field simulation model and performing multi-physical field and aging coupling processing, the performance degradation process of power electrical appliances caused by aging under the combined action of multiple physical fields can be more realistically simulated. For example, the aging process of insulation under the combined action of the electric field and temperature field can be obtained, and simulation results closer to the actual situation can be obtained.

[0053] Preferably, step S4 includes the following steps:

[0054] Step S41: Generate electrical performance evaluation indicators according to the multi-source information model of power electrical appliances to obtain an electrical performance evaluation index system;

[0055] Step S42: Obtain electrical performance index evaluation standard data; set evaluation index thresholds for the electrical performance evaluation index system according to the electrical performance index evaluation standard data to obtain an electrical performance evaluation index threshold table;

[0056] Step S43: Calculate electrical performance evaluation indicators for the multi-physical field coupled aging simulation result dataset according to the electrical performance evaluation index system to obtain an electrical performance evaluation index result table;

[0057] Step S44: Conduct an electrical performance status evaluation on the electrical performance evaluation index result table and the electrical performance evaluation index threshold table to obtain an electrical performance status evaluation report; conduct a sensitivity analysis according to the electrical performance status evaluation report to obtain a sensitivity analysis report;

[0058] Step S45: Conduct a factor interaction effect analysis according to the electrical performance status evaluation report and the sensitivity analysis report to obtain interaction effect analysis data;

[0059] Step S46: Generate an electrical performance interaction analysis report according to the interaction effect analysis data to obtain an electrical performance interaction analysis report.

[0060] Through generating an electrical performance evaluation index system based on the multi-source information model of electrical appliances, the present invention can comprehensively and systematically evaluate the electrical performance of electrical appliances, avoid missing key indicators, and improve the reliability and comprehensiveness of evaluation results. By obtaining the evaluation standard data of electrical performance indicators and setting the evaluation index threshold according to the standard, the evaluation results can be quantitatively compared with the industry standards to determine whether the electrical appliances meet the requirements for safe operation, providing a basis for subsequent decision-making. According to the electrical performance evaluation index system, calculating the simulation result data set can convert complex simulation data into intuitive electrical performance indicators, facilitating the quantitative evaluation of the electrical performance of electrical appliances. By comparing the electrical performance index results with the threshold, the electrical performance status can be evaluated to determine whether the operating status of the electrical appliances is normal and whether there are potential risks, and the key factors with greater influence on the performance can be identified through sensitivity analysis, providing a direction for subsequent factor interaction effect analysis. By conducting factor interaction effect analysis, the comprehensive influence of multiple factors on the electrical performance of electrical appliances can be deeply analyzed, such as the combined effect of ambient temperature and load current on insulation performance, identifying the main factors and interaction relationships leading to the performance change of electrical appliances, providing a basis for formulating more targeted measures. By generating an electrical performance interaction analysis report, the complex analysis results can be presented in an intuitive and easy-to-understand manner.

[0061] Preferably, step S45 includes the following steps:

[0062] Step S451: Determine the analysis factors according to the electrical performance status evaluation report and the sensitivity analysis report to obtain the data of factors to be analyzed;

[0063] Step S452: Extract the causal analysis data from the preset historical operation database of electrical appliances according to the data of factors to be analyzed to obtain the causal analysis data set;

[0064] Step S453: Infer the causal relationship for the causal analysis data set to obtain a causal relationship diagram; perform quantitative processing on the causal effect according to the causal relationship diagram to obtain a causal effect quantification table;

[0065] Step S454: Identify the root cause and the influencing factors according to the causal effect quantification table to obtain the root cause data and the list of influencing factors;

[0066] Step S455: Conduct factor interaction effect analysis according to the root cause data and the list of influencing factors to obtain the interaction effect analysis data.

[0067] By referring to the electrical performance status evaluation report and the sensitivity analysis report, the present invention can more specifically determine the key factors that need to be analyzed for factor interaction effects, avoid too broad or too narrow analysis scope, and improve the analysis efficiency. According to the factors to be analyzed, relevant data are extracted from the historical operation database of electrical appliances to form a causal analysis data set, which can provide a sufficient data basis for subsequent causal analysis and ensure the reliability of the analysis results. By inferring the causal relationship from the causal analysis data set, the causal relationship between various factors can be identified, and a causal relationship diagram can be constructed to intuitively display the influence path between factors, providing a basis for in-depth analysis of factor interaction effects. By analyzing the causal effect quantification table, the root causes that have the greatest impact on the electrical performance of electrical appliances can be identified, such as long-term overload operation, too high ambient temperature, etc., providing a basis for formulating more targeted solutions. By conducting an interaction effect analysis on the root causes and influencing factors, the combined effects of multiple factors can be quantitatively analyzed, such as the combined effect of poor heat dissipation and ambient temperature, more comprehensively revealing the mutual relationship between factors, and providing a reference for formulating more effective measures.

[0068] Preferably, step S5 includes the following steps:

[0069] Step S51: Identify key degradation indicators for the preset historical operation database of electrical appliances according to the electrical performance interaction analysis report, and obtain a set of key degradation indicators;

[0070] Step S52: Construct an electrical performance prediction model according to the set of key degradation indicators and the preset historical operation database of electrical appliances, and obtain an electrical performance prediction model;

[0071] Step S53: Evaluate the electrical performance of the electrical appliance according to the electrical performance prediction model and the preset electrical appliance operation safety standard data, and obtain an electrical performance evaluation report of the electrical appliance.

[0072] By analyzing the electrical performance interaction analysis report, the present invention can identify the indicators that play a key role in the degradation of the electrical performance of electrical appliances, and can more specifically select the input variables of the prediction model, improving the accuracy and reliability of the prediction model. Using the historical data of key degradation indicators to construct an electrical performance prediction model can predict the changing trend of the electrical performance of electrical appliances in the future for a period of time, such as the decreasing trend of insulation resistance, the increasing trend of dielectric loss angle, etc. By comparing the predicted results of the future electrical performance of electrical appliances with the safety standards, it can be evaluated whether the electrical appliances can operate safely and stably in the future for a period of time, and potential risks can be discovered in a timely manner, providing decision-making support for the operation and maintenance of electrical appliances, and avoiding power outages and economic losses caused by equipment failures.

[0073] Through data source identification and multi-dimensional data association, the present invention can break information silos, establish a multi-source information model for power electrical appliances, integrate heterogeneous data from links such as design, manufacturing, and operation, and provide a comprehensive and real data basis for subsequent analysis. By constructing a power electrical twin, information such as the geometric structure, material properties, and boundary conditions of power electrical appliances is mapped to a simulation platform, providing a highly realistic digital model for multi-physical field coupling simulation and improving the accuracy and reliability of simulation results. Through multi-physical field and aging coupling simulation, the performance degradation process of power electrical appliances under the combined action of multiple physical fields and aging factors during long-term operation can be more realistically simulated, such as insulation aging and increased contact resistance, providing more accurate data support for electrical performance evaluation. Through multi-index evaluation, sensitivity analysis, and factor interaction effect analysis, the key factors affecting the electrical performance of power electrical appliances and their interaction relationships can be more comprehensively and deeply revealed, providing a scientific basis for formulating more targeted maintenance strategies. By constructing an electrical performance prediction model, the changing trend of the electrical performance of power electrical appliances in the future period can be predicted and evaluated in combination with safety standards, providing decision-making support for the preventive maintenance of power electrical appliances and avoiding power outages and economic losses caused by equipment failures. Therefore, the present invention provides a method for evaluating the electrical performance of power electrical appliances. By constructing a power electrical twin and coupling multi-physical fields with aging factors for simulation, the problem of traditional methods ignoring the multi-physical field coupling effect is solved. At the same time, through means such as sensitivity analysis and causal analysis, the interaction effects among various evaluation indexes are deeply analyzed, the root causes and key factors affecting the performance of electrical appliances are revealed, and the limitation of traditional methods in analyzing indexes in isolation is overcome, generally realizing a more comprehensive and accurate evaluation of the performance of electrical appliances. Description of the Drawings

[0074] Figure 1 It is a schematic flow chart of the steps of a method for evaluating the electrical performance of a power electrical appliance;

[0075] Figure 2 is Figure 1 a detailed implementation step flow chart of step S3 in

[0076] Figure 3 is Figure 1 a detailed implementation step flow chart of step S4 in

[0077] The realization of the object, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiment

[0078] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0079] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus the repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0080] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0081] To achieve the above object, please refer to Figures 1 to 3 , a method for evaluating the electrical performance of an electrical appliance, comprising the following steps:

[0082] Step S1: Identify the data source of the electrical appliance device and perform multi-dimensional data association to obtain an electrical appliance association data set; construct an electrical appliance static and dynamic information model based on the electrical appliance association data set to obtain a multi-source information model of the electrical appliance, where the multi-source information model of the electrical appliance includes an electrical appliance static information model and an electrical appliance dynamic information model;

[0083] Step S2: Perform component material attribute assignment processing on the electrical appliance static information model to obtain an electrical appliance material mapping model; generate an electrical appliance grid model based on the electrical appliance material mapping model to obtain an electrical appliance grid model; apply boundary conditions to the electrical appliance dynamic information model to obtain an electrical appliance boundary condition model; integrate the electrical appliance grid model and the electrical appliance boundary condition model to obtain an electrical and electrical twin body;

[0084] Step S3: Define the coupled physical fields based on the power electrical twin to obtain the coupled physical field list data; associate the aging variable model according to the multi-source information model of the power electrical appliance to obtain the integrated aging simulation model; perform multi-physical field and aging coupling processing based on the integrated aging simulation model and the coupled physical field list data and conduct multi-physical field coupled aging simulation to obtain the multi-physical field coupled aging simulation result dataset;

[0085] Step S4: Evaluate the electrical performance state based on the multi-source information model of the power electrical appliance and the multi-physical field coupled aging simulation result dataset to obtain an electrical performance state evaluation report; analyze the factor interaction effect based on the electrical performance state evaluation report to obtain the interaction effect analysis data; generate an electrical performance interaction analysis report based on the interaction effect analysis data to obtain the electrical performance interaction analysis report;

[0086] Step S5: Construct an electrical performance prediction model based on the electrical performance interaction analysis report and the preset historical operation database of the power electrical appliance to obtain the electrical performance prediction model; evaluate the electrical performance of the power electrical appliance based on the electrical performance prediction model and the preset operation safety standard data of the power electrical appliance to obtain an electrical performance evaluation report of the power electrical appliance.

[0087] In the embodiment of the present invention, with reference to Figure 1 as described, it is a schematic diagram of the step flow of the electrical performance evaluation method of the power electrical appliance of the present invention. In this example, the electrical performance evaluation method of the power electrical appliance includes the following steps:

[0088] Step S1: Identify the data sources of the power electrical appliance equipment and perform multi-dimensional data association to obtain the power electrical appliance association dataset; construct an electrical appliance static and dynamic information model based on the power electrical appliance association dataset to obtain a multi-source information model of the power electrical appliance, where the multi-source information model of the power electrical appliance includes an electrical appliance static information model and an electrical appliance dynamic information model;

[0089] In the embodiment of the present invention, for a certain power electrical appliance equipment, its relevant data sources are identified, such as design documents, operation logs, sensor data, etc., and the original dataset is collected. After data cleaning and association processing, a power electrical appliance association dataset containing multi-dimensional information such as structure, material, and operation state is formed. Based on this dataset, a multi-source information model of the power electrical appliance containing static information (such as structure, material) and dynamic information (such as operation state, environmental parameters) is constructed.

[0090] Step S2: Perform component material property assignment processing on the electrical appliance static information model to obtain a power electrical appliance material mapping model; generate a power electrical appliance grid model according to the power electrical appliance material mapping model; apply boundary conditions to the electrical appliance dynamic information model to obtain a power electrical appliance boundary condition model; integrate the power electrical appliance grid model and the power electrical appliance boundary condition model to obtain a power electrical twin body;

[0091] In the embodiment of the present invention, according to the power electrical appliance static information model, a three-dimensional geometric model of the power electrical appliance is constructed using three-dimensional modeling software, and accurate material properties are assigned to each component to form a power electrical appliance material mapping model. Subsequently, the model is meshed, and different precisions are adopted according to the geometric complexity, and finally a power electrical appliance grid model is generated. At the same time, according to the power electrical appliance dynamic information model, the boundary conditions required for simulation, such as voltage, current, temperature, etc., are determined to form a power electrical appliance boundary condition model. Finally, the grid model and the boundary condition model are integrated into the simulation platform to construct a power electrical twin body.

[0092] Step S3: Define the coupled physical fields according to the power electrical twin body to obtain the coupled physical field list data; associate the aging variable model according to the power electrical appliance multi-source information model to obtain an integrated aging simulation model; perform multi-physical field and aging coupling processing according to the integrated aging simulation model and the coupled physical field list data and conduct a multi-physical field coupled aging simulation to obtain a multi-physical field coupled aging simulation result data set;

[0093] In the embodiment of the present invention, according to the power electrical twin body, the physical fields to be coupled, such as the electric field, magnetic field, temperature field, etc., are determined to form a coupled physical field list. Then, according to the power electrical appliance multi-source information model, a suitable model is selected to describe the aging process of the power electrical appliance and the model parameters are determined to form an integrated aging simulation model. The aging model is coupled with the multi-physical field simulation model, the simulation parameters are set, and finally a multi-physical field coupled aging simulation is conducted to obtain a multi-physical field coupled aging simulation result data set including results such as temperature field distribution and electric field strength distribution.

[0094] Step S4: Evaluate the electrical performance state according to the power electrical appliance multi-source information model and the multi-physical field coupled aging simulation result data set to obtain an electrical performance state evaluation report; analyze the factor interaction effect according to the electrical performance state evaluation report to obtain interaction effect analysis data; generate an electrical performance interaction analysis report according to the interaction effect analysis data to obtain an electrical performance interaction analysis report;

[0095] In the embodiments of the present invention, according to the multi-source information model of power electrical appliances, indicators for evaluating their electrical performance are determined, such as insulation resistance, temperature rise, partial discharge quantity, etc., and an electrical performance evaluation index system is constructed. Referring to relevant standards, thresholds for each indicator are set to form an electrical performance evaluation index threshold table. Specific values of each indicator are calculated based on the simulation result data set to form an electrical performance evaluation index result table. By comparing the result table with the threshold table, the overall electrical performance status of the power electrical appliance is evaluated to form an electrical performance status evaluation report. On this basis, the influence degree of each indicator on the overall performance status of the power electrical appliance is analyzed to identify key indicators and form a sensitivity analysis report. Finally, various factors affecting the electrical performance status of the power electrical appliance and their interaction relationships are analyzed to form an electrical performance interaction analysis report.

[0096] Step S5: Construct an electrical performance prediction model based on the electrical performance interaction analysis report and the preset historical operation database of power electrical appliances to obtain an electrical performance prediction model; evaluate the electrical performance of the power electrical appliance according to the electrical performance prediction model and the preset power electrical appliance operation safety standard data to obtain an electrical performance evaluation report of the power electrical appliance;

[0097] In the embodiments of the present invention, according to the electrical performance interaction analysis report, the historical operation database of power electrical appliances is analyzed to identify key indicators that can reflect the degradation trend of the electrical performance of power electrical appliances, forming a key degradation index set. Using the key degradation index set and historical operation data, a prediction model capable of predicting the future electrical performance change trend of power electrical appliances is trained and constructed. Finally, the change trend of key indicators of the power electrical appliance in a future period of time is predicted using this model and compared with the preset safety standards to evaluate whether the power electrical appliance can operate safely and stably in the future period of time, forming an electrical performance evaluation report of the power electrical appliance.

[0098] Preferably, step S1 includes the following steps:

[0099] Step S11: Identify data sources for power electrical appliance equipment to obtain an original data set of power electrical appliances;

[0100] Step S12: Clean the original data set of power electrical appliances to obtain a cleaned data set of power electrical appliances;

[0101] Step S13: Extract electrical appliance structure and function data from the cleaned data set of power electrical appliances to obtain electrical appliance structure and function data; perform multi-dimensional data association on the cleaned data set of power electrical appliances according to the electrical appliance structure and function data to obtain an associated data set of power electrical appliances;

[0102] Step S14: Construct an electrical appliance static and dynamic information model based on the associated data set of power electrical appliances to obtain a multi-source information model of power electrical appliances, where the multi-source information model of power electrical appliances includes an electrical appliance static information model and an electrical appliance dynamic information model.

[0103] In an embodiment of the present invention, for the electrical performance evaluation of a certain type of circuit breaker, it is first necessary to identify the data sources related to the circuit breaker, including design documents, product manuals, operation and maintenance records, online monitoring system data, historical fault records, etc. Then, collect the original data from these data sources, such as the structural dimensions, material parameters, operating environment parameters, operating state data, fault event records, etc. of the circuit breaker, to form an original dataset of electrical appliances. Clean the original dataset of electrical appliances collected, including dealing with missing values, outliers, and noise data, etc. For example, use the interpolation method to supplement the missing operating data, eliminate the obviously incorrect measurement data, and smooth the signals disturbed by noise, etc. After data cleaning, a more accurate and reliable cleaned dataset of electrical appliances is obtained. Extract the structural and functional data of the circuit breaker from the cleaned dataset of electrical appliances, such as contact materials, insulation medium types, operating mechanism types, etc. Then, according to the structural and functional data, associate the data from different sources and of different types. For example, associate the operating temperature data with the contact materials, and associate the number of operating times data with the operating mechanism types, to form an associated dataset of electrical appliances. According to the associated dataset of electrical appliances, construct the static information model and the dynamic information model of the circuit breaker respectively. The static information model describes the information that does not change with time, such as the structure and materials of the circuit breaker, such as 3D models, material properties, etc. The dynamic information model describes the information that changes with time, such as the operating state and environmental parameters of the circuit breaker, such as temperature fields, electric fields, mechanical stresses, etc. Finally, associate the static information model and the dynamic information model to form a multi-source information model of electrical appliances.

[0104] Preferably, step S14 includes the following steps:

[0105] Step S141: Identify the data source types of the associated dataset of electrical appliances to obtain multi-source heterogeneous data; perform data unification and spatio-temporal alignment on the multi-source heterogeneous data to obtain a spatio-temporally consistent standardized dataset;

[0106] Step S142: Mine multi-source fusion features from the spatio-temporally consistent standardized dataset to obtain a multi-source fusion feature set;

[0107] Step S143: Construct an electrical appliance holographic information library according to the multi-source fusion feature set and a preset multi-source information fusion framework to obtain an electrical appliance holographic information library;

[0108] Step S144: Extract static information from the electrical appliance holographic information library and construct an electrical appliance static information model to obtain an electrical appliance static information model;

[0109] Step S145: Extract dynamic information from the electrical appliance holographic information library and construct an electrical appliance dynamic information model to obtain an electrical appliance dynamic information model;

[0110] Step S146: Perform mapping atlas association on the electrical appliance static information model and the electrical appliance dynamic information model to obtain a multi-source information model of the power electrical appliance.

[0111] In the embodiment of the present invention, analyze the associated data set of the power electrical appliance, identify different data source types included in the data set, such as real-time monitoring data from sensors, historical operation data from databases, structural parameters from design files, etc. These data have different data formats, sampling frequencies, time spans, etc., constituting multi-source heterogeneous data. Perform processing such as unified data format, unit conversion, timestamp alignment, data interpolation, etc. on the multi-source heterogeneous data to eliminate data differences, and finally obtain a spatio-temporally consistent standardized data set. Based on the spatio-temporally consistent standardized data set, use multi-source information fusion technologies, such as principal component analysis (PCA), independent component analysis (ICA), etc., to perform feature extraction and dimensionality reduction processing on the data, and mine key features that can reflect the operating state of the power electrical appliance, such as temperature change trend, vibration frequency characteristics, current-voltage correlation, etc., to form a multi-source fusion feature set. Select a suitable multi-source information fusion framework, such as Bayesian network, evidence theory, Kalman filter, etc., to fuse the multi-source fusion feature set and construct an electrical appliance holographic information library containing complete information of the power electrical appliance. This information library not only contains the original data, but also includes high-level information after feature extraction and information fusion, and can more comprehensively reflect the operating state of the power electrical appliance. Extract static information independent of time, such as the geometric structure, material properties, manufacturing process, etc. of the power electrical appliance, from the electrical appliance holographic information library, and use this information to construct an electrical appliance static information model. For example, a 3D modeling software can be used to construct a 3D model of the power electrical appliance and associate the material property information to the corresponding components. Extract dynamic information related to time, such as the operating state data, environmental parameters, fault information, etc. of the power electrical appliance, from the electrical appliance holographic information library, and use this information to construct an electrical appliance dynamic information model. For example, a time series analysis method can be used to establish a model of the key parameters of the power electrical appliance changing with time, or a state space model can be used to describe the dynamic behavior of the power electrical appliance. Associate the constructed electrical appliance static information model and the electrical appliance dynamic information model, establish a mapping relationship between the two, and form a complete multi-source information model of the power electrical appliance. For example, the temperature field data in the dynamic information model can be mapped to the 3D model in the static information model to realize the visualization of the temperature field distribution of the power electrical appliance.

[0112] Preferably, step S2 includes the following steps:

[0113] Step S21: Extract geometric information from the electrical appliance static information model to obtain electrical appliance geometric information; perform 3D geometric modeling of the power electrical appliance using 3D modeling software according to the electrical appliance geometric information to obtain a 3D geometric model of the power electrical appliance;

[0114] Step S22: Extract material information from the electrical appliance static information model to obtain electrical appliance material information; perform material modeling based on the electrical appliance material information to obtain a material model; map the material model into the three-dimensional geometric model of the power electrical appliance and perform component material attribute assignment processing to obtain a power electrical appliance material mapping model;

[0115] Step S23: Divide the structure regions of the power electrical appliance material mapping model to obtain structure region division data, where the structure region division data includes simple structure region data and complex structure region data; perform regular hexahedron mesh division on the simple structure region data to obtain low-precision mesh data; perform unstructured tetrahedron mesh division on the complex structure region data and perform local mesh encryption processing to obtain high-precision mesh data; generate a power electrical appliance mesh model based on the low-precision mesh data and the high-precision mesh data to obtain a power electrical appliance mesh model;

[0116] Step S24: Extract the operating conditions and environmental parameters from the electrical appliance dynamic information model to obtain the operating conditions and environmental parameters; apply boundary conditions to the electrical appliance dynamic information model according to the operating conditions and environmental parameters to obtain a power electrical appliance boundary condition model;

[0117] Step S25: Integrate the power electrical appliance mesh model and the power electrical appliance boundary condition model to obtain a power electrical twin.

[0118] In the embodiments of the present invention, geometric information of key components, such as dimensions, shapes, positional relationships, etc., is extracted from the static information model of electrical appliances. Then, a three-dimensional geometric model of the electrical appliance is constructed using 3D modeling software based on the extracted geometric information to ensure that the model accurately reflects the structural characteristics of the actual device. Material information of each component, such as material type, conductivity, thermal conductivity, etc., is extracted from the static information model of the electrical appliance. According to the material information, the corresponding material model is selected or created in the material library of the simulation software, and the material model is assigned to the corresponding components on the three-dimensional geometric model to form a material mapping model of the electrical appliance containing material properties. According to the geometric structure and simulation accuracy requirements of the material mapping model of the electrical appliance, the model is divided into different regions, such as simple structure regions and complex structure regions. Regular hexahedron meshing is used for the simple structure regions to improve the calculation efficiency; unstructured tetrahedron meshing is used for the complex structure regions, and local mesh refinement is performed on the key parts to improve the calculation accuracy. Finally, the mesh data of different regions are integrated to generate a complete mesh model of the electrical appliance. Operating conditions and environmental parameters related to the simulation, such as voltage, current, temperature, humidity, etc., are extracted from the dynamic information model of the electrical appliance. According to the extracted parameter information, corresponding boundary conditions, such as voltage excitation, current load, thermal radiation, etc., are applied to the electrical appliance model to form a boundary condition model of the electrical appliance containing boundary condition information. The mesh model of the electrical appliance generated in step S23 is integrated with the boundary condition model of the electrical appliance generated in step S24, and the model is imported into the selected simulation platform to finally form an electrical and electrical twin body containing a complete geometric structure, material properties, and boundary conditions, preparing for subsequent multi-physics field coupling simulation.

[0119] Preferably, step S3 includes the following steps:

[0120] Step S31: Perform electrical and electrical twin body import processing on the electrical and electrical twin body to obtain a simulation platform model; define the coupled physical fields for the simulation platform model to obtain coupled physical field list data;

[0121] Step S32: Select physical field interfaces according to the coupled physical field list data to obtain a physical field interface solution;

[0122] Step S33: Associate the aging variable model according to the multi-source information model of the electrical appliance and the simulation platform model to obtain an integrated aging simulation model; perform multi-physics field and aging coupling processing according to the physical field interface solution, the integrated aging simulation model, and the coupled physical field list data to obtain a multi-physics field-aging model coupling solution;

[0123] Step S34: Perform multi-physics field coupled aging simulation according to the multi-physics field-aging model coupling solution to obtain a multi-physics field coupled aging simulation result data set.

[0124] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0125] Step S31: Perform power electrical twin import processing based on the power electrical twin to obtain a simulation platform model; perform coupling physical field definition on the simulation platform model to obtain coupling physical field list data;

[0126] In an embodiment of the present invention, a suitable simulation platform (such as COMSOL, ANSYS, etc.) is selected, and the constructed power electrical twin is imported into the simulation platform, and necessary format conversion and settings are performed to obtain a simulation platform model. Then, according to the objectives and requirements of electrical performance evaluation, determine the physical fields that need to be coupled, such as electric field, magnetic field, temperature field, flow field, etc., to form coupling physical field list data.

[0127] Step S32: Select physical field interfaces according to the coupling physical field list data to obtain a physical field interface scheme;

[0128] In an embodiment of the present invention, according to the coupling physical field list data, select suitable physical field interfaces to describe the interaction relationships between different physical fields. For example, the "electromagnetic heating" interface can be selected to describe the coupling relationship between the electric field and the temperature field, and the "fluid-solid interaction" interface can be selected to describe the coupling relationship between the flow field and structural mechanics. Finally, determine the interface types and parameter settings used for each coupling physical field to form a physical field interface scheme.

[0129] Step S33: Perform aging variable model association based on the power electrical multi-source information model and the simulation platform model to obtain an integrated aging simulation model; perform multi-physical field and aging coupling processing according to the physical field interface scheme, the integrated aging simulation model, and the coupling physical field list data to obtain a multi-physical field-aging model coupling scheme;

[0130] In an embodiment of the present invention, according to the aging information included in the power electrical multi-source information model, select a suitable model to describe the aging process of power electrical appliances, such as the Arrhenius model, inverse power law model, etc., and determine the model parameters. Associate the aging model with the simulation platform model, for example, map the material parameter changes calculated by the aging model to the simulation model to form an integrated aging simulation model. Then, according to the physical field interface scheme, couple the aging model with the multi-physical field simulation model, for example, introduce the influence of the temperature field on material parameters into the electric field calculation, and finally obtain a multi-physical field-aging model coupling scheme.

[0131] Step S34: Perform multi-physical field coupling aging simulation according to the multi-physical field-aging model coupling scheme to obtain a multi-physical field coupling aging simulation result dataset;

[0132] In the embodiment of the present invention, in the simulation platform, simulation parameters are set according to the multi-physical field-aging model coupling scheme, such as simulation time, solution method, grid accuracy, etc. Then, the simulation program is run to obtain the multi-physical field coupling simulation results of the electrical appliance under different operating conditions and aging states, such as temperature field distribution, electric field strength distribution, mechanical stress distribution, etc., to form a multi-physical field coupling aging simulation result dataset.

[0133] Preferably, step S31 includes the following steps:

[0134] Step S311: Determine the simulation objectives and scope according to the electrical power twin to obtain a simulation objective scheme;

[0135] Step S312: Perform electrical power twin import processing on the electrical power twin according to the simulation objective scheme to obtain a simulation platform model;

[0136] Step S313: Set the environmental parameters for the simulation platform model according to the simulation objective scheme to obtain an environmental parameter setting table;

[0137] Step S314: Apply boundary conditions and excitations to the simulation platform model according to the simulation objective scheme to obtain a boundary condition and excitation setting table;

[0138] Step S315: Define the coupled physical fields according to the environmental parameter setting table and the boundary condition and excitation setting table to obtain the list data of the coupled physical fields.

[0139] In the embodiments of the present invention, analyze the information contained in the power electrical twin, combine with specific electrical performance evaluation requirements, and clarify the objectives of this simulation. For example, evaluate the temperature field distribution of power electrical appliances under specific working conditions, analyze the impact of aging on insulation performance, etc. At the same time, determine the scope of the simulation. For example, whether to simulate the entire power electrical appliance or perform local simulation on key components, and form a document with the simulation objectives and scope, that is, the simulation objective plan. According to the simulation objective plan, select appropriate simulation software and modules, and import the power electrical twin into the simulation platform. According to the simulation requirements, perform necessary simplification, modification, or supplementation on the model. For example, remove components irrelevant to the simulation, simplify complex geometric structures, add necessary boundary conditions, etc., and finally obtain a simulation platform model that can run on the simulation platform. According to the environmental conditions set in the simulation objective plan, set the environmental parameters for the simulation platform model, such as environmental temperature, humidity, air pressure, etc. Record all environmental parameters and their values in the environmental parameter setting table to ensure that the simulation environment is consistent with the actual operating environment. According to the operating conditions set in the simulation objective plan, apply corresponding boundary conditions and excitations to the simulation platform model, such as voltage, current, load, heat source, etc. Record information such as the types, parameters, and application positions of all boundary conditions and excitations in the boundary condition and excitation setting table to ensure that the simulation model can simulate the stress, heat, etc. of the power electrical appliance during actual operation. According to the environmental parameter setting table and the boundary condition and excitation setting table, analyze the physical fields involved in the simulation of the power electrical appliance, such as electric field, magnetic field, temperature field, flow field, etc., and their interaction relationships. Finally, determine the physical fields that need to be coupled, add them to the simulation model, and form a list of coupled physical field data to prepare for subsequent multi-physical field coupling simulation.

[0140] Preferably, step S33 includes the following steps:

[0141] Step S331: Conduct aging mechanism analysis based on the multi-source information model of power electrical appliances to obtain aging mechanism analysis data; select aging model parameters based on the aging mechanism analysis data to obtain an aging model parameter table;

[0142] Step S332: Perform an association between the aging model parameter table and the simulation platform model for the aging variable model to obtain an integrated aging simulation model;

[0143] Step S333: Construct a multi-physical field simulation model based on the integrated aging simulation model and the list data of coupled physical fields to obtain a multi-physical field simulation model;

[0144] Step S334: Embed the aging model parameter table into the multi-physical field simulation model, and perform multi-physical field and aging coupling processing according to the physical field interface scheme to obtain a multi-physical field-aging model coupling scheme.

[0145] In the embodiments of the present invention, according to the material information, operation history data, environmental information, etc. included in the multi-source information model of power electrical appliances, the aging mechanisms occurring in power electrical appliances are analyzed, such as insulation aging, contact ablation, mechanical fatigue, etc., and the main influencing factors of each aging mechanism are determined. Then, based on the aging mechanism analysis data, a suitable model is selected to describe the influence of each aging mechanism on the performance of power electrical appliances, such as the Arrhenius model, inverse power law model, etc., and the value range or initial value of the model parameters is determined to form an aging model parameter table. The aging model parameter table is associated with the simulation platform model, that is, the parameters describing the change of material properties in the aging model are associated with the corresponding material attributes in the simulation model. For example, the change value of the insulation resistance calculated by the insulation aging model is updated in real time to the corresponding insulation material attribute in the simulation model, so as to realize the influence of the aging effect on the simulation result. According to the coupled physical field list data, the corresponding physical field interfaces and modules are selected in the simulation platform, and the integrated aging simulation model is imported into the multi-physical field simulation environment. According to the interaction relationship between physical fields, the boundary conditions, initial conditions and solution parameters of each physical field are set, and finally a simulation model containing multiple coupled physical fields is constructed. The aging model parameter table is embedded into the multi-physical field simulation model, and according to the physical field interface scheme, the coupling relationship between the aging model and each physical field is established. For example, the influence of the temperature field on the aging rate of insulation materials is introduced into the insulation aging model, and the influence of the electric field strength on contact ablation is introduced into the contact ablation model, and finally a multi-physical field-aging model coupling scheme including multi-physical field coupling and aging effect is obtained.

[0146] Preferably, step S4 includes the following steps:

[0147] Step S41: Generate electrical performance evaluation indicators according to the multi-source information model of power electrical appliances to obtain an electrical performance evaluation index system;

[0148] Step S42: Obtain electrical performance index evaluation standard data; set evaluation index thresholds for the electrical performance evaluation index system according to the electrical performance index evaluation standard data to obtain an electrical performance evaluation index threshold table;

[0149] Step S43: Calculate electrical performance evaluation indicators for the multi-physical field coupling aging simulation result data set according to the electrical performance evaluation index system to obtain an electrical performance evaluation index result table;

[0150] Step S44: Conduct an electrical performance status evaluation on the electrical performance evaluation index result table and the electrical performance evaluation index threshold table to obtain an electrical performance status evaluation report; conduct a sensitivity analysis according to the electrical performance status evaluation report to obtain a sensitivity analysis report;

[0151] Step S45: Perform a factor interaction effect analysis based on the electrical performance status evaluation report and the sensitivity analysis report to obtain interaction effect analysis data;

[0152] Step S46: Generate an electrical performance interaction analysis report based on the interaction effect analysis data to obtain an electrical performance interaction analysis report.

[0153] As an example of the present invention, referring to Figure 3 as shown, in this example, step S4 includes:

[0154] Step S41: Generate electrical performance evaluation indicators according to the multi-source information model of electrical appliances to obtain an electrical performance evaluation indicator system;

[0155] In the embodiment of the present invention, according to the structure information, material information, operating state information, etc. included in the multi-source information model of electrical appliances, combined with the functional characteristics and failure mechanisms of electrical appliances, the indicators for evaluating their electrical performance are determined, such as insulation resistance, dielectric loss angle, partial discharge amount, temperature rise, etc., and an electrical performance evaluation indicator system including each indicator and its weight is constructed.

[0156] Step S42: Obtain electrical performance indicator evaluation standard data; set evaluation indicator thresholds for the electrical performance evaluation indicator system according to the electrical performance indicator evaluation standard data to obtain an electrical performance evaluation indicator threshold table;

[0157] In the embodiment of the present invention, referring to national standards, industry standards or enterprise internal standards, the evaluation standard data of each electrical performance indicator is obtained, such as the qualified range, warning value, danger value, etc. According to the evaluation standard data, combined with the actual operating environment and requirements of electrical appliances, corresponding thresholds are set for each indicator in the electrical performance evaluation indicator system to form an electrical performance evaluation indicator threshold table.

[0158] Step S43: Calculate electrical performance evaluation indicators for the multi-physical field coupling aging simulation result data set according to the electrical performance evaluation indicator system to obtain an electrical performance evaluation indicator result table;

[0159] In the embodiment of the present invention, according to the electrical performance evaluation indicator system, the corresponding simulation data is extracted from the multi-physical field coupling aging simulation result data set, and the specific values of each indicator are calculated to form an electrical performance evaluation indicator result table. For example, the electric field strength of the insulating material is calculated according to the electric field distribution obtained by simulation, and the temperature rise of the electrical appliance is calculated according to the temperature field distribution obtained by simulation.

[0160] Step S44: Perform an electrical performance status evaluation on the electrical performance evaluation indicator result table and the electrical performance evaluation indicator threshold table to obtain an electrical performance status evaluation report; perform a sensitivity analysis according to the electrical performance status evaluation report to obtain a sensitivity analysis report;

[0161] In the embodiments of the present invention, the results table of electrical performance evaluation indicators is compared and analyzed with the threshold table of electrical performance evaluation indicators to determine whether each indicator is within the normal range, and the overall electrical performance state of the power electrical appliance is evaluated to form an electrical performance state evaluation report. On this basis, the influence degree of each indicator on the overall performance state of the power electrical appliance is analyzed, and the key indicators that are more sensitive to the change of the performance state are identified to form a sensitivity analysis report.

[0162] Step S45: Perform a factor interaction effect analysis based on the electrical performance state evaluation report and the sensitivity analysis report to obtain interaction effect analysis data;

[0163] In the embodiments of the present invention, based on the electrical performance state evaluation report and the sensitivity analysis report, various factors affecting the electrical performance state of the power electrical appliance are analyzed, such as material aging, ambient temperature, operating load, etc., as well as their interaction relationships. Methods such as correlation analysis and principal component analysis can be used to quantify the influence degree of each factor on the performance indicators, and identify the main influencing factors and interaction relationships to form interaction effect analysis data.

[0164] Step S46: Generate an electrical performance interaction analysis report based on the interaction effect analysis data to obtain an electrical performance interaction analysis report;

[0165] In the embodiments of the present invention, an electrical performance interaction analysis report is generated based on the interaction effect analysis data, which details the influence laws, interaction relationships, and main influence paths of various factors on the electrical performance of the power electrical appliance. This report can provide a reference basis for the design optimization, operation and maintenance, and fault diagnosis of the power electrical appliance.

[0166] Preferably, step S45 includes the following steps:

[0167] Step S451: Determine the analysis factors based on the electrical performance state evaluation report and the sensitivity analysis report to obtain the data of the factors to be analyzed;

[0168] Step S452: Extract causal analysis data from the preset historical operation database of the power electrical appliance according to the data of the factors to be analyzed to obtain a causal analysis data set;

[0169] Step S453: Infer the causal relationship of the causal analysis data set to obtain a causal relationship diagram; perform causal effect quantification processing on the causal relationship diagram to obtain a causal effect quantification table;

[0170] Step S454: Identify the root cause and the influencing factors based on the causal effect quantification table to obtain the root cause data and the list of influencing factors;

[0171] Step S455: Perform factor interaction effect analysis based on the root cause data and the list of influencing factors to obtain interaction effect analysis data.

[0172] In the embodiment of the present invention, combining the potential problems identified in the electrical performance status evaluation report and the key indicators determined in the sensitivity analysis report, the specific factors that need to be analyzed for factor interaction effects are determined, such as ambient temperature, load current, operating time, etc., and the specific data of these factors are extracted from the multi-source information model of the electrical appliance and the simulation result dataset to form the data of the factors to be analyzed. According to the factors to be analyzed, relevant historical operation data, such as ambient temperature, load current, operating time, fault records, etc., are extracted from the preset historical operation database of the electrical appliance to form a causal analysis dataset. This dataset should contain a sufficient number of samples for effective causal analysis. Use causal inference algorithms (such as Bayesian networks, PC algorithms, etc.) to analyze the causal analysis dataset, identify the causal relationships between various factors, and construct a causal relationship graph. For example, the analysis results show that an increase in ambient temperature will cause a decrease in insulation resistance, and an increase in load current will accelerate insulation aging. Then, perform a quantitative analysis on the causal relationship graph, calculate the influence degree of each factor on the target index, and form a causal effect quantification table. According to the influence degrees of various factors in the causal effect quantification table, identify the root causes that have the greatest impact on the electrical performance status of the electrical appliance, such as long-term overload operation, too high ambient temperature, etc. At the same time, identify the main factors that affect the root causes, such as poor heat dissipation, control system failure, etc., to form a list of influencing factors. For the root causes and influencing factors, analyze their interaction relationships, such as the relationship between poor heat dissipation and ambient temperature, the relationship between control system failure and load current, etc. Quantitative analysis methods (such as regression analysis, variance analysis, etc.) can be used to quantify the interaction between various factors and evaluate their comprehensive impact on the electrical performance status of the electrical appliance, and finally obtain the interaction effect analysis data.

[0173] Preferably, step S5 includes the following steps:

[0174] Step S51: Identify key degradation indicators for the preset historical operation database of the electrical appliance according to the electrical performance interaction analysis report to obtain a set of key degradation indicators;

[0175] Step S52: Construct an electrical performance prediction model according to the set of key degradation indicators and the preset historical operation database of the electrical appliance to obtain an electrical performance prediction model;

[0176] Step S53: Evaluate the electrical performance of the electrical appliance according to the electrical performance prediction model and the preset electrical appliance operation safety standard data to obtain an electrical performance evaluation report of the electrical appliance.

[0177] In the embodiments of the present invention, according to the main influencing factors and interaction relationships identified in the electrical performance interaction analysis report, the preset historical operation database of electrical appliances is analyzed to find out the key indicators that can reflect the electrical performance degradation trend of electrical appliances, such as insulation resistance, dielectric loss angle, partial discharge amount, etc., to form a key degradation index set. A suitable prediction model (such as time series analysis model, grey prediction model, neural network model, etc.) is selected, and using the key degradation index set and the historical data of the corresponding indicators in the preset historical operation database of electrical appliances, a prediction model that can predict the future electrical performance change trend of electrical appliances is trained and constructed. Using the electrical performance prediction model, the change trend of the key degradation indicators of electrical appliances in the future period of time is predicted, and compared and analyzed with the preset electrical appliance operation safety standard data to evaluate whether the electrical appliances can operate safely and stably in the future period of time, and the evaluation results are sorted out to form an electrical performance evaluation report of electrical appliances.

[0178] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0179] The above are only the specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the electrical performance of an electrical appliance, characterized in that, Including the following steps: Step S1: Identify the data sources of power electrical equipment and perform multi-dimensional data association to obtain a power electrical association data set; construct an electrical static and dynamic information model based on the power electrical association data set to obtain a multi-source information model of power electrical equipment, where the multi-source information model of power electrical equipment includes an electrical static information model and an electrical dynamic information model; Step S2: Perform component material attribute assignment processing on the electrical static information model to obtain a power electrical material mapping model; generate a power electrical grid model based on the power electrical material mapping model to obtain a power electrical grid model; apply boundary conditions to the electrical dynamic information model to obtain a power electrical boundary condition model; integrate the power electrical grid model and the power electrical boundary condition model to obtain a power electrical twin; Step S3: Define the coupled physical fields based on the power electrical twin to obtain the list data of the coupled physical fields; associate the aging variable model according to the multi-source information model of power electrical equipment to obtain an integrated aging simulation model; perform multi-physics field and aging coupling processing and multi-physics field coupled aging simulation based on the integrated aging simulation model and the list data of the coupled physical fields to obtain a multi-physics field coupled aging simulation result data set; Step S4: Evaluate the electrical performance state based on the multi-source information model of power electrical equipment and the multi-physics field coupled aging simulation result data set to obtain an electrical performance state evaluation report; analyze the factor interaction effect based on the electrical performance state evaluation report to obtain the interaction effect analysis data; generate an electrical performance interaction analysis report based on the interaction effect analysis data to obtain an electrical performance interaction analysis report; Step S5: Construct an electrical performance prediction model based on the electrical performance interaction analysis report and the preset historical operation database of power electrical equipment to obtain an electrical performance prediction model; evaluate the electrical performance of power electrical equipment based on the electrical performance prediction model and the preset power electrical equipment operation safety standard data to obtain an electrical performance evaluation report of power electrical equipment.

2. The electrical performance evaluation method of the power electrical appliance according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Identify the data sources of power electrical equipment to obtain a raw data set of power electrical equipment; Step S12: Clean the raw data set of power electrical equipment to obtain a cleaned data set of power electrical equipment; Step S13: Extract electrical structure and function data from the cleaned data set of power electrical equipment to obtain electrical structure and function data; perform multi-dimensional data association on the cleaned data set of power electrical equipment according to the electrical structure and function data to obtain a power electrical association data set; Step S14: Construct an electrical static and dynamic information model based on the power electrical association data set to obtain a multi-source information model of power electrical equipment, where the multi-source information model of power electrical equipment includes an electrical static information model and an electrical dynamic information model.

3. The electrical performance evaluation method of the power electrical appliance according to claim 2, wherein Step S14 includes the following steps: Step S141: Identify the data source types of the power electrical association data set to obtain multi-source heterogeneous data; unify and align the data in time and space for the multi-source heterogeneous data to obtain a spatio-temporally consistent standardized data set; Step S142: Mine the multi-source fusion features of the spatio-temporally consistent standardized data set to obtain a multi-source fusion feature set; Step S143: Construct an electrical appliance holographic information library based on the multi-source fusion feature set and the preset multi-source information fusion framework to obtain the electrical appliance holographic information library; Step S144: Extract static information from the electrical appliance holographic information library and construct an electrical appliance static information model to obtain the electrical appliance static information model; Step S145: Extract dynamic information from the electrical appliance holographic information library and construct an electrical appliance dynamic information model to obtain the electrical appliance dynamic information model; Step S146: Correlate the electrical appliance static information model and the electrical appliance dynamic information model with the electrical appliance static and dynamic information mapping atlas to obtain the multi-source information model of power electrical appliances.

4. The electrical performance evaluation method of the power electrical appliance according to claim 1, characterized in that Step S2 includes the following steps: Step S21: Extract geometric information from the electrical appliance static information model to obtain the electrical appliance geometric information; use 3D modeling software to perform 3D geometric modeling of power electrical appliances based on the electrical appliance geometric information to obtain the 3D geometric model of power electrical appliances; Step S22: Extract material information from the electrical appliance static information model to obtain the electrical appliance material information; perform material modeling based on the electrical appliance material information to obtain the material model; map the material model to the 3D geometric model of power electrical appliances and perform component material attribute assignment processing to obtain the power electrical appliance material mapping model; Step S23: Divide the structural regions of the power electrical appliance material mapping model to obtain the structural region division data, where the structural region division data includes simple structural region data and complex structural region data; perform regular hexahedron mesh division on the simple structural region data to obtain low-precision mesh data; perform unstructured tetrahedron mesh division on the complex structural region data and perform local mesh encryption processing to obtain high-precision mesh data; generate a power electrical appliance mesh model based on the low-precision mesh data and the high-precision mesh data to obtain the power electrical appliance mesh model; Step S24: Extract the operating conditions and environmental parameters from the electrical appliance dynamic information model to obtain the operating conditions and environmental parameters; apply boundary conditions to the electrical appliance dynamic information model according to the operating conditions and environmental parameters to obtain the power electrical appliance boundary condition model; Step S25: Integrate the power electrical appliance mesh model and the power electrical appliance boundary condition model to obtain the power electrical twin.

5. The electrical performance evaluation method of the power electrical appliance according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Perform power electrical twin import processing based on the power electrical twin to obtain the simulation platform model; define the coupled physical fields of the simulation platform model to obtain the coupled physical field list data; Step S32: Select physical field interfaces according to the coupled physical field list data to obtain the physical field interface scheme; Step S33: Correlate the aging variable model based on the multi-source information model of power electrical appliances and the simulation platform model to obtain the integrated aging simulation model; perform multi-physical field and aging coupling processing according to the physical field interface scheme, the integrated aging simulation model, and the coupled physical field list data to obtain the multi-physical field-aging model coupling scheme; Step S34: Perform multi-physical field coupled aging simulation according to the multi-physical field-aging model coupling scheme to obtain the multi-physical field coupled aging simulation result dataset.

6. The electrical performance evaluation method of the power electrical appliance according to claim 5, characterized in that Step S31 includes the following steps: Step S311: Determine the simulation objectives and scope based on the power and electrical digital twin to obtain a simulation objective scheme; Step S312: Perform power and electrical digital twin import processing on the power and electrical digital twin according to the simulation objective scheme to obtain a simulation platform model; Step S313: Set the environmental parameters for the simulation platform model according to the simulation objective scheme to obtain an environmental parameter setting table; Step S314: Apply boundary conditions and excitations to the simulation platform model according to the simulation objective scheme to obtain a boundary condition and excitation setting table; Step S315: Define the coupled physical fields based on the environmental parameter setting table and the boundary condition and excitation setting table to obtain the coupled physical field list data.

7. The electrical performance evaluation method of the power electrical appliance according to claim 5, characterized in that Step S33 includes the following steps: Step S331: Analyze the aging mechanism based on the power electrical multi-source information model to obtain aging mechanism analysis data; Select aging model parameters based on the aging mechanism analysis data to obtain an aging model parameter table; Step S332: Associate the aging variable model with the aging model parameter table and the simulation platform model to obtain an integrated aging simulation model; Step S333: Construct a multi-physical field simulation model based on the integrated aging simulation model and the coupled physical field list data to obtain a multi-physical field simulation model; Step S334: Embed the aging model parameter table into the multi-physical field simulation model, and perform multi-physical field and aging coupling processing according to the physical field interface scheme to obtain a multi-physical field - aging model coupling scheme.

8. The electrical performance evaluation method of the power electrical appliance according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Generate electrical performance evaluation indicators based on the power electrical multi-source information model to obtain an electrical performance evaluation index system; Step S42: Obtain electrical performance index evaluation standard data; Set the evaluation index thresholds for the electrical performance evaluation index system according to the electrical performance index evaluation standard data to obtain an electrical performance evaluation index threshold table; Step S43: Calculate the electrical performance evaluation indicators for the multi-physical field coupled aging simulation result dataset according to the electrical performance evaluation index system to obtain an electrical performance evaluation index result table; Step S44: Evaluate the electrical performance status of the electrical performance evaluation index result table and the electrical performance evaluation index threshold table to obtain an electrical performance status evaluation report; Perform sensitivity analysis based on the electrical performance status evaluation report to obtain a sensitivity analysis report; Step S45: Analyze the factor interaction effects based on the electrical performance status evaluation report and the sensitivity analysis report to obtain interaction effect analysis data; Step S46: Generate an electrical performance interaction analysis report based on the interaction effect analysis data to obtain an electrical performance interaction analysis report.

9. The electrical performance evaluation method of the power electrical appliance according to claim 8, characterized in that Step S45 includes the following steps: Step S451: Determine the analysis factors based on the electrical performance status evaluation report and the sensitivity analysis report to obtain the data of factors to be analyzed; Step S452: Extract causal analysis data from the preset power electrical historical operation database according to the data of factors to be analyzed to obtain a causal analysis dataset; Step S453: Infer the causal relationship of the causal analysis dataset to obtain a causal relationship diagram; Perform causal effect quantification processing based on the causal relationship diagram to obtain a causal effect quantification table; Step S454: Identify the root cause and influencing factors according to the causal effect quantification table to obtain the root cause data and the list of influencing factors; Step S455: Conduct factor interaction effect analysis based on the root cause data and the list of influencing factors to obtain the interaction effect analysis data.

10. The electrical performance evaluation method of the power electrical appliance according to claim 1, characterized in that Step S5 includes the following steps: Step S51: Identify the key degradation indicators for the preset historical operation database of electrical appliances according to the electrical performance interaction analysis report to obtain the key degradation indicator set; Step S52: Construct an electrical performance prediction model based on the key degradation indicator set and the preset historical operation database of electrical appliances to obtain the electrical performance prediction model; Step S53: Evaluate the electrical performance of electrical appliances according to the electrical performance prediction model and the preset electrical appliance operation safety standard data to obtain the electrical performance evaluation report of electrical appliances.

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