Multi-physics coupling simulation analysis method and system for power business

Abstract

The invention discloses a multi-physical field coupling simulation analysis method and system for power business, and relates to the technical field of power business environment change analysis, and the method comprises the following steps: 1, obtaining physical field data of a power business scene, the physical field data comprising a temperature field, a stress field, a displacement field, a magnetic field and an electric field of power equipment; and step 2, carrying out coupling calculation on the physical field data by using the multi-physical field simulation model, according to the multi-physical field coupling simulation analysis method and system for the power business, through coupling and building of different models in the multi-physical field simulation model, coupling simulation of power equipment in a complex multi-physical field environment can be realized, and the coupling simulation efficiency is improved. The simulation model can accurately predict dynamic changes of multiple physical fields such as electromagnetism, heat effect and mechanical stress of the equipment, help to optimize design and operation strategies of the power equipment, and improve reliability and safety of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power business environment change analysis, and in particular to a multi-physical field coupling simulation analysis method and system for power business. Background Art

[0002] With the rapid development of the power business, power equipment and power grid systems involve complex multi-physical field phenomena in actual operation, which usually include the mutual coupling of multiple physical fields such as electromagnetic fields, thermal fields, and mechanical fields. The interaction between these multi-physical fields may have a significant impact on the performance and life of power equipment. For example, eddy current losses caused by electromagnetic fields will cause the temperature of the equipment to rise, which in turn will cause thermal expansion of the material, leading to mechanical stress and deformation, which may cause equipment failure or even shutdown in severe cases. In addition, environmental conditions such as temperature, humidity, wind force and other external factors will also have different degrees of influence on the operating performance of power equipment. Therefore, the research and establishment of an effective multi-physical field coupling simulation and analysis system is of great significance to the design, operation and maintenance, and fault diagnosis of power equipment.

[0003] Traditional power business simulation mainly focuses on the analysis of a single physical field, and it is difficult to fully consider the coupling between multiple physical fields; electromagnetic field simulation is usually limited to the calculation of electromagnetic wave propagation and current distribution, and it is impossible to accurately evaluate the thermal effect caused by electromagnetic loss; similarly, thermal simulation is also difficult to accurately couple electromagnetic field effects and mechanical stress; in addition, most existing simulation systems are static simulations, lacking dynamic data feedback and real-time adjustment capabilities, and cannot respond to complex changes in the power business environment in a timely manner; this leads to many uncertainties in the design and operation and maintenance of power equipment, affecting the safety and stability of the equipment, which is a problem we need to solve. Therefore, there is an urgent need for an analysis method and system that can realize multi-physical field coupling simulation, which can not only comprehensively consider the interactive effects of electromagnetic, thermal, mechanical and other physical fields, but also dynamically adjust and optimize in combination with the actual business environment to improve the operating efficiency and reliability of power equipment. This can not only improve the simulation accuracy of power business, but also play an important role in equipment design, optimization control, fault prediction and diagnosis. Summary of the invention

[0004] The object of the present invention is to provide a multi-physics field coupling simulation analysis method and system for electric power business.

[0005] The purpose of the present invention can be achieved by the following technical solution: a multi-physics field coupling simulation analysis method and system for power business, the method comprising the following steps:

[0006] Step 1: Obtain the physical field data of the power business scenario, which includes the temperature field, stress field, displacement field, magnetic field and electric field of the power equipment;

[0007] Step 2: Use the multi-physics field simulation model to perform coupling calculations on the physical field data, construct a physical field coupling equation group according to the operating status and environmental parameters of the power equipment, and numerically solve the physical field coupling equation group to obtain simulation results;

[0008] Step 3: Analyze the simulation results, evaluate the operating reliability and performance of power equipment in a multi-physics environment, and determine potential fault locations and risks;

[0009] Step 4: Based on the simulation results, optimize and adjust the equipment layout and working parameters in the power business, and transmit the adjustment results to the system control module to optimize the operation plan of the power business;

[0010] Step 5: Use the equipment monitoring module to compare the real-time data obtained during the operation of the power business with the simulation results. If the difference between the actual data and the simulation results exceeds the preset threshold, the alarm mechanism is triggered and a secondary simulation analysis is performed.

[0011] Furthermore, the multi-physics field simulation model includes a coupled model of temperature field, stress field, displacement field, magnetic field and electric field, and the construction process of the coupled model includes the following steps:

[0012] Step 1: Obtain the initial physical field conditions of the power equipment and its surrounding environment, and construct the corresponding physical field coupling equations;

[0013] Step 2: Discretize the physical field coupling equations through the finite element method to obtain the solution matrix;

[0014] Step 3: numerically solve the physical field coupling equations through an iterative algorithm to obtain the simulation results, that is, the physical field distribution results of the power equipment;

[0015] Step 4: Control the accuracy of the solution by adjusting the solution step size and convergence accuracy to ensure the accuracy of the simulation results;

[0016] Among them, the establishment of the temperature field model:

[0017] Power equipment will produce thermal effects under the action of electromagnetic fields, so it is necessary to establish a temperature field model to describe the distribution and conduction of heat;

[0018] The temperature field model is based on the heat conduction equation: Where: T is temperature; α is the thermal diffusivity; q is the heat source term; ρ is the material density; c p is the specific heat capacity of the material, where the heat source term q is caused by electromagnetic loss;

[0019] Boundary conditions: Similarly, boundary conditions are defined for the temperature field, and Newton's law of cooling is applied at the device boundary to describe the heat loss, that is, to describe the convection, radiation and other forms of heat transfer between the device surface and the environment;

[0020] Heat source terms: Electromagnetic losses of power equipment, such as eddy current losses, iron losses or copper losses, will become heat source terms in the temperature field. The above heat sources are calculated through electromagnetic field simulation results and used as inputs to the temperature field equations.

[0021] Furthermore, the iterative algorithm adopts a multi-grid method and an adaptive step size optimization algorithm, specifically including:

[0022] Based on the multi-grid method, the solution area is divided into several levels of grids, and preliminary solutions are performed on the levels of grids with different resolutions.

[0023] Adaptive step size optimization algorithm is applied on each level of grid to adjust the solution step size to improve solution efficiency and accuracy;

[0024] The solutions of each level of grid are calculated comprehensively to obtain the simulation results, which can obtain high-precision global physical field coupling solution results.

[0025] Furthermore, in the multi-physics field simulation model, the coupling relationship between the electric field and the magnetic field is described as an electromagnetic field coupling model through the Maxwell equations, and the electromagnetic field coupling model is used to describe the electromagnetic behavior of the power equipment under the action of current and voltage; the coupling equation of the electromagnetic field coupling model is constructed based on the Maxwell equations, and the coupling equation is in the following form:

[0026] The numerical solution formula for the electric field equation is: Among them, E represents the electric field intensity, and B represents the magnetic induction intensity;

[0027] The numerical solution formula for the magnetic field equation is: H is the magnetic field intensity, D is the electric displacement vector, and J is the current density;

[0028] Stress field model: Construct a calculation model of the stress field based on the mechanical equilibrium equation: in, Among them, σ is the stress tensor, F is the external force, and ρ is the free charge density;

[0029] Multi-physics coupling model: By introducing coupling terms, the electromagnetic field and the stress field are combined to establish multi-physics coupling equations, and the equations are discretized using the finite element method;

[0030] The coupled equations are iteratively solved using a multi-grid method and an adaptive step size optimization algorithm to ensure computational accuracy and efficiency;

[0031] Boundary conditions: In order to solve the electromagnetic field, boundary conditions are defined: the electric field value or magnetic field value is specified at the boundary of the power equipment;

[0032] Finite element discretization: Since Maxwell's equations are difficult to solve analytically, the finite element method is used to discretize them and introduce numerical solutions. The simulation area is divided into multiple grid units through the finite element method, and the electromagnetic field equations are approximately solved on each unit to obtain the electromagnetic field distribution of the entire area.

[0033] Furthermore, the method also includes a visualization process of the simulation results, which specifically includes the following steps:

[0034] Generate a corresponding three-dimensional scene model based on the physical field data of the simulation results;

[0035] The distribution of temperature, stress, displacement, electric field and magnetic field is displayed in the form of contour lines and field intensity distribution diagrams through visualization software;

[0036] Perform scenario analysis based on visualization results, identify anomalies in physical field distribution, and generate corresponding simulation reports.

[0037] Furthermore, the method also includes real-time monitoring of the equipment in the power business system and comparing it with the simulation results. The specific process is as follows:

[0038] Use data acquisition module to obtain real-time data of power equipment;

[0039] Compare the real-time data with the simulation results to determine whether the equipment is operating normally;

[0040] If the difference between the real-time data and the simulation results exceeds the set threshold, the alarm mechanism will be triggered to perform abnormal diagnosis and secondary simulation analysis.

[0041] A multi-physics field coupling simulation and analysis system for electric power business, the system comprises a data acquisition module, a simulation and analysis module, a system control module, an optimization and adjustment module, an equipment monitoring module and a visualization module; wherein:

[0042] The data acquisition module is used to obtain the physical field data of temperature, stress, displacement, electric field and magnetic field of the power equipment;

[0043] The simulation analysis module is used to receive the physical field data from the data acquisition module, and perform simulation analysis using a multi-physical field coupling model to obtain simulation results, wherein the simulation results include the physical field distribution results and operating status of the power equipment;

[0044] The optimization and adjustment module optimizes and adjusts the equipment layout and operating parameters in the power business based on the simulation results, generates an optimized control strategy, and transmits the control strategy to the system control module to optimize the operation plan of the power business;

[0045] The visualization module is used to display the simulation results in three dimensions and generate visualization reports;

[0046] The equipment monitoring module is used to compare the real-time data obtained during the operation of the power business with the simulation results. If the difference between the actual data and the simulation results exceeds the preset threshold, the alarm mechanism is triggered, and abnormal diagnosis and secondary simulation analysis are performed.

[0047] Furthermore, the multi-physics field coupling model in the simulation analysis module includes an electromagnetic field coupling model and a stress field coupling model, and the model construction process includes:

[0048] Establish Maxwell's equations for electromagnetic fields and mechanical equilibrium equations for stress fields;

[0049] The two are coupled and solved to obtain the physical state of the power equipment under the dual effects of electromagnetic field and stress field;

[0050] The convergence speed of the solution is improved through numerical solution optimization algorithm to ensure the stability and accuracy of the analysis.

[0051] Furthermore, the simulation analysis module in the system also includes a fault prediction unit, which is used to perform fault prediction based on the simulation results and historical data of the equipment. The specific process is as follows:

[0052] Conduct statistical analysis on the historical operating data of power equipment to determine the equipment's failure modes and key influencing factors;

[0053] Combine simulation results to predict the operating status of the equipment and identify potential fault locations and types;

[0054] Maintenance recommendations are generated based on the fault prediction results and transmitted to the system control module for operator reference.

[0055] Furthermore, the system also includes an intelligent control module, which is used to receive the results of the optimization and adjustment module and the fault prediction unit, adjust the working state of the power equipment in real time, and optimize the overall operating efficiency of the system.

[0056] Compared with the prior art, the beneficial effects of the present invention are: through the coupling and construction of different models in the multi-physical field simulation model, the coupled simulation of power equipment in a complex multi-physical field environment can be realized. The simulation model can accurately predict the dynamic changes of multi-physical fields such as electromagnetic, thermal effects and mechanical stress of the equipment, help optimize the design and operation strategies of power equipment, and improve the reliability and safety of the equipment.

[0057] Through multi-physics field coupling simulation, the system can predict in advance the response of equipment under extreme environmental conditions and prevent potential failures caused by electromagnetic, stress, temperature and other factors, thereby greatly improving the safety and reliability of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of a flow chart of a multi-physics field coupling simulation analysis method for electric power business according to the present invention;

[0059] Figure 2 A structural block diagram for the simulation analysis, fault prediction and multi-physics field coupling model in the present invention;

[0060] Figure 3 A structural block diagram of numerical solution of the iterative algorithm of the present invention;

[0061] Figure 4 A structural block diagram of the visualization of the equipment monitoring and simulation results of the present invention;

[0062] Figure 5 This is a structural block diagram of the optimization adjustment and intelligent control process of the simulation results and maintenance suggestions of the present invention. DETAILED DESCRIPTION

[0063] like Figures 1 to 5 As shown, a multi-physics field coupling simulation analysis method and system for power business include.

[0064] It should be further explained that, in the specific implementation process, the method includes the following steps:

[0065] Step 1: Obtain the physical field data of the power business scenario, which includes the temperature field, stress field, displacement field, magnetic field and electric field of the power equipment;

[0066] Step 2: Use the multi-physics field simulation model to perform coupling calculations on the physical field data, construct a physical field coupling equation group according to the operating status and environmental parameters of the power equipment, and numerically solve the physical field coupling equation group to obtain simulation results;

[0067] Step 3: Analyze the simulation results, evaluate the operating reliability and performance of power equipment in a multi-physics environment, and determine potential fault locations and risks;

[0068] Step 4: Based on the simulation results, optimize and adjust the equipment layout and working parameters in the power business, and transmit the adjustment results to the system control module to optimize the operation plan of the power business;

[0069] Step 5: Use the equipment monitoring module to compare the real-time data obtained during the operation of the power business with the simulation results. If the difference between the actual data and the simulation results exceeds the preset threshold, the alarm mechanism is triggered and a secondary simulation analysis is performed.

[0070] The multi-physics field simulation model includes a coupled model of temperature field, stress field, displacement field, magnetic field and electric field. The construction process of the coupled model includes the following steps:

[0071] Step 1: Obtain the initial physical field conditions of the power equipment and its surrounding environment, and construct the corresponding physical field coupling equations;

[0072] Step 2: Discretize the physical field coupling equations through the finite element method to obtain the solution matrix;

[0073] Step 3: numerically solve the physical field coupling equations through an iterative algorithm to obtain the simulation results, that is, the physical field distribution results of the power equipment;

[0074] Step 4: Control the accuracy of the solution by adjusting the solution step size and convergence accuracy to ensure the accuracy of the simulation results;

[0075] The iterative algorithm uses a multi-grid method and an adaptive step size optimization algorithm, which includes:

[0076] Based on the multi-grid method, the solution area is divided into several levels of grids, and preliminary solutions are performed on the levels of grids with different resolutions.

[0077] Adaptive step size optimization algorithm is applied on each level of grid to adjust the solution step size to improve solution efficiency and accuracy;

[0078] The solutions of each level of grid are calculated comprehensively to obtain the simulation results, which can obtain high-precision global physical field coupling solution results.

[0079] In the multi-physics field simulation model, the coupling relationship between the electric field and the magnetic field is described as an electromagnetic field coupling model through the Maxwell equations. The electromagnetic field coupling model is used to describe the electromagnetic behavior of power equipment under the action of current and voltage. The coupling equation of the electromagnetic field coupling model is constructed based on the Maxwell equations. The coupling equation is as follows:

[0080] The numerical solution formula for the electric field equation is: Among them, E represents the electric field intensity, and B represents the magnetic induction intensity;

[0081] The numerical solution formula for the magnetic field equation is: H is the magnetic field intensity, D is the electric displacement vector, and J is the current density;

[0082] Stress field model: Construct a calculation model of the stress field based on the mechanical equilibrium equation: in, Here, σ is the stress tensor, F is the external force, and ρ is the free charge density.

[0083] The method also includes a visualization process of the simulation results, which specifically includes the following steps:

[0084] Generate a corresponding three-dimensional scene model based on the physical field data of the simulation results;

[0085] The distribution of temperature, stress, displacement, electric field and magnetic field is displayed in the form of contour lines and field intensity distribution diagrams through visualization software;

[0086] Perform scenario analysis based on visualization results, identify anomalies in physical field distribution, and generate corresponding simulation reports.

[0087] The method also includes real-time monitoring of the equipment in the power business system and comparing it with the simulation results. The specific process is as follows:

[0088] Use data acquisition module to obtain real-time data of power equipment;

[0089] Compare the real-time data with the simulation results to determine whether the equipment is operating normally;

[0090] If the difference between real-time data and simulation results exceeds the set threshold, an alarm mechanism is triggered to perform abnormal diagnosis and secondary simulation analysis;

[0091] By comparing real-time monitoring data with simulation results, abnormal conditions in equipment operation can be discovered in a timely manner and the alarm mechanism can be triggered. This real-time monitoring and early warning function can predict risks before a failure occurs, avoid major losses caused by equipment failure, and ensure the safe operation of power equipment.

[0092] Among them, the establishment of the temperature field model:

[0093] Power equipment will produce thermal effects under the action of electromagnetic fields, so it is necessary to establish a temperature field model to describe the distribution and conduction of heat;

[0094] The temperature field model is based on the heat conduction equation: Where: T is temperature; α is the thermal diffusivity; q is the heat source term; ρ is the material density; c p is the specific heat capacity of the material, where the heat source term q is caused by electromagnetic loss;

[0095] Boundary conditions: Similarly, boundary conditions are defined for the temperature field, and Newton's law of cooling is applied at the device boundary to describe the heat loss, that is, to describe the convection, radiation and other forms of heat transfer between the device surface and the environment;

[0096] Heat source term: The electromagnetic loss of the power equipment, such as eddy current loss, iron loss or copper loss, will become the heat source term in the temperature field. The above heat source is calculated through the electromagnetic field simulation results and used as the input of the temperature field equation; that is, according to the heat source generated by the electromagnetic loss, the distribution of the internal temperature of the equipment is simulated by the heat conduction equation. The equation is as given above, and the distribution of the internal temperature of the equipment can be obtained.

[0097] Establishment of stress field model:

[0098] Electrical equipment will generate mechanical stress under the action of temperature changes and electromagnetic forces, and this mechanical behavior needs to be described by a stress field model. The stress field model is based on the stress-strain relationship and Newton's second law:

[0099] Stress-strain equation: σ=C:ε; where: σ is the stress tensor; C is the elastic modulus tensor; ε is the strain tensor;

[0100] Equations of motion: Where: u is the displacement vector; f is the body density;

[0101] Heat stress:

[0102] Changes in the temperature field will cause thermal expansion, resulting in additional stress. The thermal stress is calculated by the thermal expansion coefficient: σ T =α T (T-T0); where T0 is the initial temperature;

[0103] The establishment of coupling equations:

[0104] Multi-physics coupling simulation couples the above electromagnetic field, temperature field and stress field models to form a comprehensive simulation model. The coupling equation is established in the following way:

[0105] The coupling between electromagnetic field and temperature field, i.e. the coupling mechanism, is to input the heat conduction equation through electromagnetic loss as the heat source term to form an electromagnetic-thermal coupling model;

[0106] Specifically, the eddy current loss generated by the electromagnetic field can be used as the heat source input of the temperature field to form an electrothermal coupling model;

[0107] The loss density J·E in the electromagnetic field simulation results is introduced into the heat conduction equation of the temperature field to form an electromagnetic-thermal coupling model. The specific coupling relationship is as follows: q = J·E;

[0108] Coupling of temperature field and stress field:

[0109] Changes in temperature field cause thermal expansion, which leads to changes in mechanical stress. Thermal expansion stress is an additional term in the stress field, coupling stress and temperature field. The coupling equation is as follows: σ=C:ε+σ T ;

[0110] Coupling of electromagnetic field and stress field:

[0111] The Lorentz force and electromagnetic force in the electromagnetic field will act on the equipment, causing mechanical deformation and stress. The distribution of these forces can be calculated by the electric and magnetic fields in Maxwell's equations and used as the body force input of the stress field to form an electric coupling relationship: elec = J × B;

[0112] Numerical solution and simulation: After the coupling model is established, the coupling equations are solved by the numerical solution method of the finite element method. The solution process includes the following steps:

[0113] Meshing: Meshing the simulation area of ​​the power equipment and discretizing the continuous area into finite units;

[0114] Discrete equation solving: Discretize the coupled equations using the finite element method and transform the partial differential equations into a system of algebraic equations;

[0115] Iterative solution: The Newton iteration iterative solution method is used to solve the discretized equations and obtain the simulation results of the equipment under the coupled multi-physics field conditions;

[0116] Post-processing and analysis: Post-process the simulation results to generate visualization results of stress distribution, temperature field distribution, and electromagnetic field distribution of power equipment for evaluation and optimization of equipment performance.

[0117] By coupling multi-physical field data such as temperature field, stress field, displacement field, magnetic field and electric field, the system can comprehensively and accurately simulate the operating status of power equipment in a complex environment; this multi-physical field coupling calculation method effectively improves the accuracy of the simulation results and provides a reliable foundation for the operation analysis of power equipment.

[0118] The system uses advanced coupled simulation models, combined with finite element methods and multi-grid algorithms, to efficiently solve the coupled equations of complex physical fields; through adaptive step optimization algorithms and numerical solutions, the system not only ensures the accuracy of the simulation results, but also greatly improves the computing efficiency, and can quickly respond to the multi-physics field simulation needs of power equipment.

[0119] Through the construction process of the above model, the coupled simulation of power equipment in a complex multi-physical field environment can be realized. The simulation model can accurately predict the dynamic changes of the electromagnetic, thermal effects and mechanical stress of the equipment, help optimize the design and operation strategies of the power equipment, and improve the reliability and safety of the equipment.

[0120] A multi-physics field coupling simulation and analysis system for electric power business, the system comprises a data acquisition module, a simulation and analysis module, a system control module, an optimization and adjustment module, an equipment monitoring module and a visualization module; wherein:

[0121] The data acquisition module is used to obtain the physical field data of temperature, stress, displacement, electric field and magnetic field of the power equipment;

[0122] The simulation analysis module is used to receive the physical field data from the data acquisition module, and use the multi-physical field coupling model to perform simulation analysis to obtain simulation results, where the simulation results include the physical field distribution results and operating status of the power equipment; during specific use, the simulation unit and the analysis unit can be integrated in the simulation analysis module to intelligently screen the feasibility results of the power business, avoid meaningless simulation operations, and improve simulation efficiency;

[0123] When the feasibility of implementing the power business reaches the threshold, the system conducts simulation analysis of power equipment operation and maintenance based on the actual situation, taking into account the impact of environmental factors on the power business, so as to reasonably optimize the installation location, model and quantity of the equipment; realize intelligent simulation of power business, ensure the stable operation of power equipment in a variety of complex environments, and reduce ineffective configuration and waste of resources;

[0124] The optimization and adjustment module optimizes and adjusts the equipment layout and operating parameters in the power business based on the simulation results, generates an optimized control strategy, and transmits the control strategy to the system control module to optimize the operation plan of the power business;

[0125] The visualization module is used to display the simulation results in three dimensions and generate visualization reports;

[0126] The equipment monitoring module is used to compare the real-time data obtained during the operation of the power business with the simulation results. If the difference between the actual data and the simulation results exceeds the preset threshold, the alarm mechanism is triggered, and abnormal diagnosis and secondary simulation analysis are performed.

[0127] The system can realize comprehensive multi-physical field coupling simulation analysis of power equipment by integrating the data acquisition module, simulation analysis module, system control module, optimization and adjustment module, equipment monitoring module and visualization module of the power business. This modular design can accurately predict the feasibility of the implementation of the power business and simulate according to the prediction results, which can improve the response efficiency and accuracy of the system and reduce possible failures during equipment operation and maintenance. At the same time, the system control module can centrally control the equipment and centrally manage the simulation results and adjustment results to ensure the stability and consistency of equipment operation and improve the operating efficiency of the power business.

[0128] The multi-physics field coupling model in the simulation analysis module includes the electromagnetic field coupling model and the stress field coupling model. The model construction process includes:

[0129] Establish Maxwell's equations for electromagnetic fields and mechanical equilibrium equations for stress fields;

[0130] The two are coupled and solved to obtain the physical state of the power equipment under the dual effects of electromagnetic field and stress field;

[0131] The convergence speed of the solution is improved through numerical solution optimization algorithm to ensure the stability and accuracy of the analysis.

[0132] Among them, the value-solving optimization algorithm is an algorithm introduced to improve the accuracy of power equipment simulation and control. When used, the simulation-based optimization algorithm can be introduced to achieve model optimization and parameter adjustment; the value-solving optimization algorithm includes genetic algorithm and particle swarm optimization algorithm (PSO):

[0133] The specific composition of the value-solving optimization algorithm is as follows:

[0134] Genetic Algorithm:

[0135] Genetic algorithms optimize the parameters in the simulation model and find the global optimal solution by simulating the biological evolution process: selection, crossover, mutation, etc. The main process of genetic algorithms is:

[0136] Initialization: Generate an initial population of random solutions;

[0137] Fitness function: The fitness function is defined by simulation results to measure the quality of the solution;

[0138] Selection: Select high-quality individuals for reproduction based on fitness values;

[0139] Crossover and mutation: Perform gene crossover and mutation on selected individuals to generate new individuals;

[0140] Termination condition: When the fitness function reaches the preset threshold or the number of iterations reaches the set value, the algorithm stops.

[0141] Particle Swarm Optimization (PSO):

[0142] The particle swarm optimization algorithm finds the optimal solution to the problem by simulating the mutual cooperation of individuals in the group; each particle adjusts its position according to its own experience and the optimal experience in the group; the particle swarm optimization formula is as follows:

[0143] v i (t+1)=wv i (t)+c1r1(p best,i -x i (t))+c2r2(g best -x i (t));

[0144] x i (t+1)=x i (t)+v i (t+1);

[0145] Among them, v i (t) represents the velocity of the i-th particle at time t, x i (t) is the position of the particle, p best,i is the historical optimal position of the particle, g best is the global optimal position of the group, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers; by optimizing the parameters in the simulation model, the optimal solution to the problem is obtained, so as to improve the accuracy of power equipment simulation and control and optimize the physical field distribution of power equipment.

[0146] Through multi-physics field coupling simulation, the system can predict in advance the response of equipment under extreme environmental conditions and prevent potential failures caused by electromagnetic, stress, temperature and other factors, thereby greatly improving the safety and reliability of power equipment.

[0147] The simulation analysis module in the system also includes a fault prediction unit, which is used to predict faults based on simulation results and equipment historical data. The specific process is as follows:

[0148] Conduct statistical analysis on the historical operating data of power equipment to determine the equipment's failure modes and key influencing factors;

[0149] Combine simulation results to predict the operating status of the equipment and identify potential fault locations and types;

[0150] Maintenance recommendations are generated based on the fault prediction results and transmitted to the system control module for operator reference.

[0151] The system also includes an intelligent control module, which is used to receive the results of the optimization and adjustment module and the fault prediction unit, adjust the working state of the power equipment in real time, and optimize the overall operating efficiency of the system;

[0152] Through the intelligent control module, the system controls the working status of power equipment in real time based on simulation analysis and fault prediction results to ensure that the equipment is always in the best working condition; this intelligent automatic control reduces the need for human intervention and improves the operating efficiency and reliability of the system.

[0153] The above technical solutions provide strong support for the operation, management and optimization of power business, and improve the overall safety, reliability and intelligence level of the system.

[0154] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A multi-physics field coupling simulation analysis method for power business, characterized in that: The method comprises the following steps: Step 1: Obtain the physical field data of the power business scenario, which includes the temperature field, stress field, displacement field, magnetic field and electric field of the power equipment; Step 2: Use the multi-physics field simulation model to perform coupling calculations on the physical field data, construct a physical field coupling equation group according to the operating status and environmental parameters of the power equipment, and numerically solve the physical field coupling equation group to obtain simulation results; Step 3: Analyze the simulation results, evaluate the operating reliability and performance of power equipment in a multi-physics environment, and determine potential fault locations and risks; Step 4: Based on the simulation results, optimize and adjust the equipment layout and working parameters in the power business, and transmit the adjustment results to the system control module to optimize the operation plan of the power business; Step 5: Use the equipment monitoring module to compare the real-time data obtained during the operation of the power business with the simulation results. If the difference between the actual data and the simulation results exceeds the preset threshold, the alarm mechanism is triggered and a secondary simulation analysis is performed.

2. A multi-physics field coupling simulation analysis method for electric power business according to claim 1, characterized in that: The multi-physics field simulation model includes a coupled model of temperature field, stress field, displacement field, magnetic field and electric field. The construction process of the coupled model includes the following steps: Step 1: Obtain the initial physical field conditions of the power equipment and its surrounding environment, and construct the corresponding physical field coupling equations; Step 2: Discretize the physical field coupling equations through the finite element method to obtain the solution matrix; Step 3: numerically solve the physical field coupling equations through an iterative algorithm to obtain the simulation results, that is, the physical field distribution results of the power equipment; Step 4: Control the accuracy of the solution by adjusting the solution step size and convergence accuracy.

3. A multi-physics field coupling simulation analysis method for electric power business according to claim 2, characterized in that: The iterative algorithm adopts a multi-grid method and an adaptive step size optimization algorithm, specifically including: Based on the multi-grid method, the solution area is divided into several levels of grids, and preliminary solutions are performed on the levels of grids with different resolutions. Apply adaptive step size optimization algorithm to adjust the solution step size at each level of grid; The solutions of each level of grid are comprehensively calculated to obtain the simulation results.

4. A multi-physics field coupling simulation analysis method for electric power business according to claim 3, characterized in that: In the multi-physics field simulation model, the coupling relationship between the electric field and the magnetic field is described as an electromagnetic field coupling model through the Maxwell equations. The electromagnetic field coupling model is used to describe the electromagnetic behavior of power equipment under the action of current and voltage. The coupling equation of the electromagnetic field coupling model is constructed based on the Maxwell equations. The coupling equation is as follows: The numerical solution formula for the electric field equation is: Among them, E represents the electric field intensity, and B represents the magnetic induction intensity; The numerical solution formula for the magnetic field equation is: H is the magnetic field intensity, D is the electric displacement vector, and J is the current density; Stress field model: Construct a calculation model of the stress field based on the mechanical equilibrium equation: in, Here, σ is the stress tensor, F is the external force, and ρ is the free charge density.

5. A multi-physics field coupling simulation analysis method for electric power business according to claim 4, characterized in that: The method also includes a visualization process of the simulation results, which specifically includes the following steps: Generate a corresponding three-dimensional scene model based on the physical field data of the simulation results; The distribution of temperature, stress, displacement, electric field and magnetic field is displayed in the form of contour lines and field intensity distribution diagrams through visualization software; Perform scenario analysis based on visualization results, identify anomalies in physical field distribution, and generate corresponding simulation reports.

6. A multi-physics field coupling simulation analysis method for electric power business according to claim 5, characterized in that: The method also includes real-time monitoring of the equipment in the power business system and comparing it with the simulation results. The specific process is: Use data acquisition module to obtain real-time data of power equipment; Compare the real-time data with the simulation results to determine whether the equipment is operating normally; If the difference between the real-time data and the simulation results exceeds the set threshold, the alarm mechanism will be triggered to perform abnormal diagnosis and secondary simulation analysis.

7. A multi-physics field coupling simulation analysis system for electric power business, characterized in that: The system includes a data acquisition module, a simulation analysis module, a system control module, an optimization and adjustment module, an equipment monitoring module and a visualization module; among which: The data acquisition module is used to obtain the physical field data of temperature, stress, displacement, electric field and magnetic field of the power equipment; The simulation analysis module is used to receive the physical field data from the data acquisition module, and use the multi-physical field coupling model to perform simulation analysis to obtain simulation results; The optimization and adjustment module optimizes and adjusts the equipment layout and operating parameters in the power business based on the simulation results, generates an optimized control strategy, and transmits the control strategy to the system control module to optimize the operation plan of the power business; The visualization module is used to display the simulation results in three dimensions and generate visualization reports; The equipment monitoring module is used to compare the real-time data obtained during the operation of the power business with the simulation results. If the difference between the actual data and the simulation results exceeds the preset threshold, the alarm mechanism is triggered, and abnormal diagnosis and secondary simulation analysis are performed.

8. A multi-physics field coupling simulation analysis system for electric power business according to claim 7, characterized in that: The multi-physics field coupling model in the simulation analysis module includes an electromagnetic field coupling model and a stress field coupling model, and the model construction process includes: Establish Maxwell's equations for electromagnetic fields and mechanical equilibrium equations for stress fields; The two are coupled and solved to obtain the physical state of the power equipment under the dual effects of electromagnetic field and stress field; The convergence speed of the solution is improved through numerical solution optimization algorithm to ensure the stability and accuracy of the analysis.

9. A multi-physics field coupling simulation analysis system for electric power business according to claim 8, characterized in that: The simulation analysis module in the system also includes a fault prediction unit, which is used to perform fault prediction based on the simulation results and the historical data of the equipment. The specific process is as follows: Conduct statistical analysis on the historical operating data of power equipment to determine the equipment's failure modes and key influencing factors; Combine simulation results to predict the operating status of the equipment and identify potential fault locations and types; Maintenance recommendations are generated based on the fault prediction results and transmitted to the system control module for operator reference.

10. A multi-physics field coupling simulation analysis system for electric power business according to claim 9, characterized in that: The system also includes an intelligent control module, which is used to receive the results of the optimization and adjustment module and the fault prediction unit, adjust the working state of the power equipment in real time, and optimize the overall operating efficiency of the system.

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