Simulation system and simulation method for power distribution automation

Abstract

The invention relates to the technical field of power device simulation, in particular to a simulation system and a simulation method for power distribution automation. The method comprises the following steps: constructing a power distribution device test platform; performing automatic driving operation on the power distribution device to be tested by using the power distribution device test platform, synchronously monitoring a device circuit electromagnetic field, and generating transient excitation electromagnetic field distribution data; acquiring a three-dimensional model of the power distribution device; thermal resistance values among thermal resistance units of the three-dimensional model of the power distribution device are calculated through transient excitation electromagnetic field distribution data, so that a dynamic thermal impedance matrix of the device is constructed; performing internal circulation heat conduction simulation of the device based on the dynamic thermal impedance matrix of the device to generate temperature circulation simulation data of the device; and performing thermal damage value life evaluation according to the device temperature cycle simulation data to obtain power distribution device life evaluation data. According to the invention, through automatic simulation of the temperature response of the power distribution device under the dynamic working condition, the accuracy of power distribution device life evaluation is significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power device simulation, and particularly to a distribution automation simulation system and a simulation method therefor. Background Art

[0002] During the long-term operation of distribution devices, due to the combined action of electromagnetic stress, thermal stress, and mechanical stress, their performance will degenerate or even fail. Among them, temperature is one of the key factors affecting the performance and lifespan of distribution devices. Since distribution devices generate a large amount of heat under working conditions, if heat dissipation is not timely or the temperature distribution is uneven, it will lead to too high a hot spot temperature inside the device, accelerating the aging of insulating materials, causing the accumulation of thermal stress, and ultimately resulting in device failure, seriously threatening the safe and stable operation of the power grid. Due to factors such as the flow of current, the action of the electric field, and the loss of materials, complex and non-uniformly distributed heat sources will be generated inside them. Distribution devices often face transient operating conditions such as load fluctuations and short-circuit impacts during actual operation. These dynamic changes will cause complex changes in the internal temperature distribution of the device, and the intensity and distribution of these heat sources will change dynamically with the operating state of the device (such as load current, ambient temperature, etc.) and time. However, traditional distribution automation simulation methods lack the analysis of the temperature response characteristics of devices under dynamic operating conditions, cannot effectively capture these dynamic characteristics, cannot accurately reflect the temperature gradient and thermal stress distribution inside the device, resulting in inaccurate life assessment results. Summary of the Invention

[0003] Based on this, the present invention provides a distribution automation simulation system and a simulation method therefor to solve at least one of the above technical problems.

[0004] To achieve the above object, a distribution automation simulation method includes the following steps:

[0005] Step S1: Construct a distribution device test platform; use the distribution device test platform to perform automated driving operations on the distribution device to be tested, and use an electromagnetic sensor to synchronously monitor the electromagnetic field of the device circuit to generate transient excitation electromagnetic field distribution data;

[0006] Step S2: Obtain a three-dimensional model of the distribution device; perform device heat source distribution analysis on the three-dimensional model of the distribution device through the transient excitation electromagnetic field distribution data to generate device structure heat source distribution data; divide thermal resistance units according to the device structure heat source distribution data, and then calculate the thermal resistance values between the thermal resistance units to obtain the static thermal resistance values between the units and the equivalent thermal resistance values between the units respectively; construct a device dynamic thermal impedance matrix based on the static thermal resistance values between the units and the equivalent thermal resistance values between the units;

[0007] Step S3: Establish a multi-scale thermal simulation model based on the device dynamic thermal impedance matrix and the three-dimensional model of the power distribution device; perform internal cyclic heat conduction simulation of the device using the multi-scale thermal simulation model to generate device temperature cycle simulation data;

[0008] Step S4: Conduct a thermal damage value life assessment based on the device temperature cycle simulation data to obtain the power distribution device life assessment data.

[0009] The present invention realizes the real-time and accurate acquisition of electromagnetic field data of distribution electrical components under actual operating conditions by constructing a dedicated test platform for distribution electrical components and synchronously monitoring with electromagnetic sensors. It avoids the disconnection between the simulation results and the actual situation caused by overly idealized assumption conditions. Through the analysis of the heat source distribution of the three-dimensional model of the distribution electrical component based on the transient excitation electromagnetic field distribution data, the heat source position and intensity inside the component can be accurately identified. The complexity of internal heat conduction of the component is considered. In particular, the introduction of the equivalent thermal resistance value reflects the influence of transient working conditions such as load fluctuation and short-circuit impact during actual operation on the thermal resistance, enabling the thermal impedance matrix to dynamically reflect the change of the thermal characteristics of the component. This is closer to the actual operating situation compared with the traditional method that usually uses a fixed thermal resistance value. The constructed dynamic thermal impedance matrix of the component provides key parameters for subsequent thermal simulation, significantly improving the accuracy of the simulation. A multi-scale thermal simulation model is established to achieve the accurate simulation of the temperature field and thermal stress field inside the distribution electrical component. Based on the dynamic thermal impedance matrix of the component and the three-dimensional model, this model can comprehensively consider various heat transfer mechanisms such as internal heat conduction, convection, and radiation of the component, and can simulate the temperature response of the component under various complex working conditions, and can more accurately predict the temperature distribution and thermal stress distribution inside the component. In particular, it can capture the areas with hot spot temperature and thermal stress concentration. The internal cyclic heat conduction simulation of the component using the multi-scale thermal simulation model can simulate the temperature cyclic change during the long-term operation of the distribution electrical component. By analyzing the device temperature cycle simulation data and combining with the thermal damage assessment model, the life assessment data of the distribution electrical component can be obtained. Since this method fully considers the actual operating state and dynamic characteristics of the component, the obtained life assessment results are more accurate and reliable. This has significant advantages compared with the traditional method that often conducts life assessment based on simplified models and empirical formulas. This helps to more accurately predict the remaining life of the distribution electrical component, provides a scientific basis for the operation and maintenance of the power grid and equipment replacement, thus avoiding safety accidents caused by premature failure of the component or resource waste caused by overly conservative maintenance strategies, and is of great significance for ensuring the safe and stable operation of the power grid. Therefore, a distribution automation simulation method of the present invention realizes the accurate simulation of the internal cyclic heat conduction process of the component and accurately predicts the remaining life of the distribution electrical component by constructing a dedicated test platform for distribution electrical components, conducting automated testing on the distribution electrical components, considering the dynamic nature of the internal heat source distribution of the electrical component, constructing a dynamic thermal impedance matrix of the component, and establishing a multi-scale thermal simulation model based on the dynamic thermal impedance matrix and the three-dimensional model of the distribution electrical component.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Build a test platform for distribution electrical components including the distribution electrical component to be tested, a drive circuit, a DC power supply, a load resistor, and an inductor;

[0012] Step S12: Set the gate drive signal through a preset test period. The test period is 0.1 ms to 1 ms, and the pulse duty cycle is 10% to 40%, to obtain a gate drive signal sequence;

[0013] Step S13: Apply an excitation current using a distributor device test platform based on the gate drive signal sequence. The current rising rate is 10 A / μs to 50 A / μs to drive the distributor device under test to operate;

[0014] Step S14: Synchronously monitor the circuit electromagnetic field during the device operation using an electromagnetic sensor based on the gate drive signal sequence to generate transient excitation electromagnetic field distribution data.

[0015] The present invention ensures the standardization and repeatability of the test environment by clearly defining the composition of the test platform, including the distributor device under test, drive circuit, DC power supply, load resistor or inductor. By presetting the test period and pulse duty cycle and precisely controlling the gate drive signal, precise regulation of the working state of the distributor device under test is achieved. The setting ranges of the test period and duty cycle (0.1 ms to 1 ms and 10% to 40%) ensure that the excitation signal can cover the typical working frequency range of the distributor device under test and simulate the dynamic process in actual operation. By controlling the rising rate of the excitation current (10 A / μs to 50 A / μs), the switching characteristics of the distributor device under test under different working conditions are simulated. The control range of the current rising rate covers the normal operation and potential fault states of the distributor device under test, enabling the collected data to reflect the response of the device under different stress levels. Synchronously monitoring the circuit electromagnetic field during the device operation using an electromagnetic sensor realizes the real-time and accurate acquisition of transient electromagnetic field distribution data, ensures the precise correspondence between the electromagnetic field data and the working state of the distributor device under test, and avoids time deviation.

[0016] Preferably, the analysis of the device heat source distribution based on the three-dimensional model of the distributor device in step S2 includes:

[0017] Conduct a circuit topology structure analysis on the three-dimensional model of the distributor device to obtain device circuit topology structure data;

[0018] Based on the device circuit topology structure data, conduct circuit region grid cell division to generate a three-dimensional model of the device with grid division;

[0019] Process the internal current density distribution of the device with grid division three-dimensional model through the transient excitation electromagnetic field distribution data to obtain device current density distribution data;

[0020] Calculate the Joule heat in each grid cell according to the device current density distribution data to obtain the Joule heat power density;

[0021] The device heat source distribution analysis is carried out on the three-dimensional model of the meshed device through the Joule heat power density, and the device structure heat source distribution data is generated.

[0022] In the present invention, the circuit topology structure analysis is carried out on the three-dimensional model of the distribution device, which ensures that the mesh division can accurately reflect the internal circuit structure of the device and avoids subsequent calculation errors caused by model simplification or distortion. Based on the device circuit topology structure data, the mesh unit division of the circuit region is carried out, realizing the discretization of the internal calculation region of the device. The device internal current density distribution processing is carried out on the three-dimensional model of the meshed device through the transient excitation electromagnetic field distribution data. By combining the previously collected electromagnetic field data with the geometric model of the device, the current density in each mesh unit is calculated, realizing the conversion from the macroscopic electromagnetic field to the microscopic current distribution. According to the device current density distribution data, the Joule heat in each mesh unit is calculated to obtain the Joule heat power density, converting the current density into the heat power density and completing the conversion from the electrical quantity to the thermal quantity. The calculated Joule heat power density is used as the heat source and loaded onto the three-dimensional model to form a complete heat source distribution model. This heat source distribution data not only contains the intensity information of the heat source but also the spatial distribution information of the heat source, and can more realistically reflect the heat generation situation inside the device.

[0023] Preferably, the step of dividing the thermal resistance units according to the device structure heat source distribution data and then calculating the thermal resistance values between the thermal resistance units includes:

[0024] The isothermal line extraction is carried out on the three-dimensional model of the distribution device through the device structure heat source distribution data to obtain the isothermal line of the heat source power density;

[0025] The thermal resistance region is divided with the isothermal line of the heat source power density as the boundary to obtain the initial thermal resistance unit division region;

[0026] The closed-loop geometric regular shape processing is carried out on the initial thermal resistance unit division region to generate the device thermal resistance unit data;

[0027] According to the device thermal resistance unit data, the thermal resistance values between adjacent thermal resistance units are calculated to generate the static thermal resistance values between units;

[0028] According to the static thermal resistance values between units, the equivalent thermal resistance of non-adjacent thermal resistance units is calculated to obtain the equivalent thermal resistance values between units.

[0029] The present invention extracts the isothermal lines of the heat source power density, which reflect the gradient change of the heat source distribution inside the device. By dividing with the isothermal lines as the boundary, it can ensure that the heat source distribution inside each thermal resistance unit is relatively uniform. Dividing the thermal resistance region with the isothermal lines of the heat source power density realizes the close combination of the thermal resistance unit and the heat source distribution. This division method ensures that each thermal resistance unit corresponds to a relatively independent heat source region, making the thermal characteristics of the thermal resistance unit more representative and avoiding calculation errors caused by uneven heat source distribution. Performing closed-loop geometric regular shape processing on the initial divided region of the thermal resistance unit improves the regularity and calculation efficiency of the thermal resistance unit. Processing the irregular initial region into a regular shape simplifies the geometric description of the thermal resistance unit and reduces the complexity of subsequent thermal resistance calculations. At the same time, the thermal resistance unit with a regular shape is also easier to perform mesh division and numerical calculation. Calculating the thermal resistance value between adjacent thermal resistance units according to the device thermal resistance unit data completes the basic construction of the thermal resistance network. Calculating the equivalent thermal resistance of non-adjacent thermal resistance units according to the static thermal resistance value between units takes into account the comprehensive effect of heat transfer through multiple paths, enabling the thermal impedance network to more comprehensively reflect the internal heat conduction characteristics of the device.

[0030] Preferably, the calculating the thermal resistance value between adjacent thermal resistance units according to the device thermal resistance unit data includes:

[0031] Extracting the geometric data of adjacent thermal resistance units according to the device thermal resistance unit data;

[0032] Conducting a connection line in the centroid direction of the thermal resistance unit based on the geometric data of adjacent thermal resistance units to obtain the unit centroid connection line data;

[0033] Calculating the centroid distance between adjacent thermal resistance units according to the unit centroid connection line data to obtain the unit centroid distance data;

[0034] Conducting an analysis of the contact surface type according to the geometric data of adjacent thermal resistance units. When the judgment result of the contact surface type is full contact, the contact surface area is the heat conduction area. When the judgment result of the contact surface type is partial contact, calculate the effective contact area as the heat conduction area;

[0035] Performing thermal conductivity matching of the unit contact material on the geometric data of adjacent thermal resistance units through a preset material thermal conductivity database to generate the unit contact material thermal conductivity;

[0036] Performing thermal resistance calculation based on the unit centroid distance data, the heat conduction area, and the unit contact material thermal conductivity to generate the static thermal resistance value between units.

[0037] In the present invention, by extracting the geometric data of adjacent thermal resistance units, such geometric data as the size and shape of the units, etc., serve as the basis for calculating the heat conduction area and the centroid distance. Based on the geometric data of adjacent thermal resistance units, a connection line in the centroid direction of the thermal resistance units is determined, thereby determining the main direction of heat conduction. The centroid connection line represents the shortest path for heat transfer between adjacent units and can more accurately reflect the distance of heat conduction. According to the centroid connection line data of the units, the centroid distance between adjacent thermal resistance units is calculated, quantifying the length of the heat conduction path. According to the geometric data of adjacent thermal resistance units, the contact surface type analysis is carried out, and the heat conduction area is determined according to the contact type, taking into account the influence of the actual contact situation on heat conduction. The full contact and partial contact are distinguished, and the effective contact area is calculated for the partial contact situation, avoiding the calculation error caused by the idealized assumption (assuming that the contact surface is perfectly fitted). This processing method is closer to the actual situation and improves the accuracy of thermal resistance calculation. By matching the thermal conductivity of the unit contact materials for the geometric data of adjacent thermal resistance units through a preset material thermal conductivity database, the influence of material properties on heat conduction is considered. Using the database for material thermal conductivity matching avoids the cumbersome manual search and input, improving the calculation efficiency. By comprehensively considering the geometric parameters, contact situation and material properties, the calculated thermal resistance value has more physical significance and practical value.

[0038] Preferably, the calculation of the equivalent thermal resistance of non-adjacent thermal resistance units according to the static thermal resistance value between units includes:

[0039] Constructing the connection relationship of thermal resistance units according to the device thermal resistance unit data;

[0040] Analyzing the shortest heat flow path between non-adjacent thermal resistance units based on the connection relationship of thermal resistance units to obtain the shortest heat flow path data;

[0041] Performing series thermal resistance calculation between non-adjacent units on the shortest heat flow path data through the static thermal resistance value between units to generate the series thermal resistance value between units;

[0042] Calculating the total thermal resistance of the heat flow path according to the series thermal resistance value between units, and then performing thermal conductance value processing to generate the thermal conductance value of the heat flow path;

[0043] Performing parallel equivalent thermal resistance processing according to the series thermal resistance value between units to generate the initial value of the equivalent thermal resistance between units;

[0044] Taking the thermal conductance value of the heat flow path as the heat flow distribution coefficient and performing weighted average on the initial value of the equivalent thermal resistance between units to obtain the equivalent thermal resistance value between units.

[0045] The present invention constructs the connection relationship of thermal resistance units according to the device thermal resistance unit data, and this connection relationship clarifies the topological structure between the thermal resistance units. Based on the connection relationship of thermal resistance units, the shortest heat flow path between non-adjacent thermal resistance units is analyzed, and the main path for heat transfer between non-adjacent units is determined. Searching for the shortest path reflects the physical law that heat tends to transfer along the path with the smallest thermal resistance. Through the static thermal resistance values between units, the series thermal resistance calculation between non-adjacent units is carried out on the shortest heat flow path data, and the static thermal resistance values on the shortest path are accumulated to obtain the thermal resistance between non-adjacent units along this path. According to the series thermal resistance value between units, the total thermal resistance of the heat flow path is calculated, which represents the heat conduction ability of the heat flow path. According to the series thermal resistance value between units, the parallel equivalent thermal resistance treatment is carried out. The parallel equivalent treatment reflects the characteristic that heat can be transferred through multiple paths, and the equivalent thermal resistance between non-adjacent units is initially estimated. Taking the heat conductance value of the heat flow path as the heat flow distribution coefficient, the weighted average of the initial value of the equivalent thermal resistance between units is carried out, which reflects the contribution of different paths to the heat flow distribution, making the finally obtained equivalent thermal resistance value more accurately reflect the comprehensive heat conduction characteristics between non-adjacent units.

[0046] Preferably, constructing the device dynamic thermal impedance matrix based on the static thermal resistance values between units and the equivalent thermal resistance values between units in step S2 includes:

[0047] Extract the volume and material properties of each thermal resistance unit according to the device thermal resistance unit data to obtain the unit volume-material data;

[0048] Calculate the heat capacity value of each thermal resistance unit according to the unit volume-material data to obtain the unit heat capacity value data;

[0049] Associate each thermal resistance unit with its corresponding unit heat capacity value data to obtain the heat capacity associated unit data;

[0050] Construct a thermal impedance unit network based on the heat capacity associated unit data, the static thermal resistance values between units, and the equivalent thermal resistance values between units;

[0051] Construct a node admittance matrix according to the thermal impedance unit network;

[0052] Perform Laplace transform on the node admittance matrix, and then perform matrix inversion to generate the device dynamic thermal impedance matrix.

[0053] By extracting the volume and material properties of each thermal resistance unit, the present invention calculates the unit heat capacity value data, which reflects the ability of an object to store or release heat when its temperature changes. Associating the heat capacity with the thermal resistance unit endows each unit with thermal inertia, thereby enabling the simulation of the temperature response of the device under transient conditions. Associating each thermal resistance unit with its corresponding unit heat capacity value data ensures that each thermal resistance unit contains two key properties, namely thermal resistance and heat capacity, enabling the thermal impedance unit network to reflect both the steady-state and transient thermal characteristics of the device. Based on the heat capacity-associated unit data, the static thermal resistance values between units, and the equivalent thermal resistance values between units, a thermal impedance unit network is constructed, integrating thermal resistance and heat capacity into a unified network model. This network model not only considers the conduction of heat between units (through thermal resistance) but also the storage of heat within units (through heat capacity), thus enabling a more comprehensive description of the thermal behavior of the device. According to the thermal impedance unit network, a nodal admittance matrix is constructed, transforming the thermal impedance network into a mathematical model. The nodal admittance matrix describes the thermal conductance and heat capacity relationships between the nodes in the network. Performing a Laplace transform on the nodal admittance matrix achieves the conversion from the time domain to the frequency domain, directly reflecting the thermal response characteristics of the device at different frequencies, especially for predicting the temperature response of the device under complex operating conditions.

[0054] Preferably, step S3 includes the following steps:

[0055] Step S31: Perform a state-space transformation based on the dynamic thermal impedance matrix of the device to obtain the internal heat conduction state data of the device;

[0056] Step S32: Perform finite element volume processing on the three-dimensional model of the power distribution device with a mesh density of 0.5 mm to 2 mm, and then perform simulation mesh node mapping according to the internal heat conduction state data of the device to obtain a multi-scale thermal simulation model;

[0057] Step S33: Obtain the input boundary conditions of the device operating conditions;

[0058] Step S34: Transmit the boundary conditions of the device operating conditions to the multi-scale thermal simulation model for internal cyclic heat conduction simulation of the device. The simulation time step is 0.1 ms to 1 s, and the total simulation duration is 5 s to 300 s to generate device temperature cyclic simulation data.

[0059] The present invention performs state - space conversion based on the dynamic thermal impedance matrix of the device. The state - space conversion transforms the thermal impedance matrix into a state - space model that is more suitable for dynamic simulation analysis, enabling the simulation model to more effectively handle time - varying inputs and transient responses. Finite - element volume processing is carried out according to the three - dimensional model of the distribution device, and simulation grid node mapping is performed, realizing refined modeling of the device's geometric structure and spatial mapping of the thermal impedance network. The finite - element processing discretizes the continuous physical model, enabling complex geometric shapes and heat - conduction processes to be solved through numerical calculations. A grid density of 0.5 mm to 2 mm is set, taking into account both calculation accuracy and efficiency. Mapping the heat - conduction state data onto the simulation grid nodes realizes the integration of the thermal impedance network and the finite - element model, enabling the multi - scale model to simultaneously consider the macroscopic thermal impedance characteristics and microscopic geometric details. Obtain the input boundary conditions of the device operating conditions, such as ambient temperature, convective heat - transfer coefficient, load current, etc. Transmit the device operating condition boundary conditions to the multi - scale thermal simulation model for internal - cycle heat - conduction simulation of the device, realizing the simulation of the dynamic temperature change of the device under actual operating conditions. The setting of a simulation time step of 0.1 ms to 1 s and a total simulation duration of 5 s to 300 s enables the simulation model to capture the temperature changes of the device at different time scales, reflecting both rapid transient responses and long - term steady - state trends.

[0060] Preferably, step S4 includes the following steps:

[0061] Step S41: Mark the key positions of the distribution device according to the three - dimensional model of the distribution device to generate the key positions of the distribution device;

[0062] Step S42: Process the key - position temperature cycle curve of the key positions of the distribution device through the device temperature cycle simulation data to generate key - position temperature cycle data;

[0063] Step S43: Perform cycle - counting processing according to the key - position temperature cycle data to generate the temperature cycle mean value;

[0064] Step S44: Calculate the thermal expansion stress according to the temperature cycle mean value to generate thermal expansion stress distribution data;

[0065] Step S45: Extract the maximum stress value of the key area of the device based on the thermal expansion stress distribution data to generate key - area maximum stress data;

[0066] Step S46: Calculate the damage caused by each stress cycle according to the key - area maximum stress data to obtain the single - cycle thermal damage value;

[0067] Step S47: Perform linear cumulative damage based on the single-cycle thermal damage value, and then conduct weighted life assessment for critical positions to obtain the life assessment data of the distribution device components.

[0068] In the present invention, by marking critical positions on the 3D model of the distribution device components, the key areas for life assessment are determined. Critical positions are usually the areas with the highest temperature and the greatest stress inside the device, and are also the areas most prone to failure. Marking critical positions enables subsequent analysis to focus on these key areas, improving the pertinence and efficiency of life assessment. Through the device temperature cycle simulation data, the critical position temperature cycle curve processing is carried out for the critical positions of the distribution device components. These temperature cycle data directly reflect the temperature fluctuations of the critical positions under actual working conditions. Through the cycle counting process based on the critical position temperature cycle data, key parameters such as the amplitude and period of the temperature cycle can be identified. Calculate the thermal expansion stress based on the temperature cycle mean value, and convert the temperature change into stress distribution. Thermal expansion stress is one of the main causes of device fatigue failure, and accurately calculating the thermal expansion stress is the key to life assessment. Based on the thermal expansion stress distribution data, extract the maximum stress value of the critical area of the device to determine the location and degree of stress concentration. The maximum stress value is a key parameter for evaluating material fatigue damage, and extract the maximum stress value. Calculate the damage caused by each stress cycle based on the maximum stress data of the critical area to quantify the damage degree of each temperature cycle to the device. Perform linear cumulative damage based on the single-cycle thermal damage value, and then conduct weighted life assessment for critical positions. The linear cumulative damage assumes that the damage caused by each cycle is independent and can be accumulated. The weighted critical position takes into account the influence of different critical positions on the overall life, making the life assessment result more representative.

[0069] Preferably, the present invention also provides a distribution automation simulation system for executing the distribution automation simulation method as described above. The distribution automation simulation system includes:

[0070] An automated test module for constructing a test platform for distribution device components; using the test platform for distribution device components to perform automated driving operations on the distribution device components to be tested, and synchronously monitoring the electromagnetic field of the device circuit using an electromagnetic sensor to generate transient excitation electromagnetic field distribution data;

[0071] A device impedance analysis module for obtaining the 3D model of the distribution device components; performing device heat source distribution analysis on the 3D model of the distribution device components through the transient excitation electromagnetic field distribution data to generate device structure heat source distribution data; dividing the thermal resistance units according to the device structure heat source distribution data, and then calculating the thermal resistance values between the thermal resistance units to obtain the static thermal resistance value between units and the equivalent thermal resistance value between units respectively; constructing a device dynamic thermal impedance matrix based on the static thermal resistance value between units and the equivalent thermal resistance value between units;

[0072] A heat conduction simulation module, which is used to establish a multi-scale heat simulation model based on the device dynamic thermal impedance matrix and the three-dimensional model of the distribution device; use the multi-scale heat simulation model to perform internal cyclic heat conduction simulation of the device, and generate device temperature cycle simulation data;

[0073] A thermal damage life assessment module, which is used to perform thermal damage value life assessment according to the device temperature cycle simulation data to obtain the distribution device life assessment data. Description of the Drawings

[0074] Figure 1 It is a schematic flow chart of the steps of a method for power distribution automation simulation according to the present invention;

[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, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Specific Embodiments

[0078] The technical method of the present invention will be clearly and completely described below with reference to the 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 of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the protection scope of the present invention.

[0079] In addition, the 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 their repeated description 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", "second", etc. may be used herein 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 herein 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 , the present invention provides a method for distribution automation simulation, including the following steps:

[0082] Step S1: Build a distribution device test platform; use the distribution device test platform to perform automated driving operations on the distribution device to be tested, and use an electromagnetic sensor to synchronously monitor the electromagnetic field of the device circuit to generate transient excitation electromagnetic field distribution data;

[0083] Step S2: Obtain a three-dimensional model of the distribution device; perform device heat source distribution analysis on the three-dimensional model of the distribution device through the transient excitation electromagnetic field distribution data to generate device structure heat source distribution data; divide the thermal resistance units according to the device structure heat source distribution data, and then calculate the thermal resistance values between the thermal resistance units to obtain the static thermal resistance value between the units and the equivalent thermal resistance value between the units respectively; construct a device dynamic thermal impedance matrix based on the static thermal resistance value between the units and the equivalent thermal resistance value between the units;

[0084] Step S3: Establish a multi-scale thermal simulation model based on the device dynamic thermal impedance matrix and the three-dimensional model of the distribution device; use the multi-scale thermal simulation model to perform device internal circulating heat conduction simulation to generate device temperature cycle simulation data;

[0085] Step S4: Perform thermal damage value life assessment according to the device temperature cycle simulation data to obtain the distribution device life assessment data.

[0086] In the embodiment of the present invention, the method for distribution automation simulation includes the following steps:

[0087] Step S1: Build a distribution device test platform; use the distribution device test platform to perform automated driving operations on the distribution device to be tested, and use an electromagnetic sensor to synchronously monitor the electromagnetic field of the device circuit to generate transient excitation electromagnetic field distribution data;

[0088] In an embodiment of the present invention, an automated test platform for distribution device equipment (including but not limited to hybrid switch equipment) is constructed. The core of this platform is a customized PCB (printed circuit board), on which there are slots for installing the device under test, a drive circuit (using a dedicated gate drive chip, such as IXDN614), and interfaces for connecting a DC power supply (such as Keysight N6705C) and a load (power resistor or wound inductor). An arbitrary waveform generator (such as Tektronix AFG31000) is used to generate a periodic square wave signal as the gate drive signal, with a period set to 0.5 ms, a duty cycle of 20%, a high level of +15 V, and a low level of -5 V, and the waveform is monitored through an oscilloscope (such as Rigol DS7000). The DC power supply is started, and at the same time, the waveform generator output is triggered to drive the device under test to switch between the on and off states. By adjusting the voltage rise time of the DC power supply and the current-limiting resistor of the gate drive circuit, the current rise rate is controlled to be between 10 A / μs and 50 A / μs, and a current probe (such as Tektronix TCP0030A) is used for monitoring. A three-dimensional magnetic field sensor array (composed of multiple Honeywell MLX90393 sensors) is arranged near the device under test, and the sensor outputs are connected to a multi-channel data acquisition card (such as National Instruments PXIe-6363). The clock of the data acquisition card is synchronized with the waveform generator. During the operation of the device, the data acquisition card continuously acquires the output signals of each sensor at a high sampling rate (such as 1 MHz), generating transient excitation electromagnetic field distribution data containing the magnetic field intensity values in three directions measured by each sensor at each sampling moment and the corresponding timestamps.

[0089] Step S2: Obtain a three-dimensional model of the distribution device; perform device heat source distribution analysis on the three-dimensional model of the distribution device through the transient excitation electromagnetic field distribution data to generate device structure heat source distribution data; perform thermal resistance unit division according to the device structure heat source distribution data, and then calculate the thermal resistance values between the thermal resistance units to obtain the static thermal resistance values between the units and the equivalent thermal resistance values between the units respectively; construct a device dynamic thermal impedance matrix based on the static thermal resistance values between the units and the equivalent thermal resistance values between the units;

[0090] In the embodiments of the present invention, a 3D CAD model of a hybrid switch device is obtained. The model source can be a STEP or IGES format file provided by the device manufacturer, or obtained by three-dimensional scanning of the actual device (using devices such as FARO ScanArm) and reconstruction using reverse engineering software (such as Geomagic Design X). For each conductor component, determine its connection relationship in the circuit. For example, clarify which section of the busbar the drain of the solid-state switch module is connected to, which section of the busbar the source is connected to, and which output terminal of which drive circuit the gate is connected to. Record the material properties (selected from the material library, such as copper, aluminum, silicon, etc.) and geometric parameters (such as length, width, thickness, cross-sectional area, etc., which can be directly measured by CAD software) of each conductor component. Import the CAD model into finite element analysis software (such as ANSYS Maxwell). In the software, set the material properties and circuit connection relationships of each component according to the device circuit topology data (such as setting electrical connections, defining voltage sources, etc.). Import the transient excitation electromagnetic field distribution data (including the magnetic field intensity values and timestamps of each sensor at each sampling moment) into the software. Use the coordinate transformation matrix (determined by measuring the relative positions of the sensor array and the 3D model) to transform the magnetic field data from the sensor coordinate system to the model coordinate system. Use the inverse distance weighted interpolation method (or other spatial interpolation methods) to interpolate the discrete magnetic field data onto the grid nodes of the 3D model. Set up the transient electromagnetic field analysis to solve Maxwell's equations. The boundary condition is the interpolated magnetic field data. The solver will calculate the current density distribution within each grid cell. Output the current density vector (including the X, Y, and Z components) data for each grid cell at each moment. Calculate the Joule heat power density based on the current density distribution data obtained in the previous step. For each grid cell, at each moment, calculate according to Joule's law P = J^2 × ρ, where P is the Joule heat power density, J is the magnitude of the current density (obtained by taking the square root of the sum of the squares of the three components of the current density vector), and ρ is the material resistivity (obtained from the material property database and considering the temperature effect, by looking up tables or using formulas). Set the boundary conditions for the thermal analysis (convection heat transfer coefficient, ambient temperature, etc., which can be estimated by experimental measurements or empirical formulas). Conduct a transient thermal analysis, and the solver will calculate the temperature of each grid cell at each moment according to the heat conduction equation. Output the temperature data for each grid cell at each moment, as well as the temperature distribution contour map of the entire device, to form the device structure heat source distribution data. Set the contour levels and values (such as 10 levels, 100 W / m 3 、200 W / m 3etc.). Export the isothermal lines in vector graphic format (such as SVG). In CAD software (such as AutoCAD), divide the area with the isothermal lines as the boundary and define the initial thermal resistance units. Perform geometric regularization on the initial thermal resistance units (simplify them into cuboids or cylinders), and record the shape parameters, materials, identifiers, and adjacent relationships of each thermal resistance unit. Calculate the thermal resistance values between adjacent units according to the shapes and materials of the thermal resistance units. Cuboid unit: R = L / (k×A), where L is the centroid distance (measured by CAD software), k is the thermal conductivity of the material (obtained from the material library), and A is the heat conduction area (the contact surface area when in full contact, and the overlapping area measured by CAD software when in partial contact). Cylindrical units use corresponding formulas according to the heat flow direction. Construct a thermal resistance network (the nodes are thermal resistance units and the edges are thermal resistance values). Use circuit analysis methods (such as nodal voltage method or loop current method) to calculate the equivalent thermal resistance between non-adjacent units. Calculate the heat capacity C = ρ×V×c of each unit using the volume of each thermal resistance unit (measured by CAD software) and material properties (density and specific heat capacity, obtained from the material library). Connect the heat capacity in parallel with the thermal resistance unit to construct a thermal impedance network. According to the thermal impedance network, construct a nodal admittance matrix. The diagonal elements are the sum of the conductances of all the thermal resistances connected to this node plus jωC, and the non-diagonal elements are the negative values of the branch conductances connecting two nodes. Perform Laplace transform on the nodal admittance matrix (replace jω with s), and then perform matrix inversion (using software such as MATLAB) to obtain the device dynamic thermal impedance matrix.

[0091] Step S3: Establish a multi-scale thermal simulation model based on the device dynamic thermal impedance matrix and the three-dimensional model of the power distribution device; use the multi-scale thermal simulation model to perform internal cyclic heat conduction simulation of the device and generate device temperature cycle simulation data;

[0092] In the embodiment of the present invention, use control theory methods to convert it into a state space model, use finite element analysis software (such as ANSYS Workbench) for mesh generation, set the global mesh size to 0.5mm - 2mm, and perform local refinement in key areas. Write a script (such as a Python script) to perform spatial mapping between the state variables (temperatures of thermal resistance units) in the state space model and the nodes of the finite element model to form a multi-scale thermal simulation model. Obtain the boundary conditions of the device operating conditions, including the power losses, ambient temperatures, convective heat transfer coefficients, radiative heat transfer coefficients, and cooling medium parameters (if any) of each thermal resistance unit changing with time. Set transient thermal analysis in the finite element analysis software (such as ANSYS Thermal), apply the power losses as volume heat sources, and the ambient temperature, convective and radiative heat transfer coefficients as boundary conditions to the model. Set the simulation time step to 0.1ms - 1s, and the total simulation duration to 5s - 300s. Start the simulation, calculate the temperature of each node at each time step, and generate device temperature cycle simulation data.

[0093] Step S4: Perform a thermal damage value life assessment based on the device temperature cycle simulation data to obtain the life assessment data of the distribution device.

[0094] In the embodiment of the present invention, for example, mark the key positions on the three-dimensional CAD model (such as in SolidWorks) of the hybrid switchgear, including the solder joints of the solid-state switch module, the busbar connection points, the weak insulation points, etc., and record the marked positions and identifiers. Extract the temperature-time curve of each key position from the device temperature cycle simulation data. Use the rainflow counting method to perform cycle counting on the temperature-time curve, identify all temperature cycles, count the number of cycles in different temperature ranges, and calculate the average temperature cycle. According to the material mechanics formula or finite element analysis software (such as ANSYS Mechanical), calculate the thermal expansion stress of each key position. Extract the maximum stress value of each key position from the stress distribution data. According to the S-N curve of the material and the Miner linear damage accumulation rule (D = 1 / Nf), calculate the damage caused by each stress cycle. Accumulate the damage caused by all cycles to obtain the total damage of each key position. According to the device design and reliability requirements, assign a weight factor to each key position. Multiply the total damage of each key position by its weight factor, and then sum the weighted damage of all key positions to obtain the total weighted damage. Take the reciprocal of the total weighted damage as the expected life of the device, or compare it with a preset threshold to output the life assessment data of the distribution device.

[0095] Preferably, step S1 includes the following steps:

[0096] Step S11: Build a distribution device test platform including the distribution device to be tested, a drive circuit, a DC power supply, a load resistor, and an inductor;

[0097] Step S12: Set the gate drive signal through a preset test period. The test period is 0.1 ms to 1 ms, and the pulse duty cycle is 10% to 40% to obtain a gate drive signal sequence;

[0098] Step S13: Apply an excitation current based on the gate drive signal sequence using the distribution device test platform. The current rising rate is 10 A / μs to 50 A / μs to drive the distribution device to be tested to operate;

[0099] Step S14: Synchronously monitor the circuit electromagnetic field during the device operation process using an electromagnetic sensor based on the gate drive signal sequence to generate transient excitation electromagnetic field distribution data.

[0100] In the embodiments of the present invention, a test platform is built. The core of the test platform is a customized PCB (printed circuit board), on which slots or pads for installing the power distribution device under test (including but not limited to the solid-state switch module in the hybrid switch device) are designed. A drive circuit is arranged around the slots. The drive circuit uses a dedicated gate drive chip, such as IXDN614 in model. The input end of the chip is connected to the output of the signal generator, and the output end is connected to the gate of the power distribution device under test through a current-limiting resistor. The DC power supply selects a programmable DC power supply, such as Keysight N6705C in model. Its positive pole is connected to the drain (or collector) of the power distribution device under test through a wire, and the negative pole is connected to the common ground of the circuit. For the load part, according to the test requirements, a power resistor or a wound inductor is selected. The power resistor adopts the way of parallel connection of multiple high-power non-inductive resistors to achieve the required resistance value and power capacity; the wound inductor adopts a hollow coil, and the inductance is accurately calculated and wound according to the test needs. The two ends of the resistor or inductor are respectively connected to the source (or emitter) of the power distribution device under test and the common ground of the circuit. All connections use wires with low impedance and high-frequency characteristics, and the wire connection length is minimized as much as possible to reduce the influence of parasitic parameters. All components on the platform are fixed on an anti-static test bench. Set the gate drive signal. Use an arbitrary waveform generator (such as Tektronix AFG31000 in model) to generate the required gate drive signal. According to the specification requirements, set the test period. The test period is set to 0.5 ms, corresponding to a frequency of 2 kHz. The duty cycle is set to 20%. According to the period and duty cycle, the high-level duration is calculated to be 0.1 ms, and the low-level duration is 0.4 ms. Edit a periodic square wave signal in the waveform generator, set the high-level voltage to +15 V (determined according to the data sheet of the device under test to ensure that the device is fully turned on), and the low-level voltage to -5 V (to ensure reliable turn-off of the device). Download the edited waveform into the memory of the waveform generator and set it to the continuous output mode. Use an oscilloscope (such as Rigol DS7000 in model) to monitor the output of the waveform generator, and confirm that the period, duty cycle, and high and low level voltages of the output waveform all meet the set values to form a gate drive signal sequence. Input the gate drive signal sequence generated in step S12 into the input end of the drive circuit of the test platform built in step S11. The output voltage of the programmable DC power supply is set according to the load and the expected peak current. For example, if the load resistance is 1 R and the expected peak current is 500 A, the DC power supply voltage is set to 500 V. Start the DC power supply and simultaneously trigger the waveform generator to output the gate drive signal. After receiving the gate drive signal, the drive circuit amplifies and drives the power distribution device under test. Since the gate drive signal is a periodic square wave, the power distribution device under test will quickly switch between the on and off states. The current rise rate is achieved by adjusting the voltage rise rate of the DC power supply and the driving ability of the gate drive circuit.The specific operations are as follows: First, on the control panel of the DC power supply, set the voltage rise time to a specific value so that the current rise rate reaches the middle value of the preset range, such as 30 A / μs. Then, observe the current waveform through an oscilloscope and measure the time of the current rising edge using a current probe (model such as Tektronix TCP0030A). If the current rise rate does not meet the requirements of 10 A / μs to 50 A / μs, fine-tune the voltage rise time of the DC power supply or replace the current-limiting resistor in the gate drive circuit until the current rise rate meets the requirements. Use a three-dimensional magnetic field sensor array for electromagnetic field measurement. The array consists of multiple (such as 9) independent magnetic field sensors (such as Honeywell MLX90393), which are fixed on a non-metallic bracket in a 3x3 orthogonal arrangement. Each sensor can independently measure the magnetic field intensity in three orthogonal directions (X, Y, Z) at its location. Place the sensor array near the distribution device to be measured, adjust its position and orientation so that it can cover the main electromagnetic field area around the device to be measured. Connect the output signals of the sensors to a multi-channel data acquisition card (such as National Instruments PXIe-6363). The clock of the data acquisition card is synchronized with the waveform generator to ensure the time correspondence between the electromagnetic field data and the gate drive signal. During the operation of the distribution device to be measured, the data acquisition card continuously acquires the output signals of each sensor at a high sampling rate (such as 1 MHz). The acquired data is processed to generate transient excitation electromagnetic field distribution data. This data includes the magnetic field intensity values in three directions measured by each sensor at each sampling moment, as well as the corresponding timestamps.

[0101] Preferably, the device heat source distribution analysis based on the three-dimensional model of the distribution device in step S2 includes:

[0102] Perform a circuit topology analysis on the three-dimensional model of the distribution device to obtain device circuit topology structure data;

[0103] Based on the device circuit topology structure data, perform circuit region grid cell division to generate a grid-divided device three-dimensional model;

[0104] Perform device internal current density distribution processing on the grid-divided device three-dimensional model through transient excitation electromagnetic field distribution data to obtain device current density distribution data;

[0105] Calculate the joule heat in each grid cell according to the device current density distribution data to obtain the joule heat power density;

[0106] Perform device heat source distribution analysis on the grid-divided device three-dimensional model through the joule heat power density to generate device structure heat source distribution data.

[0107] In an embodiment of the present invention, for example, a three-dimensional CAD model of a hybrid switch device is obtained. This model can be from a STEP or IGES format file provided by the device manufacturer, or can be obtained by scanning and reconstructing the physical device using a three-dimensional scanner. Open the three-dimensional model using professional CAD software (such as SolidWorks). In the CAD software, carefully check the integrity and accuracy of the model to ensure that all key components are included in the model, such as solid-state switch modules, busbars, connectors, insulators, etc. Then, use the geometric analysis function of the CAD software to identify different conductor components in the model. For each conductor component, determine its connection relationship in the circuit. For example, determine which section of the busbar the drain of the solid-state switch module is connected to, which section of the busbar the source is connected to, and which output terminal of which drive circuit the gate is connected to, to form device circuit topology structure data. According to the device circuit topology structure data, define the circuit connection relationship in the software. For example, set the drain region of the solid-state switch module to be electrically connected to the corresponding busbar region and the source region to be electrically connected to another section of the busbar region in the software. Then, perform mesh generation. Select tetrahedral mesh elements as the main mesh type. For key regions, such as the inside of the solid-state switch module and the connection of the busbar, use a finer mesh. Control the density of the mesh by setting mesh density control parameters (such as maximum element size, minimum element size, growth rate, etc.). For non-critical regions, use a coarser mesh to reduce the amount of calculation. After the mesh generation is completed, check the mesh quality to ensure that the shape and size of the mesh elements meet the requirements and avoid the occurrence of overly distorted or stretched elements. Establish a spatial correspondence relationship between the electromagnetic field data and the meshed three-dimensional model of the device. This can be achieved by defining a coordinate transformation matrix that maps the coordinate system of the magnetic field sensor array to the coordinate system of the three-dimensional model. Then, interpolate the magnetic field data at each moment to the mesh nodes of the three-dimensional model. The inverse distance weighted interpolation method is selected as the interpolation method. According to Maxwell's equations, use the finite element method to solve the relationship between the magnetic field and the current density. Specifically, set the transient electromagnetic field analysis type in the software and apply the interpolated magnetic field data as the boundary condition to the model. The solver will calculate the current density distribution in each mesh element according to Faraday's law of electromagnetic induction and Ampere's circuital law. Output the current density vector (including three components of X, Y, and Z) data of each mesh element at each moment to form device current density distribution data. For each mesh element, calculate the Joule heat power density at each moment according to Joule's law. The formula of Joule's law is: P = J^2 × ρ, where P is the Joule heat power density (unit: W / m 3 )), J is the magnitude of the current density (unit: A / m 2) where ρ is the resistivity of the material (unit: Ω·m). The current density J is obtained by taking the square root of the sum of the squares of the three components of the current density vector. The resistivity ρ of the material is obtained from the material property database, and the influence of temperature on the resistivity is considered (by looking up tables or fitting formulas). By using a loop to traverse each grid cell and each moment, the joule heat power density of each grid cell at each moment is calculated. These joule heat power density data are applied as volume heat sources to the corresponding grid cells, ensuring that the time step is consistent with the time steps of the electromagnetic field analysis and the joule heat calculation. Set the boundary conditions for the thermal analysis, such as the convective heat transfer coefficient, ambient temperature, etc. These parameters can be obtained through experimental measurements or estimated using empirical formulas. The temperature of each grid cell at each moment will be calculated according to the heat conduction equation. Output the temperature data of each grid cell at each moment, as well as the temperature distribution contour map of the entire device. These data and contour maps constitute the heat source distribution data of the device structure, reflecting the distribution of the heat generated by the device in space and time.

[0108] Preferably, the step of dividing the thermal resistance units according to the heat source distribution data of the device structure and then calculating the thermal resistance values between the thermal resistance units includes:

[0109] Extract the isothermal lines from the three-dimensional model of the distribution device through the heat source distribution data of the device structure to obtain the isothermal lines of the heat source power density;

[0110] Divide the thermal resistance regions with the isothermal lines of the heat source power density as the boundaries to obtain the initial thermal resistance unit division regions;

[0111] Perform closed-loop geometric regular shape processing on the initial thermal resistance unit division regions to generate the thermal resistance unit data of the device;

[0112] Calculate the thermal resistance values between adjacent thermal resistance units according to the thermal resistance unit data of the device to generate the static thermal resistance values between units;

[0113] Calculate the equivalent thermal resistance of non-adjacent thermal resistance units according to the static thermal resistance values between units to obtain the equivalent thermal resistance values between units.

[0114] In the embodiment of the present invention, the joule heat power density is selected as the variable for which the isothermal lines are to be extracted. Set the levels and values of the isothermal line extraction. For example, set to extract 10 isothermal lines, and the power density values are 100W / m 3 , 200W / m 3 , 300W / m 3 ,..., 1000W / m 3。The number of levels and specific values are determined according to the range and distribution characteristics of the heat source distribution data, with the principle of being able to clearly reflect the gradient change of the heat source distribution. The software will automatically generate isothermal lines corresponding to the set values on the three-dimensional model. These isothermal lines connect all points with the same Joule heat power density value in space. In CAD software, use the isothermal lines as reference lines for region division. Take the region between adjacent isothermal lines as an initial thermal resistance unit. For example, the region between the 100W / m 3 isothermal line and the 200W / m 3 isothermal line is defined as a thermal resistance unit, and the region between the 200W / m 3 isothermal line and the 300W / m 3 isothermal line is defined as another thermal resistance unit, and so on. For the case where the isothermal line is not closed, draw auxiliary lines manually according to the heat flow direction to enclose the region. The auxiliary lines should be drawn as perpendicular to the isothermal line as possible and along the main path of the heat flow. In CAD software, assign a unique identifier (such as a number or name) to each initial thermal resistance unit. Record the boundary lines (including isothermal lines and auxiliary lines) and identifiers of each initial thermal resistance unit. Since the shape of the initial thermal resistance unit is irregular, in order to facilitate subsequent thermal resistance calculations, it needs to be simplified to a regular shape. The regular shapes adopted are cuboids or cylinders. The specific operation is as follows: In CAD software, measure the dimensions and shape characteristics of each initial thermal resistance unit. For a unit whose shape is close to a cuboid, approximate it with a cuboid, and the length, width, and height of the cuboid are determined according to the dimensions of the initial unit, so that the volume of the cuboid is approximately equal to the volume of the initial unit. For a unit whose shape is close to a cylinder (such as a bus bar), approximate it with a cylinder, and the diameter and length of the cylinder are determined according to the dimensions of the initial unit, so that the volume of the cylinder is approximately equal to the volume of the initial unit. For an irregularly shaped unit, decompose it into a combination of multiple cuboids or cylinders. Record the shape parameters (cuboid: length, width, height; cylinder: diameter, length), material properties, identifiers, and adjacent relationships with other thermal resistance units of each regularized thermal resistance unit to form device thermal resistance unit data. Based on the device thermal resistance unit data, calculate the thermal resistance values between adjacent thermal resistance units. The calculation formula of thermal resistance depends on the shape of the thermal resistance unit and the heat conduction direction. For a cuboid unit, if the heat flow direction is perpendicular to one face of the cuboid, the thermal resistance calculation formula is: R = L / (k×A), where R is the thermal resistance (unit: K / W), L is the length in the heat flow direction (unit: m), k is the thermal conductivity of the material (unit: W / (m·K)), and A is the cross-sectional area in the direction perpendicular to the heat flow (unit: m 2) For a cylindrical unit, if the heat flow direction is along the axial direction, the formula for calculating the thermal resistance is: R = ln(r2 / r1) / (2×π×k×L), where R is the thermal resistance, r1 is the inner diameter, r2 is the outer diameter, L is the length, and k is the thermal conductivity. If the heat flow direction is radial, the calculation formula is more complex and a modified formula needs to be used. According to the thermal resistance unit data of the device, determine the heat flow direction and contact area between each pair of adjacent thermal resistance units. Obtain the thermal conductivity k from the material property database. Use the corresponding thermal resistance calculation formula to calculate the thermal resistance value between each pair of adjacent thermal resistance units and generate the static thermal resistance value between units. Treat each thermal resistance unit as a node and the thermal resistance between adjacent thermal resistance units as a branch connecting the nodes. For non-adjacent thermal resistance units, calculate their equivalent thermal resistance according to the series-parallel relationship of the thermal resistance network. If there are multiple paths between two thermal resistance units, the thermal resistances on these paths need to be combined in series and parallel. For example, if the thermal resistance unit A and the thermal resistance unit C are connected through the thermal resistance unit B, the equivalent thermal resistance between A and C is the sum of the thermal resistance between A and B and the thermal resistance between B and C (in series). If there are two parallel paths between the thermal resistance unit A and the thermal resistance unit D, with the thermal resistance of one path being R1 and the thermal resistance of the other path being R2, the equivalent thermal resistance between A and D is (R1×R2) / (R1 + R2) (in parallel). Use the series-parallel resistance calculation method in circuit analysis to calculate the equivalent thermal resistance between all non-adjacent thermal resistance units and obtain the equivalent thermal resistance value between units.

[0115] Preferably, the calculating the thermal resistance value between adjacent thermal resistance units according to the thermal resistance unit data of the device includes:

[0116] Extract the geometric data of adjacent thermal resistance units according to the thermal resistance unit data of the device;

[0117] Based on the geometric data of adjacent thermal resistance units, connect the centroid directions of the thermal resistance units to obtain the centroid connection line data of the units;

[0118] Calculate the centroid distance between adjacent thermal resistance units according to the centroid connection line data of the units to obtain the centroid distance data of the units;

[0119] Analyze the contact surface type according to the geometric data of adjacent thermal resistance units. When the judgment result of the contact surface type is full contact, the contact surface area is the heat conduction area. When the judgment result of the contact surface type is partial contact, calculate the effective contact area as the heat conduction area;

[0120] Match the thermal conductivity of the unit contact material for the geometric data of adjacent thermal resistance units through a preset material thermal conductivity database to generate the thermal conductivity of the unit contact material;

[0121] Based on the centroid distance data of the units, the heat conduction area, and the thermal conductivity of the unit contact material, perform thermal resistance calculation to generate the static thermal resistance value between units.

[0122] In the embodiments of the present invention, from the device thermal resistance unit data (which records the shape parameters, identifiers, and adjacent relationships with other thermal resistance units of each thermal resistance unit), all pairs of adjacent thermal resistance units are screened out. For each pair of adjacent thermal resistance units, their geometric data is extracted. The geometric data includes: if it is a cuboid unit, its length, width, and height are extracted; if it is a cylinder unit, its diameter and length are extracted. At the same time, the information used to describe the relative positions of the two adjacent units is extracted. For example, if two cuboid units are adjacent, record which faces of them are in contact with each other, and the coordinate ranges of these two faces in their respective unit coordinate systems. According to the geometric data of the adjacent thermal resistance units, the coordinates of the geometric center (centroid) of each thermal resistance unit are calculated. For a cuboid unit, the centroid coordinates are the midpoint coordinates in the three directions of its length, width, and height. For a cylinder unit, the centroid coordinates are the coordinates of the projection point of the midpoint of its axis on the center of the bottom circle and the midpoint of the axis. In three-dimensional space, connect the centroids of two adjacent thermal resistance units to form a straight line segment. This straight line segment represents the main direction of heat flow from one unit to another. Record the starting point coordinates (the centroid coordinates of the first unit) and the ending point coordinates (the centroid coordinates of the second unit) of this straight line segment, as well as the direction vector of the straight line segment. These data constitute the data of the connection line between unit centroids and are associated with the identifiers of the corresponding pairs of thermal resistance units. Using the data of the connection line between unit centroids, calculate the distance between the centroids of two adjacent thermal resistance units. The distance calculation uses the Euclidean distance formula. Assume that the centroid coordinates of the first thermal resistance unit are (x1, y1, z1) and the centroid coordinates of the second thermal resistance unit are (x2, y2, z2), then the distance d between them is: d = sqrt((x2 - x1)^2 + (y2 - y1)^2 + (z2 - z1)^2). Associate the calculated centroid distance with the identifier of the corresponding pair of thermal resistance units to form the data of the centroid distance between units. According to the geometric data and relative position information of the adjacent thermal resistance units, analyze the contact surface type between the two thermal resistance units. First, determine whether the two units are in full contact. The definition of full contact is: the contact surfaces of the two units completely coincide, and the shape and size of the contact surface are the same. For example, for two cuboid units, if one of their same-sized faces completely fits together, it is considered to be in full contact. If the contact surfaces of the two units do not completely coincide, or the shape or size of the contact surface is different, it is considered to be in partial contact. For the case of full contact, the area of the contact surface is the heat conduction area. For the case of partial contact, the effective contact area needs to be calculated. The calculation method of the effective contact area depends on the specific shape of the contact surface. For example, if two cuboids are in partial contact and the overlapping area is still a rectangle, directly calculate the area of the overlapping rectangle; if the overlapping area is an irregular shape, it needs to be measured in CAD software or calculated using the numerical integration method. Associate the calculated heat conduction area (the contact surface area in the case of full contact and the effective contact area in the case of partial contact) with the identifier of the corresponding pair of thermal resistance units.Establish a database of material thermal conductivities. This database contains thermal conductivity data of various common materials (such as copper, aluminum, silicon, epoxy resin, etc.) at different temperatures. These data can be obtained by referring to material handbooks or through experimental measurements. For each pair of adjacent thermal resistance units, according to the material properties recorded in the device thermal resistance unit data, look up the corresponding thermal conductivity value from the material thermal conductivity database. Considering the influence of temperature on thermal conductivity, according to the expected operating temperature range of the device, select the thermal conductivity value at an appropriate temperature, or use the method of temperature interpolation to calculate the thermal conductivity value at a specific temperature. According to the unit centroid distance data (as the length L in the thermal resistance calculation formula), the heat conduction area (as the area A in the thermal resistance calculation formula), and the thermal conductivity of the unit contact material (as the thermal conductivity k in the thermal resistance calculation formula), use the thermal resistance calculation formula to calculate the thermal resistance value between adjacent thermal resistance units. For a cuboid unit, if the heat flow direction is perpendicular to the contact surface, the thermal resistance calculation formula is: R = L / (k × A). For a cylindrical unit, if the heat flow is along the axial direction, the formula is R = ln(r2 / r1) / (2πk × L), where r1 and r2 are the inner and outer diameters, and L is the length. According to the shape of the thermal resistance unit, select the corresponding thermal resistance calculation formula to generate the static thermal resistance value between units.

[0123] Preferably, the calculation of the equivalent thermal resistance of non-adjacent thermal resistance units based on the static thermal resistance value between units includes:

[0124] Construct the connection relationship of thermal resistance units according to the device thermal resistance unit data;

[0125] Analyze the shortest heat flow path between non-adjacent thermal resistance units based on the thermal resistance unit connection relationship to obtain the shortest heat flow path data;

[0126] Perform the series thermal resistance calculation between non-adjacent units on the shortest heat flow path data through the static thermal resistance value between units to generate the series thermal resistance value between units;

[0127] Calculate the total thermal resistance of the heat flow path according to the series thermal resistance value between units, and then perform the thermal conductance value processing to generate the thermal conductance value of the heat flow path;

[0128] Perform the parallel equivalent thermal resistance processing according to the series thermal resistance value between units to generate the initial value of the equivalent thermal resistance between units;

[0129] Use the thermal conductance value of the heat flow path as the heat flow distribution coefficient to perform weighted averaging on the initial value of the equivalent thermal resistance between units to obtain the equivalent thermal resistance value between units.

[0130] In the embodiments of the present invention, based on the device thermal resistance unit data generated in the previous steps (including the identifiers of each thermal resistance unit and the adjacent relationships with other thermal resistance units), a thermal resistance unit connection relationship graph is constructed. This graph can be represented by an adjacency matrix or an adjacency list in graph theory. If an adjacency matrix is used, the rows and columns of the matrix respectively represent the identifiers of the thermal resistance units, and the value of the matrix element indicates whether two thermal resistance units are adjacent (for example, 1 means adjacent, 0 means not adjacent). If an adjacency list is used, a list is established for each thermal resistance unit, and the list contains the identifiers of all the thermal resistance units adjacent to it. This connection relationship graph clearly shows the topological connection relationships among all the thermal resistance units in the entire hybrid switching device. Use the shortest path algorithm in graph theory (such as Dijkstra's algorithm or Floyd-Warshall algorithm) to find the shortest heat flow path between non-adjacent thermal resistance units. For each pair of non-adjacent thermal resistance units, the algorithm will find the path with the fewest intermediate thermal resistance units passed between them. This path represents the path with the smallest thermal resistance and the most main heat flow when heat is transferred from one unit to another. Record the identifiers of all the thermal resistance units passed by this shortest path, as well as the total length of the path (that is, the number of thermal resistance units passed). These information constitute the shortest heat flow path data and are associated with the identifiers of the corresponding non-adjacent thermal resistance unit pairs. According to the shortest heat flow path data, extract the identifiers of all the thermal resistance units passed by each shortest path. Then, according to these identifiers, find the static thermal resistance values between adjacent thermal resistance units from the previously calculated static thermal resistance values between units. Accumulate these static thermal resistance values in the order on the shortest path. For example, if the shortest path passes through thermal resistance units A, B, and C, the series thermal resistance value is the static thermal resistance value between A and B plus the static thermal resistance value between B and C. The accumulated result is the series thermal resistance value between units corresponding to this shortest path. For each pair of non-adjacent thermal resistance units, there may be multiple shortest heat flow paths (that is, the path lengths are the same, but the intermediate thermal resistance units passed are different). According to the series thermal resistance values of each shortest path, accumulate these series thermal resistance values to obtain the total thermal resistance of the heat flow path between this pair of non-adjacent thermal resistance units. Then, take the reciprocal of the total thermal resistance to obtain the heat flow path thermal conductance value. The thermal conductance value is the reciprocal of the thermal resistance and represents the strength of the heat transfer ability. For each pair of non-adjacent thermal resistance units, if there are multiple shortest heat flow paths, these paths need to be regarded as in parallel relationship, and their parallel equivalent thermal resistance is calculated. Use the calculation formula for parallel resistors: 1 / R_eq = 1 / R1 + 1 / R2 +... + 1 / Rn, where R_eq is the parallel equivalent thermal resistance, and R1, R2,..., Rn are the series thermal resistance values (that is, the series thermal resistance values between units) of each shortest path. Calculate the parallel equivalent thermal resistance as the initial value of the equivalent thermal resistance between units and associate it with the identifiers of the corresponding non-adjacent thermal resistance unit pairs.If there is only one shortest path, the series thermal resistance value of this path is directly used as the initial value of the equivalent thermal resistance between units. The thermal conductance value of the heat flow path of each shortest path is used as the weight of this path. Then, the series thermal resistance values of each path are weighted and averaged. The formula is as follows: R_eq = (Σ(Gi × Ri)) / (ΣGi). Where R_eq is the final equivalent thermal resistance between units, Gi is the thermal conductance value of the heat flow path of the i-th shortest path, and Ri is the series thermal resistance value of the i-th shortest path. Σ represents the summation of all shortest paths. The result of the weighted average is the final equivalent thermal resistance value between units.

[0131] Preferably, the constructing the device dynamic thermal impedance matrix based on the static thermal resistance value between units and the equivalent thermal resistance value between units in step S2 includes:

[0132] Extract the volume and material properties of each thermal resistance unit according to the device thermal resistance unit data to obtain unit volume - material data;

[0133] Calculate the heat capacity value of each thermal resistance unit according to the unit volume - material data to obtain unit heat capacity value data;

[0134] Associate each thermal resistance unit with its corresponding unit heat capacity value data to obtain heat capacity - associated unit data;

[0135] Construct a thermal impedance unit network based on the heat capacity - associated unit data, the static thermal resistance value between units, and the equivalent thermal resistance value between units;

[0136] Construct a node admittance matrix according to the thermal impedance unit network;

[0137] Perform Laplace transform on the node admittance matrix, and then perform matrix inversion to generate the device dynamic thermal impedance matrix.

[0138] In the embodiment of the present invention, according to the device thermal resistance unit data, this data includes the geometric shape parameters of each thermal resistance unit (such as the length, width, and height of a cuboid, the diameter and length of a cylinder) and material properties (such as copper, aluminum, silicon, epoxy resin, etc.). For each thermal resistance unit, calculate its volume according to its geometric shape parameters. For example, the volume of a cuboid is length × width × height, and the volume of a cylinder is π × (diameter / 2)2 × length, where π is the circumference ratio. Associate the calculated volume value with the corresponding thermal resistance unit identifier. At the same time, associate the material property of each thermal resistance unit with the corresponding thermal resistance unit identifier. Integrate these volume values and material property data together to form unit volume - material data. Based on the unit volume - material data, calculate the heat capacity value of each thermal resistance unit. The calculation formula of the heat capacity value is: C = ρ × V × c, where C is the heat capacity value (unit: J / K), ρ is the density of the material (unit: kg / m 3 ),V is the volume of the thermal resistance unit (unit: m 3) where c is the specific heat capacity of the material (unit: J / (kg·K)). Look up the density ρ and specific heat capacity c of the material used for each thermal resistance unit from the material property database. These values can be constants or functions that vary with temperature (obtained by looking up tables or fitting formulas). Substitute the found density and specific heat capacity values into the heat capacity calculation formula to calculate the heat capacity value of each thermal resistance unit. Correlate each thermal resistance unit with its heat capacity value one by one to form heat capacity correlation unit data. Based on the heat capacity correlation unit data, the static thermal resistance value between units (between adjacent thermal resistance units) and the equivalent thermal resistance value between units (between non-adjacent thermal resistance units), construct a thermal impedance unit network. This network takes each thermal resistance unit as a node. For adjacent thermal resistance units, connect their corresponding nodes with a thermal resistance element, and the resistance value of the thermal resistance element is the static thermal resistance value between units. For non-adjacent thermal resistance units, connect their corresponding nodes with a thermal resistance element, and the resistance value of the thermal resistance element is the equivalent thermal resistance value between units. A heat capacity element with a capacitance value equal to the heat capacity value of the thermal resistance unit is connected in parallel to each thermal resistance unit node. The nodal admittance matrix is a square matrix, and its number of rows and columns is equal to the number of nodes in the thermal impedance unit network (i.e., the number of thermal resistance units). The diagonal elements of the matrix are the sum of the conductances (reciprocals of the thermal resistances) of all the thermal resistances connected to this node, plus the product of the heat capacity of this node and the angular frequency ω multiplied by the imaginary unit j (jωC). The off-diagonal elements of the matrix are the negative values of the branch conductances (reciprocals of the thermal resistances) connecting two nodes. If there is no direct connection between two nodes, the corresponding off-diagonal element is 0. When constructing the nodal admittance matrix, the nodes need to be arranged in a certain order and ensure the correct calculation of the matrix elements. In thermal impedance analysis, the Laplace transform converts the influence of heat capacity into an impedance related to frequency. The specific operation is to replace jω in the nodal admittance matrix with the complex variable s. Then, perform an inverse operation on the obtained matrix. Matrix inversion can be completed using the matrix inversion function (such as the inv() function) in numerical calculation software (such as MATLAB). The matrix obtained after inversion is the dynamic thermal impedance matrix of the device. This matrix is a complex matrix, and its elements are functions of the frequency s. The diagonal elements of the matrix represent the self-thermal impedance of each thermal resistance unit, and the off-diagonal elements represent the mutual thermal impedance between different thermal resistance units.

[0139] As an example of the present invention, refer to Figure 2 shown in Figure 1 which is a detailed implementation step flow diagram of step S3 in

[0140] Step S31: Perform state space conversion based on the device dynamic thermal impedance matrix to obtain the internal heat conduction state data of the device;

[0141] In the embodiments of the present invention, the dynamic thermal impedance matrix Z(s) is written in the rational fraction form: Z(s) = N(s) / D(s), where N(s) is a matrix polynomial and D(s) is a scalar polynomial. Then, state variables are selected. Usually, the temperatures of the thermal resistance units are selected as the state variables. The number of state variables is equal to the number of thermal resistance units. Input variables are defined. The input variables are usually the power losses of each thermal resistance unit. The number of input variables is also equal to the number of thermal resistance units. According to the rational fraction form of Z(s), and the definitions of the state variables and input variables, the four matrices A, B, C, and D of the state space model are derived. Matrix A describes the interaction between state variables, matrix B describes the influence of input variables on state variables, matrix C describes how state variables are combined into output variables (in this example, the output variables are usually also the temperatures of the thermal resistance units), and matrix D describes the direct influence of input variables on output variables (in heat conduction problems, D is usually a zero matrix). The elements of these matrices can be calculated from the coefficients of Z(s). The obtained matrices A, B, C, and D are combined to form the state space model: x_dot = Ax + Bu, y = Cx + Du, where x is the state vector (temperatures of the thermal resistance units), x_dot is the derivative of the state vector, u is the input vector (power losses of the thermal resistance units), and y is the output vector (usually the same as x).

[0142] Step S32: Perform finite element volume processing on the three-dimensional model of the power distribution device with a mesh density of 0.5 mm to 2 mm, and then perform simulation mesh node mapping according to the internal heat conduction state data of the device to obtain a multi-scale thermal simulation model;

[0143] In the embodiments of the present invention, tetrahedral mesh elements are selected as the main mesh type. The global mesh size is set to be between 0.5 mm and 2 mm. For key areas, such as the inside of the solid-state switch module and the connection of the busbars, local mesh refinement can be adopted to set a smaller mesh size. The density of the mesh should be determined according to the change of the heat flow gradient, and the mesh should be denser in the area with a large heat flow gradient. After the mesh generation is completed, check the mesh quality to ensure that the shape and size of the mesh elements meet the requirements. Then, map the state variables (temperatures of the thermal resistance units) in the state space model obtained in the previous step to the nodes of the finite element model. The specific operation is as follows: First, determine the position and range of each thermal resistance unit in the three-dimensional model. Then, assign the state variable corresponding to each thermal resistance unit to all the finite element nodes in this area. This mapping relationship can be realized by writing a script (such as a Python script), which reads the data of the state space model and the node data of the finite element model, and then performs matching and assignment according to the spatial position relationship. After the mapping is completed, a multi-scale thermal simulation model is formed.

[0144] Step S33: Obtain the boundary conditions of the operating conditions of the input device;

[0145] In the embodiments of the present invention, the boundary conditions of the operating conditions of the hybrid switchgear are obtained. These boundary conditions include: 1. The curve of the power loss of each thermal resistance unit changing with time. These data can be obtained through circuit simulation or experimental measurement. 2. The curve of the ambient temperature around the device changing with time. 3. The convective heat transfer coefficient on the surface of the device. These coefficients can be obtained through experimental measurement or estimated by empirical formulas, and different surfaces have different convective heat transfer coefficients. 4. The radiative heat transfer coefficient between the device and other devices or the environment. These coefficients can be obtained through calculation or by looking up tables. 5. If there is a cooling medium (such as air cooling or liquid cooling) inside the device, parameters such as the flow rate and temperature of the cooling medium need to be provided.

[0146] Step S34: Transmit the boundary conditions of the device operating conditions to the multi-scale thermal simulation model for internal cyclic heat conduction simulation of the device. The simulation time step is 0.1 ms to 1 s, and the total simulation duration is 5 s to 300 s to generate device temperature cyclic simulation data.

[0147] In the embodiments of the present invention, in a finite element analysis software (such as ANSYS Thermal), the type of transient thermal analysis is set. The curve of the power loss of each thermal resistance unit changing with time is applied as a volumetric heat source to the corresponding finite element nodes (according to the mapping relationship in step S32). The curve of the ambient temperature changing with time is applied as the ambient temperature boundary condition to the corresponding surface of the model. The convective heat transfer coefficient is applied as the convective boundary condition to the corresponding surface of the model. The radiative heat transfer coefficient is applied as the radiative boundary condition to the corresponding surface of the model. If there is a cooling medium, the corresponding cooling medium boundary condition is set. Set the simulation time step to be between 0.1 ms and 1 s. The selection of the time step should be determined according to the change rate of the power loss and the time constant of heat conduction. For a faster change rate or a smaller time constant, the time step should be smaller. Set the total simulation duration to be between 5 s and 300 s. The selection of the total simulation duration should be based on actual needs to ensure that the simulation can cover the main thermal cycles during the operation of the device. Start the simulation calculation. During the simulation process, the finite element analysis software will calculate the temperature of each node at each time step according to the heat conduction equation. Since the heat capacity information of the thermal resistance units is already included in the model, the simulation can simulate the internal temperature dynamic change process of the device. After the simulation is completed, the temperature data of each node at each time step is output. These data constitute the device temperature cyclic simulation data, reflecting the change of the internal temperature of the device with time and space under the given operating conditions.

[0148] As an example of the present invention, refer to Figure 3 as shown, for Figure 1Schematic diagram of the detailed implementation steps of step S4. In this example, step S4 includes:

[0149] Step S41: Mark the key positions of the distribution device based on the 3D model of the distribution device to generate the key positions of the distribution device.

[0150] In the embodiment of the present invention, according to the structural characteristics and failure mode analysis of the hybrid switchgear, the key positions that need to be focused on are determined. These key positions usually include: 1. The solder joints or connection terminals of the solid-state switch module. These positions are areas where current is concentrated and heat generation is large, and are prone to thermal fatigue failure. 2. The connection or bending points of the busbars. These positions are prone to stress concentration, resulting in mechanical fatigue failure. 3. The weak points of the insulating material or the interface in contact with the conductor. These positions are prone to electrical breakdown or thermal breakdown. 4. The roots of the fins of the radiator. These positions are areas with high heat flux density and are prone to thermal stress concentration. In the CAD model, these key positions are marked. The marking method can be to create marker points, marker lines or marker surfaces on the model, and assign a unique identifier (such as a number or name) to each marker. Record the position coordinates, associated components and identifiers of each marker. These markers and data constitute the key positions of the distribution device.

[0151] Step S42: Process the key position temperature cycle curve of the key positions of the distribution device through the device temperature cycle simulation data to generate the key position temperature cycle data.

[0152] In the embodiment of the present invention, according to the identifier and position coordinates of the key positions of the distribution device, the data of the temperature change with time at the corresponding positions are extracted from the temperature cycle simulation data. If the key position is a point, the temperature data of the node where the point is located is directly extracted. If the key position is a line or a surface, the temperature data of all nodes on the line or surface are extracted, and the average value or maximum value is calculated as the temperature of the key position. The temperature data of each key position extracted are arranged in chronological order to form a temperature-time curve. These curves reflect the temperature cycle conditions of each key position during the simulation time.

[0153] Step S43: Perform cycle counting processing based on the key position temperature cycle data to generate the temperature cycle mean value.

[0154] In the embodiments of the present invention, the rainflow counting method is adopted for the cyclic counting method. The rainflow counting method is a method of converting an irregular load-time history into a series of equivalent constant amplitude cyclic loads. The specific operation is as follows: The temperature-time curve at each key position is regarded as the load-time history. Applying the rainflow counting method, all temperature cycles in the curve are identified. Each temperature cycle is defined by a temperature valley value, a temperature peak value, and a temperature mean value. Record the temperature peak value, temperature valley value, and temperature mean value of each temperature cycle. Count the number of cycles in different temperature ranges. For example, count the number of cycles in the temperature ranges of 50°C - 60°C, 60°C - 70°C, 70°C - 80°C, etc. Represent the statistical results in the form of a table or a histogram. Calculate the average temperature of all temperature cycles as the temperature cycle mean value.

[0155] Step S44: Calculate the thermal expansion stress based on the temperature cycle mean value to generate thermal expansion stress distribution data;

[0156] In the embodiments of the present invention, the coefficient of thermal expansion can be obtained by referring to a materials handbook or through experimental measurement. Then, according to the thermal stress calculation formula in mechanics of materials, the thermal expansion stress is calculated. For components with simple shapes, the formula can be directly applied. For example, for a one-dimensional constrained rod, the thermal stress σ = E×α×ΔT, where σ is the thermal stress, E is the elastic modulus of the material, α is the coefficient of thermal expansion, and ΔT is the temperature change (relative to the reference temperature, usually room temperature). For components with complex shapes, a finite element analysis software (such as ANSYS Mechanical) needs to be used for thermal stress analysis. In the finite element model, the temperature cycle mean value is applied as the temperature load to the corresponding key positions (according to the markings in step S41). Set the elastic modulus and coefficient of thermal expansion of the material. Conduct a static analysis to calculate the stress distribution at each key position.

[0157] Step S45: Extract the maximum stress value in the critical area of the device based on the thermal expansion stress distribution data to generate critical area maximum stress data;

[0158] In the embodiments of the present invention, for each key position, the maximum stress value is extracted from the stress distribution data. The maximum stress value usually appears in stress concentration areas, such as sharp corners, hole edges, or material interfaces. If the key position is a point, the stress value at that point is directly extracted. If the key position is a line or a surface, the stress values of all nodes on that line or surface are extracted, and the maximum value is found. Record the maximum stress value of each key position and associate it with the corresponding key position identifier. These maximum stress values constitute the critical area maximum stress data, which reflects the weakest link where the device fails under temperature cycling.

[0159] Step S46: Calculate the damage caused by each stress cycle based on the maximum stress data of the key area to obtain the single-cycle thermal damage value;

[0160] In the embodiment of the present invention, based on the maximum stress data of the key area, the damage caused by each stress cycle is calculated. The damage calculation adopts the Miner linear damage accumulation rule. The Miner rule assumes that the damage caused by each stress cycle is independent and the damage can be linearly accumulated. First, it is necessary to determine the S-N curve (stress-life curve) of the material used at each key position. The S-N curve describes the fatigue life of the material under different stress levels. The S-N curve can be obtained by referring to material manuals, conducting fatigue tests or estimating using empirical formulas. Then, according to the maximum stress value at each key position, the corresponding fatigue life Nf (number of cycles) is found from the S-N curve. Calculate the damage D = 1 / Nf caused by each stress cycle. This value represents the proportion of damage caused by a single cycle to the material.

[0161] Step S47: Perform linear accumulation of damage based on the single-cycle thermal damage value, and then conduct weighted life assessment of key positions to obtain the life assessment data of the distribution device.

[0162] In the embodiment of the present invention, the damage values (1 / Nf) of each cycle are added. The accumulated result is the total damage D_total of this key position. Then, weighted life assessment of key positions is carried out. According to the design requirements and reliability requirements of the hybrid switchgear, a weight factor is assigned to each key position. The weight factor reflects the importance of this key position to the reliability of the entire device. For example, the solder joints of the solid-state switch module have a higher weight factor, while a non-critical position on the shell has a lower weight factor. Multiply the total damage D_total of each key position by its weight factor, and then sum the weighted damages of all key positions. The total weighted damage obtained can be used to evaluate the life of the entire hybrid switchgear. For example, the total weighted damage can be compared with a preset threshold to determine whether the device meets the life requirements. Or, the reciprocal of the total weighted damage can be used as the expected life of the device. Output the evaluated life data (such as total weighted damage, expected life, etc.) as the life assessment data of the distribution device.

[0163] Preferably, the present invention also provides a distribution automation simulation system for executing the distribution automation simulation method as described above. The distribution automation simulation system includes:

[0164] An automated test module for constructing a test platform for distribution devices; using the test platform for distribution devices to perform automated driving operations on the distribution devices to be tested, and synchronously monitoring the electromagnetic field of the device circuit using electromagnetic sensors to generate transient excitation electromagnetic field distribution data;

[0165] A device impedance analysis module, which is used to obtain a three-dimensional model of a distribution device; perform device heat source distribution analysis on the three-dimensional model of the distribution device through transient excitation electromagnetic field distribution data, and generate device structure heat source distribution data; divide heat resistance units according to the device structure heat source distribution data, and then calculate the heat resistance values between the heat resistance units, respectively obtaining the static heat resistance value between the units and the equivalent heat resistance value between the units; construct a device dynamic thermal impedance matrix based on the static heat resistance value between the units and the equivalent heat resistance value between the units;

[0166] A heat conduction simulation module, which is used to establish a multi-scale heat simulation model based on the device dynamic thermal impedance matrix and the three-dimensional model of the distribution device; perform device internal circulating heat conduction simulation using the multi-scale heat simulation model, and generate device temperature cycle simulation data;

[0167] A thermal damage life assessment module, which is used to perform thermal damage value life assessment according to the device temperature cycle simulation data to obtain distribution device life assessment data.

[0168] The present application lies in that by constructing a special distribution device test platform and using electromagnetic sensors to synchronously monitor the electromagnetic field of the device circuit, it is possible to achieve real-time and accurate acquisition of electromagnetic field data of the distribution device under actual operating conditions. This precise data acquisition method avoids the problem that the simulation results are out of touch with the actual situation caused by overly idealized assumptions in traditional methods. By performing heat source distribution analysis on the three-dimensional model of the distribution device through transient excitation electromagnetic field distribution data, the heat source position and intensity inside the device can be accurately identified. By calculating the static heat resistance value and the equivalent heat resistance value between the units, a device dynamic thermal impedance matrix is constructed. Compared with the traditional method that usually uses a fixed heat resistance value, the dynamic thermal impedance matrix can be closer to the actual operating conditions. Especially under transient conditions such as load fluctuations and short-circuit impacts, it can dynamically reflect the changes in the thermal characteristics of the device. This dynamic characteristic makes the simulation results more accurate and can better predict the temperature response of the device under complex working conditions. A multi-scale heat simulation model is established, which can comprehensively consider various heat transfer mechanisms such as heat conduction, convection, and radiation inside the device. This model can not only simulate the temperature response of the device under various complex working conditions, but also capture the areas with hot spot temperatures and thermal stress concentrations. The device internal circulating heat conduction simulation performed through the multi-scale heat simulation model can simulate the temperature cycle changes during the long-term operation of the distribution device, can more accurately predict the remaining life of the distribution device, and avoid safety accidents caused by premature device failure or resource waste caused by overly conservative maintenance strategies.

[0169] 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, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0170] The above are only 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, and 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 to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for simulation of power distribution automation, characterized in that: The following steps are involved: Step S1: construct a power distribution device test platform; use the power distribution device test platform to automatically drive the power distribution device to be tested, and use electromagnetic sensors to synchronously monitor the electromagnetic field of the device circuit to generate transient excitation electromagnetic field distribution data; Step S2: obtaining a three-dimensional model of a power distribution device; performing a device heat source distribution analysis on the three-dimensional model of the power distribution device through transient excitation electromagnetic field distribution data to generate device structure heat source distribution data; dividing the thermal resistance units according to the device structure heat source distribution data, and then calculating the thermal resistance values ​​between the thermal resistance units to obtain the static thermal resistance values ​​between the units and the equivalent thermal resistance values ​​between the units respectively; Construct a device dynamic thermal impedance matrix based on the static thermal resistance value between units and the equivalent thermal resistance value between units; Step S3: establishing a multi-scale thermal simulation model based on the device dynamic thermal impedance matrix and the three-dimensional model of the power distribution device; using the multi-scale thermal simulation model to simulate the internal cyclic heat conduction of the device to generate device temperature cycle simulation data; Step S4: Perform a thermal damage value life assessment based on the device temperature cycle simulation data to obtain the distribution device life assessment data.

2. The method for power distribution automation simulation according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: constructing a power distribution device test platform including a power distribution device to be tested, a drive circuit, a DC power supply, a load resistor and an inductor; Step S12: setting the gate drive signal through a preset test cycle, the test cycle is 0.1ms to 1ms, the pulse duty cycle is 10% to 40%, and a gate drive signal sequence is obtained; Step S13: applying an excitation current using a power distribution device test platform based on a gate drive signal sequence, with a current rise rate of 10A / μs to 50A / μs, to drive the power distribution device to be tested to operate; Step S14: Based on the gate drive signal sequence, the electromagnetic sensor is used to synchronously monitor the circuit electromagnetic field during the device operation process to generate transient excitation electromagnetic field distribution data.

3. The method for power distribution automation simulation according to claim 1, characterized in that: The heat source distribution analysis of the device based on the three-dimensional model of the power distribution device in step S2 includes: Conduct circuit topology analysis on the three-dimensional model of the power distribution device to obtain the device circuit topology data; Based on the device circuit topology data, the circuit area is divided into grid units to generate a grid-divided device three-dimensional model; The device current density distribution data is obtained by processing the device internal current density distribution on the three-dimensional model of the meshed device through the transient excitation electromagnetic field distribution data; The Joule heat in each grid unit is calculated according to the device current density distribution data to obtain the Joule heat power density; The device heat source distribution is analyzed by using the Joule heat power density to mesh the device three-dimensional model and generate the device structure heat source distribution data.

4. The method for power distribution automation simulation according to claim 1, characterized in that: The step S2 of dividing the thermal resistance units according to the heat source distribution data of the device structure and then calculating the thermal resistance values ​​between the thermal resistance units includes: The isoline of the three-dimensional model of the power distribution device is extracted through the device structure heat source distribution data to obtain the heat source power density isoline; The thermal resistance area is divided using the heat source power density contour as the boundary to obtain the initial thermal resistance unit division area; Perform closed-loop geometric regular shape processing on the initial thermal resistance unit division area to generate device thermal resistance unit data; Calculate the thermal resistance value between adjacent thermal resistance units according to the device thermal resistance unit data, and generate the static thermal resistance value between the units; The equivalent thermal resistance of non-adjacent thermal resistance units is calculated according to the static thermal resistance value between units to obtain the equivalent thermal resistance value between units.

5. The method for power distribution automation simulation according to claim 4, characterized in that: Calculating the thermal resistance value between adjacent thermal resistance units according to the device thermal resistance unit data includes: Extracting adjacent thermal resistance unit geometric data according to device thermal resistance unit data; Based on the geometric data of adjacent thermal resistance units, the centroid directions of the thermal resistance units are connected to obtain the unit centroid connection data; The centroid distances of adjacent thermal resistance units are calculated based on the unit centroid connection data to obtain the unit centroid distance data; The contact surface type is analyzed based on the geometric data of adjacent thermal resistance units. When the contact surface type is judged as full contact, the contact surface area is the heat conduction area. When the contact surface type is judged as partial contact, the effective contact area is calculated as the heat conduction area. The thermal conductivity of the unit contact material is matched with the geometric data of the adjacent thermal resistance units through the preset material thermal conductivity database to generate the thermal conductivity of the unit contact material; The thermal resistance is calculated based on the unit centroid distance data, the heat conduction area, and the thermal conductivity of the unit contact material to generate the static thermal resistance value between units.

6. The method for power distribution automation simulation according to claim 4, characterized in that: Calculating the equivalent thermal resistance of non-adjacent thermal resistance units according to the static thermal resistance value between units includes: Constructing a thermal resistance unit connection relationship according to the device thermal resistance unit data; Analyze the shortest heat flow path between non-adjacent thermal resistance units based on the connection relationship of thermal resistance units to obtain the shortest heat flow path data; The series thermal resistance between non-adjacent units is calculated for the shortest heat flow path data by using the static thermal resistance between units to generate the series thermal resistance between units; The total thermal resistance of the heat flow path is calculated based on the series thermal resistance between units, and then the thermal conductivity value is processed to generate the thermal conductivity value of the heat flow path; Perform parallel equivalent thermal resistance processing according to the series thermal resistance value between units to generate the initial value of the equivalent thermal resistance between units; The thermal conductivity of the heat flow path is used as the heat flow distribution coefficient, and the initial value of the equivalent thermal resistance between units is weighted averaged to obtain the equivalent thermal resistance value between units.

7. The method for power distribution automation simulation according to claim 1, characterized in that: The step S2 of constructing a device dynamic thermal impedance matrix based on the static thermal resistance values ​​between units and the equivalent thermal resistance values ​​between units includes: Extract the volume and material properties of each thermal resistance unit according to the device thermal resistance unit data to obtain unit volume-material data; Calculate the heat capacity value of each thermal resistance unit according to the unit volume-material data to obtain unit heat capacity value data; Associating each thermal resistance unit with its corresponding unit heat capacity value data to obtain heat capacity associated unit data; Construct a thermal impedance unit network based on heat capacity associated unit data, static thermal resistance values ​​between units, and equivalent thermal resistance values ​​between units; Construct a node admittance matrix based on the thermal impedance unit network; The node admittance matrix is ​​Laplace transformed and then matrix inverted to generate the device dynamic thermal impedance matrix.

8. The method for power distribution automation simulation according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing state space conversion based on the device dynamic thermal impedance matrix to obtain device internal heat conduction state data; Step S32: Perform finite element volume processing according to the three-dimensional model of the power distribution device, with a grid density of 0.5 mm to 2 mm, and then perform simulation grid node mapping according to the internal heat conduction state data of the device, so as to obtain a multi-scale thermal simulation model; Step S33: obtaining input device operating condition boundary conditions; Step S34: transmitting the device operating condition boundary conditions to the multi-scale thermal simulation model to perform device internal cyclic heat conduction simulation, with a simulation time step of 0.1ms to 1s and a total simulation time of 5s to 300s, to generate device temperature cycle simulation data.

9. The method for power distribution automation simulation according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: marking the key positions of the power distribution device according to the three-dimensional model of the power distribution device to generate the key positions of the power distribution device; Step S42: performing key position temperature cycle curve processing on key positions of the power distribution device through device temperature cycle simulation data to generate key position temperature cycle data; Step S43: performing cycle counting processing according to the temperature cycle data of the key position to generate a temperature cycle mean; Step S44: Calculate thermal expansion stress according to the temperature cycle mean value to generate thermal expansion stress distribution data; Step S45: extracting the maximum stress value of the key area of ​​the device based on the thermal expansion stress distribution data to generate the maximum stress data of the key area; Step S46: Calculate the damage caused by each stress cycle according to the maximum stress data of the key area to obtain a single-cycle thermal damage value; Step S47: perform linear accumulation of damage according to the single-cycle thermal damage value, and then perform weighted life assessment of key positions to obtain life assessment data of the power distribution device.

10. A simulation system for power distribution automation, characterized in that: Used to execute the method for distribution automation simulation as claimed in claim 1, the distribution automation simulation system comprises: The automated test module is used to construct a test platform for power distribution devices; the test platform for power distribution devices is used to automatically drive the power distribution devices to be tested, and electromagnetic sensors are used to synchronously monitor the electromagnetic field of the device circuit to generate transient excitation electromagnetic field distribution data; The device impedance analysis module is used to obtain the three-dimensional model of the power distribution device; perform device heat source distribution analysis on the three-dimensional model of the power distribution device through transient excitation electromagnetic field distribution data to generate device structure heat source distribution data; divide the thermal resistance units according to the device structure heat source distribution data, and then calculate the thermal resistance values ​​between the thermal resistance units to obtain the static thermal resistance values ​​between the units and the equivalent thermal resistance values ​​between the units; construct the device dynamic thermal impedance matrix based on the static thermal resistance values ​​between the units and the equivalent thermal resistance values ​​between the units; The heat conduction simulation module is used to establish a multi-scale thermal simulation model based on the device dynamic thermal impedance matrix and the three-dimensional model of the power distribution device; the multi-scale thermal simulation model is used to simulate the internal cycle heat conduction of the device and generate the device temperature cycle simulation data; The thermal damage life assessment module is used to perform thermal damage value life assessment based on device temperature cycle simulation data to obtain distribution device life assessment data.

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