Method for optimizing magnetic thermal stress coupling performance of bus duct in complex electromagnetic environment

Abstract

The invention provides a magnetic thermal stress coupling performance optimization method for a bus duct in a complex electromagnetic environment, and relates to the technical field of bus ducts, and the method comprises the steps: building a bus duct health performance vector and a magnetic thermal stress coupling performance loss vector through carrying out health performance parameter mining and performance loss testing on the bus duct; determining a sensitive design factor and adjusting a bus duct design scheme through an abnormal electromagnetic environment performance optimization sensitive test, and establishing an optimization first space; and performing performance improvement optimization on the optimized first space according to the performance loss vector, establishing an optimized second space, performing variation expansion optimization according to the health performance vector, and generating a bus duct optimization strategy. The technical problem that the existing bus duct design is mainly optimized based on a single physical field and lacks comprehensive consideration on the magnetic-thermal-stress coupling effect, so that the performance of the bus duct is seriously degraded in a complex electromagnetic environment is solved, and the comprehensive performance of the bus duct in the complex electromagnetic environment is improved.

Description

Technical Field

[0001] The present application relates to the technical field of bus ducts, and in particular to a method for optimizing the magnetothermal stress coupling performance of bus ducts in a complex electromagnetic environment. Background Art

[0002] As a highly efficient power transmission device, busbar ducts are widely used in various buildings and industrial sites. In actual operation, busbar ducts are often exposed to complex electromagnetic environments such as high current, high frequency, and strong magnetic fields. These environments affect the performance of busbar ducts through a combination of electromagnetic forces, thermal effects, and mechanical stress.

[0003] Existing bus duct optimization design methods are primarily based on the optimization of a single physical field, namely, improvements are made from a single perspective, such as electromagnetics, thermal management, or mechanical strength. For example, to address electromagnetic compatibility issues, shielding and reasonable wiring designs are usually adopted; to optimize heat dissipation performance, more efficient thermal conductive materials or improved structural heat dissipation designs are used; and to address mechanical strength, fatigue resistance is mainly improved by strengthening the supporting structure or optimizing materials. However, these methods only focus on improving performance in a single aspect, while ignoring the problems that may arise from the interaction of multiple physical fields. This makes it difficult to effectively improve the overall performance of the bus duct, resulting in poor adaptability of the optimization results in complex actual environments. The bus duct is prone to problems such as reduced heat dissipation performance, increased mechanical fatigue, and poor electromagnetic compatibility. Summary of the Invention

[0004] This application provides a method for optimizing the magnetic-thermal-stress coupling performance of bus ducts in complex electromagnetic environments. It solves the technical problem that the existing bus duct design is mainly based on single physical field optimization and lacks comprehensive consideration of the magnetic-thermal-stress coupling effect, resulting in serious performance degradation of the bus duct in complex electromagnetic environments, thereby affecting its long-term stability and reliability. It achieves the technical effect of improving the comprehensive performance of the bus duct and improving the long-term stability and reliability of the bus duct in complex electromagnetic environments.

[0005] In view of the above problems, the present application provides a method for optimizing the magnetothermal stress coupling performance of bus duct in a complex electromagnetic environment, the method comprising: mining health performance parameters of the bus duct to establish a bus duct health performance vector; based on the bus duct health performance vector, performing performance loss tests on the bus duct under different electromagnetic environments to determine the magnetothermal stress coupling performance loss vector corresponding to the abnormal electromagnetic environment; performing performance optimization sensitivity tests on the multi-dimensional design indicators of the bus duct according to the abnormal electromagnetic environment to determine sensitive design factors; adjusting the bus duct design scheme according to the sensitive design factors to establish a first bus duct optimization space; optimizing the performance improvement of the first bus duct optimization space according to the magnetothermal stress coupling performance loss vector to establish a second bus duct optimization space; performing variation expansion optimization on the second bus duct optimization space according to the bus duct health performance vector to generate a bus duct optimization strategy.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By mining the health performance parameters of the bus duct and establishing a health performance vector, basic data support is provided for subsequent performance evaluation and optimization, enabling a comprehensive assessment of the bus duct's health from multiple dimensions. By testing the bus duct's performance loss under different electromagnetic environments, identifying abnormal electromagnetic environments and their corresponding performance loss vectors, the performance issues of the bus duct in complex electromagnetic environments are precisely located, providing a basis for optimization. Performance optimization sensitivity testing is conducted on multi-dimensional design indicators to identify sensitive design factors, identifying key factors affecting bus duct performance, and providing guidance for subsequent optimization design, improving optimization efficiency and targeting. The bus duct design is optimized based on the identified sensitive factors, establishing a first optimization space, reducing interference from non-critical factors, and improving the effectiveness of the optimization direction. The performance improvement of the first optimization space is optimized based on the magnetothermal stress coupling performance loss vector, establishing a second optimization space, and further screening and optimizing the design solution to adapt it to more complex electromagnetic environments. The second optimization space is mutated and expanded according to the bus duct health performance vector to generate the final optimization strategy. By introducing the variation factor, the diversity and innovation of the optimization scheme are increased, so that the bus duct can maintain better performance under different working conditions, ensuring the applicability and long-term stability of the optimization scheme.

[0008] To sum up, this application uses a systematic optimization process, from healthy performance parameter mining to multi-stage optimization and optimization, to comprehensively consider the magnetic thermal stress coupling effect of the bus duct in a complex electromagnetic environment, accurately locate performance problems, and determine key influencing factors. It also effectively improves the design scheme of the bus duct through step-by-step optimization and variation expansion methods, solves the performance degradation problem of the bus duct in a complex electromagnetic environment caused by the magnetic, thermal, and stress coupling effects, and significantly improves the comprehensive performance of the bus duct in a complex electromagnetic environment, thereby improving the long-term stability and reliability of the bus duct in a complex electromagnetic environment.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of a method for optimizing the magnetic thermal stress coupling performance of a bus duct in a complex electromagnetic environment provided in an embodiment of the present application.

[0011] Figure 2 A schematic flow chart of determining the magnetothermal stress coupling performance loss vector corresponding to an abnormal electromagnetic environment in the magnetothermal stress coupling performance optimization method of a bus duct in a complex electromagnetic environment provided in an embodiment of the present application.

[0012] Figure 3 A schematic flow chart of determining sensitive design factors in a method for optimizing the magnetothermal stress coupling performance of a bus duct in a complex electromagnetic environment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a method for optimizing the magnetothermal stress coupling performance of bus ducts in complex electromagnetic environments, thereby solving the technical problem that the existing bus duct design is mainly based on single physical field optimization and lacks comprehensive consideration of the magneto-thermal-stress coupling effect, resulting in serious performance degradation of the bus duct in complex electromagnetic environments, thereby affecting its long-term stability and reliability. The embodiment of the present application achieves the technical effect of improving the comprehensive performance of the bus duct and enhancing the long-term stability and reliability of the bus duct in complex electromagnetic environments.

[0014] like Figure 1 As shown, an embodiment of the present application provides a method for optimizing the magnetic thermal stress coupling performance of a bus duct in a complex electromagnetic environment, the method comprising:

[0015] Step S1: mining the health performance parameters of the bus duct and establishing a bus duct health performance vector.

[0016] Specifically, health performance parameters refer to various performance indicators exhibited by busduct during operation, such as current carrying capacity, insulation resistance, temperature rise, and mechanical strength. A health performance vector combines multiple health performance parameters into a single vector for comprehensive evaluation of busduct health.

[0017] First, the health performance parameters of the bus duct are comprehensively collected through on-site testing, equipment monitoring, and historical data statistics. For example, an ammeter is used to measure the current carrying capacity of the bus duct, a megohmmeter is used to measure the insulation resistance, an infrared thermal imager is used to monitor the temperature rise, and mechanical strength is evaluated using mechanical performance testing equipment. These collected health performance parameters are arranged in a pre-set order to form a bus duct health performance vector. For example: health performance vector = (I, R, T, S), where I represents current carrying capacity, R represents insulation resistance, T represents temperature rise, and S represents mechanical strength. This vector provides basic data support for subsequent performance evaluation and optimization, so that the health status of the bus duct can be comprehensively considered from multiple dimensions, and performance problems and optimization directions can be accurately identified.

[0018] Step S2: Based on the bus duct health performance vector, the bus duct is subjected to performance loss tests under different electromagnetic environments to determine the magnetothermal stress coupling performance loss vector corresponding to the abnormal electromagnetic environment.

[0019] Specifically, based on the health performance vector established in step S1, the bus duct is placed in different electromagnetic environments for performance loss testing. Different electromagnetic environments can be simulated by using an adjustable current source, frequency regulator, and magnetic field generator in the electromagnetic compatibility laboratory. The bus duct is then placed in different electromagnetic environments to test the changes in performance parameters such as the current carrying capacity, insulation resistance, temperature rise, and mechanical strength of the bus duct. By comparing the health performance vectors in different electromagnetic environments, it is determined which electromagnetic environments will cause the performance of the bus duct to significantly degrade. These performance loss data caused by magneto-thermal stress coupling are combined into vectors in a certain order to obtain the magneto-thermal stress coupling performance loss vector corresponding to the abnormal electromagnetic environment. For example: magneto-thermal stress coupling performance loss vector = (ΔI, ΔR, ΔT, ΔS), where ΔI represents the loss of current carrying capacity, ΔR represents the loss of insulation resistance, ΔT represents the increase in temperature rise, and ΔS represents the loss of mechanical strength.

[0020] The performance loss test accurately located the performance loss of the bus duct in abnormal electromagnetic environments, providing a clear direction for subsequent optimization design.

[0021] Step S3: performing a performance optimization sensitivity test on the multi-dimensional design indicators of the bus duct according to the abnormal electromagnetic environment to determine sensitive design factors.

[0022] Specifically, multidimensional design indicators are the multiple parameters involved in bus duct design, such as conductor cross-sectional area, insulation material thickness, heat dissipation structure design, mechanical fixing method, etc. Sensitive design factors are design indicators that significantly affect bus duct performance and are key factors in optimizing design.

[0023] Based on the abnormal electromagnetic environment determined in step S2, a performance optimization sensitivity test is performed on the multi-dimensional design indicators of the bus duct. A control variable method can be used to keep other design indicators unchanged, change a single design indicator, and then test the performance changes of the bus duct under an abnormal electromagnetic environment. For example, finite element analysis (FEA) software can be used to simulate the electromagnetic field distribution and temperature rise under different conductor cross-sectional areas, or the insulation resistance and temperature rise under different insulation material thicknesses can be actually tested. By analyzing the test results, it is determined which design indicators have a greater impact on the performance of the bus duct, thereby determining the sensitive design factors.

[0024] Determining sensitive design factors can make subsequent optimization work more targeted, focusing on adjusting the design parameters that have a key impact on bus duct performance, improving optimization efficiency, and avoiding unnecessary optimization of some design indicators that have little impact on performance.

[0025] Step S4: adjusting the bus duct design scheme according to the sensitive design factors to establish a first optimized bus duct space.

[0026] Specifically, the design scheme of the bus duct is adjusted according to the sensitive design factors determined in step S3. For example, if the conductor cross-sectional area and the thickness of the insulating material are sensitive design factors, then you can try to increase the conductor cross-sectional area, optimize the heat dissipation structure, select more high-temperature resistant insulating materials, etc. This can be achieved through computer-aided design (CAD) software and finite element analysis (FEA) software. For example, use CAD software to draw bus duct models with different conductor cross-sectional areas, and then use FEA software to simulate their performance in abnormal electromagnetic environments. Through these adjustment measures, a number of different design schemes are generated, and these schemes are combined to optimize the first space. This bus duct optimization first space is a range composed of adjusted bus duct design schemes.

[0027] Establishing the first space for bus duct optimization provides a targeted scope for subsequent further optimization, narrows the optimization search area, improves the optimization efficiency, and enables subsequent optimization work to be carried out within a more reasonable range.

[0028] Step S5: optimizing the performance improvement of the first bus duct optimization space according to the magnetothermal stress coupling performance loss vector, and establishing a second bus duct optimization space.

[0029] Specifically, performance improvement optimization refers to the process of finding a design solution that can maximize the performance of the bus duct (mainly in terms of magnetothermal stress coupling performance loss) within the first space of bus duct optimization. According to the magnetothermal stress coupling performance loss vector obtained in step S2, the performance of each design solution in the optimized first space established in step S4 is evaluated. By comparing the performance of each solution in an abnormal electromagnetic environment, such as the improvement of current carrying capacity, the improvement of insulation resistance, the reduction of temperature rise and the enhancement of mechanical strength, the solution that can minimize performance loss is screened out to establish an optimized second space.

[0030] This optimization process can be achieved through multi-objective optimization algorithms, such as genetic algorithms (GAs) and particle swarm optimization (PSO). Various design solutions within the first busbar duct optimization space are used as the algorithm's initial population or particles. The magnetothermal stress coupling performance loss vector is used as the objective function. Through continuous algorithm iteration, the performance improvement of each design solution is calculated. For example, in a genetic algorithm, new design solutions are generated through operations such as crossover and mutation. The improvement in reducing magnetothermal stress coupling performance loss compared to the original solution is then calculated. Ultimately, the design solution with the highest performance improvement is found, thus establishing the second busbar duct optimization space.

[0031] By further screening and optimizing the design scheme based on the first space of bus duct optimization and establishing the second space of bus duct optimization, the targetedness and effectiveness of the optimization are improved, resulting in a more significant performance improvement of the optimized bus duct scheme.

[0032] Step S6: performing mutation, expansion, and optimization on the second bus duct optimization space according to the bus duct health performance vector to generate a bus duct optimization strategy.

[0033] Specifically, based on the bus duct health performance vector established in step S1, the optimized second space established in step S5 is subjected to mutation and expansion optimization. Based on the existing design solution, the mutation operation in the multi-objective optimization algorithm is used to make minor adjustments to the solution in the optimized second space. Some random variation factors are introduced, such as slight changes in conductor shape or adjustments to the layout of the heat dissipation structure. New design solutions are generated. The performance of these variant solutions is then evaluated again to determine the optimal design solution and generate a bus duct optimization strategy. This bus duct optimization strategy is the final bus duct optimization design solution, including specific design parameters and improvement measures.

[0034] By introducing variation factors to conduct variation expansion and optimization based on the optimization of the second space, the diversity of design schemes is increased, and the performance improvement potential of the bus duct is further explored, so that the final bus duct design scheme has better performance in complex electromagnetic environments.

[0035] Further, such as Figure 2 As shown, step S2 includes:

[0036] Step S21: Based on the bus duct health performance vector, a performance loss test is performed on the bus duct according to M electromagnetic environments to obtain M bus duct performance loss coefficients, where M is a positive integer greater than 1.

[0037] Step S22: Determine whether the performance loss coefficients of the M bus ducts are greater than or equal to a predetermined performance loss coefficient.

[0038] Step S23: If any bus duct performance loss coefficient among the M bus duct performance loss coefficients is greater than or equal to the predetermined performance loss coefficient, the abnormal electromagnetic environment is obtained.

[0039] Step S24: performing loss identification on the bus duct test performance vector corresponding to the abnormal electromagnetic environment according to the bus duct health performance vector, and generating the magnetothermal stress coupling performance loss vector.

[0040] Specifically, based on the bus duct health performance vector established in step S1, the bus duct is placed in M ​​different electromagnetic environments for performance loss testing. Where M is a positive integer greater than 1, which refers to the number of pre-set electromagnetic environments. Each electromagnetic environment is simulated by devices such as an adjustable current source, a frequency regulator, and a magnetic field generator. For example, different current intensities (such as 100A, 200A, 300A), frequencies (such as 50Hz, 60Hz, 100Hz), and magnetic field intensities (such as 0.1T, 0.5T, 1.0T) can be set. In each electromagnetic environment, the health performance parameters of the bus duct are measured by testing equipment (such as ammeter, megohmmeter, infrared thermal imager, mechanical performance testing equipment), and the performance loss coefficient is calculated. This performance loss coefficient is an indicator that quantifies the degree of performance degradation of the bus duct in a specific electromagnetic environment. The higher the performance loss coefficient, the higher the degree of performance degradation of the bus duct.

[0041] The predetermined performance loss coefficient is a pre-set threshold value used to determine whether the performance loss of the bus duct in a specific electromagnetic environment exceeds an acceptable range. It can be set according to the design standards of the bus duct and actual application requirements. The predetermined performance loss coefficient is compared one by one with the M bus duct performance loss coefficients obtained to determine the size relationship between each performance loss coefficient and the predetermined performance loss coefficient. If any one of the M bus duct performance loss coefficients is found to be greater than or equal to the predetermined performance loss coefficient, the corresponding electromagnetic environment is marked as an abnormal electromagnetic environment. For example, if the predetermined performance loss coefficient is 0.10, then any electromagnetic environment with a performance loss coefficient greater than or equal to 0.10 is considered to be an abnormal electromagnetic environment. By comparing with the predetermined performance loss coefficient, possible abnormal electromagnetic environments are preliminarily screened out, providing a basis for the subsequent determination of abnormal electromagnetic environments.

[0042] Based on the bus duct health performance vector established in step S1 and the bus duct test performance vector determined under an abnormal electromagnetic environment, loss identification is performed. The health performance vector under a normal environment is compared with the test performance vector under an abnormal electromagnetic environment. The loss value of each performance parameter under the abnormal electromagnetic environment is calculated, and a magnetothermal stress coupling performance loss vector corresponding to the abnormal electromagnetic environment is generated. For example, if the health performance vector under a normal environment is (I0, R0, T0, S0) and the test performance vector under a certain abnormal electromagnetic environment is (I1, R1, T1, S1), then the magnetothermal stress coupling performance loss vector (ΔI, ΔR, ΔT, ΔS) = (I0-I1, R0-R1, T1-T0, S0-S1). For example, the health performance vector under a normal environment is (300A, 100MΩ, 50°C, 1000N), and the test performance vector under a high current environment is (255A, 90MΩ, 57.5°C, 950N). Then the magnetothermal stress coupling performance loss vector (ΔI, ΔR, ΔT, ΔS) = (45A, 10MΩ, 7.5°C, 50N).

[0043] By generating the magnetothermal stress coupling performance loss vector, the performance loss of the bus duct caused by the magnetic, thermal and stress coupling in an abnormal electromagnetic environment can be quantified in detail, providing accurate data support for subsequent optimization work.

[0044] Furthermore, step S21 includes:

[0045] Step S211: extracting the mth electromagnetic environment according to the M electromagnetic environments, where m is a positive integer and 1≤m≤M.

[0046] Step S212: performing a performance test on the bus duct according to the mth electromagnetic environment to obtain an mth bus duct test performance vector.

[0047] Step S213: Input the bus duct health performance vector and the mth bus duct test performance vector into P bus duct performance loss assessment models to obtain P bus duct performance loss assessment coefficients, where P is a positive integer greater than 1.

[0048] Step S214: Calculate the average of the P bus duct performance loss evaluation coefficients to obtain the mth bus duct performance loss coefficient, and add the mth bus duct performance loss coefficient to the M bus duct performance loss coefficients.

[0049] Specifically, the mth electromagnetic environment is extracted from a pre-defined set of M electromagnetic environments. Here, m is a positive integer (1≤m≤M) and represents the electromagnetic environment number. For example, in a data center busbar system, M = 3 electromagnetic environments: High current environment: 300A current, 50Hz frequency, 0.5T magnetic field strength; High frequency environment: 200A current, 100Hz frequency, 0.5T magnetic field strength; Strong magnetic field environment: 200A current, 50Hz frequency, 1.0T magnetic field strength. Each electromagnetic environment is extracted in turn for subsequent performance testing.

[0050] The bus duct is placed in the mth electromagnetic environment and its performance parameters are measured using test equipment. For example, an ammeter is used to measure current carrying capacity, a megohmmeter to measure insulation resistance, an infrared thermal imager to measure temperature rise, and mechanical performance testing equipment to measure mechanical strength. These performance parameters are combined into a vector, which is the mth bus duct test performance vector. For example, in a high current environment (m = 1), the bus duct performance parameters obtained through testing are: current carrying capacity: 255A; insulation resistance: 90MΩ; temperature rise: 57.5°C; mechanical strength: 950N. The mth bus duct test performance vector is (255A, 90MΩ, 57.5°C, 950N).

[0051] The bus duct performance loss assessment model is a pre-established mathematical model used to assess the degree of bus duct performance loss based on the bus duct health performance vector and the test performance vector under a specific electromagnetic environment. These models assess the degree of loss based on different algorithms (such as regression analysis, neural networks, etc.). The bus duct health performance vector and the m-th bus duct test performance vector are used as input and substituted into these P models for calculation. Each model calculates a performance loss assessment coefficient based on the input vectors to reflect the degree of performance loss of the bus duct under the m-th electromagnetic environment. For example, if it is an assessment model based on a neural network, a trained neural network model can be used to normalize the vector data and input it into the model to obtain P bus duct performance loss assessment coefficients. By obtaining P bus duct performance loss assessment coefficients through multiple assessment models, the performance loss of the bus duct under the m-th electromagnetic environment can be comprehensively assessed from different perspectives and methods, thereby improving the accuracy and reliability of the assessment.

[0052] For example, a busbar trunking performance loss assessment model (P = 2) is pre-built using regression analysis and neural networks. First, health performance data for the busbar trunking under different electromagnetic environments, including indicators such as current carrying capacity, insulation resistance, temperature rise, and mechanical strength, is collected as feature data. Performance loss data under the corresponding environments is also collected as target data. The collected data is pre-processed through cleaning and normalization, and the pre-processed data is divided into training and test sets. For the busbar trunking performance loss assessment model based on regression analysis, a loss assessment model is established using linear regression, multivariate regression, or nonlinear regression, for example, P = ω1(I0-I1)+ω2(R0-R1)+ω3(T1-T0)+ω4(S0-S1)+b. The trained regression model is evaluated using the test set, and the model's prediction error, such as mean squared error (MSE) and mean absolute error (MAE), is calculated. Based on the evaluation results, the model is optimized to obtain a busbar trunking performance loss assessment model based on regression analysis. For the neural network-based busbar performance loss assessment model, determine the number of neurons in the input, hidden, and output layers of the neural network. The number of neurons in the input layer corresponds to the number of health performance parameters of the busbar, and the number of neurons in the output layer corresponds to the number of performance loss assessment indicators. Select an appropriate activation function, such as ReLU or Sigmoid, and train the neural network using the training set. Use the backpropagation algorithm to adjust the network's weights and bias to minimize the error between the predicted value and the true value. Use the test set to evaluate the performance of the trained neural network model and calculate its prediction accuracy and generalization ability. If the model performance meets the requirements, it can be applied to the actual busbar performance loss assessment to make predictions on new data.

[0053] Calculate the mean of P performance loss evaluation coefficients as the bus duct performance loss coefficient under the mth electromagnetic environment. For example, if P = 2, and the two evaluation coefficients are 0.15 and 0.16 respectively, the mean is 0.155, that is, the performance loss coefficient of the mth bus duct is 0.155. Add the bus duct performance loss coefficient under the mth electromagnetic environment to the set of M bus duct performance loss coefficients. For example, for M = 3 electromagnetic environments, calculate the performance loss coefficient in each environment in turn: the mean value of the high current environment (m = 1) is 0.155; the mean value of the high frequency environment (m = 2) is 0.205; the mean value of the strong magnetic field environment (m = 3) is 0.255, then the M bus duct performance loss coefficients are (0.155, 0.205, 0.255). By combining the results of multiple evaluation models through mean calculation, a reliable performance loss coefficient is obtained, which is added to the overall performance loss coefficient set, providing data support for subsequent abnormal electromagnetic environment judgment.

[0054] Further, such as Figure 3 As shown, step S3 includes:

[0055] Step S31: Based on the abnormal electromagnetic environment, a performance optimization sensitivity test is performed on the multi-dimensional design indicators according to a predetermined step size set to obtain multiple indicator performance optimization sensitive sequences.

[0056] Step S32: performing centralized value calculation on the multiple indicator performance optimization sensitive sequences respectively to obtain multiple indicator performance optimization sensitivity indexes.

[0057] Step S33: Based on the multiple indicator performance optimization sensitivity indexes, the multi-dimensional design indicators are screened according to the performance optimization sensitivity threshold to obtain the sensitive design factors.

[0058] Specifically, the predetermined step set is a set of pre-set adjustment amplitudes used to gradually change the values ​​of multi-dimensional design indicators. The indicator performance optimization sensitive sequence is a performance change sequence corresponding to each design indicator under different adjustment amplitudes. Based on the abnormal electromagnetic environment determined in step S2, a performance optimization sensitivity test is performed on the multi-dimensional design indicators of the bus duct. First, the value of each design indicator is gradually adjusted according to the predetermined step set. For example, for the conductor cross-sectional area, the step size can be set to 10%, 20%, 30%, etc., to gradually increase or decrease the conductor cross-sectional area. Under each adjustment amplitude, the performance changes of the bus duct are tested through simulation or experiment, and the performance of each design indicator under different adjustment amplitudes is recorded to form an indicator performance optimization sensitive sequence.

[0059] Using data processing software such as Excel and Python, we calculated the performance optimization sensitivity series for each indicator, such as the arithmetic mean and median, to obtain the performance optimization sensitivity index for each design indicator. This performance optimization sensitivity index reflects the degree of impact of each design indicator on busduct performance. The larger the index value, the greater the impact of the corresponding design indicator on busduct performance.

[0060] The performance optimization sensitivity threshold is a pre-set value used to determine whether a design indicator is a sensitive design factor. The calculated performance optimization sensitivity indexes of multiple indicators are compared with the performance optimization sensitivity threshold. If the performance optimization sensitivity index of an indicator exceeds this threshold, the indicator is considered to be a factor that is sensitive to performance optimization and is marked as a sensitive design factor.

[0061] For example, in a bus duct system in a data center, the conductor cross-sectional area and the thickness of the insulating material are determined as multi-dimensional design indicators. The predetermined step size set is 10%, 20%, and 30%, corresponding to the cases where the conductor cross-sectional area increases by 10%, 20%, and 30%, respectively. Under an abnormal electromagnetic environment, the bus duct performance under each step size is tested, and the performance optimization sensitive sequence of the conductor cross-sectional area is obtained as follows: (performance improvement of 5%, performance improvement of 10%, performance improvement of 15%), and the performance optimization sensitive sequence of the insulating material thickness is obtained as follows: (performance improvement of 3%, performance improvement of 6%, performance improvement of 9%). For the performance optimization sensitive sequence of the conductor cross-sectional area, the average value is calculated to obtain a performance optimization sensitivity index of 10%; for the performance optimization sensitive sequence of the insulating material thickness, the average value is calculated to obtain a performance optimization sensitivity index of 6%. The performance optimization sensitivity threshold is set to 8%, then the conductor cross-sectional area is screened as a sensitive design factor, and the insulating material thickness does not reach the threshold and is eliminated.

[0062] Through the above-mentioned performance optimization sensitivity test and indicator screening, the design indicators that have a significant impact on the bus duct performance are accurately determined, which points out the direction for subsequent optimization design and makes the optimization process more targeted and efficient.

[0063] Furthermore, step S31 includes:

[0064] Step S311: extracting a first design indicator according to the multi-dimensional design indicator.

[0065] Step S312: Based on the predetermined step size set, randomly perturb the bus duct design scheme according to the first design indicator to obtain multiple bus duct design perturbation schemes.

[0066] Step S313: Modeling is performed according to the multiple bus duct design disturbance schemes to obtain multiple bus duct models.

[0067] Step S314: performing performance tests on the multiple bus duct models according to the abnormal electromagnetic environment to obtain multiple bus duct performance test data.

[0068] Step S315: performing change intensity evaluation on the plurality of bus duct performance test data respectively according to the bus duct test performance vector corresponding to the abnormal electromagnetic environment, and obtaining a plurality of bus duct performance change intensities.

[0069] Step S316: Output the multiple bus duct performance change intensities as a first indicator performance optimization sensitive sequence, and add the first indicator performance optimization sensitive sequence to the multiple indicator performance optimization sensitive sequences.

[0070] Specifically, each design indicator is sequentially extracted from the multi-dimensional design indicators as the first design indicator. For example, if the multi-dimensional design indicators include conductor cross-sectional area, insulation material thickness, and heat sink spacing, then the conductor cross-sectional area, insulation material thickness, and heat sink spacing are sequentially extracted as the first design indicators for subsequent performance optimization sensitivity testing.

[0071] Based on a predetermined set of step sizes, the first design indicator is randomly perturbed. For example, if the predetermined set of step sizes is 10%, 20%, and 30%, then multiple perturbation values ​​are randomly generated based on each step size to obtain multiple bus duct design perturbation schemes. For example, for the design indicator of conductor cross-sectional area, the predetermined set of step sizes is 10%, 20%, and 30%. Based on each step size, three random perturbation schemes are generated: 10% step size: generates conductor cross-sectional area schemes of 90%, 100%, and 110%; 20% step size: generates conductor cross-sectional area schemes of 80%, 100%, and 120%; 30% step size: generates conductor cross-sectional area schemes of 70%, 100%, and 130%.

[0072] Based on the perturbation scheme designed for each bus duct, modeling tools are used to create corresponding bus duct models, resulting in multiple bus duct models. For example, finite element analysis (FEA) software such as ANSYS and COMSOL can be used to build electromagnetic, thermal, and mechanical bus duct models based on design parameters, including parameters such as conductor geometry, material properties, and current distribution. These modeling tools can help simulate bus duct performance under different electromagnetic environments, facilitating subsequent performance testing.

[0073] Placing multiple bus duct models in an abnormal electromagnetic environment for performance testing can be achieved through the simulation function of finite element analysis software. For example, the parameters of the abnormal electromagnetic environment (such as high current, high frequency, and strong magnetic field) can be set in ANSYS, and then the simulation can be run to obtain the performance test data of each model in each abnormal electromagnetic environment.

[0074] The obtained bus duct performance test data for each design is compared with the bus duct test performance vector corresponding to the abnormal electromagnetic environment (before the perturbation) to calculate the performance change intensity. For example, evaluation methods such as relative rate of change and standardized difference can be used. This change intensity evaluation quantifies the impact of each design perturbation scheme on bus duct performance, providing data support for the subsequent generation of sensitive sequences for performance optimization.

[0075] The obtained busbar performance variation intensities are combined into a sequence, which serves as the first indicator performance optimization sensitive sequence. This sequence is then added to the set of multiple indicator performance optimization sensitive sequences. This can be implemented using data structures such as lists and arrays, with data management using programming languages ​​such as Python. By integrating the performance optimization sensitive sequence for each design indicator into the overall sensitive sequence set, data support is provided for subsequent concentrated value calculation and sensitive design factor screening.

[0076] Furthermore, step S5 includes:

[0077] Step S51: extracting a first bus duct optimization decision based on the bus duct optimization first space.

[0078] Step S52: Evaluate the performance improvement of the first bus duct optimization decision according to the magnetothermal stress coupling performance loss vector to obtain a first bus duct performance improvement.

[0079] Step S53: Determine whether the first bus duct performance improvement is greater than or equal to a predetermined bus duct performance improvement.

[0080] Step S54: If the first bus duct performance improvement is greater than or equal to the predetermined bus duct performance improvement, the first bus duct optimization decision is added to the second bus duct optimization space.

[0081] Step S55: If the performance improvement of the first bus duct is less than the predetermined bus duct performance improvement, the first bus duct optimization decision is eliminated.

[0082] Step S56: Based on the magnetothermal stress coupling performance loss vector, continue to optimize the performance improvement of the first bus duct optimization space according to the predetermined bus duct performance improvement, and generate the second bus duct optimization space.

[0083] Specifically, a design scheme is extracted from the first bus duct optimization space in a storage order or randomly as the first bus duct optimization decision. The first bus duct optimization decision includes specific bus duct design parameters, such as conductor cross-sectional area, insulation material thickness, etc.

[0084] The first bus duct performance improvement is a quantitative indicator of the degree of performance improvement for a single optimized first decision. Analyze the various performance indicators in the magnetothermal stress coupling performance loss vector, such as magnetic loss, thermal loss, stress-related performance changes, etc. Then, apply the first bus duct optimization decision to a bus duct model (which can be a theoretical model or a simulation model built based on actual data) to observe the changes in these performance indicators under this decision. By comparing with the performance indicators before the decision was applied, the proportion or value of the performance improvement is calculated, and this value is the first bus duct performance improvement. For example, if the magnetic loss index in the magnetothermal stress coupling performance loss vector is 10% before the decision is applied and becomes 8% after the decision is applied, then the performance improvement in terms of magnetic loss is (10%-8%) / 10%=20%.

[0085] The predetermined busduct performance improvement is a pre-set threshold used to determine whether the optimized design achieves the desired performance improvement. The calculated first busduct performance improvement is compared with the predetermined busduct performance improvement. For example, if the predetermined busduct performance improvement is 0.10, any first optimization decision with a performance improvement greater than or equal to 0.10 is considered valid.

[0086] If the first bus duct performance improvement is greater than or equal to the predetermined bus duct performance improvement, the first bus duct optimization decision is added to the second bus duct optimization space. If the first bus duct performance improvement is less than the predetermined bus duct performance improvement, the first optimization decision is eliminated.

[0087] Continue searching for other possible optimization decisions in the first busduct optimization space. Based on the performance indicators in the magnetothermal stress coupling performance loss vector and the predetermined busduct performance improvement requirements, evaluate the performance improvement of the remaining decisions. Decisions that meet the requirements are added to the second busduct optimization space. Repeat this process until the optimization of the first busduct optimization space is complete, ultimately generating the second busduct optimization space.

[0088] Through continuous performance improvement optimization, a more optimal busbar duct optimization second space was generated, providing a high-quality solution set for subsequent variation and expansion optimization, ensuring the effectiveness and superiority of the final optimization strategy.

[0089] Furthermore, step S52 includes:

[0090] Step S521: Modeling is performed according to the first bus duct optimization decision to obtain a first bus duct optimization model.

[0091] Step S522: performing a performance test on the first bus duct optimization model according to the abnormal electromagnetic environment to obtain a first optimization decision performance test vector.

[0092] Step S523: performing loss identification on the first optimization decision performance test vector according to the bus duct health performance vector to obtain a first optimization decision performance loss vector.

[0093] Step S524: performing an improvement evaluation on the first optimization decision performance loss vector according to the magnetothermal stress coupling performance loss vector to obtain the first bus duct performance improvement.

[0094] Specifically, based on the extracted first bus duct optimization decision, a corresponding first bus duct optimization model is established using a modeling tool. For example, finite element analysis (FEA) software such as ANSYS or COMSOL can be used to establish electromagnetic, thermal, and mechanical models of the bus duct based on the design parameters.

[0095] The established first bus duct optimization model is placed in an abnormal electromagnetic environment for performance testing. This can be achieved through the simulation function of finite element analysis software. For example, in ANSYS, the parameters of the abnormal electromagnetic environment (such as high current, high frequency, and strong magnetic field) are set, and then the simulation is run to obtain performance test data. This performance test data is then used to form the first optimization decision performance test vector.

[0096] Compare the bus duct health performance vector with the first optimization decision performance test vector to calculate the loss value of each performance parameter. For example, if the standard ampacity in the bus duct health performance vector is 100A, while the ampacity in the first optimization decision performance test vector is 90A, a loss of 10A in ampacity can be identified. By comparing various performance indicators, all performance loss scenarios are identified, resulting in the first optimization decision performance loss vector.

[0097] The performance indicators in the magnetothermal stress coupling performance loss vector were compared and analyzed with the corresponding indicators in the performance loss vector of the first optimization decision. The improvement results of each performance indicator were combined to obtain the performance improvement of the first busbar trunking. For example, performance improvement = ∑(original loss - optimized loss) / ∑original loss. This performance improvement evaluation quantified the degree of improvement in busbar trunking performance achieved by optimizing the first decision, providing data support for subsequent screening.

[0098] Furthermore, step S6 includes:

[0099] Step S61: mutate the bus duct optimization second space to obtain a bus duct optimization variation domain.

[0100] Step S62: Optimizing the performance improvement of the bus duct optimization variation domain according to the magnetothermal stress coupling performance loss vector to obtain the bus duct optimization variation optimization domain.

[0101] Step S63: Expand the second bus duct optimization space according to the bus duct optimization variation optimization domain to generate a third bus duct optimization space.

[0102] Step S64: Based on the bus duct health performance vector, the bus duct optimization third space is optimized to minimize the bus duct performance loss to obtain the bus duct optimization strategy.

[0103] Specifically, a mutation operation is performed on each design solution in the second busbar duct optimization space. This can be achieved using mutation operators in genetic algorithms or by adding random perturbations to design parameters through Monte Carlo simulation. For example, parameters such as conductor cross-sectional area and insulation material thickness can be subjected to random values ​​within a certain range. All mutated solutions constitute the busbar duct optimization mutation domain.

[0104] For each element in the bus duct optimization variation domain (the bus duct optimization scheme after mutation), the performance improvement evaluation is performed according to a process similar to step S52. That is, firstly, a model is built based on the scheme after mutation, and a performance test is performed under an abnormal electromagnetic environment to obtain a performance test vector, which is compared with the bus duct health performance vector to obtain a performance loss vector, and finally, the improvement evaluation is performed based on the magnetothermal stress coupling performance loss vector. The elements whose performance improvement meets certain requirements (such as being greater than a certain threshold) are screened out to form the bus duct optimization variation search domain. The elements in this domain are optimization schemes that have been screened for performance improvement in the bus duct optimization variation domain, and are more likely to be schemes with better performance, providing a better choice for expanding the second space of bus duct optimization.

[0105] The elements in the bus duct optimization variation search domain are added to the bus duct optimization second space to generate the bus duct optimization third space. For each element (bus duct optimization scheme) in the bus duct optimization third space, the performance evaluation is performed again. The performance loss of each scheme relative to the bus duct health performance vector is calculated. For example, the difference or ratio of performance indicators such as power loss, temperature rise, and stress change with the corresponding indicators in the healthy state of the bus duct is calculated, and these performance indicators are combined to obtain an overall performance loss value. Then, find the solution with the smallest performance loss, which is the bus duct optimization strategy.

[0106] Furthermore, step S61 includes:

[0107] Step S611: performing curve fitting according to the bus duct performance improvement corresponding to each bus duct optimization decision in the second bus duct optimization space, and constructing a bus duct performance improvement curve.

[0108] Step S612: performing a variation value evaluation on each bus duct optimization decision according to the bus duct performance improvement curve to obtain a variation value coefficient of each decision.

[0109] Step S613: mutate the second bus duct optimization space according to the decision variation value coefficients to obtain the bus duct optimization variation domain.

[0110] Specifically, the busbar performance improvement data corresponding to each busbar optimization decision within the second busbar optimization space is obtained, generating multiple data points. Each data point is a binary array of a busbar optimization decision and its corresponding performance improvement. Common curve fitting algorithms, such as polynomial fitting (using least squares to determine the coefficients of the polynomial), are then used to fit these discrete data points into a continuous curve, thereby constructing the busbar performance improvement curve.

[0111] Based on the constructed busbar performance improvement curve, we analyze the points on the curve corresponding to each busbar optimization decision, as well as the shape of the curve nearby. For example, if the point corresponding to a busbar optimization decision is located in a portion of the curve with a steep slope and a clear upward trend, then mutating it has the potential to bring significant performance improvement, and the variation value coefficient will be high. Conversely, if it is located in a flat or downward trending portion of the curve, the variation value coefficient will be low. Obtaining the variation value coefficient for each decision quantifies the potential value of mutating each busbar optimization decision, providing a basis for targeted mutation operations.

[0112] The second space of bus duct optimization is mutated according to the variation value coefficient of each decision. For bus duct optimization decisions with higher variation value coefficients, larger or more complex variation operations can be performed; for decisions with lower variation value coefficients, smaller or simpler variation operations can be performed. For example, if the variation value coefficient of a bus duct optimization decision is greater than a certain threshold value T1, a combination of multiple variation methods can be used (such as changing multiple parameters at the same time, changing parameters significantly, etc.); if the variation value coefficient is between T2 and T1 (T2 < T1), a single, smaller variation method can be used; if the variation value coefficient is less than T2, only very small random adjustments can be made or even no variation can be performed. Through this variation value coefficient-based variation operation, resources can be used more efficiently, mutations can be performed in directions that are more likely to produce better results, and the probability of obtaining a better bus duct optimization solution can be increased.

[0113] Furthermore, step S1 includes:

[0114] Step S11: performing a normal performance parameter sample search on the bus duct to obtain a normal performance sample set of the bus duct.

[0115] Step S12: performing classification according to the bus duct normal performance sample set to obtain a plurality of bus duct performance sample areas.

[0116] Step S13: performing centralized value calculation on the plurality of bus duct performance sample areas respectively to obtain a bus duct performance health parameter set.

[0117] Step S14: constructing the bus duct health performance vector according to the bus duct performance health parameter set.

[0118] Specifically, by accessing databases, reviewing historical records, and collecting on-site monitoring data, we retrieve samples of bus duct performance parameters under normal electromagnetic conditions. These performance parameters include current carrying capacity, insulation resistance, temperature rise, and mechanical strength. The retrieved samples are stored in a collection to form a normal bus duct performance sample set.

[0119] Classify the bus duct normal performance sample set. Classification algorithms such as cluster analysis and decision trees can be used to divide the sample set into multiple sample areas based on the similarity of performance parameters. For example, classification can be performed based on characteristics such as the current carrying capacity range and the insulation resistance level to obtain multiple bus duct performance sample areas. Each bus duct performance sample area is a sample subset divided according to the classification rules, and the samples within each subset have similar performance characteristics. For example, the bus duct normal performance sample set can be divided into three sample areas based on current carrying capacity: low current area (0-200A), medium current area (201-400A), and high current area (401-600A).

[0120] Centralized values ​​are calculated for each busbar trunking performance sample zone. For example, the mean or median of performance parameters such as current carrying capacity, insulation resistance, temperature rise, and mechanical strength are calculated for each sample zone. These centralized values ​​constitute the busbar trunking performance health parameter set, reflecting the health of the busbar trunking in different performance ranges. For example, for samples in the low current zone, the mean current carrying capacity is calculated to be 150A, the mean insulation resistance is 120MΩ, the mean temperature rise is 45°C, and the mean mechanical strength is 900N. For samples in the medium current zone, the mean current carrying capacity is calculated to be 300A, the mean insulation resistance is 100MΩ, the mean temperature rise is 50°C, and the mean mechanical strength is 950N. For samples in the high current zone, the mean current carrying capacity is calculated to be 500A, the mean insulation resistance is 80MΩ, the mean temperature rise is 55°C, and the mean mechanical strength is 1000N. These mean values ​​constitute the busbar trunking performance health parameter set.

[0121] The concentrated values ​​of the busbar trunking performance health parameter set are arranged in a specific order to form a busbar trunking health performance vector. For example, by arranging the concentrated values ​​in the order of current carrying capacity, insulation resistance, temperature rise, and mechanical strength, the busbar trunking health performance vector is constructed as follows: (150A, 120MΩ, 45°C, 900N), (300A, 100MΩ, 50°C, 950N), and (500A, 80MΩ, 55°C, 1000N). The busbar trunking health performance vector comprehensively reflects the health of the busbar trunking in different performance ranges, providing a comparison standard for subsequent performance evaluation and optimization.

[0122] In summary, the method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment provided by the embodiment of the present application has the following beneficial effects:

[0123] The embodiment of the present application comprehensively quantifies the performance degradation of the bus duct in a complex electromagnetic environment through the mining of health performance parameters and the construction of the magnetothermal stress coupling performance loss vector, and accurately identifies the key optimization factors through sensitive design factor analysis, avoids blind adjustments, and improves the optimization efficiency. By establishing a two-layer optimization space, targeted optimization is performed first, and then global optimization is performed to ensure the stability and effectiveness of the optimization results in a complex electromagnetic environment. Finally, through the variation and expansion optimization strategy, the optimization scheme has stronger adaptability and robustness, and the electromagnetic compatibility, heat dissipation capacity and mechanical strength of the bus duct in complex electromagnetic environments such as high current, high frequency, and strong magnetic field are improved, thereby enhancing its long-term stability and reliability, and extending the service life of the bus duct.

[0124] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment, characterized in that: The method comprises: Mining the health performance parameters of bus duct and establishing the bus duct health performance vector; Based on the bus duct health performance vector, the bus duct is subjected to performance loss tests under different electromagnetic environments to determine a magnetothermal stress coupling performance loss vector corresponding to the abnormal electromagnetic environment; Performing a performance optimization sensitivity test on the multi-dimensional design indicators of the bus duct according to the abnormal electromagnetic environment to determine the sensitive design factors; Adjusting the bus duct design scheme according to the sensitive design factors to establish a first bus duct optimization space; Optimizing the performance improvement of the first bus duct optimization space according to the magnetothermal stress coupling performance loss vector, and establishing a second bus duct optimization space; The second bus duct optimization space is mutated, expanded, and optimized according to the bus duct health performance vector to generate a bus duct optimization strategy.

2. The method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment according to claim 1, characterized in that: Based on the bus duct health performance vector, a performance loss test is performed on the bus duct under different electromagnetic environments to determine a magnetothermal stress coupling performance loss vector corresponding to an abnormal electromagnetic environment, including: Based on the bus duct health performance vector, a performance loss test is performed on the bus duct according to M electromagnetic environments to obtain M bus duct performance loss coefficients, where M is a positive integer greater than 1; Determining whether the performance loss coefficients of the M bus ducts are greater than or equal to a predetermined performance loss coefficient; If any one of the M bus duct performance loss coefficients is greater than or equal to the predetermined performance loss coefficient, the abnormal electromagnetic environment is obtained; The bus duct test performance vector corresponding to the abnormal electromagnetic environment is subjected to loss identification according to the bus duct health performance vector to generate the magnetothermal stress coupling performance loss vector.

3. The method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment according to claim 2, characterized in that: Based on the bus duct health performance vector, a performance loss test is performed on the bus duct according to M electromagnetic environments to obtain M bus duct performance loss coefficients, including: Extracting the mth electromagnetic environment according to the M electromagnetic environments, where m is a positive integer and 1≤m≤M; Performing a performance test on the bus duct according to the mth electromagnetic environment to obtain an mth bus duct test performance vector; Inputting the bus duct health performance vector and the mth bus duct test performance vector into P bus duct performance loss assessment models to obtain P bus duct performance loss assessment coefficients, where P is a positive integer greater than 1; Calculate the average of the P bus duct performance loss evaluation coefficients to obtain the mth bus duct performance loss coefficient, and add the mth bus duct performance loss coefficient to the M bus duct performance loss coefficients.

4. The method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment according to claim 1, characterized in that: Performing performance optimization sensitivity testing on the multi-dimensional design indicators of the bus duct according to the abnormal electromagnetic environment to determine sensitive design factors, including: Based on the abnormal electromagnetic environment, performing a performance optimization sensitivity test on the multi-dimensional design indicators according to a predetermined step size set to obtain multiple indicator performance optimization sensitive sequences; Performing centralized value calculations on the multiple indicator performance optimization sensitive sequences respectively to obtain multiple indicator performance optimization sensitivity indexes; Based on the multiple indicator performance optimization sensitivity indexes, the multi-dimensional design indicators are screened according to the performance optimization sensitivity threshold to obtain the sensitive design factors.

5. The method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment according to claim 4, characterized in that: Based on the abnormal electromagnetic environment, a performance optimization sensitivity test is performed on the multi-dimensional design indicators according to a predetermined step set to obtain multiple indicator performance optimization sensitive sequences, including: Extracting a first design indicator according to the multidimensional design indicator; Based on the predetermined step size set, randomly perturbing the bus duct design scheme according to the first design indicator to obtain multiple bus duct design perturbation schemes; Modeling is performed according to the multiple bus duct design disturbance schemes to obtain multiple bus duct models; Performing performance tests on the multiple bus duct models according to the abnormal electromagnetic environment to obtain multiple bus duct performance test data; According to the bus duct test performance vector corresponding to the abnormal electromagnetic environment, respectively evaluating the change intensity of the plurality of bus duct performance test data to obtain a plurality of bus duct performance change intensities; The multiple bus duct performance change intensities are output as a first indicator performance optimization sensitive sequence, and the first indicator performance optimization sensitive sequence is added to the multiple indicator performance optimization sensitive sequences.

6. The method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment according to claim 1, characterized in that: Optimizing the performance improvement of the first bus duct optimization space according to the magnetothermal stress coupling performance loss vector to establish a second bus duct optimization space includes: Extracting a first bus duct optimization decision according to the bus duct optimization first space; Performing a performance improvement evaluation on the first bus duct optimization decision according to the magnetothermal stress coupling performance loss vector to obtain a first bus duct performance improvement; Determining whether the first bus duct performance improvement is greater than or equal to a predetermined bus duct performance improvement; If the first bus duct performance improvement is greater than or equal to the predetermined bus duct performance improvement, adding the first bus duct optimization decision to the second bus duct optimization space; If the performance improvement of the first bus duct is less than the predetermined bus duct performance improvement, eliminating the bus duct and optimizing the first decision; Based on the magnetothermal stress coupling performance loss vector, the performance improvement degree of the first bus duct optimization space is continuously optimized according to the predetermined bus duct performance improvement degree to generate the second bus duct optimization space.

7. The method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment according to claim 6, characterized in that: Evaluating the performance improvement of the first bus duct optimization decision according to the magnetothermal stress coupling performance loss vector to obtain a first bus duct performance improvement includes: Modeling is performed according to the first bus duct optimization decision to obtain a first bus duct optimization model; Performing a performance test on the first bus duct optimization model according to the abnormal electromagnetic environment to obtain a first optimization decision performance test vector; Performing loss identification on the first optimization decision performance test vector according to the bus duct health performance vector to obtain a first optimization decision performance loss vector; An improvement degree of the first optimization decision performance loss vector is evaluated according to the magnetothermal stress coupling performance loss vector to obtain a performance improvement degree of the first bus duct.

8. The method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment according to claim 1, characterized in that: The bus duct optimization second space is mutated, expanded, and optimized according to the bus duct health performance vector to generate a bus duct optimization strategy, including: Mutating the bus duct optimization second space to obtain a bus duct optimization variation domain; Optimize the performance improvement of the bus duct optimization variation domain according to the magnetothermal stress coupling performance loss vector to obtain the bus duct optimization variation optimization domain; Expanding the second bus duct optimization space according to the bus duct optimization variation optimization domain to generate a third bus duct optimization space; The bus duct performance loss is minimized in the bus duct optimization third space based on the bus duct health performance vector to obtain the bus duct optimization strategy.

9. The method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment according to claim 8, characterized in that: Mutating the bus duct optimization second space to obtain a bus duct optimization variation domain includes: Performing curve fitting based on the bus duct performance improvement corresponding to each bus duct optimization decision in the second bus duct optimization space to construct a bus duct performance improvement curve; Performing a variation value evaluation on each bus duct optimization decision according to the bus duct performance improvement curve to obtain a variation value coefficient of each decision; The bus duct optimization second space is mutated according to the decision variation value coefficients to obtain the bus duct optimization variation domain.

10. The method for optimizing the magnetic thermal stress coupling performance of bus duct in a complex electromagnetic environment according to claim 1, characterized in that: Mining the health performance parameters of the bus duct and establishing the bus duct health performance vector include: Performing a normal performance parameter sample search on the bus duct to obtain a normal performance sample set of the bus duct; Classify the bus duct normal performance sample set to obtain multiple bus duct performance sample areas; Performing centralized value calculations on the plurality of bus duct performance sample areas respectively to obtain a bus duct performance health parameter set; The bus duct health performance vector is constructed according to the bus duct performance health parameter set.

Images