Design method and device of low inductance value energy consumption resistor based on swarm intelligence algorithm

Abstract

The application discloses a low-inductance energy consumption resistor design method and device based on a swarm intelligence algorithm, and belongs to the field of high-power inductive energy storage load design. First, initial design parameters of the resistor are randomly generated according to a design resistance value of a laminated energy consumption resistor, mainly including the number of resistor sheets, the length, width and thickness of the resistor sheets. The inductance value under the parameters is calculated according to a stray inductance calculation formula, and is input into a grey wolf optimization algorithm as an adaptive value, and iterative calculation is performed on the adaptive value, and finally the design parameter with the minimum inductance value is found. Before iteration, corresponding boundary conditions are set according to physical limitations of resistor design. After a set number of iterations, finite element simulation software is used for verification to prevent model distortion. The application can realize the optimal design of the energy consumption resistor, reduce the stray inductance and improve the discharge performance on the basis of ensuring the accuracy of the resistance value. The application has a simple design process, higher calculation accuracy and convenient engineering implementation.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of high-power inductive energy storage load design, and particularly relates to a low-inductance energy consumption resistor design method and device based on a swarm intelligence algorithm. BACKGROUND

[0002] A superconducting magnet can withstand a much larger current than a conventional conductor in normal operation, and it stores a huge amount of electromagnetic energy. In order to ensure the safety of the superconducting magnet, when the circuit fails or other emergency situations require the circuit to be broken, the energy stored in the magnet must be quickly reduced at the same time. Energy consumption resistor is the best low-cost solution and has been widely used in various superconducting magnet operating circuits.

[0003] The energy consumption resistor will introduce stray inductance into the circuit, which will cause a high induced overvoltage in the operating circuit of the superconducting magnet when it is broken, causing damage to the circuit breaker and the magnet. Therefore, it is the primary goal of resistor design to minimize the stray inductance of the energy consumption resistor as much as possible. How to reduce the stray inductance in the design process has always been one of the difficult problems that the academic and industrial communities need to solve. SUMMARY

[0004] The purpose of the present application is to address the shortcomings of the existing layered energy consumption resistor design method, and to propose a low-inductance energy consumption resistor design method and device based on a swarm intelligence algorithm. This method can greatly reduce the distribution of stray inductance of the layered energy consumption resistor, and the design process is simple, which can effectively reduce the workload of engineering design personnel.

[0005] In order to solve the above technical problems, the present application is realized by the following technical scheme:

[0006] A low-inductance energy consumption resistor design method based on a swarm intelligence algorithm, the method comprising the following steps:

[0007] A. According to the design needs of the layered energy consumption resistor, determine the boundary of the resistor sheet parameters, including the number, length, width and thickness of the resistor sheet;

[0008] B. Take the design resistance value of the resistor as the input parameter, and randomly generate n groups of resistor sheet parameters within the boundary conditions, where n is the population number of the grey wolf optimization algorithm;

[0009] C. Calculate the stray inductance value under the n groups of resistor sheet parameters according to the stray inductance calculation formula, and take it as the fitness value of different individuals in the population;

[0010] D. According to the fitness value of different individuals in the population, alpha wolves, beta wolves and delta wolves are screened out, and the remaining individuals are omega wolves, the number, length, width and thickness of the resistor sheet are taken as the parameter coordinates of the individuals in the population, and the parameter coordinates of the omega wolves are updated according to the coordinates of the prey, that is, the optimal solution, until the stray inductance value is approached;

[0011] E. The finite element model is called to check the current optimal solution after every x iterations, and if the distortion is found, the evolution is continued from the last effective population;

[0012] F. Steps C to E are repeated until the maximum optimization iteration number is met; the coordinates of the alpha wolves in the result of the last iteration are output, and the coordinates of the alpha wolves in the result of the last iteration are the resistance parameters with the lowest stray inductance value.

[0013] A low-inductance energy consumption resistor design device based on a swarm intelligence algorithm, comprising:

[0014] A parameter determination module determines the boundary of the resistor sheet parameters according to the design needs of the laminated energy consumption resistor, and the resistor sheet parameters include the number, length, width and thickness of the resistor sheet;

[0015] A parameter generation module randomly generates n groups of resistor sheet parameters within the boundary conditions with the design resistance value of the resistor as the input parameter, wherein n is the population number of the grey wolf optimization algorithm;

[0016] An inductance calculation module calculates the stray inductance value under n groups of resistor sheet parameters according to the stray inductance calculation formula, and takes it as the fitness value of different individuals in the population;

[0017] A population screening module screens out alpha wolves, beta wolves and delta wolves according to the fitness value of different individuals in the population, and the remaining individuals are omega wolves, the number, length, width and thickness of the resistor sheet are taken as the parameter coordinates of the individuals in the population, and the parameter coordinates of the omega wolves are updated according to the coordinates of the prey, that is, the optimal solution, until the stray inductance value is approached;

[0018] A checking module calls the finite element model to check the current optimal solution after every x iterations, and if the distortion is found, the evolution is continued from the last effective population;

[0019] An iteration module repeats steps C to E until the maximum optimization iteration number is met; the coordinates of the alpha wolves in the result of the last iteration are output, and the coordinates of the alpha wolves in the result of the last iteration are the resistance parameters with the lowest stray inductance value.

[0020] An electronic device comprising: one or more processors; memory storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to carry out the method.

[0021] A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to carry out the method.

[0022] Compared with the prior art, the present application has the following advantages:

[0023] The present application can significantly reduce the stray inductance of the laminated energy consumption resistor, improve the discharge performance of the resistor, and has a simple design process, which can effectively reduce the workload of the designers. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The present application is a low-inductance energy consumption resistor design method based on swarm intelligence algorithm.

[0025] Figure 2 The present application is a typical structure diagram of the laminated energy consumption resistor.

[0026] In the drawing, the reference numerals are: current lead 1, resistor sheet 2, and tip bending 3. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other. In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme.

[0028] The embodiments of the present application will be described in detail below, and examples of the embodiments are shown in the drawings. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and cannot be understood as limiting the present application.

[0029] Reference is made to Figure 1 The present application is a low-inductance energy consumption resistor design method based on swarm intelligence algorithm. Taking the superconducting magnet quench protection system fast discharge resistor of the fusion reactor main key system comprehensive research facility as an example, the low-inductance energy consumption resistor design method and device based on swarm intelligence algorithm proposed by the present application are implemented.

[0030] A. According to the design needs of the layered energy dissipation resistor, the boundary of the parameters is determined. Limited by the site constraints, the number of resistor sheets is maximally 238, the total length is maximally 1 m, and the width is maximally 0.1 m. In order to reduce the cost, the thickness is the common size of the market, which is fixed at three values, 1 mm, 3 mm and 5 mm.

[0031] B. The design resistance of the resistor is taken as an input parameter, and n groups of resistor sheet parameters within the boundary conditions are randomly generated according to the parameter. Wherein n is the population number of the grey wolf optimization algorithm.

[0032] C. The fitness value corresponding to the n groups of parameters is calculated.

[0033] D. According to the fitness value of different individuals in the population, alpha wolf, beta wolf and delta wolf are selected, and the remaining individuals are omega wolf. The number, length, width and thickness of the resistor sheet are taken as the parameter coordinates of the individuals in the population. The parameter coordinates of the omega wolf are updated according to the coordinates of the prey, i.e. the optimal solution. It can be expressed by formula (1):

[0034] (1)

[0035] In the formula, is the difference vector; t represents the iteration number, represents the position of the current prey, is the current position of the omega wolf, is the position of the omega wolf to be moved to in the next iteration, and are coefficient vectors, which can be obtained by formula (2):

[0036] (2)

[0037] In the formula, and are random numbers between 0 and 1, is linearly reduced from 2 to 0 during the iteration process. When , the wolf pack is in the state of searching for prey, and the omega wolf is far away from the prey; when , the wolf pack is in the state of surrounding the prey, and the omega wolf approaches the prey, and finally completes the hunting.

[0038] Usually, the coordinates of the optimal solution are uncertain, so the coordinates of the individual with higher fitness value are used to replace the coordinates of the prey during the iteration calculation process. The overall mathematical description of the algorithm can be illustrated by formula (3):

[0039] (3)

[0040] In the above formula, are the intermediate update vectors, respectively, , , are the coefficient vectors of alpha wolf, beta wolf, delta wolf, respectively; , , , , , is the coefficient vector, which is obtained by formula (2); , , are the position coordinates of alpha wolf, beta wolf, delta wolf at this time, is the position coordinate of omega wolf after this iteration.

[0041] E. After each iteration calculation, use finite element software for simulation verification to prevent mathematical model distortion.

[0042] F. Repeat steps C to E until the maximum optimization iteration number is met. Output the coordinates of alpha wolf in the last iteration result, which is the resistance parameter with the lowest stray inductance value.

[0043] Preferably, the step B generates the initial parameters of the resistance randomly according to the following steps:

[0044] B1: The structure diagram of the laminated energy consumption resistance is as shown in Figure 2 , which contains current lead 1 and resistance sheet 2. When welding the end, the technology of sharp end bending welding is adopted to form a sharp end bending 3, which can be equivalent to a metal cuboid as a whole. Therefore, when calculating the resistance, formula (4) can be used:

[0045] (4)

[0046] In the formula, is the resistance value, is the resistivity of the material, is the length of the single-layer resistance sheet, is the number of resistance sheets, is the thickness of the resistance sheet, is the width of the resistance sheet.

[0047] B2: After determining the thickness of the resistance sheet, the width of the resistance sheet is randomly generated according to the heat dissipation requirement and boundary conditions of the resistance sheet .

[0048] B3: After determining the above parameters, the value of can be calculated by formula (4). According to the boundary conditions, the value is randomly generated in the available range and The parameter of the resistance is generated randomly.

[0049] Preferably, the step C evaluates the fitness value of each individual in the population according to the following steps:

[0050] C1: The fitness value of the individual in this scenario is the inductance value of the stray inductance. In the stacked power resistor, the stray inductance of each layer of the resistor sheet can be divided into self-inductance and mutual inductance. The self-inductance can be calculated by formula (5):

[0051] (5)

[0052] In the formula, is the geometric mean distance of the single layer of the resistor sheet; is the self-inductance of the single layer of the resistor sheet.

[0053] C2: The mutual inductance value of the two layers of the resistor sheet can be calculated by formula (6):

[0054] (6)

[0055] In the formula, is the geometric mean distance of the two layers of the resistor sheet; is the mutual inductance of the two layers of the resistor sheet.

[0056] C3: The inductance value of the connection between the stacked resistor and the external circuit through the current lead cannot be ignored and needs to be considered in the design and optimization process. The inductance value of the connection can be calculated by formula (7):

[0057] (7)

[0058] In the formula, the current lead is usually in the form of a square block, and the length and width are both , is the distance between the two current leads, is the width of the jth current lead, is the thickness of the jth current lead, is the geometric mean distance of the current lead; is the self-inductance of the current lead, is the mutual inductance of the current lead.

[0059] C4: The total stray inductance value of the power resistor can be obtained by formula (8):

[0060] (8)

[0061] Compared with the traditional experience design method, the stray inductance value of the laminated energy dissipation resistor can be reduced by more than 5%. Compared with the prior art, the advantages of the present application are: the stray inductance of the laminated energy dissipation resistor can be significantly reduced by using the present application, and the discharge performance of the resistor is improved. And the design process is simple, which can effectively reduce the workload of the designers. The present application has the following advantages which the prior art does not have: simple design process, higher calculation accuracy, and convenient engineering implementation.

[0062] The above only describes specific embodiments of the present application, but the technical features of the present application are not limited thereto, and any changes or modifications made by those skilled in the art within the scope of the present application are covered by the protection scope of the present application.

Claims

1. A design method for low-inductance power-consuming resistors based on swarm intelligence algorithms, characterized in that, The method includes the following steps: A. Based on the design requirements of the stacked energy-dissipating resistor, determine the boundaries of the resistor parameters, including the number, length, width, and thickness of the resistors; B. Using the designed resistance value as the input parameter, randomly generate n sets of resistor parameters within the boundary conditions, where n is the population size of the Grey Wolf optimization algorithm; C. Calculate the stray inductance values ​​under the parameters of n sets of resistors according to the stray inductance calculation formula, and use them as the fitness values ​​of different individuals in the population; D. Based on the fitness values ​​of different individuals in the population, alpha wolves, beta wolves, and delta wolves are selected, and the remaining individuals are omega wolves. The number, length, width, and thickness of the resistor sheet are used as the parameter coordinates of individuals in the population. The parameter coordinates of omega wolves are updated according to the coordinates of the prey, i.e. the optimal solution, until they approach the stray inductance value. E. After every x iterations, the finite element model is called to verify the current optimal solution. If the solution is distorted, it is rolled back to the most recent valid population and continues to evolve. F. Repeat steps C to E until the maximum number of optimization iterations is met; output the coordinates of alpha wolf in the result of the last iteration. The coordinates of alpha wolf in the result of the last iteration are the resistor parameter with the lowest stray inductance value. Step C evaluates the fitness value of each individual in the population according to the following steps: C1: Individual fitness in this scenario is the value of stray inductance. In a stacked energy-dissipating resistor, the stray inductance of each layer of resistor is divided into self-inductance and mutual inductance. Self-inductance is calculated by formula (5): (5) In the formula, denoted as , where is the geometric mean distance between single-layer resistor elements, and b is the width of the resistor element. The self-inductance of a single-layer resistor; C2: The mutual inductance of the two resistive layers is calculated using formula (6): (6) In the formula, d is the thickness of the resistor element. The geometric mean distance between the two layers of resistive elements; This represents the mutual inductance between the two resistive layers. C3: The stacked resistor is connected to the external circuit through the current lead, and the inductance value at the connection point is calculated using formula (7): (7) In the above formula, the current lead adopts a square structure, with both length and width being [missing information]. , This is the distance between the two current leads. Let j be the width of the j-th current lead. Let the thickness be the thickness of the j-th current lead. This is the geometric mean distance between the current leads; The self-inductance of the current lead. Mutual inductance of current leads; C4: The total stray inductance of the energy-consuming resistor is obtained by formula (8): (8)。 2. The low-inductance power consumption resistor design method based on swarm intelligence algorithm according to claim 1, characterized in that, In step D, the parameter coordinates of the omega wolf are updated based on the coordinates of the prey, i.e., the optimal solution, as expressed by formula (1): (1) In the formula, The difference vector; t represents the number of iterations. Indicates the current location of the prey. This indicates the current location of the omega wolf. For the coefficient vector, This represents the position the omega wolf should move to in the next iteration. and The coefficient vector is obtained using equation (2): (2) In the above formula, and It is a random number between (0,1). During the iteration process, it decreases linearly from 2 to 0, when... At this time, the wolf pack is in a hunting state, and the omega wolves stay away from the prey; when At this time, the wolf pack is in a state of surrounding and hunting prey. The omega wolf moves closer to the prey and eventually completes the hunt.

3. The low-inductance power consumption resistor design method based on swarm intelligence algorithm according to claim 2, characterized in that, During the iterative calculation process, the coordinates of individuals with higher fitness values ​​are used as equivalent substitutes for the coordinates of the prey, which is mathematically described as formula (3): (3) In the above formula, These are the intermediate update vectors, , , These are the coefficient vectors of the alpha wolf, beta wolf, and delta wolf, respectively. , , , , , The coefficient vector is obtained using formula (2); , , These are the current coordinates of the alpha wolf, beta wolf, and delta wolf. These are the coordinates of the omega wolf's position after this iteration.

4. The low-inductance power consumption resistor design method based on swarm intelligence algorithm according to claim 3, characterized in that, Step B generates the basic parameters of the resistor element according to the following steps: B1: The stacked energy-dissipating resistor sheet is equivalent to a metal cuboid. When calculating the resistance, formula (4) is used: (4) In the formula, The resistance value is... The resistivity of the material, The length of a single-layer resistor element. The number of resistors, The thickness of the resistor element. The width of the resistor element; B2: After determining the thickness of the resistor element, randomly generate the width of the resistor element based on its heat dissipation requirements and boundary conditions. ; B3: After determining the above parameters, calculate using formula (4) The value is randomly generated within a range of possible values ​​based on the boundary conditions. and With this value, the random generation process of the resistor parameters is now complete.

5. The low-inductance power consumption resistor design method based on swarm intelligence algorithm according to claim 1, characterized in that, The maximum number of resistors is 238, the maximum total length is 1m, and the maximum width is 0.1m.

6. The low-inductance power consumption resistor design method based on swarm intelligence algorithm according to claim 1, characterized in that, The thickness of the resistor sheet is fixed at three values: 1mm, 3mm, and 5mm.

7. A design apparatus for a low-inductance power-consuming resistor design method based on swarm intelligence algorithms as described in any one of claims 1-6, characterized in that, include: The parameter determination module determines the boundaries of the resistor parameters based on the design requirements of the stacked energy-dissipating resistors. The resistor parameters include the number, length, width, and thickness of the resistors. The parameter generation module takes the designed resistance value of the resistor as the input parameter and randomly generates n sets of resistor parameters within the boundary conditions, where n is the population size of the Grey Wolf optimization algorithm. The inductance calculation module calculates the stray inductance value under the parameters of n sets of resistors according to the stray inductance calculation formula, and uses it as the fitness value of different individuals in the population. The population selection module selects alpha wolves, beta wolves, and delta wolves based on the fitness values ​​of different individuals in the population. The remaining individuals are omega wolves. The number, length, width, and thickness of the resistors are used as the parameter coordinates of individuals in the population. The parameter coordinates of omega wolves are updated based on the coordinates of the prey, i.e. the optimal solution, until they approach the stray inductance value. The verification module calls the finite element model to verify the current optimal solution after every x iterations. If the solution is distorted, it will revert to the most recent valid population and continue to evolve. The iteration module repeats steps C to E until the maximum number of optimization iterations is met; it outputs the coordinates of the alpha wolf in the result of the last iteration, which is the resistance parameter with the lowest stray inductance value.

8. An electronic device, characterized in that, include: One or more processors; A memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, cause the processor to perform the method described in any one of claims 1 to 6.

Images