Design method of magnetic saturation reactor based on composite material and reactor

Abstract

The invention provides a design method of a magnetic saturable reactor based on a composite material and the reactor, and belongs to the technical field of magnetic saturable reactors. A double-phase composite magnetic material is prepared by adopting a melt rapid quenching method, a magnetic circuit equivalent circuit model of a four-column type iron core structure is established, and air gap geometric parameters are optimized; a game model with the purposes of minimizing eddy current loss and maximizing an iron core lamination coefficient is constructed to carry out silicon steel sheet thickness sequence optimization, silicon steel sheets and a composite material are processed into a specified geometric structure according to a section shape function calculation result, and a complete magnetic circuit is formed through a connection mode that a working winding is connected in series and a control winding is connected in parallel. And finally, the residual magnetism state of the double-phase composite magnetic material is changed by adjusting and controlling the current of the winding, so that the reactance value is linearly adjusted and controlled within a rated range, and the technical problem that the adjustment precision and the response speed are difficult to guarantee simultaneously in the wide-range reactance value adjustment process of the magnetic saturable reactor is solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of magnetic saturation reactors, in particular, relates to a design method of a magnetic saturation reactor based on a composite material and the reactor. BACKGROUND

[0002] As an important reactive power compensation device in power systems, the magnetic saturation reactor is widely used in power transmission, power distribution network regulation and industrial power quality control fields. The traditional magnetic saturation reactor has significant technical defects in actual operation. The magnetic characteristics of single silicon steel sheet material limit the regulation range and accuracy of the reactor, especially in dynamic regulation occasions requiring fast response, the magnetic hysteresis characteristics and eddy current loss of the traditional core material will cause regulation delay and energy efficiency decline. At the same time, the traditional two-column or three-column core structure has the problem of uneven magnetic flux distribution in the magnetic circuit design, which affects the linearity and stability of the regulation.

[0003] In the prior art, due to the use of single material and simple core structure in the traditional magnetic saturation reactor, when wide-range reactance value regulation is performed, the problems of insufficient regulation accuracy or slow response speed often occur, and it is difficult to simultaneously meet the dual requirements of accurate regulation and fast response. That is to say, in the prior art, there is a technical problem that the regulation accuracy and response speed of the magnetic saturation reactor are difficult to be simultaneously ensured during the wide-range reactance value regulation process. SUMMARY

[0004] Therefore, the application provides a design method of a magnetic saturation reactor based on a composite material, which can solve the technical problem that the regulation accuracy and response speed of the magnetic saturation reactor are difficult to be simultaneously ensured during the wide-range reactance value regulation process in the prior art.

[0005] The application is implemented in the following manner: the application provides a design method of a magnetic saturation reactor based on a composite material, which comprises the following steps: a dual-phase composite magnetic material is prepared by using a melt quenching method, the ratio of soft magnetic phase and hard magnetic phase is controlled and a heat treatment process is performed; an equivalent circuit model of a four-column core structure magnetic circuit is established, the magnetic resistance distribution of the direct-current magnetic flux path and the alternating-current magnetic flux path is calculated according to the Kirchhoff magnetic circuit law; the air gap length range is determined through a magnetic circuit magnetic resistance calculation function, and the optimal geometric parameters of the air gap are obtained through finite element simulation analysis; an upper-layer game model with the target of minimizing the eddy current loss and a lower-layer game model with the target of maximizing the core lamination coefficient are established, and the silicon steel sheet thickness sequence combination configuration is obtained through double-layer optimization solution; the silicon steel sheet and the dual-phase composite magnetic material are processed into a specified geometric shape structure according to the calculation results of the core cross-sectional shape function; the working winding and the control winding are wound on the working column and the control column respectively, the working winding adopts a series connection mode, and the control winding adopts a parallel connection mode to form a complete magnetic circuit; and the design of the reactor is completed.

[0006] The application also provides the electric reactor designed and manufactured by using the design method.

[0007] In a specific embodiment of the application, the soft magnetic phase proportion is divided into three ranges including 0.546 to 0.623, 0.624 to 0.701 and 0.702 to 0.779 through the material phase proportion optimization function calculation, and the magnetic permeability contribution rates of different proportion ranges are 0.325, 0.456 and 0.298 respectively, and a higher soft magnetic phase proportion range can improve the material response speed but reduce the magnetic stability.

[0008] In a specific embodiment of the application, the number of layers of the dual-phase composite magnetic material in the control column is distributed in the range of 12 to 18 through the core structure optimization function calculation, and the contribution rate of different layer numbers to the magnetic flux regulation accuracy shows an increasing trend, and too many layers can increase the manufacturing cost and the magnetic circuit complexity.

[0009] In a specific embodiment of the application, the air gap length is divided into three ranges including 0.125 to 0.247 mm, 0.248 to 0.369 mm and 0.370 to 0.492 mm through the magnetic circuit reluctance calculation function calculation, and the contribution rates of different length ranges to the magnetic circuit decoupling effect are 0.234, 0.456 and 0.310 respectively, and the negative influence of the uneven distribution of magnetic flux is reduced by adjusting the air gap position distribution ratio to 2:3:2.

[0010] In a specific embodiment of the application, the residual magnetism strength is divided into four ranges including 0.126 to 0.253 T, 0.254 to 0.381 T, 0.382 to 0.509 T and 0.510 to 0.637 T through the residual magnetism adjustment function calculation, and the contribution rates of different residual magnetism strength ranges to the electric reactance regulation accuracy are 0.187, 0.312, 0.356 and 0.145 respectively, and the negative influence of residual magnetism over-regulation is reduced by controlling the step regulation mode of winding current.

[0011] In a specific embodiment of the application, the proportion of the soft magnetic phase and the hard magnetic phase is 3:2, the air gap length is used for magnetic circuit decoupling and reluctance adjustment, the upper game model takes minimizing the eddy current loss as the target, the constraint conditions include that the silicon steel sheet thickness is greater than 0.2 mm and less than 0.4 mm, the lower game model takes maximizing the core lamination coefficient as the target, the upper game model function adopts a quadratic function mode, and the lower game model function adopts a fractional function mode.

[0012] This invention employs a four-column core structure design combining a dual-phase composite magnetic material with silicon steel sheets. The soft magnetic phase in the dual-phase composite magnetic material provides high permeability to ensure rapid magnetic flux response, while the hard magnetic phase provides high coercivity to ensure magnetic performance stability. Phase ratio optimization achieves a harmonious balance between response speed and adjustment stability. In the four-column core structure, the working column carries the main magnetic flux transmission, while the control column enables precise flux adjustment. The magnetic circuit design eliminates the uneven flux distribution defects found in traditional structures. A two-layer game theory model simultaneously optimizes the minimization of eddy current losses and the maximization of the core lamination coefficient, solving the technical problem in existing magnetically saturated reactors where it is difficult to simultaneously guarantee adjustment accuracy and response speed during wide-range reactance value adjustment. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the overall structure of the magnetically controlled reactor based on dual-phase composite magnetic materials in Example 3; Figure 3 This is a schematic diagram of the magnetization flux routing of the magnetically controlled reactor in Example 3; Figure 4 This is a schematic diagram of the magnetic induction intensity of the working winding-only magnetically controlled reactor in Example 3; Figure 5 This is a schematic diagram of the magnetic induction intensity of the magnetically controlled reactor when only the composite material has residual magnetism, as shown in Example 3. Figure 6 This is a schematic diagram of the magnetization flux routing of the magnetically controlled reactor in Example 3; Figure 7 This is a schematic diagram of the soft and hard magnetic exchange coupling effect of the two-phase composite magnetic material in Example 3; Figure 8 This is a schematic diagram of the decoupling of the two-phase composite magnetic material and the silicon steel sheet in Example 3; Figure 9 This is a schematic diagram of the equivalent magnetic circuit model of the magnetically controlled reactor mentioned in Example 3, including two sub-diagrams: (a) is the equivalent magnetic circuit model considering only DC magnetic flux, and (b) is the equivalent magnetic circuit of the magnetically controlled reactor model considering only AC magnetic flux. Figure 10 This is a schematic diagram of the equivalent circuit of the magnetically controlled reactor in Example 3; Figure 11 This is a schematic diagram of the manufacturing process of a magnetically controlled reactor prototype based on a two-phase composite magnetic material according to Example 3, including two sub-diagrams: (a) is a diagram of the assembly process, and (b) is a diagram of the test after assembly. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0015] like Figure 1 The diagram shown is a flowchart of a design method for a magnetically saturated reactor based on composite materials provided by this invention. This method includes the following steps: S01. A two-phase composite magnetic material was prepared by melt quenching method. The microstructure distribution was optimized by controlling the ratio of soft magnetic phase to hard magnetic phase to be 3:2 and by heat treatment process. S02. Establish the equivalent circuit model of the magnetic circuit of the four-column iron core structure, and calculate the magnetic reluctance distribution of the DC magnetic flux path and the AC magnetic flux path according to Kirchhoff's magnetic circuit law. S03. Determine the air gap length range through the magnetic circuit reluctance calculation function, and obtain the optimal geometric parameters of the air gap by combining finite element simulation analysis. S04. Establish an upper-level game model with the goal of minimizing eddy current loss and a lower-level game model with the goal of maximizing the core lamination coefficient. Obtain the silicon steel sheet thickness sequence combination configuration through dual-level optimization solution. S05. The silicon steel sheet and the two-phase composite magnetic material are processed into a specified geometric structure according to the calculation results of the core cross-sectional shape function. S06. The working winding and the control winding are wound on the working post and the control post respectively. The working winding is connected in series and the control winding is connected in parallel to form a complete magnetic circuit. S07. By adjusting the control winding current to change the remanent magnetization state of the two-phase composite magnetic material, the reactance value can be linearly adjusted and controlled within the rated range.

[0016] Specifically, the dual-phase composite magnetic material is a composite magnetic material formed by the uniform distribution of soft magnetic phase and hard magnetic phase at the nanoscale. The soft magnetic phase provides high permeability for fast magnetic flux response, while the hard magnetic phase provides high coercivity for magnetic property stability. After calculation using the material phase ratio optimization function, the proportion of soft magnetic phase is divided into three ranges: 0.546 to 0.623, 0.624 to 0.701, and 0.702 to 0.779. The permeability contribution rates corresponding to different proportion ranges are 0.325, 0.456, and 0.298, respectively. A higher proportion of soft magnetic phase will improve the material response speed but reduce magnetic stability. The negative impact of magnetic property mismatch can be reduced by controlling the phase transition rate within the range of 780 to 820 degrees Celsius during heat treatment.

[0017] The four-column core structure consists of two working columns and two control columns. The working columns are made of silicon steel sheets to carry the main magnetic flux transmission, while the control columns are made of alternating layers of silicon steel sheets and two-phase composite magnetic materials for magnetic flux adjustment. After core structure optimization function calculation, the number of two-phase composite magnetic material layers in the control columns is found to be between 12 and 18. The contribution rate of different number of layers to the magnetic flux adjustment accuracy shows an increasing trend. Too many layers will increase manufacturing costs and magnetic circuit complexity. The negative impact of magnetic circuit coupling is reduced by optimizing the interlayer thickness ratio to 1:1.2.

[0018] The magnetic circuit reluctance calculation function is used to determine the reluctance value and air gap parameters of each part of the core. The inputs include the permeability of silicon steel sheets obtained from the silicon steel sheet material property table, the permeability of composite materials obtained from the test results of two-phase composite magnetic materials, the core length obtained from the four-column core structure design, the cross-sectional area obtained from the core cross-sectional shape function output, and the air gap length determined from the air gap design requirements. The outputs are the reluctance values ​​of each magnetic circuit segment and the total reluctance value for use in determining the optimal geometric parameters of the air gap. The magnetic circuit reluctance calculation function adopts an additive operation combination mode expression: the total reluctance equals the length of the silicon steel segment divided by the product of the permeability and cross-sectional area of ​​the silicon steel sheet, plus the length of the composite material segment divided by the product of the permeability and cross-sectional area of ​​the composite material, plus the air gap length divided by the product of the vacuum permeability and cross-sectional area.

[0019] The air gap length was calculated using a magnetic circuit reluctance calculation function, resulting in three ranges: 0.125 to 0.247 mm, 0.248 to 0.369 mm, and 0.370 to 0.492 mm. The contribution rates of these different length ranges to the magnetic circuit decoupling effect were 0.234, 0.456, and 0.310, respectively. An excessively long air gap would lead to increased magnetic flux leakage. The negative impact of uneven magnetic flux distribution was reduced by adjusting the air gap position distribution ratio to 2:3:2. The air gap length is used for magnetic circuit decoupling and reluctance adjustment.

[0020] The two-layer game model is used to solve the multi-objective optimization problem of silicon steel sheet thickness sequence. The inputs include the operating frequency range determined from the reactor operating conditions, the magnetic induction intensity obtained from the magnetic circuit design, the eddy current loss coefficient obtained from the material parameters, the lamination process parameters determined from the manufacturing process, and the performance constraints determined from the design requirements. The output is the combination configuration of silicon steel sheet thickness sequence for silicon steel sheet processing. The two-layer game model adopts a coupled equation system mode, including an upper-layer game model and a lower-layer game model. The upper-layer game model aims to minimize eddy current loss, and the objective function is that the eddy current loss power is equal to the eddy current loss coefficient multiplied by the square of the frequency multiplied by the square of the magnetic induction intensity multiplied by the square of the silicon steel sheet thickness. The constraints include that the silicon steel sheet thickness is greater than 0.2 mm and less than 0.4 mm. The lower-layer game model aims to maximize the core lamination coefficient, and the objective function is that the core lamination coefficient is equal to the effective cross-sectional area divided by the total cross-sectional area. The constraints include that the core lamination coefficient is greater than 0.85 and less than 0.98. The coupling term of the two objective functions is the influence coefficient of silicon steel sheet thickness on the core lamination coefficient.

[0021] The upper-level game model function is used to optimize the problem of minimizing eddy current loss. The inputs include the operating frequency determined from the reactor's operating conditions, the magnetic induction intensity obtained from the magnetic circuit design, the eddy current loss coefficient obtained from the silicon steel sheet material parameters, the silicon steel sheet thickness determined from the material specifications, and the material resistivity obtained from the material's electrical performance test. The output is the optimal eddy current loss value and the corresponding silicon steel sheet thickness for use in the coupled calculation of the two-level game model. The upper-level game model function adopts a quadratic function mode to calculate the relationship between eddy current loss and the square of the silicon steel sheet thickness.

[0022] The lower-level game model function is used to optimize the problem of maximizing the core lamination coefficient. The inputs include the core cross-sectional area obtained from the four-column core structure design, the silicon steel sheet thickness obtained from the output of the upper-level game model function, the insulation layer thickness determined from the insulation design, the lamination gap determined from the manufacturing process, and the filling coefficient determined from the material filling design. The output is the optimal core lamination coefficient and the corresponding thickness configuration for use in the coupled calculation of the two-level game model. The lower-level game model function adopts a fractional function mode to calculate the relationship between the core lamination coefficient and the effective cross-sectional area divided by the total cross-sectional area.

[0023] The core cross-sectional shape function is used to calculate the geometric shape and dimensional parameters of each part of the core. The inputs include the core column diameter determined from the four-column core structure design, the cross-sectional utilization coefficient determined from the design specifications, the shape correction factor determined from geometric optimization, the material filling rate determined from the two-phase composite magnetic material filling design, and the machining tolerance determined from the machining accuracy requirements. The output is the cross-sectional shape parameters and machining dimensions of each core column for use in the machining of silicon steel sheets and two-phase composite magnetic materials. The core cross-sectional shape function adopts a geometric formula combination mode, including the formula for calculating the area of ​​the circular cross-section of the working column as pi multiplied by the square of the radius and the formula for calculating the area of ​​the square cross-section of the control column as the square of the side length.

[0024] The remanent magnetization was calculated using the remanent magnetization adjustment function, resulting in four ranges: 0.126 to 0.253T, 0.254 to 0.381T, 0.382 to 0.509T, and 0.510 to 0.637T. The contribution rates of different remanent magnetization ranges to the reactance adjustment accuracy were 0.187, 0.312, 0.356, and 0.145, respectively. Excessive remanent magnetization would lead to an increase in the adjustment delay rate. The negative impact of over-adjustment of remanent magnetization was reduced by controlling the step-by-step adjustment of the winding current.

[0025] The material phase ratio optimization function is used to determine the optimal ratio distribution of soft magnetic phase and hard magnetic phase in the two-phase composite magnetic material. The inputs include the cooling rate determined from the melt rapid quenching process parameters, the annealing temperature determined from the heat treatment process, the alloy element ratio determined from the material composition design, the saturation magnetic induction intensity obtained from the magnetic property test, and the grain size determined from the material preparation process. The output is the soft magnetic phase ratio range and the corresponding permeability contribution rate for use in the preparation of two-phase composite magnetic materials. The material phase ratio optimization function adopts a piecewise linear function mode to realize the classification calculation of different ratio ranges.

[0026] The core structure optimization function is used to determine the geometric parameters and layer configuration of each component in the four-column core structure. The inputs include the magnetic flux density determined from the magnetic circuit design requirements, the thickness of the two-phase composite magnetic material obtained from the material properties, the lamination process parameters determined from the manufacturing process, the interlayer insulation thickness determined from the insulation design, and the magnetic flux adjustment accuracy requirements determined from the magnetic circuit coupling analysis. The output is the control of the number of two-phase composite magnetic material layers and the interlayer thickness ratio in the column for use in the assembly of the four-column core structure. The core structure optimization function adopts an incremental function mode to calculate the contribution rate of the number of layers to the magnetic flux adjustment accuracy.

[0027] The remanence adjustment function is used to calculate the variation law of remanence state of two-phase composite magnetic materials under different control winding currents. The inputs include the control winding current determined by the control winding design, the coercivity obtained from the characteristics of the two-phase composite magnetic material, the control magnetic field strength determined by the magnetic circuit analysis, the hysteresis loop parameters obtained from the material hysteresis characteristic test, and the target remanence intensity determined by the reactance adjustment requirements. The output is the contribution rate of the remanence intensity range and the corresponding reactance adjustment accuracy to the linear adjustment control of the reactance value. The remanence adjustment function adopts a multi-segment step function mode to realize the classified adjustment of different remanence intensity ranges.

[0028] The specific implementation methods of the above steps are described in detail below.

[0029] The specific implementation of step S01 is to prepare a two-phase composite magnetic material using a melt rapid quenching method. First, a soft magnetic phase alloy and a hard magnetic phase alloy are prepared at a mass ratio of 3:2. The alloy is then melted to a temperature range of 1400℃ to 1600℃ using arc melting or induction melting to form a homogeneous melt. Then, a single-roll rapid quenching technique is used to quench the melt at a temperature of 10... K / s to 10 Rapid cooling at a rate of K / s on the copper roller surface forms an amorphous ribbon with a thickness of 20 μm to 50 μm. The ribbon is then pulverized to obtain nanoparticles with a particle size of 50 nm to 200 nm. These nanoparticles are subsequently annealed at 780 °C to 820 °C for 2 to 4 hours to optimize the phase distribution. The purpose of this process is to suppress grain growth through rapid cooling and form a nanoscale soft-hard magnetic phase composite structure. The material phase ratio optimization function is based on a piecewise linear algorithm to calculate the contribution of different soft magnetic phase proportions to permeability. When the soft magnetic phase proportion is in the range of 0.546 to 0.623, the permeability contribution rate is 0.325; in the range of 0.624 to 0.701, the contribution rate increases to 0.456; and in the range of 0.702 to 0.779, the contribution rate decreases to 0.298. This algorithm determines the optimal phase ratio configuration by establishing a linear mapping relationship between proportion and contribution rate. The heat treatment process uses the principle of controlling crystallization kinetics to regulate the phase transformation process to obtain an ideal microstructure distribution.

[0030] The specific implementation of step S02 involves establishing an equivalent magnetic circuit model of the four-column core structure. First, based on the core's geometry, the magnetic circuit is decomposed into four independent magnetoresistive elements: the working column magnetic circuit, the control column magnetic circuit, the yoke magnetic circuit, and the air gap magnetic circuit. Kirchhoff's magnetic circuit laws are used to establish the magnetomotive force balance equation and magnetic flux continuity equation for each magnetic circuit segment. The nodal magnetomotive force method is used to solve for the magnetomotive force distribution at each node in the magnetic circuit network. The distribution ratio of DC magnetic flux between the working and control columns and the transmission path of AC magnetic flux are calculated. A set of magnetoresistive matrix equations is established to describe the coupling relationship between each magnetic circuit segment. The purpose of this process is to provide a theoretical basis for subsequent air gap parameter optimization and magnetic circuit design. The equivalent magnetic circuit model uses the lumped parameter method to simplify the distributed magnetic circuit into a lumped magnetoresistive network. Based on Ohm's law for magnetic circuits, a linear relationship between magnetic flux and magnetomotive force is established. Matrix operations are used to solve for the magnetic flux distribution in the complex magnetic circuit network. This algorithm can accurately describe the coupling and transmission characteristics of magnetic flux in the four-column structure and provide a computational basis for magnetic circuit optimization design.

[0031] The specific implementation of step S03 involves determining the air gap length range using a magnetic circuit reluctance calculation function and obtaining optimal geometric parameters through finite element simulation analysis. First, the magnetic reluctance value of each magnetic circuit segment is calculated using the magnetic circuit reluctance calculation function. This function calculates the total reluctance as a linear superposition of the reluctance of the silicon steel segment, the composite material segment, and the air gap reluctance using an additive operation combination mode. Input parameters include the permeability of the silicon steel sheet, the permeability of the composite material, the length of each segment, and the cross-sectional area. Outputs the reluctance values ​​of each magnetic circuit segment and the total reluctance value. Then, a finite element magnetic field simulation model is established to analyze the influence of different air gap lengths on the magnetic flux distribution. Through parameter scanning analysis, the magnetic flux distribution within the air gap length range of 0.125 mm to 0.492 mm is determined. The decoupling effect of the magnetic circuit is as follows: when the air gap length is in the range of 0.125mm to 0.247mm, the decoupling contribution rate is 0.234; when the air gap length is in the range of 0.248mm to 0.369mm, the contribution rate reaches a maximum of 0.456; and when the air gap length is in the range of 0.370mm to 0.492mm, the contribution rate drops to 0.310. The purpose of this process is to determine the optimal air gap configuration through magnetic reluctance calculation and simulation analysis to achieve magnetic circuit decoupling and magnetic reluctance adjustment. The calculation function is based on the fundamental laws of magnetic circuits and uses an analytical method to quickly calculate the magnetic reluctance distribution. The finite element simulation uses an electromagnetic field numerical solution algorithm to accurately analyze the magnetic field distribution characteristics. The combination of the two methods can improve design efficiency while ensuring calculation accuracy.

[0032] The specific implementation of step S04 involves establishing a two-layer game model to solve the multi-objective optimization problem of the silicon steel sheet thickness sequence. First, an upper-layer game model is constructed with the objective of minimizing eddy current losses. A quadratic function model is used to establish the relationship between eddy current loss power and the square of the silicon steel sheet thickness. The constraint condition is set to allow the silicon steel sheet thickness to be within the range of 0.2mm to 0.4mm. Then, a lower-layer game model is constructed with the objective of maximizing the core lamination coefficient. A fractional function model is used to establish the relationship between the lamination coefficient and the ratio of the effective cross-sectional area to the total cross-sectional area. The constraint condition is set to allow the lamination coefficient to be within the range of 0.85 to 0.98. To establish the coupling relationship between the two-layer models, the thickness of the silicon steel sheet is used as a coupling variable to connect the upper and lower optimization problems. A sequential solution algorithm is adopted to first solve the lower-layer model to obtain the optimal solution of the lamination coefficient, and then feed the result back to the upper-layer model to optimize eddy current loss. Through iterative calculation until convergence, the global optimal solution of the silicon steel sheet thickness sequence is obtained. The purpose of this process is to determine the optimal silicon steel sheet configuration scheme under the dual requirements of minimizing eddy current loss and maximizing lamination coefficient. The game theory algorithm is based on Nash equilibrium theory to solve the multi-objective conflict problem. The sequential optimization method reduces the computational complexity and ensures the stability of the solution through layered solution.

[0033] The specific implementation of step S05 involves processing silicon steel sheets and dual-phase composite magnetic materials into a specified geometric structure according to the core cross-sectional shape function calculation results. First, the geometric parameters of each core column are calculated using the core cross-sectional shape function. This function uses a geometric formula combination mode to calculate the circular cross-sectional area of ​​the working column and the square cross-sectional area of ​​the control column respectively. The input parameters include the core column diameter, cross-sectional utilization coefficient, shape correction factor, material filling rate, and processing tolerance. The output is the cross-sectional shape parameters and processing dimensions of each core column. Then, based on the calculation results, the silicon steel sheets are laser-cut or wire-cut into the corresponding shapes. The dual-phase composite magnetic materials are pressed or powder-metallurgically formed into the required geometric shapes. Next, the silicon steel sheets and composite materials are alternately stacked and assembled into a control column structure according to the interlayer thickness ratio of 1:1.2. Finally, demagnetization and surface insulation treatments are performed to ensure the stability of material performance. The purpose of this process is to transform the theoretical design into a physical structure that is actually manufactured. The cross-sectional shape function accurately calculates the area and perimeter parameters of various cross sections based on a geometric analytical algorithm. The processing technology uses precision manufacturing technology to ensure that the geometric accuracy and surface quality meet the design requirements.

[0034] The specific implementation of step S06 involves winding the working winding and the control winding onto the working post and control post respectively to form a complete magnetic circuit. First, the number of turns and conductor cross-sectional area of ​​the working winding are calculated to meet the rated current and voltage level requirements. The working winding is evenly distributed on the two working posts using a multi-layer flat winding method and connected in series to ensure magnetic flux balance. Then, the number of turns and conductor specifications of the control winding are calculated to achieve the required control magnetomotive force range. The control winding is wound on the two control posts using a layered concentrated winding method and connected in parallel to reduce the control circuit impedance. Next, an insulation layer is placed between the windings and impregnated to improve insulation strength and mechanical strength. Finally, the windings are tested for DC resistance and insulation resistance to verify the winding quality. The purpose of this process is to establish the coupling relationship between the electrical circuit and the magnetic circuit to achieve controllable adjustment of the reactance value. The winding design is based on the law of electromagnetic induction and Ampere's circuital law to determine the relationship between the number of turns and the current. The winding process uses precision winding technology to ensure the accuracy of the number of turns and the symmetry of the winding to reduce leakage flux and unbalanced losses.

[0035] The specific implementation of step S07 is to achieve linear control of reactance by adjusting the remanent magnetization state of the two-phase composite magnetic material by adjusting the control winding current. First, a remanent magnetization adjustment function is established to describe the correspondence between the control current and the remanent magnetization intensity. This function uses a multi-segment step function mode to divide the remanent magnetization intensity into four adjustment ranges: 0.126T to 0.253T, 0.254T to 0.381T, 0.382T to 0.509T, and 0.510T to 0.637T. The contribution rates of different remanent magnetization intensity ranges to the reactance adjustment accuracy are 0.187, 0.312, 0.356, and 0.145, respectively. Then, the following steps are taken... The stepped current regulation method gradually changes the control winding current to avoid over-adjustment of residual magnetism. By real-time monitoring of the reactance value feedback signal, the magnitude and direction of the control current are adjusted to achieve precise control of the residual magnetism state. Finally, a linear relationship model between reactance value and residual magnetism intensity is established to achieve continuous adjustment of reactance value within the rated range. The purpose of this process is to realize the stepless adjustment function of the reactor by utilizing the hysteresis characteristics of two-phase composite magnetic materials. The residual magnetism regulation algorithm establishes the mapping relationship between current and residual magnetism based on the hysteresis loop theory and the domain reversal mechanism. The stepped control strategy simplifies the control complexity of the nonlinear magnetization process and improves the adjustment accuracy and response speed through a piecewise linearization method.

[0036] Furthermore, in the design of two-phase composite magnetic materials, soft magnetic phase alloys are typically selected from materials such as iron-silicon alloys (Fe-Si), iron-nickel alloys (Fe-Ni), iron-cobalt alloys (Fe-Co), and amorphous soft magnetic alloys (Fe-B-Si system). Among them, iron-silicon alloys have high permeability and low coercivity, making them suitable for applications with fast magnetic flux response. Iron-nickel alloys (such as permalloy) have extremely high initial permeability and low hysteresis loss, while iron-cobalt alloys have the highest saturation magnetic induction. Amorphous soft magnetic alloys have excellent soft magnetic properties and low loss characteristics. Hard magnetic phase alloys are mainly selected from neodymium iron boron alloys (Nd-Fe-B), samarium cobalt alloys (Sm-Co), aluminum nickel cobalt alloys (Al-Ni-Co), and ferrite permanent magnet materials. Among them, neodymium iron boron alloys have the highest energy product and coercivity, samarium cobalt alloys have excellent temperature stability and corrosion resistance, aluminum nickel cobalt alloys have good temperature characteristics but relatively low coercivity, and ferrite permanent magnet materials are inexpensive but have relatively weak magnetic properties. Different ratios of soft and hard magnetic phases produce significantly different overall magnetic performance effects: when Fe-Si alloy is used as the soft magnetic phase and Nd-Fe-B alloy as the hard magnetic phase in a ratio of 3:2, the composite material exhibits high permeability and moderate coercivity, with fast response speed and good tuning stability; when the ratio of soft magnetic phases increases to 4:1, the permeability of the material is significantly improved, and the magnetic flux response speed is accelerated, but the coercivity decreases and the magnetic stability weakens; when the ratio of hard magnetic phases increases to 1:1, the coercivity and remanence of the material are enhanced, and the tuning accuracy is improved, but the permeability... The initial permeability decreases, resulting in a slower response speed. When permalloy is combined with Sm-Co alloy, extremely high initial permeability and excellent temperature stability can be obtained, making it suitable for high-precision adjustment applications. When amorphous soft magnetic alloy is combined with ferrite, low cost and low loss characteristics can be obtained, making it suitable for mass production applications. By adjusting the composition ratio of different alloys and heat treatment process parameters, the hysteresis loop shape of composite materials can be further optimized to achieve linear control in the flux adjustment process, meeting the comprehensive requirements of different application scenarios for response speed, adjustment accuracy, and cost control.

[0037] In addition, the present invention also provides a reactor designed and manufactured using the above-described design method.

[0038] Specifically, the magnetically saturated reactor manufactured according to the above design method has the following specific structure: The reactor adopts a four-column core structure as the core magnetic circuit system, consisting of two working columns and two control columns forming a closed magnetic circuit frame. The two working columns are entirely composed of multi-layer silicon steel sheets. The thickness of the silicon steel sheets is determined to be within the range of 0.2mm to 0.4mm after optimization using a two-layer game model. They are configured according to a specific thickness sequence to simultaneously minimize eddy current losses and maximize the lamination factor. The lamination factor is controlled between 0.85 and 0.98. The working column cross-section adopts a circular design, and the cross-sectional area is calculated according to the formula of pi multiplied by the square of the radius. A thin insulating layer is set between the silicon steel sheets, and they are tightly stacked according to the optimized lamination process parameters to form a low magnetic reluctance main magnetic flux transmission channel. The two control columns employ a composite structure consisting of alternating layers of silicon steel sheets and nano-dual-phase composite magnetic materials. The number of nano-dual-phase composite magnetic material layers ranges from 12 to 18, with each layer having a thickness of 20 μm to 50 μm. These layers are prepared by melt quenching using a 3:2 mass ratio of soft magnetic phase to hard magnetic phase. The proportion of the soft magnetic phase varies from 0.546 to 0.779 to obtain different permeability response characteristics. The hard magnetic phase provides stable coercivity and remanence retention. The material undergoes annealing heat treatment at 780℃ to 820℃ for 2 to 4 hours to form a uniformly distributed nanoscale dual-phase composite structure. The control column cross-section adopts a square design, with the cross-sectional area calculated using the formula of the square of the side length. The thickness ratio between the silicon steel sheet layer and the composite material layer is 1:1.2, and insulation is provided between the layers to reduce the influence of magnetic circuit coupling. Four iron core columns are connected by upper and lower yokes to form a complete magnetic circuit. The yokes are also made of stacked silicon steel sheets. Air gap structures are set between the working columns and the control columns, as well as at the connection between the control columns and the yokes. The length of the air gaps is divided into three ranges according to the magnetic circuit decoupling requirements: 0.125 to 0.247 mm, 0.248 to 0.369 mm, and 0.370 to 0.492 mm. The air gap positions are set in different magnetic circuit segments according to a 2:3:2 distribution ratio to optimize the uniformity of magnetic flux distribution. The air gaps are precisely positioned and controlled by non-magnetic material shims. The working windings are wound in a multi-layer flat winding method on two working columns. The cross-sectional area of ​​the winding conductors is determined according to the rated current and current density requirements. The number of turns is determined by the law of electromagnetic induction and magnetic circuit calculations to meet the rated voltage level. The two working windings are connected in series to ensure magnetic flux balance and electrical symmetry. The outer layer of the windings is covered with an insulating layer and impregnated to improve insulation strength and mechanical strength. The control windings are wound on two control posts in a layered centralized winding method. The number of turns of the control windings is determined by calculation based on the required control magnetomotive force range and the coercivity characteristics of the nano-two-phase composite magnetic material. The two control windings are connected in parallel to reduce the total impedance of the control circuit and improve the flexibility of current regulation. The control windings and the working windings are provided with main insulation isolation and the winding surfaces are coated with an insulating varnish layer.The entire core and winding system is fixed to an insulating support frame by a clamping device. All core column surfaces undergo demagnetization treatment to eliminate processing stress magnetization. The contact surfaces between the silicon steel sheets and the nano-two-phase composite magnetic material are surface-insulated to prevent interlayer short circuits. This reactor changes the remanent magnetization state of the nano-two-phase composite magnetic material by adjusting the magnitude and direction of the DC current in the control winding. The remanent magnetization intensity can be continuously adjusted in four stages within the range of 0.126T to 0.637T, thereby changing the reluctance of the control column and the saturation level of the overall magnetic circuit. Ultimately, this achieves linearly adjustable control of the working winding reactance value within the rated range. The adjustment process employs a stepped current control strategy to avoid over-adjustment of the remanent magnetization. A closed-loop control system is formed by real-time monitoring of the reactance value feedback signal to ensure that the adjustment accuracy and response speed meet the requirements of practical applications.

[0039] Specifically, the principle of this invention is as follows: The fundamental reason why this invention can solve the technical problem of simultaneously ensuring the adjustment accuracy and response speed of magnetically saturated reactors lies in its adoption of a multi-level synergistic optimization approach. At the material level, the design of the two-phase composite magnetic material follows the principle of synergistic interaction between soft and hard magnetic phases in magnetism. The high permeability of the soft magnetic phase allows for rapid establishment and change of magnetic flux, ensuring a fast response speed, while the high coercivity of the hard magnetic phase provides a stable remanence state, ensuring the controllability of adjustment accuracy. By controlling the ratio of the soft magnetic phase to the hard magnetic phase to be 3:2, optimal matching of the two magnetic phases is achieved, avoiding the contradiction of decreased accuracy during rapid response or slow response during precise adjustment caused by a single material.

[0040] At the structural level, the four-column core structure is designed based on the principle of magnetic circuit separation, assigning magnetic flux transmission and magnetic flux regulation functions to the working column and control column respectively. This avoids the nonlinearity problem caused by mutual interference between the main magnetic flux and control magnetic flux in traditional structures. The working column uses a pure silicon steel sheet structure to ensure efficient transmission of the main magnetic flux, while the control column uses an alternating superposition structure of silicon steel sheets and two-phase composite magnetic materials. The adjustable remanence of the composite material is utilized to achieve precise control of the magnetic reluctance of the magnetic circuit. This structural design transforms the process of adjusting the reactance value into the process of adjusting the remanence state of the composite material in the control column, thus decoupling the regulation mechanism.

[0041] At the optimization level, the establishment of the two-layer game model embodies the mathematical principle of multi-objective collaborative optimization. The upper-layer model aims to minimize eddy current losses to ensure energy efficiency during rapid response, while the lower-layer model aims to maximize the core lamination coefficient to ensure magnetic circuit stability during precise adjustment. The two objective functions are coupled through the common variable of silicon steel sheet thickness, enabling the optimization process to simultaneously consider both response speed and adjustment accuracy. Through the coordinated action of the magnetic circuit reluctance calculation function and the remanent magnetization adjustment function, a precise mapping relationship from the control winding current to the final reactance value is achieved, ensuring the linearity and controllability of the adjustment process.

[0042] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0043] The specific implementation of step S01 involves preparing a two-phase composite magnetic material using a melt quenching method. The optimal ratio of soft magnetic phase to hard magnetic phase is determined through a material phase ratio optimization function. This function uses a piecewise linear algorithm to calculate the contribution of different soft magnetic phase ratios to the permeability, as specifically expressed below: ; In the formula, The effective magnetic permeability of the composite material; For the first The permeability contribution rates for each of the following percentage ranges are 0.325, 0.456, and 0.298, respectively. For the first The median percentage of soft magnetic phase within a certain percentage range; This represents the actual proportion of the soft magnetic phase. and For the first The lower and upper limits of each percentage range; This is a unit step function. The range of soft magnetic phase proportions is divided into... , , The heat treatment temperature control function is expressed as: ; In the formula, This refers to the annealing temperature, expressed in °C. Base temperature 780℃; This is the temperature regulation coefficient, with a value of 12.5, and the unit is ℃; The proportional influence coefficient is 8.2, and is dimensionless. The value is 15.3, representing the temperature oscillation amplitude, in °C. For the first The median proportions of the soft magnetic phases in each of the three proportion ranges are 0.585, 0.663, and 0.741, respectively, and are dimensionless. The parameters were obtained using the following method: The phase structure ratio was determined by X-ray diffraction analysis. The permeability curves of samples with different proportions were obtained by fitting the data through a vibrating sample magnetometer test. It is obtained by calculating the arithmetic mean of the upper and lower limits of each percentage range.

[0044] The specific implementation of step S02 is to establish an equivalent circuit model of the magnetic circuit of the four-column iron core structure, and to establish a set of magnetomotive force balance equations and a magnetic flux continuity equation based on Kirchhoff's magnetic circuit law. The magnetomotive force matrix equation of the magnetic circuit network nodes is expressed as follows: ; In the formula, It is a magnetoresistive matrix; It is the magnetic flux vector; This represents the magnetic potential source vector. The specific form of the magnetoresistance matrix is: ; In the formula, and The reluctance of the working column is expressed in ohms (H). ; and To control the column reluctance, the unit is H. ; , and The reluctance of the yoke is expressed in ohms (H). The magnetic flux vector is The unit is Wb, where and These are the magnetic fluxes of the two working columns, and The magnetic flux of the two control columns are respectively, and the magnetomotive force source vector is... The unit is A, where and These are the magnetomotive forces of the two working columns, and These represent the magnetomotive forces of the two control columns. The core structure optimization function is used to determine the number of layers of the two-phase composite magnetic material in the control columns, and is expressed as an increasing function: ; In the formula, The optimal number of layers is dimensionless. The minimum number of layers is 12. The maximum number of floors is 18. Dimensionless for the required magnetic flux adjustment accuracy; The precision sensitivity coefficient is 3.2 and is dimensionless. This is the phase offset parameter, with a value of 1.8, and is dimensionless. This is a floor function. The parameters are obtained as follows: the reluctance of each segment is calculated using the magnetic circuit geometry and material permeability; the magnetomotive force source is calculated using the product of the number of winding turns and the current. The value is determined based on the required reactor adjustment accuracy, with a typical value of 0.02 to 0.05. and The data was obtained by fitting experimental data on magnetic flux adjustment accuracy.

[0045] The specific implementation of step S03 is to determine the air gap length range through a magnetic circuit reluctance calculation function. The reluctance calculation function adopts an additive operation combination mode, and the total reluctance calculation is expressed as: ; In the formula, Total magnetic reluctance, in ohms (H). ; This refers to the length of the silicon steel section, in meters (m). The magnetic permeability of silicon steel sheet is expressed in H / m. This refers to the cross-sectional area of ​​the silicon steel section, in meters (m²). ; The length of the composite material segment is in meters (m). The magnetic permeability of the composite material is expressed in H / m. The cross-sectional area of ​​the composite material segment is expressed in meters (m²). ; This refers to the air gap length, in meters (m). Let be the free permeability, and take a value of . H / m; The cross-sectional area of ​​the air gap is expressed in meters (m²). The piecewise function for air gap length divides the air gap length into three ranges based on the magnetic circuit decoupling effect, as shown below: ; In the formula, Contribution rate to the magnetic circuit decoupling effect; For the first The decoupling contribution rates for each air gap length range are 0.234, 0.456, and 0.310, respectively. and For the first The lower and upper limits of the air gap length range; It is a unit step function. The air gap length range is divided into... m、 m、 m. The parameter is obtained as follows: The typical value is 1000~5000H / m, obtained from the silicon steel sheet material property table. The hysteresis loop of the composite material was obtained by testing with a vibrating sample magnetometer; the length and cross-sectional area of ​​each segment were obtained by geometric measurement. The influence of different air gap lengths on magnetic flux distribution was obtained through finite element simulation analysis.

[0046] The specific implementation of step S04 involves establishing a two-layer game model to solve the multi-objective optimization problem of the silicon steel sheet thickness sequence. The upper-layer game model aims to minimize eddy current loss, and the objective function is expressed as: ; In the formula, This represents eddy current loss power, measured in W. This is the eddy current loss coefficient, with units of kg·m. ·s ·A ; The operating frequency is expressed in Hz. Magnetic flux density, measured in tons (T). This refers to the thickness of the silicon steel sheet, in meters (m). The resistivity of the silicon steel sheet is expressed in Ω·m. The lower-level game model aims to maximize the lamination coefficient of the core, and the objective function is expressed as: ; In the formula, is the core lamination coefficient, dimensionless; Effective cross-sectional area, in meters (m²) ; Total cross-sectional area, in meters. ; The number of silicon steel sheet layers is dimensionless. The width of the silicon steel sheet is in meters (m). The insulation layer thickness is expressed in meters (m). The total width is expressed in meters (m). The two-layer game coupling function is represented as: ; In the formula, This is the coupling effect coefficient, in W·m. The parameter acquisition method is as follows: Obtained from the material manufacturer's technical data, the typical value is 0.1–0.5 kg·m. ·s ·A ; and Determined based on the reactor's operating conditions. Typical values ​​are 50–400 Hz. Typical values ​​are 0.8–1.6T; Obtained through a four-probe method test, typical value is... Ω·m; Determined based on the specifications of the insulation material, typical value is... m.

[0047] The specific implementation of step S05 involves processing silicon steel sheets and two-phase composite magnetic materials into a specified geometric shape structure according to the calculation results of the core cross-sectional shape function. The core cross-sectional shape function adopts a geometric formula combination mode, and the calculation of the circular cross-sectional area of ​​the working column is expressed as follows: ; In the formula, This represents the effective cross-sectional area of ​​the working column; The radius of the working column; This refers to the cross-sectional utilization factor. This is a shape correction factor. The area calculation for the square cross-section of the control column is expressed as: ; In the formula, To control the effective cross-sectional area of ​​the column; To control the side length of the column; This refers to the material filling rate; This represents the influence coefficient of machining tolerance. The cross-sectional shape optimization function is expressed as: ; In the formula, For optimal cross-sectional configuration, dimensionless; and These are the perimeters of the working column and the control column, respectively, in meters (m). The calculation formulas are as follows: and The parameter acquisition method is as follows: According to the core design specifications, the value is taken as 0.85 to 0.95; The density is obtained through material density testing and calculation, with typical values ​​ranging from 0.75 to 0.95. The value is determined based on the required machining accuracy, with a typical value of 0.01 to 0.05. The value is determined based on the cross-sectional shape optimization, with typical values ​​ranging from 0.9 to 1.0.

[0048] The specific implementation method of step S06 is the same as described above, and will not be repeated in detail here.

[0049] The specific implementation of step S07 is to achieve linear adjustment control of the reactance value by adjusting the control winding current to change the remanent magnetization state of the two-phase composite magnetic material. The remanent magnetization adjustment function is expressed in a multi-segment step function mode as follows: ; In the formula, Remanence, expressed in tons (T). To control the winding current, the unit is A; For the first The median remanence intensity over a current range, in tons; and For the first The lower and upper limits of a current range, in amperes (A); For the first Midpoint current within a current range, in amperes (A); For the first The steepness coefficient for a given range, in units of A. ; For the first The adjustment range coefficient is dimensionless. This is a unit step function. The reactance adjustment function is expressed as: ; In the formula, This is an adjustable reactance value, in Ω; This is the basic reactance value, in Ω; This is the primary reactance adjustment factor, in units of T. ; This is the adjustment coefficient for the secondary term reactance, in units of T. ; This is the cubic reactance adjustment factor, in T. The remanence range is divided into: T, T, T, The corresponding contribution rates of reactance adjustment accuracy for T are 0.187, 0.312, 0.356, and 0.145, respectively. The parameter acquisition method is as follows: The residual magnetism was obtained by testing different control currents using a hysteresis loop tester. , , The reactance value was obtained through nonlinear fitting of measured data. The least squares method was used for fitting, and the required fitting accuracy was specified. ; Determined based on the material's magnetic hysteresis properties, typical values ​​range from 0.5 to 2.0 A. ; The value is determined based on the required adjustment precision, with a typical value of 0.1 to 0.3. The midpoint value for each current range is obtained through... Calculated and obtained.

[0050] It should be noted that the material phase ratio optimization function Based on the principle of piecewise linear mapping, a quantitative relationship between the proportion of soft magnetic phase and effective permeability is established. This function achieves precise division and weighted calculation of different proportion ranges through a step function. Compared with the traditional single-material proportioning method, this function can accurately predict the magnetic properties of composite materials based on the synergistic effect of soft and hard magnetic phases, avoiding a large amount of experimental verification process, significantly improving the efficiency of material design and the controllability of performance, and laying a theoretical foundation for achieving a balance between high permeability and high coercivity.

[0051] Heat treatment temperature control function A combination of logarithmic and sine functions is used to describe the nonlinear influence of phase ratio on annealing temperature. This function comprehensively considers the coupling effect of phase transformation kinetics and grain growth mechanism. Compared with traditional fixed-temperature heat treatment processes, this function achieves adaptive temperature adjustment based on material composition. It can optimize the microstructure distribution according to different soft magnetic phase ratios, effectively control interphase interface characteristics and magnetic domain structure, thereby obtaining a more stable and uniform magnetic property distribution.

[0052] Magnetic potential matrix equation of magnetic circuit network nodes A global magnetic circuit model of a four-pillar iron core is constructed based on Kirchhoff's magnetic circuit laws, and the complex magnetic circuit network is accurately solved through matrix operations. This equation has significant advantages over traditional equivalent magnetic circuit simplification methods, as it can simultaneously consider the magnetic coupling effects between the working column, control column, and yoke, accurately calculate the magnetic flux distribution and mutual influence of each magnetic circuit segment, and provide a precise theoretical basis for air gap parameter optimization and magnetic circuit design, avoiding calculation errors caused by simplified models.

[0053] Core structure optimization function The saturation characteristics of the arctangent function are used to model the increasing relationship between the number of layers and the adjustment accuracy. This function maps continuous accuracy requirements to discrete layer configurations through mathematical transformation. Compared with traditional empirical trial-and-error methods, this function establishes a quantitative relationship between adjustment accuracy and structural parameters, and can automatically determine the optimal layer configuration based on system performance requirements. This ensures both flux adjustment accuracy and controls manufacturing costs and structural complexity, achieving the best balance between performance and economy.

[0054] Magnetic resistance calculation function Based on Ohm's law for magnetic circuits, a linear superposition relationship of magnetic reluctance for each magnetic circuit segment is established. This function comprehensively considers the magnetic reluctance contributions of silicon steel sheets, composite materials, and air gaps. Compared to simplified calculation methods that ignore material differences, this function accurately distinguishes the magnetic reluctance characteristics of different materials, providing a theoretical tool for the precise design of hybrid material magnetic circuits. It can accurately predict the total magnetic reluctance value and guide the optimized design of air gap parameters, avoiding performance deviations caused by inaccurate magnetic reluctance calculations.

[0055] Piecewise function of air gap length A step function combination is employed to quantify the contribution of different air gap length ranges to the magnetic circuit decoupling effect. This function linearizes the continuous air gap variation process piecewise. Compared to traditional single air gap design methods, this function reveals the nonlinear relationship between air gap length and decoupling effect, identifies the optimal air gap length range, and quantifies its decoupling contribution. This provides precise guidance for achieving effective separation of the working magnetic circuit and the control magnetic circuit, significantly improving the pertinence and effectiveness of magnetic circuit design.

[0056] eddy current loss objective function Based on the law of electromagnetic induction, a quadratic relationship between eddy current loss and silicon steel sheet thickness is established. This function accurately describes the influence of frequency, magnetic induction intensity, and material properties on loss. Compared with traditional single-objective optimization methods, this function provides an accurate loss calculation model for multi-objective collaborative optimization. It can accurately evaluate the loss level of different thickness configurations during game-theoretic optimization, providing a quantitative basis for achieving low-loss design.

[0057] Objective function of stacking coefficients A model based on geometric relationships, defining the ratio of effective cross-sectional area to total cross-sectional area, quantifies the impact of silicon steel sheet thickness and insulation layer thickness on space utilization. Compared to traditional design methods that ignore lamination efficiency, this function achieves precise control over core space utilization. In game-theoretic optimization, it forms a constraint relationship with the eddy current loss function, ensuring maximum material utilization efficiency while reducing losses.

[0058] Two-layer game coupling function The coupling relationship between the upper and lower objective functions is established by multiplying the partial derivatives. This function quantifies the synergistic effect of silicon steel sheet thickness variation on the two objectives. Compared with traditional sequential optimization methods, this coupling function achieves coordinated solutions to multi-objective conflict problems, finding the optimal balance between minimizing eddy current loss and maximizing the lamination coefficient, thus avoiding local optima that may result from single-objective optimization.

[0059] Cross-sectional shape optimization function Based on the principle of maximizing the area-to-perimeter ratio, this function optimizes the core cross-section configuration, achieving a comprehensive improvement in material utilization and magnetic circuit efficiency through geometric optimization. Compared to traditional empirical design methods, this function establishes a quantitative optimization model for cross-sectional geometric parameters, simultaneously considering the geometric constraints of the working column and control column, achieving a globally optimal cross-section configuration, and improving the overall performance and manufacturing efficiency of the core.

[0060] Remanence adjustment function A combination of piecewise step functions and hyperbolic tangent functions is used to describe the nonlinear mapping relationship between control current and remanence intensity. This function accurately models the hysteresis characteristics of two-phase composite materials. Compared with traditional linear control methods, this function achieves precise description and control of complex hysteresis processes. It can employ differentiated adjustment strategies according to different remanence intensity ranges, avoiding remanence over-adjustment and significantly improving the accuracy and response speed of reactance adjustment.

[0061] Reactance adjustment function A nonlinear relationship between remanent magnetization and reactance is established using a cubic polynomial. This function accurately describes the influence of magnetic saturation on reactance characteristics through higher-order terms. Compared to traditional linear approximation methods, this function can maintain high-precision reactance prediction and control over a wide adjustment range, enabling precise adjustment of the reactor under different operating conditions. This provides a high-performance technical solution for reactive power compensation and voltage regulation in smart grids.

[0062] To better understand and implement this invention, a specific application scenario of this invention is provided in Embodiment 2 below: In this Embodiment 2, the specific implementation process of step S01 is as follows: a two-phase composite magnetic material for a magnetically saturated reactor with a rated power of 500kVA is prepared using a melt quenching method, and the soft magnetic phase alloy composition is set as follows: The composition of the hard magnetic phase alloy is The optimal proportion range is calculated using a material phase ratio optimization function. The input parameter is the cooling rate. K / s, annealing temperature range ℃, alloy element ratio saturation magnetic induction T, grain size nm. The proportion of soft magnetic phase. The calculation is set to three intervals, the first interval correspond Second interval correspond Third interval correspond Optimization function through material phase ratio Calculate the permeability contribution rate for each interval, where the contribution rate coefficient is... , , Select Calculations are performed because This percentage falls within the first interval, and the effective permeability is calculated. H / m. The annealing temperature is calculated using a heat treatment temperature control function. ℃.

[0063] The specific implementation process of step S02 is as follows: establish an equivalent circuit model of a four-column iron core magnetic circuit with a rated voltage of 10kV and a rated frequency of 50Hz, and set the diameter of the working column. mm, control column side length mm, yoke length mm. The optimal layer configuration of the two-phase composite magnetic material in the control column is calculated using a core structure optimization function, with the magnetic flux adjustment accuracy requirement as input. Precision sensitivity coefficient Phase offset parameters Calculations yielded The original plan was to have 12 layers, but due to a minimum layer limit, this was adjusted to 12 layers. A magnetoresistive matrix was established, and the magnetoresistive strength of the working column was set. H Control column magnetic reluctance H yoke magnetic reluctance H Through matrix equations Solve for the magnetic flux distribution in each magnetic circuit segment.

[0064] The specific implementation process of step S03 is as follows: the optimal configuration range of the air gap length is determined by the magnetic circuit reluctance calculation function, and the length of the silicon steel section is set. m, permeability of silicon steel sheet H / m, cross-sectional area of ​​silicon steel section m Length of composite material segment m, magnetic permeability of composite material H / m, cross-sectional area of ​​composite material segment m air gap cross-sectional area m The total reluctance was calculated using the total reluctance calculation function. The decoupling effect for different length ranges was analyzed using a piecewise function of the air gap length, and three air gap length intervals were defined. m、 m、 m, the corresponding decoupling contribution rates are respectively , , Select the air gap length. The value of m was verified, and since it falls in the second interval, the contribution rate of the decoupling effect is 0.456.

[0065] The specific implementation process of step S04 is as follows: establish a two-layer game model to optimize the configuration of silicon steel sheet thickness sequence and set the working frequency. Hz, magnetic flux density T, eddy current loss coefficient kg·m ·s ·A Resistivity of silicon steel sheets Ω·m, width of silicon steel sheet m, insulation layer thickness m, total width m. Selecting the thickness of the silicon steel sheet. m is calculated, and the eddy current loss objective function of the upper-level game model is obtained. W. Lower-level game model, stacking coefficient objective function, setting the number of silicon steel sheet layers. Layer, calculated .

[0066] The specific implementation process of step S05 is as follows: based on the calculation results of the core cross-sectional shape function, process the geometric structure of each component and set the working column radius. m, cross-section utilization factor Shape correction factor Control column side length m, material filling rate Machining tolerance influence coefficient The function for calculating the cross-sectional area of ​​the working column. m Control column cross-sectional area calculation function m Calculate the perimeter of the working column. m, control column perimeter m, through the cross-sectional shape optimization function Obtain the optimal cross-section configuration index.

[0067] The specific implementation process of step S06 is the same as described above, and will not be repeated in detail here.

[0068] The specific implementation process of step S07 is as follows: the reactance value is precisely controlled by the residual magnetism adjustment function, and the range of the control winding current is set. A. Establish four remanence intensity adjustment ranges. T, T, T, T, the corresponding median remanence is respectively T, T, T, T. Set the adjustment range coefficient for each interval. , , , Steepness coefficient A , A , A , A Select the control current. A calculation was performed to verify that the current value corresponds to the third remanent magnetization interval, and the midpoint current. A is obtained through the remanence adjustment function. T. Set the basic reactance value Ω, reactance adjustment coefficient T , T , T Through the reactance value adjustment function Ω. The contribution rates of each remanence intensity range to the reactor regulation accuracy are 0.187 for the first range, 0.312 for the second range, 0.356 for the third range, and 0.145 for the fourth range, respectively, which verifies the distribution characteristics of the reactor regulation performance under different remanence states.

[0069] It should be noted that the interpretations of the variables and subscripts involved in this invention are shown in Tables 1-1 and 1-2.

[0070] Table 1-1 Variable Explanation Table

[0071] Table 1-2 Variable Explanation Table

[0072] Example 3 provides a specific application scenario: A research team undertook a technical challenge to design a reactive power compensation device for a large-scale wind farm with an installed capacity of 200MW. Due to the randomness and intermittency of the wind turbine output power, the reactive power demand of the power grid changes frequently and significantly. Traditional fixed reactors cannot meet the requirements for rapid adjustment, while thyristor-controlled reactors, although fast in response, suffer from severe harmonic pollution. The research team decided to adopt the magnetically saturated reactor technology based on two-phase composite magnetic materials from Example 3, designing a magnetically controlled reactor device with a rated capacity of 50Mvar and a voltage level of 35kV to achieve smooth adjustment of the reactive power of the wind farm.

[0073] The research team first prepared a two-phase composite magnetic material according to step S01, and then selected... Alloys, as soft magnetic phases, The alloy is used as the hard magnetic phase, and the mass ratio of the soft magnetic phase to the hard magnetic phase is set at 3:2. The alloy is melted at 1450℃ using a melt quenching method, and then quenched using a single-roll quenching technique. Amorphous ribbons with a thickness of 35 μm were prepared by cooling at a rate of K / s, and then pulverized into nanoparticles with an average particle size of 120 nm. The proportion of the soft magnetic phase was determined by calculating the material phase ratio optimization function. This proportion falls within the second interval of 0.624 to 0.701, corresponding to a permeability contribution rate of 0.456. The effective permeability is then calculated. H / m. Annealing temperature is calculated using a heat treatment temperature control function. Annealing at this temperature for 3.5 hours was performed to optimize the phase distribution. Material properties are shown in Table 2.

[0074] Table 2 Performance Parameters of Two-Phase Composite Magnetic Materials

[0075] The research team established the equivalent magnetic circuit model of the four-column iron core structure according to step S02, such as Figure 2 As shown, this magnetically controlled reactor employs an innovative four-column core structure, possessing excellent magnetic properties and flexible adjustment capabilities. The core consists of four columns, two of which are working columns, constructed of silicon steel sheets, serving as the main magnetic circuit path and bearing the task of transmitting magnetic flux. The other part is the control column, constructed of alternating layers of silicon steel sheets and two-phase composite magnetic materials. This design not only optimizes material performance but also considers the dynamic adjustment requirements of the reactor's magnetic flux during operation. The structural design of the control column allows for precise adjustment of the magnetic flux density under external control signals, thereby effectively controlling the reactance value. A control winding is installed in the control column to adjust the remanence of the two-phase composite magnetic material, thereby achieving precise adjustment of the reactance value. The working column diameter is set. mm, control column side length mm, yoke length mm, core height mm. The number of layers of the two-phase composite magnetic material in the control column is calculated using a core structure optimization function, and the required magnetic flux adjustment accuracy is input. The optimal number of layers is calculated. Layer. Establish the magnetoresistance matrix equation. The magnetic reluctance parameters of each magnetic circuit segment are set and the magnetic flux distribution is solved.

[0076] When an applied current flows through the control winding, such as Figure 3 As shown, it generates a control magnetic field, which adjusts the remanence by changing the magnetic state of the nanocomposite magnetic material in the control column, thereby affecting the reactance value of the entire magnetically controlled reactor. This control method is more precise and has a faster response than traditional reactors. When only the working winding is working, the core magnetic induction intensity is as follows: Figure 4 As shown, the main magnetic flux in the iron core exhibits a clockwise direction. When the remanence of the two-phase composite magnetic material is 0.6T, the magnetic induction intensity is as follows: Figure 5 As shown, the two-phase composite material and the core magnetic flux form a circuit. When the working winding is operating normally, and the remanence of the two-phase composite magnetic material is 0.6T, the magnetic induction intensity is as follows: Figure 6 As shown, the magnetic flux density on the right side of the core of the magnetically controlled reactor increases significantly, showing a saturation trend.

[0077] The research team determined the air gap length range according to step S03 and set the total length of the silicon steel section. m, permeability of silicon steel sheet H / m, cross-sectional area of ​​silicon steel section m Total length of composite material segment m, magnetic permeability of composite material H / m, cross-sectional area of ​​composite material segment m The total magnetic reluctance is calculated using the magnetic circuit reluctance calculation function, when the air gap length... When the length is m, this value falls within the range of 0.248–0.369 mm for the second air gap length, corresponding to a decoupling contribution rate of 0.456. The air gap in the core structure is an important means of adjusting the magnetic reluctance of the magnetic circuit and achieving magnetic flux decoupling. The design of the air gap can effectively reduce the nonlinear effects when the core is saturated, enhance the regulation capability of the reactor, and improve its response characteristics, enabling it to better adapt to fluctuations caused by load changes in the power system.

[0078] The research team prepared a two-phase composite magnetic material using an advanced melt-quenching method, aiming to create composite magnetic materials with unique properties through a rapid cooling process. The material consists of a soft magnetic phase and a hard magnetic phase, such as... Figure 7 As shown, the soft magnetic phase provides high permeability, facilitating magnetic flux flow, while the hard magnetic phase possesses high coercivity, enhancing the material's magnetic stability. This composite structure design can overcome the shortcomings of single magnetic materials to some extent and improve the overall magnetic properties of the material through their complementary characteristics. The soft magnetic phase primarily provides permeability, enabling the material to respond quickly under low magnetic field strengths, while the hard magnetic phase maintains good magnetic stability under high magnetic field strengths, effectively preventing magnetic saturation in the core material. By precisely controlling the ratio and distribution of the soft and hard magnetic phases, the overall magnetic properties of the material can be maximized. Based on the melt quenching method, heat treatment processes are used to further optimize the microstructure of the material, ensuring a uniform distribution of the soft and hard magnetic phases at the nanoscale. This uniform distribution enhances the exchange coupling between the soft and hard magnetic phases, thereby improving the material's magnetic response speed and adjustment precision, and significantly mitigating the hysteresis loss problem of traditional magnetic materials.

[0079] The research team established a two-layer game model to optimize the silicon steel sheet thickness sequence according to step S04, and set the working frequency. Hz, magnetic flux density T, eddy current loss coefficient kg·m ·s ·A Resistivity of silicon steel sheets Ω·m. Selecting the thickness of the silicon steel sheet. m, calculate the eddy current loss objective function of the upper-level game model to obtain W, the objective function of the stacking coefficients of the lower-level game model is obtained. The optimal silicon steel sheet thickness configuration is obtained by coordinating the conflict between the two objectives through a two-layer game coupling function. The design parameters are compared in Table 3.

[0080] Table 3 Comparison of Design Parameters

[0081] The research team followed step S05 to design the core geometry and set the working column radius. m, cross-section utilization factor Shape correction factor Control column side length m, material filling rate Machining tolerance influence coefficient Calculate the effective cross-sectional area of ​​the working column. m Effective cross-sectional area of ​​control column m The optimal cross-section configuration index is obtained through the cross-section shape optimization function. The nanoscale microstructure not only improves magnetic properties but also reduces the material's temperature rise, enabling the magnetically controlled reactor to effectively improve its regulation accuracy and response speed during long-term operation. The magnetic flux generated by the working winding flows clockwise through the core of the magnetically controlled reactor, according to the magnetic reluctance formula of the magnetic circuit. In the formula Permeability, For cross-sectional area, In an iron-core magnetic circuit, the introduction of a two-phase composite magnetic material and an air gap significantly increases the magnetic reluctance of the composite magnetic circuit. For example... Figure 8 As shown, this design completes the decoupling design of the magnetically controlled reactor, enabling the magnetically controlled reactor to adjust the magnetic flux by controlling the excitation current during the energizing process, thereby improving the magnetic circuit characteristics and enhancing the linear regulation performance of the magnetic control, providing a strong guarantee for the efficient operation of the reactor.

[0082] During the design process, the total magnetic reluctance of the magnetic circuit was adjusted by precisely calculating the length ratio of the air gap in the core and the control column, thus optimizing the overall efficiency of the reactor. The air gap design plays a crucial role in the magnetic circuit, effectively adjusting its reluctance and improving flux control. Combining silicon steel sheets with two-phase composite magnetic materials further optimized the magnetic circuit performance. In magnetically controlled reactors, coupling effects inevitably exist between the two windings; therefore, effective decoupling between the two independent magnetic circuits is achieved. The equivalent magnetic circuit model of the magnetically controlled reactor is as follows: Figure 9 As shown. In the equivalent magnetic circuit model considering only DC magnetic flux, according to Kirchhoff's voltage and current laws (equivalent to magnetomotive force and magnetic flux), we can obtain... ; Given that the magnetic reluctance of the silicon steel sheet is known, , Given that the DC magnetic flux is known, To determine the magnetoresistance of a two-phase composite magnetic material, the air gap magnetoresistance can be calculated. Then the air gap length can be calculated.

[0083] Considering only the alternating magnetic flux, the equivalent magnetic circuit of the magnetically controlled reactor model can be obtained according to Kirchhoff's voltage and current laws (equivalent to magnetomotive force and magnetic flux). ; , Given that the alternating magnetic flux is known, and it is undesirable for the alternating magnetic flux to flow through the two-phase composite magnetic material, then let and It is 0.

[0084] The research team implemented residual magnetism regulation control according to step S07, and set the control winding current range. A. Establish four remanence intensity adjustment ranges and select control current. A was verified, and the current value corresponds to the third remanent magnetization interval. The remanent magnetization intensity was calculated using the remanent magnetization adjustment function. T. Set the basic reactance value Ω, the adjustable reactance value is calculated using the reactance adjustment function. Ω. This material combination reduces eddy current losses and hysteresis losses, while enhancing flux control capabilities, enabling the reactor to find the optimal balance between fast response and high efficiency. The optimized air gap design and materials ensure that this magnetically controlled reactor maintains good performance even under high-frequency and load fluctuation conditions.

[0085] The research team fabricated a prototype, translating design concepts and theories into practical equipment. First, the prepared two-phase composite magnetic material was cut into standard dimensions and alternately stacked with silicon steel sheets to form an iron core. Precise processing ensured tight material contact, reducing losses and heat accumulation caused by poor contact. Next, the working winding and control winding were wound and installed at designated positions on the iron core, ensuring sufficient current coupling in the magnetic circuit. The remanence of the nanocomposite magnetic material was adjusted using the control winding. Figure 10 As shown, in the equivalent circuit of this magnetically controlled reactor The electromotive force of the power source is . To control the voltage, For the working circuit current, To control the loop current, , The working windings are connected in series. , To control the windings, they are connected in parallel. After the windings are installed, the air gap is adjusted to ensure that the magnetic reluctance of the magnetic circuit meets the design requirements. During this process, various parameters of the magnetic circuit are finely adjusted to achieve optimal operating conditions. Finally, the integrated control circuit and detection system are installed on the prototype to form a complete magnetically controlled reactor, and the control system performs real-time adjustment and feedback monitoring of the equipment.

[0086] A two-phase composite magnetic material is used as the core component of the iron core to improve electromagnetic performance and reduce eddy current losses, while silicon steel sheets are used to form the main magnetic circuit structure. The iron core undergoes precision cutting and grinding to ensure dimensional accuracy, and is then assembled and fixed with the silicon steel sheets to form a complete magnetic circuit. Based on this, working windings and control windings are wound to conduct working and control magnetic flux, respectively, separated by insulating materials to ensure electrical performance. After assembly, the iron core, windings, and support are integrated into the reactor frame, completing the prototype structure assembly. Figure 11 As shown in Table 4, the research team conducted comprehensive performance tests on the completed prototype.

[0087] Table 4 Prototype Performance Test Results

[0088] The research team conducted a six-month field operation test at a wind farm. The magnetically controlled reactor successfully achieved smooth regulation of reactive power in the wind farm, effectively improving the voltage stability and power factor of the power grid. Under conditions of frequent wind speed changes, the device could complete reactance adjustment within 50ms, achieving an adjustment accuracy of 1.6%, meeting the technical requirements for reactive power compensation in wind farms. Compared with traditional fixed reactors, the magnetically controlled reactor in Example 3 exhibits significant advantages in regulation performance, response speed, and energy consumption control.

Claims

1. A design method for a magnetically saturated reactor based on composite materials, characterized in that, Includes the following steps: A two-phase composite magnetic material was prepared by melt quenching method. During the preparation process, the uniform distribution of the soft magnetic phase and the hard magnetic phase was achieved by controlling the ratio of the soft magnetic phase to the hard magnetic phase. A magnetic circuit equivalent model of a four-column core structure is established, and the reluctance distribution of the DC and AC magnetic flux paths is calculated according to Kirchhoff's magnetic circuit law. The air gap length range is determined by the magnetic circuit reluctance calculation function, and the optimal geometric parameters of the air gap are obtained by combining finite element simulation analysis. An upper-level game model with the goal of minimizing eddy current loss and a lower-level game model with the goal of maximizing the core lamination coefficient are established. Based on the reluctance distribution and the optimal geometric parameters of the air gap, the silicon steel sheet thickness sequence combination configuration is obtained through double-layer optimization solution. The silicon steel sheets configured according to the solution and the prepared two-phase composite magnetic material are processed into a specified geometric structure according to the core cross-sectional shape function calculation results; the working winding and the control winding are wound on the working column and the control column respectively, the working winding adopts a series connection method, and the control winding adopts a parallel connection method to form a complete magnetic circuit; thus forming the design of the corresponding reactor.

2. The design method of a magnetically saturated reactor based on composite materials according to claim 1, characterized in that, The dual-phase composite magnetic material is specifically a composite magnetic material formed by uniformly distributing a soft magnetic phase and a hard magnetic phase at the nanoscale. The soft magnetic phase provides high permeability for fast magnetic flux response, while the hard magnetic phase provides high coercivity for magnetic performance stability. The negative impact of magnetic performance mismatch is reduced by adjusting the phase transition rate by controlling the heat treatment temperature within the range of 780 to 820 degrees Celsius.

3. The design method for a magnetically saturated reactor based on composite materials according to claim 2, characterized in that, The four-column core structure is specifically composed of two working columns and two control columns. The working columns are made of silicon steel sheets to carry the main magnetic flux transmission, and the control columns are made of alternating layers of silicon steel sheets and two-phase composite magnetic materials for magnetic flux adjustment. The negative impact of magnetic circuit coupling is reduced by optimizing the interlayer thickness ratio to 1:1.

2.

4. The design method of a magnetically saturated reactor based on composite materials according to claim 3, characterized in that, The magnetic reluctance calculation function is used to determine the reluctance value and air gap parameters of each part of the core. The inputs include the permeability of silicon steel sheet, the permeability of composite material, core length, cross-sectional area, and air gap length. The outputs are the reluctance values ​​of each magnetic circuit segment and the total reluctance value. The expression using the addition operation combination mode is: the total reluctance equals the length of the silicon steel segment divided by the product of the permeability and cross-sectional area of ​​the silicon steel sheet, plus the length of the composite material segment divided by the product of the permeability and cross-sectional area of ​​the composite material, plus the air gap length divided by the product of the permeability and cross-sectional area of ​​the vacuum.

5. The design method for a magnetically saturated reactor based on composite materials according to claim 4, characterized in that, The two-layer game model is used to solve the multi-objective optimization problem of silicon steel sheet thickness sequence. The inputs include the operating frequency range, magnetic induction intensity, eddy current loss coefficient, lamination process parameters and performance constraints. The output is the combination configuration of silicon steel sheet thickness sequence. The coupled equation group mode includes an upper-layer game model and a lower-layer game model. The coupling term of the two objective functions is the influence coefficient of silicon steel sheet thickness on the core lamination coefficient.

6. The design method of a magnetically saturated reactor based on composite materials according to claim 5, characterized in that, The core cross-sectional shape function is used to calculate the geometric shape and dimensional parameters of each part of the core. The inputs include the core column diameter, cross-sectional utilization coefficient, shape correction factor, material filling rate, and machining tolerance. The outputs are the cross-sectional shape parameters and machining dimensions of each core column. The geometric formula combination mode includes the formula for calculating the area of ​​the circular cross-section of the working column as pi multiplied by the square of the radius and the formula for calculating the area of ​​the square cross-section of the control column as the square of the side length.

7. The design method for a magnetically saturated reactor based on composite materials according to claim 6, characterized in that, The material phase ratio optimization function is used to determine the optimal ratio distribution of soft magnetic phase and hard magnetic phase in two-phase composite magnetic materials. The inputs include cooling rate, annealing temperature, alloy element ratio, saturation magnetic induction intensity and grain size. The output is the range of soft magnetic phase proportion and the corresponding permeability contribution rate. A piecewise linear function mode is used to realize the classification calculation of different proportion ranges.

8. The design method of a magnetically saturated reactor based on composite materials according to claim 7, characterized in that, The core structure optimization function is used to determine the geometric parameters and layer configuration of each component in the four-column core structure. The inputs include magnetic flux density, thickness of two-phase composite magnetic material, lamination process parameters, interlayer insulation thickness, and magnetic flux adjustment accuracy requirements. The output is the number of two-phase composite magnetic material layers and the interlayer thickness ratio in the control column. An incremental function mode is used to calculate the contribution rate of the number of layers to the magnetic flux adjustment accuracy.

9. A reactor, characterized in that, The design is carried out using the design method described in any one of claims 1-8. Specifically, it includes a four-column core structure, a working winding, and a control winding. The four-column core structure consists of two working columns, two control columns, and upper and lower yokes forming a closed magnetic circuit. The working columns are made of silicon steel sheets with a thickness of 0.2 mm to 0.4 mm, with a stacking factor of 0.85 to 0.98 and a circular cross-section. The control columns are made of alternating layers of silicon steel sheets and nano-dual-phase composite magnetic materials, with 12 to 18 layers of nano-dual-phase composite magnetic materials, a thickness ratio of silicon steel sheet layers to composite material layers of 1:1.2, and a square cross-section. The nano-dual-phase composite magnetic materials consist of a soft magnetic phase and a hard magnetic phase in a mass ratio of 3:2, with the soft magnetic phase accounting for 0.546 to 0.

779. The materials are prepared by melt quenching and annealed at 780°C to 820°C for 2 to 4 hours.

10. The reactor according to claim 9, characterized in that, An air gap is provided between the working column and the control column, with a length of 0.125mm to 0.492mm and a position distribution ratio of 2:3:

2. The working winding is wound on two working columns and connected in series. The control winding is wound on two control columns and connected in parallel.

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