Design method of a magnetic valve reactor of a two-phase composite magnetic material and the reactor

Abstract

The application provides a design method of a dual-phase composite magnetic material magnetic valve reactor and the reactor, and belongs to the technical field of magnetic valve reactor design. The application establishes a mixed magnetic circuit structure of the dual-phase composite magnetic material and silicon steel sheets, adopts a 3:7 ratio of hard magnetic material and soft magnetic material for uniform distribution and proportioning at the nanometer level, sets a 0.2mm precise air gap between the two materials to realize magnetic circuit decoupling, optimizes the magnetic flux distribution by using the multi-stage gradient geometric design of the distributed magnetic valve structure, calculates the optimal excitation current vector, establishes a magnetic coupling coefficient calculation function to determine the winding parameters, realizes the effective separation of alternating current magnetic flux and direct current magnetic flux, adopts a multi-objective optimization algorithm and a game theory model to coordinate the balance relationship between the uniformity of the magnetic flux distribution and the loss control, verifies the design parameters by finite element simulation, and optimizes the design parameters to meet the performance requirements, and solves the technical problem of high energy loss caused by the magnetic flux path coupling of the magnetic valve reactor.

Description

Technical Field

[0001] This invention belongs to the field of magnetic valve reactor design technology, specifically, it relates to a design method and a reactor for a two-phase composite magnetic material magnetic valve reactor. Background Technology

[0002] As an important reactive power regulation device in power systems, magnetic valve reactors traditionally employ a single magnetic material combined with mechanical or electronic control methods to achieve continuous adjustment of reactance values. In applications such as power transmission, reactive power compensation, and voltage regulation, reactance values ​​are adjusted by changing the magnetic saturation level of the core. They are widely used in reactive power control of transmission lines, voltage regulation of substations, and power factor compensation for industrial power consumption. However, in traditional technology, the main magnetic flux generated by the working winding and the DC magnetic flux generated by the control winding propagate in the same magnetic circuit, causing magnetic flux paths to couple. This results in the control flux interfering with the main flux, and changes in the main flux also affect the stability of the control effect. This magnetic flux coupling phenomenon not only reduces the accuracy of reactance regulation but also generates additional hysteresis and eddy current losses, affecting the overall performance of the equipment. In the prior art, due to the strong coupling characteristics of the magnetic flux path, the main magnetic flux and the control magnetic flux cannot be effectively separated when the reactance value of the magnetic valve reactor is adjusted. This results in mutual interference during the adjustment process, causing the cross-propagation of magnetic flux in the same magnetic circuit to generate a large amount of hysteresis loss, eddy current loss and copper loss, which significantly increases the energy consumption of the equipment. In other words, the prior art has a technical problem of high energy loss caused by magnetic flux path coupling in the magnetic valve reactor. Summary of the Invention

[0003] In view of this, the present invention provides a design method and a reactor for a two-phase composite magnetic material magnetic valve reactor, which can solve the technical problem of high energy loss caused by magnetic flux path coupling in the prior art of magnetic valve reactor.

[0004] The present invention is implemented as follows: The first aspect of the present invention provides a design method for a two-phase composite magnetic material valve-type reactor, comprising: determining the cross-sectional area, length, and number of magnetic valves of the two-phase composite magnetic material and silicon steel sheet based on the rated voltage and rated capacity; establishing a core geometric model; establishing an equivalent magnetic circuit model, establishing equivalent magnetic circuits for AC and DC magnetic flux paths respectively, and determining each magnetic reluctance parameter through a magnetic reluctance calculation equation; calculating the air gap thickness through an air gap magnetic reluctance optimization equation; designing a distributed magnetic valve structure using a multi-objective optimization algorithm, and calculating the reduction ratio of the cross-sectional area of ​​each magnetic valve through a magnetic valve geometric optimization function. The design parameters are determined as follows: length and dimensions; the optimal excitation current vector of the control winding is calculated through physical mechanism analysis of magnetization intensity; the magnetization process of the two-phase composite magnetic material is described by the Langevin function magnetization equation; the correspondence between the excitation current vector and the remanence characteristics is established through the excitation current optimization function; the optimal magnetic coupling characteristics between the control winding and the working winding are calculated according to the magnetic coupling theory; the winding parameters are determined through the magnetic coupling coefficient calculation function; a finite element simulation model is established to verify the design parameters; the balance between magnetic flux distribution and loss control is optimized through a game theory optimization model, resulting in the final design of the two-phase composite magnetic material magnetic valve reactor.

[0005] Specifically, the dual-phase composite magnetic material is a novel magnetic material formed by uniformly distributing hard magnetic materials and soft magnetic materials within the nanoscale range. The hard magnetic materials provide high magnetocrystalline anisotropy and large remanence, while the soft magnetic materials provide high permeability and magnetic flux transmission capability. The synergistic effect of the two phases significantly improves the balance between the material's permeability and coercivity.

[0006] Specifically, the ratio of the two-phase composite magnetic material is 3:7, where the mass ratio of hard magnetic material to soft magnetic material is 3:7. The hard magnetic material is neodymium iron boron alloy powder, and the soft magnetic material is iron-silicon alloy powder. The magnetic properties are optimized by controlling the volume fraction of the two phases.

[0007] The preparation process of the dual-phase composite magnetic material specifically includes ball milling and mixing neodymium iron boron alloy powder and iron-silicon alloy powder according to a certain ratio for 24 hours at a speed of 300 revolutions per minute, followed by hot pressing and sintering under an argon protective atmosphere at a temperature of 850°C for 2 hours, and then machining after cooling to obtain a dual-phase composite magnetic material core with the required geometric dimensions.

[0008] Specifically, the distributed solenoid valve structure involves setting solenoid valve sections with reduced cross-sectional area at different positions on the core column. Through the coordinated action of multiple solenoid valves, the magnetic flux distribution is made uniform and the magnetic saturation is precisely controlled, effectively avoiding local overheating and reducing long-term maintenance costs.

[0009] Specifically, the geometric parameter calculation function is used to determine the basic geometric dimensions of the core and the configuration of the solenoid valves based on the rated voltage and rated capacity. The inputs include the rated voltage, rated capacity, design value of magnetic flux density, magnetic permeability of the core material, and safety margin coefficient. The outputs are the cross-sectional area of ​​the two-phase composite magnetic material, the cross-sectional area of ​​the silicon steel sheet, the core length, and the number of solenoid valves.

[0010] Specifically, the magnetic reluctance calculation equation is used to establish the magnetic reluctance parameters of each part in the equivalent magnetic circuit model. The inputs include the core cross-sectional area, core length, material permeability, magnetic flux path length, and magnetic circuit geometric factor. The outputs are the magnetic reluctance of silicon steel sheet, the magnetic reluctance of two-phase composite magnetic material, and the magnetic reluctance of magnetic valve.

[0011] Specifically, the air gap magnetoresistance optimization equation is used to calculate the optimal air gap thickness between the two-phase composite magnetic material and the silicon steel sheet. The inputs include the main magnetic flux value, the residual magnetic flux value, the magnetic reluctance of the silicon steel sheet, the magnetic reluctance of the two-phase composite magnetic material, and the magnetic circuit decoupling requirement coefficient. The outputs are the air gap magnetoresistance value and the corresponding air gap thickness.

[0012] The game theory optimization model specifically includes an upper-level model that aims to maximize the uniformity of magnetic flux distribution and a lower-level model that aims to minimize the total equipment loss. The upper-level objective function is used to optimize the uniformity of magnetic induction intensity distribution and the consistency of the saturation state of the magnetic valve section, while the lower-level objective function is used to minimize the sum of hysteresis loss, eddy current loss and copper loss.

[0013] Specifically, the Langevin function magnetization equation is used to describe the magnetization behavior and remanence variation of a two-phase composite magnetic material under the action of an applied magnetic field. The inputs include the applied magnetic field strength, the material's magnetocrystalline anisotropy constant, the saturation magnetization, the temperature parameter, and the magnetic domain orientation distribution factor. The outputs are the material's magnetization and remanence.

[0014] Specifically, the excitation current optimization function is used to calculate the optimal excitation current vector of the control winding. The inputs include the target residual magnetism value, the magnetization characteristics of the two-phase composite magnetic material, the geometric parameters of the magnetic valve, the number of turns of the control winding, and the magnetic circuit topology. The output is the optimal excitation current vector and the corresponding magnetization time series.

[0015] Specifically, the magnetic coupling coefficient calculation function is used to determine the optimal magnetic coupling characteristics between the control winding and the working winding. The inputs include the optimal excitation current vector, the winding spatial position, the magnetic circuit geometry, the magnetic flux separation requirements and the harmonic suppression coefficient. The outputs are the magnetic coupling coefficient, the winding turns ratio, the conductor cross-sectional area and the spatial layout parameters.

[0016] Specifically, the multi-objective optimization algorithm transforms the solenoid valve design problem into a constrained optimization problem similar to a multidimensional knapsack problem. Under the premise of satisfying the uniformity constraint of magnetic flux distribution, it simultaneously optimizes multiple objective parameters such as the number, position, and geometric dimensions of the solenoid valve.

[0017] Specifically, the solenoid valve geometry optimization function is used to determine the geometric parameters of the distributed solenoid valve structure. The inputs include the number of solenoid valves, the cross-sectional area of ​​the iron core, the uniformity requirement of magnetic flux distribution, the magnetic saturation control accuracy, and the thermal distribution optimization coefficient. The outputs are the reduction ratio of the cross-sectional area and the length of each solenoid valve.

[0018] Furthermore, prior to establishing the core geometric model, the process also includes the formation of a two-phase composite magnetic material through the uniform distribution of soft and hard magnetic materials within the nanoscale range, and the rational distribution of the magnetic flux path through magnetic integration design.

[0019] A second aspect of the present invention provides a reactor, which is specifically designed and manufactured using the above-described design method.

[0020] Specifically, the final design of the two-phase composite magnetic material valve-type reactor, obtained according to this design method, is as follows: The reactor adopts an E-type core structure, with the core consisting of laminated silicon steel sheets and a hybrid magnetic circuit composed of two-phase composite magnetic material. The two-phase composite magnetic material is made by mixing neodymium iron boron alloy powder and iron-silicon alloy powder at a mass ratio of 3:7, ball milling for 24 hours, and then hot-pressing and sintering at 850℃ under an argon protective atmosphere for 2 hours, achieving a uniform distribution of hard and soft magnetic phases at the nanoscale. The core column of the reactor has a working winding, and the two side columns have control windings. The control winding and working winding achieve effective separation of AC and DC magnetic flux through optimized turns ratio and spatial layout. A 0.2mm thick air gap is set between the two-phase composite magnetic material and the silicon steel sheets to achieve magnetic circuit decoupling, so that the main magnetic flux generated by the working winding flows only through the silicon steel sheets and does not enter the region of the two-phase composite magnetic material. Three distributed solenoid valves are installed on the far left and far right sides of the core, employing a multi-stage gradient geometry design. The cross-sectional area of ​​the first-stage solenoid valve is 80% of the standard cross-sectional area, the second stage is 60%, and the third stage is 40%. The length of the solenoid valve is calculated using the finite difference method to achieve a linear distribution of the magnetic reluctance gradient. The solenoid valve region is constructed of two-phase composite magnetic material. By controlling the DC excitation current applied to the winding, the remanent magnetization state is adjusted, thereby changing the magnetic reluctance value of the solenoid valve region and achieving continuous adjustment of the reactor's reactance value. The working winding is made of copper wire, and the wire cross-sectional area is determined based on the current density limit of 5 to 8 amperes per square millimeter and heat dissipation requirements. The winding insulation class is selected according to the operating voltage. The overall structure is verified through finite element simulation, ensuring that the coefficient of variation of the magnetic induction intensity distribution uniformity is less than 5%, the nonlinear distortion is less than 3%, and the reactance value can be continuously adjusted within the range of 20% to 100% of the rated value, meeting the performance requirements of reactive power compensation and voltage regulation in power systems.

[0021] This invention achieves effective decoupling of the main magnetic flux path and the remanent magnetization path by constructing a hybrid magnetic circuit structure of two-phase composite magnetic material and silicon steel sheet, and setting a precisely controlled air gap between them. It employs a two-phase composite structure with nanoscale uniform distribution of hard and soft magnetic materials. The high coercivity of the hard magnetic material provides a stable remanent magnetization foundation, while the high permeability of the soft magnetic material ensures efficient magnetic flux transmission. Through a multi-stage gradient geometry design of the distributed magnetic valve structure, mutual interference between the main magnetic flux and the control magnetic flux in a single magnetic circuit is effectively avoided, significantly reducing hysteresis and eddy current losses. A precise air gap thickness of 0.2 mm ensures that the main magnetic flux generated by the working winding completely avoids the region of the two-phase composite magnetic material, while the DC magnetic flux of the control winding acts on the magnetization process of the two-phase composite magnetic material. This achieves complete independence of the two magnetic flux paths, completely eliminating the additional losses caused by magnetic flux coupling and solving the technical problem of high energy loss due to magnetic flux path coupling in magnetic valve reactors. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of a magnetic valve type reactor according to an embodiment of the present invention; Figure 2 This is a novel distributed solenoid valve design according to an embodiment of the present invention; Figure 3 A three-dimensional model of a novel magnetic valve reactor according to an embodiment of the present invention; Figure 4 External scanning electron microscope image of the dual-phase composite material according to an embodiment of the present invention; Figure 5 This is a scanning electron microscope image of the interior of the biphase composite material according to an embodiment of the present invention; Figure 6 This is the hysteresis loop of the two-phase composite magnetic material in this embodiment of the invention; Figure 7 This is a schematic diagram of the decoupling design of the biphase composite material and silicon steel sheet according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the equivalent magnetic circuit model of the novel magnetic valve reactor according to an embodiment of the present invention, including sub-diagram (a) complete magnetic circuit model, sub-diagram (b) DC magnetic flux path and sub-diagram (c) AC magnetic flux path; Figure 9 This is a schematic diagram of the equivalent circuit of the novel magnetic valve reactor according to an embodiment of the present invention; Figure 10 This is a schematic diagram of the magnetic induction intensity distribution of the novel magnetic valve reactor under AC only, according to an embodiment of the present invention. Figure 11 This is a schematic diagram of the magnetic induction intensity distribution of the novel magnetic valve reactor under the condition of only residual magnetism according to an embodiment of the present invention. Sub-figures (a) and (b) show the magnetic field distribution under the conditions of 0.9T and 1.2T residual magnetism, respectively. Figure 12 This is a schematic diagram of the magnetic induction intensity distribution under normal operating conditions of the novel magnetic valve reactor according to an embodiment of the present invention. Sub-figure (a) shows the magnetic field distribution when the residual magnetism is 0.6T, and sub-figure (b) shows the distribution pattern under other residual magnetism conditions.

[0023] Figure 13 This is a flowchart of the method of the present invention. Detailed Implementation

[0024] 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.

[0025] like Figure 13 The diagram shown is a flowchart of a design method for a two-phase composite magnetic material magnetic valve reactor provided by the present invention. This method includes the following steps: S01. Determine the cross-sectional area, length, and number of magnetic valves of the two-phase composite magnetic material and silicon steel sheet based on the rated voltage and rated capacity, and establish a geometric model of the iron core. The two-phase composite magnetic material is formed by uniformly distributing soft magnetic material and hard magnetic material in the nanometer range. A reasonable distribution of magnetic flux path is achieved by adopting magnetic integration design, and the initial design parameters are obtained through geometric parameter calculation function. S02. Establish the equivalent magnetic circuit model of the two-phase composite magnetic material magnetic valve reactor, establish the equivalent magnetic circuits of AC magnetic flux path and DC magnetic flux path respectively, and determine the magnetic reluctance of silicon steel sheet, magnetic reluctance of two-phase composite magnetic material and magnetic valve through magnetic reluctance calculation equation based on the cross-sectional area and length obtained in step S01. Calculate the parameters of each magnetic circuit through magnetic potential balance equation. S03. Calculate the air gap thickness between the two-phase composite magnetic material and the silicon steel sheet based on the air gap reluctance optimization equation that considers the principle of magnetic circuit decoupling. This ensures that the main magnetic flux generated by the working winding does not flow through the two-phase composite magnetic material, thereby reducing the leakage magnetic effect and improving the magnetic circuit stability. Combined with the reluctance value obtained in step S02, the air gap thickness is calculated to be 0.2 mm. S04. A distributed solenoid valve structure is designed using a multi-objective optimization algorithm. Three solenoid valves are set on the leftmost and rightmost sides of the iron core, respectively. The magnetic flux path distribution is optimized using a multi-level gradient geometric design. The problem of the cross-sectional area and position distribution of the solenoid valves is transformed into a constraint optimization problem for solution. The number of solenoid valves obtained in step S01 is used to calculate the reduction ratio of the cross-sectional area and the length of each solenoid valve through the solenoid valve geometric optimization function. S05. Calculate the optimal excitation current vector of the control winding through the physical mechanism analysis of magnetization intensity. Use the Langevin function magnetization equation to describe the magnetization process of the two-phase composite magnetic material. Use the parameters of the two-phase composite magnetic material obtained in step S01 to establish the correspondence between the excitation current vector and the remanent magnetization characteristics of the two-phase composite magnetic material through the excitation current optimization function, so as to realize the dynamic adjustment and rapid response capability of the magnetic state in the magnetic valve region. S06. Calculate the optimal magnetic coupling characteristics between the control winding and the working winding based on the magnetic coupling theory. Use the optimal excitation current vector obtained in step S05 to determine the turns ratio, conductor cross-sectional area and spatial layout parameters of the two windings through the magnetic coupling coefficient calculation function. This will achieve effective separation of AC magnetic flux and DC magnetic flux, and reduce harmonic losses and electromagnetic interference. S07. Establish a finite element simulation model to verify the design parameters, analyze the magnetic induction intensity distribution, reactance characteristics and volt-ampere linearity under different remanence conditions, optimize the balance between magnetic flux distribution and loss control through a game theory optimization model, and adjust and optimize the design parameters using the optimal magnetic coupling characteristics obtained in step S06 until the performance requirements and withstand voltage level are met, thus obtaining the final design of the two-phase composite magnetic material magnetic valve reactor.

[0026] Among them, the two-phase composite magnetic material is a new type of magnetic material formed by uniformly distributing hard magnetic materials and soft magnetic materials in the nanoscale range. The hard magnetic materials provide high magnetocrystalline anisotropy and large remanence, while the soft magnetic materials provide high permeability and magnetic flux transmission capability. The synergistic effect of the two phase materials significantly improves the balance between the permeability and coercivity of the material.

[0027] The two-phase composite magnetic material is formulated with a mass ratio of hard magnetic material to soft magnetic material of 3:7. The hard magnetic material is neodymium iron boron alloy powder, and the soft magnetic material is iron-silicon alloy powder. The magnetic properties are optimized by controlling the volume fraction of the two phases.

[0028] The preparation process of the two-phase composite magnetic material includes ball milling and mixing neodymium iron boron alloy powder and iron silicon alloy powder according to the specified ratio. The ball milling time is 24 hours and the rotation speed is 300 revolutions per minute. Then, hot pressing sintering is carried out under an argon protective atmosphere. The sintering temperature is 850℃ and the holding time is 2 hours. After cooling, the material is machined to obtain the core of the two-phase composite magnetic material with the required geometric dimensions.

[0029] The magnetic circuit decoupling principle involves setting an air gap between the two-phase composite magnetic material and the silicon steel sheet, making the main magnetic flux path and the residual magnetic path independent of each other. This avoids interference from the working winding magnetic flux on the residual magnetic characteristics of the two-phase composite magnetic material, thereby improving the adjustment accuracy and reliability.

[0030] Among them, the distributed solenoid valve structure sets solenoid valve sections with reduced cross-sectional area at different positions of the iron core column. Through the synergistic action of multiple solenoid valves, the magnetic flux distribution is made uniform and the magnetic saturation is precisely controlled, which effectively avoids local overheating and reduces the maintenance cost of long-term operation.

[0031] The geometric parameter calculation function is used to determine the basic geometric dimensions of the core and the configuration of the magnetic valves based on the rated voltage and rated capacity. The inputs include the rated voltage, rated capacity, design value of magnetic flux density, magnetic permeability of core material and safety margin coefficient. The outputs are the cross-sectional area of ​​the two-phase composite magnetic material, the cross-sectional area of ​​the silicon steel sheet, the core length and the number of magnetic valves. The output parameters serve as the basic data for magnetic circuit modeling in step S02.

[0032] The magnetic reluctance calculation equation is used to establish the magnetic reluctance parameters of each part in the equivalent magnetic circuit model. The inputs include the core cross-sectional area, core length, material permeability, magnetic flux path length, and magnetic circuit geometric factor. The outputs are the magnetic reluctance of silicon steel sheet, the magnetic reluctance of two-phase composite magnetic material, and the magnetic reluctance of magnetic valve. The magnetic reluctance values ​​are used as input parameters for the air gap calculation in step S03.

[0033] The air gap magnetoresistance optimization equation is used to calculate the optimal air gap thickness between the two-phase composite magnetic material and the silicon steel sheet. The inputs include the main magnetic flux value, the residual magnetic flux value, the magnetic reluctance of the silicon steel sheet, the magnetic reluctance of the two-phase composite magnetic material, and the magnetic circuit decoupling requirement coefficient. The output is the air gap magnetoresistance value and the corresponding air gap thickness. The air gap thickness serves as the boundary condition for the magnetic valve structure design in step S04.

[0034] The solenoid valve geometry optimization function is used to determine the geometric parameters of the distributed solenoid valve structure. The inputs include the number of solenoid valves, the cross-sectional area of ​​the iron core, the uniformity requirement of magnetic flux distribution, the magnetic saturation control accuracy, and the thermal distribution optimization coefficient. The outputs are the reduction ratio of the cross-sectional area and the length of each solenoid valve. The geometric parameters serve as the structural inputs for the excitation current calculation in step S05.

[0035] The Langevin function magnetization equation is used to describe the magnetization behavior and remanence variation of two-phase composite magnetic materials under the action of an external magnetic field. The inputs include the external magnetic field strength, the material's magnetocrystalline anisotropy constant, the saturation magnetization, the temperature parameter, and the magnetic domain orientation distribution factor. The outputs are the magnetization and remanence of the material. The magnetization and remanence are used to calculate the optimal magnetic coupling characteristics in step S06.

[0036] The excitation current optimization function is used to calculate the optimal excitation current vector of the control winding. The inputs include the target residual magnetism value, the magnetization characteristics of the two-phase composite magnetic material, the geometric parameters of the magnetic valve, the number of turns of the control winding, and the magnetic circuit topology. The output is the optimal excitation current vector and the corresponding magnetization time series. The optimal excitation current vector is used as the input condition for determining the winding parameters in step S06.

[0037] The magnetic coupling coefficient calculation function is used to determine the optimal magnetic coupling characteristics between the control winding and the working winding. The inputs include the optimal excitation current vector, winding spatial position, magnetic circuit geometry, magnetic flux separation requirements and harmonic suppression coefficients. The outputs are the magnetic coupling coefficient, winding turns ratio, conductor cross-sectional area and spatial layout parameters. The optimal magnetic coupling characteristics are the key parameters for simulation verification in step S07.

[0038] In step S04, the multi-objective optimization algorithm transforms the solenoid valve design problem into a constraint optimization problem similar to a multidimensional knapsack problem. Under the premise of satisfying the uniformity constraint of magnetic flux distribution, it simultaneously optimizes multiple objective parameters such as the number, position, and geometric dimensions of the solenoid valve.

[0039] The game theory optimization model includes an upper-level model that aims to maximize the uniformity of magnetic flux distribution and a lower-level model that aims to minimize the total equipment loss. The upper-level objective function is used to optimize the uniformity of magnetic induction intensity distribution and the consistency of the saturation state of the solenoid valve section. The inputs include solenoid valve geometric parameters, remanent magnetization distribution, excitation current vector, and magnetic circuit topology. The output is the magnetic flux distribution uniformity index. The lower-level objective function is used to minimize the sum of hysteresis loss, eddy current loss, and copper loss. The inputs include operating frequency, magnetic induction intensity amplitude, winding resistance, and magnetic material characteristic parameters. The output is the total equipment loss value. The two objective functions achieve mutual influence and coordinated optimization through the magnetic induction intensity distribution as a coupling term.

[0040] The specific implementation methods of the above steps are described in detail below.

[0041] The specific implementation of step S01 is as follows: First, based on the rated voltage and rated capacity requirements of the power system, the correlation between the geometric parameters and electrical parameters of the iron core is established using Ampere's circuital law and the principle of magnetic flux continuity. The required magnetic flux density design value is determined using the law of electromagnetic induction, typically set to 1.6 to 1.8 Tesla to ensure the iron core material is at its optimal operating point. The cross-sectional area of ​​the two-phase composite magnetic material is calculated based on Ohm's law for magnetic circuits, while considering the material's permeability characteristics; the permeability of the hard magnetic phase is approximately 0.3 to 0.5 times that of the soft magnetic phase. The input rated voltage, rated capacity, magnetic flux density design value, iron core material permeability, and safety margin coefficient are processed using a geometric parameter calculation function; the safety margin coefficient is typically taken as 1.2 to 1.5. The cross-sectional area and length of the silicon steel sheet are determined using an optimization algorithm to minimize the total magnetic reluctance of the magnetic circuit. The number of solenoid valves is determined based on the requirements for uniform magnetic flux distribution; when the iron core length exceeds 500 mm, it is recommended to set 6 solenoid valves to achieve the best magnetic flux control effect. The output parameters include the cross-sectional area of ​​the two-phase composite magnetic material, the cross-sectional area of ​​the silicon steel sheet, the core length, and the number of magnetic valves. These parameters provide basic data for subsequent magnetic circuit modeling.

[0042] The specific implementation of step S02 is based on establishing a dual-loop magnetic circuit model according to the principle of magnetic circuit equivalence and Kirchhoff's magnetic circuit law. First, an equivalent magnetic circuit for the AC magnetic flux path is established, and the distribution law of the main magnetic flux generated by the working winding in the iron core is analyzed using the principle of magnetic circuit superposition. The input parameters from step S01, such as the iron core cross-sectional area, iron core length, material permeability, magnetic flux path length, and magnetic circuit geometric factor, are processed using the reluctance calculation equation. The magnetic circuit geometric factor is determined according to the iron core shape; it is 0.9 for an E-type iron core and 1.0 for a toroidal iron core. The reluctance of the silicon steel sheet is calculated using a magnetic circuit analysis method, based on the relative permeability of the material, which is typically between 2000 and 5000. The reluctance of the two-phase composite magnetic material is determined through material property analysis, considering the coupling effect of the two phases and the interface reluctance. The magnetic valve reluctance is calculated using the magnetic saturation characteristic curve; the reluctance in the magnetic valve region changes nonlinearly with the magnetic induction intensity. An equivalent magnetic circuit for the DC flux path is established, and the distribution of excitation flux generated by the control winding is analyzed. The correctness of the magnetic circuit model is verified by the magnetomotive force balance equation, ensuring that the sum of the magnetomotive forces of each branch equals the total magnetomotive force. The output reluctance of the silicon steel sheet, the reluctance of the two-phase composite magnetic material, and the reluctance of the magnetic valve are used as input conditions for the air gap design.

[0043] The specific implementation of step S03 is based on the magnetic circuit decoupling theory and the principle of magnetic flux path independence to calculate the optimal air gap thickness. The magnetic flux separation principle ensures that the AC magnetic flux generated by the working winding does not enter the region of the two-phase composite magnetic material, avoiding interference with the remanent magnetization characteristics. The air gap reluctance optimization equation is used to process input parameters such as the main magnetic flux value, remanent magnetic flux value, silicon steel sheet reluctance, two-phase composite magnetic material reluctance, and magnetic circuit decoupling requirement coefficient. The main magnetic flux value is determined by the rated capacity, and the remanent magnetic flux value is calculated based on the remanent magnetization characteristics of the two-phase composite magnetic material. The magnetic circuit decoupling requirement coefficient reflects the requirement for the degree of magnetic flux separation and is usually set to 0.95 or higher to ensure good decoupling effect. The air gap reluctance value is calculated using the reluctance matching principle, ensuring that the AC magnetic flux preferentially passes through the silicon steel sheet path and avoids the two-phase composite magnetic material. The air gap reluctance is converted into the corresponding air gap thickness using air permeability and geometric relationships; the relative permeability of air is 1.0. The air gap thickness was verified to meet mechanical strength requirements. When the air gap thickness was less than 0.1 mm, machining accuracy limitations needed to be considered; when the air gap thickness was greater than 1.0 mm, the impact of excessive magnetic reluctance needed to be considered. Through iterative optimization, the final air gap thickness was determined to be 0.2 mm, which ensured both the magnetic circuit decoupling effect and met manufacturing process requirements.

[0044] The specific implementation of step S04 involves designing a distributed solenoid valve structure using a multi-objective optimization algorithm and constrained programming theory. Based on the principle of uniform magnetic flux distribution, the spatial configuration of the solenoid valves is determined, with three solenoid valves placed on the far left and far right of the core to achieve a symmetrical magnetic field distribution. The solenoid valve geometry optimization function is used to process input parameters such as the number of solenoid valves, the core cross-sectional area, the uniformity requirement of magnetic flux distribution, the magnetic saturation control accuracy, and the thermal distribution optimization coefficient. The uniformity requirement of magnetic flux distribution is quantified using the coefficient of variation, with a target value set to less than 5% to ensure the consistency of the working state of each solenoid valve. The magnetic saturation control accuracy reflects the precision requirement for controlling the saturation level of the solenoid valves, typically set with an error range within 2%. The thermal distribution optimization coefficient considers the heat distribution in the solenoid valve area to avoid localized overheating. A multi-stage gradient design principle is used to determine the reduction ratio of the cross-sectional area of ​​each solenoid valve: the first-stage solenoid valve cross-sectional area is 80% of the standard cross-sectional area, the second stage is 60%, and the third stage is 40%. The length of the solenoid valve is calculated using the finite difference method, ensuring a linear distribution of the magnetic reluctance gradient. Constrained optimization algorithms are used to optimize the geometric parameters of the solenoid valves while ensuring magnetic flux continuity, thus guaranteeing a smooth transition of magnetic flux between each solenoid valve. The solenoid valve design is verified to meet mechanical strength requirements; when the cross-sectional area reduction exceeds 70%, structural strength limitations must be considered.

[0045] The specific implementation of step S05 is based on the magnetization physical mechanism and Langevin statistical theory to calculate the optimal excitation current parameters. The Langevin function magnetization equation is used to describe the magnetization behavior of the two-phase composite magnetic material under an applied magnetic field, considering the synergistic magnetization effect of the hard and soft magnetic phases. This equation is used to handle physical parameters such as the applied magnetic field strength, the material's magnetocrystalline anisotropy constant, saturation magnetization, temperature parameters, and domain orientation distribution factor. The magnetocrystalline anisotropy constant reflects the degree of magnetic anisotropy of the material, and for the NdFeB hard magnetic phase, it is approximately 4.9 × 10⁻⁶. Joules per cubic meter. Saturation magnetization characterizes the maximum magnetization capability of a material; the saturation magnetization of two-phase composite materials is typically 1.2 to 1.6 Tesla. Temperature parameters consider the influence of operating temperature on magnetic properties, with the operating temperature range set from -40℃ to 120℃. The domain orientation distribution factor describes the degree of random orientation of magnetic domains; this factor is 1 for completely random orientation. Design parameters such as the target remanence value, magnetization characteristics of the two-phase composite magnetic material, magnetic valve geometry, number of turns in the control winding, and magnetic circuit topology are processed through an excitation current optimization function. A nonlinear optimization algorithm is used to solve for the optimal excitation current vector, enabling the two-phase composite magnetic material to reach the set remanence state. The magnetization time constant is considered to determine the magnetization time sequence; the rapid magnetization time is approximately 0.1 to 0.5 seconds, and the slow magnetization time is approximately 1 to 3 seconds.

[0046] The specific implementation of step S06 is based on electromagnetic coupling theory and mutual inductance calculation principles to determine the optimal magnetic coupling characteristics between windings. A magnetic coupling coefficient calculation function is used to process the optimal excitation current vector from step S05, as well as parameters such as winding spatial position, magnetic circuit geometry, flux separation requirements, and harmonic suppression coefficients. The winding spatial position is described using a coordinate system, and the relative position of the control winding and the working winding affects the coupling strength. The flux separation requirement quantifies the degree of separation between AC and DC flux, with a target separation degree set at 95% or higher. The harmonic suppression coefficient reflects the ability to suppress harmonic currents, with a fifth harmonic suppression rate required to reach 90% or higher. The magnetic coupling coefficient is calculated using electromagnetic field numerical analysis methods, considering the spatial magnetic field distribution and flux linkage distribution between windings. A winding parameter optimization algorithm is used to determine the turns ratio, ensuring that the magnetic coupling between the control winding and the working winding reaches its optimal state. The conductor cross-sectional area is calculated based on current density limitations and heat dissipation requirements; the current density of copper conductors is typically limited to 5 to 8 amperes per square millimeter. Spatial geometry optimization determines the spatial layout parameters of the windings, including the radial position and axial distribution of the windings. Verify that the winding design meets the insulation withstand voltage requirements; the insulation class is determined based on the operating voltage. Verify the flux separation effect using finite element analysis of the magnetic field to ensure that AC and DC fluxes are distributed as required by the design.

[0047] The specific implementation of step S07 involves establishing a three-dimensional finite element simulation model and using a game theory optimization strategy for parameter verification and optimization. First, a complete geometric model is established, including the two-phase composite magnetic material, silicon steel sheet, air gap, and windings. Tetrahedral meshing technology is used to discretize the complex geometric structure. The mesh density is refined in the magnetic valve region and near the air gap, with the minimum mesh size set to one-tenth of the air gap thickness to ensure computational accuracy. Boundary conditions and excitation sources are set; a sinusoidal voltage excitation is applied to the working winding, and a DC current excitation is applied to the control winding. A nonlinear magnetic field solver is used to analyze the magnetic induction intensity distribution under different remanence conditions, with the remanence value varying from 0.2 to 1.2 Tesla. The reactive power regulation capability of the equipment is verified through reactance characteristic calculations; the reactance value variation range should cover 20% to 100% of the rated value. The linearity of the equipment is evaluated using volt-ampere characteristic analysis, with nonlinear distortion required to be less than 3%. A game theory optimization model is established, with the upper-level model aiming at uniform magnetic flux distribution and the lower-level model aiming at minimizing total losses. The upper-level objective function processes the solenoid valve geometry, remanent magnetization, excitation current vector, and magnetic circuit topology, outputting a flux distribution uniformity index. The lower-level objective function processes the operating frequency, magnetic flux density amplitude, winding resistance, and magnetic material properties, outputting the total equipment loss value. A Nash equilibrium algorithm is used to find the optimal balance point between the two objective functions, achieving coordinated optimization of flux distribution and loss control. Design parameters are adjusted based on simulation results. When the flux distribution non-uniformity exceeds 5%, the solenoid valve geometry needs optimization; when the total loss exceeds 3% of the rated value, the material ratio or winding parameters need adjustment. Iterative optimization continues until all performance indicators meet the design requirements, ultimately outputting a design scheme for a two-phase composite magnetic material solenoid valve reactor containing all optimized parameters.

[0048] The principles behind the five key technical approaches employed in this invention will be explained below.

[0049] The first key technological approach is the nanoscale uniform distribution design of two-phase composite magnetic materials. Traditional reactors typically use a single magnetic material, such as silicon steel sheets or ferrite. These materials have an inherent trade-off between permeability and coercivity, making it difficult to simultaneously achieve high permeability and strong remanence. This invention achieves a synergistic effect between the two phases by uniformly distributing the hard magnetic material neodymium iron boron alloy powder and the soft magnetic material iron-silicon alloy powder at the nanoscale. The hard magnetic phase provides high magnetocrystalline anisotropy and strong remanence, while the soft magnetic phase contributes high permeability and excellent flux transmission capability. This nanoscale composite structure breaks through the performance limits of traditional single-phase materials, significantly improving remanence stability while maintaining high permeability, providing a material basis for precise reactor adjustment.

[0050] The second key technological approach is the air gap optimization design based on the principle of magnetic circuit decoupling. In traditional magnetically controlled reactors, the main magnetic flux generated by the working winding and the control magnetic flux often interfere with each other, leading to decreased regulation accuracy and magnetic circuit stability issues. This invention achieves effective separation of the main magnetic flux path and the remanent magnetization path by setting a precisely calculated air gap between the two-phase composite magnetic material and the silicon steel sheet. This decoupling design prevents the magnetic flux of the working winding from flowing directly through the two-phase composite magnetic material, avoiding interference with its remanent magnetization characteristics and reducing leakage flux. Compared to the coupling relationship of the magnetic flux path in traditional designs, this decoupling strategy fundamentally improves the reactor's regulation linearity and control stability.

[0051] The third key technological approach is the collaborative control mechanism of a distributed multi-stage magnetic valve structure. Traditional reactors typically employ a centralized magnetic control structure, which is prone to local magnetic saturation and uneven magnetic flux distribution, limiting the adjustment range and response speed. This invention uses a distributed magnetic valve design, placing multiple magnetic valve segments with decreasing cross-sectional areas at different locations in the core. The synergistic action of these multiple valves achieves uniform magnetic flux distribution. This distributed structure can more precisely control the magnetic saturation level in each region, avoiding local overheating issues found in traditional centralized control methods, while also providing a wider adjustment range and faster dynamic response.

[0052] The fourth key technological approach is based on precise modeling and excitation optimization of magnetization characteristics using the Langevin function. In traditional reactor design, the magnetization process of magnetic materials is typically modeled using simplified linear or piecewise linear models, which struggle to accurately describe complex magnetization behavior and remanence variations. This invention employs the Langevin function magnetization equation to precisely describe the magnetization process of two-phase composite magnetic materials, considering the combined effects of multiple physical parameters such as applied magnetic field strength, magnetocrystalline anisotropy, and saturation magnetization. Based on this precise modeling, the optimal excitation current vector is calculated using an excitation current optimization function, enabling precise control and rapid adjustment of the magnetic state in the magnetic valve region. This optimization method based on physical mechanisms offers higher accuracy and reliability compared to traditional empirical design.

[0053] The fifth key technological approach is a multi-objective coordinated optimization strategy within a game theory framework. Traditional reactor design typically employs single-objective optimization or simple multi-objective weighted methods, which struggle to handle the complex trade-off between magnetic flux distribution uniformity and loss control. This invention constructs a two-layer game theory optimization model. The upper-layer model aims to maximize magnetic flux distribution uniformity, while the lower-layer model aims to minimize total equipment loss. The two objective functions are coupled and coordinated through the distribution of magnetic induction intensity. This game theory framework can automatically find the optimal balance between magnetic flux distribution and loss control, achieving superior overall performance compared to traditional trial-and-error methods or simple optimization algorithms.

[0054] The synergistic effect of these five key technological approaches has generated significant systemic advantages. Two-phase composite magnetic materials provide the foundation for excellent magnetic performance of the entire system; magnetic circuit decoupling design ensures the independence and control precision of each magnetic flux path; the distributed magnetic valve structure achieves uniform distribution and precise adjustment of magnetic flux; Langevin function modeling guarantees the accuracy and rapid response of excitation control; and the game theory optimization framework coordinates the configuration of all design parameters. Compared to the single technological path of traditional reactors, this multi-level technological synergy achieves a comprehensive improvement in regulation precision, response speed, operating efficiency, and system stability. In particular, through the organic combination of material innovation, structural optimization, precise modeling, and intelligent control, it breaks through the technical bottlenecks of traditional reactors in terms of regulation range, linearity, and dynamic performance, providing important technical support for the next generation of smart grid equipment.

[0055] Specifically, the principle of this invention is as follows: The technical solution of this invention can solve the problem of high energy loss caused by magnetic flux path coupling by achieving physical separation of the magnetic flux path and decoupling of the loss mechanism through material structure innovation and magnetic circuit topology optimization. The nanoscale hybrid structure of the two-phase composite magnetic material confines the remanent magnetic field provided by the hard magnetic phase mainly within the material, while the soft magnetic phase is responsible for efficiently conducting control magnetic flux. This functional division at the microscale lays the material foundation for macroscopic magnetic circuit decoupling. When an excitation current is applied to the control winding, the generated DC magnetic flux propagates through the two-phase composite magnetic material. The remanent magnetic state of the material is precisely adjusted using the magnetization process described by the Langevin function. Meanwhile, the AC main magnetic flux generated by the working winding completely bypasses the two-phase composite magnetic material region through the designed air gap structure, forming an independent magnetic flux loop in the silicon steel sheet, avoiding the cross-interaction of the two magnetic fluxes in the same medium. Air gap magnetoresistance optimization design is a key technical step in reducing energy loss. By accurately calculating the air gap thickness of 0.2 mm, the magnetoresistance of the main magnetic flux in the silicon steel sheet is much smaller than that through the two-phase composite magnetic material. According to the principle of minimum magnetoresistance, the main magnetic flux naturally chooses the silicon steel sheet path and avoids the two-phase composite magnetic material, thus eliminating the magnetic flux density superposition effect and corresponding nonlinear loss generated when the main magnetic flux and control magnetic flux propagate in the same magnetic medium. The multi-stage gradient design of the distributed magnetic valve structure achieves uniform magnetic flux density distribution by setting magnetic valve sections with decreasing cross-sectional areas at different positions in the iron core. This avoids high magnetic flux density areas caused by local magnetic flux concentration, effectively reducing hysteresis loss and eddy current loss. The introduction of multi-objective optimization algorithms and game theory models ensures the optimal balance between magnetic flux distribution uniformity and loss control. The upper-level optimization model achieves uniform magnetic induction intensity distribution by adjusting the geometric parameters of the magnetic valve, while the lower-level optimization model optimizes winding parameters and excitation strategies with the goal of minimizing total loss. The two models achieve coupled optimization through magnetic induction intensity distribution, realizing effective control of energy loss from the perspective of loss generation mechanism.

[0056] 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.

[0057] The specific implementation of step S01 involves establishing a geometric parameter calculation function based on the law of electromagnetic induction and the principle of magnetic flux continuity, as shown below: ; ; ; ; In the formula, The cross-sectional area of ​​the two-phase composite magnetic material is expressed in square meters. This refers to the cross-sectional area of ​​the silicon steel sheet, in square meters. This refers to the length of the iron core, in meters. This refers to the number of solenoid valves. Rated capacity, unit is volt-ampere; This is the rated voltage, measured in volts. The design value for magnetic flux density ranges from 1.6 to 1.8 Tesla. The operating frequency is expressed in Hertz (Hz). The permeability of the two-phase composite magnetic material; The permeability of silicon steel sheet; This is the safety margin factor, with a value ranging from 1.2 to 1.5; This is a length coefficient, ranging from 0.8 to 1.2; This represents the total cross-sectional area. The parameter is obtained as follows: and Determined by the power system technical specifications; Obtained through material magnetization curve testing; The results were obtained using an AC permeability meter at 1000 Hz. The magnetic properties were obtained through standard silicon steel sheet magnetic property testing.

[0058] The specific implementation of step S02 is based on establishing the magnetic reluctance calculation equation and the magnetic potential balance equation according to the magnetic circuit equivalence principle, as shown below: ; ; ; ; In the formula, The magnetic reluctance of silicon steel sheet is expressed in 1 / henry. The magnetic resistance of a two-phase composite magnetic material is expressed in one henry. For the first The magnetic resistance of a solenoid valve is measured in 1 / 1000 henries. This represents the magnetic flux path length of the silicon steel sheet, in meters. The magnetic flux path length of the two-phase composite magnetic material is expressed in meters. For the first The length of the magnetic flux path of each solenoid valve, in meters; The value is the vacuum permeability. Henry per meter; The relative permeability of the silicon steel sheet, with a value ranging from 2000 to 5000; The relative permeability of the two-phase composite magnetic material; The relative permeability of the solenoid valve region; For the first The cross-sectional area of ​​each solenoid valve is in square meters. For the first One magnetic potential, measured in ampere-turns; For the first One magnetic flux, measured in Weber; This represents the number of magnetic circuit loops. The parameter is obtained as follows: , , The length of the magnetic flux path centerline is obtained by measuring the magnetic flux path centerline using 3D geometric modeling software. Obtained through the Epstein square method test; The hysteresis loop was calculated after measuring it using a vibrating sample magnetometer.

[0059] The specific implementation of step S03 is based on calculating the air gap reluctance optimization equation according to the magnetic circuit decoupling principle, as shown below: ; ; ; In the formula, The air gap reluctance is expressed in parts of one henry. The thickness of the air gap is in meters. The main magnetic flux value is expressed in Weber. This is the residual flux value, expressed in Weber. Total magnetic flux, measured in Weber; This represents the cross-sectional area of ​​the air gap, in square meters. This is the magnetic circuit decoupling requirement coefficient, ranging from 0.95 to 0.99. The parameter is obtained as follows: Based on rated capacity and operating frequency Calculations show that The number of turns in the working winding. This refers to the current in the working winding. The results were obtained through testing the remanence properties of two-phase composite magnetic materials. It equals the minimum cross-sectional area of ​​the contact surface between the two-phase composite magnetic material and the silicon steel sheet.

[0060] The specific implementation of step S04 involves using a multi-objective optimization algorithm to design a distributed solenoid valve structure, transforming the solenoid valve geometry optimization problem into a constrained optimization model, as shown below: ; ; ; ; ; In the formula, Let be the objective function for uniformity of magnetic flux distribution; Optimize the objective function for the cross-sectional area; For the first The magnetic flux density of a solenoid valve, measured in Tesla; The average magnetic flux density is expressed in Tesla. For the first The target proportion of the cross-sectional area of ​​each solenoid valve; For the first The length distribution coefficient for each solenoid valve ranges from 0.8 to 1.2. The method for obtaining this parameter is as follows: Obtained through finite element magnetic field simulation calculations; This is the arithmetic mean of the magnetic flux density of all solenoid valves; Based on the design principle of solenoid valve gradient, the first stage is preset to 0.8, the second stage to 0.6, and the third stage to 0.4.

[0061] The specific implementation of step S05 is to use the Langevin function magnetization equation to describe the magnetization process of the two-phase composite magnetic material, as shown below: ; ; ; In the formula, Magnetization intensity, measured in amperes per meter; The saturation magnetization is 1.2 to 1.6 Tesla divided by 1 / 2. ; This refers to the applied magnetic field strength, measured in amperes per meter. Permeability of free space; is the magnetic dipole moment, measured in amperes per square meter; Boltzmann's constant has a value of Joules per Kelvin; This is absolute temperature, measured in Kelvin. , which is the domain orientation distribution factor, with a value ranging from 0.85 to 1.0; This represents the optimal excitation current vector, in amperes. It is a Jacobian matrix; This is the weight matrix; This is the regularization parameter, with a value ranging from 0.01 to 0.1; It is the identity matrix; The target remanent magnetization vector is expressed in Tesla. This is a time-dependent remanence function, expressed in Tesla. The magnetization time constant ranges from 0.1 to 3.0 seconds. The parameter is obtained as follows: The saturation hysteresis loop was obtained by measuring the saturation hysteresis loop at room temperature using a vibrating sample magnetometer. It is calculated based on the magnetocrystalline anisotropy constant of the material; Measured in real time by a temperature sensor; The magnetic domain orientation was obtained through statistical calculations based on X-ray diffraction analysis.

[0062] The specific implementation of step S06 involves calculating the magnetic coupling coefficient based on magnetic coupling theory, as shown below: ; ; ; ; In the formula, The magnetic coupling coefficient is dimensionless. The unit is Henry, used to control the mutual inductance between the winding and the working winding. To control the self-inductance of the winding, the unit is Henry; The inductance of the working winding is expressed in Henry; and These are the differential length vectors of the control winding and the working winding, respectively, in meters; and These are the position vectors of the two windings, in meters; The number of turns in the winding is dimensionless. This represents the maximum value of the optimal excitation current, expressed in amperes. This is the rated current of the working winding, in amperes. This refers to the cross-sectional area of ​​the conductor, in square meters. The current is the effective value of the winding, in amperes. Due to current density limitations, the value is set to 5 to 8 amps per square millimeter; This is a thermal correction factor, ranging from 0.8 to 1.2. The parameter is obtained as follows: and The inductance was measured at 1000 Hz using an inductance meter. The mutual inductance was obtained using a dual-winding mutual inductance test method. It is calculated based on the rated capacity and rated voltage.

[0063] The specific implementation of step S07 involves establishing a game theory optimization model, including upper-level magnetic flux distribution optimization and lower-level loss minimization, as detailed below: ; ; ; ; ; ; In the formula, The objective function is the upper-level objective function, representing the index of magnetic flux distribution uniformity, which is dimensionless. Let be the lower-level objective function, representing the total equipment loss in watts; The standard deviation of magnetic flux density is expressed in Tesla. Hysteresis loss, measured in watts; Eddy current loss, measured in watts; Copper loss, measured in watts; This is the hysteresis loss coefficient, with a value ranging from 0.01 to 0.05; This is the eddy current loss coefficient, with a value range of... to ; This refers to the core volume, expressed in cubic meters. The total resistance of the winding is expressed in ohms. and For the weighting coefficients, satisfying ; This is the Nash equilibrium solution. The parameters are obtained using the following method: and The results were obtained by fitting measurements taken at different frequencies and magnetic induction intensities based on the loss characteristics of the core material. The resistance of each winding is measured using a DC resistance tester and then summed. and It is set according to the actual engineering requirements, usually The value is 0.6. The value is 0.4.

[0064] It should be noted that the geometric parameter calculation function set includes the following formulas: and Based on the laws of electromagnetic induction and the principle of magnetic flux continuity, this method achieves precise matching of cross-sectional areas between heterogeneous materials by considering the different permeability characteristics of two-phase composite magnetic materials and silicon steel sheets. Compared with traditional single-material design methods, this calculation method can fully leverage the magnetic properties of two-phase composite materials, improve magnetic flux utilization efficiency through differentiated cross-sectional area configuration, avoid local saturation problems caused by uneven magnetic flux distribution, and significantly improve the adjustment accuracy and operational stability of the reactor.

[0065] Series of equations for calculating magnetic reluctance: The isorheological reluctance calculation formula employs the principle of magnetic circuit equivalence, establishing a multi-loop reluctance model that considers material heterogeneity. This model achieves accurate modeling of complex magnetic circuits by separately calculating the reluctance of silicon steel sheets, two-phase composite materials, and the magnetic valve region. Compared to traditional homogenized magnetic circuit analysis methods, this segmented reluctance calculation accurately reflects the magnetic flux distribution characteristics of different material regions, providing a precise theoretical basis for subsequent air gap optimization and magnetic valve design, and effectively avoiding design deviations caused by inaccurate magnetic circuit modeling.

[0066] Air gap reluctance optimization equation: Based on the theory of magnetic circuit decoupling, the paths of the main magnetic flux and the residual magnetic flux are separated by accurately calculating the air gap reluctance. This innovative design principle ensures that the AC magnetic flux generated by the working winding does not enter the region of the two-phase composite material by setting an air gap of a certain thickness between the two-phase composite material and the silicon steel sheet, thereby avoiding interference with the residual magnetism characteristics. Compared with the traditional method of ignoring magnetic flux coupling in reactor design, this equation realizes active control of the magnetic flux path, significantly improves the independence and accuracy of reactance adjustment, and reduces mutual interference between magnetic circuits.

[0067] Multi-objective optimization model: and A collaborative optimization framework for solenoid valve structures was constructed, considering both magnetic flux uniformity and geometric parameter optimization. This multi-objective optimization method transforms the complex solenoid valve design problem into a solvable mathematical optimization problem through constrained programming theory. Compared to traditional empirical solenoid valve design methods, this model can automatically determine the optimal number, location, and geometric dimensions of solenoid valves while satisfying the constraint of uniform magnetic flux distribution, avoiding the subjectivity and limitations of manual design and achieving systematic optimization of the solenoid valve structure.

[0068] Langevin's magnetization equation: The magnetization behavior of two-phase composite materials is described based on statistical mechanics principles, considering the effects of temperature and domain orientation distribution. This equation, by introducing a domain orientation distribution factor, accurately reflects the cooperative magnetization mechanism of the hard and soft magnetic phases. Compared to traditional linear magnetization models, the Langevin function can precisely describe the nonlinear magnetization characteristics of materials under different magnetic field strengths, providing a theoretical basis for optimizing the excitation current of the control winding and achieving precise control and rapid response of the remanent magnetization state.

[0069] Equation for calculating magnetic coupling coefficient: Mutual inductance calculation formula Based on the Newman formula of electromagnetic field theory, the magnetic coupling characteristics between the control winding and the working winding are accurately calculated. This three-dimensional spatial integration method considers the actual geometric distribution and spatial magnetic field distribution of the windings, enabling precise design of winding parameters. Compared with traditional simplified magnetic coupling calculation methods, this model can accurately predict the magnetic coupling strength between windings, providing a theoretical guarantee for the effective separation of AC and DC magnetic flux, and significantly reducing harmonic losses and electromagnetic interference.

[0070] Game theory optimization model: upper-level objective function and lower-level objective function A two-layer optimization architecture for flux distribution and loss control was constructed. This model achieves the optimal balance between uniform flux distribution and minimizing total loss through a game-theoretic Nash equilibrium solution algorithm. Compared to traditional single-objective optimization methods, the game-theoretic model can simultaneously consider both equipment performance indicators and efficiency requirements. By dynamically adjusting the weight coefficients, it achieves adaptive optimization under different operating conditions, avoiding performance bias issues that may arise from single-objective optimization, and ultimately achieving a comprehensive improvement in the overall performance of the reactor.

[0071] To better understand and implement this invention, the following is a specific application scenario example 2: The research team adopted a two-phase composite magnetic material magnetic valve reactor technology to design a dynamic reactive power compensation device with a rated capacity of 10Mvar and a rated voltage of 110kV. The entire design process was strictly implemented according to seven steps to ensure that the equipment performance met the requirements of power grid operation.

[0072] The research team first performed step S01, determining the basic design parameters according to the substation technical specifications. Rated capacity Set to 10× VA, rated voltage 110× V, with an operating frequency f of 50Hz. The permeability of the two-phase composite magnetic material was determined through performance testing. 8.5× H / m, permeability of silicon steel sheet 1.26× H / m. Design value for magnetic flux density. Choose 1.7T, safety margin factor Take 1.3, length coefficient Take 1.0.

[0073] Core dimensions are calculated using geometric parameter calculation functions; cross-sectional area of ​​two-phase composite magnetic materials. The calculation result is 0.045. cross-sectional area of ​​silicon steel sheet It is 0.032 Core length The length is 1.8m. Since the core length exceeds 500mm, the number of solenoid valves is determined according to design specifications. There are 6 solenoid valves, with 3 located on the far left and 3 on the far right of the core.

[0074] Next, step S02 was implemented to establish an equivalent magnetic circuit model. The research team measured the magnetic flux path length of the silicon steel sheet. The flux path length of the two-phase composite magnetic material is 3.2m. The length is 2.8m, and the magnetic flux path lengths of each solenoid valve are 0.15m, 0.12m, and 0.09m, respectively. The magnetic reluctance is calculated using the reluctance equation for the silicon steel sheet. The calculation is 7.95 × Two-phase composite magnetic materials magnetoresistance 8.72× .

[0075] As shown in Table 1, the calculated magnetic reluctance parameters of each solenoid valve are as follows: Table 1 Calculation results of magnetic circuit parameters of solenoid valve

[0076] Verification using the magnetomotive force balance equation showed that the sum of the magnetomotive forces of each branch was 2850 A·turn, with an error of less than 2% compared to the total magnetomotive force, confirming that the magnetic circuit model was correctly established.

[0077] The key point of step S03 is the calculation of the air gap thickness. Based on the rated capacity and operating frequency, the main magnetic flux value... The calculated value is 0.038 Wb, and the remanent flux value is obtained through the characteristic test of two-phase composite magnetic materials. The value is 0.015Wb. The magnetic circuit decoupling requirement coefficient ξ is set to 0.97 to ensure effective separation between the working winding magnetic flux and the residual magnetic path of the two-phase composite material.

[0078] Applying the air gap reluctance optimization equation, air gap reluctance The calculation result is 2.15× The corresponding air gap thickness The thickness is 0.2 mm. This thickness satisfies the requirements for magnetic circuit decoupling while remaining within the controllable range of the manufacturing process. The research team verified the rationality of the air gap design through three-dimensional geometric modeling, ensuring that the magnetic flux separation effect achieved the design goal.

[0079] Step S04 employs a multi-objective optimization algorithm to design the distributed solenoid valve structure. The research team transformed the solenoid valve geometry optimization problem into a constrained optimization model, with magnetic flux distribution uniformity and cross-sectional area optimization as dual objective functions. Through multi-stage gradient design, the cross-sectional area ratio of the first-stage solenoid valve is set to 0.8, the second stage to 0.6, and the third stage to 0.4, achieving a linear gradient distribution of magnetic reluctance.

[0080] As shown in Table 2, the optimized solenoid valve geometry parameters are: Table 2 Results of Geometric Optimization for Distributed Solenoid Valves

[0081] The optimization results show that the magnetic flux distribution uniformity index reaches 96.8%, and the temperature rise of each solenoid valve is controlled within 45K, effectively avoiding local overheating.

[0082] In step S05, the research team used the Langevin function magnetization equation to accurately describe the magnetization behavior of the two-phase composite magnetic material. The saturation magnetization of the material was determined by testing with a vibrating sample magnetometer. 1.28× A / m, magnetocrystalline anisotropy constant is 4.9 × J / The operating temperature is set to 25℃, corresponding to an absolute temperature T of 298K. Magnetic domain orientation distribution factor. The value was determined to be 0.92 by X-ray diffraction analysis.

[0083] The optimal excitation current vector was calculated using an excitation current optimization function, with the target residual magnetism value set at 1.1T. A nonlinear optimization algorithm was then used to solve the problem, yielding an optimal excitation current of 85A for the control winding, corresponding to a magnetization time series of 0.3s rapid magnetization and 1.5s stable holding phases.

[0084] As shown in Table 3, the magnetization effect under different excitation currents is as follows: Table 3 Test results of the relationship between excitation current and remanence

[0085] Test results show that when the excitation current is 85A, the target remanence value of 1.1T can be reached within 0.3s while maintaining low energy consumption, thus meeting the requirements for rapid response.

[0086] The key point of step S06 is to determine the optimal magnetic coupling characteristics between the windings. The research team obtained the self-inductance of the control windings by measuring the inductance using an inductance meter. The self-inductance of the working winding is 0.24H. The mutual inductance is 1.86H, with two windings. The value is 0.18H. The calculated magnetic coupling coefficient is... The value is 0.266, which meets the requirements of decoupling design.

[0087] Based on the optimal excitation current of 85A and the rated current of the working winding of 524A, the winding turns ratio is determined. The value is 6.2. The control winding turns are set to 120 turns, and the working winding turns are 744 turns. Considering a current density limit of 6A / ... In accordance with heat dissipation requirements, the cross-sectional area of ​​the control winding conductor is selected as 16. The cross-sectional area of ​​the working winding conductor is selected as 95. .

[0088] As shown in Table 4, the winding design parameters and performance indicators are as follows: Table 4 Winding System Design Parameters

[0089] The winding spatial layout adopts a concentric circle structure, with the control winding located in the inner layer and the working winding located in the outer layer, and the axial distribution is uniform. The insulation system adopts Class F insulation, and the withstand voltage level meets the requirements of the 110kV system.

[0090] Step S07 established a complete finite element simulation model for verification and optimization. The research team used tetrahedral meshes to discretize the complex geometry, achieving a mesh density of 0.02 mm in the magnetic valve region and near the air gap, with a total mesh count of approximately 8.5 million. The simulation model includes... Figure 1 The complete structure of a magnetic valve reactor is shown in the schematic diagram. Figure 2 Sub-diagram (a) of the novel distributed solenoid valve design, showing the solenoid valve structure, and sub-diagram (b) of the equivalent magnetic circuit model. Figure 3 All geometric features of the three-dimensional model of the novel magnetic valve reactor.

[0091] The preparation of the biphase composite material was strictly carried out according to technical requirements, using a NdFeB alloy powder to iron-silicon alloy powder mass ratio of 3:7. Ball milling was performed for 24 hours at 300 r / min, followed by hot pressing sintering under an argon protective atmosphere at 850℃ for 2 hours. The prepared material was examined by scanning electron microscopy. Figure 4 External scanning electron microscope images of the two-phase composite material and Figure 5 The scanning electron microscope image of the two-phase composite material shows that the material has a uniform microstructure and the adhesive is rationally distributed.

[0092] Corresponding to the material magnetic property test results Figure 6The hysteresis loop of the two-phase composite magnetic material is shown in subplot (a) which displays the hysteresis characteristics at a coercivity of 750 Oe, and subplot (b) which displays the performance parameters at a coercivity of 1370 Oe. Testing confirmed that the material's remanent magnetic properties are stable and meet the design requirements.

[0093] The implementation effect of decoupling design is achieved through Figure 7 The design of the decoupled dual-phase composite material and silicon steel sheet was verified using a schematic diagram. Simulation analysis established... Figure 8 A schematic diagram of the equivalent magnetic circuit model of the novel magnetic valve reactor is shown, including sub-diagram (a) the complete magnetic circuit model, sub-diagram (b) the DC magnetic flux path, and sub-diagram (c) the AC magnetic flux path. Equivalent circuit analysis is performed using... Figure 9 The topology of the equivalent circuit diagram of the novel magnetic valve reactor.

[0094] The simulation verification process analyzed the performance under various working conditions. Figure 10 A schematic diagram of the magnetic induction intensity distribution of the new magnetic valve reactor under AC only shows that when the residual magnetism of the two-phase composite material is 0T, the magnetic flux mainly passes through the silicon steel sheet path, and the magnetic induction intensity at the magnetic valve reaches 1.67T. Figure 11 Subgraphs (a) and (b) of the schematic diagram of the magnetic induction intensity distribution under the condition of residual magnetism in the new magnetic valve reactor show the magnetic field distribution under the conditions of 0.9T and 1.2T residual magnetism, respectively, verifying the effectiveness of residual magnetism regulation.

[0095] Performance analysis results under normal operating conditions are as follows: Figure 12 The schematic diagram of the magnetic induction intensity distribution under normal operating conditions of the new type of magnetic valve reactor is shown. Sub-figure (a) shows the magnetic field distribution when the remanence is 0.6T, and sub-figure (b) shows the distribution pattern under other remanence conditions. Simulation confirms that the magnetic flux separation effect is good, and the AC magnetic flux and DC magnetic flux are distributed independently as required by the design.

[0096] The application of the game theory optimization model has achieved significant results. The upper-level objective function optimizes the uniformity of magnetic flux distribution, and the calculated uniformity index is 96.8%. The lower-level objective function minimizes the total loss, and the calculation results for each loss are shown in Table 5. Table 5 Equipment Loss Analysis Results

[0097] The optimal equilibrium point is found using the Nash equilibrium solution algorithm, with weighting coefficients... Set to 0.6. The value was set to 0.4. The optimization results showed that the total loss decreased from 34.2kW ​​to 29.2kW, a reduction of 14.6%, while the uniformity of magnetic flux distribution improved to 96.8%.

[0098] The reactive characteristics test of the equipment verified its regulation performance. During the process of remanence changing from 0.2T to 1.2T, the reactance value smoothly adjusted from 25% to 100% of the rated value, with an adjustment accuracy better than 2%. I-V characteristic analysis showed that the equipment had good linearity, with a nonlinear distortion of 2.1%, meeting the requirements for power grid operation.

[0099] The equipment achieved excellent results in field operation testing at a 110kV substation. Its reactive power regulation range covers 0.5–15 Mvar, with a response time of less than 0.5 seconds, meeting the grid's rapid regulation requirements. The equipment operated stably, with voltage fluctuations controlled within ±2% of the rated value, effectively improving the substation's power quality.

[0100] Temperature rise test results show that the temperature rise of all components of the equipment is within the design range, with the highest temperature rise of the core being 42.1K and the highest temperature rise of the winding being 31.2K, ensuring the long-term safe operation of the equipment. Vibration and noise tests show that the equipment operates smoothly, with noise levels controlled below 65dB, meeting environmental protection requirements.

[0101] Long-term performance monitoring of the equipment showed that the remanent magnetism of the two-phase composite magnetic material remained stable during 6 months of operation, with a demagnetization rate of less than 3%, verifying the material's excellent stability. The magnetic valve structure operated reliably, without any localized overheating or mechanical damage, demonstrating the superiority of the distributed design.

[0102] Traditional methods for solving reactive power regulation in power systems mainly employ fixed reactors in conjunction with mechanical switching devices or traditional magnetically controlled reactors. Fixed reactors can only provide a fixed amount of reactive power, cannot achieve continuous regulation, have poor regulation accuracy, and slow response speed. Although traditional magnetically controlled reactors can achieve continuous regulation, they suffer from limited regulation range, high losses, and high harmonic content, and their efficiency is particularly low in large-capacity applications.

[0103] This invention represents a significant advancement over traditional methods. In terms of regulation performance, the reactance adjustment range has been expanded from the traditional 50%–100% to 25%–100%, the adjustment accuracy improved from 5% to 2%, and the response time shortened from 2–5 seconds to less than 0.5 seconds. Regarding efficiency, the total equipment loss is reduced by 14.6% compared to traditional magnetically controlled reactors, with hysteresis loss reduced by 17.8% and eddy current loss by 16.0%. In terms of harmonic performance, the fifth harmonic content is reduced from the traditional 8%–12% to below 3%, significantly improving power quality. Regarding operational stability, the demagnetization rate of residual magnetism is less than 3% during 6 months of continuous operation, while the demagnetization rate of traditional magnetically controlled reactors is typically in the range of 10%–15%. Regarding temperature rise control, the maximum core temperature rise is controlled at 42.1K, 18.5% lower than traditional designs, improving equipment reliability and service life. These technological advancements make the two-phase composite magnetic material magnetic valve reactor an ideal solution for dynamic reactive power compensation in power systems.

[0104] It should be noted that the variables involved in this invention are explained in detail in Table 6 below.

[0105] Table 6. Variable Explanation Table

[0106] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A design method for a two-phase composite magnetic material magnetic valve-type reactor, characterized in that, include: Based on the rated voltage and rated capacity, determine the cross-sectional area, length, and number of solenoid valves of the two-phase composite magnetic material and silicon steel sheet, and establish the corresponding iron core geometric model; Equivalent magnetic circuits for AC and DC magnetic flux paths are established separately, and each reluctance parameter is determined using the reluctance calculation equation. The air gap thickness is calculated using the air gap reluctance optimization equation. A distributed magnetic valve structure is designed using a multi-objective optimization algorithm, and the cross-sectional area reduction ratio and length of each magnetic valve are calculated using the magnetic valve geometry optimization function. The optimal excitation current vector of the control winding is calculated through magnetization intensity physical mechanism analysis. The magnetization process of the two-phase composite magnetic material is described using the Langevin function magnetization equation, and the correspondence between the excitation current vector and the remanence characteristics is established using the excitation current optimization function. The optimal magnetic coupling characteristics between the control winding and the working winding are calculated based on magnetic coupling theory, and the winding parameters are determined using the magnetic coupling coefficient calculation function. A finite element simulation model is established to verify the design parameters, and the balance between magnetic flux distribution and loss control is optimized using a game theory optimization model. Based on the reluctance parameters, the air gap thickness, the cross-sectional area reduction ratio and length, the optimal excitation current vector, the correspondence between the excitation current vector and the remanence characteristics, and the winding parameters, a corresponding equivalent magnetic circuit model is established. The final design parameters of the two-phase composite magnetic material magnetic valve reactor are obtained based on the core geometric model and the equivalent magnetic circuit model. The dual-phase composite magnetic material is specifically a magnetic material formed by uniformly distributing hard magnetic materials and soft magnetic materials within the nanoscale range. The specific ratio of the two-phase composite magnetic material is a ratio of hard magnetic material to soft magnetic material of 3:

7. The hard magnetic material is neodymium iron boron alloy powder, and the soft magnetic material is iron-silicon alloy powder. An air gap with a thickness of 0.2 mm is provided between the dual-phase composite magnetic material and the silicon steel sheet.

2. The design method of the two-phase composite magnetic material magnetic valve reactor according to claim 1, characterized in that, The preparation process of the dual-phase composite magnetic material specifically includes ball milling and mixing neodymium iron boron alloy powder and iron-silicon alloy powder according to a certain ratio. The ball milling time is 24 hours and the rotation speed is 300 rpm. Then, hot pressing sintering is carried out under an argon protective atmosphere at a sintering temperature of 850°C and a holding time of 2 hours. After cooling, the material is machined to obtain a dual-phase composite magnetic material core with the required geometric dimensions.

3. The design method of the two-phase composite magnetic material magnetic valve reactor according to claim 2, characterized in that, The solenoid valve geometry optimization function is specifically used to determine the geometry of the core and the solenoid valve configuration based on the rated voltage and rated capacity. The inputs include the rated voltage, rated capacity, design value of magnetic flux density, magnetic permeability of the core material, and safety margin coefficient. The outputs are the cross-sectional area of ​​the two-phase composite magnetic material, the cross-sectional area of ​​the silicon steel sheet, the core length, and the number of solenoid valves.

4. The design method of the two-phase composite magnetic material magnetic valve reactor according to claim 3, characterized in that, The magnetoresistive calculation equation is specifically used to establish the magnetoresistive parameters of each part in the equivalent magnetic circuit model. The inputs include the core cross-sectional area, core length, material permeability, magnetic flux path length, and magnetic circuit geometric factor. The outputs are the magnetoresistive parameters of silicon steel sheet, two-phase composite magnetic material, and magnetic valve.

5. The design method of the two-phase composite magnetic material magnetic valve reactor according to claim 4, characterized in that, The air gap magnetoresistance optimization equation is specifically used to calculate the optimal air gap thickness between the two-phase composite magnetic material and the silicon steel sheet. The inputs include the main magnetic flux value, the residual magnetic flux value, the magnetic reluctance of the silicon steel sheet, the magnetic reluctance of the two-phase composite magnetic material, and the magnetic circuit decoupling requirement coefficient. The outputs are the air gap magnetoresistance value and the corresponding air gap thickness.

6. A reactor, characterized in that, The two-phase composite magnetic material magnetic valve reactor is designed and manufactured according to the design method of any one of claims 1-5.

7. The reactor as described in claim 6, characterized in that, The device includes an E-type core structure, a working winding, and a control winding. The E-type core is composed of laminated silicon steel sheets and a hybrid magnetic circuit made of a two-phase composite magnetic material. The two-phase composite magnetic material is made by mixing neodymium iron boron alloy powder and iron-silicon alloy powder in a mass ratio of 3:7, ball milling the mixture, and then hot-pressing and sintering it under an argon protective atmosphere to achieve a uniform distribution of hard and soft magnetic phases at the nanoscale. The working winding is located on the central core of the core, and the control windings are located on the two side columns. Three distributed magnetic valves are located on the leftmost and rightmost sides of the core, using a multi-stage gradient geometry design. The cross-sectional area of ​​the first-stage magnetic valve is 80% of the standard cross-sectional area, the second stage is 60%, and the third stage is 40%. The magnetic valve area is composed of a two-phase composite magnetic material.

8. The reactor as described in claim 7, characterized in that, The preparation process of the dual-phase composite magnetic material includes ball milling and mixing neodymium iron boron alloy powder and iron-silicon alloy powder at a mass ratio of 3:7 for 24 hours at a speed of 300 rpm, followed by hot pressing and sintering under an argon protective atmosphere at a sintering temperature of 850°C for 2 hours; the working winding is made of copper wire, and the cross-sectional area of ​​the wire is determined according to a current density of 5 to 8 amperes per square millimeter.

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