Step type magnetic lead screw power generation device and parameter optimization selection method thereof

Abstract

The invention discloses a step type magnetic lead screw power generation device and a parameter optimization selection method thereof, the device comprises a V-shaped embedded step rotor, a spiral rotor damping modulation ring and a step stator which are coaxially arranged in sequence from inside to outside, and low-speed reciprocating motion of waves can be efficiently converted into electric energy. The parameter optimization method comprises the following steps: setting a to-be-optimized parameter and generating an initial population by using a Halton sequence; performance indexes are extracted through finite element simulation, and the fitness is calculated; on the basis of the nonlinear escape energy model, selecting an exploration or development mode of the Harris eagle algorithm to update parameters; and differential variation is introduced to enhance the search capability, and iterative optimization is carried out until an optimal parameter combination is obtained. According to the method, multi-parameter collaborative automatic optimization is realized, the air gap flux density, the thrust stability and the output efficiency of the power generation device are remarkably improved, and the defects that a traditional trial-and-error method is long in design period and prone to falling into local optimum are overcome.

Description

Technical Field

[0001] This invention relates to the field of wave power generation new energy technology, specifically to a step-type magnetic screw power generation device and a method for optimizing the selection of its parameters. Background Technology

[0002] In response to the continuous deterioration of the marine environment, my country has been strengthening its monitoring and management efforts. Offshore marine observation equipment, as an important carrier of marine environmental monitoring, can provide real-time and comprehensive ocean water quality data, which is of great significance for marine ecological environment protection and sustainable resource utilization. However, since offshore marine observation equipment usually floats on the sea surface for long periods, its power supply mainly relies on built-in batteries, which have limited capacity and cannot provide continuous power for extended periods. Replacing batteries in the complex marine environment is extremely difficult, and the output power of traditional power generation devices fluctuates greatly due to wave motion, making it difficult to achieve stable power supply. Therefore, how to provide a continuous and stable power supply for observation equipment in the marine environment has become an urgent technical challenge to be solved.

[0003] Magnetic lead screws, as a non-contact transmission device, achieve motion transmission through magnetic field coupling. They can replace traditional mechanical transmission structures, effectively reducing friction loss, noise and heat generation, and improving system efficiency and dynamic stability, making them particularly suitable for high-speed and high-precision applications. Applying them to wave energy generation devices holds promise for achieving direct and efficient conversion of low-speed reciprocating linear motion into electrical energy. However, the electromagnetic performance and mechanical characteristics of step-magnetic lead screw power generation devices are influenced by multiple structural parameters, which are strongly coupled, resulting in a high-dimensional and nonlinear design space. Traditional parameter design methods often rely on trial-and-error adjustments based on engineer experience or limited analysis based on single factors, making it difficult to find the optimal parameter combination in the global design space. This leads to long design cycles, low optimization efficiency, and often results in getting stuck in local optima, limiting further improvements in the overall performance of the device. Summary of the Invention

[0004] The purpose of this invention is to provide a step-type magnetic screw power generation device and its parameter optimization method, so as to solve the problems of long parameter design cycle, difficulty in global optimization, unstable power generation performance and low efficiency caused by relying on experience trial and error and univariate analysis in the prior art.

[0005] To achieve the above objectives, the technical solution provided by this invention is: a method for optimizing the selection of parameters for a step-type magnetic screw power generation device, comprising the following steps: S1: Set the parameters to be optimized for the step-magnetic screw generator; S2: Based on the parameters to be optimized and their corresponding constraints, an initial population that satisfies the constraints is generated using the Halton sequence. The initial population represents the initial vector of parameters to be optimized. S3: Drive finite element simulation based on the parameter vector of each individual in the current population and extract the corresponding target performance index; the current population represents the current parameter vector to be optimized; calculate the fitness of the individual based on the target performance index; compare all fitness values ​​and determine the current optimal individual as the prey position; where the current population represents the current parameters to be optimized; the prey position represents the optimal parameter combination. S4: Calculate the prey escape energy based on the nonlinear adjustable energy decay model. The prey escape energy represents the control amount of the parameter vector adjustment method. Select the exploration or development mode according to the escape energy, and update the individual parameter vector by applying the corresponding Harris Eagle strategy. Then, perform differential mutation operation and update the population by comparing fitness. The updated population represents the optimized parameter vector. S5: Use the differential mutation method to replace the parameter vectors in the original population and generate new parameter vector combinations; S6: Determine if the algorithm has reached the maximum number of iterations. If the condition is met, output the optimal parameter vector combination; otherwise, return to step S3.

[0006] To optimize the above technical solution, the specific measures also include: In step S1, the parameters to be optimized for the step-type magnetic screw power generation device include:

[0007] in, The groove depth coefficient of the step subyoke (101) is represented by . This represents the axial length of the single-stage step-jump subyoke (101). Indicates the length of the permanent magnet (102), This indicates the width of the permanent magnet (102). Indicates the radial thickness of the magnetically conductive spiral ring. This indicates the thickness of the cladding layer of the damping winding. This indicates the thickness of the non-magnetic metal layer filling the pole pair gap. This indicates the opening angle of the V-shaped magnetic guide hole. The circumferential distribution angle of the step stator yoke (301) is indicated.

[0008] Further, in step S2, the parameters to be optimized and their corresponding constraints are specifically as follows: Magnetic spiral ring thickness With the thickness of the non-magnetic metal layer The ratio lies in the interval Inside, and the thickness of the magnetic spiral ring Damping winding thickness and the thickness of the non-magnetic metal layer The sum of the values ​​is not greater than the axial gap between the V-shaped embedded step mover (1) and the step stator (3) of the step magnetic screw generator. Groove depth ratio factor V-shaped magnetic hole angle ; Length of permanent magnet (102) With width Limited by the size of the groove opened on the step yoke (101), and the length-to-width ratio of the permanent magnet is in the range The circumferential distribution angle of the stepped inclined groove of the inner step stator yoke (301) Satisfy constraints: ,and It is a positive integer.

[0009] Further, in step S3, the step of driving finite element simulation based on the parameter vector of each individual in the current population and extracting the corresponding target performance index; the current population represents the current parameter vector to be optimized; and calculating the fitness of individuals based on the target performance index is specifically as follows: Based on the parameter vector of the initial step magnetic screw power generation device It drives the parametric modeling script to automatically generate the corresponding three-dimensional electromagnetic field simulation model in the finite element analysis software; Submit and solve the finite element model, and automatically extract the target performance indicators, including the target air gap average magnetic flux density, from the simulation results file. Thrust fluctuation Thrust base wave amplitude ; Calculate the fitness of this individual. The individual with the lowest fitness is designated as the optimal individual, and its location is the prey location.

[0010] in, , , The weighting coefficient is greater than zero.

[0011] Further, in step S4, the prey escape energy is calculated based on a nonlinear adjustable energy decay model, where the prey escape energy represents the control quantity of the parameter vector adjustment method; the exploration or exploitation mode is selected based on the escape energy, and the corresponding Harris eagle strategy is applied to update the individual parameter vector, specifically as follows: The prey escape energy is calculated based on a nonlinear adjustable energy decay model, using the following formula:

[0012] when When, select exploration mode; when decay to When choosing a development mode; in, This represents the initial escape energy of the prey. It is an adjustable parameter. This represents the current iteration number. This represents the maximum number of iterations.

[0013] When exploration mode is selected, the individual parameter vector is updated as follows:

[0014] in, This represents the individual parameter vector for the next iteration. This represents a parameter vector for a random individual among all individuals. It is the currently discovered optimal position parameter vector. It is the current parameter vector of this individual. and These are the upper and lower bounds of the variable. and Is Random numbers between; It is the current average parameter vector of all individuals.

[0015] Furthermore, when selecting a development mode, the probability of escape is considered. Choose different strategies to update the parameter vector: when and At that time, a hovering and encirclement strategy is adopted, and the parameter vector is updated as follows:

[0016]

[0017] when and At that time, a strong surprise attack strategy is adopted, and the parameter vector is updated as follows:

[0018] when and At that time, a spiraling, gradual strategy is adopted, and the parameter vector is updated as follows:

[0019] when and At that time, a strong asymptotic strategy is adopted, and the parameter vector is updated as follows:

[0020] Furthermore, after updating the parameter vector using the hovering and aggressive gradualist strategies, if the fitness still does not decrease, a Levy flight is performed:

[0021]

[0022]

[0023] Furthermore, if the fitness level still does not decrease, then the original position is used:

[0024] in, This indicates the current iteration number is... At that time, the difference between the optimal solution and the individual parameter vector; Is Random numbers within a certain range represent the optimization strength of the parameter vector; It is the current parameter vector of this individual. This represents the currently discovered optimal position parameter vector; It is the dimension for solving the problem. yes A dimensional random parameter vector, It is the Lévy flight function. , All are intervals Random numbers in the data, It is a constant. For the fitness function, This is the Gamma function.

[0025] Further, in step S5, the differential mutation method is used to replace the parameter vectors in the original population and generate new parameter vector combinations. The specific process is as follows: For the The first generation of the population parameter vectors Generate a vector of mutated parameters , mutation parameter vector The parameter vector of the newly obtained step-type magnetic screw generator is as follows:

[0026] in, A random integer, and ; , and Let be the parameter vectors of three distinct step-magnetic screw generators randomly selected from the current population. The scaling factor represents the proportional coefficient of the adjustment range of the control parameter in the differential variation. Furthermore, regarding the mutation parameter vector Components exceeding the boundary undergo boundary processing:

[0027] Generate experimental parameter vectors :

[0028] in, and They represent the first The lower and upper bounds of the values ​​of each component; For crossover probability, Let be the dimension of the random parameter vector.

[0029] Furthermore, the following formula is used to determine whether to replace the original parameter vector with the experimental parameter vector: .

[0030] As another important technical solution, the present invention also provides a step-type magnetic screw power generation device, comprising: a tubular component arranged coaxially, which consists of, from the inside to the outside: a V-shaped embedded step mover (1), a helical mover damping modulation ring (2) and a step stator (3). The V-shaped embedded stepper (1) includes a cylindrical stepper yoke (101) and a permanent magnet (102) embedded in the magnetic guide hole of the stepper yoke (101); the stepper yoke (101) is provided with a pair of stepped grooves every 90 degrees, the extension line of the short side of the stepped groove passes through the center of the stepper yoke (101), and the long side of the stepped groove is perpendicular to the short side; each pair of permanent magnets (102) is symmetrically distributed about the magnetic pole center line of the stepper yoke (101) and arranged in a V-shape. The helical rotor damping modulation ring (2) includes a magnetic helical ring and a damping winding covering it, and the pole pair intervals of the magnetic helical ring are filled with a non-magnetic metal layer. The stepped stator (3) includes a stepped stator yoke (301), a winding (302), and a housing (303). The inner wall of the stepped stator yoke (301) is provided with stepped grooves and rings that are alternately distributed in the circumferential direction. The winding (302) is located on the protrusions on the outside of the stepped grooves and rings. The housing (303) is fitted on the outermost side of the stepped stator yoke (301).

[0031] Compared with the prior art, the beneficial effects of the present invention are: The step magnetic screw of this invention is easy to manufacture, can reduce problems such as friction, noise and heat generation to improve the dynamic stability of the entire transmission system, and can greatly improve the efficiency of the energy conversion device.

[0032] This invention uses Halton sequence mapping to initialize the population, effectively avoiding the population clustering problem that may be caused by random initialization, and significantly improving the efficiency and diversity of the global exploration in the early stages of the algorithm. Compared with traditional random methods, Halton sequences ensure that all regions of the knowledge space are fully covered, laying a good foundation for the subsequent optimization process and improving the overall convergence speed and stability of the algorithm. This invention, based on a nonlinear adjustable energy decay model, allows for dynamic control of the escape energy decay rate. This enables the algorithm to maintain a high energy value in the early stages of iteration to enhance global exploration, while rapidly decaying energy in the later stages to accelerate local convergence. Furthermore, the escape energy decay curve can be adjusted according to problem characteristics, achieving a precise transition between the exploration and development phases. This effectively avoids premature convergence while improving convergence speed, significantly enhancing the algorithm's adaptability compared to a fixed decay mode. This invention applies the differential mutation method to parameter optimization. By performing differential operations on the current parameter vector, it generates mutated individuals, effectively enhancing the ability to explore and develop in complex parameter spaces. This method uses population difference information to guide the search direction and can dynamically adjust the search step size, avoiding parameter optimization from getting trapped in local optima.

[0033] This invention establishes a seamless automated interface between the Harris Eagle optimization algorithm and finite element simulation software, forming an efficient closed loop of design, simulation, and optimization. The optimization output includes not only specific parameter values ​​but also parameter relationship ranges that guide engineering applications, enhancing the practical value of the results. It realizes global automated optimization of multi-parameter collaborative design of step magnetic screw generators, overcoming the limitations of traditional trial-and-error methods. Attached Figure Description

[0034] Figure 1 : A schematic diagram of the workflow of this invention. Figure 2 : A schematic diagram of the structure of an embodiment of the present invention.

[0035] Figure 3 : Schematic diagram of the step-jump subyoke structure in an embodiment of the present invention.

[0036] Figure 4 : Schematic diagram of the step stator yoke structure in an embodiment of the present invention.

[0037] Figure 5 : A schematic diagram of the structure of the step stator in an embodiment of the present invention.

[0038] Among them, 1-V-type embedded step mover; 2-helical mover damping modulation ring; 3-step stator; 101-step mover yoke; 102-permanent magnet; 301-step stator yoke; 302-winding; 303-shell. Detailed Implementation

[0039] The present invention will be further described in detail below through specific embodiments, but it should not be construed as limiting the scope of the subject matter of the present invention to the following embodiments. All technologies implemented based on the above content of the present invention fall within the scope of the present invention.

[0040] In some embodiments, the present invention provides a method for optimizing the selection of parameters for a step-type magnetic screw power generation device, comprising the following steps: S1: Set the parameters to be optimized for the step-magnetic screw generator; The parameters to be optimized for the step-type magnetic screw generator include:

[0041] Among them, groove depth ; The groove depth coefficient of the step subyoke (101) is represented by . This represents the axial length of the single-stage step-jump subyoke (101). Indicates the length of the permanent magnet (102), This indicates the width of the permanent magnet (102). Indicates the radial thickness of the magnetically conductive spiral ring. This indicates the thickness of the cladding layer of the damping winding. This indicates the thickness of the non-magnetic metal layer filling the pole pair gap. This indicates the opening angle of the V-shaped magnetic guide hole. The circumferential distribution angle of the step stator yoke (301) is indicated.

[0042] S2: Based on the parameters to be optimized and their corresponding constraints, an initial population that satisfies the constraints is generated using the Halton sequence. The initial population represents the initial vector of parameters to be optimized. In some implementations, the parameters to be optimized and their corresponding constraints are specifically as follows: Magnetic spiral ring thickness With the thickness of the non-magnetic metal layer The ratio lies in the interval Inside, and the thickness of the magnetic spiral ring Damping winding thickness and the thickness of the non-magnetic metal layer The sum of the thicknesses is not greater than the axial gap between the V-shaped embedded step mover (1) and the step stator (3) of the step magnetic screw generator; this thickness ratio range is beneficial to suppress inter-pole leakage magnetic flux while ensuring effective modulation of the magnetic field.

[0043] Groove depth ratio factor This range balances the significant change in magnetic reluctance with the mechanical strength of the moving yoke. V-shaped magnetic guide hole angle. This angular range optimizes the direction and focusing effect of the permanent magnet flux.

[0044] Length of permanent magnet (102) With width Limited by the size of the groove opened on the step yoke (101), and the length-to-width ratio of the permanent magnet is in the range Internally, the length-to-width ratio helps achieve optimal electromagnetic thrust and stability. The circumferential distribution angle of the stepped skewers of the stepped stator yoke (301) Satisfy constraints: ,and It is a positive integer to ensure that the windings are evenly and symmetrically distributed in the circumferential direction and that operation is feasible.

[0045] Initializing the population using Halton sequence mapping leverages its low dissimilarity to generate highly uniformly distributed initial individuals in the solution space. This initialization method effectively avoids population aggregation problems that may occur with random initialization, significantly improving the efficiency and diversity of the algorithm's early global exploration. Compared to traditional random methods, Halton sequences ensure that all regions of the solution space are fully covered, laying a solid foundation for subsequent optimization processes and improving the overall convergence speed and stability of the algorithm.

[0046] Determine the parameter vector of the initial step-magnetic screw generator:

[0047] Initial parameter vector and subsequent iteration parameters All satisfy the boundary constraints:

[0048]

[0049] in, Describes the population generated in the t-th iteration as the th... The first individual parameter vector of the _th One component; and They represent the first The lower bound and upper bound of the values ​​of each component.

[0050] In some implementations, a base-prime system is used. The sequence generates an initial population, which represents the initial vector of parameters to be optimized, and is then transformed into manufactureable parameters through interval mapping.

[0051] in, Represents the initial population parameter matrix; Representing the Initialization scheme for parameters of a stepped magnetic lead screw; The population size represents the set of parameter vectors to be optimized.

[0052] In some implementations, basis vectors Defined as:

[0053] in: represent The exponential basis vector of the sequence is used to control the distribution of the initial points of each parameter of the step magnetic screw; each prime number Fixed structural parameters corresponding to a single characteristic; for the first parameter vectors The 3D structural parameters , The first of the sequence The dimensional components are:

[0054] in, yes At the base The next digits; of The base expansion is: .

[0055] The matrix form representing the sequence point set is as follows:

[0056] Through linear mapping Sequence matrix mapping to parameter range:

[0057]

[0058] in, Each component is represented as , Indicates by The initial parameter vector set for sequence generation; Indicates the first parameter vectors The 3D structural parameters Normalized decision variables.

[0059] S3: Drive finite element simulation based on the parameter vector of each individual in the current population and extract the corresponding target performance index; the current population represents the current parameter vector to be optimized; calculate the fitness of the individual based on the target performance index; compare all fitness values ​​and determine the current optimal individual as the prey position; where the current population represents the current parameters to be optimized; the prey position represents the optimal parameter combination. In step S3, the finite element simulation is driven based on the parameter vector of each individual in the current population, and the corresponding target performance index is extracted; the current population represents the current parameter vector to be optimized; the fitness of the individual is calculated based on the target performance index, specifically as follows: In some implementations, based on the parameter vector of the initial step-magnetic screw generator... It drives the parametric modeling script to automatically generate the corresponding three-dimensional electromagnetic field simulation model in the finite element analysis software; Submit and solve the finite element model, and automatically extract the target performance indicators, including the target air gap average magnetic flux density, from the simulation results file. Thrust fluctuation Thrust base wave amplitude ; Calculate the fitness of this individual. This is used to guide the movement of the entire population, and it needs to be recalculated in each iteration. To update the location of prey The individual with the lowest fitness is designated as the optimal individual, and its location is the prey location. :

[0060] in, , , These are weighting coefficients greater than zero, used to balance the pursuit of high magnetic field strength, low thrust fluctuation (high stability), and high thrust, respectively.

[0061] S4: Calculate the prey escape energy based on the nonlinear adjustable energy decay model. The prey escape energy represents the control amount of the parameter vector adjustment method. Select the exploration or development mode according to the escape energy, and update the individual parameter vector by applying the corresponding Harris Eagle strategy. Then, perform differential mutation operation and update the population by comparing fitness. The updated population represents the optimized parameter vector. The nonlinear adjustable energy decay model allows for dynamic control of the escape energy decay rate, enabling the algorithm to maintain a high energy value in the early stages of iteration to enhance global exploration, and then rapidly decay energy in the later stages to accelerate local convergence. This strategy can adjust the decay curve according to the problem characteristics, achieving a precise transition between the exploration and development phases, effectively avoiding premature convergence while improving convergence speed. Compared to a fixed decay mode, it significantly enhances the algorithm's adaptability.

[0062] The prey escape energy is calculated based on a nonlinear adjustable energy decay model, using the following formula:

[0063] in, This represents the initial escape energy of the prey. It is an interval The random number in it, It is an adjustable parameter. This represents the current iteration number. This is the maximum number of iterations (set manually).

[0064] In some implementations, the escape energy decay curve can be adjusted according to the characteristics of the problem to achieve a precise transition between the exploration and development phases. This is especially useful for models that may have multiple locally optimal parameter vectors. The initial settings are relatively small, allowing for the longest possible preliminary exploration, while for models where the range of the potential optimal point is relatively clear, The setting is larger to accelerate convergence.

[0065] In some implementations... It is a number of iterations The decrease in attenuation as the value increases, when When the exploration mode is selected, the algorithm globally searches the parameter vector space of the step magnetic screw to avoid getting trapped in local electromagnetic thrust peaks; when decay to When selecting the development mode, fine-tuning is performed on the optimal parameter neighborhood to improve the positioning accuracy and thrust stability of the lead screw.

[0066] When exploration mode is selected, the individual parameter vector is updated as follows:

[0067] in, This represents the individual parameter vector for the next iteration. This represents a parameter vector for a random individual among all individuals. It is the prey vector, representing the currently discovered optimal position parameter vector. It is the current parameter vector of this individual. and These are the upper and lower bounds of the variable. and Is Random numbers between; It is the average parameter vector of all individuals, calculated using the following formula: .

[0068] In some implementations, the development mode is selected based on the probability of escape. Different strategies are selected to update the parameter vector, where the escape probability is... for Random numbers in the array.

[0069] when and At that time, a hovering and encirclement strategy was adopted (to robustly adjust the initially discovered high-thrust parameter vector), and the parameter vector was updated as follows:

[0070]

[0071] when and At that time, a strong surprise attack strategy was adopted (after locking in the optimal parameter region, the final fine-tuning was quickly completed), and the parameter vector was updated as follows:

[0072] when and When this happens, a spiraling, gradual approach is adopted (if adjusting the parameter vector no longer improves performance, try jumping to a new parameter vector). The parameter vector update method is as follows:

[0073] when and At that time, a strong asymptotic strategy is adopted (in the final stage of optimization, the optimal parameter vector is evaluated to eliminate any overlooked better solutions). The parameter vector update method is as follows:

[0074] In some implementations, if the fitness does not decrease after updating the parameter vector using a hovering and aggressive gradualism strategy, a Levy flight is performed:

[0075]

[0076]

[0077] If fitness does not decrease, then use the original position:

[0078] in, This indicates the current iteration number is... At that time, the difference between the optimal solution and the individual parameter vector; Is Random numbers within a certain range represent the optimization strength of the parameter vector; It is the current parameter vector of this individual. This represents the currently discovered optimal position parameter vector; It is the dimension for solving the problem. yes A dimensional random parameter vector, It is the Lévy flight function. , All are intervals Random numbers in the data, It is a constant. For the fitness function, This is the Gamma function.

[0079] S5: Use the differential mutation method to replace the parameter vectors in the original population and generate new parameter vector combinations; Applying the differential mutation method to parameter optimization, this method generates mutated individuals by performing differential operations on the current parameter vector, effectively enhancing the exploration and development capabilities in complex parameter spaces. This method utilizes population difference information to guide the search direction and can dynamically adjust the search step size, avoiding parameter optimization from getting trapped in local optima. For the multi-parameter coupling characteristics of magnetic lead screws, differential mutation can efficiently and collaboratively optimize structural parameters such as groove depth, permanent magnet size, and magnetic ring thickness. For the The first generation of the population parameter vectors Generate a vector of mutated parameters , mutation parameter vector The parameter vector of the newly obtained step-type magnetic screw generator is as follows:

[0080] in, A random integer, and ; , and Let be the parameter vectors of three distinct step-magnetic screw generators randomly selected from the current population. The scaling factor represents the proportional coefficient of the control parameter adjustment range in the differential variation, corresponding to the step size scale of the step magnetic screw structure parameter optimization. The default value is... ; For the mutation parameter vector Components exceeding the boundary undergo boundary processing:

[0081] In some implementations, an experimental parameter vector is generated. :

[0082] in, and They represent the first The lower and upper bounds of the values ​​of each component; For crossover probability, the preferred method is... ; , Let be the dimension of the random parameter vector.

[0083] The following formula is used to determine whether to replace the original parameter vector with the experimental parameter vector: .

[0084] S6: Determine if the algorithm has reached the maximum number of iterations. If the condition is met, output the optimal parameter vector combination; otherwise, return to step S3.

[0085] In some implementations, such as Figure 2 As shown, the present invention also provides a step-type magnetic screw power generation device, which consists of three coaxial tubular components with a coaxial mounting design, from the inside to the outside, namely a V-shaped embedded step mover (1), a helical mover damping modulation ring (2) and a step stator (3).

[0086] The helical rotor damping modulation ring (2) is installed between the V-shaped embedded step rotor (1) and the step stator (3), and there are internal air gaps between the V-shaped embedded step rotor (1) and the helical rotor damping modulation ring (2) and between the helical rotor damping modulation ring (2) and the step stator (3) with the nested V-shaped permanent magnet.

[0087] In some embodiments, the V-shaped embedded stepper (1) includes a cylindrical stepper yoke (101) and a permanent magnet (102). The stepper yoke (101) is provided with a pair of stepped grooves every 90°. The extension line of the short side of the stepped groove passes through the center of the stepper yoke (101), and the long side is perpendicular to the short side. The permanent magnet (102) is embedded with the magnetic guide hole of the stepper yoke (101). The magnetic guide holes of the permanent magnets located on the same permanent magnet pole are V-shaped. Each pair of permanent magnets has a magnetic pole center line, and each pair of permanent magnets is symmetrically distributed about its respective magnetic pole center line. There are a total of four pairs of V-shaped permanent magnets, which are evenly distributed along the circumference. In some embodiments, the helical mover damping modulation ring (2) includes a helical magnetic helical ring and a damping winding. The magnetic helical ring is made of ferromagnetic material, and each pole pair is filled with a non-magnetic metal layer. The magnetic helical ring and the non-magnetic metal layer are alternately arranged to prevent the magnetic field between adjacent pole pairs of the helical mover damping modulation ring (2) from penetrating. The outer side of the magnetic helical ring is wrapped with the damping winding. The magnetic helical part of the helical mover damping modulation ring (2) is made of ferromagnetic material, and each pole pair is filled with a non-magnetic metal layer such as copper or aluminum. The magnetic material and the non-magnetic material are alternately filled to prevent the magnetic field between adjacent pole pairs of the modulation ring from penetrating, so that the magnetic field forms a helical magnetic circuit through the helical mover damping modulation ring (2). The side of the magnetic helical ring is wrapped with the damping winding to adjust the transient oscillation amplitude of torque and speed caused by the sudden change in wave speed, thereby improving the dynamic stability of the entire transmission system. Due to the up-and-down movement of the waves, the V-shaped embedded stepper (1) is subjected to force and performs low-speed, high-thrust linear reciprocating motion. The static axial permanent magnet magnetic field generated by the permanent magnet (102) in the inner air gap is modulated into a high-speed spiral magnetic field by the helical mover damping modulation ring (2), thereby accelerating the magnetic field of the inner air gap once.

[0088] The stepped stator (3) includes a stepped stator yoke (301), windings (302), and a housing (303). Each section of the stepped stator yoke (301) is annular with stepped grooves on the inner wall and uniform groove thickness. Each stepped groove extends a certain distance toward the center and engages with the stepped stator yoke (101). The stepped groove of each section of the stepped stator yoke (301) occupies half of the circumference, and the remaining part of the inner wall is a conventional ring. The circumferential stepped grooves and conventional rings alternate. The axial installation also alternates between stepped grooves and conventional rings. Furthermore, each stepped groove and conventional ring has a square protrusion on the outer side, which can be used to place the windings (302) and perform a fixing function. The housing (303) is installed on the outermost side.

[0089] In some ways, such as Figure 3 The diagram shown is a schematic of the step-jump subyoke structure in this invention; as shown... Figure 4 The diagram shown is a schematic of the step stator yoke structure in this invention.

[0090] In some implementations, the step yoke (101) on the outside of the V-shaped embedded step actuator (1) and the step stator yoke (301) on the inside of the step stator (3) both use a single-slot step structure. The step structure can reduce the high-order harmonic components of the induced voltage waveform and make the induced voltage waveform a smooth sine wave, thereby reducing torque fluctuation and improving the running stability of the magnetic screw.

[0091] The embedded permanent magnet (102) in the V-shaped embedded step mover (1) not only serves as the magnetic source of the magnetic screw mover and generates a spiral magnetic field under the influence of the helical mover damping modulation ring (2), but also serves as the magnetic source of the outer winding and generates an alternating permanent magnetic field. This alternating magnetic field induces electrical energy on the outermost motor winding, thereby converting the kinetic energy of the wave undulation motion into electrical energy, realizing energy conversion, and ultimately improving the wave energy conversion efficiency. At the same time, it can compensate for the magnetic loss of the embedded permanent magnet compared to the helical permanent magnet.

[0092] In some implementations, such as Figure 5 As shown, each stepped stator yoke (301) is divided into stepped grooves and conventional rings that alternate. When installed axially, it is necessary to ensure that each stepped groove is followed by a conventional ring to form a stepped structure with varying heights.

[0093] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent substitutions, and improvements made by those skilled in the art to the above embodiments without departing from the scope of the technical solution of the present invention, based on the technical essence of the present invention, shall still fall within the protection scope of the technical solution of the present invention.

Claims

1. A step magnetic wire screw power generation device, characterized by, include: The coaxial tubular components, from the inside out, are: V-shaped embedded step mover (1), helical mover damping modulation ring (2), and step stator (3). The V-shaped embedded stepper (1) includes a cylindrical stepper yoke (101) and a permanent magnet (102) embedded in the magnetic guide hole of the stepper yoke (101); the stepper yoke (101) is provided with a pair of stepped grooves every 90 degrees, the extension line of the short side of the stepped groove passes through the center of the stepper yoke (101), and the long side of the stepped groove is perpendicular to the short side; each pair of permanent magnets (102) is symmetrically distributed about the magnetic pole center line of the stepper yoke (101) and arranged in a V-shape. The helical rotor damping modulation ring (2) includes a magnetic helical ring and a damping winding covering it, and the pole pair intervals of the magnetic helical ring are filled with a non-magnetic metal layer. The stepped stator (3) includes a stepped stator yoke (301), a winding (302), and a housing (303). The inner wall of the stepped stator yoke (301) is provided with stepped grooves and rings that are alternately distributed in the circumferential direction. The winding (302) is located on the protrusions on the outside of the stepped grooves and rings. The housing (303) is fitted on the outermost side of the stepped stator yoke (301).

2. The parameter optimization selection method for the step-type magnetic screw power generation device according to claim 1, characterized in that, Includes the following steps: S1: Set the parameters to be optimized for the step-magnetic screw generator; S2: Based on the parameters to be optimized and their corresponding constraints, an initial population that satisfies the constraints is generated using the Halton sequence. The initial population represents the initial vector of parameters to be optimized. S3: Drive finite element simulation based on the parameter vector of each individual in the current population and extract the corresponding target performance index; the current population represents the current parameter vector to be optimized; calculate the fitness of the individual based on the target performance index; compare all fitness values ​​and determine the current optimal individual as the prey position; where the current population represents the current parameter to be optimized. The prey position indicates the optimal combination of parameters; S4: Calculate the prey escape energy based on the nonlinear adjustable energy decay model. The prey escape energy represents the control amount of the parameter vector adjustment method. Select the exploration or development mode according to the escape energy, and update the individual parameter vector by applying the corresponding Harris Eagle strategy. Then, perform differential mutation operation and update the population by comparing fitness. The updated population represents the optimized parameter vector. S5: Use the differential mutation method to replace the parameter vectors in the original population and generate new parameter vector combinations; S6: Determine if the algorithm has reached the maximum number of iterations. If the condition is met, output the optimal parameter vector combination; otherwise, return to step S3.

3. The parameter optimization selection method for the step-type magnetic screw power generation device according to claim 2, characterized in that, Includes the following steps: In step S1, the parameters to be optimized for the step-type magnetic screw generator include: in, The groove depth coefficient of the step subyoke (101) is represented by . This represents the axial length of the single-stage step-jump subyoke (101). Indicates the length of the permanent magnet (102), This indicates the width of the permanent magnet (102). Indicates the radial thickness of the magnetically conductive spiral ring. This indicates the thickness of the cladding layer of the damping winding. This indicates the thickness of the non-magnetic metal layer filling the pole pair gap. This indicates the opening angle of the V-shaped magnetic guide hole. The circumferential distribution angle of the step stator yoke (301) is indicated.

4. The parameter optimization selection method for the step-type magnetic screw power generation device according to claim 2, characterized in that: In step S2, the parameters to be optimized and their corresponding constraints are specifically as follows: Magnetic spiral ring thickness With the thickness of the non-magnetic metal layer The ratio lies in the interval Inside, and the thickness of the magnetic spiral ring Damping winding thickness and the thickness of the non-magnetic metal layer The sum of the values ​​is not greater than the axial gap between the V-shaped embedded step mover (1) and the step stator (3) of the step magnetic screw generator. Groove depth ratio factor V-shaped magnetic hole angle ; Length of permanent magnet (102) With width Limited by the size of the groove opened on the step yoke (101), and the length-to-width ratio of the permanent magnet is in the range The circumferential distribution angle of the stepped inclined groove of the inner step stator yoke (301) Satisfy constraints: ,and It is a positive integer.

5. The parameter optimization selection method for the step-type magnetic screw power generation device according to claim 4, characterized in that: In step S3, the finite element simulation is driven based on the parameter vector of each individual in the current population, and the corresponding target performance index is extracted; the current population represents the current parameter vector to be optimized; the fitness of the individual is calculated based on the target performance index, specifically as follows: Based on the parameter vector of the initial step magnetic screw power generation device It drives the parametric modeling script to automatically generate the corresponding three-dimensional electromagnetic field simulation model in the finite element analysis software; Submit and solve the finite element model, and automatically extract the target performance indicators, including the target air gap average magnetic flux density, from the simulation results file. Thrust fluctuation Thrust base wave amplitude ; Calculate the fitness of this individual. The individual with the lowest fitness is designated as the optimal individual, and its location is the prey location. in, , , The weighting coefficient is greater than zero.

6. The parameter optimization selection method for the step-type magnetic screw power generation device according to claim 2, characterized in that: In step S4, the prey escape energy is calculated based on a nonlinear adjustable energy decay model, where the prey escape energy represents the control quantity of the parameter vector adjustment method; based on the escape energy, either the exploration or exploitation mode is selected, and the corresponding Harris Hawk strategy is applied to update the individual parameter vector, specifically as follows: The prey escape energy is calculated based on a nonlinear adjustable energy decay model, using the following formula: when When, select exploration mode; when decay to When choosing a development mode; in, This represents the initial escape energy of the prey. It is an adjustable parameter. This represents the current iteration number. This represents the maximum number of iterations.

7. The parameter optimization selection method for the step-type magnetic screw power generation device according to claim 6, characterized in that: When exploration mode is selected, the individual parameter vector is updated as follows: in, This represents the individual parameter vector for the next iteration. This represents a parameter vector for a random individual among all individuals. It is the currently discovered optimal position parameter vector. It is the current parameter vector of this individual. and These are the upper and lower bounds of the variable. and Is Random numbers between; It is the current average parameter vector of all individuals.

8. The parameter optimization selection method for the step-type magnetic screw power generation device according to claim 6, characterized in that, include: When selecting a development mode, consider the probability of escape. Choose different strategies to update the parameter vector: when and At that time, a hovering and encirclement strategy is adopted, and the parameter vector is updated as follows: when and At that time, a strong surprise attack strategy is adopted, and the parameter vector is updated as follows: when and At that time, a spiraling, gradual strategy is adopted, and the parameter vector is updated as follows: when and At that time, a strong asymptotic strategy is adopted, and the parameter vector is updated as follows: If the fitness does not decrease after updating the parameter vector using a hovering and aggressive gradualism strategy, then perform a Levy flight: If fitness does not decrease, then use the original position: in, This indicates the current iteration number is... At that time, the difference between the optimal solution and the individual parameter vector; Is Random numbers within a certain range represent the optimization strength of the parameter vector; It is the current parameter vector of this individual. This represents the currently discovered optimal position parameter vector; It is the dimension for solving the problem. yes A dimensional random parameter vector, It is the Lévy flight function. , All are intervals Random numbers in the data, It is a constant. For the fitness function, This is the Gamma function.

9. The parameter optimization selection method for the step-type magnetic screw power generation device according to claim 6, characterized in that, include: In step S5, the differential mutation method is used to replace the parameter vectors in the original population and generate new parameter vector combinations. The specific process is as follows: For the The first generation of the population parameter vectors Generate a vector of mutated parameters , mutation parameter vector The parameter vector of the newly obtained step-type magnetic screw generator is as follows: in, A random integer, and ; , and Let be the parameter vectors of three distinct step-magnetic screw generators randomly selected from the current population. The scaling factor represents the proportional coefficient of the adjustment range of the control parameter in the differential variation. For the mutation parameter vector Components exceeding the boundary undergo boundary processing: Generate experimental parameter vectors : in, and They represent the first The lower and upper bounds of the values ​​of each component; For crossover probability, Let be the dimension of the random parameter vector.

10. The parameter optimization selection method for the step-type magnetic screw power generation device according to claim 9, characterized in that, include: The following formula is used to determine whether to replace the original parameter vector with the experimental parameter vector: 。

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