CN104283393B - Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine - Google Patents

Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine Download PDF

Info

Publication number
CN104283393B
CN104283393B CN201410499891.XA CN201410499891A CN104283393B CN 104283393 B CN104283393 B CN 104283393B CN 201410499891 A CN201410499891 A CN 201410499891A CN 104283393 B CN104283393 B CN 104283393B
Authority
CN
China
Prior art keywords
rotor
magnetic suspension
parameter
torque
initial value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201410499891.XA
Other languages
Chinese (zh)
Other versions
CN104283393A (en
Inventor
朱志莹
孙玉坤
胡文宏
卢冰洋
王锴
李峤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Institute of Technology
Original Assignee
Nanjing Institute of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing Institute of Technology filed Critical Nanjing Institute of Technology
Priority to CN201410499891.XA priority Critical patent/CN104283393B/en
Publication of CN104283393A publication Critical patent/CN104283393A/en
Application granted granted Critical
Publication of CN104283393B publication Critical patent/CN104283393B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K29/00—Motors or generators having non-mechanical commutating devices, e.g. discharge tubes or semiconductor devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02N—ELECTRIC MACHINES NOT OTHERWISE PROVIDED FOR
    • H02N15/00—Holding or levitation devices using magnetic attraction or repulsion, not otherwise provided for
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K2213/00—Specific aspects, not otherwise provided for and not covered by codes H02K2201/00 - H02K2211/00
    • H02K2213/03—Machines characterised by numerical values, ranges, mathematical expressions or similar information

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Geometry (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Physiology (AREA)
  • Evolutionary Biology (AREA)
  • Biomedical Technology (AREA)
  • Software Systems (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Power Engineering (AREA)
  • Computer Hardware Design (AREA)
  • General Health & Medical Sciences (AREA)
  • Control Of Electric Motors In General (AREA)
  • Synchronous Machinery (AREA)

Abstract

The invention discloses a method for optimizing the structure parameter of a single-winding magnetic suspension switch reluctance machine, and belongs to the technical field of magnetic suspension switch reluctance machines. The method comprises the steps that the initial structure parameter of the single-winding magnetic suspension switch reluctance machine is calculated; finite element simulation is conducted on a motor model, the structure parameter to be optimized is selected, and a sample data set is determined; an extreme learning machine algorithm is used for training the sample data set to establish the motor model; the parameter to be optimized is used as an optimized object, and suspension force and torque are used as an optimizing object for conducting parameter optimizing on the motor model. According to the method, an extreme learning machine of a single implicit strata feedforward neural network is used for conducting model identification, iteration is of no need, the motor model can be quickly trained in a high-precision mode, and the parameter design that collaboration of the suspension force and the torque is optimum is achieved by a multi-objective optimizing algorithm.

Description

A kind of optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters
Technical field
The invention discloses a kind of optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters, belong to magnetic suspension The technical field of switched reluctance machines.
Background technology
Start to develop from 20 end of the centurys, magnetic suspension switched reluctance motor receives the extensive concern of research worker, period, grinds Study carefully most be double-winding structure magnetic suspension switched reluctance motor, its structure is similar with switched reluctance machines, difference be by Radial force winding is made to be not take up independent for producing the winding of radial force lap wound together with torque winding in same stator poles Axial space.Due to this kind of motor have the advantages that unlubricated, no abrasion, the operating of high-power and ultrahigh speed can be realized, very It is applied to the fields such as Aero-Space, high-speed machine tool, flywheel energy storage.However, in the magnetic suspension switched reluctance motor of double-winding structure Main winding is with the strong coupling of suspending windings so that motor is increasingly complex in terms of mathematical modeling, control algolithm；Extra suspension Winding increases the difficulty of motor construction；It is electric with match that the increase of suspending windings leads to extra power amplifier Subsystem, increased design on control circuit complexity.For the disadvantages mentioned above of double-winding structure magnetic suspension motor, American National is navigated Empty space agency, Dresden, Germany polytechnical university and Qing Xing university of Korea S have carried out simplex winding magnetic levitation switch magnetic resistance electricity in succession The research of machine.Simplex winding magnetic suspension switched reluctance motor structure is basically identical with switched reluctance machines, main difference is that, opens Close multiple stator poles coil series winding excitations in reluctance motor, and any one in simplex winding magnetic suspension switched reluctance motor is fixed The coil that son is extremely gone up is all independent actuation, to reach the purpose that rotor suspends, rotates.
For the Optimal Structure Designing of magnetic suspension switched reluctance motor, the method being suggested has：Using theory analysis The method for designing combining with finite element simulation is optimized design to magnetic suspension switched reluctance motor structure, but because parameter is excellent Change needs are substantial amounts of to call computation model to obtain its output, so computational efficiency is low；Other method is：Using support vector machine Learning algorithm training set up magnetic suspension switched reluctance motor model, for this motor model recycle genetic algorithm with a certain electricity Machine runnability carries out optimizing as optimization aim, this method solves the low problem of the former computational efficiency, but calculates in no intelligence In the case that method is optimized to support vector machine parameter, support vector machine training set up motor model precision general, it is hard to Meet actual demand.In addition, genetic algorithm as a kind of single object optimization algorithm, can only realize the single of suspending power or torque Maximize, the unavoidable effect of both single optimization weakens mutually.
Content of the invention
The technical problem to be solved is the deficiency for above-mentioned background technology, there is provided a kind of simplex winding magnetcisuspension The optimization method of floation switch reluctance motor structural parameters.Carry out the number of model with the extreme learning machine of Single hidden layer feedforward neural networks According to training, realize multi-objective optimization using non-dominated sorted genetic algorithm.
The present invention adopts the following technical scheme that for achieving the above object：
A kind of optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters, comprises the steps：
Step 1, calculates the initial structure parameter of simplex winding magnetic suspension switched reluctance motor, and initial structure parameter includes：Turn Sub- external diameter initial value Da0, rotor internal diameter initial value Di0, iron core physical length initial value la0, stator outer diameter initial value Ds0, air gap long Degree initial value g0, stator polar arc initial value βs0, rotor pole arc initial value βr0, stator yoke thickness initial value hcs0, rotor yoke thickness initial value hcr0；
Step 2, carries out finite element simulation to motor model, chooses structural parameters to be optimized, determines sample data set；
Step 3, builds motor model using extreme learning machine Algorithm for Training sample data set,
Described extreme learning machine algorithm：Number of training is less than as principle with node in hidden layer, with fixed step size search Mode determines node in hidden layer, selects Sigmoid or RBF as excitation function, with parameter to be optimized as extreme learning machine Input data, the output data with suspending power corresponding with parameter values to be optimized and torque as extreme learning machine start instruct Practice sample data set；
Step 4, with parameter to be optimized as optimization object, is joined to motor model with suspending power, torque for optimization aim Number optimizes.
Further, step 2 carries out finite element simulation as follows：
Step 2-1, sets up having of simplex winding magnetic suspension switched reluctance motor with the calculated initial structure parameter of step 1 Limit meta-model, obtains torque current component i to FEM (finite element) model emulationm：Set up in Ansoft finite element with initial structure parameter Simplex winding magnetic suspension switched reluctance motor model in software, turns in turn in three-phase and applies rated voltage UNIn the case of, Emulation obtains current effective value only during turning on for the phase winding, remains to the follow-up simplex winding magnetic levitation switch magnetic resistance set up Motor FEM (finite element) model enters row energization, and current effective value only during turning on for the phase winding is torque current component im；
Step 2-2, sets up switched reluctance machines model with initial structure parameter in the Rmxprt module of Ansoft, record The rotor quality m that lower software is calculated automatically fromr, with rotor quality mrTo simplex winding magnetic levitation switch in Matlab/Simulink In the suspension system of reluctance motor, rotor quality is configured, and is succeeded by the emulation of this suspension system afterwards and realizes rotor suspension Radial suspension force numerical intervals F, in Ansoft, simplex winding magnetic suspension switched reluctance motor is set up with initial structure parameter Model, emulates, according to radial suspension force numerical intervals F, the levitating current component i obtaining substantiallysα、isβNumerical value；
Step 2-3, in iron core physical length la, stator outer diameter DsIn the case of constant, apply to the winding in FEM (finite element) model Plus exciting current, exciting current includes torque current component imWith levitating current component isα、isβ, change rotor diameter D respectivelya、 Rotor internal diameter Di, gas length g, stator polar arc βs, rotor pole arc βr, stator yoke thickness hcs, rotor yoke thickness hcrValue emulation point The rule that analysis obtains suspending power, torque changes with remaining structural parameters, chooses the structural parameters different on suspending power, torque impact As parameter x to be optimized1,x2,…xi,…,xn, i≤n, n=1 ..., 7, xiFor rotor diameter, rotor internal diameter, gas length, determine Any one structural parameters in sub- polar arc, rotor pole arc, stator yoke thickness, rotor yoke thickness, suspending power, torque are same with structural parameters For monotone increasing or be all monotone decreasing structural parameters do not need optimize, suspending power monotone increasing (or monotone decreasing) therewith should be chosen And the structural parameters of torque monotone decreasing (or monotone increasing) therewith are as parameter to be optimized；
Step 2-4, by different parameter values to be optimized and its corresponding suspending power Fs, torque T assemble sample data set (x1,x2,…,xn,Fs, T), using this training dataset as the sample data set of extreme learning machine.
Further, step 4 carries out multi-objective optimization using non-dominated sorted genetic algorithm to motor model.
As the further prioritization scheme of the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters, according to biography System switched reluctance machines structural parameters computational methods are calculated simplex winding magnetic suspension switched reluctance motor structural parameters, in order to enter One step explanation Conventional switched reluctance electric machine structure calculation method of parameters, structural parameters are defined as follows：
Application scenario is designed according to simplex winding magnetic suspension switched reluctance motor and determines rated power, rated speed, efficiency, according to According to each variable experience span obtain magnetic loading, electric load, winding current coefficient, square wave current coefficient, coefficient 1, coefficient 2, Coefficient 3, coefficient 4, coefficient 5, the concrete numerical value of gas length, step 1 is according to formula (1)：
Determine structural parameters initial value, kiFor winding current coefficient, kmFor square wave current coefficient, PNFor rated power, nNFor Rated speed, η is efficiency, BδFor magnetic loading, A is electric load, bpsFor stator pole-core width, bprFor rotor pole-core width, λ1、 λ2、λ3、λ4、λ5For coefficient, λ1=0.5～3.0, λ2=0.5～0.55, λ3=0.4～0.5, λ4=1.2～1.4, λ5=1.2～ 1.4, km≈ 0.8, ki≈ 0.5, Bδ=0.3～0.6, A=15000～50000,
For meeting the requirement of simplex winding magnetic suspension switched reluctance motor self-startup ability, rotor pole arc βr, stator polar arc βsTake Value must is fulfilled for formula (2):
NrFor rotor tooth pole number, m is the electric current number of phases,
So far, just obtain each structural parameters of specific simplex winding magnetic suspension switched reluctance motor, and each structural parameters are designated as： Rotor diameter initial value Da0, rotor internal diameter initial value Di0, iron core physical length initial value la0, stator outer diameter initial value Ds0, air gap Length initial value g0, stator polar arc initial value βs0, rotor pole arc initial value βr0, stator yoke thickness initial value hcs0, rotor yoke thick initial Value hcr0, wherein, Dsi0=Da0+2g0, each structural parameters composition initial structure parameter.
The present invention adopts technique scheme, has the advantages that：
(1) carry out the data training of model with the extreme learning machine of Single hidden layer feedforward neural networks, without iteration；
(2) achieve suspending power, optimum parameter designing is worked in coordination with torque.
Brief description
Fig. 1 shows simplex winding magnetic suspension switched reluctance motor Optimal Structure Designing idiographic flow proposed by the invention.
Fig. 2 (a), Fig. 2 (b), Fig. 2 (c), Fig. 2 (d), Fig. 2 (e), Fig. 2 (f) are stator and rotor pole centrage overlapping positions, turn Son without acceptance of persons in the case of suspending power and each parameters relationship curve chart.
Fig. 3 (a), Fig. 3 (b), Fig. 3 (c), Fig. 3 (d), Fig. 3 (e), Fig. 3 (f) are that average torque is bent with the relation of each parameter Line.
Fig. 4 (a), Fig. 4 (b), Fig. 4 (c) are respectively and optimize front motor sectional view, the sectional view of optimization motor 1, optimize motor 2 sectional view.
Fig. 5 shows the motor output suspending power contrast before and after optimization.
Before and after Fig. 6 is shown that to optimize, motor output torque contrasts.
Specific embodiment
With reference to Fig. 2 to Fig. 6 to idiographic flow shown in Fig. 1 technical scheme be described in detail.
1. when motor pre-determined characteristicss parameter is：Rated power PN=1.1kW, rated speed nN=2000r/min, specified electricity Pressure UN=220V, rated efficiency η=0.8, according to SRM (Switched Reluctance Motor, switched reluctance machines) structure Parameter Traditional calculating methods can obtain 12/8 structure SWBSRM (Single Winding Bearingless Switched Reluctance Motor, simplex winding magnetic suspension switched reluctance motor) structural parameters initial value be：Rotor diameter Ds=137mm, Rotor diameter Da=70mm, rotor internal diameter Di=31.5mm, stator yoke thickness hcs=10mm, rotor yoke thickness hcr=10mm, air gap are long Degree g=0.3mm, stator polar arc βs=15 °, rotor pole arc βr=15 °, iron core physical length la=70mm, every pole stator winding N =85 circles.
2. method described above determines torque current component im=4.7A, because two suspension force component principle under vertical coordinate system Unanimously, so, herein only arrange x coordinate direction of principal axis levitating current component isx=1.88A.
3. with im=4.7A, isx=1.88A, isyMotor energization in=0A setting finite element simulation, analyzes each structural parameters During change, the Changing Pattern of gained suspending power and torque increase with the parameters, specially：
(1) suspending power is with rotor internal diameter Di, rotor diameter Da, stator yoke thickness hcs, rotor yoke thickness hcrIncrease and monotone increasing Plus；Suspending power increases and dull reduction with gas length g；In addition, stator polar arc βsIncrease, suspending power first increases and tends to steady afterwards Fixed, and suspending power is with rotor pole arc βrChanging Pattern and βsUnanimously, wherein, stator and rotor pole centrage overlapping positions, rotor be no Suspending power in the case of bias and each parameters relationship curve such as Fig. 2 (a), Fig. 2 (b), Fig. 2 (c), Fig. 2 (d), Fig. 2 (e), Fig. 2 (f) Shown；
(2) torque is with rotor diameter Da, stator yoke thickness hcsIncrease and monotone increasing；Torque is with rotor internal diameter Di, rotor yoke Thick hcr, gas length g increase and reduce；Torque is with stator polar arc βs, rotor pole arc βrIncrease, variation tendency is first to increase to subtract afterwards Little, wherein, relation curve such as Fig. 3 (a) of average torque and each parameter, Fig. 3 (b), Fig. 3 (c), Fig. 3 (d), Fig. 3 (e), Fig. 3 (f) Shown；
Final choice rotor internal diameter Di, rotor yoke thickness hcr, stator polar arc βs, rotor pole arc βrFor optimization object.
4. finite element simulation, changes internal diameter Di, rotor yoke thickness hcr, stator polar arc βs, rotor pole arc βr, it is output as (Fs, T), synthesis obtains sample data (Di,hcr,βs,βr,Fs,T).
5. using ELM (Extrem Learning Machine, extreme learning machine), above-mentioned sample data is trained To motor model, this mode input is (Di,hcr,βs,βr), it is output as (Fs,T).
6. (Non dominated Sorting Genetic Algorithm, non-dominated ranking heredity is calculated to utilize NSGA Method) optimizing optimized after simplex winding magnetic suspension switched reluctance motor because optimizing the optimal result that obtains is that a set is (many Objective optimization gained " optimal solution " is " Generalized optimal ", because being likely to conflicting between each optimization aim, obtain " optimal solution " often set), wherein concrete 2 are：
(1) optimize motor 1：Rotor diameter Ds=137mm, rotor diameter Da=70mm, rotor internal diameter Di=31.5mm, fixed Sub- yoke thickness hcs=10mm, rotor yoke thickness hcr=12mm, gas length g=0.3mm, stator polar arc βs=23.42 °, rotor pole arc βr=20.53 °, iron core physical length la=70mm, every pole stator winding N=85 circle；
(2) optimize motor 2：Rotor diameter Ds=137mm, rotor diameter Da=70mm, rotor internal diameter Di=40mm, stator Yoke thickness hcs=10mm, rotor yoke thickness hcr=11.329mm, gas length g=0.3mm, stator polar arc βs=23.287 °, rotor pole Arc βr=20.14 °, iron core physical length la=70mm, every pole stator winding N=85 circle.
7. Fig. 4 (a), Fig. 4 (b), Fig. 4 (c) show motor sectional view before and after optimization, before and after Fig. 5, Fig. 6 show optimization Suspending power, torque with rotor angle change curve, contrast understand,
8. show suspending power before and after optimization, torque with rotor angle change curve, contrast understands, optimizes motor 1 Curve is essentially coincided with the curve optimizing motor 2, optimizes motor 1,2 compared to original motor in the range of the complete period, that is, all At rotor position angle, (- 22.5 °～22.5 °) suspending power all increased about 200N, and it is radially outstanding that this greatly enhances motor Floating stability, in other words, inputs under same suspending power, reducing required electric current in motor resuspension procedure, because And the radial suspension reducing motor runs power consumption；Meanwhile, rotor position angle (- 22.5 °～-13.5 °) with (13.5～ 22.5) in the range of, optimizing motor 1,2 about increases 2N m than the output torque of original motor, and this shows that optimizing motor is adapted to more Big load torque, and torque pulsation has obtained very big improvement compared to original motor, this motor speed after making to optimize is more Steadily, reduce the restriction to motor application for the big torque pulsation to a certain extent.Therefore, by the enforcement of this optimization method, greatly Enhance greatly motor radial suspension force and output torque, improve motor radial suspension performance and reduce operation power consumption and torque Pulsation.
Above-described embodiment is only 12/8 structure simplex winding magnetic suspension switched reluctance motor parameter optimization, remaining structure simplex winding The parameter of magnetic suspension switched reluctance motor all can be optimized design using technical scheme.
In sum, the invention has the advantages that：
(1) carry out the data training of model with the extreme learning machine of Single hidden layer feedforward neural networks, without iteration, can be fast Speed, accurately motor model is trained；
(2) algorithm must be had to achieve suspending power, turn and refuse simultaneously optimum parameter designing using multiple target.

Claims (4)

1. a kind of optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters is it is characterised in that comprise the steps：
Step 1, calculates the initial structure parameter of simplex winding magnetic suspension switched reluctance motor, and initial structure parameter includes：Outside rotor Footpath initial value Da0, rotor internal diameter initial value Di0, iron core physical length initial value la0, stator outer diameter initial value Ds0, at the beginning of gas length Initial value g0, stator polar arc initial value βs0, rotor pole arc initial value βr0, stator yoke thickness initial value hcs0, rotor yoke thickness initial value hcr0；
Step 2, carries out finite element simulation to motor model, chooses structural parameters to be optimized, determines sample data set；
Step 3, builds motor model using extreme learning machine Algorithm for Training sample data set,
Described extreme learning machine algorithm：Number of training is less than as principle with node in hidden layer, in the way of fixed step size search Determine node in hidden layer, select Sigmoid or RBF as excitation function, defeated with parameter to be optimized as extreme learning machine Enter data, the output data with suspending power corresponding with parameter values to be optimized and torque as extreme learning machine starts to train sample Notebook data collection；
Step 4, with parameter to be optimized as optimization object, with suspending power, that torque enters line parameter for optimization aim to motor model is excellent Change.
2. the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters according to claim 1, its feature exists In step 2 carries out finite element simulation as follows：
Step 2-1, sets up the finite element of simplex winding magnetic suspension switched reluctance motor with the calculated initial structure parameter of step 1 Model, obtains torque current component i to FEM (finite element) model emulationm；
Step 2-2, obtains suspending power numerical intervals F with the rotor quality emulation in FEM (finite element) model, then by suspending power numerical value area Between F emulation obtain levitating current component isα、isβ；
Step 2-3, in the case that iron core physical length, stator outer diameter are constant, applies excitation to the winding in FEM (finite element) model Electric current, exciting current includes torque current component imWith levitating current component isα、isβ, simulation analysis obtain suspending power, torque with The rule of remaining structural parameters change, chooses the structural parameters different on suspending power, torque impact as parameter x to be optimized1, x2,…xi,…,xn, i<N, n=1 ..., 7, xiFor rotor diameter, rotor internal diameter, gas length, stator polar arc, rotor pole arc, Any one structural parameters in stator yoke thickness, rotor yoke thickness；
Step 2-4, by different parameter values to be optimized and its corresponding suspending power Fs, torque T assemble sample data set (x1, x2,…,xn,Fs,T).
3. the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters according to claim 1 and 2, its feature It is, step 4 carries out multi-objective optimization using non-dominated sorted genetic algorithm to motor model.
4. the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters according to claim 3, its feature exists In step 1 is according to formula：
D a = 6.1 1.05 &lambda; 1 B &delta; A k i k m P N ( 1 + &eta; ) / 2 &eta; n N 3 l a = &lambda; 1 D a D s = D a / &lambda; 2 D i = &lambda; 3 D a D s i = D a + 2 g b p s = D s i sin &beta; s 2 b p r = D a sin &beta; r 2 h c r = &lambda; 4 b p r 2 h c s = &lambda; 5 b p s 2
Determine structural parameters initial value, kiFor winding current coefficient, kmFor square wave current coefficient, PNFor rated power, nNFor specified Rotating speed, η is efficiency, BδFor magnetic loading, A is electric load, bpsFor stator pole-core width, bprFor rotor pole-core width, λ1、λ2、λ3、 λ4、λ5For coefficient,
For meeting the requirement of simplex winding magnetic suspension switched reluctance motor self-startup ability, rotor pole arc βr, stator polar arc βsValue must Must meet:
NrFor rotor tooth pole number, m is the electric current number of phases.
CN201410499891.XA 2014-09-25 2014-09-25 Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine Active CN104283393B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201410499891.XA CN104283393B (en) 2014-09-25 2014-09-25 Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201410499891.XA CN104283393B (en) 2014-09-25 2014-09-25 Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine

Publications (2)

Publication Number Publication Date
CN104283393A CN104283393A (en) 2015-01-14
CN104283393B true CN104283393B (en) 2017-02-15

Family

ID=52257979

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201410499891.XA Active CN104283393B (en) 2014-09-25 2014-09-25 Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine

Country Status (1)

Country Link
CN (1) CN104283393B (en)

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104598686A (en) * 2015-01-24 2015-05-06 安徽大学 Water pump motor modeling and optimizing method based on electromagnetic calculation and neural network
CN106208433B (en) * 2016-08-01 2018-09-28 湘潭大学 A method of improving switching magnetic-resistance wind driven generator output power
CN106407559B (en) * 2016-09-19 2019-06-04 湖南科技大学 Switched reluctance machines structure parameter optimizing method and device
CN108073755B (en) * 2017-05-25 2021-04-02 烟台仙崴机电有限公司 Multi-objective optimization design method for switched reluctance motor system of electric vehicle
CN108258867A (en) * 2018-01-24 2018-07-06 淄博京科电气有限公司 A kind of novel super-low speed large torque switched reluctance motor
CN109245449B (en) * 2018-11-12 2020-11-03 南京工程学院 Optimization design method of axial split-phase magnetic suspension switched reluctance flywheel motor
CN110059348B (en) * 2019-03-12 2023-04-25 南京工程学院 Axial split-phase magnetic suspension flywheel motor suspension force numerical modeling method
CN110007232B (en) * 2019-05-23 2021-09-03 广东工业大学 Method and related device for predicting running efficiency of squirrel-cage asynchronous motor
CN110661390B (en) * 2019-09-24 2021-05-25 江苏大学 Accurate modeling method for suspension force of 12/14 pole magnetic suspension switched reluctance motor
CN111797495B (en) * 2020-05-19 2024-02-20 国网浙江瑞安市供电有限责任公司 Single-winding magnetic suspension switch reluctance motor simulink modeling method
CN111859574B (en) * 2020-07-22 2024-05-03 合肥工业大学 Synchronous reluctance motor rotor optimization design method for reducing torque pulsation
CN113094952B (en) * 2021-04-06 2022-05-13 哈尔滨工业大学（威海） Stray magnetic field-based static eccentricity detection method for permanent magnet motor

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101441728A (en) * 2007-11-21 2009-05-27 新乡市起重机厂有限公司 Neural network method of crane optimum design
CN102819651A (en) * 2012-08-20 2012-12-12 西北工业大学 Simulation-based parameter optimizing method for precise casting process of single crystal turbine blade
CN103065191A (en) * 2011-10-19 2013-04-24 西安邮电学院 Rapid neural network leaning method
CN103106305A (en) * 2013-02-01 2013-05-15 北京工业大学 Space grid structure model step-by-step correction method based on actual measurement mode
CN103888037A (en) * 2014-02-25 2014-06-25 江苏大学 Construction method for inverse decoupling controller of extreme learning machine

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103236769A (en) * 2013-04-24 2013-08-07 江苏大学 Method for optimizing key parameters of bearingless permanent magnet motor
CN103414292B (en) * 2013-07-23 2015-08-26 江苏大学 Based on the Induction-type bearingless motor Optimization Design of dichotomy
CN103678783B (en) * 2013-11-26 2016-08-17 上海交通大学 Permanent magnet brushless direct-current motor with closed windings Optimization Design

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101441728A (en) * 2007-11-21 2009-05-27 新乡市起重机厂有限公司 Neural network method of crane optimum design
CN103065191A (en) * 2011-10-19 2013-04-24 西安邮电学院 Rapid neural network leaning method
CN102819651A (en) * 2012-08-20 2012-12-12 西北工业大学 Simulation-based parameter optimizing method for precise casting process of single crystal turbine blade
CN103106305A (en) * 2013-02-01 2013-05-15 北京工业大学 Space grid structure model step-by-step correction method based on actual measurement mode
CN103888037A (en) * 2014-02-25 2014-06-25 江苏大学 Construction method for inverse decoupling controller of extreme learning machine

Also Published As

Publication number Publication date
CN104283393A (en) 2015-01-14

Similar Documents

Publication Publication Date Title
CN104283393B (en) Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine
Sun et al. Multiobjective design optimization of an IPMSM for EVs based on fuzzy method and sequential Taguchi method
Sun et al. Driving-cycle-oriented design optimization of a permanent magnet hub motor drive system for a four-wheel-drive electric vehicle
Cheng et al. Average torque control of switched reluctance machine drives for electric vehicles
Navardi et al. Efficiency improvement and torque ripple minimization of switched reluctance motor using FEM and seeker optimization algorithm
Sun et al. Torque ripple reduction for a 12/8 switched reluctance motor based on a novel sliding mode control strategy
US20230412098A1 (en) Efficiency optimization control method and system for permanent magnet synchronous motor
CN106407559B (en) Switched reluctance machines structure parameter optimizing method and device
CN113177341B (en) Magnetic suspension flywheel motor multi-objective optimization design method based on kriging approximate model
Diao et al. Robust-oriented optimization of switched reluctance motors considering manufacturing fluctuation
Liu et al. Direct instantaneous torque control system for switched reluctance motor in electric vehicles
CN104467596A (en) Method for reducing torque ripples of switched reluctance motor
Sharifian et al. Self-adaptive RBF neural network PID controller in linear elevator
Jie et al. Research on Control Strategy of the Electric Power Steering System for All‐Terrain Vehicles Based on Model Predictive Current Control
Wang et al. Flux-weakening fuzzy adaptive ST-SMO sensorless control algorithm for PMSM in EV
Campeanu et al. Optimization of startup characteristics of medium power asynchronous motors with short circuit rotor
CN116722772A (en) Permanent magnet synchronous motor control method and system based on improved particle swarm
Jing et al. Research on the cloud computing fuzzy proportion integration differentiation control strategy for permanent-magnet homopolar motor with salient pole solid rotor used on new-energy vehicle
Xie et al. Super‐spiral sliding mode controller design for single‐winding bearingless switched reluctance motor
CN113343592B (en) Intelligent control method for DQN (direct current) of permanent magnet synchronous motor of new energy aircraft
Fatemi et al. Fast multi-objective CMODE-type optimization of electric machines for multicore desktop computers
Li et al. Optimization of an induction motor for loss reduction considering manufacturing tolerances
Xie et al. Rotor Multidisciplinary Optimization of High Speed PMSM based on Multi-fidelity Surrogate Model and Gradient Sequential Sampling
Mazloum et al. Wound field synchronous drive cycle current parameters optimization: A metamodel-based approach
Xu et al. Rotor topology optimization of a synchronous reluctance motor based on deep neural network model

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant