CN104283393A - Method for optimizing structure parameter of single-winding magnetic suspension switch reluctance machine - Google Patents

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 the technical field of magnetic suspension switched reluctance motor.

Background technology

Development is started from 20 end of the centurys, magnetic suspension switched reluctance motor receives the extensive concern of researcher, period, most study be the magnetic suspension switched reluctance motor of double-winding structure, its structure and switched reluctance machines similar, difference is the lap wound together with torque winding of the winding for generation of radial load in same stator poles, to make radial load winding not take independently axial space.Because this kind of motor has unlubricated, that nothing is worn and torn, it is high-power to realize and ultrahigh speed operates advantage, be highly suitable for the fields such as Aero-Space, high-speed machine tool, flywheel energy storage.But in the magnetic suspension switched reluctance motor of double-winding structure, main winding and suspending windings is strong coupling, makes motor more complicated in mathematical modeling, control algolithm; Extra suspending windings increases the difficulty of motor construction; The increase of suspending windings causes extra power amplifier and the electrical subsystem matched, and adds design on control circuit complexity.For the above-mentioned shortcoming of double-winding structure magnetic suspension motor, US National Aeronautics and Space Administration, Dresden, Germany polytechnical university and Qing Xing university of Korea S have carried out the research of simplex winding magnetic suspension switched reluctance motor in succession.Simplex winding magnetic suspension switched reluctance motor structure and switched reluctance machines basically identical, main difference is, coil series winding excitation in multiple stator poles in switched reluctance machines, and the coil in any one stator poles in simplex winding magnetic suspension switched reluctance motor is all independent actuation, to reach the object that rotor suspends, rotates.

For the Optimal Structure Designing of magnetic suspension switched reluctance motor, the method be suggested has: the method for designing adopting theory analysis to combine with finite element simulation is optimized design to magnetic suspension switched reluctance motor structure, but because parameter optimization needs a large amount of computation models that calls to export, so computational efficiency is low to obtain it; Other method is: utilize the learning algorithm of SVMs to train and set up magnetic suspension switched reluctance motor model, optimizing is carried out using a certain motor runnability as optimization aim for this motor model recycling genetic algorithm, this method solve the problem that the former computational efficiency is low, but when being optimized SVMs parameter without intelligent algorithm, the motor model precision that SVMs training is set up is general, is difficult to practical requirement.In addition, genetic algorithm, as a kind of single object optimization algorithm, can only realize the single largest of suspending power or torque, and the unavoidable effect of both single optimization weakens mutually.

Summary of the invention

Technical problem to be solved by this invention is the deficiency for above-mentioned background technology, provides a kind of optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters.Carry out the data training of model with the extreme learning machine of Single hidden layer feedforward neural networks, utilize non-dominated sorted genetic algorithm to realize multi-objective optimization.

The present invention adopts following technical scheme for achieving the above object:

An optimization method for simplex winding magnetic suspension switched reluctance motor structural parameters, comprises the steps:

Step 1, calculates the initial structure parameter (D of simplex winding magnetic suspension switched reluctance motor _a0, D _i0, l _a0, D _s0, g ₀, β _s0, β _r0, h _cs0, h _cr0), wherein, D _a0, D _i0, l _a0, D _s0, g ₀, β _s0, β _r0, h _cs0, h _cr0be respectively rotor diameter, rotor internal diameter, iron core physical length, stator outer diameter, gas length, stator polar arc, rotor pole arc, stator yoke is thick, rotor yoke is thick initial value;

Step 2, carries out finite element simulation to motor model, chooses structural parameters to be optimized, determines sample data collection;

Step 3, adopts extreme learning machine Algorithm for Training sample data collection to build motor model;

Step 4, with parameter to be optimized for optimization object, carries out parameter optimization to motor model with suspending power, torque for optimization aim.

Further, step 2 carries out finite element simulation as follows:

Step 2-1, the initial structure parameter calculated with step 1 sets up the FEM (finite element) model of simplex winding magnetic suspension switched reluctance motor, obtains torque current component i to FEM (finite element) model emulation _m: with (D _a0, D _i0, l _a0, D _s0, g ₀, β _s0, β _r0, h _cs0, h _cr0) be based upon simplex winding magnetic suspension switched reluctance motor model in Ansoft finite element software, in three-phase conducting apply rated voltage U in turn _nwhen, emulation obtains a phase winding only at the current effective value of conduction period, and wait until and encourage the simplex winding magnetic suspension switched reluctance motor FEM (finite element) model of follow-up foundation, a phase winding is only torque current component i at the current effective value of conduction period _m;

Step 2-2, with (D in the Rmxprt module of Ansoft _a0, D _i0, l _a0, D _s0, g ₀, β _s0, β _r0, h _cs0, h _cr0) set up switched reluctance machines model, record the rotor quality m that software calculates automatically _r, with m _rarrange the suspension system rotor quality of simplex winding magnetic suspension switched reluctance motor in Matlab/Simulink, being succeeded by the emulation of this suspension system afterwards realizes the radial suspension force numerical intervals F of rotor suspension, with (D in Ansoft _a0, D _i0, l _a0, D _s0, g ₀, β _s0, β _r0, h _cs0, h _cr0) set up the model of simplex winding magnetic suspension switched reluctance motor, the levitating current component i obtained roughly is emulated according to radial suspension force numerical intervals F _{s α}, i _{s β}numerical value;

Step 2-3, at iron core physical length l _a, stator outer diameter D _swhen constant, apply exciting current (i to the winding in FEM (finite element) model _m, i _{s α}, i _{s β}), change parameter D respectively _a, D _i, g, β _s, β _r, h _cs, h _crthe rule that simulation analysis obtains suspending power, torque changes with all the other structural parameters, chooses and affects different structural parameters as parameter x to be optimized to suspending power, torque ₁, x ₂... x _i..., x _n, i≤n, n=1 ..., 7, x _ifor rotor diameter, rotor internal diameter, gas length, stator polar arc, rotor pole arc, stator yoke are thick, rotor yoke thick in any one structural parameters, the structural parameters that suspending power, torque are all monotone increasing along with structural parameters or are all monotone decreasing do not need to optimize, and should choose the structural parameters of suspending power torque monotone decreasing (or monotone increasing) thereupon thereupon monotone increasing (or monotone decreasing) as parameter to be optimized;

Step 2-4, by the suspending power F of parameter values to be optimized for difference and correspondence thereof _s, torque T assembles training dataset (x ₁, x ₂..., x _n, F _s, T), using this training dataset as the training data of extreme learning machine.

Further, step 4 utilizes non-dominated sorted genetic algorithm to carry out multi-objective optimization to motor model.

As the further prioritization scheme of the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters, extreme learning machine algorithm in step 3, number of training N is less than for principle with node in hidden layer L, with the mode determination node in hidden layer of fixed step size search, select Sigmoid or Sine or RBF as excitation function, with parameter x to be optimized ₁, x ₂... x _i..., x _nfor the input data of extreme learning machine, with the suspending power corresponding with parameter values to be optimized and torque F _s, T is that the output data of extreme learning machine start training sample data collection.

As the further prioritization scheme of the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters, simplex winding magnetic suspension switched reluctance motor structural parameters are calculated according to Conventional switched reluctance electric machine structure calculation method of parameters, in order to further illustrate Conventional switched reluctance electric machine structure calculation method of parameters, structural parameters are defined as follows:

According to simplex winding magnetic suspension switched reluctance motor design ap-plication occasion determination rated power, rated speed, efficiency, obtain the concrete numerical value of magnetic loading, electric loading, winding current coefficient, square wave current coefficient, coefficient 1, coefficient 2, coefficient 3, coefficient 4, coefficient 5, gas length according to each variable experience span, step 1 is according to formula (1):

$\left\{\begin{array}{c}{D}_a=\sqrt[3]{\frac{6.1}{1.05{\mathrm{\λ}}_1{B}_{\mathrm{\δ}}A}\frac{k_i}{k_m}\frac{P_N(1+\mathrm{\η})/2\mathrm{\η}}{n_N}}\\ l_a={\mathrm{\λ}}_1{D}_a\\ D_s=D_a/{\mathrm{\λ}}_2\\ D_i={\mathrm{\λ}}_3{D}_a\\ D_{\mathrm{si}}=D_a+2g\\ b_{\mathrm{ps}}=D_{\mathrm{si}}\sin \frac{{\mathrm{\β}}_s}{2}\\ b_{\mathrm{pr}}=D_a\sin \frac{{\mathrm{\β}}_r}{2}\\ h_{\mathrm{cr}}={\mathrm{\λ}}_4\frac{b_{\mathrm{pr}}}{2}\\ h_{\mathrm{cs}}={\mathrm{\λ}}_5\frac{b_{\mathrm{ps}}}{2}\end{array}\right.---\left(1\right),$

Determine structural parameters initial value, k _ifor winding current coefficient, k _mfor square wave current coefficient, P _nfor rated power, n _nfor rated speed, η is efficiency, B _δfor magnetic loading, A is electric loading, b _psfor stator pole-core width, b _prfor rotor pole-core width, λ ₁, λ ₂, λ ₃, λ ₄, λ ₅for coefficient, λ ₁=0.5 ~ 3.0, λ ₂=0.5 ~ 0.55, λ ₃=0.4 ~ 0.5, λ ₄=1.2 ~ 1.4, λ ₅=1.2 ~ 1.4, k _m≈ 0.8, k _i≈ 0.5, B _δ=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 β _r, stator polar arc β _svalue must meet formula (2):

$\left\{\begin{array}{c}{\mathrm{\β}}_s+{\mathrm{\β}}_r\≤\frac{2\mathrm{\π}}{N_r}\\ {\mathrm{\β}}_s\≤{\mathrm{\β}}_r\\ {\mathrm{\β}}_s\≥\frac{2\mathrm{\π}}{{\mathrm{mN}}_r}\end{array}\right.---,\left(2\right)$

N _rfor rotor tooth pole number, m is the electric current number of phases,

So far, just obtain the concrete each structural parameters of simplex winding magnetic suspension switched reluctance motor, and each structural parameters are designated as (D _a0, D _i0, l _a0, D _s0, D _si0, g ₀, β _s0, β _r0, h _cs0, h _cr0), wherein, there is relation in parameters: D _si0=D _a0+ 2g ₀, so initial structure parameter can be designated as (D _a0, D _i0, l _a0, D _s0, g ₀, β _s0, β _r0, h _cs0, h _cr0).

The present invention adopts technique scheme, has following beneficial effect:

(1) the data training of model is carried out with the extreme learning machine of Single hidden layer feedforward neural networks, without the need to iteration;

(2) achieve suspending power, torque works in coordination with optimum parameter designing.

Accompanying drawing explanation

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 the suspending power under stator and rotor pole center line overlapping positions, rotor without acceptance of persons situation and each parameters relationship curve chart.

The relation curve that Fig. 3 (a), Fig. 3 (b), Fig. 3 (c), Fig. 3 (d), Fig. 3 (e), Fig. 3 (f) are average torque and each parameter.

Fig. 4 (a), Fig. 4 (b), Fig. 4 (c) are respectively and optimize front motor sectional view, the sectional view of optimization motor 1, the sectional view of optimization motor 2.

Fig. 5 shows the motor before and after optimizing and exports suspending power contrast.

What Fig. 6 showed is motor output torque contrast before and after optimizing.

Embodiment

Below in conjunction with Fig. 2 to Fig. 6 to idiographic flow shown in Fig. 1 technical scheme be described in detail.

1. when motor pre-determined characteristics parameter is: rated power P _n=1.1kW, rated speed n _n=2000r/min, rated voltage U _n=220V, rated efficiency η=0.8, according to SRM (Switched Reluctance Motor, switched reluctance machines) the structural parameters Traditional calculating methods structural parameters initial value that can obtain 12/8 structure SWBSRM (Single Winding Bearingless Switched Reluctance Motor, simplex winding magnetic suspension switched reluctance motor) is: rotor diameter D _s=137mm, rotor diameter D _a=70mm, rotor internal diameter D _ithe thick h of=31.5mm, stator yoke _csthe thick h of=10mm, rotor yoke _cr=10mm, gas length g=0.3mm, stator polar arc β _s=15 °, rotor pole arc β _r=15 °, iron core physical length l _a=70mm, every pole stator winding N=85 circle.

2. with said method determination torque current component i _m=4.7A, because two suspending power component principles are consistent under vertical coordinate system, so, x change in coordinate axis direction levitating current component i is only set herein _sx=1.88A.

3. with i _m=4.7A, i _sx=1.88A, i _sy=0A arranges motor energization in finite element simulation, and when analyzing the change of each structural parameters, the Changing Pattern that gained suspending power and torque increase with parameter, is specially:

(1) suspending power is with rotor internal diameter D _i, rotor diameter D _a, the thick h of stator yoke _cs, the thick h of rotor yoke _crincrease and monotone increasing; Suspending power increases and dull reduction with gas length g; In addition, stator polar arc β _sincrease, suspending power first increases and tends towards stability afterwards, and suspending power is with rotor pole arc β _rchanging Pattern and β _sunanimously, wherein, suspending power without acceptance of persons in situation of stator and rotor pole center line overlapping positions, rotor and each parameters relationship curve are as shown in Fig. 2 (a), Fig. 2 (b), Fig. 2 (c), Fig. 2 (d), Fig. 2 (e), Fig. 2 (f);

(2) torque is with rotor diameter D _a, the thick h of stator yoke _csincrease and monotone increasing; Torque is with rotor internal diameter D _i, the thick h of rotor yoke _cr, gas length g increase and reduce; Torque is with stator polar arc β _s, rotor pole arc β _rincrease, variation tendency first increases rear reduction, wherein, the relation curve of average torque and each parameter is as shown in Fig. 3 (a), Fig. 3 (b), Fig. 3 (c), Fig. 3 (d), Fig. 3 (e), Fig. 3 (f);

Final selection rotor internal diameter D _i, the thick h of rotor yoke _cr, stator polar arc β _s, rotor pole arc β _rfor optimization object.

4. finite element simulation, changes internal diameter D _i, the thick h of rotor yoke _cr, stator polar arc β _s, rotor pole arc β _r, obtain exporting as (F _s, T), synthesis obtains sample data (D _i, h _cr, β _s, β _r, F _s, T).

5. utilize ELM (Extrem Learning Machine, extreme learning machine) to carry out training to above-mentioned sample data and obtain motor model, this mode input is (D _i, h _cr, β _s, β _r), export as (F _s, T).

6. utilize NSGA (Non dominated Sorting Genetic Algorithm, non-dominated sorted genetic algorithm) optimizing be optimized after simplex winding magnetic suspension switched reluctance motor, because optimizing the optimal result obtained is that (multiple-objection optimization gained " optimal solution " is " Generalized optimal " in a set, because probably conflicting between each optimization aim, so, " optimal solution " that obtain be a set often), wherein concrete 2 examples are:

(1) motor 1 is optimized: rotor diameter D _s=137mm, rotor diameter D _a=70mm, rotor internal diameter D _ithe thick h of=31.5mm, stator yoke _csthe thick h of=10mm, rotor yoke _cr=12mm, gas length g=0.3mm, stator polar arc β _s=23.42 °, rotor pole arc β _r=20.53 °, iron core physical length l _a=70mm, every pole stator winding N=85 circle;

(2) motor 2 is optimized: rotor diameter D _s=137mm, rotor diameter D _a=70mm, rotor internal diameter D _ithe thick h of=40mm, stator yoke _csthe thick h of=10mm, rotor yoke _cr=11.329mm, gas length g=0.3mm, stator polar arc β _s=23.287 °, rotor pole arc β _r=20.14 °, iron core physical length l _a=70mm, every pole stator winding N=85 circle.

7. Fig. 4 (a), Fig. 4 (b), Fig. 4 (c) show and optimize front and back motor sectional view, and Fig. 5, the suspending power that Figure 6 shows that optimization front and back, torque, with the change curve of rotor angle, contrast known,

8. be depicted as the suspending power before and after optimizing, torque with the change curve of rotor angle, contrast known, the curve optimizing motor 1 overlaps substantially with the curve optimizing motor 2, optimize motor 1,2 compared to original motor within the complete period, namely about 200N is all added at all rotor position angle places (-22.5 ° ~ 22.5 °) suspending power, this greatly enhances motor radial suspension stability, in other words, for under same suspending power, can reduce electric current input required in motor resuspension procedure, the radial suspension thus reducing motor runs power consumption; Simultaneously, in rotor position angle (-22.5 ° ~-13.5 °) with (13.5 ~ 22.5) scope, optimize motor 1,2 and about increase 2Nm than the Driving Torque of original motor, this shows that optimizing motor can adapt to larger load torque, and torque pulsation obtains very large improvement compared to original motor, this makes the motor speed after optimizing more steady, reduces the restriction of large torque pulsation to motor application to a certain extent.Therefore, by the enforcement of this optimization method, greatly strengthen motor radial suspension force and Driving Torque, improve motor radial suspension performance and reduce and run power consumption and torque pulsation.

Above-described embodiment is only 12/8 structure simplex winding magnetic suspension switched reluctance motor parameter optimization, and the parameter of all the other structure simplex winding magnetic suspension switched reluctance motors all can utilize technical scheme of the present invention to be optimized design.

In sum, the present invention has following beneficial effect:

(1) carry out the data training of model with the extreme learning machine of Single hidden layer feedforward neural networks, without the need to iteration, can train motor model fast, accurately;

(2) utilize multiple target must have algorithm realization suspending power, turn and refuse simultaneously optimum parameter designing.

Claims (5)

1. an optimization method for simplex winding magnetic suspension switched reluctance motor structural parameters, is characterized in that comprising the steps:

Step 1, calculates the initial structure parameter of simplex winding magnetic suspension switched reluctance motor: (D _a0, D _i0, l _a0, D _s0, g ₀, β _s0, β _r0, h _cs0, h _cr0), wherein, D _a0, D _i0, l _a0, D _s0, g ₀, β _s0, β _r0, h _cs0, h _cr0be respectively rotor diameter, rotor internal diameter, iron core physical length, stator outer diameter, gas length, stator polar arc, rotor pole arc, stator yoke is thick, rotor yoke is thick initial value;

Step 2, carries out finite element simulation to motor model, chooses structural parameters to be optimized, determines sample data collection;

Step 3, adopts extreme learning machine Algorithm for Training sample data collection to build motor model;

Step 4, with parameter to be optimized for optimization object, carries out parameter optimization to motor model with suspending power, torque for optimization aim.

2. the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters according to claim 1, it is characterized in that, step 2 carries out finite element simulation as follows:

Step 2-1, the initial structure parameter calculated with step 1 sets up the FEM (finite element) model of simplex winding magnetic suspension switched reluctance motor, obtains torque current component i to FEM (finite element) model emulation _m;

Step 2-2, obtains suspending power numerical intervals F with the rotor quality emulation in FEM (finite element) model, then obtains levitating current component i by suspending power numerical intervals F emulation _{s α}, i _{s β};

Step 2-3, when iron core physical length, stator outer diameter are constant, applies exciting current (i to the winding in FEM (finite element) model _m, i _{s α}, i _{s β}), the rule that simulation analysis obtains suspending power, torque changes with all the other structural parameters, chooses and affects different structural parameters as parameter x to be optimized to suspending power, torque ₁, x ₂... x _i..., x _n, i<n, n=1 ..., 7, x _ifor rotor diameter, rotor internal diameter, gas length, stator polar arc, rotor pole arc, stator yoke are thick, rotor yoke thick in any one structural parameters;

Step 2-4, by the suspending power F of parameter values to be optimized for difference and correspondence thereof _s, torque T assembles training dataset (x ₁, x ₂..., x _n, F _s, T).

3. the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters according to claim 1 and 2, is characterized in that, step 4 utilizes non-dominated sorted genetic algorithm to carry out multi-objective optimization to motor model.

4. the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters according to claim 3, it is characterized in that, extreme learning machine algorithm in step 3, number of training is less than for principle with node in hidden layer, with the mode determination node in hidden layer of fixed step size search, select Sigmoid or RBF as excitation function, with parameter x to be optimized ₁, x ₂... x _i..., x _nfor the input data of extreme learning machine, with the suspending power corresponding with parameter values to be optimized and torque F _s, T is that the output data of extreme learning machine start training sample data collection.

5. the optimization method of simplex winding magnetic suspension switched reluctance motor structural parameters according to claim 4, it is characterized in that, step 1 is according to formula:

$\left\{\begin{array}{c}{D}_a=\sqrt[3]{\frac{6.1}{1.05{\mathrm{\&lambda;}}_1{B}_{\mathrm{\&delta;}}A}\frac{k_i}{k_m}\frac{P_N(1+\mathrm{\&eta;})/2\mathrm{\&eta;}}{n_N}}\\ l_a={\mathrm{\&lambda;}}_1{D}_a\\ D_s=D_a/{\mathrm{\&lambda;}}_2\\ D_i={\mathrm{\&lambda;}}_3{D}_a\\ D_{\mathrm{si}}=D_a+2g\\ b_{\mathrm{ps}}=D_{\mathrm{si}}\sin \frac{{\mathrm{\&beta;}}_s}{2}\\ b_{\mathrm{pr}}=D_a\sin \frac{{\mathrm{\&beta;}}_r}{2}\\ h_{\mathrm{cr}}={\mathrm{\&lambda;}}_4\frac{b_{\mathrm{pr}}}{2}\\ h_{\mathrm{cs}}={\mathrm{\&lambda;}}_5\frac{b_{\mathrm{ps}}}{2}\end{array}\right.$

Determine structural parameters initial value, k _ifor winding current coefficient, k _mfor square wave current coefficient, P _nfor rated power, n _nfor rated speed, η is efficiency, B _δfor magnetic loading, A is electric loading, b _psfor stator pole-core width, b _prfor rotor pole-core width, λ ₁, λ ₂, λ ₃, λ ₄, λ ₅for coefficient,

For meeting the requirement of simplex winding magnetic suspension switched reluctance motor self-startup ability, rotor pole arc β _r, stator polar arc β _svalue must meet:

$\left\{\begin{array}{c}{\mathrm{\&beta;}}_s+{\mathrm{\&beta;}}_r\&le;\frac{2\mathrm{\&pi;}}{N_r}\\ {\mathrm{\&beta;}}_s\&le;{\mathrm{\&beta;}}_r\\ {\mathrm{\&beta;}}_s\&GreaterEqual;\frac{2\mathrm{\&pi;}}{{\mathrm{mN}}_r}\end{array}\right.,$ N _rfor rotor tooth pole number, m is the electric current number of phases.