CN107862143B - Linear motor line point type connecting structure for magnetic flux switching and transverse impulse suppression method - Google Patents
Linear motor line point type connecting structure for magnetic flux switching and transverse impulse suppression method Download PDFInfo
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- CN107862143B CN107862143B CN201711112398.8A CN201711112398A CN107862143B CN 107862143 B CN107862143 B CN 107862143B CN 201711112398 A CN201711112398 A CN 201711112398A CN 107862143 B CN107862143 B CN 107862143B
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Abstract
The invention discloses a linear point type connecting structure of a magnetic flux switching linear motor and a transverse impulse suppression method, wherein a LFSPM motor is connected with a rail door in a three-point mode, and a primary rotor and the rail door are ensured to synchronously operate while a secondary stator actively bears high load of the rail door; the method adopts a digital-analog disturbance observer to be matched with a wavelet neural network algorithm to realize accurate prediction of the transverse impulse force at the rail gate, adopts a reinforcement current harmonic injection method to compensate to realize transverse fixation of the rail gate, and reduces loss. The invention combines a three-point connecting structure, a digital-analog disturbance observer, a wavelet neural network algorithm and a reinforced current harmonic injection method together, so that the predicted value of the lateral bearing capacity of the LFSPM motor can quickly and accurately follow the true value, and the contradiction between loss reduction and control performance is solved through the three-point connecting structure and rotor flux linkage optimization control, so that the LFSPM motor is suitable for a large-bearing track door system.
Description
Technical Field
The invention relates to a method for applying an LFSPM motor to a large-bearing track door system, in particular to a linear point type connecting structure of a magnetic flux switching linear motor and a transverse impulse force restraining method.
Background
The traditional elevator door motor is generally a direct current or alternating current rotating motor, so a mechanical transmission device which can change the rotating motion into the linear motion must be additionally arranged to be matched with the rotating motor to drive the elevator door leaf to operate. Due to the existence of the intermediate mechanical transmission devices, the transmission efficiency of the system is low, the failure rate is increased, and in addition, the noise is increased due to abrasion. The linear motor can directly drive the elevator door leaf to do linear motion without an intermediate mechanical transmission device, so that the door leaf can be more quickly acted and responded compared with the traditional elevator door machine based on the rotating motor, and the linear motor has the advantages of large thrust, high acceleration and no abrasion and silence caused by the characteristic of non-contact driving.
The flux switching linear motor (LFSPM) inherits the advantages of simple and firm structure of a rotor of a switched reluctance motor, high torque density and high efficiency of a permanent magnet synchronous motor (a rotor permanent magnet motor), a permanent magnet is placed on a primary stator and is not subjected to centrifugal force, the heat dissipation condition is good, the rotor is not provided with a winding or a permanent magnet, the structure is simple, the linear motor is suitable for high-speed operation, and the linear motor is very suitable for a track door control system. The LFSPM is applied to the rail door control system, and the following technical difficulties need to be solved:
(1) how to construct a structural mode that a primary level and a secondary level of a magnetic flux switching linear motor (LFSPM) are directly connected with a rail door, and synchronous operation is realized while the magnetic flux switching linear motor bears the rail door;
(2) how to accurately apply weather forecast data to transverse impact force prediction with high accuracy requirement;
(3) the modeling of the transverse impact force is influenced by a series of factors such as a terrain elevation map, roughness, operation stability, tunnel space and the like, and how to establish an accurate physical model capable of reflecting the actual stress condition of the rail door;
(4) how to accurately predict transverse impact force based on physical and statistical models based on meteorological data and historical data
(5) How to build a reinforcement current model according to the predicted transverse impact force and determine the reinforcement current value which can be used for counteracting the transverse impact force.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a linear point type connecting structure of a magnetic flux switching linear motor and a transverse impulse suppression method, wherein a three-point type connecting structure, a digital-analog disturbance observer, a wavelet neural network algorithm and a reinforcing current harmonic injection method are combined together, so that the predicted value of the transverse bearing capacity of the LFSPM motor can quickly and accurately follow the true value, and the contradiction between loss reduction and control performance is solved through the optimized control of the three-point type connecting structure and a rotor flux linkage, so that the LFSPM motor is suitable for a large-bearing track door system.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a magnetic flux switching linear motor line point type connection structure and a transverse impulse suppression method are disclosed, wherein an LFSPM motor is connected with a rail door in a three-point mode, and a primary rotor and the rail door are ensured to synchronously operate while a secondary stator actively bears high load of the rail door; aiming at the flux linkage loss caused by the transverse impulse force borne by the rail door under the working conditions of meeting of trains, hurricanes and the like, the digital-analog disturbance observer is matched with the wavelet neural network algorithm to realize the accurate prediction of the transverse impulse force at the rail door, and the reinforced current harmonic injection method is adopted to compensate to realize the transverse fixation of the rail door, so that the loss is reduced.
Preferably, the primary rotor of the LFSPM motor comprises a winding, a permanent magnet and a magnetic barrier, the magnetic barrier at the most middle position is widened, and the primary rotor and the rail door are synchronously operated by hooking the magnetic barrier and the rail door by using a hooking component B; a secondary stator of the LFSPM motor is provided with a track, a hook component A with a pulley is used for hooking the track and a track door, and a sliding rail reciprocates along the track, so that the secondary stator actively bears the high load of the track door.
Preferably, a digital-analog disturbance observer is adopted to be matched with a wavelet neural network algorithm to realize accurate prediction of transverse impulse, and the prediction comprises three parts of data acquisition, model building and prediction judgment;
firstly, training a wavelet neural network algorithm by adopting historical weather forecast data and historical track door wind power data, and carrying out wind power comprehensive evaluation according to real-time meteorological data to obtain predicted wind speed capable of reflecting actual wind conditions; because wind power modeling can be influenced by unmodeled dynamic factors except mechanism models such as relative wind speed, a geographical elevation map, train attitude change, different road excitation and the like, the transverse force prediction is accurately carried out on the basis of physical and mechanism models by adopting historical weather forecast data and historical track door wind power data;
secondly, establishing a wind farm physical model by utilizing the train running speed and the geographical elevation map to obtain a wind ratio relation number matrix capable of reflecting the flow field force distribution characteristics in the wind farm;
and finally, outputting the predicted transverse force based on a dynamic factor modeling method of the train attitude and the excitation model with the road.
Preferably, in the wavelet neural network algorithm, time-interval normalization processing is firstly performed on historical weather forecast data and historical orbit gate wind data; then, performing wavelet transformation on the normalization result, wherein the wavelet transformation quantity is used as the input quantity of the wavelet neural network, so as to determine the structure of the wavelet neural network; and finally, establishing a mapping relation between the real-time weather forecast data and the real-time wind power data at the track gate according to the historical weather forecast data.
Preferably, on the basis of the prediction of the transverse force at the rail door, a reinforcement current i is added to the q-axis current of the corresponding electromagnetic thrust in the control systemqcSo that iqcThe generated reinforcing force is equivalent to the transverse force after being decomposed, has the same amplitude and the opposite direction, and can be used for offsetting the transverse impact force and reinforcing the rail door.
Has the advantages that: the invention provides a linear point type connecting structure and a transverse impulse force restraining method for a magnetic flux switching linear motor, which combine a three-point type connecting structure, a digital-analog disturbance observer, a wavelet neural network algorithm and a reinforcement current harmonic wave injection method together, so that a predicted value of the transverse bearing force of an LFSPM motor can quickly and accurately follow a true value, and the LFSPM motor is suitable for a large-bearing track door system by optimizing and controlling the three-point type connecting structure and a rotor flux linkage to solve the contradiction between loss reduction and control performance.
Drawings
FIG. 1 is a schematic view of a linear point type connection structure of a magnetic flux switching linear motor; the magnetic suspension type magnetic suspension device comprises a 1-secondary stator, a 2-primary rotor, a 21-winding, a 23-magnetic barrier, a 22-permanent magnet, a 3-track door, a 4-hollow suspension gap, a 5-hook component A, a 51-pulley, a 6-hook component B and a 7-track;
FIG. 2 is a block diagram of a digital-to-analog disturbance observer;
FIG. 3 is a flow chart of a wavelet hybrid neural network algorithm;
fig. 4 is a schematic diagram of a wavelet neural network structure.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, a linear point type connection structure and a transverse impulse suppression method for a magnetic flux switching linear motor are provided, in which an LFSPM motor is connected with a rail door in a three-point manner, and a primary rotor and the rail door are ensured to operate synchronously while a secondary stator actively bears high load of the rail door; aiming at the flux linkage loss caused by the transverse impulse borne by the rail door under the working conditions of train encounter, hurricane and the like, the digital-analog disturbance observer is adopted to be matched with the wavelet neural network algorithm to realize the accurate prediction of the transverse impulse, and a reinforcement current harmonic injection method is adopted to compensate, so that the transverse fixation of the rail door is realized, and the loss is reduced. The invention combines a three-point fixed structure, a digital-analog disturbance observer and a wavelet neural network algorithm with a reinforced current harmonic injection method, so that the predicted value of the lateral bearing capacity of the LFSPM motor can quickly and accurately follow the true value, and the contradiction between loss reduction and control performance is solved through the three-point fixed structure and rotor flux linkage optimization control, so that the LFSPM motor is suitable for a large-bearing track door system.
As shown in fig. 1, the LFSPM motor mainly includes two parts, a secondary stator and a primary rotor, the primary rotor includes a winding, a permanent magnet and a magnetic barrier (high carbon alloy steel), the magnetic barrier at the middle position is widened, and the magnetic barrier and the rail door are hooked by using a hooking component B, so as to realize the synchronous operation of the primary rotor and the rail door; a secondary stator of the LFSPM motor is provided with a track, a hook component A with a pulley is used for hooking the track and a track door, and a sliding rail reciprocates along the track, so that the secondary stator actively bears the high load of the track door.
Aiming at flux linkage loss caused by transverse impulse borne by a rail door under working conditions of train encounter, hurricane and the like, a digital-analog disturbance observer is adopted to be matched with a wavelet neural network algorithm to realize accurate prediction of the transverse impulse, and the method comprises three parts of data acquisition, model building and prediction judgment as shown in figure 2; firstly, training a wavelet neural network algorithm by adopting historical weather forecast data and historical track door wind power data, and carrying out wind power comprehensive evaluation according to real-time meteorological data to obtain predicted wind speed capable of reflecting actual wind conditions; secondly, establishing a wind farm physical model by utilizing the train running speed and the geographical elevation map to obtain a wind ratio relation number matrix capable of reflecting the flow field force distribution characteristics in the wind farm; and finally, outputting the predicted transverse force based on a dynamic factor modeling method of the train attitude and the excitation model with the road.
The wavelet neural network algorithm flow is shown in fig. 3, and time-interval normalization processing is firstly carried out on historical weather forecast data and historical orbit door wind data; then, performing wavelet transformation on the normalization result, wherein the wavelet transformation quantity is used as the input quantity of the wavelet neural network, so as to determine the structure of the wavelet neural network; and finally, establishing a mapping relation between the real-time weather forecast data and the real-time wind power data at the track gate according to the historical weather forecast data.
The wavelet neural network structure is as follows: setting the number of elements of an input layer as N, the number of elements of an output layer as m, and the number of neurons of a hidden layer as N, the excitation function in the hidden layer of the wavelet neural network is as follows:
the excitation function of the output layer selects a Sigmoid function as follows:
fi(X)＝1/[1+exp(-X)]
the wavelet neural network output is:
where psi is the excitation coefficient, and X is [ X ]1,X2,…，Xn]TIs an input vector; the weight from the output layer to the hidden layer is wij(ii) a The scale parameter and the translation parameter of the wavelet function of the hidden layer are respectively aij、bij；i＝1,2,…,m；j＝1,2,…,N。
Considering the transverse induction factor, the formula of the fluid velocity of the wind field is as follows:
ur＝(1-a)u∞
wherein u is∞Is a free flow velocity; u. ofrThe wind speed at the rail door is adopted; a is a transverse induction factor. The transverse impact force born by the rail door is as follows:
wherein rho is the stress density coefficient of the rail door; h is the bearing transverse force area of the rail door; cTIs the wind speed thrust coefficient.
For the prediction of the transverse impact force, the influence of unmodeled dynamics such as different train postures (up-down vibration, lateral movement, longitudinal movement, rolling movement, pitching movement, yawing movement and the like) and road excitation on the rail door is unmodeled dynamics except a mechanism model, so that the complete transverse force prediction should be fused with the mechanism model and the dynamic factor model.
Coefficient of wind speed thrust CTCan be represented by the lateral induction factor a as:
CT＝4a(1-a)
considering the wind speed and thrust coefficient, 3 scenarios are established: scenario 1 is a train attitude model; scenario 2 is a dynamic model considering road excitation; scenario 3 is a dynamic model that considers train attitude and road excitation. Considering the acceleration ratio correlation coefficient under 3 scenarios, the lateral force prediction result is finally expressed as:
wherein, CTAnd
the actual value and the maximum value of the thrust coefficient are respectively;
the wind power predicted value is scene 3; f1 aAnd
respectively, the predicted values of the transverse wind power based on the scenes 1 and 2 are obtained by a curve of the predicted wind speed corresponding to the constant wind power, FaIs the actual wind force.
On the basis of the prediction of the transverse force at the rail door, a reinforcement current i is added to the q-axis current of the corresponding electromagnetic thrust in the control systemqc，iqcThe generated reinforcing force is equivalent to the transverse force after being decomposed, has the same amplitude and the opposite direction, and can be used for offsetting the transverse impact force and reinforcing the rail door.
For LFSPM motors with the same shaft inductance, the electromagnetic thrust FeCan be simplified as follows:
from the above two equations, the compensating reinforcement current i can be determinedqcComprises the following steps:
wherein, taupFor LFSPM motor pole pitch, psiPMIs a permanent magnetic linkage; i.e. iqIs q-axis current
Thus, on the basis of the prediction of the transverse force at the rail door, a q-axis current corresponding to the electromagnetic thrust is added to the control systemReinforcement current iqcAnd the reinforcing force with the same amplitude and the opposite direction with the transverse force is generated, so that the reinforcing rail door can be used for offsetting the transverse impact force and reinforcing the rail door.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (4)
1. A linear point type connecting structure and a transverse impulse restraining method of a magnetic flux switching linear motor are characterized in that: the LFSPM motor is connected with the rail door in a three-point mode, and the synchronous operation of the primary rotor and the rail door is ensured while the secondary stator actively bears the high load of the rail door; the method adopts a digital-analog disturbance observer to be matched with a wavelet neural network algorithm to realize accurate prediction of the transverse impulse force at the rail gate, and adopts a reinforcement current harmonic injection method to compensate to realize transverse fixation of the rail gate, thereby reducing loss;
the method comprises the following steps of adopting a digital-analog disturbance observer to be matched with a wavelet neural network algorithm to realize accurate prediction of transverse impulse, wherein the prediction comprises three parts of data acquisition, model building and prediction judgment;
firstly, training a wavelet neural network algorithm by adopting historical weather forecast data and historical track door wind power data, and carrying out wind power comprehensive evaluation according to real-time meteorological data to obtain predicted wind speed capable of reflecting actual wind conditions;
secondly, establishing a wind farm physical model by utilizing the train running speed and the geographical elevation map to obtain a wind ratio relation number matrix capable of reflecting the flow field force distribution characteristics in the wind farm;
and finally, outputting the predicted transverse force based on a dynamic factor modeling method of the train attitude and the excitation model with the road.
2. The linear-point connecting structure and the lateral impulse suppressing method for the flux switching linear motor according to claim 1, wherein: the primary rotor of the LFSPM motor comprises a winding, a permanent magnet and a magnetic barrier, the magnetic barrier at the most middle position is widened, and the primary rotor and the rail door are hooked by using a hooking component B to realize synchronous operation of the primary rotor and the rail door; a secondary stator of the LFSPM motor is provided with a track, a hook component A with a pulley is used for hooking the track and a track door, and a sliding rail reciprocates along the track, so that the secondary stator actively bears the high load of the track door.
3. The linear-point connecting structure and the lateral impulse suppressing method for the flux switching linear motor according to claim 1, wherein: in the wavelet neural network algorithm, firstly, time-interval normalization processing is carried out on historical weather forecast data and historical orbit door wind power data; then, performing wavelet transformation on the normalization result, wherein the wavelet transformation quantity is used as the input quantity of the wavelet neural network, so as to determine the structure of the wavelet neural network; and finally, establishing a mapping relation between the real-time weather forecast data and the real-time wind power data at the track gate according to the historical weather forecast data.
4. The linear-point connecting structure and the lateral impulse suppressing method for the flux switching linear motor according to claim 1, wherein: on the basis of the prediction of the transverse force at the rail door, a reinforcement current i is added to the q-axis current of the corresponding electromagnetic thrust in the control systemqcSo that iqcThe generated reinforcing force is equivalent to the transverse force after being decomposed, has the same amplitude and the opposite direction, and can be used for offsetting the transverse impact force and reinforcing the rail door.
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