CN115411991A - Inverter nonlinear self-learning method of synchronous reluctance motor driver - Google Patents

Inverter nonlinear self-learning method of synchronous reluctance motor driver Download PDF

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CN115411991A
CN115411991A CN202211227201.6A CN202211227201A CN115411991A CN 115411991 A CN115411991 A CN 115411991A CN 202211227201 A CN202211227201 A CN 202211227201A CN 115411991 A CN115411991 A CN 115411991A
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synchronous reluctance
axis
reluctance motor
current
inverter
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CN115411991B (en
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杨贵杰
钟本诚
苏健勇
谭凯文
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Harbin Institute of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/22—Current control, e.g. using a current control loop
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/08—Reluctance motors
    • H02P25/092—Converters specially adapted for controlling reluctance motors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier

Abstract

An inverter nonlinear self-learning method of a synchronous reluctance motor driver relates to the technical field of motor control. The invention aims to solve the problem of poor compensation effect of the traditional inverter nonlinear compensation method. The invention relates to a nonlinear self-learning method of an inverter of a synchronous reluctance motor driver, which comprises the steps of injecting step current into a d axis or a q axis of a synchronous reluctance motor under the offline working condition of the synchronous reluctance motor to obtain a dq axis current given value under a rotating coordinate system of the synchronous reluctance motor, and recording dq axis voltage given values corresponding to different current given values; converting the dq axis current set value under the rotating coordinate system into a three-phase current set value under the static coordinate system; and calculating by adopting a particle swarm optimization algorithm according to the three-phase current given value and the dq-axis voltage given value to obtain saturation voltage drop, a shape coefficient and the resistance of the synchronous reluctance motor, and completing the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.

Description

Inverter nonlinear self-learning method of synchronous reluctance motor driver
Technical Field
The invention belongs to the technical field of motor control.
Background
The synchronous reluctance motor has a large number of applications in the industrial application field mainly including fans and pumps due to the advantages of simple structure, high reliability, low cost and the like. With the improvement of the industrial application field on the energy conservation and emission reduction requirements of the motor system, the high-performance synchronous reluctance motor driver is paid high attention. However, since the synchronous reluctance motor employs a voltage-type inverter, the motor control effect is affected by the nonlinear characteristics of the inverter. The inverter nonlinearity causes five and seven current harmonics in the motor phase current, and the motor loss is increased. In addition, voltage errors caused by the nonlinearity of the inverter can cause errors of motor inductance, resistance parameter identification and position angle estimation, negative effects are generated on the efficiency optimal operation track planning and the position-free operation of the synchronous reluctance motor, and the efficiency of a motor system is further reduced. In order to improve the control performance of the motor, the research on the inverter nonlinear self-learning method and the compensation method which aim at the synchronous reluctance motor and have high universality has great application value.
Most of the traditional inverter nonlinear compensation methods acquire nonlinear characteristics through offline learning, and perform real-time compensation by adopting a description function method, wherein the description function mainly adopts a sign function and a trapezoidal function. However, both of these functions are difficult to describe the characteristic of the non-linear change of the error voltage with the current in the small current region, resulting in poor compensation effect of the conventional method. In addition, in order to avoid the influence of the zero sequence voltage on the accuracy of the nonlinear self-learning result of the inverter, self-learning needs to be carried out under a specific angle position, so that the motor has to be positioned before self-learning. Therefore, the traditional method is not suitable for the condition that the motor is connected with a load, and the application occasions are limited. In addition, as the nonlinear offline learning of the inverter is generally carried out in a steady state and is limited by the rated current of the motor, the applicable voltage amplitude is low, and the nonlinear self-learning accuracy of the inverter is obviously influenced by the resistance parameter error at the moment.
Disclosure of Invention
The invention aims to solve the problem that the compensation effect of the traditional inverter nonlinear compensation method is poor due to the fact that the sign function and the trapezoidal function are difficult to describe the characteristic that error voltage changes along with the current nonlinearity in a small current region; the traditional nonlinear compensation method of the inverter has limited application occasions; the inverter nonlinear offline learning is limited by the rated current of the motor, so that the resistance parameter error influences the accuracy of the inverter nonlinear self-learning.
The inverter nonlinear self-learning method of the synchronous reluctance motor driver comprises the following steps:
the method comprises the following steps: under the offline working condition of the synchronous reluctance motor, injecting step current into a d axis or a q axis of the synchronous reluctance motor to obtain a dq axis current given value under a rotating coordinate system of the synchronous reluctance motor, and recording dq axis voltage given values corresponding to different current given values;
step two: converting the dq axis current set value under the rotating coordinate system into a three-phase current set value under a static coordinate system;
step three: and calculating by adopting a particle swarm optimization algorithm according to the three-phase current given value and the dq-axis voltage given value to obtain saturation voltage drop, a shape coefficient and the resistance of the synchronous reluctance motor, and completing the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.
Further, in the first step, the expression of the step current injected into the synchronous reluctance motor is as follows:
Figure BDA0003880208490000021
wherein ,
Figure BDA0003880208490000022
injecting a current value for any one of the d-axis and the q-axis of the synchronous reluctance motor,
Figure BDA0003880208490000023
for the value of the current injected into the other shaft of the synchronous reluctance machine, Δ i step N is the step size of the current step change, N is the current step index value, N =1,2Total number of ladders.
Further, in the step current injection process, the voltage of the synchronous reluctance motor satisfies the following formula:
Figure BDA0003880208490000024
wherein ,ud and uq D-axis and q-axis voltages, i, of synchronous reluctance machines, respectively d and iq D-axis and q-axis currents, L, of synchronous reluctance machines, respectively d and Lq The inductance of the d axis and the q axis of the synchronous reluctance motor are respectively, R is the resistance of the synchronous reluctance motor, p is a differential operator, and omega is the electrical angular velocity of the synchronous reluctance motor.
Further, in the second step, the dq-axis current set value in the rotating coordinate system is converted into the abc-phase current set value in the stationary coordinate system by the following formula:
Figure BDA0003880208490000025
wherein ,
Figure BDA0003880208490000026
and
Figure BDA0003880208490000027
respectively are a current given value of a phase, b phase and c phase under a static coordinate system,
Figure BDA0003880208490000028
and
Figure BDA0003880208490000029
the current set values of a d axis and a q axis under a rotating coordinate system are respectively, and theta is a rotor position angle.
Further, the specific process of the third step is as follows:
initialization: randomly taking the position and the speed of the particle when the iteration number k =0,1, 2.. And k = 0;
s1: in the particle search space range and the particle swarm velocity range, the position and the velocity of the particle s are updated by the following formula to obtain the position X of the kth iteration of the particle s s (k) And velocity V s (k)：
Figure BDA0003880208490000031
wherein ,Xs (k) Is the position of the kth iteration of the particle s, and
Figure BDA0003880208490000032
V s (k) Is the velocity of the kth iteration of the particle s, and V s (k)＝[v s1 (k) v s2 (k) v s3 (k)]，v s1 (k)、v s2(k) and vs3 (k) Are respectively as
Figure BDA0003880208490000033
And
Figure BDA0003880208490000034
the speed of change of (a) is,
Figure BDA0003880208490000035
is the resistance estimated value of the synchronous reluctance motor at the kth iteration of the particle s,
Figure BDA0003880208490000036
and
Figure BDA0003880208490000037
respectively a saturation pressure drop estimated value and a shape coefficient estimated value of the particles s at the kth iteration,
m is the dimension of the particle position and m =1,2,3, gamma is the inertia factor, c 1 and c2 All acceleration constants, rand (0, 1) is a random number between 0 or 1, gb m (k-1) is the m-dimensional parameter of the global optimal particle position at the k-1 iteration, pb sm (k-1) is the m-dimensional parameter, x, of the optimal position of the particle s at the k-1 iteration sm (k-1) is the m-dimension parameter of the particle s at the k-1 iteration position;
s2: mixing X s (k) The estimated value of saturation voltage drop and the estimated value of shape coefficient contained in (1) are substituted as a saturation voltage drop Δ U and a shape coefficient ξ into the following expressions, respectively, and the voltage error of the particle s under each phase current is calculated
Figure BDA0003880208490000038
Figure BDA0003880208490000039
Wherein r = a, b, c;
s3: obtained in S2
Figure BDA00038802084900000310
Substituting the formula to obtain the voltage estimated value of the r phase in the stationary coordinate system corresponding to the particle s
Figure BDA00038802084900000311
Figure BDA00038802084900000312
wherein ,
Figure BDA00038802084900000313
is a given value of r-phase current in a static coordinate system, u 0 Is a zero-sequence voltage component and is,
Figure BDA00038802084900000314
s4: obtained in S3
Figure BDA00038802084900000315
Substituting the following formula to obtain d-axis and q-axis voltage estimated values corresponding to the particles s
Figure BDA00038802084900000316
And
Figure BDA00038802084900000317
Figure BDA0003880208490000041
wherein θ is a rotor position angle;
s5: obtained in S4
Figure BDA0003880208490000042
And
Figure BDA0003880208490000043
substituting the formula to calculate the fitness fit of the kth iteration of the particle s s (k)：
Figure BDA0003880208490000044
wherein ,
Figure BDA0003880208490000045
and
Figure BDA0003880208490000046
respectively setting voltage values of a d axis and a q axis under a rotating coordinate system;
s6: judgment of fit s (k) Whether less than the optimal position X of the current particle s s Corresponding fitness fit s If yes, then X is s (k) As X s Will fit s (k) As fit s Then S7 is executed, otherwise, X is not executed s and fits Updating, and then executing S8;
s7: determining fit s (k) Whether the fitness is smaller than the fitness fit corresponding to the global optimal particle position X or not is judged, if so, the X is judged to be less than the fitness fit corresponding to the global optimal particle position X s (k) As X, fit s (k) As fit, then executing S8, otherwise, not updating X and fit, and then executing S8;
s8: judging whether k +1 exceeds an iteration threshold, if so, executing S9, and if not, enabling k = k +1 and returning to S1;
s9: and taking the parameters in the X as the final saturated voltage drop, the shape coefficient and the resistance of the synchronous reluctance motor to complete the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.
Further, the nonlinear error voltages of the phases of the inverter are respectively calculated by using the nonlinear saturation voltage drop and the shape coefficient of the inverter obtained in the step three, the nonlinear error voltages of the phases of the inverter are converted into a required coordinate system according to the control requirement, and the nonlinear compensation is performed on the inverter by using the converted nonlinear error voltages.
The invention provides a nonlinear self-learning method for an inverter of a synchronous reluctance motor driver, which self-learns the nonlinearity of the inverter through current injection under the offline working condition of the motor, does not need additional detection equipment in the whole self-learning process, and has the advantages of simple operation and high practicability. And aiming at a specific inverter, only one self-learning is needed by the method, so that the nonlinearity of the inverter can be compensated in real time in the subsequent online operation process of the motor, and the operation performance of the motor is improved.
The invention adopts Sigmoid function to describe the nonlinear characteristic of the inverter, thereby improving the depicting capability of the error voltage of the small current area along with the nonlinear change of the motor current and improving the nonlinear compensation effect of the inverter. In addition, in the process of determining the inversion nonlinear related parameters, the resistance parameters of the motor are synchronously identified, and the negative influence of resistance parameter errors on the accuracy of the self-learning result is inhibited.
The invention also considers the influence of zero sequence voltage and provides a nonlinear self-learning method of the inverter, which is not influenced by the position angle. Compared with the traditional self-learning method, the method can realize accurate learning of the nonlinearity of the inverter at any angle position, further avoid motor positioning operation before self-learning, is beneficial to application on occasions where the motor is connected with a load, and expands the application range of the self-learning method.
The invention is automatically completed in the digital control chip, does not need manual adjustment, and is simple and convenient. The whole self-learning process is completed in an off-line state, the algorithm has no real-time requirement, the performance requirement on the digital control chip is low, and the self-learning method is convenient for transplanting and applying among different systems.
Drawings
FIG. 1 is a schematic block diagram of an inverter nonlinear self-learning method of a synchronous reluctance motor driver according to the present invention;
FIG. 2 is a schematic diagram of current setting in a nonlinear self-learning process of an inverter;
FIG. 3 is a flow chart of a particle swarm algorithm;
FIG. 4 is the estimated voltage set value obtained by self-learning under different electrical angles
Figure BDA0003880208490000051
With true set point
Figure BDA0003880208490000052
A comparison graph therebetween;
FIG. 5 is a comparison graph of the self-learned nonlinear phase voltage error curves of the inverter at different electrical angles;
FIG. 6 is a graph of phase current waveforms before nonlinear compensation of an inverter and Fourier analysis thereof;
fig. 7 is a graph of a phase current waveform after nonlinear compensation of an inverter and a fourier analysis result thereof.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
First embodiment, the present embodiment is described in detail with reference to fig. 1 to 7, and the inverter nonlinear self-learning method of the synchronous reluctance motor driver according to the present embodiment includes the following steps:
the method comprises the following steps: referring to fig. 1, under an offline condition of the synchronous reluctance motor, injecting a step current into a d axis or a q axis of the synchronous reluctance motor to obtain a dq axis current given value under a rotating coordinate system of the synchronous reluctance motor, and recording dq axis voltage given values corresponding to different current given values.
Specifically, the expression of the step current injected into the synchronous reluctance motor is as follows:
Figure BDA0003880208490000053
wherein ,
Figure BDA0003880208490000054
injecting current value for any axis of d axis or q axis of the synchronous reluctance motor,
Figure BDA0003880208490000055
value of current, Δ i, injected into the other axis of the synchronous reluctance machine step Is the step size of the current step change, N is the current step index value, N =1, 2.
During the step current injection process, the voltage of the synchronous reluctance motor satisfies the following formula:
Figure BDA0003880208490000061
wherein ,ud and uq D-axis and q-axis voltages, i, of synchronous reluctance machines, respectively d and iq D-axis and q-axis currents, L, of synchronous reluctance machines, respectively d and Lq The inductance of a d axis and the inductance of a q axis of the synchronous reluctance motor are respectively, R is the resistance of the synchronous reluctance motor, p is a differential operator, and omega is the electrical angular velocity of the synchronous reluctance motor.
During the above current injection, the motor remains stationary and the electrical angular velocity ω is equal to 0. In addition, to avoid the influence of introducing a current differential term, the calculation of the voltage given magnitude is performed in the current plateau phase. With reference to FIG. 2, inAveraging the real-time voltage given signal in the region I, filtering out high-frequency noise in the signal to obtain current given signal
Figure BDA0003880208490000062
And
Figure BDA0003880208490000063
lower corresponding dq-axis voltage give
Figure BDA0003880208490000064
And
Figure BDA0003880208490000065
data; in region II, the current step process is completed.
Step two: converting the dq-axis current set value in the rotating coordinate system into the abc phase current set value in the stationary coordinate system by the following formula:
Figure BDA0003880208490000066
wherein ,
Figure BDA0003880208490000067
and
Figure BDA0003880208490000068
respectively are a current given value of a phase, b phase and c phase under a static coordinate system,
Figure BDA0003880208490000069
and
Figure BDA00038802084900000610
the current set values of a d axis and a q axis under a rotating coordinate system are respectively, and theta is a rotor position angle.
Step three: and calculating by adopting a particle swarm optimization algorithm according to the three-phase current given value and the dq-axis voltage given value to obtain saturation voltage drop, a shape coefficient and the resistance of the synchronous reluctance motor, and completing the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.
As shown in fig. 3, the specific process is as follows:
initialization: the position and velocity of the particle are randomly taken when the number of iterations k =0,1, 2.
S1: in the particle search space range and the particle swarm velocity range, the position and the velocity of the particle s are updated by the following formula to obtain the position X of the kth iteration of the particle s s (k) And velocity V s (k)：
Figure BDA00038802084900000611
wherein ,Xs (k) Is the position of the kth iteration of the particle s, and
Figure BDA0003880208490000071
V s (k) Is the velocity of the kth iteration of the particle s, and V s (k)＝[v s1 (k) v s2 (k) v s3 (k)]，v s1 (k)、v s2(k) and vs3 (k) Are respectively as
Figure BDA0003880208490000072
And
Figure BDA0003880208490000073
the speed of change of (a) is,
Figure BDA0003880208490000074
is the resistance estimated value of the synchronous reluctance motor at the kth iteration of the particle s,
Figure BDA0003880208490000075
and
Figure BDA0003880208490000076
the estimated saturation pressure drop and the estimated shape factor of the particle s at the kth iteration are respectively.
m is the dimension of the particle position and m =1,2,3, gamma is the inertia factor, c 1 and c2 Are all addedVelocity constant, rand (0, 1) is a random number between 0 or 1, gb m (k-1) is the m-dimension parameter, pb, of the globally optimal particle position at the k-1 iteration sm (k-1) is the m-dimensional parameter, x, of the optimal position of the particle s at the k-1 iteration sm (k-1) is the m-dimension parameter of the particle s at the k-1 iteration position.
S2: the nonlinearity of the inverter is related to the voltage amplitude of a bus, the conduction voltage drop of a switching tube, the conduction voltage drop of an anti-parallel diode, dead time, the switching delay of a device and other factors; in addition, parasitic capacitance effects can cause the nonlinear voltage error of the low-current area inverter to change along with the nonlinear current change. In general, the inverter nonlinearity can be modeled as a linear section in a large current region and a nonlinear section in a small current region. In the linear section, the nonlinear voltage error of the inverter can be regarded as a constant, and the voltage drop at the moment is called saturation voltage drop; and in the nonlinear section, the change relation of the voltage error with the current is described by adjusting the shape correlation coefficient of the nonlinear function.
Mixing X s (k) The estimated value of saturation voltage drop and the estimated value of shape coefficient contained in (1) are substituted as a saturation voltage drop Δ U and a shape coefficient ξ into the following expressions, respectively, and the voltage error of the particle s under each phase current is calculated
Figure BDA0003880208490000077
Figure BDA0003880208490000078
Where r = a, b, c, e denotes an exponential function.
S3: obtained in S2
Figure BDA0003880208490000079
Substituting the formula to obtain the voltage estimated value of the r phase in the stationary coordinate system corresponding to the particle s
Figure BDA00038802084900000710
Figure BDA00038802084900000711
wherein ,
Figure BDA00038802084900000712
a given value of r-phase current in a static coordinate system, u 0 Is a zero-sequence voltage component and is,
Figure BDA00038802084900000713
s4: obtained by S3
Figure BDA0003880208490000081
Substituting the following formula to obtain d-axis and q-axis voltage estimated values corresponding to the particles s
Figure BDA0003880208490000082
And
Figure BDA0003880208490000083
Figure BDA0003880208490000084
wherein θ is a rotor position angle;
s5: obtained in S4
Figure BDA0003880208490000085
And
Figure BDA0003880208490000086
substituting the formula to calculate the fitness fit of the kth iteration of the particle s s (k)：
Figure BDA0003880208490000087
wherein ,
Figure BDA0003880208490000088
and
Figure BDA0003880208490000089
respectively setting voltage values of a d axis and a q axis under a rotating coordinate system;
s6: judgment of fit s (k) Whether it is smaller than the optimal position X of the current particle s s Corresponding fitness fit s If yes, then X is s (k) As X s Will fit s (k) As fit s Then S7 is executed, otherwise, X is not executed s and fits Updating, and then executing S8;
s7: determining fit s (k) Whether the fitness is smaller than the fitness fit corresponding to the global optimal particle position X or not is judged, if so, the X is judged s (k) As X, fit s (k) As fit, then executing S8, otherwise, not updating X and fit, and then executing S8;
s8: judging whether k +1 exceeds an iteration threshold, if so, executing S9, and if not, enabling k = k +1 and returning to S1;
s9: and taking the parameters in the X as the final saturated voltage drop, the shape coefficient and the resistance of the synchronous reluctance motor to complete the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.
In a second embodiment, the present embodiment is further configured to compensate for the inverter nonlinearity based on the inverter nonlinearity self-learning method for the synchronous reluctance motor driver according to the first embodiment, and specifically includes:
step four: and C, respectively calculating the nonlinear error voltage of each phase of the inverter by using the nonlinear saturation voltage drop and the shape coefficient of the inverter obtained in the step three, converting the nonlinear error voltage of each phase of the inverter into a required coordinate system according to a control requirement, and performing nonlinear compensation on the inverter by using the converted nonlinear error voltage.
Taking nonlinear compensation of the inverter in an α β coordinate system as an example, a method for calculating a nonlinear voltage error in a corresponding coordinate system is as follows:
Figure BDA00038802084900000810
wherein ,Δuα and Δuβ And the nonlinear compensation voltage of the inverter is an alpha axis and a beta axis under an alpha beta coordinate system.
To verify the effectiveness of the method proposed in this embodiment, an experiment was performed:
firstly, the nonlinear self-learning of the inverter is performed under four random angles, and the obtained dq axis real voltage given and estimated voltage given are shown in fig. 4. As can be seen from FIG. 4, the inverter nonlinear self-learning algorithm can realize accurate reconstruction of given voltage under different angle positions. Further, the self-learning of the inverter nonlinear phase voltage error curves at different angles is shown in fig. 5, and it can be seen that the self-learning is performed at different angle positions, and the obtained results are basically consistent, which proves that the method of the present invention can realize the accurate learning of the inverter nonlinearity at different angle positions.
Secondly, comparing the current waveforms before and after compensation and the fourier analysis results thereof, as shown in fig. 6 and 7, the fifth and seventh harmonic components in the phase current are significantly suppressed, and the current sine is improved.
In summary, the method for nonlinear self-learning and compensation of the synchronous reluctance motor driver inverter according to the embodiment is not affected by zero sequence voltage, and can accurately learn the nonlinear characteristics of the inverter at any electrical angle position; in addition, harmonic components in the current can be effectively reduced through voltage compensation, so that the negative influence of the nonlinearity of the inverter on the motor control is corrected.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.

Claims (6)

1. The inverter nonlinear self-learning method of the synchronous reluctance motor driver is characterized by comprising the following steps of:
the method comprises the following steps: under the offline working condition of the synchronous reluctance motor, injecting step current into a d axis or a q axis of the synchronous reluctance motor to obtain a dq axis current given value under a rotating coordinate system of the synchronous reluctance motor, and recording dq axis voltage given values corresponding to different current given values;
step two: converting the dq axis current set value under the rotating coordinate system into a three-phase current set value under the static coordinate system;
step three: and calculating by adopting a particle swarm optimization algorithm according to the three-phase current given value and the dq-axis voltage given value to obtain a saturation voltage drop, a shape coefficient and the resistance of the synchronous reluctance motor, and completing the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.
2. The inverter nonlinear self-learning method of the synchronous reluctance motor driver as claimed in claim 1, wherein the step current injected into the synchronous reluctance motor in the first step is expressed as follows:
Figure FDA0003880208480000011
wherein ,
Figure FDA0003880208480000012
injecting current value for any axis of d axis or q axis of the synchronous reluctance motor,
Figure FDA0003880208480000013
for the value of the current injected into the other shaft of the synchronous reluctance machine, Δ i step Is the step size of the current step change, N is the current step index value, N =1, 2.
3. The inverter nonlinear self-learning method of a synchronous reluctance motor driver of claim 2, wherein, during the step current injection, the voltage of the synchronous reluctance motor satisfies the following equation:
Figure FDA0003880208480000014
wherein ,ud and uq D-axis and q-axis voltages, i, of synchronous reluctance machines, respectively d and iq D-axis and q-axis currents, L, of synchronous reluctance machines, respectively d and Lq The inductance of the d axis and the q axis of the synchronous reluctance motor are respectively, R is the resistance of the synchronous reluctance motor, p is a differential operator, and omega is the electrical angular velocity of the synchronous reluctance motor.
4. The inverter nonlinear self-learning method of the synchronous reluctance motor driver as claimed in claim 1, wherein in the second step, the given value of dq-axis current in the rotating coordinate system is transformed into the given value of abc phase current in the stationary coordinate system by the following formula:
Figure FDA0003880208480000015
wherein ,
Figure FDA0003880208480000016
and
Figure FDA0003880208480000017
respectively the given current values of a phase, b phase and c phase under a static coordinate system,
Figure FDA0003880208480000018
and
Figure FDA0003880208480000019
the current given values of a d axis and a q axis under a rotating coordinate system are respectively, and theta is a rotor position angle.
5. The inverter nonlinear self-learning method of the synchronous reluctance motor driver as claimed in claim 1, wherein the specific process of the third step is as follows:
initialization: randomly taking the position and the speed of the particle when the iteration number k =0,1, 2.. And k = 0;
s1: in the particle search space range and the particle swarm velocity range, the position and the velocity of the particle s are updated by the following formula to obtain the position X of the kth iteration of the particle s s (k) And velocity V s (k)：
Figure FDA0003880208480000021
wherein ,Xs (k) Is the position of the kth iteration of the particle s, and
Figure FDA0003880208480000022
V s (k) Is the velocity of the kth iteration of the particle s, and V s (k)＝[v s1 (k) v s2 (k) v s3 (k)]，v s1 (k)、v s2(k) and vs3 (k) Are respectively as
Figure FDA0003880208480000023
And
Figure FDA0003880208480000024
the speed of change of (a) is,
Figure FDA0003880208480000025
is the resistance estimated value of the synchronous reluctance motor at the kth iteration of the particle s,
Figure FDA0003880208480000026
and
Figure FDA0003880208480000027
respectively a saturation pressure drop estimated value and a shape coefficient estimated value of the particles s at the kth iteration,
m is the dimension of the particle position and m =1,2,3, gamma is the inertia factor, c 1 and c2 Are all acceleration constants, rand (0, 1) is a random number between 0 or 1, gb m (k-1) is the m-dimensional parameter of the global optimal particle position at the k-1 iteration, pb sm (k-1) is the m-dimensional parameter, x, of the optimal position of the particle s at the k-1 iteration sm (k-1) is the m-dimension parameter of the particle s at the k-1 iteration position;
s2: mixing X s (k) The estimated value of saturation voltage drop and the estimated value of shape coefficient contained in (1) are substituted as a saturation voltage drop Δ U and a shape coefficient ξ into the following expressions, respectively, and the voltage error of the particle s under each phase current is calculated
Figure FDA0003880208480000028
Figure FDA0003880208480000029
Wherein r = a, b, c;
s3: obtained in S2
Figure FDA00038802084800000211
Substituting the formula to obtain the voltage estimated value of the r phase in the stationary coordinate system corresponding to the particle s
Figure FDA00038802084800000212
Figure FDA00038802084800000210
wherein ,
Figure FDA0003880208480000031
a given value of r-phase current in a static coordinate system, u 0 Is a component of the zero-sequence voltage,
Figure FDA0003880208480000032
s4: obtained by S3
Figure FDA0003880208480000033
Substituting the following formula to obtain d-axis and q-axis voltage estimated values corresponding to the particles s
Figure FDA0003880208480000034
And
Figure FDA0003880208480000035
Figure FDA0003880208480000036
wherein θ is a rotor position angle;
s5: obtained by S4
Figure FDA0003880208480000037
And
Figure FDA0003880208480000038
substituting the formula to calculate the fitness fit of the kth iteration of the particle s s (k)：
Figure FDA0003880208480000039
wherein ,
Figure FDA00038802084800000310
and
Figure FDA00038802084800000311
respectively setting voltage values of a d axis and a q axis under a rotating coordinate system;
s6: judgment of fit s (k) Whether it is smaller than the optimal position X of the current particle s s Corresponding fitness fit s If yes, then X is s (k) As X s Will fit s (k) As fit s Then S7 is executed, otherwise, X is not executed s and fits Updating, and then executing S8;
s7: judgment of fit s (k) Whether the fitness is smaller than the fitness fit corresponding to the global optimal particle position X or not is judged, if so, the X is judged s (k) As X, fit s (k) As fit, then executing S8, otherwise, not updating X and fit, and then executing S8;
s8: judging whether k +1 exceeds an iteration threshold value, if so, executing S9, and if not, enabling k = k +1 and returning to S1;
s9: and taking the parameters in the X as the final saturated voltage drop, the shape coefficient and the resistance of the synchronous reluctance motor to complete the nonlinear self-learning of the inverter of the synchronous reluctance motor driver.
6. The inverter nonlinear self-learning method for the synchronous reluctance motor driver as claimed in claim 1, wherein the nonlinear error voltages of the phases of the inverter are calculated by using the nonlinear saturation voltage drop and the shape factor of the inverter obtained in the third step, and are transformed into a demand coordinate system according to the control demand, and the transformed nonlinear error voltages are used to perform nonlinear compensation on the inverter.
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