CN116073713A - Model-free predictive current control method for variable vector sequence induction motor - Google Patents

Model-free predictive current control method for variable vector sequence induction motor Download PDF

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CN116073713A
CN116073713A CN202211309764.XA CN202211309764A CN116073713A CN 116073713 A CN116073713 A CN 116073713A CN 202211309764 A CN202211309764 A CN 202211309764A CN 116073713 A CN116073713 A CN 116073713A
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vector
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张永昌
张昊男
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North China Electric Power University
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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
    • H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter estimation
    • 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
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/18—Estimation of position or speed
    • 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

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Abstract

The application provides a model-free prediction current control method for an induction motor super-local model variable vector sequence, which comprises the following steps: step A: obtaining a dead beat predictive current control complex vector model according to the motor mathematical model; and (B) step (B): estimating system parameters F and alpha on line according to a super local model of the motor, wherein in order to ensure that the switching frequency is fixed, the pulse width modulation period of the sequence 012 is 2/3 times of that of other three sequences; step C: according to the estimated F and alpha, compensating one-beat delay and calculating a voltage vector reference value based on a dead beat principle; step D: according to a calculation formula of the effective value of the current harmonic, the per unit value of the effective value of the current harmonic of different vector sequences in one Pulse Width Modulation (PWM) period is obtained; the vector sequence to be selected is expanded from one to three, and the vector sequence which minimizes the current harmonic is selected on line according to the modulation ratio M, so that the current harmonic is reduced. The method can remarkably improve the control effect, has excellent robustness, and meanwhile remarkably reduces current harmonic waves in the full modulation ratio.

Description

Model-free predictive current control method for variable vector sequence induction motor
Technical Field
The invention relates to the field of electricity, in particular to a model-free prediction current control method for an induction motor super-local model-type variable vector sequence.
Background
The model predictive control (model predictive Control, MPC) method is a power controller switching sequence generation method that has received attention from a large number of scholars in recent years, and the goal is to select the most suitable switching state from among all the switching state sets of the power controller. Compared with the traditional asynchronous asymmetric pulse width modulation method and the synchronous optimal pulse width modulation strategy, the model predictive control has better control effect and dynamic characteristic. But the parameters of the motor will change due to temperature and magnetic saturation. And a large number of motor parameters are required to be used in the prediction and control process, MPC has high dependence on the accuracy of the motor parameters, and the robustness is poor. And when only one voltage vector acts in the whole control period, the traditional model predictive control has larger current pulsation, and limits the application of the current pulsation in high-performance motor control.
In order to improve the robustness of parameters of model predictive control, various methods have been proposed in the prior art. The literature, "Predictive-Control-Based Direct Power Control With an Adaptive Parameter Identification Technique for Improved AFE Performance" describes a method for online identification of parameters based on the least squares method, which uses sampled input current and input voltage to calculate the input inductance and input resistance of the AFE in each sampling period. The document Design and Implementation of Disturbance Compensation-Based Enhanced Robust Finite Control Set Predictive Torque Control for Induction Motor Systems utilizes a disturbance observer to observe the disturbance quantity caused by external environment influence, parameter mismatch and other factors, and then compensates the selected voltage vector by applying a feedforward link, thereby weakening the influence of parameter change on the system. However, the method of on-line identification of parameters and adoption of a disturbance observer improves the robustness of the parameters, but the method is still based on a system model in the implementation process, and the implementation process is slightly complicated. In order to further solve the parameter dependence problem, the Linet et al propose a model-free predictive current control method which does not require any motor parameters and achieves good effects in the permanent magnet synchronous motor. However, this method requires two current samples in one control period, which increases the complexity of the system implementation.
To improve the steady state performance of model predictive control, document Performance Improvement of Model-Predictive Current Control of Permanent Magnet Synchronous Motor Drives applies a zero vector along with a voltage vector obtained from a conventional MPCC during one control period, improving the steady state performance of the system by obtaining an optimal duty cycle. However, the method applies a large number of motor parameters, and the robustness effect is poorer than that of model-free prediction control. The literature of induction motor speedless sensor model prediction flux linkage control based on space vector modulation introduces deadbeat control, but the method adopts single vector action whole control period based on basic 7-segment vector sequence, and current harmonic wave is relatively larger at high modulation ratio. The document 'induction motor three-vector model prediction flux linkage control' proposes vector selection similar to SVM by using two effective voltage vectors and a zero vector in one control period, but also brings the problems of complex vector selection, large calculated amount, high switching frequency and the like.
In summary, no better method can simultaneously meet the following conditions: (1) has good robustness; (2) The full modulation ratio has good steady-state performance and lower current harmonic wave; (3) The method is simple to realize, the vector selection mode is easy to understand, and complex calculation is not needed; the modulation mode is easy to be unified with other control methods, and different control modes are easy to be realized under a unified control program framework.
Disclosure of Invention
The method can remarkably improve the control effect, has excellent robustness and simultaneously remarkably reduces current harmonics in the full modulation ratio. The invention provides a model-free predictive current control method for a variable vector sequence induction motor based on a super local model on the basis of analyzing and deducing current harmonic waves of space vector modulation based on 4 voltage vector sequences.
The application provides a model-free prediction current control method for an induction motor super-local model variable vector sequence, which comprises the following steps:
and step A, obtaining a state equation expression of the stator current according to the mathematical model of the motor. Taking one beat delay compensation into consideration, a new stator current state equation is obtained.
Step B, combining the principle of the super local model with a state equation of motor stator current to obtain a super local model equation related to the stator current, and estimating system parameters F and alpha on line according to input current information and output voltage information; meanwhile, in order to maintain the consistency of the switching frequency, the pulse width modulation period of the sequence 012 is 2/3 times that of the other three sequences (0127, 0121, 1012);
step C: according to F and alpha obtained by online estimation in the last step, taking one beat delay compensation into consideration, and calculating a voltage vector reference value according to a dead beat principle and a super local model related to stator current;
step D: according to a calculation formula of the effective value of the current harmonic wave, the per unit value of the effective value of the current harmonic wave of four different voltage vector sequences (0127, 0121, 012, 1012) in one Pulse Width Modulation (PWM) period is obtained; the vector sequence to be selected is expanded from one to three, and the vector sequence which minimizes the current harmonic is selected according to the modulation ratio M, so that the current harmonic is reduced.
In some embodiments, the formula in step a is as follows:
Figure BDA0003906744890000031
in the formula ,
Figure BDA0003906744890000032
stator currents at the k moment and the k+1 moment respectively; />
Figure BDA0003906744890000033
A voltage vector applied from the time k to the time k+1; r is R s 、R r The resistances of the stator and the rotor are respectively; l (L) s 、L r 、L m Respectively representing the mutual inductance among the stator inductance, the rotor inductance and the stator and the rotor; psi phi type r Is rotor flux linkage; omega r The motor rotation speed; t (T) r ＝L r /R r ，/>
Figure BDA0003906744890000034
Considering one beat delay compensation, one beat delay compensation can be obtained:
Figure BDA0003906744890000035
in the formula ,
Figure BDA0003906744890000036
stator current at time k+2; />
Figure BDA0003906744890000037
A voltage vector applied from time k+1 to time k+2;
in some embodiments, the step B comprises:
super local model of motor:
Figure BDA0003906744890000038
by comparing this formula with the stator current state equation mentioned in the previous step, the +.>
Figure BDA0003906744890000039
F is a disturbance variable of the whole system, including disturbance caused by all known factors in the system and other disturbance introduced by unknown conditions.
Estimating F and alpha according to the voltage and current values at the past moment;
defining the variation of the current:
Figure BDA0003906744890000041
Figure BDA0003906744890000042
as mentioned in the previous step, the sequence 012 is different from the control periods corresponding to the remaining three sequences, so T sc With different superscripts, the following formula is specifically selected:
Figure BDA0003906744890000043
according to the voltage and current information and the super local model at the past two moments, estimated values of F and alpha can be obtained:
Figure BDA0003906744890000044
Figure BDA0003906744890000045
wherein ：
Figure BDA0003906744890000046
and />
Figure BDA0003906744890000047
The current value at the moment k-1, the average voltage vector at the moment k-1 to the moment k and the control period are respectively; />
Figure BDA0003906744890000048
and />
Figure BDA0003906744890000049
The current value at the moment k-2, the average voltage vector at the moment k-2 to the moment k-1 and the control period are respectively; />
Figure BDA00039067448900000410
The current value at time k.
In some embodiments, the step C comprises:
in an actual digital control system, there is a one-beat delay between the output voltage and the command voltage, and in order to eliminate the influence of the one-beat delay, the current value at time k is compensated, and the current predicted value at time k+1 is:
Figure BDA00039067448900000411
calculating a reference voltage vector according to the dead beat principle:
Figure BDA00039067448900000412
in some embodiments, the step D comprises:
calculating a modulation ratio M, and selecting a vector sequence which minimizes current harmonics according to the current harmonic expression in the step A:
Figure BDA00039067448900000413
Figure BDA00039067448900000414
calculating the per unit value of four sequence current harmonic effective values according to the modulation ratio M
Figure BDA0003906744890000051
Figure BDA0003906744890000052
/>
Figure BDA0003906744890000053
Figure BDA0003906744890000054
Figure BDA0003906744890000055
Per unit value of the current harmonic effective value corresponding to sequences 0127, 012, 0121, 1012; m is modulation ratioThe method comprises the steps of carrying out a first treatment on the surface of the Pi is a mathematical symbol, and its value is 3.1415926. After comparing the current harmonic effective values, the 1012 sequence is rejected.
According to the selected vector sequence, the three-phase duty ratio is calculated:
Figure BDA0003906744890000056
Figure BDA0003906744890000057
Figure BDA0003906744890000058
Figure BDA0003906744890000059
wherein ,
Figure BDA00039067448900000510
for standardized three-phase reference voltage, +.>
Figure BDA00039067448900000511
Is a zero sequence component;
and obtaining the signal of each switching tube of the inverter through the vector sequence and the three-phase duty ratio obtained in the steps.
The invention has the following characteristics and advantages:
1. the variable vector sequence induction motor model-free predictive current control based on the super local model does not depend on any motor parameter, and has extremely strong robustness.
2. Compared with the traditional dead beat prediction current control, the method expands the alternative voltage vector sequences from one to three, selects the optimal vector sequence on line, has easy understanding of the vector selection mode, effectively reduces current harmonic waves, and improves the steady state performance of the system.
The values of F and alpha are updated online, no priori knowledge is needed, the switching frequency is fixed, and the requirement on the sampling frequency is not high.
4. The method is simple to realize and does not need complex calculation; the modulation mode is easy to be unified with other control methods, and different control modes are easy to be realized under a unified control program framework.
Drawings
FIG. 1 is a hardware block diagram of an induction motor speed regulation control system;
FIG. 2 is a block diagram of a model-free predictive current control architecture for a variable vector sequence induction motor based on a super local model;
FIG. 3 is a per unit value of the current harmonic effective value of four voltage vector sequences at one fundamental period at full modulation ratio;
FIG. 4 is an experimental result of using dead beat predicted current control with accurate parameters, a switching frequency of 5kHz, and a motor operating at 600r/min with rated load;
FIG. 5 is an experimental result of motor operation at 600r/min with rated load using dead beat predictive current control with 3 times overall motor parameter expansion at a switching frequency of 5 kHz;
FIG. 6 is an experimental result of a model-free predictive current control of an induction motor with a variable vector sequence based on a super local model, a switching frequency of 5kHz, and a motor running at 600r/min with rated load;
FIG. 7 is an experimental result of motor operation at 1200r/min with nominal load using dead beat predicted current control with accurate parameters at a switching frequency of 5 kHz;
FIG. 8 is an experimental result of motor operation at 1200r/min with rated load using dead beat predictive current control with 3-fold expansion of all motor parameters with a switching frequency of 5 kHz;
FIG. 9 is an experimental result of using a superlocal model-based variable vector sequence induction motor model-free predictive current control with a switching frequency of 5kHz and a motor operating at 1200r/min with rated load;
FIG. 10 is an experimental result with dead beat predicted current control using accurate parameters, switching frequency of 5kHz, motor operating at 1350r/min with rated load;
FIG. 11 is an experimental result of motor operation at 1350r/min with rated load using dead beat predicted current control with 3 times the overall motor parameter expansion at a switching frequency of 5 kHz;
FIG. 12 is an experimental result of model-free predictive current control of a variable vector sequence induction motor based on a super local model, with a switching frequency of 5kHz, and with a rated load of 1350 r/min;
FIG. 13 is an experimental result of motor operation at 1500r/min with nominal load using dead beat predicted current control with accurate parameters at a switching frequency of 5 kHz;
FIG. 14 is an experimental result of motor operation at 1500r/min with nominal load using dead beat predicted current control with 3-fold expansion of all motor parameters with a switching frequency of 5 kHz;
FIG. 15 is an experimental result of model-free predictive current control of a variable vector sequence induction motor based on a super local model, with a switching frequency of 5kHz, and with a nominal load of 1500 r/min;
FIG. 16 is a comparison plot of current Total Harmonic Distortion (THD) for dead beat predicted current control of accurate parameters, dead beat predicted current control of 3 times extension of all motor parameters, and dead beat predicted current control of a superlocal model-based variable vector sequence induction motor at different average modulation ratios;
FIG. 17 is an experimental result of direct motor start from 0 to 1500r/min with dead beat predicted current control with accurate parameters at a switching frequency of 5 kHz;
FIG. 18 is an experimental result of experimental results from 0 direct start to 1500r/min for a switching frequency of 5kHz using dead beat predictive current control with a 3-fold expansion of all motor parameters;
FIG. 19 is an experimental result of experimental results of a variable vector sequence induction motor model-free predictive current control based on a super local model, with a switching frequency of 5kHz, and a motor starting directly from 0 to 1500 r/min;
FIG. 20 is an experimental result of motor sudden load rating with dead beat predicted current control using accurate parameters with a switching frequency of 5 kHz;
FIG. 21 is an experimental result of motor burst load rating using dead beat predicted current control with 3-fold expansion of all motor parameters with a switching frequency of 5 kHz;
FIG. 22 is an experimental result of model-free predictive current control of a variable vector sequence induction motor based on a superlocal model, a switching frequency of 5kHz, and a sudden increase of rated load of the motor.
Detailed Description
The following examples will enable those skilled in the art to more fully understand the present invention and are not intended to limit the same in any way.
The technical scheme adopted by the invention is as follows:
a model-free predictive current control method for a variable vector sequence induction motor based on a super local model comprises the following steps:
step 1: the whole system adopts a series control structure, and a q-axis current reference value is obtained through a speed outer loop Proportional Integral (PI) regulator
Figure BDA0003906744890000071
The d-axis current reference value is set to a nominal value.
Step 2: slip ω by q-axis current reference value and d-axis current reference value sl Then the electric angular velocity omega is obtained e Integrating to obtain the magnetic field position angle theta e 。
Step 3: q-axis current reference value obtained according to step 1
Figure BDA0003906744890000081
Given d-axis current reference value +.>
Figure BDA0003906744890000082
The magnetic field position angle theta obtained in the step 2 e Obtaining current vector reference value +.>
Figure BDA0003906744890000083
Step 4: model-free control parameters F and α are estimated from the first-order superlocal model and the voltage vector, current vector and control period of the past two moments.
Step 5: according to the current vector at the current moment
Figure BDA0003906744890000084
F and alpha estimated in the step 4 are subjected to one-beat delay compensation to obtain a current vector +.>
Figure BDA0003906744890000085
Step 6: according to the current vector reference value obtained in the step 3
Figure BDA0003906744890000086
F and alpha obtained in step 4, current vector +.f at time k+1 obtained in step 5>
Figure BDA0003906744890000087
By combining with a super-local model of the motor, the reference voltage vector +.>
Figure BDA0003906744890000088
Step 7: according to the reference voltage vector in step 6
Figure BDA0003906744890000089
The modulation ratio M is obtained.
Step 8: selecting a vector sequence minimizing current harmonics from three alternative voltage vector sequences in combination with step 7, and determining a three-phase duty cycle d a,b,c 。
Step 9: the vector sequence selected according to step 8 and the three-phase duty cycle d a,b,c The driving signal of each switching tube of the inverter is constructed.
Fig. 1 is a block diagram of a hardware circuit of the invention, which comprises a three-phase voltage source, an asynchronous motor, a three-phase diode rectifier bridge, a direct-current side capacitor, an asynchronous motor, a voltage and current sampling circuit, a DSP controller and a driving circuit. The voltage and current sampling circuit respectively collects direct-current side voltage and a phase current of the asynchronous motor by using a voltage Hall sensor and a current Hall sensor, and sampling signals enter a Digital Signal Processing (DSP) controller to be converted into digital signals after passing through the signal conditioning circuit. The DSP controller completes the operation of the method provided by the invention, outputs six paths of switching pulses, and then obtains the final driving signals of six switching tubes of the inverter after passing through the driving circuit.
Fig. 2 is a control schematic block diagram of the present invention, and the control method is implemented on the DSP controller of fig. 1 in sequence according to the following steps:
step 1: obtaining q-axis current reference value through speed outer loop PI regulator
Figure BDA00039067448900000810
Is specifically shown as
Figure BDA0003906744890000091
(k p and ki Proportional gain and integral gain in the PI regulator respectively), and d-axis current reference value is set as a rated value; in (1) the->
Figure BDA0003906744890000092
Reference current for q-axis; />
Figure BDA0003906744890000093
ω r Respectively a reference rotating speed and an actual rotating speed of the motor; />
Figure BDA0003906744890000094
Representing the integral.
Step 2: slip ω by q-axis current reference value and d-axis current reference value sl Then the electric angular velocity omega is obtained e Integrating to obtain the magnetic field position angle theta e ；
Step 3: according to the reference current instruction obtained in the step 1
Figure BDA0003906744890000095
Solving for a reference current vector +.>
Figure BDA0003906744890000096
The magnetic field position angle theta obtained according to the step 2 e Converting the reference current vector from a synchronous coordinate system to a static coordinate system through coordinate transformation;
Figure BDA0003906744890000097
/>
Figure BDA0003906744890000098
in the formula ,
Figure BDA0003906744890000099
the reference value is a stator current vector under a synchronous coordinate system; />
Figure BDA00039067448900000910
Reference current for d-axis; />
Figure BDA00039067448900000911
The reference value is a stator current vector under a static coordinate system; e, e jθ Is an angle change.
Step 4: the estimation system has no model control parameters F and α.
According to the mathematical model of the induction motor, the traditional dead beat prediction current control complex vector model is as follows:
Figure BDA00039067448900000912
in the formula ,
Figure BDA00039067448900000913
stator currents at the k moment and the k+1 moment respectively; />
Figure BDA00039067448900000914
A voltage vector applied from the time k to the time k+1; r is R s 、R r The resistances of the stator and the rotor are respectively; l (L) s 、L r 、L m Respectively representing the mutual inductance among the stator inductance, the rotor inductance and the stator and the rotor; psi phi type r Is rotor flux linkage; omega r The motor rotation speed; t (T) r ＝L r /R r ，/>
Figure BDA00039067448900000915
Super local model of motor:
Figure BDA00039067448900000916
voltage at current time
Figure BDA00039067448900000917
When the voltage and current vectors at the past two moments are stored, the variation of the stator current can be obtained as follows:
Figure BDA00039067448900000918
Figure BDA00039067448900000919
wherein ：
Figure BDA0003906744890000101
and />
Figure BDA0003906744890000102
The current value and the control period at the moment k-1 are respectively; />
Figure BDA0003906744890000103
and />
Figure BDA0003906744890000104
The current value and the control period at the moment k-2 are respectively; />
Figure BDA0003906744890000105
The current value at time k.
It can be seen from the above that the current prediction formula of the induction motor is very complex and uses a large number of motor parameters, in order to simplify the control complexity, indirect magnetic field directional control is adopted, according to the model-free theory, the whole control system is regarded as an input/output system described by complex vectors, and the parameters in the system are regarded as a black box, so that only model-free control parameters can be used without any controlled object information. The equation of the complex vector model and the superlocal model of the traditional dead beat predictive current control is compared, so that the equation in the traditional dead beat predictive current control can be compared
Figure BDA0003906744890000106
Written as F->
Figure BDA0003906744890000107
Alpha is written to obtain a model-free complex vector motor mathematical model
Figure BDA0003906744890000108
wherein ：
Figure BDA0003906744890000109
for the voltage vector acting from time k to time k+1, T sc For the control period, α is the input variable weight coefficient, and F is the disturbance variable of the whole system, including the disturbance caused by all known factors in the system and other disturbances introduced by unknown conditions.
After the super-local model-free control thinking is combined with the prediction current control, we can calculate the parameters alpha and F of the position in the super-local model
Figure BDA00039067448900001010
Figure BDA00039067448900001011
wherein ：
Figure BDA00039067448900001012
and />
Figure BDA00039067448900001013
The stator current vector value at the moment k-1, the average voltage vector at the moment k-1 to the moment k and the control period are respectively; />
Figure BDA00039067448900001014
and />
Figure BDA00039067448900001015
The current value at the moment k-2, the average voltage vector at the moment k-2 to the moment k-1 and the control period are respectively; />
Figure BDA00039067448900001016
The stator current vector value at time k.
Step 5: based on F and alpha estimated in the step 4, combining with a super-local model of the motor, performing one-beat delay compensation on the control system
Figure BDA00039067448900001017
Step 6: calculation of reference voltage vector based on dead beat principle
Figure BDA00039067448900001018
And the modulation ratio M is found.
Figure BDA0003906744890000111
Figure BDA0003906744890000112
wherein ：Udc Is the bus voltage.
Step 7: and (3) selecting a vector sequence according to the modulation ratio M obtained in the step (6) and obtaining the three-phase duty ratio. The sequence selection is based on the following principle, firstly, calculating the expression of the per unit value of the effective value of the current harmonic in a fundamental wave period, wherein the expression is as follows:
Figure BDA0003906744890000113
Figure BDA0003906744890000114
Figure BDA0003906744890000115
Figure BDA0003906744890000116
Figure BDA0003906744890000117
per unit value of the current harmonic effective value corresponding to sequences 0127, 012, 0121, 1012; m is modulation ratio; pi is a mathematical symbol, and its value is 3.1415926. After comparing the current harmonic effective values, the 1012 sequence is rejected.
From fig. 3, when M <0.72, the current harmonic corresponding to sequence 0127 is the lowest, sequence 0127 is selected, and the subsequent sequence selection also uses the lowest current harmonic as the selection criterion; sequence 012 is selected when 0.72< m < 0.92; when M >0.92, sequence 0121 is selected. The vector sequence which minimizes the current harmonic can be selected only according to the range of M without substituting M into the expression to carry out complicated calculation, and the realization is simple. According to the selected vector sequence, three-phase duty ratio is obtained
Figure BDA0003906744890000118
Figure BDA0003906744890000119
/>
Figure BDA00039067448900001110
Figure BDA00039067448900001111
wherein ,
Figure BDA00039067448900001112
for standardized three-phase reference voltage, +.>
Figure BDA00039067448900001113
Is a zero sequence component.
Step 8: according to the vector sequence and the three-phase duty cycle d in step 7 a,b,c The driving signal of each switching tube of the inverter is constructed.
The effectiveness of the proposed method can be obtained by comparing the experimental results of the three working conditions of fig. 4 to 15 with the THD comparison result of fig. 16. The experimental results are all 5kHz switching frequency, and waveforms from top to bottom are respectively a motor rotating speed, q-axis current, d-axis current, A-phase current and voltage vector sequence. In the figure seq is 1, 2, 3 representing the sequences 0127, 012 and 0121 respectively. The average modulation ratio of fig. 4, 5 and 6 is 0.43, and the three methods all select the sequence 0127, and the steady-state effect of current control is obviously deteriorated by dead beat prediction of error parameters. The average modulation ratio of fig. 7, 8 and 9 is 0.78. The voltage vector sequences selected by the three methods are different, the dead beat prediction current control method is sequence 0127, and most of the sequences of the variable vector sequence induction motor dead beat prediction current control method based on the super local model are selected as 012. As can be seen from the graph, the q-axis current pulsation of the model-free predictive current control method of the variable vector sequence induction motor based on the super local model is smaller, and the q-axis current pulsation is obviously increased by adopting dead beat predictive current control of error parameters. The average modulation ratio of fig. 10, 11 and 12 is 0.86. The variable vector sequence induction motor model-free predictive current control method based on the super local model has the advantages that the q-axis current pulsation is minimum, and the A-phase current is sinusoidal. The current THD of the dead beat prediction current control method adopting accurate parameters is 4.5911%, the current THD of the dead beat prediction current control method adopting error parameters is 17.3334%, and the current THD of the dead beat prediction current control method of the variable vector sequence induction motor based on the super local model is 3.7432%. Compared with a dead beat prediction current control method adopting accurate parameters, the dead beat prediction current control method for the variable vector sequence induction motor based on the super local model has the advantages that the current THD is reduced by 18.47%, and more excellent steady state performance and robustness are shown. The average modulation ratio of fig. 13, 14 and 15 was 0.95. The three methods select voltage vector sequences to be different, the dead beat prediction current control method is sequence 0127, and most of the sequences of the variable vector sequence induction motor dead beat prediction current control method based on the super local model are selected as sequence 0121. The current THD of the dead beat prediction current control method adopting accurate parameters is 4.8216%, the current THD of the dead beat prediction current control method adopting error parameters is 29.9616%, and the current THD of the dead beat prediction current control method of the variable vector sequence induction motor based on the super local model is 3.6898%. Compared with a dead beat prediction current control method adopting accurate parameters, the dead beat prediction current control method for the variable vector sequence induction motor based on the super local model has the advantages that the current THD is reduced by 23.47%, and more excellent steady state performance and robustness are shown. Fig. 16 is a summary of the current THD at different modulation ratios for the three methods. Compared with the prior art, the model-free prediction current control method of the variable vector sequence induction motor based on the super local model is independent of motor parameters, has good robustness, and has the minimum current THD under the full modulation ratio; the current THD is also lower at high modulation ratios than the dead beat predicted current control. FIGS. 17, 18 and 19 show experimental waveforms for three methods of motor start-up from 0 directly to 1500 r/min; fig. 20, 21 and 22 show experimental waveforms of sudden loading of the motor at rated rotation speed by three methods, and it can be seen that the variable vector sequence non-model predictive current control method of the induction motor based on the super local model has the advantages of rapid no-load starting and the same effect as the traditional non-model predictive current control; the method has stronger anti-interference capability for external sudden load, excellent robustness and good dynamic performance.
In summary, compared with dead beat prediction current control, the method disclosed by the invention has the advantages of strong robustness and more excellent steady-state performance, and can reduce the THD of current by up to 23.47% at a high modulation ratio. The method disclosed by the invention has good universality and practicability.
The method solves the problems that the traditional dead beat prediction current control scheme depends on motor parameters, the robustness is poor, and current harmonic waves of a single vector sequence are relatively large under the working condition of high-speed heavy-load equal-height modulation ratio. According to the method, a first-order super local model of the induction motor is generated according to a mathematical model of the motor, the model does not need any controlled object information, the robustness is very strong, the control parameters are updated on line by applying voltage and current at the past moment, and meanwhile, the stator voltage vector reference value is obtained according to the dead beat principle. The method calculates the per unit value of the current harmonic effective values of three voltage vector sequences under the pure inductive load, selects the vector sequence with the smallest current harmonic at different modulation ratios, updates the vector selection of the single traditional dead beat control to 3 vector sequences, reduces the current THD at the full modulation ratio, does not need any priori knowledge to control the motor, can reduce the current THD, has simple and practical algorithm, is easy to unify with other control methods, and is easy to realize different control modes under a unified control program framework.
It should be understood by those skilled in the art that the above embodiments are exemplary embodiments only and that various changes, substitutions and alterations can be made hereto without departing from the spirit and scope of the invention.

Claims (5)

1. The model-free prediction current control method for the induction motor super-local model-type variable vector sequence is characterized by comprising the following steps of:
step A, obtaining a traditional dead beat prediction current control complex vector model according to a motor mathematical model, and obtaining the dead beat prediction current control complex vector model by considering one beat delay compensation;
step B, combining the principle of the super local model with a state equation of motor stator current to obtain a super local model equation related to the stator current, and estimating system parameters F and alpha on line according to input current information and output voltage information; meanwhile, in order to maintain the consistency of the switching frequency, the control period of the sequence 012 is 2/3 times that of the other three sequences 0127, 0121 and 1012;
step C: according to F and alpha obtained by online estimation in the last step, taking one beat delay compensation into consideration, and calculating a voltage vector reference value according to a dead beat principle and a super local model related to stator current;
step D: according to a calculation formula of the effective value of the current harmonic, the per unit value of the effective value of the current harmonic of four different voltage vector sequences 0127, 0121, 012 and 1012 in one Pulse Width Modulation (PWM) period is obtained; the vector sequence to be selected is expanded from one to three, and the vector sequence which minimizes the current harmonic is selected according to the modulation ratio M, so that the current harmonic is reduced.
2. The method according to claim 1, wherein the step a step comprises:
dead beat predictive current control complex vector model:
Figure FDA0003906744880000011
in the formula ,
Figure FDA0003906744880000012
stator currents at the k moment and the k+1 moment respectively; />
Figure FDA0003906744880000013
A voltage vector applied from the time k to the time k+1; r is R s 、R r The resistances of the stator and the rotor are respectively; l (L) s 、L r 、L m Respectively are provided withIs the mutual inductance among the stator inductance, the rotor inductance and the stator and the rotor; psi phi type r Is rotor flux linkage; omega r The motor rotation speed; t (T) r ＝L r /R r ，/>
Figure FDA0003906744880000014
Considering one beat delay compensation, one beat delay compensation can be obtained:
Figure FDA0003906744880000015
in the formula ,
Figure FDA0003906744880000016
stator current at time k+2; />
Figure FDA0003906744880000017
A voltage vector applied from time k+1 to time k+2.
3. The method according to claim 1, wherein said step B comprises:
super local model of motor:
Figure FDA0003906744880000018
comparing the formula with the dead beat predictive current control complex vector model mentioned in the previous step to obtain +.>
Figure FDA0003906744880000021
F is a disturbance variable of the whole system, and comprises disturbance caused by all known factors in the system and other disturbance introduced by unknown conditions;
estimating F and alpha according to the voltage and current values at the past moment;
defining the variation of the current:
Figure FDA0003906744880000022
Figure FDA0003906744880000023
wherein ：
Figure FDA0003906744880000024
and />
Figure FDA0003906744880000025
The current value and the control period at the moment k-1 are respectively; />
Figure FDA0003906744880000026
and />
Figure FDA0003906744880000027
The current value and the control period at the moment k-2 are respectively; />
Figure FDA0003906744880000028
A current value at time k;
since the control period of the sequence 012 is different from that of the other three sequences, T is sc With different superscripts, the following formula is specifically selected:
Figure FDA0003906744880000029
according to the voltage and current information and the super local model at the past two moments, estimated values of F and alpha can be obtained:
Figure FDA00039067448800000210
Figure FDA00039067448800000211
wherein ：
Figure FDA00039067448800000212
and />
Figure FDA00039067448800000213
The current value at the moment k-1, the average voltage vector at the moment k-1 to the moment k and the control period are respectively; />
Figure FDA00039067448800000214
and />
Figure FDA00039067448800000215
The current value at the moment k-2, the average voltage vector at the moment k-2 to the moment k-1 and the control period are respectively; />
Figure FDA00039067448800000216
The current value at time k.
4. The method according to claim 1, wherein said step C comprises:
in an actual digital control system, there is a one-beat delay between the output voltage and the command voltage, and in order to eliminate the influence of the one-beat delay, the current value at time k is compensated, and the current predicted value at time k+1 is:
Figure FDA00039067448800000217
calculating a reference voltage vector based on the dead beat principle:
Figure FDA0003906744880000031
5. the method according to claim 1, wherein said step D comprises:
calculating a modulation ratio M, and selecting a vector sequence which minimizes current harmonics according to the current harmonic expression in the step A:
Figure FDA0003906744880000032
Figure FDA0003906744880000033
in the formula ,
Figure FDA0003906744880000034
as a reference voltage vector, θ is the angle of the reference voltage vector;
calculating the per unit value of four sequence current harmonic effective values according to the modulation ratio M
Figure FDA0003906744880000035
Figure FDA0003906744880000036
Figure FDA0003906744880000037
Figure FDA0003906744880000038
/>
in the formula ,
Figure FDA0003906744880000039
per unit of current harmonic effective values corresponding to sequences 0127, 012, 0121, 1012, respectivelyA value; m is modulation ratio; pi is a mathematical symbol, and the value of pi is 3.1415926; after comparing the current harmonic effective values, eliminating 1012 sequences;
according to the selected vector sequence, the three-phase duty ratio is calculated:
Figure FDA00039067448800000310
Figure FDA00039067448800000311
Figure FDA00039067448800000312
Figure FDA00039067448800000313
wherein ,
Figure FDA00039067448800000314
for standardized three-phase reference voltage, +.>
Figure FDA00039067448800000315
Is a zero sequence component;
and obtaining the signal of each switching tube of the inverter through the vector sequence and the three-phase duty ratio obtained in the steps.
CN202211309764.XA 2022-10-25 2022-10-25 Model-free predictive current control method for variable vector sequence induction motor Pending CN116073713A (en)

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