CN111064408A - Method for controlling prediction torque of asynchronous motor model without weight value - Google Patents

Method for controlling prediction torque of asynchronous motor model without weight value Download PDF

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CN111064408A
CN111064408A CN202010000351.8A CN202010000351A CN111064408A CN 111064408 A CN111064408 A CN 111064408A CN 202010000351 A CN202010000351 A CN 202010000351A CN 111064408 A CN111064408 A CN 111064408A
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stator
flux linkage
torque
voltage
vector
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CN111064408B (en
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卢子广
刘朦
林靖宇
杨帅帅
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Guangxi 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
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/20—Estimation of torque
    • 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
    • H02P21/141—Flux 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
    • 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
    • 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
    • H02P2207/01—Asynchronous machines

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Abstract

The invention discloses a method for controlling a model prediction torque of an asynchronous motor without a weight value, which comprises the steps of data acquisition, calculation of three-phase stator voltage, calculation of stator flux linkage under αβ axes and calculation of machine end virtual flux linkage, and a standard torque reference value tau is obtained through a PI regulator*And reactive torque reference η*The method for controlling the torque of the non-weight value asynchronous motor model prediction is reasonable in design and strong in universality, completely describes the dynamic characteristics of flux linkage and torque, unifies the time scale and the dimension of variables in a value function, and omits the value functionThe complex weighting factors are designed in the number, the calculation of the reactive torque η only depends on the terminal current and the terminal voltage, and is not influenced by motor parameters, so that the robustness of control is improved, and the steady-state performance of the control is improved.

Description

Method for controlling prediction torque of asynchronous motor model without weight value
Technical Field
The invention relates to the technical field of motor control, in particular to a weighted-value-free asynchronous motor model prediction torque control method.
Background
In recent years, induction motors have become the first choice of drive motors in motor drive systems for electric power transportation, such as chinese high-speed rail traction and electric automobiles.
The asynchronous motor has the advantages of simple structure, convenience in manufacturing, low cost, reliability in operation, little maintenance, capability of being used in severe environment and the like, and is widely applied to the fields of industrial transportation, electric power, coal, petrifaction, plastic cement, metallurgy, textile chemical fiber, food industry and the like. And the control performance of the asynchronous motor is required to be safely, efficiently, stably and quickly realized, and a high-performance control strategy is indispensable.
At present, widely used high-performance control schemes of asynchronous motors comprise Field Oriented Control (FOC) and Direct Torque Control (DTC), but the torque response speed of the Field Oriented Control (FOC) is limited by the current loop bandwidth, the rotor flux linkage orientation angle is sensitive to the parameter change of the motor, and the Direct Torque Control (DTC) has the problems of overhigh switching frequency, large torque ripple and the like.
The model predictive control is a computer control method appearing in the field of industrial engineering control in the later 70 th of the 20 th century, and because the model predictive control algorithm needs too long calculation time and the control of electrical variables needs very high processing speed, the model predictive control algorithm is limited by the calculation capacity of a microprocessor at the moment, the control technology cannot be used in a system with higher switching frequency, and only the model predictive control method is considered to be applied to a high-power system with low switching frequency. With the rapid development of microprocessor technology in recent years, and because model predictive control has the advantages of intuitive and simple concept, fast dynamic response, easy inclusion of multiple control variables, easy processing of various nonlinear constraints and the like, the application of the model predictive control technology in the field of power electronics is widely concerned by learners, especially in the field of motor control. . Although model predictive control has many advantages, in the traditional model predictive control method, the cost function is generally formed by linearly combining a torque error and a flux linkage amplitude error, and the two errors have difference in dimension and are not uniform in time scale, so that a complicated weighting factor design is introduced, and an undesirable control effect is caused. And the stator flux linkage and the electromagnetic torque prediction are calculated based on a system mathematical model, so that the algorithm is dependent on the system model and is sensitive to the parameter change of the motor.
In order to solve the complicated design problem of weight coefficients, some researchers have proposed some solutions, for example, in the document, "multi objective Switching State Selector for finish-States model predictive control Based on fuzzy y Decision Making in a Matrix Converter", a fuzzy logic Decision Making process is adopted to select an optimal Converter Switching State, so that the problem of weight coefficient selection in multi-target tracking control can be solved well, but the algorithm complexity is increased. The literature, "Predictive Torque and Flux control with weighting Factors" adopts two objective functions of calculating the Flux linkage and the Torque first, and then sequences the values of all vectors under the two objective functions to obtain the optimal voltage vector comprehensively. In patent CN106301127A "a method and apparatus for controlling flux linkage by model prediction of asynchronous motor", a mathematical model of the motor is used to obtain a reference value of a stator flux linkage vector and then a reference value of a stator voltage vector, in the process, rotation transformation and angle and trigonometric function calculation are used, and the program calculation load is large. In a word, most of the existing methods are complex, the practicability is not strong, and the control effect is not ideal.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a weight-free asynchronous motor model prediction torque control method, which provides a concept of reactive torque η, completely describes the dynamic characteristics of torque and flux linkage by standardizing torque tau and reactive torque η, designs a new cost function by adopting tau and η with the same time scale and the same dimension, omits a weighting factor, simplifies the design and realizes high-dynamic and decoupled flux linkage and torque control.
The technical scheme of the invention is as follows: a method for controlling the model prediction torque of an asynchronous motor without weight values specifically comprises the following steps:
s1), collecting rotor speed omega (k) and three-phase stator current i of the asynchronous motor at the moment ksa(k)、isb(k)、isc(k) And the DC side voltage U of the converterdc；
S2) according to the converter drive signal Sa，Sb，ScAnd the DC side voltage U of the converterdcAnd calculating to obtain the three-phase stator voltage u at the moment ksa(k)、usb(k)、usc(k) The calculation formula is as follows:
Figure BDA0002352909020000021
s3), obtaining voltage u under αβ axes by coordinate change of three-phase stator voltage and current at the time ksα(k)、usβ(k) And current isα(k)、isβ(k) The calculation formulas are respectively as follows:
Figure BDA0002352909020000022
Figure BDA0002352909020000031
s4), stator flux linkage under αβ axis is estimated through a voltage model, terminal virtual flux linkage under αβ axis is estimated through terminal voltage, and the calculation formula is as follows:
Figure BDA0002352909020000032
Figure BDA0002352909020000033
wherein R issIs stator resistance, #sα，ψsβAre respectively stator flux linkage vectors
Figure BDA0002352909020000034
Stator flux linkage, psi, under axis αβvα，ψvβRespectively machine end virtual flux linkage vector
Figure BDA0002352909020000035
The component below the αβ axis;
s5), comparing the rotation speed ω with the given rotation speed ω*Obtaining a standard torque reference value tau through the output of a PI regulator after difference making*Will give a stator flux linkage
Figure BDA00023529090200000315
And estimating stator flux linkage psisObtaining a reactive torque reference value η through a PI regulator after difference is made*；
S6), calculating to obtain a standardized torque tau through a cross product of the stator flux linkage and the stator current, and calculating to obtain a reactive torque η through a dot product of the generator-end virtual flux linkage and the stator current, wherein the calculation formula is as follows:
Figure BDA0002352909020000036
Figure BDA0002352909020000037
in the formula,
Figure BDA0002352909020000038
ψsα、ψsβthe stator flux linkage vector and the component of the stator flux linkage in αβ coordinate system,
Figure BDA0002352909020000039
ψvα、ψvβthe virtual flux linkage vector and the component of the virtual flux linkage in αβ coordinate system,
Figure BDA00023529090200000310
isβ、isαis the division of stator current vector and stator current in αβ coordinate systemAn amount;
s7), obtaining the stator current vector at the k +1 moment according to the first-order Euler discrete prediction
Figure BDA00023529090200000311
Stator flux linkage vector
Figure BDA00023529090200000312
End-of-the-book virtual flux linkage vector
Figure BDA00023529090200000313
The calculation formula is as follows:
Figure BDA00023529090200000314
Figure BDA0002352909020000041
Figure BDA0002352909020000042
in the formula, TsIn order to be the sampling period of time,
Figure BDA0002352909020000043
for all the possible voltage vectors it is possible to,
Figure BDA0002352909020000044
Lσ＝σLs，krand τσTo define intermediate variables, kr＝Lm/Lr，τσ＝Lσ/Rσ，τrIs the rotor electromagnetic time constant, τr＝Lr/Rr，Ls、LrStator and rotor inductors respectively; l ismMutual inductance between stator and rotor; rs、RrRespectively stator and rotor resistances, j is an imaginary symbol;
the normalized torque predicted value τ (k +1) and the reactive torque predicted value η (k +1) at time k +1 are:
Figure BDA0002352909020000045
Figure BDA0002352909020000046
s8), predicting the standardized torque and the reactive torque at the moment of k +1 for 8 possible switch state combinations, substituting each prediction result into a cost function for evaluation, and respectively obtaining 8 different values of the cost function g, wherein the calculation formula is as follows:
g＝|τ*-τ(k+1)|+|η*-η(k+1)|；
s9), selecting the voltage vector which minimizes the cost function through rolling optimization, storing the corresponding switch state output, and obtaining the corresponding switch driving signal Sa、Sb、ScAnd the on-off of the switch tube is controlled, so that the control of the converter on the motor is realized.
Preferably, in step S1), the rotor of the asynchronous motor is obtained by an encoder, and the three-phase stator current i is obtainedsa(k)、isb(k)、isc(k) And the DC side voltage U of the converterdcRespectively collected by a current sensor and a voltage sensor.
The invention has the beneficial effects that:
1. the invention has reasonable design and strong universality, the proposed standardized torque tau and the reactive torque η completely describe the dynamic characteristics of flux linkage and torque, the time scale and the dimension of variables in the value function are unified, the complicated weighting factor designed in the value function is omitted, the calculation of the reactive torque η only depends on the current and the voltage at the generator end and is not influenced by motor parameters, the robustness of control is improved, and the steady-state performance of control is improved.
2. The method is simple, simplifies the design by omitting the weighting factor, and realizes the high-dynamic and decoupled flux linkage and torque control.
Drawings
FIG. 1 is a schematic flow chart of a control method according to the present invention;
FIG. 2 is a schematic structural diagram of a control method according to the present invention;
FIG. 3 is a schematic diagram of the relationship between 8 voltage vectors and driving signals according to the present invention;
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings:
as shown in fig. 1 and 2, the present embodiment provides a method for controlling a predicted torque of an asynchronous machine model without a weight, which provides a concept of reactive torque η, completely describes torque and flux linkage dynamics characteristics by normalizing torque τ and reactive torque η, designs a new cost function by using τ and η having the same time scale and the same dimension, omits a weighting factor, thereby simplifying the design and realizing high-dynamic decoupled flux linkage and torque control.
The method specifically comprises the following steps:
s1), collecting rotor speed omega (k) and three-phase stator current i of the asynchronous motor at the moment ksa(k)、isb(k)、isc(k) And the DC side voltage U of the converterdc；
Wherein, the rotor of the asynchronous motor is obtained by an encoder, and the three-phase stator current isa(k)、isb(k)、isc(k) And the DC side voltage U of the converterdcRespectively collected by a current sensor and a voltage sensor.
S2) based on the driving signal S of the current transformera，Sb，ScAnd the DC side voltage U of the converterdcAnd calculating to obtain the three-phase stator voltage u at the moment ksa(k)、usb(k)、usc(k) The calculation formula is as follows:
Figure BDA0002352909020000051
s3), obtaining a voltage u under the αβ axis by coordinate change of the three-phase stator voltage and the three-phase stator current at the time ksα(k)、usβ(k) And current isα(k)、isβ(k) The calculation formulas are respectively as follows:
Figure BDA0002352909020000052
Figure BDA0002352909020000061
s4), estimating stator flux linkage under αβ axis through a voltage model, estimating terminal virtual flux linkage under αβ axis through terminal voltage, and calculating the formula as follows:
Figure BDA0002352909020000062
Figure BDA0002352909020000063
wherein R issIs stator resistance, #sα，ψsβAre respectively stator flux linkage vectors
Figure BDA0002352909020000064
Stator flux linkage, psi, under axis αβvα，ψvβRespectively machine end virtual flux linkage vector
Figure BDA0002352909020000065
The component below the αβ axis;
s5), comparing the rotation speed ω with the given rotation speed ω*Obtaining a standard torque reference value tau through the output of a PI regulator after difference making*Will give a stator flux linkage
Figure BDA0002352909020000066
And estimating stator flux linkage psisObtaining a reactive torque reference value η through a PI regulator after difference is made*；
S6), by stator flux linkage
Figure BDA0002352909020000067
And stator current vector
Figure BDA0002352909020000068
The cross product of the two-dimensional data is calculated to obtain a standardized torque tau, and a machine-end virtual flux linkage vector is obtained
Figure BDA0002352909020000069
And stator current
Figure BDA00023529090200000610
The dot product of (a) to obtain the reactive torque η, the calculation formula is as follows:
Figure BDA00023529090200000611
Figure BDA00023529090200000612
in the formula,
Figure BDA00023529090200000613
ψsα、ψsβthe stator flux linkage vector and the component of the stator flux linkage in αβ coordinate system,
Figure BDA00023529090200000614
ψvα、ψvβthe virtual flux linkage vector and the component of the virtual flux linkage in αβ coordinate system,
Figure BDA00023529090200000615
isβ、isαthe stator current vector and the component of the stator current in αβ coordinate system are respectively;
s7), obtaining the stator current vector at the k +1 moment according to the first-order Euler discrete prediction
Figure BDA00023529090200000616
Stator flux linkage vector
Figure BDA00023529090200000617
End-of-the-book virtual flux linkage vector
Figure BDA00023529090200000618
The calculation formula is as follows:
Figure BDA00023529090200000619
Figure BDA0002352909020000071
Figure BDA0002352909020000072
in the formula, TsIn order to be the sampling period of time,
Figure BDA0002352909020000073
for all the possible voltage vectors it is possible to,
Figure BDA0002352909020000074
Lσ＝σLs，krand τσTo define intermediate variables, kr＝Lm/Lr，τσ＝Lσ/Rσ，τrIs the rotor electromagnetic time constant, τr＝Lr/Rr，Ls、LrStator and rotor inductors respectively; l ismMutual inductance between stator and rotor; rs、RrRespectively stator and rotor resistances, j is an imaginary symbol;
the normalized torque predicted value τ (k +1) and the reactive torque predicted value η (k +1) at time k +1 are:
Figure BDA0002352909020000075
Figure BDA0002352909020000076
s8), predicting the normalized torque and reactive torque at the time of k +1 for 8 possible switch state combinations, and substituting each prediction result into the cost function to evaluate, so as to obtain 8 different values of the cost function g, as shown in fig. 3, and the calculation formula is as follows:
g＝|τ*-τ(k+1)|+|η*-η(k+1)|；
wherein, tau*To normalize the torque reference value, η*In order to be the reactive torque reference value,
s9), selecting the voltage vector which minimizes the cost function through rolling optimization, storing the corresponding switch state output, and obtaining the corresponding switch driving signal Sa、Sb、ScAnd the on-off of the switch tube is controlled, so that the control of the converter on the motor is realized.
The foregoing embodiments and description have been presented only to illustrate the principles and preferred embodiments of the invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention as hereinafter claimed.

Claims (7)

1. A method for controlling the model prediction torque of an asynchronous motor without weight values is characterized in that: the method specifically comprises the following steps:
s1), collecting rotor speed omega (k) and three-phase stator current i of the asynchronous motor at the moment ksa(k)、isb(k)、isc(k) And the DC side voltage U of the converterdc；
S2) according to the converter drive signal Sa，Sb，ScAnd the DC side voltage U of the converterdcAnd calculating to obtain the three-phase stator voltage u at the moment ksa(k)、usb(k)、usc(k)；
S3), obtaining voltage u under αβ axes by coordinate change of three-phase stator voltage and three-phase stator current at the moment ksα(k)、usβ(k) And current isα(k)、isβ(k)；
S4), estimating αβ lower stator flux linkage psi through a voltage modelsα、ψsβEstimating the virtual magnetic linkage psi at the transmitter end by the voltage at the transmitter endvα、ψvβ；
S5), comparing the rotation speed ω with the given rotation speed ω*Obtaining a standard torque reference value tau through the output of a PI regulator after difference making*Will give a stator flux linkage
Figure FDA0002352909010000011
And estimating stator flux linkage psisObtaining a reactive torque reference value η through a PI regulator after difference is made*；
S6), calculating to obtain a standardized torque tau through a cross product of the stator flux linkage and the stator current, and calculating to obtain a reactive torque η through a dot product of the generator-end virtual flux linkage and the stator current, wherein the calculation formula is as follows:
Figure FDA0002352909010000012
Figure FDA0002352909010000013
in the formula,
Figure FDA0002352909010000014
ψsα、ψsβthe stator flux linkage vector and the component of the stator flux linkage in αβ coordinate system,
Figure FDA0002352909010000015
ψvα、ψvβthe virtual flux linkage vector and the component of the virtual flux linkage in αβ coordinate system,
Figure FDA0002352909010000016
isβ、isαthe stator current vector and the component of the stator current in αβ coordinate system are respectively;
s7), obtaining the stator current vector at the k +1 moment according to the first-order Euler discrete prediction
Figure FDA0002352909010000017
Stator flux linkage vector
Figure FDA0002352909010000018
End-of-the-book virtual flux linkage vector
Figure FDA0002352909010000019
The normalized torque predicted value τ (k +1) and the reactive torque predicted value η (k +1) at time k +1 are:
Figure FDA00023529090100000110
Figure FDA00023529090100000111
s8), predicting the standardized torque and the reactive torque at the moment of k +1 for 8 possible switch state combinations, substituting each prediction result into a cost function for evaluation, and respectively obtaining 8 different values of the cost function g, wherein the calculation formula is as follows:
g＝|τ*-τ(k+1)|+|η*-η(k+1)|；
s9), selecting the voltage vector which minimizes the cost function through rolling optimization, storing the corresponding switch state output, and obtaining the corresponding switch driving signal Sa、Sb、ScAnd the on-off of the switch tube is controlled, so that the control of the converter on the motor is realized.
2. The method of claim 1, wherein the model-based, unweighted torque predictive control for an asynchronous machine comprises: in step S2), in step S1), the rotor of the asynchronous motor is obtained by an encoder, and the three-phase stator current isa(k)、isb(k)、isc(k) And the DC side voltage U of the converterdcRespectively collected by a current sensor and a voltage sensor.
3. The method of claim 1, wherein the model-based, unweighted torque predictive control for an asynchronous machine comprises: in step S2), theThree-phase stator voltage usa(k)、usb(k)、usc(k) The calculation formula is as follows:
Figure FDA0002352909010000021
4. the method of claim 1, wherein the model-based, unweighted torque predictive control for an asynchronous machine comprises: in step S3), the voltage u issα(k)、usβ(k) And current isα(k)、isβ(k) The calculation formulas are respectively as follows:
Figure FDA0002352909010000022
Figure FDA0002352909010000023
5. the method of claim 1, wherein in step S4), the αβ axial lower stator flux linkage psisα、ψsβVirtual magnetic chain psi at machine endvα、ψvβThe calculation formula of (a) is as follows:
Figure FDA0002352909010000031
Figure FDA0002352909010000032
wherein R issIs stator resistance, #sα，ψsβAre respectively stator flux linkage vectors
Figure FDA0002352909010000033
Component below the αβ axis, ψvα，ψvβAre respectively terminal virtualPseudo flux linkage vector
Figure FDA0002352909010000034
The component below the αβ axis.
6. The method of claim 1, wherein in step S6), the normalized torque τ and reactive torque η are calculated as follows:
Figure FDA0002352909010000035
Figure FDA0002352909010000036
in the formula,
Figure FDA0002352909010000037
ψsα、ψsβthe stator flux linkage vector and the component of the stator flux linkage in αβ coordinate system,
Figure FDA0002352909010000038
ψvα、ψvβthe virtual flux linkage vector and the component of the virtual flux linkage in αβ coordinate system,
Figure FDA0002352909010000039
isβ、isαthe stator current vector and the component of the stator current in αβ coordinate system are respectively.
7. The method of claim 1, wherein the model-based, unweighted torque predictive control for an asynchronous machine comprises: in step S7), the stator current vector
Figure FDA00023529090100000310
Stator flux linkage vector
Figure FDA00023529090100000311
End-of-the-book virtual flux linkage vector
Figure FDA00023529090100000312
The calculation formula is as follows:
Figure FDA00023529090100000313
Figure FDA00023529090100000314
Figure FDA00023529090100000315
in the formula, TsIn order to be the sampling period of time,
Figure FDA00023529090100000316
for all the possible voltage vectors it is possible to,
Figure FDA00023529090100000317
Lσ＝σLs，krand τσTo define intermediate variables, kr＝Lm/Lr，τσ＝Lσ/Rσ，τrIs the rotor electromagnetic time constant, τr＝Lr/Rr，Ls、LrStator and rotor inductors respectively; l ismMutual inductance between stator and rotor; rs、RrStator and rotor resistances, respectively, and j is an imaginary symbol.
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CN112865643A (en) * 2021-01-15 2021-05-28 北方工业大学 Model prediction control method and device for permanent magnet synchronous motor and motor controller
CN112865643B (en) * 2021-01-15 2022-05-31 北方工业大学 Model prediction control method and device for permanent magnet synchronous motor and motor controller
CN113093542A (en) * 2021-03-31 2021-07-09 吉林大学 Motor torque optimization finite set prediction control parallel computing method
CN113093542B (en) * 2021-03-31 2022-08-12 吉林大学 Motor torque optimization finite set prediction control parallel computing method
CN115347829A (en) * 2022-08-12 2022-11-15 华北电力大学 Induction motor control method, system, device, electronic equipment and storage medium
CN116436346A (en) * 2023-03-15 2023-07-14 浙江大学 Control method of brushless direct current motor driven by storage battery-super capacitor hybrid power supply

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