CN112583315A - Three-vector model prediction torque control method for three-level permanent magnet synchronous motor - Google Patents

Three-vector model prediction torque control method for three-level permanent magnet synchronous motor Download PDF

Info

Publication number
CN112583315A
CN112583315A CN202011352040.4A CN202011352040A CN112583315A CN 112583315 A CN112583315 A CN 112583315A CN 202011352040 A CN202011352040 A CN 202011352040A CN 112583315 A CN112583315 A CN 112583315A
Authority
CN
China
Prior art keywords
vector
voltage vector
optimal
optimal voltage
voltage
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011352040.4A
Other languages
Chinese (zh)
Other versions
CN112583315B (en
Inventor
涂建军
李众
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jiangsu University of Science and Technology
Original Assignee
Jiangsu University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jiangsu University of Science and Technology filed Critical Jiangsu University of Science and Technology
Priority to CN202011352040.4A priority Critical patent/CN112583315B/en
Publication of CN112583315A publication Critical patent/CN112583315A/en
Application granted granted Critical
Publication of CN112583315B publication Critical patent/CN112583315B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • H02P21/20—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
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/34—Modelling or simulation for control purposes
    • 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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/64—Electric machine technologies in electromobility

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Ac Motors In General (AREA)

Abstract

The invention discloses a three-vector model prediction torque control method of a three-level permanent magnet synchronous motor, which adopts a vector partition method to select a voltage vector, substitutes a large vector in an alternative vector of a three-level inverter into a cost function to optimize, thereby determining a first optimal voltage vector, determining a sector according to the position of the first optimal voltage vector, substituting three non-zero vectors in the sector where the first optimal voltage vector is located into a cost function to obtain a second optimal voltage vector, controlling the action of the three-level inverter by combining the action time of the first optimal voltage vector, the second optimal voltage vector and the zero vector, therefore, the first optimal voltage vector is reduced from the original 27 times of optimization to 6 times, the optimization process not only considers the selection of two optimal voltage vectors, but also combines the action time of the two optimal voltage vectors to ensure that the two selected optimal voltage vectors can ensure the midpoint potential balance and the lower switching frequency.

Description

Three-vector model prediction torque control method for three-level permanent magnet synchronous motor
Technical Field
The invention relates to torque control of a permanent magnet synchronous motor, in particular to a three-level permanent magnet synchronous motor three-vector model prediction torque control method.
Background
Because of the advantages of simple structure, small volume, high power density, high efficiency, excellent running performance and the like, the permanent magnet synchronous motor has been widely applied in recent years, for example: the rotary mechanism is used in the fields of traffic, favorite singles, driving of vehicles and ships, pumps, compressors, high-precision servo systems and the like. And therefore the research on the control of the permanent magnet synchronous motor is also increasing.
The existing permanent magnet synchronous motor control multipurpose model prediction torque control is a classic control method in a finite set prediction algorithm, is very suitable for a permanent magnet synchronous motor control system, has the advantages of simple control strategy and good dynamic performance, and is more suitable for a multi-scalar, multi-constraint condition and nonlinear system. In the traditional model prediction torque control, a prediction model of a system is obtained, a known number of state vectors are traversed, a proper value function is set according to an expected control target, and a switching sequence which needs to be executed in the future of a power device can be obtained through comparison of the value function.
The three-level inverter has a smaller switching frequency and a wider vector selection range than the two-level inverter, but in practical application, the problem of midpoint potential balance needs to be considered. In the existing model prediction control strategy, a midpoint potential balance item is introduced into a cost function, the midpoint potential balance problem is considered in the optimization process, but the implementation difficulty of the control strategy is increased.
The prior art has the following defects:
(1) in the existing model prediction control strategy, only one voltage vector with fixed size and fixed direction can be obtained in each control period, but the reference voltage vector which needs to be tracked actually is uncertain, so that the output voltage vector cannot effectively enable the feedback torque to track the reference torque, and the permanent magnet synchronous motor directly has large magnetic flux pulsation, unstable output torque pulsation and poor motor steady-state performance.
(2) The number of the switching states of the devices of the three-level or more-level topological structure is too large, the calculation cost is too high, and complicated redundant vectors exist, so that the switching sequence combination is not easy to determine;
(3) the cost function contains a midpoint potential control item, and the setting of the weight factor becomes more complicated, so that the control precision is not high and the calculation is complicated;
(4) multi-vector predictive control results in higher switching frequency and increased switching losses.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects, the invention provides a three-level permanent magnet synchronous motor three-vector model prediction torque control method for rapidly screening vectors and reducing switching loss.
The technical scheme is as follows: in order to solve the problems, the invention adopts a three-level permanent magnet synchronous motor three-vector model prediction torque control method, which comprises the following steps:
(1) according to a state space model of the permanent magnet synchronous motor, a state equation of current and magnetic flux is constructed, a prediction model of torque and magnetic flux is obtained according to the state equation of the current and the magnetic flux, and a first capacitance voltage prediction model is obtained according to a capacitance voltage model on the direct current side of the three-level inverter; constructing a first value function according to the first capacitance voltage prediction model;
(2) sampling to obtain current at the k moment;
(3) substituting the sampled data into a torque and magnetic flux prediction model to obtain the torque and magnetic flux linkage at the next moment k + 1;
(4) substituting a large vector in a space vector diagram of the three-level inverter into a first cost function as a candidate vector of a first optimal voltage vector to select the first optimal voltage vector;
(5) taking three non-zero vectors in a sector corresponding to the first optimal voltage vector as alternative vectors of a second optimal voltage vector, and taking a zero vector as a third voltage vector;
(6) sequentially selecting three alternative vectors as a second optimal voltage vector, calculating the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector, solving the duty ratio, and modulating a new voltage vector according to the duty ratio;
(7) establishing a second capacitance voltage prediction model according to the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector, and establishing a second valence function according to the second capacitance voltage prediction model;
(8) substituting the three new voltage vectors modulated in the step 6 into a second valence function to select an optimal second optimal voltage vector;
(9) and inputting the switching sequences corresponding to the selected first optimal voltage vector, the second optimal voltage vector and the third voltage vector and the corresponding action time into a pulse modulator to control the action of the three-level inverter.
Further, the state equation of the current and the magnetic flux in step 1 is as follows:
Figure BDA0002801587600000021
Figure BDA0002801587600000022
wherein iα、iβRespectively representing the components of the stator current vector on an alpha axis and a beta axis; l issRepresenting the stator inductance; u. ofα、uβRespectively representing the components of the stator voltage vector under an alpha axis and a beta axis; rsRepresenting the stator resistance; omegarRepresenting the electrical angular speed of the rotor of the motor; psiα、ψβRespectively representing the components of the electron flux linkage vector in the alpha and beta axes.
Further, in step 1, the prediction model of the torque and the magnetic flux is:
Figure BDA0002801587600000031
ψs(k+1)＝ψs(k)+Ts[us(k)-Rsis(k)]
Figure BDA0002801587600000032
wherein, TsIs a sampling period; i.e. is(k+1)、ψs(k+1)、Te(k +1) is a stator current vector, a stator flux linkage and a torque vector at the moment k +1 respectively; i.e. is(k)、ψs(k)、us(k)、ωr(k) The stator current vector, the stator flux linkage, the stator voltage vector and the electrical angular velocity of the rotor at the moment k are respectively; j is an imaginary unit, p is a log number,
Figure BDA0002801587600000033
is a cross-product sign.
Further, in step 1, the capacitance-voltage prediction model is:
Figure BDA0002801587600000034
wherein v isc1(k)、vc2(k) The capacitor voltage sampled at the moment k; v. ofc1(k+1)、vc2(k +1) is the predicted capacitor voltage at time k + 1; i.e. ic1(k) And ic2(k) Is based on the defined value of the switching state and the output current of the inverter at time k, C1、C2Two capacitors on the direct current side.
Further, in step 4, the first cost function is:
Figure BDA0002801587600000035
wherein the content of the first and second substances,
Figure BDA0002801587600000036
the torque is given for a given value of torque,
Figure BDA0002801587600000037
for stator flux set-point, λ1、λ2、λ3Is a weight coefficient, FswThe number of times of action conversion of the on-off of the switching tube in one control period.
Further, in step 6, the action time of the first optimal voltage vector, the action time of the second optimal voltage vector and the action time of the third optimal voltage vector are t1、t2And t0：
t0＝Ts-t1-t2
Figure BDA0002801587600000038
Wherein the content of the first and second substances,
Figure BDA0002801587600000039
s is an amount, and
Figure BDA0002801587600000041
Figure BDA0002801587600000042
the change rate of the torque and the magnetic flux under the action of the first optimal voltage vector is taken as the change rate;
Figure BDA0002801587600000043
Figure BDA0002801587600000044
the change rate of the torque and the magnetic flux under the action of the second optimal voltage vector is obtained;
Figure BDA0002801587600000045
is the rate of change of torque and flux under zero vector action.
Further, t1、t2、t0At 0 to TsWithin a range of and for ensuring t1、t2、t0At 0 to TsWithin the range, the following treatments are required:
(6.1) when acting for a time t1、t2And t0Any two of them have action time less than 0 and the other has action time greater than TsIf the action time is less than 0, the action time is equal to 0 and greater than TsHas an action time equal to Ts；
(6.2) when acting for a time t1+t2≤TsIf so, the action time of each vector is unchanged;
(6.3) when acting for a time t1+t2≥TsThen, the action time of the three vectors needs to be substituted into the following formula for correction:
Figure BDA0002801587600000046
wherein: t is t1'、t'2And t'0And respectively showing the action time of the corrected first optimal voltage vector, the second optimal voltage vector and the third voltage vector.
Further, in step 6, the modulation formula of the new voltage vector is:
Figure BDA0002801587600000047
wherein u isα2、uβ2Is the alpha, beta axis component of the new voltage vector, uopt1_α、uopt1_βIs the alpha, beta axis component of the first optimum voltage vector, uopt2_n_α、uopt2_n_βThe first optimal voltage vector u is the alpha-axis component and the beta-axis component of the nth voltage vector in the second optimal voltage vector setopt1Second optimum voltage vector uopt2_nAnd zero vector u0Respectively η of duty ratioopt1、ηopt2_n、η0。
Further, the second capacitor voltage prediction model in step 7 is:
Figure BDA0002801587600000048
wherein, v'c1(k+1)、v'c2(k +1) is two capacitors at the direct current side at the k +1 moment under the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vectorVoltage, v'c1(k)、v'c2(k) The voltage of two capacitors at the k moment under the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector is represented by ic11(k)、ic21(k)、ic31(k)、ic12(k)、ic22(k)、ic32(k) The output current values when the first optimal voltage vector, the second optimal voltage vector and the third voltage vector act are respectively.
Further, the second value function g in step 7nComprises the following steps:
Figure BDA0002801587600000051
wherein, gnAs a function of the second value.
Has the advantages that: compared with the prior art, the method has the remarkable advantages that the sector is determined through the position of the first optimal voltage vector so as to select the second optimal voltage vector, the optimization times are reduced, and the action time of the two optimal vectors and the zero vector is combined, so that the midpoint potential balance and the lower switching frequency are ensured; by using the three-vector model for predictive control, the synthesized vector can not only change in direction, but also change in size, so that the achieved control effect is optimal.
Drawings
FIG. 1 is a topology diagram of an NPC three-level inverter of the present invention;
FIG. 2 is a three-level basic space vector distribution diagram of the vector partition provided by the present invention;
FIG. 3 is a block diagram of three vector model predictive control of a three level inverter driven PMSM according to the present invention;
FIG. 4 is a flow chart of vector selection in accordance with the present invention.
Detailed Description
In the embodiment, a three-vector model predicted torque control method for a three-level permanent magnet synchronous motor is provided, wherein
According to the state space model of the permanent magnet synchronous motor, the state equation of the current and the magnetic flux is constructed as follows:
Figure BDA0002801587600000052
Figure BDA0002801587600000053
wherein iα、iβRespectively representing the components of the stator current vector on an alpha axis and a beta axis; l issRepresenting the stator inductance; u. ofα、uβRespectively representing the components of the stator voltage vector under an alpha axis and a beta axis; rsRepresenting the stator resistance; omegarRepresenting the electrical angular speed of the rotor of the motor; psiα、ψβRespectively representing the components of the electron flux linkage vector in the alpha and beta axes.
Discretizing the differential terms according to the state equations of the current and the magnetic flux of the permanent magnet synchronous motor, and calculating to obtain a prediction model of the torque and the magnetic flux of the permanent magnet synchronous motor, wherein the prediction model comprises the following steps:
Figure BDA0002801587600000054
ψs(k+1)＝ψs(k)+Ts[us(k)-Rsis(k)]
Figure BDA0002801587600000061
wherein, TsIs a sampling period; i.e. is(k+1)、ψs(k+1)、Te(k +1) is a stator current vector, a stator flux linkage and a torque vector at the moment k +1 respectively; i.e. is(k)、ψs(k)、us(k)、ωr(k) The stator current vector, the stator flux linkage, the stator voltage vector and the electrical angular velocity of the rotor at the moment k are respectively; j is an imaginary unit, p is a log number,
Figure BDA0002801587600000062
is a cross-product sign.
As shown in fig. 1, the three-level inverter has three legs, A, B, C three phases, and four switching tubes on each phase. During normal operation, each phase has three switching states, for the phase A, the 'P' state corresponds to the states of Sa1 and Sa2 being turned on, and Sa3 and Sa4 being turned off; the "O" state corresponds to Sa2 and Sa3 being on, Sa1 and Sa4 being off; the "N" state corresponds to Sa3 and Sa4 being on, Sa1 and Sa2 being off. The three-phase leg thus has 27 switching states and 19 different voltage vectors. The influence of different voltage vectors on the midpoint potential is different, so that the midpoint potential balance control needs to be set in a control system. Obtaining a first capacitor voltage prediction model according to the three-level inverter direct current side capacitor voltage model as follows:
Figure BDA0002801587600000063
Figure BDA0002801587600000064
wherein v isc1(k)、vc2(k) The capacitor voltage sampled at the moment k; v. ofc1(k+1)、vc2(k +1) is the predicted capacitor voltage at time k + 1; i.e. ic1(k) And ic2(k) Is based on the defined value of the switching state and the output current of the inverter at time k, C1、C2Is a DC side dual capacitor ia(k)、ib(k)、ic(k) For three-phase stator currents, idc(k) Is a direct bus current, H1a、H1b、H1c、H2a、H2b、H2cFor three-phase state values, H for "P" state1aIs 1, the rest is 0; h in the "O" state1bIs 1, the rest is 0; h in the "N" state1cIs 1, the rest is 0.
Constructing a target first cost function g by using the absolute value of the difference between the electromagnetic torque and the magnetic flux predicted value of the permanent magnet synchronous motor and the reference value, the absolute value of the difference between the inverter direct-current side capacitor voltage and the switching frequency limit:
Figure BDA0002801587600000065
wherein the content of the first and second substances,
Figure BDA0002801587600000066
the torque is given for a given value of torque,
Figure BDA0002801587600000067
for stator flux set-point, λ1、λ2、λ3Is a weight coefficient, FswThe number of times of action conversion of switching on and switching off of the switching tube in one control period is specifically calculated as follows:
Fsw＝|Sa(k)-Sa(k-1)|+|Sb(k)-Sb(k-1)|+|Sc(k)-Sc(k-1)|
wherein Sa(k)、Sb(k)、Sc(k) The switching state of three phases of the inverter at the moment k; sa(k-1)、Sb(k-1)、ScAnd (k-1) represents the switching state of the three phases of the inverter at the previous time k-1.
As shown in fig. 2, the space vectors of the three-level inverter are respectively 3 zero vectors, 12 positive and negative redundant small vectors, 6 medium vectors and 6 large vectors, wherein the large vectors have no influence on the center potential, so that the space vector diagram is divided into six sectors with the large vectors as the center.
Substituting a large vector in a three-level inverter space vector diagram as a candidate vector of a first optimal voltage vector into a first cost function g, and selecting a voltage vector with the minimum result as a first optimal voltage vector uopt1(ii) a Will be associated with a first optimum voltage vector uopt1The non-zero vector of the same sector is included in a second optimal voltage vector alternative set, and each sector has three alternative voltage vectors; and will be aligned with the first optimum voltage vector uopt1The zero vector in the same sector is used as the third voltage vector.
In turn selectThree alternative voltage vectors are used as second optimal voltage vectors, and first optimal voltage vectors u are calculatedopt1Second optimum voltage vector uopt2And a third voltage vector (zero vector) u0According to the torque and flux linkage dead-beat control principle, after one sampling period is finished, the predicted values of the torque and the flux linkage are equal to given values, namely:
Figure BDA0002801587600000071
Figure BDA0002801587600000072
wherein
Figure BDA0002801587600000073
The change rate of the torque and the magnetic flux under the action of the first optimal voltage vector is taken as the change rate;
Figure BDA0002801587600000074
the change rate of the torque and the magnetic flux under the action of the second optimal voltage vector is obtained;
Figure BDA0002801587600000075
Figure BDA0002801587600000076
the change rate of the torque and the magnetic flux under the action of a zero vector is shown;
Figure BDA0002801587600000077
is a first optimal voltage vector uopt1Predicted values of applied torque and flux linkage; t ise(k+1)opt2_n、|ψs(k+1)opt2_nL is the nth voltage vector u in the second optimal voltage vector setopt2_nPredicted values of applied torque and flux linkage; t ise(k+1)_0、|ψS(k+1)_0I is the pre-torque and flux linkage under the action of the third voltage vector (zero vector)And (6) measuring.
The action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector is t1、t2And t0：
t0＝Ts-t1-t2
Figure BDA0002801587600000081
Wherein the content of the first and second substances,
Figure BDA0002801587600000082
s is an amount, and
Figure BDA0002801587600000083
calculate t1、t2、t0Then, to ensure t1、t2、t0At 0 to TsWithin the range, the following treatments are required:
(6.1) when acting for a time t1、t2And t0Any two of them have action time less than 0 and the other has action time greater than TsIf the action time is less than 0, the action time is equal to 0 and greater than TsHas an action time equal to Ts；
(6.2) when acting for a time t1+t2≤TsIf so, the action time of each vector is unchanged;
(6.3) when acting for a time t1+t2≥TsThen, the action time of the three vectors needs to be substituted into the following formula for correction:
Figure BDA0002801587600000084
wherein: t is t1'、t'2And t'0Respectively representing the corrected first, second and third optimal voltage vectorsThe action time.
First optimal voltage vector uopt1Second optimum voltage vector uopt2_nAnd zero vector u0Respectively η of duty ratioopt1、ηopt2_n、η0Modulating a new voltage vector according to the duty ratio:
Figure BDA0002801587600000085
wherein u isα2、uβ2Is the alpha, beta axis component of the new voltage vector, uopt1_α、uopt1_βIs the alpha, beta axis component of the first optimum voltage vector, uopt2_n_α、uopt2_n_βThe alpha and beta axis components of the nth voltage vector in the second optimal voltage vector set are obtained.
In the three-level inverter, the first optimal voltage vector, the second optimal voltage vector and the third voltage vector have different influences on the center potential, so that the predicted voltages of the two capacitors on the direct current side are reestablished according to the action time of the three voltage vectors, and the second capacitor voltage prediction model is as follows:
Figure BDA0002801587600000091
wherein, v'c1(k+1)、v'c2(k +1) is the direct-current side two-capacitor voltage at the k +1 moment under the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector, v'c1(k)、v'c2(k) The voltage of two capacitors at the k moment under the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector is represented by ic11(k)、ic21(k)、ic31(k)、ic12(k)、ic22(k)、ic32(k) The output current values when the first optimal voltage vector, the second optimal voltage vector and the third voltage vector act are respectively.
Constructing a second valence function g according to the second capacitance-voltage prediction modelnComprises the following steps:
Figure BDA0002801587600000092
three new voltage vectors which are modulated by sequentially selecting three alternative voltage vectors as second optimal voltage vectors are substituted into a second valence function gnAnd finally, inputting the switching sequences corresponding to the selected first optimal voltage vector, the second optimal voltage vector and the third voltage vector (zero vector) and action time thereof into a pulse modulator to control the action of the three-level inverter.

Claims (10)

1. A three-vector model prediction torque control method of a three-level permanent magnet synchronous motor is characterized by comprising the following steps:
(1) according to a state space model of the permanent magnet synchronous motor, a state equation of current and magnetic flux is constructed, a prediction model of torque and magnetic flux is obtained according to the state equation of the current and the magnetic flux, and a first capacitance voltage prediction model is obtained according to a capacitance voltage model on the direct current side of the three-level inverter; constructing a first value function according to the first capacitance voltage prediction model;
(2) sampling to obtain current at the k moment;
(3) substituting the sampled data into a torque and magnetic flux prediction model to obtain the torque and magnetic flux linkage at the next moment k + 1;
(4) substituting a large vector in a space vector diagram of the three-level inverter into a first cost function as a candidate vector of a first optimal voltage vector to select the first optimal voltage vector;
(5) taking three non-zero vectors in a sector corresponding to the first optimal voltage vector as alternative vectors of a second optimal voltage vector, and taking a zero vector as a third voltage vector;
(6) sequentially selecting three alternative vectors as a second optimal voltage vector, calculating the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector, solving the duty ratio, and modulating a new voltage vector according to the duty ratio;
(7) establishing a second capacitance voltage prediction model according to the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector, and establishing a second valence function according to the second capacitance voltage prediction model;
(8) substituting the three new voltage vectors modulated in the step 6 into a second valence function to select an optimal second optimal voltage vector;
(9) and inputting the switching sequences corresponding to the selected first optimal voltage vector, the second optimal voltage vector and the third voltage vector and the corresponding action time into a pulse modulator to control the action of the three-level inverter.
2. The three-vector model predicted torque control method according to claim 1, characterized in that the state equations of current and magnetic flux in step 1 are:
Figure FDA0002801587590000011
Figure FDA0002801587590000012
wherein iα、iβRespectively representing the components of the stator current vector on an alpha axis and a beta axis; l issRepresenting the stator inductance; u. ofα、uβRespectively representing the components of the stator voltage vector under an alpha axis and a beta axis; rsRepresenting the stator resistance; omegarRepresenting the electrical angular speed of the rotor of the motor; psiα、ψβRespectively representing the components of the electron flux linkage vector in the alpha and beta axes.
3. The three-vector model predicted torque control method according to claim 2, wherein in step 1, the prediction models of torque and magnetic flux are:
Figure FDA0002801587590000021
ψs(k+1)＝ψs(k)+Ts[us(k)-Rsis(k)]
Figure FDA0002801587590000022
wherein, TsIs a sampling period; i.e. is(k+1)、ψs(k+1)、Te(k +1) is a stator current vector, a stator flux linkage and a torque vector at the moment k +1 respectively; i.e. is(k)、ψs(k)、us(k)、ωr(k) The stator current vector, the stator flux linkage, the stator voltage vector and the electrical angular velocity of the rotor at the moment k are respectively; j is an imaginary unit, p is a log number,
Figure FDA0002801587590000023
is a cross-product sign.
4. The method of claim 3, wherein in step 1, the capacitor voltage prediction model is:
Figure FDA0002801587590000024
wherein v isc1(k)、vc2(k) The capacitor voltage sampled at the moment k; v. ofc1(k+1)、vc2(k +1) is the predicted capacitor voltage at time k + 1; i.e. ic1(k) And ic2(k) Is based on the defined value of the switching state and the output current of the inverter at time k, C1、C2Two capacitors on the direct current side.
5. The three-vector model predicted torque control method according to claim 4, wherein in step 4, the first cost function is:
Figure FDA0002801587590000025
wherein the content of the first and second substances,
Figure FDA0002801587590000026
the torque is given for a given value of torque,
Figure FDA0002801587590000027
for stator flux set-point, λ1、λ2、λ3Is a weight coefficient, FswThe number of times of action conversion of the on-off of the switching tube in one control period.
6. The method according to claim 5, wherein in step 6, the first optimal voltage vector, the second optimal voltage vector, and the third voltage vector have respective action times t1、t2And t0：
t0＝Ts-t1-t2
Figure FDA0002801587590000031
Wherein the content of the first and second substances,
Figure FDA0002801587590000032
s is an amount, and
Figure FDA0002801587590000033
Figure FDA0002801587590000034
the change rate of the torque and the magnetic flux under the action of the first optimal voltage vector is taken as the change rate;
Figure FDA0002801587590000035
Figure FDA0002801587590000036
the change rate of the torque and the magnetic flux under the action of the second optimal voltage vector is obtained;
Figure FDA0002801587590000037
is the rate of change of torque and flux under zero vector action.
7. The three-vector model predicted torque control method of claim 6, characterized in that t is t1、t2、t0At 0 to TsWithin a range of and for ensuring t1、t2、t0At 0 to TsWithin the range, the following treatments are required:
(6.1) when acting for a time t1、t2And t0Any two of them have action time less than 0 and the other has action time greater than TsIf the action time is less than 0, the action time is equal to 0 and greater than TsHas an action time equal to Ts；
(6.2) when acting for a time t1+t2≤TsIf so, the action time of each vector is unchanged;
(6.3) when acting for a time t1+t2≥TsThen, the action time of the three vectors needs to be substituted into the following formula for correction:
Figure FDA0002801587590000038
wherein: t'1、t'2And t'0And respectively showing the action time of the corrected first optimal voltage vector, the second optimal voltage vector and the third voltage vector.
8. The three-vector model predicted torque control method according to claim 7, characterized in that in said step 6, the modulation formula of the new voltage vector:
Figure FDA0002801587590000039
wherein u isα2、uβ2Is the alpha, beta axis component of the new voltage vector, uopt1_α、uopt1_βIs the alpha, beta axis component of the first optimum voltage vector, uopt2_n_α、uopt2_n_βThe first optimal voltage vector u is the alpha-axis component and the beta-axis component of the nth voltage vector in the second optimal voltage vector setopt1Second optimum voltage vector uopt2_nAnd zero vector u0Respectively η of duty ratioopt1、ηopt2_n、η0。
9. The three-vector model predicted torque control method according to claim 8, wherein the second capacitor voltage prediction model in step 7 is:
Figure FDA0002801587590000041
wherein, v'c1(k+1)、v'c2(k +1) is the direct-current side two-capacitor voltage at the k +1 moment under the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector, v'c1(k)、v'c2(k) The voltage of two capacitors at the k moment under the action time of the first optimal voltage vector, the second optimal voltage vector and the third voltage vector is represented by ic11(k)、ic21(k)、ic31(k)、ic12(k)、ic22(k)、ic32(k) The output current values when the first optimal voltage vector, the second optimal voltage vector and the third voltage vector act are respectively.
10. The three-vector model predicted torque control method according to claim 9, wherein the second cost function in step 7 is:
Figure FDA0002801587590000042
wherein, gnAs a function of the second value.
CN202011352040.4A 2020-11-27 2020-11-27 Three-vector model prediction torque control method for three-level permanent magnet synchronous motor Active CN112583315B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011352040.4A CN112583315B (en) 2020-11-27 2020-11-27 Three-vector model prediction torque control method for three-level permanent magnet synchronous motor

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011352040.4A CN112583315B (en) 2020-11-27 2020-11-27 Three-vector model prediction torque control method for three-level permanent magnet synchronous motor

Publications (2)

Publication Number Publication Date
CN112583315A true CN112583315A (en) 2021-03-30
CN112583315B CN112583315B (en) 2022-05-17

Family

ID=75124006

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011352040.4A Active CN112583315B (en) 2020-11-27 2020-11-27 Three-vector model prediction torque control method for three-level permanent magnet synchronous motor

Country Status (1)

Country Link
CN (1) CN112583315B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112953341A (en) * 2021-04-22 2021-06-11 哈尔滨理工大学 Three-vector model predictive control algorithm for zero common-mode voltage of permanent magnet synchronous motor
CN113179065A (en) * 2021-05-12 2021-07-27 西南交通大学 Permanent magnet synchronous motor model prediction pulse sequence control method
CN113708688A (en) * 2021-08-31 2021-11-26 西南交通大学 Permanent magnet motor vector reduction model prediction control method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2546979A1 (en) * 2011-07-15 2013-01-16 ABB Research Ltd. Method for controlling harmonics and resonances in an inverter
CN106936356A (en) * 2017-04-24 2017-07-07 东南大学盐城新能源汽车研究院 Vector is screened and dutycycle is combined motor model Predictive Control System and method
CN110350568A (en) * 2019-06-26 2019-10-18 武汉大学 A kind of universal model prediction optimal control method under the conditions of unbalanced power grid
CN110707978A (en) * 2019-10-12 2020-01-17 南通大学 Three-level permanent magnet synchronous motor model prediction control method considering vector partition
CN110867887A (en) * 2019-10-16 2020-03-06 南京理工大学 NPC three-level inverter optimal sequence model prediction control device and method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2546979A1 (en) * 2011-07-15 2013-01-16 ABB Research Ltd. Method for controlling harmonics and resonances in an inverter
CN106936356A (en) * 2017-04-24 2017-07-07 东南大学盐城新能源汽车研究院 Vector is screened and dutycycle is combined motor model Predictive Control System and method
CN110350568A (en) * 2019-06-26 2019-10-18 武汉大学 A kind of universal model prediction optimal control method under the conditions of unbalanced power grid
CN110707978A (en) * 2019-10-12 2020-01-17 南通大学 Three-level permanent magnet synchronous motor model prediction control method considering vector partition
CN110867887A (en) * 2019-10-16 2020-03-06 南京理工大学 NPC three-level inverter optimal sequence model prediction control device and method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
QI WANG ET AL.: "Modulated Model Predictive Current Control for PMSM Operating With Three-level NPC Inverter", 《2019 IEEE 15TH BRAZILIAN POWER ELECTRONICS CONFERENCE AND 5TH IEEE SOUTHERN POWER ELECTRONICS CONFERENCE (COBEP/SPEC)》 *
兰志勇 等: "永磁同步电机新型三矢量模型预测电流控制", 《中国电机工程学报》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112953341A (en) * 2021-04-22 2021-06-11 哈尔滨理工大学 Three-vector model predictive control algorithm for zero common-mode voltage of permanent magnet synchronous motor
CN113179065A (en) * 2021-05-12 2021-07-27 西南交通大学 Permanent magnet synchronous motor model prediction pulse sequence control method
CN113179065B (en) * 2021-05-12 2022-08-19 西南交通大学 Permanent magnet synchronous motor model prediction pulse sequence control method
CN113708688A (en) * 2021-08-31 2021-11-26 西南交通大学 Permanent magnet motor vector reduction model prediction control method
CN113708688B (en) * 2021-08-31 2023-06-30 西南交通大学 Permanent magnet motor vector-reduction model predictive control method

Also Published As

Publication number Publication date
CN112583315B (en) 2022-05-17

Similar Documents

Publication Publication Date Title
CN112583315B (en) Three-vector model prediction torque control method for three-level permanent magnet synchronous motor
CN106936356B (en) Vector screening and duty ratio combined motor model prediction control system and method
CN110460281B (en) Three-level permanent magnet synchronous motor double-vector model prediction flux linkage control method
CN109274303B (en) Asynchronous motor model prediction control method based on extended control set
CN110557074B (en) Prediction current control method for permanent magnet motor system of cascaded H-bridge inverter
CN113659898B (en) Model predictive torque control method for double three-phase permanent magnet synchronous motor
CN111327242B (en) Model-free prediction current control method for three-level permanent magnet synchronous motor
CN110971162B (en) Model prediction torque control method of NPC three-level converter-PMSM system
CN111555684B (en) Permanent magnet synchronous motor multi-step model prediction torque control method with variable switching points
CN113708688B (en) Permanent magnet motor vector-reduction model predictive control method
CN112865646A (en) Dead-beat prediction control method for single current sensor of permanent magnet synchronous motor
CN112821816B (en) PMSM model prediction current control method based on NPC type three-level inverter
CN112564567A (en) Three-level inverter driving permanent magnet synchronous motor system finite set prediction control method
CN115833671A (en) Double three-phase motor high-precision model prediction current control system and control method
CN113098348B (en) Predictive torque control method for double three-phase permanent magnet synchronous motor
CN113783490A (en) Permanent magnet motor model prediction control method with fixed switching frequency
CN113098349A (en) Discrete space vector modulation permanent magnet synchronous motor model prediction control method
CN116865628A (en) Torque ripple suppression method for switched reluctance motor based on current reconstruction technology
CN108649852B (en) Permanent magnet synchronous motor control method for improving current loop
CN114301361B (en) Control method of electrolytic capacitor-free permanent magnet synchronous motor driving system based on bus current control
CN112087177B (en) Control method for single current sensor of permanent magnet synchronous motor
CN116317795B (en) Permanent magnet synchronous motor current prediction control method based on discrete space vector modulation
CN117526790A (en) Five-level model predictive control method for open winding permanent magnet synchronous motor
CN116455288B (en) No-parameter prediction control method, device, equipment and medium for electric locomotive
CN113472250B (en) Method for resisting capacitor voltage deviation in three-phase four-switch inverter

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant