CN114374350A - Surface-mounted permanent magnet synchronous motor parameter identification method - Google Patents

Surface-mounted permanent magnet synchronous motor parameter identification method Download PDF

Info

Publication number
CN114374350A
CN114374350A CN202111564322.5A CN202111564322A CN114374350A CN 114374350 A CN114374350 A CN 114374350A CN 202111564322 A CN202111564322 A CN 202111564322A CN 114374350 A CN114374350 A CN 114374350A
Authority
CN
China
Prior art keywords
permanent magnet
magnet synchronous
synchronous motor
axis
position angle
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
CN202111564322.5A
Other languages
Chinese (zh)
Other versions
CN114374350B (en
Inventor
赵文祥
陈奕帆
邱先群
陶涛
吉敬华
和阳
田伟
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
Original Assignee
Jiangsu University
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 filed Critical Jiangsu University
Priority to CN202111564322.5A priority Critical patent/CN114374350B/en
Publication of CN114374350A publication Critical patent/CN114374350A/en
Application granted granted Critical
Publication of CN114374350B publication Critical patent/CN114374350B/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
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Analysis (AREA)
  • Computational Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Power Engineering (AREA)
  • Algebra (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Operations Research (AREA)
  • Control Of Ac Motors In General (AREA)
  • Control Of Motors That Do Not Use Commutators (AREA)

Abstract

The invention discloses a surface-mounted permanent magnet synchronous motor parameter identification method. The method is based on a voltage equation under a two-phase rotating coordinate system, two different d-q axis voltage states are formed by injecting periodic square waves into position angle signals, and according to the two d-q axis voltage states, a recursive least square method observer is utilized to finally identify stator inductance, stator resistance and rotor flux linkage parameters of the motor by combining the rotating speed, q axis current and the size of the injected position angle signals. The method for injecting the periodic square waves into the position angle signals can effectively solve the problem of observability of the parameters of the permanent magnet synchronous motor, the recursive least square observer has the advantages of high convergence speed, less occupied memory, higher precision and the like, and meanwhile, the online identification of the parameters of the surface-mounted permanent magnet synchronous motor can be realized.

Description

Surface-mounted permanent magnet synchronous motor parameter identification method
Technical Field
The invention belongs to the field of permanent magnet synchronous motor drive control application, and particularly relates to a surface-mounted permanent magnet synchronous motor parameter identification method.
Background
The permanent magnet synchronous motor is one kind of synchronous motor and consists of two key parts, i.e. a multi-polarization permanent magnet rotor and a stator with windings. Due to the advantages of high efficiency, large torque, small volume and the like, the method plays an important role in the operation control in the field of industrial automation. The permanent magnet synchronous motor is divided into a surface-mounted permanent magnet synchronous motor and a built-in permanent magnet synchronous motor, and for the surface-mounted permanent magnet synchronous motor, the difference of the alternating-direct axis magnetic resistance is small, and the difference of the corresponding alternating-direct axis inductance is also small, so that the use amount of the surface-mounted permanent magnet synchronous motor is small, and sine wave magnetomotive force is generated more easily. In practical applications, due to the nonlinearity and strong coupling of the permanent magnet synchronous motor, the parameters of the motor will also change along with the operation process. In the field of parameter identification, the main identification methods are divided into online parameter identification and offline parameter identification. Common parameter identification methods include a recursive least square method, a neural network algorithm, a Kalman filtering algorithm and the like. The recursive least square algorithm is simple in structure and easy to implement, but the algorithm is more suitable for a constant unknown parameter system.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the observability problem of parameter identification of the surface-mounted permanent magnet synchronous motor, a position angle signal injection-based method is provided. Compared with the traditional method of injecting negative current into the d axis, the method has the advantages that the rotating speed performance of the motor is not influenced, and in addition, the novel method also guarantees the authenticity of identification because the inductance parameter is related to the current. Therefore, a method for identifying parameters of a surface-mounted permanent magnet synchronous motor is provided, and the accuracy of identifying the parameters of the surface-mounted permanent magnet synchronous motor is ensured.
The technical scheme is as follows: in order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention provides a surface-mounted permanent magnet synchronous motor parameter identification method, which comprises the following implementation steps:
step 1, injecting a position angle signal: injecting a periodic square wave into the position signal to form two d-q axis voltage states;
step 2, verifying observability: two d-q axis voltage state equations formed in a simultaneous mode prove that the system is considerable by utilizing the lie derivative according to the observability principle of a nonlinear system;
step 3, decoupling of parameters: processing various different voltage state equations to realize decoupling of various parameters;
and 4, identifying parameters of the recursive least square method: and (3) realizing online identification of each parameter by using a recursive least square observer according to the decoupled equation.
Further, the specific process of step 1 is as follows:
the voltage equation of the surface-mounted permanent magnet motor in the two-phase rotating coordinate system can be expressed as
Figure BDA0003421684570000021
Wherein, UdAnd UqIs the voltage of d-q axis, idAnd iqCurrent of d-q axis, ωeIs the electrical angular velocity, #fFor the rotor flux of the machine, RsIs the stator resistance of the motor, LsIs the motor stator inductance.
By injecting a periodic square wave with amplitude delta theta and duty cycle of 50% into the position angle signal, the new d-q axis voltages and currents can be represented as
Figure BDA0003421684570000022
Figure BDA0003421684570000023
According to id' 0, the actual d-q axis current is
Figure BDA0003421684570000024
Figure BDA0003421684570000025
At this time idAnd iqFor d-q axis currents, i, after injection of position angle signalsd' and iq' d-q axis Current, i, before injecting the position angle Signalq *For the q-axis current of reference, Δ θ is the injected position angle signal.
Thus, the d-q axis voltage equation after injection is
Figure BDA0003421684570000026
Further, the specific process in step 2 is as follows:
according to general expressions of non-linear systems, in particular
Figure BDA0003421684570000027
y＝h(x)
Where x is the state vector,
Figure BDA0003421684570000028
is the derivative of the state vector, f, h are the corresponding functional expressions, u is the control vector, y is the output vector.
For a non-linear system in which a certain state x0Define an O matrix, expressed as
Figure BDA0003421684570000029
n is the dimension of the state vector x and p is the dimension of the output vector y.
Wherein L is an observability discrimination matrix defined as
Figure BDA0003421684570000031
Figure BDA0003421684570000032
Figure BDA0003421684570000033
Wherein L isdAnd LqAre motor quadrature-direct axis inductors respectively.
According to the general expression of the nonlinear system, because the observability of the built-in permanent magnet synchronous motor is the same as that of the surface-mounted permanent magnet synchronous motor, the general applicability is considered, and the d-q axis voltage equation of the built-in permanent magnet synchronous motor can be rewritten as follows
Figure BDA0003421684570000034
Figure BDA0003421684570000035
Figure BDA0003421684570000036
It is assumed that the resistance, inductance, flux linkage do not change in a short time. According to the definition of the nonlinear system, the following form can be constructed
x＝[id,iq,Rs,Ld,Lq,ψf]Τ
y＝h(x)＝[id,iq]Τ
u＝[ud,uq,ωe]Τ
Figure BDA0003421684570000037
Its 0 to 5 th order lie derivative
Lf 0h＝h＝[id,iq]Τ
Figure BDA0003421684570000041
Figure BDA0003421684570000042
Figure BDA0003421684570000043
Figure BDA0003421684570000044
Figure BDA0003421684570000045
Figure BDA0003421684570000046
The n-th order lie derivative of h versus f,
Figure BDA0003421684570000047
representing a gradient.
Wherein A is
Figure BDA0003421684570000048
According to the observability principle, O is required12×6Full rank, using 0 to 5 order lie derivatives, O12×6The matrix is shown below
Figure BDA0003421684570000049
Due to O12×6The first two columns of the matrix are full rank, so only O needs to be distinguished12×6Submatrix O of matrix4×4', is as follows
Figure BDA00034216845700000410
In a transient state, i.e.
Figure BDA00034216845700000411
Time, matrix O4×4' full rank, when the system is considerable. But in a steady state situation, i.e.
Figure BDA00034216845700000412
When is, O4×4' simplify as follows
Figure BDA00034216845700000413
At this time O4×4The' rank is 2. Similarly, for the surface-mounted permanent magnet synchronous motor, the O is3×3The' rank is also 2, and they are all not appreciable.
Further, the specific process of step 3 is as follows:
the d-q axis voltage equation state of the surface-mounted permanent magnet synchronous motor after the position angle signal is injected is
Figure BDA0003421684570000051
ud' and uq' is the d-q axis voltage after injecting the position angle signal.
The d-q axis voltage equation state of the surface-mounted permanent magnet synchronous motor without the position angle signal is
Figure BDA0003421684570000052
The calculation method for obtaining each parameter through correlation operation realizes decoupling operation among each parameter, and the method is as follows
Figure BDA0003421684570000053
Figure BDA0003421684570000054
Figure BDA0003421684570000055
Figure BDA0003421684570000056
In order to identify the inductance of the stator,
Figure BDA0003421684570000057
in order to identify the flux linkage of the rotor,
Figure BDA0003421684570000058
is the identified stator resistance.
Since the injection position angle signal takes a very small value, then sin Δ θ ≈ Δ θ, cos Δ θ ≈ 1, so
Figure BDA0003421684570000059
Further, the specific process of step 4 is as follows:
for a set of data yi∈R,xi∈RnI is 1, …, N, satisfying
Figure BDA00034216845700000510
Identifying n × 1-dimensional parameter vector θ ═[ θ 1, …, θ n ]
Form is rewritten as
YN＝XNθ
The goal is to minimize the estimation error, expressed as
Figure BDA00034216845700000511
To pair
Figure BDA00034216845700000512
Derivation
Figure BDA0003421684570000061
Figure BDA0003421684570000062
Figure BDA0003421684570000063
Wherein, YN∈RN，XNIs an N x N matrix, one for each row
Figure BDA0003421684570000064
Is an estimated parameter.
Defining a correction quantity M, carrying out recursion calculation, and updating parameters of the model as follows
Figure BDA0003421684570000065
The subscripts t and t-1 represent time t and t-1, respectively.
Defining a variable PN -1＝(XN TXN) Then, then
Figure BDA0003421684570000066
The same theory has the following expression
Figure BDA0003421684570000067
Figure BDA0003421684570000068
PN＝(PN-1 -1+xNxN T)-1
Solving a recursive least square method according to the matrix inverse lemma
PN＝PN-1-PN-1xN[I+xN TPN-1xN]-1xN TPN-1
Figure BDA0003421684570000069
The invention has the beneficial effects that:
1) the method based on the position angle signal injection can effectively solve the observability problem of parameter identification, does not influence the rotating speed performance, and ensures the identification accuracy;
2) the invention adopts a simple operation method among various states, is simple to realize and plays a role in decoupling parameters;
3) the invention realizes on-line parameter identification by using the recursive least square observer, and has the advantages of high convergence speed, less occupied memory, higher precision and the like.
Drawings
FIG. 1 is a schematic diagram of parameter identification of a surface-mounted permanent magnet synchronous motor
FIG. 2 is a schematic diagram of a recursive least squares method for parameter identification
FIG. 3 is a timing diagram of the position signal injection
FIG. 4 is a comparison graph of the actual value of the inductance and the identification simulation result
FIG. 5 is a comparison graph of the real value of flux linkage and the identification simulation result
FIG. 6 is a comparison graph of the actual resistance value and the identification simulation result
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the present invention provides a method for identifying parameters of a surface-mounted permanent magnet synchronous motor.
The specific implementation steps of the provided surface-mounted permanent magnet synchronous motor parameter identification method comprise:
step 1: injection of position angle signals
The voltage equation of the surface-mounted permanent magnet motor in the two-phase rotating coordinate system can be expressed as
Figure BDA0003421684570000071
Wherein, UdAnd UqIs the voltage of d-q axis, idAnd iqCurrent of d-q axis, ωeIs the electrical angular velocity, #fFor the rotor flux of the machine, RsIs the stator resistance of the motor, LsIs the motor stator inductance.
By injecting a periodic square wave with amplitude delta theta and duty cycle of 50% into the position angle signal, the new d-q axis voltages and currents can be represented as
Figure BDA0003421684570000072
Figure BDA0003421684570000073
According to id' 0, the actual d-q axis current is
Figure BDA0003421684570000074
Figure BDA0003421684570000075
At this time idAnd iqFor d-q axis currents, i, after injection of position angle signalsd' and iq' d-q axis Current, i, before injecting the position angle Signalq *For the q-axis current of reference, Δ θ is the injected position angle signal.
Thus, the d-q axis voltage equation after injection is
Figure BDA0003421684570000081
Step 2, verifying observability
According to general expressions of non-linear systems, in particular
Figure BDA0003421684570000082
y＝h(x)
Where x is the state vector,
Figure BDA0003421684570000083
is the derivative of the state vector, f, h are the corresponding functional expressions, u is the control vector, y is the output vector.
For a non-linear system in which a certain state x0Define an O matrix, expressed as
Figure BDA0003421684570000084
n is the dimension of the state vector x and p is the dimension of the output vector y.
Wherein L is an observability discrimination matrix defined as
Figure BDA0003421684570000085
Figure BDA0003421684570000086
Figure BDA0003421684570000087
Wherein L isdAnd LqAre motor quadrature-direct axis inductors respectively.
According to the general expression of the nonlinear system, because the observability of the built-in permanent magnet synchronous motor is the same as that of the surface-mounted permanent magnet synchronous motor, the general applicability is considered, and the d-q axis voltage equation of the built-in permanent magnet synchronous motor can be rewritten as follows
Figure BDA0003421684570000088
Figure BDA0003421684570000089
Figure BDA00034216845700000810
It is assumed that the resistance, inductance, flux linkage do not change in a short time. According to the definition of the nonlinear system, the following form can be constructed
x＝[id,iq,Rs,Ld,Lq,ψf]Τ
y＝h(x)＝[id,iq]Τ
u＝[ud,uq,ωe]Τ
Figure BDA0003421684570000091
Its 0 to 5 th order lie derivative
Lf 0h＝h＝[id,iq]Τ
Figure BDA0003421684570000092
Figure BDA0003421684570000093
Figure BDA0003421684570000094
Figure BDA0003421684570000095
Figure BDA0003421684570000096
Figure BDA0003421684570000097
The n-th order lie derivative of h versus f,
Figure BDA0003421684570000098
representing a gradient.
Wherein A is
Figure BDA0003421684570000099
According to the observability principle, O is required12×6Full rank, using 0 to 5 order lie derivatives, O12×6The matrix is shown below
Figure BDA00034216845700000910
Due to O12×6The first two columns of the matrix are full rank, so only O needs to be distinguished12×6Submatrix O of matrix4×4', is as follows
Figure BDA0003421684570000101
In a transient state, i.e.
Figure BDA0003421684570000102
Time, matrix O4×4' full rank, when the system is considerable. But in a steady state situation, i.e.
Figure BDA0003421684570000103
When is, O4×4' simplify as follows
Figure BDA0003421684570000104
At this time O4×4The' rank is 2. Similarly, for the surface-mounted permanent magnet synchronous motor, the O is3×3The' rank is also 2, and they are all not appreciable.
Step 3, decoupling of parameters
The d-q axis voltage equation state of the surface-mounted permanent magnet synchronous motor after the position angle signal is injected is
Figure BDA0003421684570000105
ud' and uq' is the d-q axis voltage after injecting the position angle signal.
The d-q axis voltage equation state of the surface-mounted permanent magnet synchronous motor without the position angle signal is
Figure BDA0003421684570000106
The calculation method for obtaining each parameter through correlation operation realizes decoupling operation among each parameter, and the method is as follows
Figure BDA0003421684570000107
Figure BDA0003421684570000108
Figure BDA0003421684570000109
Figure BDA00034216845700001010
In order to identify the inductance of the stator,
Figure BDA00034216845700001011
in order to identify the flux linkage of the rotor,
Figure BDA00034216845700001012
is the identified stator resistance.
Since the injection position angle signal takes a very small value, then sin Δ θ ≈ Δ θ, cos Δ θ ≈ 1, so
Figure BDA00034216845700001013
Step 4, parameter identification of recursive least square method
For a set of data yi∈R,xi∈RnI is 1, …, N, satisfying
Figure BDA0003421684570000111
Identifying n × 1-dimensional parameter vector θ ═[ θ 1, …, θ n ]
Form is rewritten as
YN＝XNθ
The goal is to minimize the estimation error, expressed as
Figure BDA0003421684570000112
To pair
Figure BDA0003421684570000113
Derivation
Figure BDA0003421684570000114
Figure BDA0003421684570000115
Figure BDA0003421684570000116
Wherein, YN∈RN，XNIs an N x N matrix, one for each row
Figure BDA0003421684570000117
Is an estimated parameter.
Defining a correction quantity M, carrying out recursion calculation, and updating parameters of the model as follows
Figure BDA0003421684570000118
The subscripts t and t-1 represent time t and t-1, respectively.
Defining a variable PN -1＝(XN TXN) Then, then
Figure BDA0003421684570000119
The same theory has the following expression
Figure BDA00034216845700001110
Figure BDA00034216845700001111
PN＝(PN-1 -1+xNxN T)-1
Solving a recursive least square method according to the matrix inverse lemma
PN＝PN-1-PN-1xN[I+xN TPN-1xN]-1xN TPN-1
Figure BDA00034216845700001112
Fig. 4, 5 and 6 are comparison diagrams of the actual inductance and the identification inductance, the actual flux linkage and the identification flux linkage, and the actual resistance and the identification resistance of the motor, respectively. As can be seen, their identified estimated values substantially match the actual values.
The above embodiments are merely illustrative of the design ideas and features of the present invention, and are intended to enable those skilled in the art to understand the contents of the present invention and to implement the present invention accordingly. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.

Claims (5)

1. A surface-mounted permanent magnet synchronous motor parameter identification method is characterized by comprising the following steps:
step 1, injecting a position angle signal: two d-q axis voltage states are formed by injecting a periodic square wave into the position angle;
step 2, verifying observability: two d-q axis voltage state equations formed in a simultaneous mode prove that the system is considerable by utilizing the lie derivative according to the observability principle of a nonlinear system;
step 3, decoupling of parameters: processing various different voltage state equations to realize decoupling of various parameters;
and 4, identifying parameters of the recursive least square method: and (3) realizing online identification of stator inductance, stator resistance and rotor flux linkage parameters by using a recursive least square observer according to an equation obtained after decoupling.
2. The method for identifying parameters of a surface-mounted permanent magnet synchronous motor according to claim 1, wherein the specific steps of step 1 comprise:
the voltage equation of the surface-mounted permanent magnet motor in the two-phase rotating coordinate system can be expressed as
Figure FDA0003421684560000011
Wherein, UdAnd UqIs the voltage of d-q axis, idAnd iqCurrent of d-q axis, ωeIs the electrical angular velocity, #fFor the rotor flux of the machine, RsIs the stator resistance of the motor, LsIs the motor stator inductance.
By injecting a periodic square wave with amplitude delta theta and duty cycle of 50% into the position angle signal, the new d-q axis voltages and currents can be represented as
Figure FDA0003421684560000012
Figure FDA0003421684560000013
According to id' 0, the actual d-q axis current is
Figure FDA0003421684560000014
Figure FDA0003421684560000015
At this time idAnd iqFor d-q axis currents, i, after injection of position angle signalsd' and iq' d-q axis Current, i, before injecting the position angle Signalq *For the q-axis current of reference, Δ θ is the injected position angle signal.
Thus obtaining the equation of the voltage of the d-q axis after injection as
Figure FDA0003421684560000021
。
3. The method for identifying parameters of a surface-mounted permanent magnet synchronous motor according to claim 1, wherein the specific steps of the step 2 comprise:
according to general expressions of non-linear systems, in particular
Figure FDA0003421684560000022
y＝h(x)
Where x is the state vector,
Figure FDA0003421684560000023
is the derivative of the state vector, f, h are the corresponding functional expressions, u is the control vector, y is the output vector.
For a non-linear system in which a certain state x0Define an O matrix, expressed as
Figure FDA0003421684560000024
n is the dimension of the state vector x and p is the dimension of the output vector y.
Wherein L is an observability discrimination matrix defined as
Figure FDA0003421684560000025
Figure FDA0003421684560000026
Figure FDA0003421684560000027
Wherein L isdAnd LqAre motor quadrature-direct axis inductors respectively.
According to the general expression of the nonlinear system, because the observability of the built-in permanent magnet synchronous motor is the same as that of the surface-mounted permanent magnet synchronous motor, the general applicability is considered, and the d-q axis voltage equation of the built-in permanent magnet synchronous motor can be rewritten as follows
Figure FDA0003421684560000028
Figure FDA0003421684560000029
Figure FDA00034216845600000210
It is assumed that the resistance, inductance, flux linkage do not change in a short time. According to the definition of the nonlinear system, the following form can be constructed
x＝[id,iq,Rs,Ld,Lq,ψf]Τ
y＝h(x)＝[id,iq]Τ
u＝[ud,uq,ωe]Τ
Figure FDA0003421684560000031
Its 0 to 5 th order lie derivative
Lf 0h＝h＝[id,iq]Τ
Figure FDA0003421684560000032
Figure FDA0003421684560000033
Figure FDA0003421684560000034
Figure FDA0003421684560000035
Figure FDA0003421684560000036
Figure FDA0003421684560000037
The n-th order lie derivative of h versus f,
Figure FDA0003421684560000038
representing a gradient.
Wherein A is
Figure FDA0003421684560000039
According to the observability principle, O is required12×6Full rank, using 0 to 5 order lie derivatives, O12×6The matrix is shown below
Figure FDA00034216845600000310
Due to O12×6The first two columns of the matrix are full rank, so only O needs to be distinguished12×6Submatrix O of matrix4×4', is as follows
Figure FDA0003421684560000041
In a transient state, i.e.
Figure FDA0003421684560000042
Time, matrix O4×4' full rank, when the system is considerable. But in a steady state situation, i.e.
Figure FDA0003421684560000043
When is, O4×4' simplify as follows
Figure FDA0003421684560000044
At this time O4×4The' rank is 2. Similarly, for the surface-mounted permanent magnet synchronous motor, the O is3×3The' rank is also 2, and they are all not appreciable.
4. The method for identifying parameters of a surface-mounted permanent magnet synchronous motor according to claim 1, wherein the specific step of step 3 comprises:
the d-q axis voltage equation state of the surface-mounted permanent magnet synchronous motor after the position angle signal is injected is
Figure FDA0003421684560000045
ud' and uq' is the d-q axis voltage after injecting the position angle signal.
The d-q axis voltage equation state of the surface-mounted permanent magnet synchronous motor without the position angle signal is
Figure FDA0003421684560000046
The calculation method for obtaining each parameter through correlation operation realizes decoupling operation among each parameter, and the method is as follows
Figure FDA0003421684560000047
Figure FDA0003421684560000048
Figure FDA0003421684560000049
Figure FDA00034216845600000410
In order to identify the inductance of the stator,
Figure FDA00034216845600000411
in order to identify the flux linkage of the rotor,
Figure FDA00034216845600000412
is the identified stator resistance.
Since the injection position angle signal takes a very small value, then sin Δ θ ≈ Δ θ, cos Δ θ ≈ 1, so
Figure FDA00034216845600000413
。
5. The method for identifying parameters of a surface-mounted permanent magnet synchronous motor according to claim 1, wherein the specific step of step 4 comprises:
for a set of data yi∈R,xi∈RnI is 1, …, N, satisfying
yi＝xi Tθ
Identifying n × 1-dimensional parameter vector θ ═[ θ 1, …, θ n ]
Form is rewritten as
YN＝XNθ
The goal is to minimize the estimation error, expressed as
Figure FDA0003421684560000051
To pair
Figure FDA0003421684560000052
Derivation
Figure FDA0003421684560000053
Figure FDA0003421684560000054
Figure FDA0003421684560000055
Wherein, YN∈RN，XNIs an N x N matrix, one for each row
Figure FDA0003421684560000056
Figure FDA0003421684560000057
Is an estimated parameter.
Defining a correction quantity M, carrying out recursion calculation, and updating parameters of the model as follows
Figure FDA0003421684560000058
The subscripts t and t-1 represent time t and t-1, respectively.
Defining a variable PN -1＝(XN TXN) Then, then
Figure FDA0003421684560000059
The same theory has the following expression
Figure FDA00034216845600000511
Figure FDA00034216845600000510
PN＝(PN-1 -1+xNxN T)-1
Solving a recursive least square method according to the matrix inverse lemma
PN＝PN-1-PN-1xN[I+xN TPN-1xN]-1xN TPN-1
Figure FDA0003421684560000061
CN202111564322.5A 2021-12-20 2021-12-20 Parameter identification method for surface-mounted permanent magnet synchronous motor Active CN114374350B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111564322.5A CN114374350B (en) 2021-12-20 2021-12-20 Parameter identification method for surface-mounted permanent magnet synchronous motor

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111564322.5A CN114374350B (en) 2021-12-20 2021-12-20 Parameter identification method for surface-mounted permanent magnet synchronous motor

Publications (2)

Publication Number Publication Date
CN114374350A true CN114374350A (en) 2022-04-19
CN114374350B CN114374350B (en) 2023-12-15

Family

ID=81139670

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111564322.5A Active CN114374350B (en) 2021-12-20 2021-12-20 Parameter identification method for surface-mounted permanent magnet synchronous motor

Country Status (1)

Country Link
CN (1) CN114374350B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103248306A (en) * 2013-05-24 2013-08-14 天津大学 Online decoupling identification method of multiple parameters of PMSM (permanent magnet synchronous motor)
JP2016025714A (en) * 2014-07-18 2016-02-08 富士電機株式会社 Control device for permanent magnet synchronous motor
CN109167545A (en) * 2018-09-14 2019-01-08 新疆大学 Magneto alternator magnetic linkage on-line identification method and system
CN112994564A (en) * 2021-03-15 2021-06-18 合肥恒大江海泵业股份有限公司 Permanent magnet synchronous motor parameter identification method based on convex optimization
CN113422546A (en) * 2021-06-30 2021-09-21 新风光电子科技股份有限公司 Permanent magnet synchronous motor initial position detection method adopting pulse vibration sinusoidal voltage scanning

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103248306A (en) * 2013-05-24 2013-08-14 天津大学 Online decoupling identification method of multiple parameters of PMSM (permanent magnet synchronous motor)
JP2016025714A (en) * 2014-07-18 2016-02-08 富士電機株式会社 Control device for permanent magnet synchronous motor
CN109167545A (en) * 2018-09-14 2019-01-08 新疆大学 Magneto alternator magnetic linkage on-line identification method and system
CN112994564A (en) * 2021-03-15 2021-06-18 合肥恒大江海泵业股份有限公司 Permanent magnet synchronous motor parameter identification method based on convex optimization
CN113422546A (en) * 2021-06-30 2021-09-21 新风光电子科技股份有限公司 Permanent magnet synchronous motor initial position detection method adopting pulse vibration sinusoidal voltage scanning

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
PENG WANG ET AL.: "An Online Estimation Method for Both Stator Inductance and Rotor Flux Linkage of SPMSM Without Dead-Time Influence", 《IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS》, pages 1627 - 1638 *
黄科元 等: "考虑逆变器非线性永磁同步电机 高频注入电感辨识方法", 《电工技术学报》, vol. 26, no. 8, pages 1607 - 1616 *

Also Published As

Publication number Publication date
CN114374350B (en) 2023-12-15

Similar Documents

Publication Publication Date Title
Jiang et al. Improved deadbeat predictive current control combined sliding mode strategy for PMSM drive system
CN108964544B (en) Double-time scale sliding mode control system and method for permanent magnet linear synchronous motor
CN108964527B (en) PMSM torque ripple suppression method under stator current vector orientation
CN112054730B (en) Permanent magnet synchronous motor online parameter identification method
CN106712618A (en) Method and system for identifying rotational inertia of permanent magnet synchronous motor
CN114337434A (en) Permanent magnet motor parameter offline identification method considering inductance saturation effect
CN114374350A (en) Surface-mounted permanent magnet synchronous motor parameter identification method
CN107681941B (en) Method for constructing radial displacement-free sensor of bearingless permanent magnet synchronous motor
CN111049446A (en) Permanent magnet synchronous motor hardware-in-loop simulation method and device, storage medium and terminal
Ananthamoorthy et al. Modelling, simulation and analysis of fuzzy logic controllers for permanent magnet synchronous motor drive
CN112468037B (en) Permanent magnet synchronous motor MTPV control current track searching method and online control method
CN110112976B (en) Permanent magnet synchronous motor parameter identification method using Newton-Raphson method
CN112468032B (en) Full-speed domain efficiency MAP graph generation method of permanent magnet synchronous motor
CN108667370B (en) Built-in permanent magnet synchronous motor weak magnetic curve tracking method and device based on autonomous learning
CN107482978A (en) A kind of permagnetic synchronous motor on-line parameter discrimination method based on finite time algorithm
CN110190782B (en) Permanent magnet motor position sensorless control method
CN107404274A (en) A kind of method based on open-loop voltage detection PMSM rotor zero-bits
CN109379014B (en) Design method of LPV (Low Power Voltage) rotating speed observer of permanent magnet synchronous motor
Huang et al. Sensorless Model Predictive Current Control for SynRM Based on Alternate High-Frequency Square-wave Voltage Injection
CN112865648A (en) Parameter identification-combined high-frequency voltage injection control method for permanent magnet synchronous motor
Dong et al. Predictive current control of PMSM based on parameter identification of combat vehicles
CN111241659A (en) Hardware-in-loop real-time simulation method and device for surface-mounted permanent magnet synchronous motor and terminal
CN113688499B (en) Modeling method of three-phase 12/8-pole switch reluctance motor based on dynamic and static test
CN112152528B (en) Permanent magnet synchronous motor speed regulation control method based on self-adaptive terminal sliding mode
Luo et al. Positionless Control of PMSM Based on Improved Sliding Mode Observer

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