Induction motor efficiency optimization prediction control method

Abstract

The invention provides an induction motor efficiency optimization prediction control method, which belongs to the field of induction motor control, and comprises the following steps: constructing an induction motor mathematical model; a voltage flux linkage model and a current flux linkage model are constructed based on the mathematical model of the induction motor, the voltage flux linkage model comprises a sliding mode compensation item, errors of the voltage flux linkage model and the current flux linkage model are obtained, the errors are limited in a preset interval through error conversion, and then the estimated stator resistance when the errors are limited is obtained; obtaining a total loss expression of the motor based on the mathematical model of the induction motor and the estimated stator resistance; a super local prediction model and a generalized proportional integral observer are constructed, and a cost function considering the total loss of the motor is designed, so that the optimal stator d-axis voltage and the optimal stator q-axis voltage are respectively obtained. According to the invention, the efficiency and robustness of the induction motor can be improved in the process of controlling the induction motor.

Description

Technical Field

[0001] The invention belongs to the field of induction motor control, and in particular relates to an induction motor efficiency optimization prediction control method. Background Art

[0002] Researching and developing new energy vehicles is a challenge and opportunity for the current automotive industry. Induction motors are widely used in new energy vehicle drive systems due to their simple structure, low cost, and durability. In the prior art, field-oriented control is generally used to control induction motors. Specifically, field-oriented control controls the currents on the AC and DC axes separately to achieve the effect of controlling the motor flux and torque. However, field-oriented control uses a proportional-integral controller, which has poor dynamic performance and robustness. In addition, 60% of the energy consumed in industrial applications comes from induction motors, and field-oriented control does not consider the efficiency of the motor. Therefore, how to improve the dynamic performance, robustness, and efficiency of induction motors in the process of controlling induction motors is currently a hot topic in induction motor control, which has certain theoretical significance and engineering application value. Summary of the invention

[0003] The purpose of the present invention is to propose an induction motor efficiency optimization predictive control method, which can improve the efficiency and robustness of the induction motor in the process of controlling the induction motor.

[0004] The present invention is achieved through the following technical solutions: An induction motor efficiency optimization prediction control method comprises the following steps: Step S1, constructing an induction motor mathematical model, wherein the induction motor mathematical model includes a d-axis stator current, a q-axis stator current, a d-axis mutual inductance current, a d-axis stator current, a q-axis stator current, and a d-axis mutual inductance current, both of which include iron loss resistance, and both of which include stator resistance; Step S2, constructing a voltage flux model and a current flux model based on a mathematical model of an induction motor, wherein the voltage flux model includes a sliding mode compensation term, obtaining errors of the voltage flux model and the current flux model, and limiting the errors within a preset interval through error conversion, thereby obtaining an estimated stator resistance when the errors are limited; Step S3, obtaining an expression for total motor loss based on a mathematical model of the induction motor and an estimated stator resistance; Step S4, constructing a super-local prediction model and a generalized proportional integral observer, and designing a cost function that takes into account the total loss of the motor, thereby obtaining the optimal stator d-axis voltage and stator q-axis voltage respectively.

[0005] Furthermore, in step S1, the mathematical model of the induction motor is as follows: It indicates that, i ds expressd Shaft stator current, express d The differential of the shaft stator current, R s is the stator resistance, L σs represents the stator leakage inductance, represents the synchronous angular velocity, i qs express q Shaft stator current, express q The differential of the shaft stator current, R t It represents the parallel value of iron loss resistance and rotor resistance, i dm express d Axis mutual inductance current, express d The differential of the shaft mutual inductance current, u ds express d Shaft stator voltage, L m Indicates mutual induction, u qs express q Shaft stator voltage, n p Indicates the number of motor pole pairs, J 1 represents the moment of inertia, T e represents the electromagnetic torque, T L represents the load torque, represents the electrical angular velocity, Represents the differential of electrical angular velocity.

[0006] Furthermore, in step S2, the voltage flux linkage model is expressed as , the current flux model is expressed as ,in, Indicates the rotor α The voltage flux of the shaft, Indicates the rotor α The differential of the voltage flux of the shaft, L r is the rotor inductance, Indicates stator α Phase voltage, Indicates stator α Phase current, Indicates stator α The differential of the phase current, σ is the leakage inductance coefficient, L s represents the stator inductance,K is the sliding mode gain, K () is the sliding mode compensation term, Indicates the rotor β The voltage flux of the shaft, Indicates the rotor β The differential of the voltage flux of the shaft, Indicates the rotor α The current flux of the shaft, Indicates the rotor α The differential of the current flux of the shaft, Indicates stator β Phase voltage, Indicates stator β Phase current, Indicates stator β The differential of the phase current, Indicates the rotor β The current flux of the shaft, Indicates the rotor β The differential of the current flux of the shaft, is the rotor time constant, Indicates the rotor α Phase flux, Indicates the rotor β Phase flux linkage.

[0007] Furthermore, in step S2, the error between the voltage flux model and the current flux model is , the differential of this error is expressed as ,in, , , represents the estimated value of the stator resistance, which is expressed as , , , k i represents the integral gain, k p represents the proportional gain, Indicates the rotor α The error components of the axes, Indicates the rotor β The error component of the axis.

[0008] Furthermore, in step S2, the specific process of limiting the error within a preset range through error conversion and then obtaining the estimated stator resistance when the error is limited is: The error bound is defined as ,in, represents the preset error boundary, is the initial boundary, is the final boundary, is the convergence rate; The conversion error is defined as ,in, is the error conversion equation, ; The estimated stator resistance when the error is limited is expressed as ,in, Depend on Through the error conversion equation, we get: Depend on Obtained through the error conversion equation.

[0009] Furthermore, in step S3, the total motor loss expression is: ,in, , , represents the iron loss resistance, Indicates the rotor resistance.

[0010] Furthermore, in step S4, the super local prediction model is constructed as ,in, F d F q It represents the lumped disturbance of dq axis, which is specifically expressed as , f d express F d The differential of f q express F q The differential of .

[0011] Furthermore, in step S4, the generalized proportional integral observer is designed as ,in, represents the differential of the estimated value of the stator current, represents the estimated value of the stator current, represents the stator current, represents the estimated value of the zeroth-order lumped disturbance, represents the differential of the estimate of the zeroth-order lumped disturbance, represents the differential of the estimated value of the nth-order lumped disturbance, Represents the control gain.

[0012] Furthermore, in step S4, the cost function is designed as , Lian Li The optimal stator is obtained by using the mathematical model of the induction motor d The shaft voltage is , Lian Li The optimal stator is obtained by using the motor mathematical model q The shaft voltage is ,in, , K Indicates K time, express q Axis reference current, when the induction motor operates in steady state, when the motor operates in steady state, the q-axis feedback current can accurately track the reference current, i dm ( K )and i ds ( K )equivalence.

[0013] The present invention has the following beneficial effects: The present invention first constructs an induction motor mathematical model including iron loss resistance, then constructs a voltage flux model and a current flux model based on the induction motor mathematical model, obtains the errors of the voltage flux model and the current flux model, and limits the errors within a preset interval through error conversion, thereby obtaining an estimated stator resistance when the error is limited. In order to facilitate compensation of the error between the voltage flux model and the current flux model, a sliding mode compensation term is set, and limiting the error within a preset interval can accelerate the convergence rate. Then, based on the induction motor mathematical model and the estimated stator resistance, an expression for the total loss of the motor is obtained. Finally, a super-local prediction model and a generalized proportional integral observer are constructed, and a cost function considering the total loss of the motor is designed, thereby obtaining the optimal stator d-axis voltage and stator q-axis voltage respectively, thereby achieving the improvement of the dynamic performance and robustness of the induction motor in the process of controlling the induction motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention is further described in detail below with reference to the accompanying drawings.

[0015] Figure 1 It is a flow chart of the present invention.

[0016] Figure 2 It is a principle block diagram of the present invention.

[0017] Figure 3 It is a circuit diagram corresponding to the mathematical model of the induction motor of the present invention. DETAILED DESCRIPTION

[0018] like Figure 1 to Figure 2 As shown, the induction motor efficiency optimization prediction control method is characterized by comprising the following steps: Step S1, constructing an induction motor mathematical model, wherein the induction motor mathematical model includes a d-axis stator current, a q-axis stator current, a d-axis mutual inductance current, a d-axis stator current, a q-axis stator current, and a d-axis mutual inductance current, both of which include iron loss resistance, and both of which include stator resistance; Specifically, the induction motor is a very complex linear system. The traditional mathematical model of the induction motor usually ignores the iron loss in order to simplify the model. However, the use of a simplified model usually affects the performance of the efficiency optimization algorithm. Therefore, the circuit diagram corresponding to the mathematical model of the induction motor constructed by the present invention is as follows: Figure 3 As shown, the mathematical model of the induction motor is as follows: It indicates that, i ds express d Shaft stator current, express d The differential of the shaft stator current, R s is the stator resistance, L σs represents the stator leakage inductance, represents the synchronous angular velocity, i qs express q Shaft stator current, express q The differential of the shaft stator current, R t It represents the parallel value of iron loss resistance and rotor resistance, i dm express d Axis mutual inductance current, express d The differential of the shaft mutual inductance current, u ds express d Shaft stator voltage, L m Indicates mutual induction, u qs express q Shaft stator voltage, n p Indicates the number of motor pole pairs, J 1 represents the moment of inertia, T e represents the electromagnetic torque, T L represents the load torque, represents the electrical angular velocity, Represents the differential of electrical angular velocity.

[0019] Step S2, constructing a voltage flux model and a current flux model based on a mathematical model of an induction motor, wherein the voltage flux model includes a sliding mode compensation term, obtaining errors of the voltage flux model and the current flux model, and limiting the errors within a preset interval through error conversion, thereby obtaining an estimated stator resistance when the errors are limited; Specifically, the change of stator resistance will affect the performance of the efficiency optimization algorithm. To overcome this problem, a preset performance control model reference adaptive (PPC-MRAS) parameter identification method is designed as described in this step. The voltage flux model is expressed as , the current flux model is expressed as In order to compensate for the error between the reference model (i.e., current flux model) and the adjustable model (i.e., voltage flux model), a sliding mode compensation term is added to the voltage flux model. and , in, Indicates the rotor α The voltage flux of the shaft, Indicates the rotor α The differential of the voltage flux of the shaft, L r is the rotor inductance, Indicates stator α Phase voltage, Indicates stator α Phase current, Indicates stator α The differential of the phase current, is the leakage inductance coefficient, L s represents the stator inductance, K is the sliding mode gain, K () is the sliding mode compensation term, Indicates the rotor β The voltage flux of the shaft, Indicates the rotor β The differential of the voltage flux of the shaft, Indicates the rotor α The current flux of the shaft, Indicates the rotor α The differential of the current flux of the shaft, Indicates stator β Phase voltage, Indicates stator β Phase current, Indicates stator β The differential of the phase current, Indicates the rotor β The current flux of the shaft, Indicates the rotor β The differential of the current flux of the shaft, is the rotor time constant, Indicates the rotor α Phase flux, Indicates the rotor β Phase flux, for Axial current type flux linkage, for Shaft voltage type flux linkage.

[0020] The error between the voltage flux model and the current flux model is , the differential of this error is expressed as ,in, , , represents the estimated value of stator resistance; A proportional-integral controller is usually used as the adaptive law. The adaptive law (estimated value) of the stator resistance can be expressed as , in order to satisfy Popov's superstability theorem ,Will g 1( t )and g 2( t ) is designed as , k i represents the integral gain, k p represents the proportional gain, Indicates the rotor α The error component of the axis, expressed as Rotor β The error component of the axis.

[0021] The specific process of limiting the error within a preset range through error conversion (to speed up the convergence rate of MRAS) and then obtaining the estimated stator resistance when the error is limited is: The error bound is defined as ,in, represents the preset error boundary, is the initial boundary, is the final boundary, is the convergence rate; The conversion error is defined as ,in, is the error conversion equation, ; Therefore g 1( t )and g 2( t ) can be rewritten as , then the estimated stator resistance when the error is limited is expressed as ,in, Depend on Through the error conversion equation, we get: Depend on Obtained through the error conversion equation.

[0022] Step S3, obtaining an expression for total motor loss based on a mathematical model of the induction motor and an estimated stator resistance; Specifically, this step implements the adaptive loss model method: Under steady-state conditions, assuming that the magnetic field orientation is accurate, , we can get the following initial conditions , then the total motor loss can be expressed as ,in, P Cus , P iron , P Cur They represent stator copper loss, iron loss, and rotor copper loss respectively. Friction loss and inverter switching loss are relatively small and are therefore ignored. Considering the initial conditions as mentioned above, the total motor loss can be simplified to The optimal excitation current with minimal loss can be obtained through the total motor loss model. ,in, , , represents the iron loss resistance, Indicates the rotor resistance.

[0023] Step S4, constructing a super-local prediction model and a generalized proportional integral observer, and designing a cost function that takes into account the total loss of the motor, thereby obtaining the optimal stator d-axis voltage and stator q-axis voltage respectively; Specifically, the generalized proportional-integral observer is designed as ,in, represents the differential of the estimated value of the stator current, represents the estimated value of the stator current, represents the stator current, represents the estimated value of the zeroth-order lumped disturbance, represents the differential of the estimate of the zeroth-order lumped disturbance, represents the differential of the estimated value of the nth-order lumped disturbance, Represents the control gain.

[0024] The traditional mathematical model of continuous set predictive current control is ,in, u ds , u qs Respectively represent stator d Shaft voltage, stator q Shaft voltage.

[0025] Since changes in parameters such as inductance and resistance will lead to inaccurate induction motor models, in order to improve the accuracy of induction motor models, a super-local prediction model is constructed. ,in, F d F q It represents the lumped disturbance of dq axis, which is specifically expressed as , f d express F d The differential of f q express F q The differential of For illustration F d F q is differentiable.

[0026] In traditional predictive current control, the cost function is usually given by dq The error term of the reference value and feedback value of the shaft current is composed of the error term of the reference value and feedback value of the shaft current, but it does not consider the motor efficiency problem. In order to improve the motor efficiency, the present invention takes the motor loss into consideration in the cost function of the predictive control. The specific cost function is designed as follows: ,in, , Lian Li The optimal stator is obtained by using the mathematical model of the induction motor d The shaft voltage is , Lian Li The optimal stator is obtained by using the mathematical model of the induction motor q The shaft voltage is ,in, , K Indicates K time, express q The q-axis reference current comes from the proportional-integral controller. When the induction motor works in a steady state, the q-axis feedback current can accurately track the reference current. i dm ( K )and i ds ( K )equivalence, R t express R r ||R Fe .

[0027] The above description is only a preferred embodiment of the present invention, and therefore cannot be used to limit the scope of implementation of the present invention. That is, equivalent changes and modifications made according to the scope of the patent application of the present invention and the contents of the specification should still fall within the scope covered by the patent of the present invention.

Claims

1. An induction motor efficiency optimization prediction control method, characterized in that: The steps include: Step S1, constructing an induction motor mathematical model, wherein the induction motor mathematical model includes a d-axis stator current, a q-axis stator current, a d-axis mutual inductance current, a d-axis stator current, a q-axis stator current, and a d-axis mutual inductance current, both of which include iron loss resistance, and both of which include stator resistance; Step S2, constructing a voltage flux model and a current flux model based on a mathematical model of an induction motor, wherein the voltage flux model includes a sliding mode compensation term, obtaining errors of the voltage flux model and the current flux model, and limiting the errors within a preset interval through error conversion, thereby obtaining an estimated stator resistance when the errors are limited; Step S3, obtaining an expression for total motor loss based on a mathematical model of the induction motor and an estimated stator resistance; Step S4, constructing a super-local prediction model and a generalized proportional integral observer, and designing a cost function that takes into account the total loss of the motor, thereby obtaining the optimal stator d-axis voltage and stator q-axis voltage respectively.

2. The induction motor efficiency optimization prediction control method according to claim 1, characterized in that: In step S1, the mathematical model of the induction motor is as follows: It indicates that, i ds express d Shaft stator current, express d The differential of the shaft stator current, R s is the stator resistance, L σs represents the stator leakage inductance, represents the synchronous angular velocity, i qs express q Shaft stator current, express q The differential of the shaft stator current, R t Represents the parallel value of the iron loss resistance and the rotor resistance, i dm express d Axis mutual inductance current, express d The differential of the shaft mutual inductance current, u ds express d Shaft stator voltage, L m Indicates mutual induction, u qs express q Shaft stator voltage, n p Indicates the number of motor pole pairs, J 1 represents the moment of inertia, T e represents the electromagnetic torque, T L represents the load torque, represents the electrical angular velocity, Represents the differential of electrical angular velocity.

3. The induction motor efficiency optimization prediction control method according to claim 2 is characterized in that: In step S2, the voltage flux linkage model is expressed as , the current flux model is expressed as ,in, Indicates the rotor α The voltage flux of the shaft, Indicates the rotor α The differential of the voltage flux of the shaft, L r is the rotor inductance, Indicates stator α Phase voltage, Indicates stator α Phase current, Indicates stator α The differential of the phase current, σ is the leakage inductance coefficient, L s represents the stator inductance, K is the sliding mode gain, K () is the sliding mode compensation term, Indicates the rotor β The voltage flux of the shaft, Indicates the rotor β The differential of the voltage flux of the shaft, Indicates the rotor α The current flux of the shaft, Indicates the rotor α The differential of the current flux of the shaft, Indicates stator β Phase voltage, Indicates stator β Phase current, Indicates stator β The differential of the phase current, Indicates the rotor β The current flux of the shaft, Indicates the rotor β The differential of the current flux of the shaft, represents the rotor time constant, Indicates the rotor α Phase flux, Indicates the rotor β Phase flux linkage.

4. The method for optimizing the efficiency prediction and control of an induction motor according to claim 3, characterized in that: In step S2, the error between the voltage flux model and the current flux model is , the differential of this error is expressed as ,in, , , represents the estimated value of the stator resistance, which is expressed as , , , k i represents the integral gain, k p represents the proportional gain, Indicates the rotor α The error components of the axes, Indicates the rotor β The error component of the axis.

5. The method for optimizing the efficiency prediction and control of an induction motor according to claim 4, characterized in that: In step S2, the specific process of limiting the error within a preset range through error conversion and then obtaining the estimated stator resistance when the error is limited is: The error bound is defined as ,in, represents the preset error boundary, is the initial boundary, is the final boundary, is the convergence rate; The conversion error is defined as ,in, is the error conversion equation, ; The estimated stator resistance when the error is limited is expressed as ,in, Depend on Through the error conversion equation, we get: Depend on Obtained through the error conversion equation.

6. The method for optimizing the efficiency prediction and control of an induction motor according to claim 5, characterized in that: In step S3, the total motor loss expression is: ,in, , , represents the iron loss resistance, Indicates the rotor resistance.

7. The induction motor efficiency optimization prediction control method according to claim 6, characterized in that: In step S4, the super local prediction model is constructed as ,in, F d F q It represents the lumped disturbance of dq axis, which is specifically expressed as , f d express F d The differential of f q express F q The differential of .

8. The induction motor efficiency optimization prediction control method according to claim 7, characterized in that: In step S4, the generalized proportional integral observer is designed as ,in, represents the differential of the estimated value of the stator current, represents the estimated value of the stator current, represents the stator current, represents the estimated value of the zeroth-order lumped disturbance, represents the differential of the estimate of the zeroth-order lumped disturbance, represents the differential of the estimated value of the nth-order lumped disturbance, Represents the control gain.

9. The method for optimizing the efficiency prediction and control of an induction motor according to claim 8, characterized in that: In step S4, the cost function is designed as , Lian Li The optimal stator is obtained by using the mathematical model of the induction motor d The shaft voltage is , Lian Li The optimal stator is obtained by using the mathematical model of the induction motor q The shaft voltage is ,in, , K Indicates K time, express q Axis reference current, under steady-state conditions, the q-axis feedback current can accurately track the q-axis reference current. i dm ( K )and i ds ( K )equivalence.

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