CN116805850A - Parameter estimation method for three-phase permanent magnet synchronous motor based on digital twin model - Google Patents

Parameter estimation method for three-phase permanent magnet synchronous motor based on digital twin model Download PDF

Info

Publication number
CN116805850A
CN116805850A CN202310700240.1A CN202310700240A CN116805850A CN 116805850 A CN116805850 A CN 116805850A CN 202310700240 A CN202310700240 A CN 202310700240A CN 116805850 A CN116805850 A CN 116805850A
Authority
CN
China
Prior art keywords
permanent magnet
magnet synchronous
synchronous motor
phase
model
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.)
Pending
Application number
CN202310700240.1A
Other languages
Chinese (zh)
Inventor
宋文胜
邹宇超
张思慧
麻宸伟
李佳耀
陈健
王青元
葛兴来
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.)
Southwest Jiaotong University
Original Assignee
Southwest Jiaotong 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 Southwest Jiaotong University filed Critical Southwest Jiaotong University
Priority to CN202310700240.1A priority Critical patent/CN116805850A/en
Publication of CN116805850A publication Critical patent/CN116805850A/en
Pending legal-status Critical Current

Links

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
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/141—Flux estimation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/022—Synchronous motors
    • 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
    • H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
    • H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
    • H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation

Abstract

The application discloses a parameter estimation method of a three-phase permanent magnet synchronous motor based on a digital twin model, relates to the field of digital twin application research of permanent magnet synchronous motors, and aims to solve the problem that the parameters of the three-phase permanent magnet synchronous motor cannot be effectively estimated in the prior art; firstly, establishing a closed loop mathematical model of a three-phase two-level inverter, a permanent magnet synchronous motor and a controller; then, using sensor data of an experimental platform, using an intelligent algorithm as a bridge, and continuously iterating to optimize key parameters, and establishing a closed-loop system digital twin model of the permanent magnet synchronous motor driven by the three-phase inverter when the output of the established model is consistent with the output of an actual physical circuit; under different working conditions, the digital twin model can simulate the running state of an actual circuit; finally, estimating the obtained key parameters of the permanent magnet synchronous motor for a plurality of times and taking an average value to finally obtain the key parameters of the permanent magnet synchronous motor; the application can realize the estimation of the key parameters of the permanent magnet synchronous motor and can obtain a plurality of key parameters at the same time.

Description

Parameter estimation method for three-phase permanent magnet synchronous motor based on digital twin model
Technical Field
The application relates to the field of digital twin application research of permanent magnet synchronous motors, in particular to a three-phase permanent magnet synchronous motor parameter estimation method based on a digital twin model.
Background
Permanent Magnet Synchronous Motors (PMSM) are widely applied to various fields such as rail traction transportation, electric automobiles and the like by high power density, excellent efficiency and excellent controllability; the parameters of the permanent magnet synchronous motor have important significance in motor control and state monitoring; however, motor manufacturers typically do not disclose key parameters of the motor, such as stator resistance, inductance, and flux linkage; in addition, the motor parameters are different according to different working conditions; therefore, it is necessary to acquire parameters of the permanent magnet synchronous motor by adopting a proper parameter estimation method.
Many parameter estimation methods have been proposed; the parameter estimation method of the three-phase permanent magnet synchronous motor based on the digital twin model is more effective; in addition, the method of parameter estimation using existing signals is the most widespread; for example, recursive Least Squares (RLS), extended Kalman Filtering (EKF), model Reference Adaptive Systems (MRAS), and observer-based methods all utilize existing signals to perform parameter estimation of a permanent magnet synchronous motor, and all perform analytical estimation primarily based on the physical model of the system; with the development of artificial intelligence, a method based on data driving is also an effective means for realizing parameter estimation and a method for estimating parameters of a three-phase permanent magnet synchronous motor based on a digital twin model, and the data driving needs a large amount of data to train the model, which is difficult to realize in the current power electronics field.
Disclosure of Invention
In order to solve the problems in the prior art, the application aims to provide a parameter estimation method of a three-phase permanent magnet synchronous motor based on a digital twin model, and aims to solve the problem that the parameters of the three-phase permanent magnet synchronous motor cannot be effectively estimated in the prior art.
The parameter estimation method of the three-phase permanent magnet synchronous motor based on the digital twin model comprises the following steps:
step 1: based on kirchhoff's law, constructing a main circuit topology mathematical model of the three-phase two-level inverter;
step 2: substituting the mathematical model of the three-phase permanent magnet synchronous motor under the dq axis coordinate into the main circuit topology mathematical model obtained in the step 1, and solving the model by using a fourth-order range-Kutta method to obtain the mathematical model of the three-phase inverter driving permanent magnet synchronous motor;
step 3: constructing a discretized mathematical model of a permanent magnet synchronous motor control system, wherein the permanent magnet synchronous motor control system is controlled by adopting a model prediction current control method;
step 4: combining the main circuit topology mathematical model of the step 1, the three-phase inverter permanent magnet synchronous motor mathematical model of the step 2 and the discretization mathematical model of the control system of the step 3, and solving the combined mathematical model by using a fourth-order Runge-Kutta method to finally obtain a discretization closed-loop mathematical model of the three-phase two-level inverter driving permanent magnet synchronous motor system;
step 5: combining the discretized closed-loop mathematical model in the step 4 with output data of an actual three-phase inverter driven permanent magnet synchronous motor experimental platform, and iteratively optimizing internal key parameters of the model by taking an intelligent algorithm as a bridge to ensure that the output characteristics of the constructed closed-loop mathematical model are consistent with the output characteristics of an actual physical circuit;
step 6: when the difference value of the output data of the discretization closed-loop mathematical model and the actual physical circuit is smaller than a set threshold value, the parameter estimation value corresponding to the discretization closed-loop mathematical model is stored, then parameter estimation is carried out for a plurality of times, the non-convergence result in the parameter estimation value is removed, and other parameter estimation values are averaged to be used as a final parameter estimation value.
Preferably, the step 6 further includes: after the final parameter estimation value is applied to the discretized closed-loop mathematical model, the discretized closed-loop mathematical model is used as a closed-loop digital twin model of the three-phase inverter driving permanent magnet synchronous motor.
Preferably, in the step 1, the mathematical model of the main circuit topology is:
wherein U is an 、U bn 、U cn Respectively three-phase voltages, S i Representative power switch T i Wherein 1 is on and 0 is off, said A 1 、A 2 、B 1 、B 2 、C 1 、C 2 The following are provided:
wherein Udc is the dc side voltage, i k,sign Is the phase current (k=a, b, c).
Preferably, in the step 2, the three-phase permanent magnet synchronous motor is powered by a three-phase inverter, and the dq axis mathematical model thereof is expressed as:
wherein i is d ,i q Respectively dq axis currents, U d ,U q Respectively dq axis voltage, R is stator flux linkage, L d ,L q Respectively dq axis inductance, ψ f Is flux linkage omega e Is the electrical angular velocity; the dq coordinate axis is transformed from the abc coordinate axis, and the transformation formula is as follows:
wherein θ e Is the rotor position angle;
substituting the model into the main circuit topology mathematical model of the three-phase two-level inverter obtained in the step 1 to obtain the mathematical model of the three-phase inverter driving permanent magnet synchronous motor, and solving the model by using a fourth-order Runge-Kutta method, wherein the method specifically comprises the following steps:
the model differential equation is rewritten as:
wherein n is the current magnitude at the current moment, n+1 is the current magnitude at the next moment, h is the calculation step length from the moment n to the moment n+1, and K a1 -K a4 ，K b1 -K b4 Calculated by the following equation:
preferably, the step 3 includes the steps of:
the rotating speed outer ring is PI control, the current inner ring adopts model prediction control, an optimal voltage vector is found out, and a discretization mathematical model of a control system is built:
wherein omega err As a rotation speed error omega ref For reference rotational speed, ω is rotational speed, K p And K I The PI parameter is adopted, and T is the control period; the subscript k +1 indicates the next time, the subscript k indicates the current time,representing i q Is a desired value of (2); />Representing i d Is a desired value of (2).
Preferably, in the step 4, the discrete closed-loop mathematical model of the permanent magnet synchronous motor driven by the three-phase two-level inverter is:
where n is the current time value and n+1 is the next time value.
Preferably, the step 5 includes the steps of:
setting up a three-phase two-level inverter driving permanent magnet synchronous motor experimental platform, collecting output characteristic data of steady state and dynamic operation of the experimental platform as input of a digital twin model, and taking a difference value of the output characteristics of the twin model and an actual system as an iterative objective function of a particle swarm algorithm, namely:
wherein f obj N is the sample size, i, as an objective function am ,i bm ,i cm I is the three-phase current actual measurement value a ,i b ,i c Outputting three-phase current for digital twinning;
calculating the current f of each particle obj Recording the individual optimal solution and the global optimal solution of the iteration, and then calculating the updated f according to the initial speed of the particles, the cognition of the particles to the optimal solution and the cognition of other particles to the optimal solution, and updating the speed and the position of the particles obj Updating the individual optimal solution and the global optimal solution; repeating the process, iteratively optimizing the internal key parameters of the model, so that the output characteristic of the closed-loop mathematical model approximates to the output characteristic of the actual physical circuit, wherein the key parameters considered here refer to the parameters selected in the step 2; the principle of each update speed and position of the particles is as follows:
where i is the number of iterations, j is the number of particles, v i,j The movement speed of the jth particle in the ith iteration; p (P) i,j Is the position of the jth particle in the ith iteration; g best And P best Representing global best particles and personal best particles, respectively; omega, r 1 、r 2 、c 1 And c 2 Is a parameter; ω is the inertia coefficient, with a larger ω meaning that the particle is more prone to select the previous motion path; c 1 For an individual learning factor, a larger value indicates that the particles are more prone to select a previous individual optimal solution; c 2 Is a group learning factor, c 2 The larger the probability that a particle selects a globally optimal solution is, the greater; r is (r) 1 And r 2 Is [0,1 ]]Random numbers in between;
in order to reduce the probability of the particle swarm algorithm being trapped in the local optimum, chaos is introduced into the particle swarm; when iterating to a certain number of times, the global optimal particle G is firstly obtained by the following formula best Transition into the (0, 1) interval:
where x is the dimension of the particle, b x And a x The upper and lower boundaries of the particles respectively;
then, the chaotic sequence is obtained through a LOGISTIC equation:
z x,y+1 ＝μ(1-z x,y )z x,y ,y＝1,2…
wherein z is x,y An xth chaotic variable representing the yth iteration; mu is a control parameter, generally 4 is taken, and the whole system is in a complete chaotic state;
finally, the chaotic sequence is returned to the original solution space by the following formula, the objective function is recalculated, and G is updated best And P best ：
P i,y ＝z y (b-a)+a。
The beneficial effects of the application include:
1) Compared with the existing modeling method, the method has the advantages that a complete closed-loop model of the three-phase two-level inverter driving permanent magnet synchronous motor with a control system is constructed, the model is solved by using a fourth-order Runge-Kutta method, the complexity of model solving is reduced, and in addition, the probability of the particle swarm algorithm falling into local optimum is reduced by using a chaotic algorithm;
2) The constructed digital twin model can effectively simulate the steady state and dynamic running state of an actual system, output external characteristic waveforms consistent with the actual physical system, help a user understand and debug the system more conveniently, and guide the control parameter design and running state prediction of a permanent magnet synchronous motor control system;
3) The proposed parameter estimation and data processing method can realize effective monitoring of key parameters of the permanent magnet synchronous motor, the final stator resistance estimation error is about 10%, the stator inductance and flux linkage estimation error are about 10%, and all parameters are acquired simultaneously; and parameters can be well estimated in both transient and steady states.
Drawings
FIG. 1 is a three-phase two-level inverter driven permanent magnet synchronous motor topology;
FIG. 2 is a model predictive current control block diagram;
FIGS. 3 (a), (b), (c), and (d) are the output results of the actual physical circuit in steady state;
fig. 4 (a), (b), (c), and (d) are comparison between the output characteristics of the digital twin model and the actual physical circuit in the steady state of the system;
FIGS. 5 (a) and (b) are the results of the output characteristics of the actual physical circuit under system acceleration and deceleration;
FIGS. 6 (a) and (b) are graphs showing the comparison between the output characteristic results of the digital twin model and the actual physical circuit under the system acceleration and deceleration;
FIGS. 7 (a) and (b) are the results of the output characteristics of the actual physical circuit under the load of the system increase or decrease;
FIGS. 8 (a) and (b) are graphs comparing the output characteristics of the digital twin model and the actual physical circuit under the system acceleration and deceleration;
FIGS. 9 (a), (b) and (c) are R, L, respectively d,q 、ψ f Is a parameter estimation result of (a);
FIGS. 10 (a), (b) and (c) are R, L at different speeds d,q 、ψ f Is a parameter estimation result of (a);
FIGS. 11 (a), (b), and (c) are R, L for different load torques d,q 、ψ f Is a parameter estimation result of (a);
FIGS. 12 (a), (b), and (c) are each R, L for different DC voltages d,q 、ψ f Is a parameter estimation result of (a);
FIGS. 13 (a), (b), and (c) are, respectively, R, L for parameter mismatch d,q 、ψ f Is a parameter estimation result of (a);
fig. 14 is a flowchart of a method for estimating parameters of a three-phase permanent magnet synchronous motor based on a digital twin model.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
Example 1
Specific embodiments of the present application will be described in detail below with reference to fig. 1-14;
the parameter estimation method of the three-phase permanent magnet synchronous motor based on the digital twin model comprises the following steps:
step 1: based on kirchhoff's law, the three-phase two-level inverter mathematical model is as follows
Wherein U is an 、U bn 、U cn Respectively three-phase voltages, S i Representative power switch T i Drive signal (1 is on, 0 is off), A 1 、A 2 、B 1 、B 2 、C 1 、C 2 Is that
Wherein Udc is the dc side voltage, i k,sign Is the phase current (k=a, b, c).
Step 2: the three-phase permanent magnet synchronous motor is powered by a three-phase inverter, and the dq axis mathematical model thereof can be expressed as
Wherein i is d,q Respectively dq axis currents, U d,q Is dq axis voltage, R is stator flux linkage, L d,q For dq axis inductance, ψ f Is flux linkage omega e Is the electrical angular velocity. The dq coordinate axis is transformed from the abc coordinate axis
Wherein θ e Is the rotor position angle.
Substituting the model into the mathematical model of the three-phase two-level inverter in the step 1 to obtain the mathematical model of the three-phase inverter driving permanent magnet synchronous motor, and solving the model by using a fourth-order Runge-Kutta method, wherein the method specifically comprises the following steps:
the model differential equation is rewritten as:
wherein n is the current magnitude at the current moment, n+1 is the current magnitude at the next moment, h is the calculation step length from the moment n to the moment n+1, and K a1 -K a4 ，K b1 -K b4 Calculated by the following equation:
step 3: the control system of the permanent magnet synchronous motor adopts model predictive current control, as shown in figure 2, i is adopted d Control=0, the rotating speed outer ring is PI control, the current inner ring adopts model prediction control, an optimal voltage vector is found, and a discretization mathematical model of the control system is built:
wherein omega err As a rotation speed error omega ref For reference rotational speed, ω is rotational speed, K p And K I For PI parameters, T is the control period, subscript k +1 indicates the next time, subscript k indicates the current time,representing i q Is a desired value of (2); />Representing i d Is not shown, and here +.>Constant equal to 0.
Step 4: combining the mathematical model of the three-phase two-level inverter in the step 1, the mathematical model of the three-phase inverter driving permanent magnet synchronous motor in the step 2 and the discretization mathematical model of the control system in the step 3 to finally obtain a discretization closed-loop mathematical model of the three-phase two-level inverter driving permanent magnet synchronous motor system;
where n is the current time value and n+1 is the next time value.
Step 5: setting up a three-phase two-level inverter driving permanent magnet synchronous motor experimental platform, collecting steady-state and dynamic operation output characteristic data as input of a digital twin model, and taking the difference value of the output characteristics of the twin model and an actual system as an iterative objective function of a particle swarm algorithm, namely
Wherein f obj N is the sample size, i, as an objective function am ,i bm ,i cm I is the three-phase current actual measurement value a ,i b I is a digital twin output three-phase current.
Calculating the current f of each particle obj Recording the individual optimal solution and the global optimal solution of the iteration, and then calculating the updated f according to the initial speed of the particles, the cognition of the particles to the optimal solution and the cognition of other particles to the optimal solution, and updating the speed and the position of the particles obj And updating the individual optimal solution and the global optimal solution. Repeating the process, iteratively optimizing the internal key parameters of the model, so that the output characteristic of the closed-loop mathematical model approximates to the output characteristic of the actual physical circuit, wherein the key parameters considered here refer to the parameters selected in the step 2. The principle of each update speed and position of the particles is as follows:
where i is the number of iterations, j is the number of particles, v i,j The movement speed of the jth particle in the ith iteration; p (P) i,j Is the position of the jth particle in the ith iteration; g best And P best Representing global best particles and personal best particles, respectively; omega, r 1 、r 2 、c 1 And c 2 Is a parameter. ω is the inertia coefficient, with a larger ω meaning that the particle is more prone to select the previous motion path; c 1 For an individual learning factor, a larger value indicates that the particles are more prone to select a previous individual optimal solution; c 2 Is a group learning factor, c 2 The larger indicates the greater likelihood that the particle will select the globally optimal solution. r is (r) 1 And r 2 Is [0,1 ]]Random numbers in between.
In order to reduce the probability of the particle swarm algorithm being trapped in a local optimum, chaos is introduced into the particle swarm. When iterating to a certain number of times, the global optimal particle G is firstly obtained by the following formula best Transition into the (0, 1) interval:
where x is the dimension of the particle, b x And a x The upper and lower boundaries of the particle, respectively.
Then, the chaotic sequence is obtained through a LOGISTIC equation:
z x,y+1 ＝μ(1-z x,y )z x,y ,y＝1,2L
wherein z is x,y An xth chaotic variable representing the yth iteration; mu is a control parameter, generally 4 is taken, and the whole system is in a complete chaotic state.
Finally, the chaotic sequence is returned to the original solution space by the following formula, the objective function is recalculated, and G is updated best And P best ：
P i,y ＝z y (b-a)+a
Step 6: when the difference value of the output data of the mathematical model and the actual physical circuit is smaller than a set threshold value or reaches the iteration times, the obtained closed-loop discretization model is the closed-loop digital twin model of the permanent magnet synchronous motor driven by the three-phase inverter. The output data of the actual physical circuit is shown in fig. 3, 5 and 7, and the obtained closed-loop digital twin model simulates the steady-state and dynamic processes of the actual physical circuit, as shown in fig. 4, 6 and 8; the comparison graph shows that the output of the closed-loop digital twin model is consistent with the output of the actual physical circuit, and the obtained closed-loop digital twin model is accurate.
In the step 5, the iterative optimization key parameter process is called parameter estimation of the permanent magnet synchronous motor, in order to avoid the influence caused by local optimum or non-convergence of the particle swarm, parameter estimation is carried out for a plurality of times, the non-convergence result is removed, other estimation results are averaged to be used as a final parameter estimation value, and the influence of an error value on the final estimation value is reduced as much as possible.
The parameter estimation results of the closed-loop digital twin model under different working conditions are shown in fig. 9-13, and the working conditions of the motor are shown in table 1.
Table 1 motor operating conditions
Taking working condition 2 as an example, collecting 5 groups of data, obtaining 10 times of estimated values for each group of data, taking an average value, and carrying out statistics and error analysis on optimizing results of key parameters, wherein as shown in table 2, it can be seen that the estimated error of the final stator resistance is about 10%, and the estimated errors of the stator inductance and the flux linkage are about 10%; in addition, all parameters are acquired simultaneously, and parameters can be well estimated in transient and steady states.
Table 2 key parameter monitoring results
The above examples merely illustrate specific embodiments of the application, which are described in more detail and are not to be construed as limiting the scope of the application. It should be noted that it is possible for a person skilled in the art to make several variants and modifications without departing from the technical idea of the application, which fall within the scope of protection of the application.

Claims (7)

1. The parameter estimation method of the three-phase permanent magnet synchronous motor based on the digital twin model is characterized by comprising the following steps of:
step 1: based on kirchhoff's law, constructing a main circuit topology mathematical model of the three-phase two-level inverter;
step 2: substituting the mathematical model of the three-phase permanent magnet synchronous motor under the dq axis coordinate into the main circuit topology mathematical model obtained in the step 1, and solving the model by using a fourth-order range-Kutta method to obtain the mathematical model of the three-phase inverter driving permanent magnet synchronous motor;
step 3: constructing a discretized mathematical model of a permanent magnet synchronous motor control system, wherein the permanent magnet synchronous motor control system is controlled by adopting a model prediction current control method;
step 4: combining the main circuit topology mathematical model of the step 1, the three-phase inverter permanent magnet synchronous motor mathematical model of the step 2 and the discretization mathematical model of the control system of the step 3, and solving the combined mathematical model by using a fourth-order Runge-Kutta method to finally obtain a discretization closed-loop mathematical model of the three-phase two-level inverter driving permanent magnet synchronous motor system;
step 5: combining the discretized closed-loop mathematical model in the step 4 with output data of an actual three-phase inverter driven permanent magnet synchronous motor experimental platform, and iteratively optimizing internal key parameters of the model by taking an intelligent algorithm as a bridge to ensure that the output characteristics of the constructed closed-loop mathematical model are consistent with the output characteristics of an actual physical circuit;
step 6: when the difference value of the output data of the discretization closed-loop mathematical model and the actual physical circuit is smaller than a set threshold value, the parameter estimation value corresponding to the discretization closed-loop mathematical model is stored, then parameter estimation is carried out for a plurality of times, the non-convergence result in the parameter estimation value is removed, and other parameter estimation values are averaged to be used as a final parameter estimation value.
2. The method for estimating parameters of a three-phase permanent magnet synchronous motor based on a digital twin model according to claim 1, wherein the step 6 further comprises: after the final parameter estimation value is applied to the discretized closed-loop mathematical model, the discretized closed-loop mathematical model is used as a closed-loop digital twin model of the three-phase inverter driving permanent magnet synchronous motor.
3. The method for estimating parameters of a three-phase permanent magnet synchronous motor based on a digital twin model according to claim 1, wherein in the step 1, the main circuit topology mathematical model is:
wherein U is an 、U bn 、U cn Respectively three-phase voltages, S i Representative power switch T i Wherein 1 is on and 0 is off, said A 1 、A 2 、B 1 、B 2 、C 1 、C 2 The following are provided:
wherein Udc is the dc side voltage, i k,sign Is the phase current (k=a, b, c).
4. The method for estimating parameters of a three-phase permanent magnet synchronous motor based on a digital twin model according to claim 1, wherein in the step 2, the three-phase permanent magnet synchronous motor is powered by a three-phase inverter, and the dq axis mathematical model thereof is represented as:
wherein i is d ,i q Respectively dq axis currents, U d ,U q Respectively dq axis voltage, R is stator flux linkage, L d ,L q Respectively dq axis inductance, ψ f Is flux linkage omega e Is the electrical angular velocity; the dq coordinate axis is defined by abc coordinateThe axes are transformed from the following transformation formula:
wherein θ e Is the rotor position angle;
substituting the model into the main circuit topology mathematical model of the three-phase two-level inverter obtained in the step 1 to obtain the mathematical model of the three-phase inverter driving permanent magnet synchronous motor, and solving the model by using a fourth-order Runge-Kutta method, wherein the method specifically comprises the following steps:
the model differential equation is rewritten as:
wherein n is the current magnitude at the current moment, n+1 is the current magnitude at the next moment, h is the calculation step length from the moment n to the moment n+1, and K a1 -K a4 ，K b1 -K b4 Calculated by the following equation:
5. the method for estimating parameters of a three-phase permanent magnet synchronous motor based on a digital twin model according to claim 1, wherein the step 3 comprises the steps of:
the rotating speed outer ring is PI control, the current inner ring adopts model prediction control, an optimal voltage vector is found out, and a discretization mathematical model of a control system is built:
wherein omega err As a rotation speed error omega ref For the reference rotational speed, ω is the rotational speed,K p and K I The PI parameter is adopted, and T is the control period; the subscript k +1 indicates the next time, the subscript k indicates the current time,representing i q Is a desired value of (2); />Representing i d Is a desired value of (2).
6. The method for estimating parameters of a three-phase permanent magnet synchronous motor based on a digital twin model according to claim 1, wherein in the step 4, a discretized closed-loop mathematical model of a three-phase two-level inverter driving permanent magnet synchronous motor system is as follows:
where n is the current time value and n+1 is the next time value.
7. The method for estimating parameters of a three-phase permanent magnet synchronous motor based on a digital twin model according to any one of claims 1 to 6, wherein the step 5 comprises the steps of:
setting up a three-phase two-level inverter driving permanent magnet synchronous motor experimental platform, collecting output characteristic data of steady state and dynamic operation of the experimental platform as input of a digital twin model, and taking a difference value of the output characteristics of the twin model and an actual system as an iterative objective function of a particle swarm algorithm, namely:
wherein f obj N is the sample size, i, as an objective function am ,i bm ,i cm I is the three-phase current actual measurement value a ,i b ,i c Outputting three-phase current for digital twinning;
calculating the current f of each particle obj Recording the individual optimal solution and the global optimal solution of the iteration, and then calculating the updated f according to the initial speed of the particles, the cognition of the particles to the optimal solution and the cognition of other particles to the optimal solution, and updating the speed and the position of the particles obj Updating the individual optimal solution and the global optimal solution; repeating the process, iteratively optimizing the internal key parameters of the model, so that the output characteristic of the closed-loop mathematical model approximates to the output characteristic of the actual physical circuit, wherein the key parameters considered here refer to the parameters selected in the step 2; the principle of each update speed and position of the particles is as follows:
where i is the number of iterations, j is the number of particles, v i,j The movement speed of the jth particle in the ith iteration; p (P) i,j Is the position of the jth particle in the ith iteration; g best And P best Representing global best particles and personal best particles, respectively; omega, r 1 、r 2 、c 1 And c 2 Is a parameter; ω is the inertia coefficient, with a larger ω meaning that the particle is more prone to select the previous motion path; c 1 For an individual learning factor, a larger value indicates that the particles are more prone to select a previous individual optimal solution; c 2 Is a group learning factor, c 2 The larger the probability that a particle selects a globally optimal solution is, the greater; r is (r) 1 And r 2 Is [0,1 ]]Random numbers in between;
in order to reduce the probability of the particle swarm algorithm being trapped in the local optimum, chaos is introduced into the particle swarm; when iterating to a certain number of times, the global optimal particle G is firstly obtained by the following formula best Transition into the (0, 1) interval:
where x is the dimension of the particle, b x And a x The upper and lower boundaries of the particles respectively;
then, the chaotic sequence is obtained through a LOGISTIC equation:
z x,y+1 ＝μ(1-z x,y )z x,y ,y＝1,2…
wherein z is x,y An xth chaotic variable representing the yth iteration; mu is a control parameter, generally 4 is taken, and the whole system is in a complete chaotic state;
finally, the chaotic sequence is returned to the original solution space by the following formula, the objective function is recalculated, and G is updated best And P best ：
P i,y ＝z y (b-a)+a。
CN202310700240.1A 2023-06-13 2023-06-13 Parameter estimation method for three-phase permanent magnet synchronous motor based on digital twin model Pending CN116805850A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310700240.1A CN116805850A (en) 2023-06-13 2023-06-13 Parameter estimation method for three-phase permanent magnet synchronous motor based on digital twin model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310700240.1A CN116805850A (en) 2023-06-13 2023-06-13 Parameter estimation method for three-phase permanent magnet synchronous motor based on digital twin model

Publications (1)

Publication Number Publication Date
CN116805850A true CN116805850A (en) 2023-09-26

Family

ID=88080264

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310700240.1A Pending CN116805850A (en) 2023-06-13 2023-06-13 Parameter estimation method for three-phase permanent magnet synchronous motor based on digital twin model

Country Status (1)

Country Link
CN (1) CN116805850A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117057255A (en) * 2023-10-10 2023-11-14 中核武汉核电运行技术股份有限公司 Pre-training model acquisition method for online synchronization of digital twin model

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117057255A (en) * 2023-10-10 2023-11-14 中核武汉核电运行技术股份有限公司 Pre-training model acquisition method for online synchronization of digital twin model
CN117057255B (en) * 2023-10-10 2024-01-02 中核武汉核电运行技术股份有限公司 Pre-training model acquisition method for online synchronization of digital twin model

Similar Documents

Publication Publication Date Title
Maiti et al. An adaptive speed sensorless induction motor drive with artificial neural network for stability enhancement
Yu et al. Adaptive fuzzy backstepping position tracking control for a permanent magnet synchronous motor
CN116805850A (en) Parameter estimation method for three-phase permanent magnet synchronous motor based on digital twin model
Zair et al. MRAS speed sensorless vector control of induction motor drives using predictive adaptation mechanism
Xie et al. Optimization research of FOC based on PSO of induction motors
Riveros et al. Five-phase induction machine parameter identification using PSO and standstill techniques
Bekiroglu et al. Comparison of trapezoidal and sinusoidal pwm techniques for speed and position control of pmsm
Mehrotra et al. Speed estimation of induction motor using artificial neural networks
Consoli et al. Adaptive vector control of induction motor drives based on a neuro-fuzzy approach
CN112019111B (en) State constraint-based fuzzy self-adaptive control method for random system of permanent magnet synchronous motor
Jegan et al. Simulation and Validation of Permanent Magnet Synchronous Motor Drives Using Reinforcement Learning
Reza et al. Stator resistance estimation scheme using fuzzy logic system for direct torque controlled induction motor drive
Anvari et al. Surrogate-Based Optimization of Firing Angles for Switched Reluctance Motor
CN112422014A (en) Super-local model-free permanent magnet synchronous motor rotating speed prediction method based on high-order sliding mode compensation
Ranjan et al. Analysis of the performance of an hcc based pmsm drive using adaptive neuro fuzzy inference
Thakare et al. Comparative Analysis of Performance of Different Speed Controllers for FOC Based SVPWM fed PMSM Drive
CN112039374B (en) Permanent magnet synchronous motor command filtering discrete control method considering input saturation
CN114094884B (en) Motor off-line parameter identification system of electric tricycle
CN117491869A (en) Permanent magnet motor health state monitoring control method and system based on digital twinning
Ge et al. A novel terminal sliding mode control based on RBF neural network for the permanent magnet synchronous motor
Ünsal et al. Investigation of Speed Control Performances of The Fuzzy Logic Controllers Having Different Membership Functions and Inference Methods
CN113176731B (en) Dual-neural-network self-learning IPMSM active disturbance rejection control method
Wen et al. A novel multi‐strategy self‐optimizing SAPSO algorithm for PMSM parameter identification
Ketaki et al. Model Predictive Control (MPC) Based BLDC Drive for Indian Drive Cycle
Younesi et al. Optimized sensor and sensorless control of PMSM modeled in discrete mode: A comparison of KH and PSO algorithms

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