CN115065294A - Switched reluctance motor model prediction torque control method based on multi-level power converter - Google Patents
Switched reluctance motor model prediction torque control method based on multi-level power converter Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
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- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
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- 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
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- 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
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Abstract
The invention provides a switched reluctance motor model prediction torque control method based on a multilevel power converter, which comprises the steps of firstly obtaining the inductance characteristic and the torque characteristic of a switched reluctance motor through off-line measurement, and manufacturing a flux linkage current torque meter of the motor; then, according to the current position, the rotating speed and the current information, the current and the position information at the next moment are predicted by combining a switch state lookup table; for the time delay compensation, the current and position information of the two following moments are further predicted, then the torque in each switching state is obtained by looking up a table and is brought into a cost function, the switching state of the optimal solution is obtained by optimizing the cost function, and then the switching states of the switching tubes of the three-phase power converter are respectively corresponding to the decoupling of the switching signals, so that the effect of torque ripple suppression is achieved.
Description
Technical Field
The invention belongs to the field of motor control, and particularly relates to a switched reluctance motor model prediction torque control method based on a multilevel power converter.
Background
In the structure of the switched reluctance motor, the stator and the rotor of the switched reluctance motor are both in a salient pole structure, a rotor winding and a permanent magnet do not exist, the structure is simple and firm, and the cost is relatively low. The torque has higher torque density and power density, and does not have cogging torque. Therefore, the structure of the reluctance motor is firmer relative to other motors, and the reluctance motor can operate in a severe environment for a longer time. In the aspect of motor control, the controllability of the switched reluctance motor is high, and the electromagnetic torque direction of the switched reluctance motor is determined by the winding excitation sequence and is not related to the current direction. Because the switched reluctance motor has many inherent advantages of high efficiency, high reliability, high starting torque, high fault-tolerant capability and the like, the switched reluctance motor is widely applied to the fields of electric automobiles, household appliances, aerospace, industrial transmission and the like. However, the switched reluctance motor has disadvantages of torque ripple and servo vibration, etc., which limit its application fields, due to its high non-linearity of electromagnetic characteristics. Therefore, in order to improve the performance of the speed regulating system of the switched reluctance motor, suppressing torque ripple and vibration has become a research hotspot of the switched reluctance motor.
The current common methods for reducing the torque ripple mainly comprise a torque distribution function, a phase current PI controller, direct torque control, direct instantaneous torque control and the like, and all the methods have respective advantages and disadvantages. The model prediction control intuitively and conveniently realizes multi-objective optimization by constructing a cost function, and is concerned more and more in the control of the switched reluctance motor. By constructing the cost function of the torque and the radial force of the switched reluctance motor, the model prediction control solves the problems of torque pulsation and vibration at the same time, and has an important effect on improving the applicability and the speed regulation performance of the switched reluctance motor.
However, the power converters applied by the current model predictive control method are all three levels of the traditional asymmetric half-bridge, and although the effect of reducing torque fluctuation is achieved, the three levels cannot meet the requirements of the motor in high speed and various application fields, so that a more flexible and efficient multi-level power converter is needed, and the requirement is provided for the control method of the multi-level power converter.
Disclosure of Invention
The invention provides a method for predicting torque control based on a multilevel power converter switched reluctance motor model, which is not the method for controlling the traditional asymmetric half-bridge three-level power converter but the method for controlling a five-level power converter.
The method does not simply carry out prediction on all the switch states when the three levels are continued, but carries out prediction through certain selection, and provides an improvement strategy aiming at a commutation algorithm and vector optimization. The method comprises the following general steps: firstly, obtaining the inductance characteristic and the torque characteristic of a switched reluctance motor through off-line measurement, and manufacturing a flux linkage current torque meter of the motor; then, according to the current position, the rotating speed and the current information, the current and the position information at the next moment are predicted by combining a switch state lookup table; for the time delay compensation, the current and position information of the two following moments are further predicted, then the torque in each switching state is obtained by looking up a table and is brought into a cost function, the switching state of the optimal solution is obtained by optimizing the cost function, and then the switching states of the switching tubes of the three-phase power converter are respectively corresponding to the decoupling of the switching signals, so that the effect of torque ripple suppression is achieved.
The technical scheme of the invention is as follows:
the method for controlling the predicted torque of the switched reluctance motor model based on the multilevel power converter comprises the following steps:
step 1: determining a reference torque T ref (ii) a Acquiring inductance characteristic, flux linkage characteristic and torque characteristic of the switched reluctance motor according to the characteristicsSex construction data table L ph (I ph ,θ)、T ph (I ph θ); wherein L is ph 、T ph 、I ph Theta respectively represents the phase inductance, the phase torque, the phase current and the rotor position of the switched reluctance motor;
step 2: collecting rotor position theta (k) and phase current i of the motor at the moment k ph (k) Speed ω (k) and phase voltage V ph (k) And flux linkage psi ph (k) A value of (d);
and step 3: predicting the rotor position theta (k +1) and the flux linkage psi at the moment k +1 according to the measurement data obtained in the step 2 ph (k + 1); and obtaining phase current i by looking up the table ph (k + 1); wherein
θ(k+1)＝θ(k)+ω(k)Ts
ψ ph (k+1)＝[V ph (k)-i ph (k)R]Ts+ψ ph (k)
Wherein R is a load resistor, Ts is a sampling frequency, and ω (k), θ (k), i ph (k)、V ph (k) The rotational speed, rotor position, phase current, and phase voltage at time k are θ (k +1), i ph (k +1) is the rotor position and phase current value at the moment of k +1, respectively;
and 4, step 4: predicting a rotor position theta (k +2) at the moment k +2 according to a formula theta (k +2) ═ 2 theta (k +1) -theta (k), wherein theta (k), theta (k +1) and theta (k +2) are rotor positions at the moments k, k +1 and k +2 respectively; judging whether the rotor is in a phase change region or not according to the rotor position theta (k +2) at the moment k +2, if so, respectively carrying out torque prediction on 12 possible switching states at the moment k +2, and if not, respectively carrying out torque prediction on 5 possible switching states at the moment k + 2;
the 5 switch states are respectively as follows:
the switching state variables of the A phase, the B phase and the C phase are (1, -1, -1), (0.5, -1, -1), (0, -1, -1), (-0.5, -1, -1) and (-1, -1, -1);
the 12 switch states are respectively as follows:
the switching state variables of the a phase, the B phase and the C phase are (1, 1, -1), (1, 0.5, -1), (1, 0, -1), (1, -0.5, -1), (1, -1, -1), (0.5, 0.5, -1), (0.5, 0, -1), (0.5, -0.5, -1), (0.5, -1, -1), (0, 0, -1), (0, -0.5, -1), (0, -1, 1);
the process of torque prediction for each switch state is:
step 4.1: obtaining the output voltage according to the three-phase switch state variable of the switch state and the relation between the switch state variable and the phase voltage, namely obtaining the predicted phase voltage V at the moment of k +1 ph (k + 1); and then use the formula
ψ ph (k+2)＝[V ph (k+1)-i ph (k+1)R]Ts+ψ ph (k+1)
Predicting k +2 time flux linkage psi ph (k +2), and then obtaining the phase current i at the time of k +2 by a table look-up method ph (k+2)；
Step 4.2: combining phase current at time k +2 and rotor position information by looking up table T ph (I ph θ) predicting phase torque T at time k +2 ph (k +2), and further calculating the total torque according to the formula:
in the formula N ph Representing the number of phases of the switched reluctance motor, ph representing the number of phases, T ph (k +2) represents the phase torque at the time of k +2, and T (k +2) represents the total torque of the switched reluctance motor;
step 4.3: predicting the total torque at the moment k +2 according to the step 4.2, and solving a cost function:
J＝q T *(|T a -T aref | 2 +|T b -T bref | 2 +|T c -T cref | 2 )+q I *(I a 2 +I b 2 +I c 2 )
where J is a cost function, q T Q is the proportion of the torque error I Is the proportion of phase current error, T a 、T b 、T c Three-phase torque resolved for the total torque T (k +2) of the switched reluctance machine obtained according to step 4.2, I a 、I b 、I c For three-phase current, T aref 、T bref 、T cref Is a three-phase reference torque;
and 5: respectively solving corresponding cost function values for the 5 switch states or 12 switch states in the step 4, and finding out the switch state corresponding to the minimum cost function value, namely the optimal turn-off signal for minimizing the torque ripple; obtaining three-phase switch state variables corresponding to the optimal solution through the obtained optimal solution, and then decoupling through the corresponding relation between the switch state variables and the switch tubes of each phase to obtain the switch state of the switch tubes of each phase;
step 6: upon completion of step 5, the process returns to step 2 to circulate.
Further, the reference torque T ref The torque distribution function is determined by adopting a cosine function as the torque distribution function.
Further, in step 5, the ratio q of the torque error T 15, the proportion q of phase current error I 0.02 is taken.
Further, in step 5, the corresponding table of the switch state variables and the switch tubes of each phase is
| Mode | S1 | S2 | S3 | |
| 1 | On | On | On | On |
| 0.5 | On | On | On | Off |
| 0 | On | On | Off | Off |
| -0.5 | Off | On | Off | Off |
| -1 | Off | Off | Off | Off |
。
Further, in step 4.1, the relationship between the switching state variable and the phase voltage is: when the state variable U is switched on or off ph When 1, the phase voltage V ph ＝2U；U ph At 0.5, phase voltage V ph ＝U；U ph When equal to 0, the phase voltage V ph ＝0；U ph At-0.5 phase voltage V ph ＝-U；U ph When equal to-1, the phase voltage V ph ＝-2U。
Advantageous effects
The invention discloses a switched reluctance motor model prediction torque control method based on a multilevel power converter. Meanwhile, the torque distribution is combined with model prediction control, and in the torque distribution, the compensation of model prediction errors is considered, so that the reference torque of each phase is obtained. In model prediction control, the inductance characteristic and the torque characteristic of the switched reluctance motor are obtained through off-line measurement, and the current and position information at the next moment are predicted by combining a switching state lookup table according to the current position, the rotating speed and the current information; in order to perform time delay compensation, current and position information are further predicted, then, the torque in each switching state is obtained by looking up a table and is brought into a cost function, a cost function containing a phase reference torque tracking error is constructed, a control signal enabling the cost function to be optimal is sought, then the optimal control signal is used as a switching signal to control a switch in a power converter, and therefore the torque fluctuation suppression of the switched reluctance motor considering the operation efficiency is achieved. The effectiveness of the method is verified by a simulation result, and the method is simple in control logic, obvious in torque ripple effect and easy to realize in engineering.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a control block diagram of a switched reluctance motor torque ripple suppression method based on model predictive control;
fig. 2 to 6 are schematic diagrams of five switching states of a single-phase bridge arm of a multilevel power converter of a switched reluctance motor; (the dotted line indicates the current flow direction);
wherein:
FIG. 2 is a circuit diagram of a fast excitation state of a multi-level power converter of a switched reluctance motor;
FIG. 3 is a circuit diagram of a normal excitation state of a multi-level power converter of a switched reluctance motor;
FIG. 4 is a circuit diagram of a zero-voltage follow current state of a multi-level power converter of a switched reluctance motor;
FIG. 5 is a circuit diagram of a normal demagnetization state of a multi-level power converter of a switched reluctance motor;
FIG. 6 is a circuit diagram of a switched reluctance motor multilevel power converter in a fast demagnetization state;
FIGS. 7 and 8 are optimization vector selection charts (underlined are deleted vectors, black are candidate vectors);
wherein:
FIG. 7 is a chart of 5 vector selections for a single pass region;
FIG. 8 is a 12 vector selection chart for the commutation zone;
FIG. 9 is a flowchart of a method for suppressing torque ripple of a switched reluctance motor based on model predictive control;
FIGS. 10 and 11 are graphs comparing torque ripple for angular position control and model predictive control at 2000rpm operation;
wherein:
FIG. 10 is an angular position control total torque pulse diagram;
FIG. 11 is a graph of model predictive control total torque pulsation.
Detailed Description
The invention is described below in connection with specific embodiments, which are intended to be illustrative, but not limiting, of the invention. The motor used in the example was a 1kW three-phase 12/8 pole switched reluctance motor.
The embodiment provides a model prediction torque control method based on a multi-level power converter switched reluctance motor, which aims at five levels to carry out model prediction control and comprises the following steps:
step 1: given reference torque T ref The reference torque is a given value that the motor is actually required to reach stably. In a closed loop system, T ref The method can be obtained by a torque distribution function, the torque distribution function can help model prediction control to commutate in a commutation zone, and a cosine function can be generally adopted as the torque distribution function. Reference torque T of demagnetization phase dref And excitation phase T eref Can be controlled by a reference torque T ref Is shown as
T eref ＝T ref *(0.5-0.5*cos(pi*(θ-θ on )/θ ov ))
T dref ＝T ref *(0.5+0.5*cos(pi*(θ-θ off )/θ ov ))
Where θ is the rotor position, θ on To the opening angle, theta off To the off angle, θ ov Is an angle of approach.
Obtaining inductance characteristic, flux linkage characteristic and torque characteristic of the switched reluctance motor by a rotor fixed clamping method, and constructing a data table L according to the characteristics ph (I ph ,θ)、T ph (I ph θ); wherein L is ph 、T ph 、I ph And theta respectively represent phase inductance, phase torque, phase current and rotor position of the switched reluctance motor.
Step 2: collecting rotor position theta (k) and phase current i of the motor at the moment k ph (k) Speed ω (k) and phase voltage V ph (k) And flux linkage psi ph (k) A value of (d);
and step 3: predicting the rotor position theta (k +1) and the flux linkage psi at the moment k +1 according to the measurement data obtained in the step 2 ph (k + 1); and obtaining phase current i by a table look-up method ph (k + 1); wherein
θ(k+1)＝θ(k)+ω(k)Ts
ψ ph (k+1)＝[V ph (k)-i ph (k)R]Ts+ψ ph (k)
Wherein R is a load resistor, Ts is a sampling frequency, and ω (k), θ (k), i ph (k)、V ph (k) The rotational speed, rotor position, phase current, and phase voltage at time k are θ (k +1), i ph (k +1) is the rotor position and phase current value at the moment of k +1, respectively;
and 4, step 4: according to the formula
θ(k+2)＝2θ(k+1)-θ(k)
Predicting a rotor position theta (k +2) at the moment k +2, wherein theta (k), theta (k +1) and theta (k +2) are rotor positions at the moments k, k +1 and k +2 respectively; judging whether the rotor is in a phase change region or not according to the rotor position theta (k +2) at the moment k +2, if so, respectively predicting the torque current of 12 possible switching states at the moment k +2, and if not, respectively predicting the torque current of 5 possible switching states at the moment k + 2;
the 5 switch states are respectively as follows:
the switching state variables of the A phase, the B phase and the C phase are respectively (1, -1, -1), (0.5, -1, -1), (0, -1, -1), (-0.5, -1, -1) and (-1, -1, -1);
the 12 switch states are respectively as follows:
the switching state variables of the a phase, the B phase and the C phase are (1, 1, -1), (1, 0.5, -1), (1, 0, -1), (1, -0.5, -1), (1, -1, -1), (0.5, 0.5, -1), (0.5, 0, -1), (0.5, -0.5, -1), (0.5, -1, -1), (0, 0, -1), (0, -0.5, -1) and (0, -1, -1), respectively.
For a certain phase, the switch state variable U ph And phase voltage V ph The relationship of (a) to (b) is as follows:
where U represents half the bus voltage,U ph representing a switch state variable, U ph 1 represents that four switching tubes of the five-level power converter are all conducted, U ph 0.5 indicates that three switching tubes of the five-level power converter are all conducted, U ph The power converter with 0 five level has only two switching tubes connected, U ph -0.5 indicates that only one switching tube of the five-level power converter is conducted, and U is ph And-1 means that all four switching tubes are closed.
The combination rule of the switch states is as follows: in the one-way conduction area, only the switch state of the current conduction phase is calculated, and the other phases are-1; in the commutation zone, only the two-phase switching states that are being commutated are predicted.
| Mode | S1 | S2 | | S4 | Uph | |
| 0 | Off | Off | Off | Off | -1 | |
| 1 | On | Off | Off | Off | × | |
| 2 | Off | On | Off | Off | -0.5 | |
| 3 | On | On | | Off | 0 | |
| 4 | Off | Off | On | Off | -0.5 | |
| 5 | On | Off | On | Off | × | |
| 6 | Off | On | On | |
0 | |
| 7 | On | On | On | Off | 0.5 | |
| 8 | Off | Off | Off | On | × | |
| 9 | On | Off | Off | On | × | |
| 10 | Off | On | Off | On | × | |
| 11 | On | On | Off | On | × | |
| 12 | Off | Off | On | On | 0 | |
| 13 | On | Off | On | On | × | |
| 14 | Off | On | On | On | 0.5 | |
| 15 | On | On | On | On | 1 |
The table above is a table of the state of the switch tube corresponding to the state variable of the switch, wherein U ph And S1, S2, S3 and S4 represent four switching tubes of each phase, wherein On represents On, and Off represents Off, which are switching state variables, namely the proportion of the output voltage at two ends of each phase of bridge arm to the bus voltage.
For a five-level power converter, the number of selectable space vectors is 125, but there are numerous redundant vectors, so a vector optimization choice is made.
The optimal selection of the vector is carried out according to the following rules:
in the single-phase conducting area, a zero-voltage follow current state is used as much as possible when the torque is reduced so as to ensure that the torque is reduced smoothly, and severe torque fluctuation caused by the use of a reverse voltage demagnetization state is avoided.
In the phase change region, the working states of the two-phase windings cannot be switched at the same time, so that the torque ripple is increased due to overshoot, and the power loss caused by frequent action of the power tube is avoided.
In the phase change area, the open-phase voltage is always greater than or equal to zero so as to quickly establish the required phase current, ensure that enough torque is provided in the single-phase conduction area, and preferentially excite the open-phase when the torque needs to be increased, so that the value of the open-phase voltage less than zero is artificially deleted; the voltage of the off-phase is not larger than the voltage of the on-phase, so that the phase current is prevented from entering the negative torque to generate the negative torque to influence the efficiency of the motor, and the off-phase is preferentially demagnetized when the torque is required to be reduced, so that the value that the voltage of the off-phase is larger than the voltage of the on-phase is artificially deleted.
In the phase change region, when the torque is reduced, the demagnetized phase is kept in a zero-voltage freewheeling state as much as possible so as to avoid causing severe torque fluctuation.
And finally obtaining 12 optimized candidate vectors.
Since the position signals cannot be immediately corresponded to one-to-one pair of these switching vector signals, we need to divide the position into regions, and then to correspond to 125 kinds of vector signals, and then to encode these vector signals and select the switching signals we need. The coding of the three phases is as follows:
the A-phase encoding formula is as follows: y1 [ -floor (u/25.9) +2]/2
In the formula, the floor function is an integer function in the negative infinity direction.
The B-phase encoding formula is as follows: y2 ═ - { mod [ floor (u/5.01),5] +2}/2
In the formula, mod function is a remainder function
The C-phase encoding formula is as follows:
and finally, the obtained coded values corresponding to the three-phase different switching signals are taken out again after the cost function calculation of minimizing the torque ripple is completed, and the coded values are decoded into the switching signals.
The process of torque prediction for each switch state is:
step 4.1: for a certain possible predicted switching state, the output voltage magnitude is obtained according to the switching state variables of the three phases and the relation between the switching state variables and the phase voltages, and the corresponding output voltage magnitude is the phase voltage magnitude, so that the predicted phase voltage V at the moment of k +1 can be obtained ph (k+1)；
Using a formula
ψ ph (k+2)＝[V ph (k+1)-i ph (k+1)R]Ts+ψ ph (k+1)
Predicting k +2 time flux linkage psi ph (k +2), and then phase current i is obtained by a table lookup method ph (k + 2); wherein R is a load resistor, Ts is a sampling frequency, and theta (k +1), i ph (k+1)、V ph (k +1) is the rotor position, phase current, and phase voltage at time k + 1, θ (k +2), i ph (k +2) are the rotor position and the phase current value at the time of k +2, respectively;
step 4.2: combining phase current at time k +2 and rotor position information by looking up table T ph (I ph θ) predicting the phase torque T at the time k +2 ph (k +2), and further calculating the total torque;
in the formula N ph Representing the number of phases of the switched reluctance motor, ph representing the numerical value of the number of phases, T ph (k +2) represents the phase torque at the time of k +2, and T (k +2) represents the total torque of the switched reluctance motor;
step 4.3: predicting the total torque at the moment of k +2 according to the step 4.2, and solving a cost function; the cost function is as follows:
J＝q T *(|T a -T aref | 2 +|T b -T bref | 2 +|T c -T cref | 2 )+q I *(I a 2 +I b 2 +I c 2 )
where J is a cost function, q T As a proportion of the torque error, q I Q is the proportion of phase current error T 、q I Given a value, the torque specific gravity q is taken T Set to 15, current density q I Set to 0.02, corresponding changes can be made to the desired motor performance. T is a 、T b 、T c Three-phase torque resolved for the total torque T (k +2) of the switched reluctance machine obtained according to step 4.2, I a 、I b 、I c Three phase current. T is aref 、T bref 、T cref Is a three-phase reference torque, which is given by a torque distribution function from the reference torque.
And 5: and respectively solving the corresponding cost function values by utilizing 5 or 12 switching states, and finding out the switching state corresponding to the minimum cost function value, namely the optimal turn-off signal for minimizing the torque ripple. Obtaining three-phase switch state variables corresponding to the optimal solution through the obtained optimal solution, and decoupling the switch state of each phase of switch tube through the switch state variables and the corresponding table of each phase of switch tube; the decoupling function is tabulated as follows:
| Mode | S1 | | S3 | S4 | |
| 1 | On | On | On | On | |
| 0.5 | On | On | On | |
|
| 0 | On | On | Off | Off | |
| -0.5 | Off | On | Off | Off | |
| -1 | Off | Off | Off | Off |
step 6: upon completion of step 5, the process returns to step 2 to circulate.
Fig. 9 is a flow chart of the control method proposed by the present invention, and fig. 10 and 11 are comparative graphs (2000rpm) of torque ripple suppression using angular position control and the method used by the present invention, respectively. When the angular position control is switched to the model prediction torque ripple suppression control method, the torque ripple is reduced from 131% to 23%. Therefore, the switched reluctance motor torque ripple suppression method based on model prediction control has obvious effect on reducing torque ripple.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Claims (5)
1. A switched reluctance motor model prediction torque control method based on a multilevel power converter is characterized by comprising the following steps: the method comprises the following steps:
step 1: determining a reference torque T ref (ii) a Acquiring inductance characteristic, flux linkage characteristic and torque characteristic of the switched reluctance motor, and constructing a data table L according to the characteristics ph (I ph ,θ)、T ph (I ph θ); wherein L is ph 、T ph 、I ph Theta respectively represents the phase inductance, the phase torque, the phase current and the rotor position of the switched reluctance motor;
step 2: collecting motor at time kRotor position θ (k) and phase current i ph (k) Rotational speed ω (k) and phase voltage V ph (k) And flux linkage psi ph (k) A value of (d);
and step 3: predicting the rotor position theta (k +1) and the flux linkage psi at the moment k +1 according to the measurement data obtained in the step 2 ph (k + 1); and obtaining phase current i by looking up the table ph (k + 1); wherein
θ(k+1)＝θ(k)+ω(k)Ts
ψ ph (k+1)＝[V ph (k)-i ph (k)R]Ts+ψ ph (k)
Wherein R is a load resistor, Ts is a sampling frequency, and ω (k), θ (k), i ph (k)、V ph (k) The rotational speed, rotor position, phase current, and phase voltage at time k are θ (k +1), i ph (k +1) is the rotor position and phase current value at the moment of k +1, respectively;
and 4, step 4: predicting a rotor position theta (k +2) at the moment k +2 according to a formula theta (k +2) ═ 2 theta (k +1) -theta (k), wherein theta (k), theta (k +1) and theta (k +2) are rotor positions at the moments k, k +1 and k +2 respectively; judging whether the rotor is in a phase conversion area or not according to the rotor position theta (k +2) at the moment k +2, if so, respectively carrying out torque prediction on 12 possible switching states at the moment k +2, and if not, respectively carrying out torque prediction on 5 possible switching states at the moment k + 2;
the 5 switch states are respectively as follows:
the switching state variables of the A phase, the B phase and the C phase are respectively (1, -1, -1), (0.5, -1, -1), (0, -1, -1), (-0.5, -1, -1) and (-1, -1, -1);
the 12 switch states are respectively as follows:
the switching state variables of the a phase, the B phase and the C phase are (1, 1, -1), (1, 0.5, -1), (1, 0, -1), (1, -0.5, -1), (1, -1, -1), (0.5, 0.5, -1), (0.5, 0, -1), (0.5, -0.5, -1), (0.5, -1, -1), (0, 0, -1), (0, -0.5, -1), (0, -1, 1);
the process of torque prediction for each switch state is:
step 4.1: three-phase switch state variable according to the switch state, and switch state variable and phase voltageThe magnitude of the output voltage is obtained, namely the predicted phase voltage V at the moment of k +1 ph (k + 1); and then use the formula
ψ ph (k+2)＝[V ph (k+1)-i ph (k+1)R]Ts+ψ ph (k+1)
Predicting k +2 time flux linkage psi ph (k +2), and then obtaining the phase current i at the time of k +2 by a table lookup method ph (k+2)；
And 4.2: combining the phase current at time k +2 and rotor position information by looking up table T ph (I ph θ) predicting the phase torque T at the time k +2 ph (k +2), and further, the total torque is calculated according to the formula:
in the formula N ph Representing the number of phases of the switched reluctance motor, ph representing the numerical value of the number of phases, T ph (k +2) represents the phase torque at the time of k +2, and T (k +2) represents the total torque of the switched reluctance motor;
step 4.3: predicting the total torque at the moment k +2 according to the step 4.2, and solving a cost function:
J＝q T *(|T a -T aref | 2 +|T b -T bref | 2 +|T c -T cref | 2 )+q I *(I a 2 +I b 2 +I c 2 )
where J is a cost function, q T As a proportion of the torque error, q I Is the proportion of phase current error, T a 、T b 、T c Three-phase torque resolved for the total torque T (k +2) of the switched reluctance machine obtained according to step 4.2, I a 、I b 、I c For three-phase current, T aref 、T bref 、T cref Is a three-phase reference torque;
and 5: respectively solving corresponding cost function values for the 5 switch states or 12 switch states in the step 4, and finding out the switch state corresponding to the minimum cost function value, namely the optimal turn-off signal for minimizing the torque ripple; obtaining three-phase switch state variables corresponding to the optimal solution through the obtained optimal solution, and then decoupling through the corresponding relation between the switch state variables and the switch tubes of each phase to obtain the switch state of the switch tubes of each phase;
step 6: upon completion of step 5, the process returns to step 2 to circulate.
2. The method for model-based predictive torque control of a switched reluctance motor based on a multilevel power converter according to claim 1, wherein: the reference torque T ref The torque distribution function is determined by adopting a cosine function as the torque distribution function.
3. The method for model-based predictive torque control of a switched reluctance motor based on a multilevel power converter according to claim 1, wherein: in step 5, the ratio q of the torque error T 15, proportion q of phase current error I 0.02 was taken.
4. The method for model-based predictive torque control of a switched reluctance motor based on a multilevel power converter according to claim 1, wherein: in step 5, the corresponding table of the switch state variable and the switch tube of each phase is
。
5. The method for model-based predictive torque control of a switched reluctance motor based on a multilevel power converter according to claim 1, wherein: step 4.1, switching off of the switching state variables and the phase voltagesThe method comprises the following steps: when the state variable U is switched on or off ph Phase voltage V equal to 1 ph ＝2U；U ph At 0.5, phase voltage V ph ＝U；U ph At 0, phase voltage V ph ＝0；U ph At-0.5 phase voltage V ph ＝-U；U ph When equal to-1, the phase voltage V ph ＝-2U。
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118611533A (en) * | 2024-08-07 | 2024-09-06 | 四川省产品质量监督检验检测院 | Four-level fault-tolerant converter for switched reluctance motor and control system and method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2943656A1 (en) * | 2014-12-15 | 2016-06-23 | China University Of Mining And Technology | Braking torque closed-loop control system and method for switch reluctance motor |
| CN107425762A (en) * | 2017-07-04 | 2017-12-01 | 大连海事大学 | A kind of three-phase switch reluctance machine prediction moment controlling system and method |
| CN109347390A (en) * | 2018-11-28 | 2019-02-15 | 大连海事大学 | Vector based on model prediction magnetic linkage control is double to choose method for suppressing torque ripple |
| CN113746397A (en) * | 2021-08-20 | 2021-12-03 | 西北工业大学 | Method for controlling predicted torque and radial force of switched reluctance motor model |
| CN113839600A (en) * | 2021-08-25 | 2021-12-24 | 西北工业大学 | Switched reluctance motor torque control method based on Fourier series model |
| CN114531078A (en) * | 2022-02-27 | 2022-05-24 | 西北工业大学 | Method for inhibiting torque pulsation and bus current pulsation of switched reluctance motor |
-
2022
- 2022-07-11 CN CN202210815318.XA patent/CN115065294B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2943656A1 (en) * | 2014-12-15 | 2016-06-23 | China University Of Mining And Technology | Braking torque closed-loop control system and method for switch reluctance motor |
| CN107425762A (en) * | 2017-07-04 | 2017-12-01 | 大连海事大学 | A kind of three-phase switch reluctance machine prediction moment controlling system and method |
| CN109347390A (en) * | 2018-11-28 | 2019-02-15 | 大连海事大学 | Vector based on model prediction magnetic linkage control is double to choose method for suppressing torque ripple |
| CN113746397A (en) * | 2021-08-20 | 2021-12-03 | 西北工业大学 | Method for controlling predicted torque and radial force of switched reluctance motor model |
| CN113839600A (en) * | 2021-08-25 | 2021-12-24 | 西北工业大学 | Switched reluctance motor torque control method based on Fourier series model |
| CN114531078A (en) * | 2022-02-27 | 2022-05-24 | 西北工业大学 | Method for inhibiting torque pulsation and bus current pulsation of switched reluctance motor |
Non-Patent Citations (1)
| Title |
|---|
| 卿龙;王惠民;葛兴来;: "一种高效率开关磁阻电机转矩脉动抑制方法", 电工技术学报, no. 09, 15 May 2020 (2020-05-15), pages 1912 - 1920 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118611533A (en) * | 2024-08-07 | 2024-09-06 | 四川省产品质量监督检验检测院 | Four-level fault-tolerant converter for switched reluctance motor and control system and method |
| CN118611533B (en) * | 2024-08-07 | 2024-10-18 | 四川省产品质量监督检验检测院 | Four-level fault-tolerant converter for switched reluctance motor and control system and method |
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