CN113315439A - Improved control method of ship propulsion motor - Google Patents

Improved control method of ship propulsion motor Download PDF

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Publication number
CN113315439A
CN113315439A CN202110615179.1A CN202110615179A CN113315439A CN 113315439 A CN113315439 A CN 113315439A CN 202110615179 A CN202110615179 A CN 202110615179A CN 113315439 A CN113315439 A CN 113315439A
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motor
state
equation
coordinate system
control method
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马继先
沈晓龙
王婷
刘英杰
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Jiangsu University of Science and Technology
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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
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/18—Estimation of position or speed
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
    • 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
    • H02P25/024—Synchronous motors controlled by supply frequency
    • H02P25/026—Synchronous motors controlled by supply frequency thereby detecting the rotor position
    • 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
    • H02P27/08—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 with pulse width modulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/34—Modelling or simulation for control purposes
    • 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

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

Abstract

An improved control method for a ship propulsion motor, comprising the steps of: establishing a PMSM mathematical model to obtain a mathematical model of a surface-mounted three-phase PMSM in a static coordinate system; based on a mathematical model of the surface-mounted three-phase PMSM in a static coordinate system, an extended Kalman filtering algorithm is designed by combining a motor model and an operating environment used by a submarine side-pushing system, and motor position information is estimated. According to the method, the reliability and the stability of a motor control system can be effectively improved, the running performance of a submarine side-pushing motor can be improved, and the working reliability of the motor can be improved by setting up a simulation model and performing simulation analysis and verifying by combining with the actual application setting parameters of the submarine; the motor speed and rotor position estimation errors are small, the fluctuation adjusting speed is high, the overshoot is small, and the system stability is high.

Description

Improved control method of ship propulsion motor
Technical Field
The invention belongs to the technical field of motor control, and particularly relates to an improved control method of a ship propulsion motor, which aims to improve the running performance of a submarine side-pushing motor and improve the working reliability of the motor.
Background
The traditional submarine side-pushing motor system has the advantages that due to the existence of the position sensor, the reliability is reduced, the sealing difficulty is increased, the requirements of the seawater working environment on the sensor are harsh, the environmental requirements are high, and the reliability of the system is low.
At present, no matter the method of using the salient pole characteristic of the motor to estimate the position information and the speed of the rotor. Or the other method is to estimate the rotor position information and the rotating speed by using the back electromotive force or the stator flux linkage information, and the advantages of the general control method are mainly reflected in a middle-high speed section and a zero-low speed section, and the detection failure is realized. Most of the current algorithms are not high enough in accuracy, are relatively sensitive to external noise interference, are not positive enough to correct the change of the system correspondingly, and the stability of the system and the performance of the motor need to be improved.
Disclosure of Invention
The invention provides an improved control method of a ship propulsion motor aiming at the problems of a position sensor of a submarine side-pushing system, which adopts a permanent magnet synchronous motor without a position sensor. The reliability is improved, the harsh condition requirement of the sensor working in the seawater environment is avoided, the running performance of the submarine side-pushing motor is improved by adopting the optimized EKF algorithm, and the reliability and the stability of a motor control system are improved.
An improved control method for a ship propulsion motor, comprising the steps of:
step 1, establishing a PMSM mathematical model to obtain a mathematical model of a surface-mounted three-phase PMSM in a static coordinate system;
and 2, designing an extended Kalman filtering algorithm based on a mathematical model of the surface-mounted three-phase PMSM in a static coordinate system and by combining a motor model and an operating environment used by the submarine side-pushing system, and estimating the motor position information.
Further, in step 1, a voltage equation of the surface-mounted PMSM in the static coordinate system is as follows:
Figure BDA0003097100160000021
in the formula ua、uβStator voltages in a stationary coordinate system a-beta, respectively; i.e. ia、iβStator currents in a stationary coordinate system a-beta, respectively; r is the stator resistance; l issRepresenting an inductance; psifRepresents a permanent magnet flux linkage; thetaeIndicating the angle of deflection of the rotation, omegaeIndicating the electrical angular velocity.
Transforming equation (1) into a current equation, one can obtain:
Figure BDA0003097100160000022
consider the relationship shown below:
Figure BDA0003097100160000023
the following equation of state is obtained:
Figure BDA0003097100160000024
y＝Cx (5)
wherein:
Figure BDA0003097100160000031
Figure BDA0003097100160000032
Figure BDA0003097100160000033
further, in step 1, since the equations (4) and (5) are non-linear, the discretized mathematical model is represented as:
x(k+1)＝f[x(k)]+B(k)u(k)+V(k) (9)
y(k)＝C(k)x(k)+W(k) (10)
wherein V (k) is system noise, W (k) is measurement noise; the above equation is a state equation, and the following equation is an output equation.
If V (k) and W (k) are both zero-mean white noise:
E{V(k)}＝0,E{W(k)}＝0 (11)
wherein E { } represents numerical expectation.
Further, in step 1, covariance matrices Q and R of the noise vectors V and W, respectively, are obtained, where Q and R are defined as:
Figure BDA0003097100160000041
assuming that V (k) and W (k) are independent, x (0) is a random vector, independent of V (k) and W (k).
Further, in step 2, the state estimation of the extended kalman filter EKF is roughly divided into two stages, namely, a prediction stage and a correction stage, and the detailed steps are as follows:
step 2-1, estimating a state vector: from the previous state estimate
Figure BDA0003097100160000042
And inputting the value u (k) to estimate the state vector at the next time (k +1) as:
Figure BDA0003097100160000043
in the formula, TsFor the sampling period, ^ represents the state estimation value, and ^ represents the prediction value.
Calculating corresponding outputs
Figure BDA0003097100160000044
Namely, it is
Figure BDA0003097100160000045
Step 2-2, calculating an error covariance matrix, namely
Figure BDA0003097100160000046
In the formula, TsFor the sampling period, ^ represents the state estimation value, and ^ represents the prediction value.
Wherein:
Figure BDA0003097100160000047
the results were:
Figure BDA0003097100160000048
step 2-3, calculating a gain matrix K (K +1) of the EKF, namely
Figure BDA0003097100160000051
Step 2-4, for the predicted state vector
Figure BDA0003097100160000052
Performing feedback correction to obtain optimized state estimation
Figure BDA0003097100160000053
Namely:
Figure BDA0003097100160000054
this step is an estimate of the corrected state.
Step 2-5, the estimation error covariance matrix is pre-calculated for the next estimation, that is
Figure BDA0003097100160000055
And (3) selecting proper covariance and initial values by combining a corresponding simulation model, determining stator voltage and stator current under a static coordinate system, and finally estimating the stator current, the rotating speed and the rotor position information.
The invention achieves the following beneficial effects: by building a simulation model and simulation analysis and verifying by combining with the practical application setting parameters of the submarine, the method can effectively improve the reliability and stability of a motor control system, improve the running performance of a submarine side-pushing motor and improve the working reliability of the motor; the motor speed and rotor position estimation errors are small, the fluctuation adjusting speed is high, the overshoot is small, and the system stability is high.
Drawings
Fig. 1 is a simulation model structure diagram of an EKF-based PMSM system in an embodiment of the present invention.
Fig. 2 is a change curve of three-phase current, actual rotating speed and torque of the motor stator in the embodiment of the invention.
Fig. 3 is a variation curve of the actual value and the estimated rotation speed value in the embodiment of the present invention.
FIG. 4 is a graph illustrating the variation of the error of the speed estimation according to the embodiment of the present invention.
Fig. 5 is a variation curve of the actual value and the estimated value of the rotor position according to the embodiment of the present invention.
FIG. 6 is a graph illustrating the variation of the rotor position estimation error in an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
Aiming at the problems of a position sensor of a submarine side-pushing system, the invention adopts a permanent magnet synchronous motor without the position sensor. Therefore, the reliability is improved to some extent, and the harsh condition requirement of the sensor working in the seawater environment is avoided.
In various algorithms controlled by a permanent magnet synchronous motor without a position sensor, an EKF algorithm is most widely applied to a high-performance servo system, can efficiently work in a wide speed range, and can accurately complete rotating speed estimation even at a lower speed. The real-time property is a feature of the extended kalman filter, so that it can track the state of the system in real time and perform effective output, and at the same time, it can reduce interference, suppress noise, and its effect is significant, even when the noise estimation is inaccurate, it can still make the observer converge. The invention adopts the optimized EKF algorithm to improve the running performance of the submarine side-pushing motor and improve the reliability and stability of a motor control system.
The method comprises the following specific contents:
1. establishing PMSM mathematical model
The voltage equation of the surface-mounted PMSM in the static coordinate system is as follows:
Figure BDA0003097100160000061
transforming equation (1) into a current equation, one can obtain:
Figure BDA0003097100160000071
consider the relationship shown below:
Figure BDA0003097100160000072
the following equation of state can be obtained:
Figure BDA0003097100160000073
y＝Cx (5)
wherein:
Figure BDA0003097100160000074
Figure BDA0003097100160000075
Figure BDA0003097100160000076
since equations (4) and (5) are non-linear, an EKF algorithm must be used, whose discretized mathematical model can be expressed as:
x(k+1)＝f[x(k)]+B(k)u(k)+V(k) (9)
y(k)＝C(k)x(k)+W(k) (10)
wherein V (k) is system noise, W (k) is measurement noise; the above equation is a state equation, and the following equation is an output equation.
If V (k) and W (k) are both zero-mean white noise, then
E{V(k)}＝0,E{W(k)}＝0 (11)
Wherein E { } represents numerical expectation.
In the EKF algorithm, the noise vectors V and W are not used directly, but rather use respective covariance matrices Q and R, where Q and R are defined as:
Figure BDA0003097100160000081
assuming that V (k) and W (k) are independent, x (0) is a random vector, independent of V (k) and W (k).
2. Extended Kalman filter design
Firstly, for the state estimation of the extended Kalman filter, the state estimation of the EKF is roughly divided into a prediction stage and a correction stage, and the detailed steps are as follows:
estimating a state vector: from the previous state estimate
Figure BDA0003097100160000082
And inputting the value u (k) to estimate the state vector at the next time (k +1) as:
Figure BDA0003097100160000083
in the formula: t iss-a sampling period; "" - - - -state estimate; "- - - - -prediction value.
Calculating corresponding output
Figure BDA0003097100160000084
Namely:
Figure BDA0003097100160000085
calculating an error covariance matrix, namely:
Figure BDA0003097100160000091
wherein:
Figure BDA0003097100160000092
the results were:
Figure BDA0003097100160000093
fourthly, calculating a gain matrix K (K +1) of the EKF, namely
Figure BDA0003097100160000094
For predicted state vector
Figure BDA0003097100160000095
Feedback correction is performed to obtain an optimized state estimate
Figure BDA0003097100160000096
Namely:
Figure BDA0003097100160000097
this step is filtering, which is a corrected state estimate.
Sixthly, an estimation error covariance matrix is calculated in advance so as to carry out the estimation at the next time, namely:
Figure BDA0003097100160000098
a block diagram of a simulation model of an EKF-based PMSM system is shown in fig. 1.
An EKF-based estimation module is constructed through an s function in MATLAB software, the reference voltage and the actual current value which are set as PMSM are input, and the rotor position information and the rotating speed information which are set as estimation are output to obtain corresponding simulation.
FIG. 2 is a curve showing the variation of three-phase current, actual rotating speed and torque of a stator of the motor; fig. 3 is a graph of variation of the estimated value of the motor speed and the actual value. As can be seen from the figure, the estimated curve deviates less from the actual curve and is finally kept at a stable value, so that the EKF control algorithm can control the error range to be small. The fluctuation adjusting speed is high, the overshoot is small, and the system stability is high; FIG. 4 is a variation curve of the estimated error of the rotation speed, wherein the estimated error is stable around 0 value, and the fluctuation is extremely small except the fluctuation existing at the moment of starting the motor; FIG. 5 is a variation curve of an actual value and an estimated value of a rotor position, where the estimated curve has a small deviation from the actual curve, a good fitting condition, and a waveform regularity of reciprocation; FIG. 6 is a variation curve of the estimation error of the rotor position, the estimation error is stable near the value of 0, and the fluctuation is small except the fluctuation existing at the moment of starting the motor, so that the stability requirement of the system is met.
The invention adopts an extended Kalman filter method, and can realize accurate tracking of the position and the rotating speed of the rotor when the motor runs at high speed. Rotor position state estimation based on extended kalman filtering enables the rotor position estimate to converge well to the actual value. Meanwhile, the method is applied to the control of the ship permanent magnet synchronous motor, so that the running performance of the submarine side-pushing motor can be well improved, and the working reliability of the motor is improved.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.

Claims (5)

1. An improved control method of a ship propulsion motor is characterized in that: the method comprises the following steps:
step 1, establishing a PMSM mathematical model to obtain a mathematical model of a surface-mounted three-phase PMSM in a static coordinate system;
and 2, designing an extended Kalman filtering algorithm based on a mathematical model of the surface-mounted three-phase PMSM in a static coordinate system and by combining a motor model and an operating environment used by the submarine side-pushing system, and estimating the motor position information.
2. An improved control method for a ship propulsion motor, according to claim 1, characterized in that: in step 1, a voltage equation of the surface-mounted PMSM in a static coordinate system is as follows:
Figure FDA0003097100150000011
in the formula: u. ofa、uβStator voltages in a stationary coordinate system a-beta, respectively; i.e. ia、iβStator currents in a stationary coordinate system a-beta, respectively; r is the stator resistance; l issRepresenting an inductance; psifRepresents a permanent magnet flux linkage; thetaeIndicating the angle of deflection of the rotation, omegaeRepresents an electrical angular velocity;
transforming equation (1) into a current equation, one can obtain:
Figure FDA0003097100150000012
consider the relationship shown below:
Figure FDA0003097100150000021
the following equation of state is obtained:
Figure FDA0003097100150000022
y＝Cx (5)
wherein:
Figure FDA0003097100150000023
Figure FDA0003097100150000024
Figure FDA0003097100150000025
3. an improved control method for a ship propulsion motor as claimed in claim 2, characterized in that: in step 1, since equations (4) and (5) are nonlinear, the discretized mathematical model is represented as:
x(k+1)＝f[x(k)]+B(k)u(k)+V(k) (9)
y(k)＝C(k)x(k)+W(k) (10)
wherein V (k) is system noise, and W (k) is measurement noise; b (k) and C (k) represent discrete forms of the B matrix and C matrix given by equations (4) and (5); the above equation is a state equation, and the following equation is an output equation.
If V (k) and W (k) are both zero-mean white noise:
E{V(k)}＝0,E{W(k)}＝0 (11)
in the formula, E { } represents numerical expectation.
4. An improved control method for a ship propulsion motor as claimed in claim 3, characterized in that: in step 1, covariance matrices Q and R of noise vectors V and W, respectively, are obtained, where Q and R are defined as:
Figure FDA0003097100150000031
assuming that V (k) and W (k) are independent, x (0) is a random vector, independent of V (k) and W (k).
5. An improved control method for a ship propulsion motor, according to claim 1, characterized in that: in step 2, firstly, the state estimation of the extended kalman filter EKF is roughly divided into two stages, namely a prediction stage and a correction stage, and the detailed steps are as follows:
step 2-1, estimating a state vector: from the previous state estimate
Figure FDA0003097100150000032
And inputting the value u (k) to estimate the state vector at the next time (k +1) as:
Figure FDA0003097100150000033
in the formula, TsRepresenting a state estimation value and representing a predicted value for a sampling period;
calculating corresponding outputs
Figure FDA0003097100150000034
Namely:
Figure FDA0003097100150000035
step 2-2, calculating an error covariance matrix, namely:
Figure FDA0003097100150000041
in the formula, TsRepresenting a state estimation value and representing a predicted value for a sampling period;
wherein:
Figure FDA0003097100150000042
the results were:
Figure FDA0003097100150000043
step 2-3, calculating a gain matrix K (K +1) of the EKF, namely:
Figure FDA0003097100150000044
step 2-4, for the predicted state vector
Figure FDA0003097100150000045
Performing feedback correction to obtain optimized state estimation
Figure FDA0003097100150000046
Namely:
Figure FDA0003097100150000047
this step is an estimation of the corrected state;
step 2-5, an estimation error covariance matrix is pre-calculated for the next estimation, that is:
Figure FDA0003097100150000048
and (3) selecting proper covariance and initial values by combining a corresponding simulation model, determining stator voltage and stator current under a static coordinate system, and finally estimating the stator current, the rotating speed and the rotor position information.
CN202110615179.1A 2021-06-02 2021-06-02 Improved control method of ship propulsion motor Pending CN113315439A (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111193448A (en) * 2020-01-20 2020-05-22 江苏新安电器股份有限公司 Surface-mounted permanent magnet synchronous motor load torque observation method based on extended Kalman filter
CN111884555A (en) * 2020-07-29 2020-11-03 江南大学 Filtering estimation method for rotating speed and position of permanent magnet synchronous motor rotor

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111193448A (en) * 2020-01-20 2020-05-22 江苏新安电器股份有限公司 Surface-mounted permanent magnet synchronous motor load torque observation method based on extended Kalman filter
CN111884555A (en) * 2020-07-29 2020-11-03 江南大学 Filtering estimation method for rotating speed and position of permanent magnet synchronous motor rotor

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王婷 等: "基于扩展卡尔曼滤波法的船舶永磁同步电机无传感器控制", 《软件》 *

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