KR101218441B1 - Control System of Interior Permanent Magnet Synchronous Motor and Method to Detect Sensor Fault thereof - Google Patents

Control System of Interior Permanent Magnet Synchronous Motor and Method to Detect Sensor Fault thereof Download PDF

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KR101218441B1
KR101218441B1 KR1020110042659A KR20110042659A KR101218441B1 KR 101218441 B1 KR101218441 B1 KR 101218441B1 KR 1020110042659 A KR1020110042659 A KR 1020110042659A KR 20110042659 A KR20110042659 A KR 20110042659A KR 101218441 B1 KR101218441 B1 KR 101218441B1
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permanent magnet
magnet synchronous
synchronous motor
model
embedded permanent
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KR20120124805A (en
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이형철
김지환
전남주
정기윤
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한양대학교 산학협력단
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Abstract

Sensor failure detection method of the control system of the embedded permanent magnet synchronous motor according to an embodiment of the present invention, the modeling step of modeling the model of the embedded permanent magnet synchronous motor; A linear model transformation step of converting the nonlinear model of the embedded permanent magnet synchronous motor into a linearized model by input-output linearization; And an error measuring step of measuring a residual from the linearized model by applying a parity equation. The current sensor and the position sensor of the system using a failure detection algorithm generated based on the steps. The fault can be detected in real time.

Description

Embedded System Permanent Magnet Synchronous Motor and Method to Detect Sensor Fault

An embedded permanent magnet synchronous motor control system and a sensor failure detection method thereof are disclosed. More specifically, a buried permanent magnet synchronous motor control system and a sensor failure detection method thereof, which can accurately detect not only a failure of a current sensor of a buried permanent magnet synchronous motor but also a failure of a position sensor by a designed algorithm, are disclosed.

Embedded Permanent Magnet Synchronous Motors (IPMSMs) have a higher power factor, higher power density, higher torque to current ratio, higher efficiency and greater force to weight ratio than other AC motors. This advantage is essential for automotive applications such as automotive motor systems and hybrid vehicles.

However, the magnetic saturation of the rotor core and the nonlinear coupling between the winding current and the rotor speed make it difficult to design the controller of the motor. Therefore, motor controllers are very different from linear motors such as PI and PID controllers, and some nonlinear controllers have been developed for the application of synchronous motors.

For automotive applications, the motor control system must have the same level of reliability of the machine part as a vehicle breakdown can threaten the safety of the passenger. In order to secure the reliability, various studies on the failure diagnosis algorithm of the motor control system have been conducted.

Most of the research uses a fault detection approach based on observers. One of the advantages of observer-based fault detection is that stiffness to model uncertainty can be easily achieved. However, this method carries the risk that failures with slow rate constants may not be detected because the improvement in stiffness may reduce the sensitivity of detection to slow rate constants.

On the other hand, a parity equation and parameter evaluation approach based on a linear model is a simpler and more direct solution for detecting failure of the control system. However, applying this approach to a motor control system without any correction has the potential for false alarms since the linear model has a large amount of model uncertainty when the controller is in a nonlinear operating state.

In order to solve this problem, it is essential to apply a method based on a nonlinear model for the motor control system.

An object according to an embodiment of the present invention, the embedded permanent magnet synchronous motor control system and its sensor that can accurately detect the failure of the current sensor as well as the position sensor of the embedded permanent magnet synchronous motor in real time by the designed algorithm It is to provide a fault detection method.

Another object according to an embodiment of the present invention, an embedded permanent magnet synchronous motor control system that can detect the failure of the current sensor and the position sensor by the algorithm to reduce the cost of detecting the failure of the sensor compared to the conventional And a sensor failure detection method thereof.

Sensor failure detection method of the control system of the embedded permanent magnet synchronous motor according to an embodiment of the present invention, the modeling step of modeling the model of the embedded permanent magnet synchronous motor; A linear model transformation step of converting a nonlinear model of the embedded permanent magnet synchronous motor into a linearized model by input-output linearization; And an error measuring step of measuring a residual from the linearized model by applying a parity equation. The current sensor of the system using a failure detection algorithm generated based on the steps. And a failure of the position sensor can be detected in real time.

The sensor failure detection method may further comprise an algorithm verification step of verifying the usefulness of the proposed failure detection algorithm by computer simulation.

The computer simulation in the algorithm verification step may be executed by a program including MATLAB or SIMULINK.

The model in the modeling step is defined by direct-quadrature (dq) transformation, and the voltage equation within the defined dq sync frame is

Figure 112011033494627-pat00001
And
Figure 112011033494627-pat00002
(here,
Figure 112011033494627-pat00003
Is the d-axis voltage, R is the stator resistance,
Figure 112011033494627-pat00004
D-axis current,
Figure 112011033494627-pat00005
D-axis inductance,
Figure 112011033494627-pat00006
Is the pole pair of the motor,
Figure 112011033494627-pat00007
Silver motor speed,
Figure 112011033494627-pat00008
Is the q-axis inductance,
Figure 112011033494627-pat00009
Is the q-axis current,
Figure 112011033494627-pat00010
Is the q-axis voltage,
Figure 112011033494627-pat00011
May be flux linkage).

The current equation in the dq sync frame defined in the modeling step is

Figure 112011033494627-pat00012
(here,
Figure 112011033494627-pat00013
Is the a-axis current,
Figure 112011033494627-pat00014
Is the b-axis current,
Figure 112011033494627-pat00015
Is the rotor position.

In the linear model conversion step, the linearized model may be obtained by applying an equation defining the nonlinear model in the modeling step to a Lie derivative.

The input-output linearization provides a nonlinear equivalent variation that linearizes the dynamic equations of the system, thereby applying the input-output linearization to account for the nonlinear characteristics of the embedded permanent magnet synchronous motor. A model to reflect can be obtained.

On the other hand, the control system of the embedded permanent magnet synchronous motor according to an embodiment of the present invention, after modeling the model of the embedded permanent magnet synchronous motor, the embedding by input-output linearization (input-output linearization) A failure detection algorithm is generated by converting a nonlinear model of the type permanent magnet synchronous motor into a linearized model, and then measuring a residual from the linearized model by applying a parity equation, and generating the failure. Detection algorithms can be used to detect faults in the current and position sensors of the system in real time.

According to the embodiment of the present invention, not only the failure of the current sensor of the embedded permanent magnet synchronous motor but also the failure of the position sensor can be accurately detected in real time by a designed algorithm.

According to the embodiment of the present invention, the failure of the current sensor and the position sensor can be detected by an algorithm, so that the cost of detecting the failure of the sensor can be reduced as compared with the related art.

1 is a block diagram schematically illustrating a configuration of an embedded permanent magnet synchronous motor according to an embodiment of the present invention.
2 is a flowchart illustrating a sensor failure detection method of a control system of an embedded permanent magnet synchronous motor according to an exemplary embodiment of the present invention.
3 to 5 are graphs showing simulation results of a control system of an embedded permanent magnet synchronous motor according to an embodiment of the present invention.

Hereinafter, configurations and applications according to embodiments of the present invention will be described in detail with reference to the accompanying drawings. The following description is one of several aspects of the patentable invention and the following description forms part of the detailed description of the invention.

In the following description, well-known functions or constructions are not described in detail for the sake of clarity and conciseness.

1 is a block diagram schematically showing the configuration of a buried permanent magnet synchronous motor according to an embodiment of the present invention, Figure 2 is a control system of a buried permanent magnet synchronous motor according to an embodiment of the present invention 3 to 5 are graphs illustrating simulation results of a control system of an embedded permanent magnet synchronous motor according to an exemplary embodiment of the present invention.

Referring to FIG. 1, the embedded permanent magnet synchronous motor 100 according to an exemplary embodiment of the present disclosure may convert the current value and the position detection information obtained by the encoder 120 to the failure detection algorithm 101. By substituting, the IPMSM controller 110 can detect the failure of the current sensor and the position sensor provided in the control system in real time. In other words, on the basis of the algorithm 101, not only the failure of the current sensor but also the failure of the position sensor can be detected in real time, thereby improving efficiency and accuracy and reducing costs.

2, the sensor failure detection method of the embedded permanent magnet synchronous motor control system according to an exemplary embodiment of the present disclosure includes a modeling step of modeling a nonlinear model of the embedded permanent magnet synchronous motor 100. (S100), a linear model conversion step (S200) for converting a nonlinear model of the embedded permanent magnet synchronous motor 100 into a linearized model by input-output linearization, and a parity equation (Parity) an error measurement step (S300) of measuring a residual from the linearized model by applying an equation, and an algorithm proof step of verifying the usefulness of the failure detection algorithm 101 proposed by the above-described steps by computer simulation. It may include (S400).

For each step, first, in the modeling step (S100), assuming that the embedded permanent magnet synchronous motor 100 has balanced windings and three phases without saturation, direct quadrature (dq, direct-quadrature) synchronization The voltage equation of the synchronous motor in the frame can be expressed as follows.

Figure 112011033494627-pat00016
(Equation 1)

Figure 112011033494627-pat00017
(Equation 2)

In equation 1 and equation 2,

Figure 112011033494627-pat00018
Is the d-axis voltage, R is the stator resistance,
Figure 112011033494627-pat00019
D-axis current,
Figure 112011033494627-pat00020
D-axis inductance,
Figure 112011033494627-pat00021
Is the pole pair of the motor,
Figure 112011033494627-pat00022
Silver motor speed,
Figure 112011033494627-pat00023
Is the q-axis inductance,
Figure 112011033494627-pat00024
Is the q-axis current,
Figure 112011033494627-pat00025
Is the q-axis voltage,
Figure 112011033494627-pat00026
Is the flux linkage.

The electromagnetic torque can be expressed as follows.

Figure 112011033494627-pat00027
(Equation 3)

On the other hand, the dynamic spinning equation of the speed is as follows.

Figure 112011033494627-pat00028
(Equation 4)

In equation 4,

Figure 112011033494627-pat00029
Is an unknown external load,
Figure 112011033494627-pat00030
,
Figure 112011033494627-pat00031
Is the standard parameter of the motor.

The dynamic equation of position is given by

Figure 112011033494627-pat00032
(Eq. 5)

Where a-axis current

Figure 112011033494627-pat00033
, b-axis current
Figure 112011033494627-pat00034
Can be measured by a current sensor, and the rotor position
Figure 112011033494627-pat00035
Can be measured by the position sensor. At this time,
Figure 112011033494627-pat00036
,
Figure 112011033494627-pat00037
from
Figure 112011033494627-pat00038
And
Figure 112011033494627-pat00039
Direct quadrature transformation (dq) can be applied to generate Direct orthogonal transformation may be defined as in Equation 6 below.

Figure 112011033494627-pat00040
(Equation 6)

On the other hand, as described above, after the modeling step (S100), a linear model conversion step (S200) reflecting the non-linear characteristics of the embedded permanent magnet synchronous motor 100 proceeds, where the input output linearization (input-output linearization) Can be made.

In the linear model transformation step (S200), a linear system model can be obtained by applying a nonlinear system model to, for example, a Lie derivative equation. Lee's differential equation is given by the following equation.

Figure 112011033494627-pat00041
(Eq. 7)

In order to obtain a linearized model here, a new state variable may be defined as in Equation 8 below.

Figure 112011033494627-pat00042
(Eq. 8)

In Equation 8

Figure 112011033494627-pat00043
ego,

Figure 112011033494627-pat00044
Is,
Figure 112011033494627-pat00045
ego,

Figure 112011033494627-pat00046
Is,

Figure 112011033494627-pat00047
ego,
Figure 112011033494627-pat00048
to be.

The system changes slowly and the current state of the system

Figure 112011033494627-pat00049
Suppose it is known that the new state variable is differentiated with respect to time and then applied to equations 1 to 5 as described above.
Figure 112011033494627-pat00050
The state space formation of the system including) can be obtained from Equation 9 below.

Figure 112011033494627-pat00051
(Eq. 9)

here,

Figure 112011033494627-pat00052
ego,

Figure 112011033494627-pat00053
,
Figure 112011033494627-pat00054
,

Figure 112011033494627-pat00055
,

Figure 112011033494627-pat00056

Figure 112011033494627-pat00057
to be.

From these equations, we can obtain a linear system model that reflects the nonlinear part.

Thereafter, an error measuring step S300 of obtaining an error by applying a linear system model to a parity equation is performed.

To detect current sensor and position sensor failures in the system,

Figure 112011033494627-pat00058
, residual) can be obtained by the following equation.

Figure 112011033494627-pat00059
(Eq. 10)

To check for the presence of a fault, the error (

Figure 112011033494627-pat00060
)
Figure 112011033494627-pat00061
Is not zero
Figure 112011033494627-pat00062
Affected by
Figure 112011033494627-pat00063
Is 0, it becomes asymptotically small. To meet these conditions
Figure 112011033494627-pat00064
,
Figure 112011033494627-pat00065
In order to find, Equation 11 below can be applied.

Figure 112011033494627-pat00066
(Eq. 11)

here,

Figure 112011033494627-pat00067
to be.

Equation 11

Figure 112011033494627-pat00068
,
Figure 112011033494627-pat00069
The basis of
Figure 112011033494627-pat00070
Indicates the remaining null space of the. therefore
Figure 112011033494627-pat00071
,
Figure 112011033494627-pat00072
To determine,
Figure 112011033494627-pat00073
It is essential to find the space to be pressed.
Figure 112011033494627-pat00074
Remaining pressed space (
Figure 112011033494627-pat00075
) Can be calculated according to the matrices from equations (9) and (11).

Figure 112011033494627-pat00076
Figure 112011033494627-pat00077
(Eq. 12)

here,

Figure 112011033494627-pat00078
to be.

As represented by Equation 12,

Figure 112011033494627-pat00079
,
Figure 112011033494627-pat00080
,
Figure 112011033494627-pat00081
And
Figure 112011033494627-pat00082
Is not appropriate, which makes it impossible to realize the error. Thus, a low pass filter may be applied.

Figure 112011033494627-pat00083
(Eq. 13)

In equation 13,

Figure 112011033494627-pat00084
And
Figure 112011033494627-pat00085
Is the time constant of the low pass filter. By applying equation 12 to equation 13
Figure 112011033494627-pat00086
,
Figure 112011033494627-pat00087
Can be calculated and the error can be calculated by applying equations 12 and 13 to equation 10. The calculated error is shown in Equation 14 below.

Figure 112011033494627-pat00088
(Eq. 14)

Current motor speed (

Figure 112011033494627-pat00089
Since is not known, it must be estimated to perform equation 14. The motor speed estimate can be obtained from the following equation (15).

Figure 112011033494627-pat00090
(Eq. 15)

here,

Figure 112011033494627-pat00091
And
Figure 112011033494627-pat00092
Is the time constant of the low pass filter. The block diagram of the proposed failure detection algorithm 101 is as shown in FIG. 1 as described above. The proposed failure detection algorithm 101 can be executed by equations (14) and (15).

On the other hand, in the algorithm verification step S400 of the present embodiment, the usefulness of the failure detection algorithm 101 determined by the above-described steps can be proved by computer simulation.

The computer simulation that may be applied in the algorithm verification step S400 may be Matlab or Simulink. However, the present invention is not limited thereto.

The parameters of the embedded permanent magnet synchronous motor that can be applied in the algorithm verification step may be given as shown in Table 1 below.

sign Justice Number (unit)

Figure 112011033494627-pat00093
Number of pole pairs 2
Figure 112011033494627-pat00094
Rotor d-axis inductance 42.44 (mH)
Figure 112011033494627-pat00095
Rotor q-axis inductance 79.57 (mH)
Figure 112011033494627-pat00096
Stator resistance 1.93 (Ω)
Figure 112011033494627-pat00097
Magnetic chain coefficient 0.311 (Vs / rad)
Figure 112011033494627-pat00098
Motor inertia 0.003 (kgm 2 )
Figure 112011033494627-pat00099
Coefficient of friction 0.001 (Nm / s / rad)

The simulator for simulation is the same as that shown in FIG. 1, but the IPMSM controller 110, the PWM generator 130, and the inverter 140 may not be executed for the convenience of simulation.

To illustrate the effect of external load on error, although not shown, the input signal generator may include a virtual external load generator. The sensor detection generator can generate a fault signal of the current sensor and encoder to confirm the detectability of the proposed algorithm.

Meanwhile, the d-axis voltage and the q-axis voltage are shown in FIG. 3 when the external load of 5 Nm is applied every 10 seconds, and the result of the simulation is shown. As shown in Figure 4 it can be seen that the effect of the external load on the error is very small.

On the other hand, Figure 5 shows the results of the simulation when a 0.5V offset fault signal of the a-axis current sensor is applied every 20 seconds and an external load of 5 Nm is applied every 10 seconds. As shown here, it is easy to see that the change in error is so great that one of the sensors is abnormal after 20 seconds.

As such, in accordance with an embodiment of the present invention, a novel sensor failure detection method of a motor control system is a failure detection algorithm designed based on input-output linearization and parity equation approach to reduce changes in system nonlinearity and load torque effects. By doing so, there is an advantage in that failure detection of both the current sensor and the position sensor can be easily and accurately performed.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention. Therefore, such modifications or variations will have to be belong to the claims of the present invention.

100: embedded permanent magnet synchronous motor 101: failure detection algorithm
110: IPMSM controller 120: encoder
130: PWM generator 140: Inverter

Claims (8)

  1. In the sensor failure detection method of the control system of the embedded permanent magnet synchronous motor,
    A modeling step of modeling a model of the embedded permanent magnet synchronous motor defined by direct-quadrature (dq) transformation;
    A linear model that transforms the nonlinear model of the embedded permanent magnet synchronous motor into a linearized model by input-output linearization providing a nonlinear equivalent deformation that linearizes the dynamic equations of the system Conversion step; And
    An error measuring step of measuring a residual from the linearized model by applying a parity equation;
    And detecting a failure of the current sensor and the position sensor of the system using a failure detection algorithm generated based on the above steps.
  2. The method of claim 1,
    A method for detecting a sensor failure of the control system of the embedded permanent magnet synchronous motor, further comprising an algorithm verification step, which is performed after the error measuring step and proves the usefulness of the proposed failure detection algorithm by computer simulation.
  3. The method of claim 2,
    And the computer simulation in the algorithm verification step is executed by a program comprising a matlab or simulink.
  4. The method of claim 1,
    In the modeling step, the voltage equation within the defined dq sync frame,
    Figure 112012068036134-pat00100
    And
    Figure 112012068036134-pat00101
    (here,
    Figure 112012068036134-pat00102
    Is the d-axis voltage, R is the stator resistance,
    Figure 112012068036134-pat00103
    D-axis current,
    Figure 112012068036134-pat00104
    D-axis inductance,
    Figure 112012068036134-pat00105
    Is the pole pair of the motor,
    Figure 112012068036134-pat00106
    Silver motor speed,
    Figure 112012068036134-pat00107
    Is the q-axis inductance,
    Figure 112012068036134-pat00108
    Is the q-axis current,
    Figure 112012068036134-pat00109
    Is the q-axis voltage,
    Figure 112012068036134-pat00110
    Is a flux linkage. A failure detection method for a control system of an embedded permanent magnet synchronous motor.
  5. 5. The method of claim 4,
    The current equation in the dq sync frame defined in the modeling step S100 is
    Figure 112011033494627-pat00111
    (here,
    Figure 112011033494627-pat00112
    Is the a-axis current,
    Figure 112011033494627-pat00113
    Is the b-axis current,
    Figure 112011033494627-pat00114
    Is the rotor position) sensor failure detection method of a control system of an embedded permanent magnet synchronous motor.
  6. The method of claim 1,
    In the linear model conversion step, a failure detection method of a control system of an embedded permanent magnet synchronous motor, which obtains the linearized model by applying an equation defining the nonlinear model in the modeling step to a Li derivative.
  7. The method according to claim 6,
    And applying the input-output linearization to obtain a model reflecting the nonlinear characteristics of the embedded permanent magnet synchronous motor.
  8. In order to detect the failure of the current sensor and the failure sensor included in the control system of the embedded permanent magnet synchronous motor, a model defined by direct-quadrature (dq) transformation of the embedded permanent magnet synchronous motor is developed. After modeling, the nonlinear model of the embedded permanent magnet synchronous motor is linearized by input-output linearization providing a nonlinear equivalent deformation that linearizes the dynamic equations of the system. Converting the model into a model, and then applying a parity equation to measure the residual from the linearized model to generate a failure detection algorithm that is proved useful by computer simulation, and generates the generated failure detection algorithm. Embedded to detect the failure of the current sensor and position sensor of the system in real time Control system of the permanent magnet synchronous motor.
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CN103513135B (en) * 2013-10-10 2016-02-24 清华大学 A kind of simulating test device and using method thereof of directly driving sea wave power generation system
KR101672131B1 (en) * 2015-04-23 2016-11-16 한양대학교 산학협력단 Fault diagnosis system method for driving motor of electric vehicle using vehicle dynamic analysis
CN105827335B (en) * 2016-06-07 2018-06-29 北京邮电大学 A kind of antenna number determines method and device

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JP2000513097A (en) * 1996-06-24 2000-10-03 アルチェリク・アノニム・シルケト Model-based fault detection system for an electric motor
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KR20090078075A (en) * 2008-01-14 2009-07-17 충북대학교 산학협력단 Fault diagnosis of inductirn motors by dft and wavelet

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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