Device and method for estimating motor parameters

Abstract

A device (1) and method (1') for estimating motor parameters, comprising receiving an operating parameter (u d,q 、i d,q 、ω e ), based on the operating parameters (u d,q 、i d,q 、ω e ) and the initially determined second motor parameter (R S,0 ) Estimate the estimated first motor parameter (Ψ PM，1 ), and based on the operating parameters (u d,q 、i d,q 、ω e ) and the initially determined first motor parameter (Ψ PM，0 ) to estimate the estimated second motor parameter (R S,1 The device (1) and method (1') further include a method based on the estimated first motor parameter (Ψ PM，1 ) and operating parameters (u d,q 、i d,q 、ω e ) estimates the corrected estimated second motor parameter (R S,2 ), and the second motor parameter (R S,1 ) and operating parameters (u d,q 、i d,q 、ω e ) estimates the corrected estimated first motor parameter (Ψ PM，2 ).

Description

Technical Field

[0001] The present application relates to a device and method for estimating motor parameters, in particular for online estimating motor parameters of an electric motor. Background Art

[0002] Online estimation of motor parameters enables condition monitoring of the motor, such as estimation of phase faults or coil temperature.

[0003] In particular, for coil temperature estimation, an accurate estimation of the phase resistance R is required. S However, the phase resistance R S The estimation depends on the permanent magnet flux linkage Ψ PM In addition, during load or speed changes, the presence of non-modeled voltage losses can cause the estimated phase resistance R S There is a deviation.

[0004] Model Reference Adaptive Controller (MRAC) and Adaline Neural Network (ANN) are well-known parameter estimation algorithms. In particular, the MRCA algorithm can be used to estimate the phase resistance R S , the ANN algorithm can be used to estimate the permanent magnet flux linkage Ψ PM , so an algorithm combining these two algorithms seems to be suitable for the required estimation.

[0005] However, due to rank deficiency, it is impossible to simultaneously estimate the permanent magnet flux linkage Ψ under reference control PM and phase resistance R S , so the DC current is set to zero (I d =0A). Therefore, in the MRCA model, the permanent magnet flux linkage Ψ PM Set to a constant value to estimate the phase resistance R S , in the ANN model, the phase resistance R S Set to a constant value to estimate the permanent magnet flux linkage Ψ PM However, these models do not account for non-modeled voltage losses during load and speed variations, thus leading to deviations in the estimated parameters from their actual values. These deviations, however, lead to errors in the coil temperature estimation. Summary of the Invention

[0006] Therefore, the purpose of the present application is to remedy the above shortcomings and provide a device and method that can accurately estimate motor parameters under the operating conditions that occur.

[0007] The object is achieved by the device according to the invention, the method according to the invention and the computer program product according to the invention. Advantageous further developments are included in the technical solution of the invention.

[0008] According to one aspect of the present application, a device for estimating motor parameters includes: an input interface configured to receive operating parameters of a motor; a first device configured to execute a first parameter estimation algorithm, the first parameter estimation algorithm estimating an estimated first motor parameter based on the operating parameters and an initially determined second motor parameter; and a second device configured to execute a second parameter estimation algorithm, the second parameter estimation algorithm estimating an estimated second motor parameter based on the operating parameters and the initially determined first motor parameter. The device also includes: a third device configured to execute a third parameter estimation algorithm, the third parameter estimation algorithm estimating a revised estimated second motor parameter based on the estimated first motor parameter and the operating parameter; and a fourth device configured to execute a fourth parameter estimation algorithm, the fourth parameter estimation algorithm estimating a revised estimated first motor parameter based on the estimated second motor parameter and the operating parameter.

[0009] By means of such a device, the effects of non-model operating parameters can be taken into account in order to provide a more accurate estimate of the motor parameters.

[0010] In an advantageous embodiment of the device, the operating parameter includes at least one of a detected voltage, a detected current and a detected electrical angular velocity, the first motor parameter includes a permanent magnet flux linkage, and the second motor parameter includes a phase resistance.

[0011] By selecting these operating parameters and the motor parameters to be estimated, estimation of the motor parameters required for estimating the coil temperature can be accurately performed.

[0012] In another advantageous embodiment of the device, the first means and the fourth means comprise artificial neural network models implementing parameter estimation algorithms, and the second means and the third means comprise model reference adaptive control models implementing the second and third parameter estimation algorithms.

[0013] By providing a corresponding parameter estimation algorithm for estimating motor parameters, a suitable parameter estimation algorithm is provided and motor parameters can be accurately and effectively estimated.

[0014] In another advantageous embodiment of the device, the initially determined first motor parameter and the initially determined second motor parameter are constant values.

[0015] This determination provides suitable starting values ​​for the estimation.

[0016] In a further advantageous embodiment of the device, the third or fourth device is configured to be able to estimate a third motor parameter.

[0017] By estimating further parameters in a more accurate manner, the estimate of the coil temperature may be improved or additional parameters may be estimated.

[0018] In a further advantageous embodiment of the device, the third motor parameter comprises inductance.

[0019] By estimating the inductance, coil faults can be determined.

[0020] According to another aspect of the present application, a method includes the following steps: estimating an estimated first motor parameter based on an operating parameter and an initially determined second motor parameter; estimating an estimated second motor parameter based on the operating parameter and the initially determined first motor parameter; estimating a corrected estimated second motor parameter based on the estimated first motor parameter and the operating parameter; and estimating a corrected estimated first motor parameter based on the estimated second motor parameter and the operating parameter.

[0021] With this approach, the effects of non-modeled operating parameters can be considered to provide a more accurate estimate of the motor parameters.

[0022] In an advantageous embodiment of the method, the operating parameters include a detected voltage, a detected current, and a detected electrical angular velocity, the first motor parameter includes a phase resistance, and the second motor parameter includes a permanent magnet flux linkage.

[0023] By selecting these operating parameters and motor parameters in the method, the estimation of the motor parameters required for estimating the coil temperature can be accurately performed.

[0024] In another advantageous embodiment of the method, the estimation of the estimated first motor parameters and the corrected estimated first motor parameters is performed with the aid of an artificial neural network model, and the estimation of the estimated second motor parameters and the corrected estimated second motor parameters is performed with the aid of a model reference adaptive control model.

[0025] By using a corresponding parameter estimation algorithm for estimating motor parameters, a suitable parameter estimation algorithm is used and the motor parameters can be accurately and efficiently estimated.

[0026] In a further advantageous embodiment of the method, the initially determined first motor parameter and the initially determined second motor parameter are set to constant values.

[0027] By means of this determination, suitable starting values ​​are used for the estimation.

[0028] In yet another advantageous embodiment, the method further comprises the step of estimating a third motor parameter.

[0029] By estimating further motor parameters, the estimate of the coil temperature may be improved or additional motor parameters may be estimated.

[0030] According to a further advantageous embodiment, the third motor parameter comprises inductance.

[0031] According to another aspect, a computer program product comprises instructions which, when the program is executed by a computer, cause the computer to perform the steps of the method.

[0032] By estimating the inductance, coil faults can be determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present application is explained below with reference to the accompanying drawings using exemplary embodiments.

[0034] In particular,

[0035] Figure 1 A block diagram showing devices and algorithms according to the present application; and

[0036] Figure 2 A diagram illustrating the efficacy of an apparatus or method according to the present application is shown. DETAILED DESCRIPTION

[0037] Figure 1 A block diagram of the device and algorithm according to the present application is shown.

[0038] In the embodiment provided as an apparatus, reference numeral 1 depicts an apparatus for estimating motor parameters of an electric motor (not shown).

[0039] The first motor parameter includes the permanent magnet flux linkage Ψ PM The second motor parameter includes the phase resistance R S Alternatively, other motor parameters may be estimated.

[0040] The device 1 comprises a first estimator 2 and a second estimator 3. Furthermore, the device 1 comprises an input interface 4.

[0041] The input interface 4 receives the operating parameters of the motor. The input interface 4 receives the detected voltage u d,q , the detected current i d,q and the detected electrical angular velocity ω e In alternative embodiments, less than all of these operating parameters are entered or additional operating parameters are entered.

[0042] The first estimator 2 includes a first device 5 including an artificial neural network (ANN) model that executes a parameter estimation algorithm accordingly, and a second device 6 including a model reference adaptive control (MRAC) model that executes a parameter estimation algorithm accordingly. Alternatively, another number of devices, only one or more than two, or another model, such as an extended Kalman filter (EKF), may be provided.

[0043] The first device 5 is configured to execute a first parameter estimation algorithm according to an artificial neural network (ANN) model, thereby estimating the first parameter based on the operating parameter u d,q 、i d,q 、ω e and the initially determined second motor parameter R S,0 Estimate the estimated first motor parameter Ψ PM，1 .

[0044] The second device 6 is configured to execute a second parameter estimation algorithm according to a model reference adaptive control (MRAC) model, thereby d,q 、i d,q 、ω e and the initially determined first motor parameter Ψ PM，0 Estimate the estimated second motor parameter R S,1 .

[0045] The second estimator 3 includes a third device 7 and a fourth device 8. The third device 7 includes a model reference adaptive control (MRAC) model that executes a parameter estimation algorithm accordingly, and the fourth device 8 includes an artificial neural network (ANN) model that executes a parameter estimation algorithm accordingly. Alternatively, another number of devices, only one or more than two, or another model, such as concurrent learning adaptive control, may be provided.

[0046] The third device 7 is configured to execute a third parameter estimation algorithm according to a model reference adaptive control (MRAC) model, thereby estimating the first motor parameter Ψ based on the estimated PM,1 and running parameters u d,q 、i d,q 、ω e Estimate the corrected estimated second motor parameter R S,2 .

[0047] The third device 7 further estimates a third motor parameter Ls. The third motor parameter includes inductance. Alternatively, another third motor parameter is estimated or no third motor parameter is estimated.

[0048] The fourth device 8 is configured to execute a fourth parameter estimation algorithm according to an artificial neural network (ANN) model, thereby estimating the fourth parameter based on the estimated second motor parameter R S,1 and running parameters u d,q 、i d,q 、ω e Estimating a corrected estimated first motor parameter Ψ PM，2 .

[0049] Initially determined first motor parameter Ψ PM，0 and the initially determined second motor parameter R S,0Alternatively, the initially determined motor parameters are variable values.

[0050] The estimators 2, 3 and the means 5, 6, 7, 8 are shown as separate modules, however, alternatively they may be fully or partially integrated in one or more modules.

[0051] In an embodiment provided as a method, reference numeral 1 ′ depicts a method for estimating motor parameters of an electric motor.

[0052] In use, the method 1 for estimating motor parameters is to input the operating parameters u d,q 、i d,q 、ω e Furthermore, the second motor parameter R is set to be initially determined. S,0 and the initially determined first motor parameter Ψ PM，0 .

[0053] Based on the operating parameter u d,q 、i d,q 、ω e and the initially determined second motor parameter R S,0 , estimated first motor parameter Ψ PM，1 Estimated by the first parameter estimation algorithm 5'. In addition, based on the operating parameter u d,q 、i d,q 、ω e and the initially determined first motor parameter Ψ PM，0 , estimated second motor parameter R S,1 Estimated by the second parameter estimation algorithm 6'.

[0054] In addition, based on the operating parameter u d,q 、i d,q 、ω e and the estimated first motor parameter Ψ PM，1 , the corrected estimated second motor parameter R S,2 Estimated by the third parameter estimation algorithm 7'. In addition, based on the operating parameter u d,q 、i d,q 、ω e and the estimated second motor parameter R S,1 , the corrected estimated first motor parameter Ψ PM，2 Estimated by the fourth parameter estimation algorithm 8'.

[0055] The first parameter estimation algorithm 5' and the second parameter estimation algorithm 6' are components of the first estimator 2', which represents a device and a software module. The third parameter estimation algorithm 7' and the fourth parameter estimation algorithm 8' are components of the second estimator 3', which also represents a device and a software module.

[0056] As mentioned above, the first motor parameter includes the permanent magnet flux linkage Ψ PM , the second motor parameter includes the phase resistance Rs. Alternatively, other motor parameters may be estimated.

[0057] The estimated first motor parameter Ψ PM,1 and the corrected estimated first motor parameter Ψ PM,2 The estimation of the second motor parameter R is performed by means of an artificial neural network (ANN) model. S,1 and the corrected estimated second motor parameter R S,2 The estimation of is performed with the aid of a Model Reference Adaptive Control (MRAC) model. As mentioned above, the estimation can be performed by only one model or by another model, such as Concurrent Learning Adaptive Control.

[0058] The first motor parameter Ψ initially determined PM,0 and the initially determined second motor parameter R S,0 Set to constant values. Alternatively, they can be variable values.

[0059] The third parameter estimation algorithm 7′ further estimates the inductance L S Alternatively, another motor parameter is estimated or no further motor parameter is estimated.

[0060] Figure 2 A diagram illustrating the efficacy of an apparatus or method according to the present application is shown.

[0061] The graph above depicts the second motor parameter R S , the diagram in the figure depicts the inductance L S , the following graph depicts the first motor parameter Ψ PM On the left, the estimation results of the first estimator (2, 2') are shown. On the right, the estimation results of the second estimator (3, 3') are shown.

[0062] The solid lines represent estimated values, and the dashed lines represent measured values.

[0063] At 12 seconds, the motor speed increases, and at 7 and 18 seconds, the applied load torque increases.

[0064] In the graphs depicting the results of the first estimator (2, 2'), it is possible to see the deviation of the estimated parameters due to the increase in motor speed and applied load. Moreover, as can be seen in the right graphs with the results of the second estimator (3, 3'), the device and algorithm according to the present application significantly improve the results, since, except for brief peaks during the variations, the corrected estimated parameter Ψ PM，2 and R S,2 Remains constant during dynamic load changes.

[0065] The present application has been described in conjunction with various embodiments herein. However, by studying the drawings, the disclosure, and the accompanying drawings, those skilled in the art may understand and implement other variations to the disclosed embodiments when practicing the claimed invention. Such modifications may involve other features known in the art and may be used instead of or in addition to the features already described herein. In the technical solutions of the present invention, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude plural number.

[0066] Reference Signs List

[0067] 1 Equipment for estimating motor parameters

[0068] 1' Method for estimating motor parameters

[0069] 2 First Estimator

[0070] 2' First Estimator

[0071] 3 Second Estimator

[0072] 3' Second Estimator

[0073] 4 Input ports

[0074] 5 First Device

[0075] 5' First parameter estimation algorithm

[0076] 6 Second Device

[0077] 6' Second parameter estimation algorithm

[0078] 7 The Third Device

[0079] 7' Third parameter estimation algorithm

[0080] 8 The Fourth Device

[0081] 8' Fourth parameter estimation algorithm

[0082] Ψ PM First motor parameter (permanent magnet flux)

[0083] Ψ PM，0 Initially determined first motor parameters

[0084] Ψ PM,1 Estimated first motor parameters

[0085] Ψ PM,2 Corrected estimated first motor parameters

[0086] R S Second motor parameter (phase resistance)

[0087] R S,0 Initially determined second motor parameters

[0088] R S,1 Estimated parameters of the second motor

[0089] R S,2 Corrected estimated second motor parameters

[0090] L S Third motor parameter (inductance)

[0091] u d,q Detected voltage

[0092] i d,q Detected current

[0093] ω e Detected electrical angular velocity

[0094] ANN Artificial Neural Network

[0095] MRAC Model Reference Adaptive Control

Claims

1. A device (1) for estimating motor parameters, the device (1) comprising: Input interface (4), which is configured to receive the operating parameters of the motor (u d,q 、i d,q 、ω e ), A first device (5) is configured to execute a first parameter estimation algorithm (5') so as to estimate the value of the first parameter based on the operating parameter (u d,q 、i d,q 、ω e ) and the initially determined second motor parameter (R S,0 ) Estimate the estimated first motor parameter (Ψ PM，1 ),and The second device (6) is configured to execute a second parameter estimation algorithm (6') so as to estimate the value of the second parameter based on the operating parameter (u d,q 、i d,q 、ω e ) and the initially determined first motor parameter (Ψ PM，0 ) to estimate the estimated second motor parameter (R S,1 ), wherein the first motor parameter comprises permanent magnet flux linkage, and the second motor parameter comprises phase resistance, Wherein, the device (1) further comprises: The third device (7) is configured to execute a third parameter estimation algorithm (7') so as to estimate the first motor parameter (Ψ PM，1 ) and operating parameters (u d,q 、i d,q 、ω e ) estimates the corrected estimated second motor parameter (R S,2 ),and The fourth device (8) is configured to execute a fourth parameter estimation algorithm (8') so as to estimate the second motor parameter (R S,1 ) and operating parameters (u d,q 、i d,q 、ω e ) estimates the corrected estimated first motor parameter (Ψ PM,2 ).

2. The device (1) according to claim 1, wherein Operation parameters (u d,q 、i d,q 、ω e ) including the detected voltage (u d,q ), the detected current (i d,q ) and the detected electrical angular velocity (ω e ) at least one of.

3. The device (1) according to claim 2, wherein The first device (5) and the fourth device (8) include artificial neural network (ANN) models that execute the first parameter estimation algorithm (5') and the fourth parameter estimation algorithm (8'), The second means (6) and the third means (7) comprise a model reference adaptive control (MRAC) model that executes a second parameter estimation algorithm (6') and a third parameter estimation algorithm (7').

4. The device (1) according to any one of claims 1 to 3, wherein The first motor parameter (Ψ PM,0 ) and the initially determined second motor parameter (R S,0 ) is a constant value.

5. The device (1) according to any one of claims 1 to 3, wherein The third device (7) is configured to estimate a third motor parameter (L S ).

6. The device (1) according to claim 5, wherein The third motor parameter (L S ) including inductance.

7. A method (1') for estimating motor parameters, the method (1') comprising the following steps: Based on the operating parameters (u d,q 、i d,q 、ω e ) and the initially determined second motor parameter (R S,0 ) Estimate the estimated first motor parameter (Ψ PM，1 ); Based on the operating parameters (u d,q 、i d,q 、ω e ) and the initially determined first motor parameter (Ψ PM，0 ) to estimate the estimated second motor parameter (R S,1 ); Based on the estimated first motor parameter (Ψ PM，1 ) and operating parameters (u d,q 、i d,q 、ω e ) estimates the corrected estimated second motor parameter (R S,2 ); and Based on the estimated second motor parameter (R S,1 ) and operating parameters (u d,q 、i d,q 、ω e ) estimates the corrected estimated first motor parameter (Ψ PM，2 ), The first motor parameter includes permanent magnet flux linkage, and the second motor parameter includes phase resistance.

8. The method (1') according to claim 7, wherein Operation parameters (u d,q 、i d,q 、ω e ) including the detected voltage (u d,q ), the detected current (i d,q ) and the detected electrical angular velocity (ω e ).

9. The method (1') according to claim 8, wherein The estimation of the estimated first motor parameter (ΨPM,1) and the corrected estimated first motor parameter (ΨPM,2) is performed by means of an artificial neural network (ANN) model, The estimated second motor parameter (R S,1 ) and the corrected estimated second motor parameter (R S,2 ) is estimated with the help of a Model Reference Adaptive Control (MRAC) model.

10. The method (1') according to any one of claims 7 to 9, wherein The first motor parameter (Ψ PM，0 ) and the initially determined second motor parameter (R S,0 ) is set to a constant value.

11. The method (1') according to any one of claims 7 to 9, wherein The method (1') further comprises the following steps: Estimate the third motor parameters (L S ).

12. The method according to claim 11, wherein The third motor parameter (L S ) including inductance.

13. A computer program product comprising instructions which, when the program is executed by a computer, cause the computer to perform the steps of the method according to any one of claims 7 to 12.

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