CN113783488B - Permanent magnet synchronous motor full-parameter identification method and permanent magnet synchronous motor system - Google Patents

Permanent magnet synchronous motor full-parameter identification method and permanent magnet synchronous motor system Download PDF

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CN113783488B
CN113783488B CN202110890790.5A CN202110890790A CN113783488B CN 113783488 B CN113783488 B CN 113783488B CN 202110890790 A CN202110890790 A CN 202110890790A CN 113783488 B CN113783488 B CN 113783488B
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axis
frequency
motor
current
inductance
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CN113783488A (en
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曲荣海
刘子睿
孔武斌
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Huazhong 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
    • 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
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0014—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks
    • 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
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/141—Flux estimation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/22—Current control, e.g. using a current control loop
    • 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
    • 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
    • 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)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Control Of Ac Motors In General (AREA)

Abstract

The invention discloses a permanent magnet synchronous motor full-parameter identification method and a permanent magnet synchronous motor system, which belong to the field of parameter identification of permanent magnet synchronous motors and comprise the following steps: injecting frequency omega into d and q axis command voltage h (ω h /ω e ∈[3,5]) High frequency voltage u of (2) dh And a high frequency voltage u qh The method comprises the steps of carrying out a first treatment on the surface of the The following steps are performed: sampling three-phase current and converting to synchronous rotation speed omega e Is rotated by the dq axis of (2) to obtain i d And i q The method comprises the steps of carrying out a first treatment on the surface of the Pair i d And i q Performing discrete Fourier transform to obtain amplitude I of high-frequency current under d and q axes mdh And I mqh And a phase theta d And theta q The method comprises the steps of carrying out a first treatment on the surface of the According to I by using Adaline single-layer neural network mdh 、I mqh 、θ d And theta q And u dh And u qh Amplitude U of (2) mdh And U mqh Identifying high-frequency parameters; according to the high-frequency parameter and d-axis direct current i, an Adaline single-layer neural network is utilized d0 Q-axis direct current i q0 D-axis DC voltage u d0 And q-axis DC voltage u q0 The fundamental frequency parameters are identified. The invention can realize accurate identification of parameters in the running process of the permanent magnet synchronous motor under the full working condition.

Description

Permanent magnet synchronous motor full-parameter identification method and permanent magnet synchronous motor system
Technical Field
The invention belongs to the field of parameter identification of permanent magnet synchronous motors, and particularly relates to a permanent magnet synchronous motor full-parameter identification method and a permanent magnet synchronous motor system.
Background
With the rapid popularization of electric vehicles and hybrid electric vehicles, the electric motor is a key part of a power transmission system of the electric vehicle, naturally faces more development opportunities and challenges, and compared with the traditional industrial electric motor, the electric vehicle motor must have higher torque and power density to meet the constraint of space in the vehicle, and must have a wider running speed range to meet the traction requirement of the vehicle. Among all motor types, the permanent magnet synchronous motor is the first choice of the main drive motor of the electric vehicle because of strong overload capacity and wide-range weak magnetic energy capacity. In addition, the electric drive control is not only required to realize high-efficiency operation, but also realized to realize accurate rotation speed and torque control in the whole operation speed range. In order to meet the above application requirements, accurate parameters in the operating state of the motor have to be obtained.
The existing permanent magnet synchronous motor parameter identification method mainly comprises an identification method utilizing a mathematical model of the permanent magnet synchronous motor and a motor parameter identification method based on high-frequency injection.
The mathematical model of the permanent magnet synchronous motor used in the traditional control, namely a voltage equation, has the following expression:
in the mathematical model, R s Represents the fundamental frequency resistance, psi f Representing permanent magnet flux linkage, ψ d and ψq Respectively represent d-axis permanent magnet flux linkage and q-axis permanent magnet flux linkage, i d and iq Respectively represent d-axis current and q-axis current, ω e Represents the fundamental frequency of motor operation, L d and Lq The d-axis inductance and the q-axis inductance are represented, respectively. The mathematical model is derived under ideal conditions, provided that saturation of the flux linkage is not assumed, i.e. that the flux linkage ψ varies linearly with the current i. In practice, the motor is a high coupling strength nonlinear system, and the permanent magnet flux linkage psi is realized by neglecting the influence of space harmonics such as cogging d and ψq Is mainly dependent on the permanent magnet flux linkage psi f And dq axis current, it can be considered that the permanent magnet flux linkage ψ f Is a function of dq-axis current as shown in the following equation:
the flux linkage and derivative terms of the flux linkage in the above voltage equation can be expressed as:
wherein , and />Respectively representing d-axis increment self-inductance, q-axis increment self-inductance and d-axis increment mutual inductance and q-axis increment mutual inductance; and />The d-axis apparent inductance and the q-axis apparent inductance are represented, respectively.
Therefore, in the traditional method for carrying out parameter identification by using the mathematical model, the influence of the iron core saturation on the motor parameter under the heavy current is ignored, the dq axis coupling quantity of the motor voltage equation under the synchronous rotation coordinate system is ignored, and the accuracy of motor parameter identification is low.
Traditional motor parameter identification methods based on high-frequency injection are based on the fact that the frequency of an injection voltage signal is far greater than the frequency omega of a motor operation fundamental wave e While ignoring omega e L d i d and ωe L q i q Since the frequency of the injected high-frequency signal cannot be infinitely increased due to the limitation of the switching frequency of the inverter, the scheme can only realize parameter identification in the zero-speed and low-speed states, and the assumption is not true when the motor operates at high speed, and the characteristic L of the embedded permanent magnet synchronous motor d <<L q Therefore atIgnoring omega e L d i d and ωe L q i q Item-time also results in a pair L d And the observation of (c) generates a large error.
Therefore, the traditional motor parameter identification method based on high-frequency injection can obtain accurate results only when the motor works at zero speed or extremely low speed, the observation error of the motor can be gradually amplified along with the increase of the motor rotating speed and the increase of current, and accurate parameter identification can not be realized in the full working condition range.
Disclosure of Invention
Aiming at the defects and improvement demands of the prior art, the invention provides a permanent magnet synchronous motor full-parameter identification method and a permanent magnet synchronous motor system, and aims to realize accurate identification of parameters in the running process of the permanent magnet synchronous motor under full-working conditions.
In order to achieve the above object, according to one aspect of the present invention, there is provided a permanent magnet synchronous motor full parameter identification method based on rotary high frequency voltage injection, comprising:
a step of rotating high-frequency voltage injection: in the running process of the motor, the d and q axis command voltages output by the current loop of the motor are respectively injected with the frequency omega h D-axis high-frequency voltage u of (2) dh And q-axis high frequency voltage u qh； wherein ,ωh /ω e ∈[3,5]，ω e Representing the fundamental frequency of the motor;
the full parameter identification step comprises the following steps:
(S1) sampling three-phase current in the running state of the motor, and converting the three-phase current into synchronous rotation speed to be motor fundamental frequency omega e Is rotated in the dq-axis to obtain d-axis current i d And q-axis current i q ；
(S2) for d-axis current i d And q-axis current i q Performing discrete Fourier transform to obtain frequency omega h Amplitude I of the current in d, q axes mdh and Imqh And a phase theta d and θq ；
(S3) utilizing an Adaline single-layer neural network according to the amplitude I mdh and Imqh Phase θ d and θq And d-axis high-frequency voltage u dh And q-axis high frequency voltage u qh Amplitude U of (2) mdh and Umqh Identifying high frequency parameters in the motor, including d-axis increment self-inductanceq-axis increment self-inductance->d. q-axis incremental mutual inductance>High-frequency resistor->
(S4) utilizing an Adaline single-layer neural network to obtain d-axis direct current i under the operation state of the motor according to high-frequency parameters d0 Q-axis direct current i q0 D-axis DC voltage u d0 And q-axis DC voltage u q0 Identifying fundamental frequency parameters in motor, including permanent magnet flux linkage psi f Apparent d-axis inductorq-axis apparent inductance->Fundamental frequency resistor R s 。
According to the invention, the motor parameter identification is performed by injecting the rotating high-frequency voltage into the current loop, the parameter identification is performed by utilizing an Adaline single-layer neural network instead of a theoretical model, and the identified motor parameter is except for a high-frequency resistorPermanent magnet flux linkage psi f Apparent d-axis inductance->q-axis apparent inductance->Fundamental frequency resistor R s Besides, the device also comprises d-axis increment self-inductance +.>q-axis increment self-inductance->And d, q axis incremental mutual inductance->The influence of iron core saturation on motor parameters under high current and dq axis coupling quantity of a motor voltage equation under a synchronous rotation coordinate system are fully considered, full-parameter identification is realized, and accuracy of motor parameter identification is effectively improved.
Further, between steps (S1) and (S2), further comprising:
for d-axis current i d And q-axis current i q Filtering to remove the frequency omega h The component of the (2) is used for obtaining d-axis direct current i under the running state of the motor d0 And q-axis direct current i q0 ；
D-axis direct current i d0 And q-axis direct current i q0 A current loop is input.
The invention injects rotating high-frequency voltage into the current loop, and then samples three-phase current of the motor and transforms coordinates to obtain d-axis current i d And q-axis current i q Comprising two parts: DC current i for generating torque during motor operation d0 and iq0 Frequency omega h High-frequency current signal i of (2) dh and iqh Before d and q axis currents are input into a current loop, the invention filters out omega frequency in the d and q axis currents by filtering h Can prevent the current loop output command voltage from generating an inhibition signal 180 degrees different from the rotation voltage signal, thereby avoiding influencing the motor control part by the injected rotation high-frequency voltage, ensuring the normal operation of the motor and simultaneously avoiding the inhibition signal generated by the current loop from interfering the motor parameterThe number identification ensures the accuracy of motor parameter identification.
Further, d-axis increment self-inductanceThe observation equation of (2) is:
wherein k represents the number of iterations; w, d, X and O respectively represent the weight, bias, input and output of the Adaline single-layer neural network; η represents a convergence coefficient;represents d-axis increment self-inductance->Is a function of the observed value of (a).
Further, q-axis incremental self-inductanceThe observation equation of (2) is:
wherein ,represents q-axis increment self-inductance->Is a function of the observed value of (a).
Further, d and q axis incremental mutual inductanceThe observation equation of (2) is:
wherein ,represents d, q axis incremental mutual inductance->Is a function of the observed value of (a).
Further, the high-frequency resistorThe observation equation of (2) is:
wherein ,representing a high frequency resistance->Is a function of the observed value of (a).
The invention uses Adaline single-layer neural network to identify high-frequency parametersThe adopted observation equations can reflect the actual running condition of the motor more accurately, and each observation equation comprises the frequency omega h The related terms also preserve the fundamental frequency omega of the motor e The term of interest, thus, in the case of a motor operating at high speed, i.e. the fundamental frequency ω of the motor e When the motor parameter is larger, the accurate identification of the motor parameter is realized; in addition, the dq axis coupling quantity of the motor voltage equation under the synchronous rotation coordinate system is substituted in the observation equation, so that the motor parameter identification is more accurate. In general, the invention can accurately identify motor parameters within the full operating range.
Further, in step (S4), according toIdentification of d-axis apparent inductance->According to->Identification base frequency resistor R s 。
Because the d-axis magnetic circuit is mainly a permanent magnet, the magnetic conductivity of the d-axis magnetic circuit is similar to that of air, the d-axis magnetic circuit is not easy to saturate, and the d-axis linearity is good, the d-axis magnetic circuit basically meets the following conditions in the normal operation range of the motorSince the injection frequency is similar to the motor frequency, the high frequency resistor and the base frequency resistor basically meet +.>The invention is based on the operating characteristics of the motor according to +.> and />Realize d-axis apparent inductance->And a fundamental frequency resistor R s The identification of the motor parameters can be ensured, the observation equations of other motor parameters can be correctly converged while the accurate identification of the two motor parameters is ensured, and the accurate identification of all motor parameters is realized.
Further, q-axis apparent inductanceThe observation equation of (2) is:
wherein k represents the number of iterations; w, d, X and O respectively represent the weight, bias, input and output of the Adaline single-layer neural network; η represents a convergence coefficient;represents q-axis apparent inductance +.>Is a function of the observed value of (a).
Further, permanent magnet flux linkage ψ f The observation equation of (2) is:
wherein ,representing permanent magnet flux linkage psi f Is>Represents d-axis apparent inductance +.>Is a function of the observed value of (a).
According to the invention, the identification of the fundamental frequency parameters is realized by using the Adaline single-layer neural network instead of the theoretical model, and the adopted observation equation can more accurately reflect the actual running condition of the motor, so that the accurate identification of the fundamental frequency parameters can be realized.
According to another aspect of the present invention, there is provided a permanent magnet synchronous motor system comprising: the device comprises a permanent magnet synchronous motor, a rotary high-frequency voltage injection module and a full-parameter identification module;
the rotary high-frequency voltage injection module is connected between the current loop of the motor and the SVPWM module and is used for respectively injecting frequency omega into d-axis command voltage and q-axis command voltage output by the current loop of the motor in the operation process of the motor h D-axis high-frequency voltage u of (2) dh And q-axis high frequency voltage u qh； wherein ,ωh /ω e ∈[3,5]，ω e Representing the fundamental frequency of the motor;
the full parameter identification module comprises: the system comprises a sampling unit, a DFT unit, a high-frequency parameter identification observer and a low-frequency parameter identification observer;
the sampling unit is used for sampling three-phase current in the running state of the motor and converting the three-phase current into synchronous rotation speed which is equal to the fundamental frequency omega of the motor e Is rotated in the dq-axis to obtain d-axis current i d And q-axis current i q ；
DFT unit for d-axis current i d And q-axis current i q Performing discrete Fourier transform to obtain frequency omega h Amplitude I of the current in d, q axes mdh and Imqh And a phase theta d and θq ；
The high-frequency parameter identification observer is used for utilizing an Adaline single-layer neural network and is used for identifying the observer according to the amplitude I mdh and Imqh Phase θ d and θq And d-axis high-frequency voltage u dh And q-axis high frequency voltage u qh Amplitude U of (2) mdh and Umqh Identifying high frequency parameters in the motor, including d-axis increment self-inductanceq-axis increment self-inductance->d. q-axis incremental mutual inductance>High-frequency resistor->
The low-frequency parameter identification observer is used for utilizing an Adaline single-layer neural network to identify d-axis direct current i under the operation state of the motor according to high-frequency parameters d0 Q-axis direct current i q0 D-axis DC voltage u d0 And q-axis DC voltage u q0 Identifying fundamental frequency parameters in motor, including permanent magnet flux linkage psi f Apparent d-axis inductorq-axis apparent inductance->Fundamental frequency resistor R s 。
In general, through the above technical solutions conceived by the present invention, the following beneficial effects can be obtained:
(1) According to the invention, motor parameter identification is performed by injecting rotary high-frequency voltage into a current loop, an Adaline single-layer neural network is utilized, and the identified motor parameters except for a high-frequency resistorPermanent magnet flux linkage psi f Apparent d-axis inductance->q-axis apparent inductance->Fundamental frequency resistor R s Besides, the device also comprises d-axis increment self-inductance +.>q-axis increment self-inductance->And d, q axis incremental mutual inductance->The influence of iron core saturation on motor parameters under high current and dq axis coupling quantity of a motor voltage equation under a synchronous rotation coordinate system are fully considered, full-parameter identification is realized, and accuracy of motor parameter identification is effectively improved.
(2) When the Adaline single-layer neural network is used for identifying the high-frequency parameters, the adopted observation equations can more accurately reflect the actual running condition of the motor, and each observation equation comprises the frequency omega h The related terms also preserve the fundamental frequency omega of the motor e The term of interest, thus, in the case of a motor operating at high speed, i.e. the fundamental frequency ω of the motor e And when the motor parameter is larger, the accurate identification of the motor parameter is also realized. Therefore, the invention can accurately identify the motor parameters in the full working condition range.
(3) When the invention identifies the fundamental frequency parameters, the invention firstly follows the running characteristics of the motor according toAndrealize d-axis apparent inductance->And a fundamental frequency resistor R s The identification of the motor parameters can be ensured, and meanwhile, the observation equations of other motor parameters can be correctly converged, so that the accurate identification of the motor parameters is realized; for the rest parameters in the fundamental frequency parameters, the Adaline single-layer neural network is utilized for identification, and the adopted observation equation can more accurately reflect the actual running condition of the motor, so that the accurate identification of the fundamental frequency parameters can be realized.
(4) Before d and q axis currents are input into a current loop, the invention filters out omega frequency in the d and q axis currents by filtering h The component of the motor control part can prevent the current loop output command voltage from generating an inhibition signal which is 180 degrees different from the rotation voltage signal, thereby avoiding influencing the motor control part by the injected rotation high-frequency voltage, ensuring the normal operation of the motor, avoiding the inhibition signal generated by the current loop from interfering the motor parameter identification, and ensuring the accuracy of the motor parameter identification.
Drawings
FIG. 1 is a schematic diagram of flux linkage as a function of current in a permanent magnet synchronous motor;
FIG. 2 is a voltage vector diagram of high frequency excitation provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating injection of a rotating high-frequency voltage and filtering of a high-frequency current signal according to an embodiment of the present invention;
fig. 4 is a full parameter identification schematic diagram according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In the present invention, the terms "first," "second," and the like in the description and in the drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
In order to solve the technical problems that the existing permanent magnet motor parameter identification method simplifies parameters, ignores the influence of the saturated iron core under high current and the dq axis coupling amount of a motor voltage equation under a synchronous rotation coordinate system on motor parameters, so that accurate identification results can be obtained only when a motor works at zero speed or extremely low speed, and accurate parameter identification can not be realized in a full working condition range, the invention provides a permanent magnet synchronous motor full-parameter identification method and a permanent magnet synchronous motor system, and the whole thought of the method is as follows: injecting a rotating high-frequency voltage into a voltage command output by a current loop in a permanent magnet motor, and under the condition that the influence of the dq axis coupling amount of a motor voltage equation under a large current and a synchronous rotating coordinate system on motor parameters is fully considered, after the rotating high-frequency voltage is injected, generating a relationship between the high-frequency current and the motor parameters in a complex domain, thereby realizing the identification of the motor high-frequency parameters by utilizing the high-frequency voltage and the high-frequency current, and further completing the identification of fundamental frequency parameters; in the parameter identification process, an Adaline single-layer neural network is used instead of an ideal voltage equation.
Before explaining the technical scheme of the invention in detail, the theoretical deduction related to the invention is briefly introduced as follows:
in the permanent magnet synchronous motor, a d-axis permanent magnet flux linkage psi d And q-axis permanent magnet flux linkage psi q The curve with current is shown in FIG. 1, wherein the d-axis permanent magnet flux linkage ψ d The linearity of the q-axis permanent magnet flux linkage psi is good q The distribution of the magnetic circuit is divided into a linear region and a nonlinear region, the q-axis magnetic circuit is mainly composed of an iron core and a yoke part, and the magnetic permeability of the q-axis magnetic circuit is larger and the magnetic flux linkage Cheng Xianxing is distributed in the linear region; under the condition of large current, the iron core and the yoke part have saturation conditions, the magnetic permeability gradually decreases along with the increase of the current, and the q-axis inductance continuously decreases.
In a permanent magnet synchronous motor, incremental inductance represents the derivative of the flux linkage at the corresponding current, and apparent inductance represents the flux linkage divided by the current; in the following examples, L is unless otherwise specified inc Represents incremental inductance, L ap Representing the apparent inductance.
The frequency of injection of the invention is omega h D-axis high-frequency voltage u of (2) dh And q-axis high frequency voltage u qh The amplitude values are U respectively mdh and Umqh Accordingly, the expression is as follows:
in order to reduce the influence on the output torque of the motor, the influence of fluctuation of a small rotating speed on the operation of the motor and the subsequent parameter identification, in the equation (1), the amplitude of the injected rotating high-frequency voltage should be as small as possible; specifically, a threshold value may be set, and the ratio of the amplitude of the rotation high-frequency voltage to the rated voltage must not exceed the threshold value; the threshold may be set according to the operation characteristics of the permanent magnet synchronous motor to be identified in practical application and the motor parameter identification requirement, and optionally, in the following embodiment, the threshold is 10%.
At the same time, in order to avoid the generation of odd harmonics in the ABC three phases of the motor, the frequency omega of the injected rotating high-frequency voltage of the invention h ≠6nω e N is a positive integer; in the present invention omega h /ω e ∈[3,5]The method comprises the steps of carrying out a first treatment on the surface of the Alternatively, in the following embodiment, the d-axis high-frequency voltage u dh And q-axis high frequency voltage u qh Frequency omega of (2) h ＝3ω e Even harmonics of 2 and 4 multiples are generated in the ABC three phases.
Considering that the d-axis magnetic circuit is mainly a permanent magnet, the magnetic permeability of the d-axis magnetic circuit is similar to that of air and is not easy to saturate, so that the d-axis magnetic circuit is in the normal operation range of the motorThus, the permanent magnet flux linkage ψ f When viewed as a function of dq axis current, the motor equation can be rewritten as:
the voltage equation considers the nonlinear state of the motor, according to the voltage equation, the voltage equation of the rotating high-frequency voltage can be written, and as the counter potential term under the dq coordinate system is direct current, the counter potential term is ignored when alternating current is considered, and therefore the voltage equation of the rotating high-frequency voltage is as follows:
wherein ,represents a high-frequency resistance, i dh and iqh Respectively represent the frequency omega generated by injecting the rotating high-frequency voltage h Is a high frequency current signal of (a);
since both voltage and current are ac, converting equation (3) to complex form can be written as:
analysis of complex planar currents due to dq axis delta mutual inductanceSmaller, for simplicity of analysis, assume +.>Can obtain
The variables A and B are defined as follows
From this, it can be seen that the high-frequency current signal i dh and iqh Is about 90 DEG, thus, a complex domain vector diagram of a high frequency current can be drawn inAs shown in fig. 2; from the complex domain vector diagram shown in fig. 2, the following equation can be obtained;
solving the equation (7) to obtain the d-axis increment self-inductanceq-axis increment self-inductance->d. q-axis incremental mutual inductance>High-frequency resistor->The expressions of (2) are respectively:
from the expressions of the above parameters, the identification result of each parameter depends on the frequency ω of the injected rotating high-frequency voltage h The present invention refers to the above parameters as high frequency parameters;
in order to ensure the identification accuracy of the high-frequency parameters, the invention proposes to utilize an Adaline single-layer neural network to replace a traditional ideal mathematical model for parameter identification; designing related parameters of the Adaline single-layer neural network according to the expression of each high-frequency parameter to obtain an observation equation of each high-frequency parameter, namely d-axis increment self-inductanceq-axis increment self-inductance->d. q-axis incremental mutual inductance/>High-frequency resistor->The observation equations of (2) are respectively:
in the above observation equation, k represents the number of iterations; w, d, X and O respectively represent the weight, bias, input and output of the Adaline single-layer neural network; η represents a convergence coefficient;represents d-axis increment self-inductance->Observation values of +.>Represents q-axis increment self-inductance->Observation values of +.>Representing d, q axis incrementMutual inductance->Is>Representing a high frequency resistance->Is a measurement of the observed value of (2);
it is easy to understand that W, d, X, O and η are basic parameters in the Adaline single-layer neural network, and the above four high-frequency parameters are respectively and independently identified, so that the parameters in the observation equation of each parameter are also independently set; in the process of iteration of the Adaline single-layer neural network, the observed value gradually converges, and the observed value at the end of iteration is the identification result corresponding to each parameter.
After the high-frequency parameter identification is completed, the remaining motor parameters mainly comprise a permanent magnet flux linkage psi f Apparent d-axis inductorq-axis apparent inductance->Fundamental frequency resistor R s These parameters are only related to the fundamental frequency ω of the motor e And are related, the present invention therefore refers to these parameters as fundamental frequency parameters.
Considering that the magnetic permeability of the d-axis magnetic circuit is similar to that of air and is not easy to saturate as the d-axis magnetic circuit is mainly a permanent magnet, the d-axis linearity is good, so that the motor basically meets the requirements in the normal operation range of the motorSince the injection frequency is similar to the motor frequency, the high frequency resistor and the base frequency resistor basically meet +.>Therefore, in the following examples the terms +.>Andrealize d-axis apparent inductance->And a fundamental frequency resistor R s The identification of the motor parameters can be ensured, the observation equations of other motor parameters can be correctly converged while the accurate identification of the two motor parameters is ensured, and the accurate identification of all motor parameters is realized.
The voltage equation at the fundamental current is:
since the motor is operating in steady state, di/dt=0 can be considered, and thus the d-axis apparent inductance is identifiedAnd a fundamental frequency resistor R s Thereafter, the voltage equation has only two unknowns: q-axis apparent inductance->And permanent magnet flux linkage psi f According to the above equation (12), the expressions of these two parameters can be found as:
likewise, in order to ensure the identification accuracy of the high-frequency parameters, the invention proposes to utilize an Adaline single-layer neural network to replace a traditional ideal mathematical model for parameter identification; apparent inductance according to q-axisAnd permanent magnet flux linkage psi f These two partsThe expression of each fundamental frequency parameter designs the relevant parameters of the Adaline single-layer neural network, and then the observation equations of the two fundamental frequency parameters can be obtained, wherein the observation equations are respectively as follows:
in the above observation equation, k represents the number of iterations; w, d, X and O respectively represent the weight, bias, input and output of the Adaline single-layer neural network; η represents a convergence coefficient;representing permanent magnet flux linkage psi f Is>Represents d-axis apparent inductance +.>Is>Represents q-axis apparent inductance +.>Is a function of the observed value of (a).
Similarly, the above two fundamental frequency parameters are respectively and independently identified, so that the parameters in the observation equation of each parameter are also independently set; in the process of iteration of the Adaline single-layer neural network, the observed value gradually converges, and the observed value at the end of iteration is the identification result corresponding to each parameter.
Based on the analysis result provided by the invention, the full-parameter identification of the permanent magnet synchronous motor can be realized.
The following are examples.
Example 1:
a permanent magnet synchronous motor full-parameter identification method based on rotary high-frequency voltage injection comprises the following steps:
a step of rotating high-frequency voltage injection: as shown in fig. 3, during the operation of the motor, the d-axis command voltage and the q-axis command voltage outputted by the current loop of the motor are respectively injected with the frequency omega h D-axis high-frequency voltage u of (2) dh And q-axis high frequency voltage u qh The method comprises the steps of carrying out a first treatment on the surface of the In this embodiment omega h ＝3ω e ；
The full parameter identification step, as shown in fig. 4, includes:
(S1) sampling three-phase current in the running state of the motor, and converting the three-phase current into synchronous rotation speed to be motor fundamental frequency omega e Is rotated in the dq-axis to obtain d-axis current i d And q-axis current i q ；
(S2) for d-axis current i d And q-axis current i q Performing discrete Fourier transform to obtain frequency omega h Amplitude I of the current in d, q axes mdh and Imqh And a phase theta d and θq ；
(S3) utilizing an Adaline single-layer neural network according to the amplitude I mdh and Imqh Phase θ d and θq And d-axis high-frequency voltage u dh And q-axis high frequency voltage u qh Amplitude U of (2) mdh and Umqh Identifying high frequency parameters in the motor, including d-axis increment self-inductanceq-axis increment self-inductance->d. q-axis incremental mutual inductance>High-frequency resistor->
In the present embodimentSpecific observation equations for identifying the high-frequency parameters are shown in the equations (9) to (12) above; in the embodiment, when the Adaline single-layer neural network is utilized to identify the high-frequency parameters, the adopted observation equations can more accurately reflect the actual running condition of the motor, and each observation equation comprises the frequency omega h The related terms also preserve the fundamental frequency omega of the motor e The term of interest, thus, in the case of a motor operating at high speed, i.e. the fundamental frequency ω of the motor e When the motor parameter identification method is large, accurate identification of the motor parameter is realized, so that the motor parameter can be accurately identified in the full working condition range;
(S4) utilizing an Adaline single-layer neural network to obtain d-axis direct current i under the operation state of the motor according to high-frequency parameters d0 Q-axis direct current i q0 D-axis DC voltage u d0 And q-axis DC voltage u q0 Identifying fundamental frequency parameters in motor, including permanent magnet flux linkage psi f Apparent d-axis inductorq-axis apparent inductance->Fundamental frequency resistor R s ；
In the present embodiment, according toIdentification of d-axis apparent inductance->According to->Identification base frequency resistor R s And the apparent inductance of q axis +.>And permanent magnet flux linkage psi f When the two fundamental frequency parameters are observed, the observation equations are respectively shown in the equations (15) - (16);according to the embodiment, the identification of the fundamental frequency parameters is realized by using the Adaline single-layer neural network instead of the theoretical model, and the adopted observation equation can more accurately reflect the actual running condition of the motor, so that the accurate identification of the fundamental frequency parameters can be realized.
Taking into account the d-axis current i obtained by sampling and coordinate transforming the three-phase current of the motor after injecting the rotating high-frequency voltage into the current loop d And q-axis current i q Comprising two parts: DC current i for generating torque during motor operation d0 and iq0 Frequency omega h High-frequency current signal i of (2) dh and iqh . If the d-axis current i after sampling and coordinate transformation is directly carried out d And q-axis current i q When the high-frequency current signal is input into the current loop, the output command voltage of the current loop generates an inhibition signal which is 180 degrees different from the rotation voltage signal in the subsequent control of the current loop, so that the original control part of the motor is influenced, and the sampling current also contains interference generated by the inhibition signal to influence the accuracy of parameter identification. In order to avoid the above two problems and ensure accuracy of parameter identification, in this embodiment, the method further includes, between steps (S1) and (S2):
for d-axis current i d And q-axis current i q Filtering to remove the frequency omega h The component of the (2) is used for obtaining d-axis direct current i under the running state of the motor d0 And q-axis direct current i q0 ；
D-axis direct current i d0 And q-axis direct current i q0 An input current loop;
as shown in fig. 3, two modules with frequency omega can be connected after the module for coordinate transformation of three-phase sampling current h Realize the band elimination filter of the current i to the d axis d And q-axis current i q The current output by the two band-stop filters is the direct current i of the torque generated by the motor during operation d0 and iq0 The method comprises the steps of carrying out a first treatment on the surface of the The signals output by the two band-stop filters are correspondingly input into the current loop, so that the operation can be realized.
In general terms, the process is carried out,the embodiment starts from a motor mathematical model in a nonlinear state, brings dq axis coupling terms in the motor mathematical model into an observation design, performs motor parameter identification by injecting rotating high-frequency voltage into a current loop, performs parameter identification by using an Adaline single-layer neural network instead of a theoretical model, and identifies motor parameters except a high-frequency resistorPermanent magnet flux linkage psi f Apparent d-axis inductance->q-axis apparent inductance->Fundamental frequency resistor R s Besides, the device also comprises d-axis increment self-inductance +.>q-axis increment self-inductance->And d, q axis incremental mutual inductance->The influence of iron core saturation on motor parameters under high current and dq axis coupling quantity of motor voltage equation under synchronous rotation coordinate system are fully considered, full parameter identification is realized, the saturation characteristic of the motor is effectively described, the error of state observation is reduced, and accurate identification of motor parameters in the full working condition range of the motor is realized.
Example 2:
a permanent magnet synchronous motor system comprising: the device comprises a permanent magnet synchronous motor, a rotary high-frequency voltage injection module and a full-parameter identification module;
as shown in fig. 3, the rotary high-frequency voltage injection module is connected between the current loop of the motor and the SVPWM module for injecting d-axis command voltage and q-axis command voltage respectively outputted from the current loop of the motor during operation of the motorFrequency of ingress omega h D-axis high-frequency voltage u of (2) dh And q-axis high frequency voltage u qh The method comprises the steps of carrying out a first treatment on the surface of the In this embodiment omega h ＝3ω e ；
The full parameter identification module comprises: the system comprises a sampling unit, a DFT unit, a high-frequency parameter identification observer and a low-frequency parameter identification observer;
the sampling unit is used for sampling three-phase current in the running state of the motor and converting the three-phase current into synchronous rotation speed which is equal to the fundamental frequency omega of the motor e Is rotated in the dq-axis to obtain d-axis current i d And q-axis current i q ；
DFT unit for d-axis current i d And q-axis current i q Performing discrete Fourier transform to obtain frequency omega h Amplitude I of the current in d, q axes mdh and Imqh And a phase theta d and θq ；
The high-frequency parameter identification observer is used for utilizing an Adaline single-layer neural network and is used for identifying the observer according to the amplitude I mdh and Imqh Phase θ d and θq And d-axis high-frequency voltage u dh And q-axis high frequency voltage u qh Amplitude U of (2) mdh and Umqh Identifying high frequency parameters in the motor, including d-axis increment self-inductanceq-axis increment self-inductance->d. q-axis incremental mutual inductance>High-frequency resistor->
The low-frequency parameter identification observer is used for utilizing an Adaline single-layer neural network to identify d-axis direct current i under the operation state of the motor according to high-frequency parameters d0 Q-axis direct current i q0 D-axis DC voltage u d0 And q-axis direct currentVoltage u q0 Identifying fundamental frequency parameters in motor, including permanent magnet flux linkage psi f Apparent d-axis inductorq-axis apparent inductance->Fundamental frequency resistor R s ；
As shown in fig. 3, in order to avoid generation of a suppression signal 180 ° different from the rotation voltage signal in the command voltage outputted from the current loop, in this embodiment, two frequency ω are also connected between the current loop and the module for coordinate transformation of the three-phase sampling current h Is used for the d-axis current i respectively d And q-axis current i q Filtering to remove the frequency omega h The component of the (2) is used for obtaining d-axis direct current i under the running state of the motor d0 And q-axis direct current i q0 The method comprises the steps of carrying out a first treatment on the surface of the D-axis direct current i output by band elimination filter d0 And q-axis direct current i q0 The current loop is input to carry out subsequent control;
in this embodiment, the specific implementation of each module and unit may refer to the description in the above method embodiment, and will not be repeated here.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (9)

1. The full-parameter identification method of the permanent magnet synchronous motor based on the rotary high-frequency voltage injection is characterized by comprising the following steps of:
a step of rotating high-frequency voltage injection: during the operation of the motor, the d-axis command voltage and the q-axis command voltage output by the current loop of the motor are respectively injected with the frequency omega h D-axis high-frequency voltage u of (2) dh And q-axis high frequency voltage u qh； wherein ,ωh /ω e ∈[3,5]，ω e Representing the fundamental frequency of the motor;
the full parameter identification step comprises the following steps:
(S1) sampling three-phase current in the running state of the motor and converting the three-phase current into synchronous rotation speed to be the fundamental frequency omega of the motor e Is rotated in the dq-axis to obtain d-axis current i d And q-axis current i q ；
(S2) for the d-axis current i d And the q-axis current i q Performing discrete Fourier transform to obtain frequency omega h Amplitude I of the current in d, q axes mdh and Imqh And a phase theta d and θq ；
(S3) deriving d-axis increment self-inductance according to a voltage equation of the injected rotating high-frequency voltageq-axis increment self-inductance->d. q-axis incremental mutual inductance>High-frequency resistor->The expressions of (2) are respectively:
using Adaline single-layer neural network according to the amplitude I mdh and Imqh The phase theta d and θq And the d-axis high-frequency voltage u dh And the q-axis high-frequency voltage u qh Amplitude U of (2) mdh and Umqh Identifying high frequency parameters in the motor, including d-axis increment self-inductanceq-axis increment self-inductance->d. q-axis incremental mutual inductance>High-frequency resistor->
(S4) deriving the q-axis apparent inductance according to the voltage equation under the fundamental currentAnd permanent magnet flux linkage psi f The method comprises the following steps:
using Adaline single-layer neural network to obtain d-axis direct current i according to the high-frequency parameter and the motor running state d0 Q-axis direct current i q0 D-axis DC voltage u d0 And q-axis DC voltage u q0 Identifying fundamental frequency parameters in motor, including permanent magnet flux linkage psi f Apparent d-axis inductorq-axis apparent inductance->Fundamental frequency resistor R s ；
Wherein, in the step (S4), according toIdentifying the d-axis apparent inductance +.>According to->Identifying the fundamental frequency resistor R s 。
2. The method for identifying all parameters of a permanent magnet synchronous motor based on rotary high frequency voltage injection according to claim 1, further comprising, between the steps (S1) and (S2):
for the d-axis current i d And the q-axis current i q Filtering to remove the frequency omega h The component of the (2) is used for obtaining d-axis direct current i under the running state of the motor d0 And q-axis direct current i q0 ；
The d-axis direct current i is applied d0 And the q-axis direct current i q0 The current loop is input.
3. The method for identifying all parameters of a permanent magnet synchronous motor based on rotary high-frequency voltage injection according to claim 1 or 2, wherein the d-axis increment self-inductanceThe observation equation of (2) is:
wherein k represents the number of iterations; w, d, X and O respectively represent the weight, bias, input and output of the Adaline single-layer neural network; η represents a convergence coefficient;represents d-axis increment self-inductance->Is a function of the observed value of (a).
4. The method for identifying all parameters of a permanent magnet synchronous motor based on rotary high-frequency voltage injection as claimed in claim 3, wherein the q-axis increment self-inductanceThe observation equation of (2) is:
wherein ,representing said q-axis increment self-inductance +.>Is a function of the observed value of (a).
5. The method for identifying all parameters of a permanent magnet synchronous motor based on rotary high-frequency voltage injection as claimed in claim 4, wherein the d-axis increment mutual inductance and the q-axis increment mutual inductance areThe observation equation of (2) is:
wherein ,representing the d, q axis increment mutual inductance +.>Is a function of the observed value of (a).
6. The method for identifying all parameters of a permanent magnet synchronous motor based on rotary high-frequency voltage injection as claimed in claim 5, wherein the high-frequency resistorThe observation equation of (2) is:
wherein ,representing the high frequency resistance->Is a function of the observed value of (a).
7. The method for identifying all parameters of a permanent magnet synchronous motor based on rotary high-frequency voltage injection as claimed in claim 1, wherein the q-axis apparent inductanceThe observation equation of (2) is:
wherein k represents the number of iterations; w, d, X and O respectively represent the weight, bias, input and output of the Adaline single-layer neural network; η represents a convergence coefficient;representing the q-axis apparent inductance +.>Is a function of the observed value of (a).
8. The method for identifying all parameters of a permanent magnet synchronous motor based on rotary high-frequency voltage injection according to claim 7, wherein the permanent magnet flux linkage ψ is as follows f The observation equation of (2) is:
wherein ,representing the permanent magnet flux linkage ψ f Is>Representing the d-axis apparent inductance +.>Is a function of the observed value of (a).
9. A permanent magnet synchronous motor system, comprising: the device comprises a permanent magnet synchronous motor, a rotary high-frequency voltage injection module and a full-parameter identification module;
the rotary high-frequency voltage injection module is connected between the current loop of the motor and the SVPWM module and is used for respectively injecting frequency omega into d-axis command voltage and q-axis command voltage output by the current loop of the motor in the running process of the motor h D-axis high-frequency voltage u of (2) dh And q-axis high frequency voltage u qh； wherein ,ωh /ω e ∈[3,5]，ω e Representing the fundamental frequency of the motor;
the full parameter identification module comprises: the system comprises a sampling unit, a DFT unit, a high-frequency parameter identification observer and a low-frequency parameter identification observer;
the sampling unit is used for sampling three-phase current in the running state of the motor and converting the three-phase current into synchronous rotation speed which is the fundamental frequency omega of the motor e Is rotated in the dq-axis to obtain d-axis current i d And q-axis current i q ；
The DFT unit is used for controlling the d-axis current i d And the q-axis current i q Performing discrete Fourier transform to obtain frequency omega h Amplitude I of the current in d, q axes mdh and Imqh And a phase theta d and θq ；
The high-frequency parameter identification observer is used for deriving d-axis increment self-inductance according to a voltage equation of the injected rotating high-frequency voltageq-axis increment self-inductance->d. q-axis incremental mutual inductance>High-frequency resistor->The expressions of (2) are respectively:
the high-frequency parameter identification observer is also used for utilizing an Adaline single-layer neural network according to the amplitude I mdh and Imqh The phase theta d and θq And the d-axis high-frequency voltage u dh And the q-axis high-frequency voltage u qh Amplitude U of (2) mdh and Umqh Identifying high frequency parameters in the motor, including d-axis increment self-inductanceq-axis increment self-inductance->d. q-axis incremental mutual inductance>High-frequency resistor->
The low-frequency parameter identification observer is used for deriving q-axis apparent inductance according to a voltage equation under fundamental wave currentAnd permanent magnet flux linkage psi f The method comprises the following steps:
the low-frequency parameter identification observer is also used for utilizing an Adaline single-layer neural network to identify the direct current i of the d-axis under the operation state of the motor according to the high-frequency parameter d0 Q-axis direct current i q0 D-axis DC voltage u d0 And q-axis DC voltage u q0 Identifying fundamental frequency parameters in motor, including permanent magnet flux linkage psi f Apparent d-axis inductorq-axis apparent inductance->Fundamental frequency resistor R s ；
Wherein the low-frequency parameter identification observer is based onIdentifying the d-axis apparent inductance +.>According to->Identifying the fundamental frequency resistor R s 。
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