CN114744938B - Full-parameter observer based on Kalman filtering and full-parameter identification method - Google Patents
Full-parameter observer based on Kalman filtering and full-parameter identification method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- 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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
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- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
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- 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
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- H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
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- H02P25/022—Synchronous motors
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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
- 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
- H02P27/12—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 pulsing by guiding the flux vector, current vector or voltage vector on a circle or a closed curve, e.g. for direct torque control
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- 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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Abstract
The invention discloses a Kalman filtering-based full-parameter observer and a full-parameter identification method, which belong to the field of parameter identification of permanent magnet synchronous motors and comprise the following steps: the system comprises a parameter prior prediction module, a parameter posterior estimation module, a state prior prediction module and a state posterior estimation module; the parameter priori prediction module is used for performing priori prediction on the parameters of the current period to obtain the parameter priori predicted value of the current periodThe state priori prediction module is used for performing priori prediction on the state of the current period to obtain a state priori predicted value of the current periodThe parameter posterior estimation module is used for performing posterior estimation on the parameters of the current period to obtain the posterior estimation value of the parameters of the current periodThe state posterior estimation module is used for performing posterior estimation on the state of the current period to obtain a state posterior estimation value of the current periodThe invention can realize accurate identification of parameters in the running process of the permanent magnet synchronous motor under the full working condition.
Description
Technical Field
The invention belongs to the field of parameter identification of permanent magnet synchronous motors, and particularly relates to a full-parameter observer based on Kalman filtering and a full-parameter identification method.
Background
At present, various industries are electrified and intelligently transformed. Among them, the electrification of household appliances, multi-electric aircraft and electric automobiles is rapidly spreading, and motors are naturally facing more challenges and opportunities as key power parts of electrification systems. Compared with the traditional industrial scene motor, the application scene motor must have a wider rotating speed operation range and stronger overload capacity to meet the speed regulation and traction requirements of a power system, and must have higher power density and efficiency due to space limitation, so that the volume ratio of a cooling system is reduced. Among the 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 running state of the motor must be obtained, so as to realize accurate control and intelligent control of the system. However, the existing methods and mathematical models have the following disadvantages:
The traditional motor driving control system and the parameter identification system are established on a mathematical model of the linear permanent magnet synchronous motor, and the expression is as follows:
wherein R s represents a motor stator winding, ψ f represents a permanent magnet flux linkage, L d and L q represent d and q axis inductances, u d and u q represent d and q axis voltages, i d and i q represent d and q axis currents, respectively, ω e represents a motor fundamental frequency.
The simplified mathematical model can bring convenience to the design of the controller, but in practice, the motor is a high coupling strong nonlinear system, and when the motor operates under a high current condition, the model cannot reflect the real electromagnetic condition inside the motor, and accordingly, the accuracy of the parameter identification result cannot be ensured.
The traditional parameter identification algorithm based on high-frequency injection injects high-frequency disturbance voltage into a voltage command output by a motor current loop, and directly ignores items omega eLdid and omega eLqiq in the parameter identification process based on the assumption that the frequency omega h of an injection voltage signal is far greater than the frequency omega e of a motor operation fundamental wave. Because 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, the assumption is not true when the motor operates at a high speed, and because the embedded permanent magnet synchronous motor has the characteristic of L d<<Lq, larger errors are generated on the observation of the inductance when omega eLdid and omega eLqiq items are ignored, so that the accuracy of parameter identification is affected.
Disclosure of Invention
Aiming at the defects and improvement demands of the prior art, the invention provides a Kalman filtering-based full-parameter observer and a full-parameter identification method, which aim to accurately identify parameters in the running process of a permanent magnet synchronous motor under full working conditions.
In order to achieve the above objective, according to one aspect of the present invention, there is provided a kalman filter-based full parameter observer for parameter identification of a permanent magnet synchronous motor, in which small signal voltages u dh and u qh with a frequency of ω h are respectively injected into d and q axis command voltages output by a current loop of the permanent magnet synchronous motor during operation; the full parameter observer comprises: a parameter extended kalman filter and a state extended kalman filter;
The parameter extended kalman filter includes: the parameter prior prediction module and the parameter posterior estimation module; the state-extended kalman filter includes: the state prior prediction module and the state posterior estimation module;
The first input end of the parameter priori prediction module is connected to the output end of the parameter posterior estimation module, and the parameter priori prediction module is used for performing priori prediction on the parameters of the current period to obtain a parameter priori predicted value theta k - of the current period;
The first input end of the state prior prediction module is connected to the output end of the state posterior estimation, the second input end of the state prior prediction module is connected to the output end of the parameter prior prediction module, and the state prior prediction module is used for carrying out prior prediction on the state of the current period to obtain a state prior prediction value of the current period
The first input end of the parameter posterior estimation module is connected to the output end of the parameter prior prediction module, the second input end of the parameter posterior estimation module is connected to the output end of the state prior prediction module, and the parameter posterior estimation module is used for carrying out posterior estimation on the parameters of the current period to obtain the parameter posterior estimation value of the current period
The first input end of the state posterior estimation module is connected to the output end of the state prior prediction module, the second input end of the state posterior estimation module is connected to the output end of the parameter prior prediction module, and the state posterior estimation module is used for carrying out posterior estimation on the state of the current period to obtain a state posterior estimation value of the current period
Wherein,The state posterior estimate value of the previous cycle is represented, and U k-1 represents the small signal voltage injected in the previous cycle; t s denotes the sampling time interval, ω e denotes the motor fundamental frequency; /(I)R s represents the resistance of the motor stator winding; expressed as the inverse of the inductance matrix,/> AndRespectively represent d-axis increment self-inductance and q-axis increment self-inductance,/>The d and q axes incremental mutual inductance is represented; θ= [ R s Tdd Tdq Tqq]T ] represents the parameter to be observed,/>F * () represents the discrete state equation of the motor.
Further, the calculation expression of the signal processing by the parameter prior prediction module is as follows:
Wherein, A parameter posterior estimate representing a previous period; /(I)Parameter prior prediction covariance matrix representing current period,/>The parameter a posteriori estimated covariance matrix representing the last period, Q p represents the system noise covariance matrix in the parameter a priori prediction.
Further, the calculation expression of the state prior prediction model for signal processing is as follows:
Wherein, Parameter prior estimation covariance matrix representing current period,/>The state posterior estimation covariance matrix of the last period is represented, and Q x represents the system noise covariance matrix in state prior prediction; f x,k-1 represents the value of the derivative of the discrete state equation F * () on the motor state X in the last cycle.
Further, the calculation expression of the signal processing by the parameter posterior estimation module is as follows:
Wherein, Representing the output of the motor in the current period, and h * () represents the discrete output equation of the motor; /(I)A parameter posterior estimation covariance matrix representing a current period; k θ,k represents the Kalman gain of the current period parameter, H θ,k represents the value of the derivative of the discrete output equation on the motor parameter theta in the current period; m represents the measurement noise covariance matrix of the system.
Further, the calculation expression of the state posterior estimation module for signal processing is:
Wherein, The state posterior estimation covariance matrix of the current period is represented, K x,k represents the kalman gain of the state of the current period, and H x,k represents the value of the derivative of the discrete output equation on the state X of the motor in the current period.
According to another aspect of the invention, there is provided a permanent magnet synchronous motor full parameter identification method based on the full parameter observer, comprising:
A small signal disturbance voltage injection step: in the running process of the motor, d-axis small signal disturbance voltage u dh and q-axis small signal disturbance voltage u qh with the frequency of omega h are respectively injected into d-axis command voltage and q-axis command voltage output by a current loop of the motor;
The full parameter identification step comprises the following steps:
(S1) sampling three-phase current in a motor running state, and transforming the three-phase current into a dq-axis rotation coordinate system with synchronous rotation speed of motor fundamental frequency omega e to obtain d-axis current i d and q-axis current i q;
(S2) extracting components with the frequency of ω h from the d-axis current i d and the q-axis current i q, respectively, to obtain a d-axis small signal disturbance current i dh and a q-axis small signal disturbance current i qh under the excitation of a d-axis small signal disturbance voltage u dh and a q-axis small signal disturbance voltage u qh;
(S3) inputting the d-axis small signal disturbance current i dh and the q-axis small signal disturbance current i qh into a full-parameter observer based on Kalman filtering to obtain a parameter posterior estimation value theta= [ R s Tdd TdqTqq]T output by the full-parameter observer based on Kalman filtering so as to calculate the motor stator winding resistance R s and d-axis increment self-inductance Q-axis delta self-inductance/>D and q axis incremental mutual inductance
(S4) calculating d-axis flux linkage ψ d0 and q-axis flux linkage ψ q0 based on the parameters calculated in (S3).
Further, in step (S4), the calculation expressions of the d-axis flux linkage ψ d0 and the q-axis flux linkage ψ q0 are:
Where u d0 and u q0 represent a d-axis fundamental frequency signal voltage and a q-axis fundamental frequency signal voltage, respectively, and i d0 and i q0 represent a d-axis fundamental frequency signal current and a q-axis fundamental frequency signal current, respectively.
Further, between steps (S1) and (S2), further comprising:
Filtering out components with the frequency omega h in the d-axis current i d and the q-axis current i q to obtain d-axis direct current i d0 and q-axis direct current i q0 in the motor running state;
D-axis direct current i d0 and q-axis direct current i q0 are input into the current loop.
In general, through the technical scheme of the invention, the used discretized motor state model f * () fully considers the high coupling strength nonlinear characteristic of the permanent magnet synchronous motor and the influence of the injected signal on the motor state under the condition of injecting the high-frequency disturbance voltage signal, so that the motor state model f * () can more accurately reflect the state in the running process of the motor, is not limited by the motor rotating speed, and can realize accurate observation of motor parameters under all working conditions; in addition, in the invention, two Kalman filters are designed for observing the state and the motor parameters of the motor respectively, and the prior prediction results of the two Kalman filters are called mutually, so that the accuracy of parameter observation can be further improved under the mutual promotion of state observation and parameter observation, and the accuracy of the subsequent motor parameter identification is further improved.
Drawings
FIG. 1 is a schematic diagram of a Kalman filtering-based full parameter observer according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a control system of a permanent magnet synchronous motor according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a permanent magnet synchronous motor full parameter identification method 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 problem that the existing parameter identification method can not realize accurate identification of parameters of the permanent magnet synchronous motor under the full working condition, the invention provides a full-parameter observer based on Kalman filtering and a full-parameter identification method, and the whole thought is as follows: when a state equation of the motor is established, fully considering the high coupling strength nonlinear characteristic of the permanent magnet synchronous motor and the influence of the injected signal on the state of the motor under the condition of injecting a high-frequency disturbance voltage signal; based on the established state equation, the characteristics of the Kalman filter are combined to correspondingly design the observation variable, so that the observer capable of accurately identifying the motor parameters is obtained. Based on the designed observer, the parameters of the permanent magnet synchronous motor can be accurately identified under the full working condition.
Before explaining the technical scheme of the invention in detail, the related principles are analyzed as follows.
The mathematical model of the linear permanent magnet synchronous motor is shown in formula (1):
wherein R s represents a motor stator winding, ψ f represents a permanent magnet flux linkage, L d and L q represent d and q axis inductances, u d and u q represent d and q axis voltages, i d and i q represent d and q axis currents, respectively, ω e represents a motor fundamental frequency.
Converting the mathematical model of the permanent magnet synchronous motor shown in the formula (1) into a synchronous rotation coordinate system, as shown in the formula (2):
wherein, ψ d and ψ q represent d-axis flux linkage and q-axis flux linkage, respectively; it is contemplated that in the case of injection of small signal perturbation voltages, d, q-axis voltages u d and u q may be represented as the sum of the fundamental frequency signal voltage and the small signal perturbation voltage, i.e., u d＝ud0+udh,uq＝uq0+uqh,ud0 and u q0 represent the d, q-axis fundamental frequency signal voltages, respectively, and u dh and u qh represent the injected d, q-axis small signal perturbation voltages, respectively; likewise, the d, q-axis currents i d and i q may be represented as the sum of the fundamental frequency current signal and the small-signal perturbation current, i.e., i d＝id0+idh,iq＝iq0+iqh,id0 and i q0 represent the d, q-axis fundamental frequency current signal, respectively, and i dh and i qh represent the d, q-axis small-signal perturbation current under the excitation of the small-signal perturbation voltage, respectively.
Considering that the permanent magnet synchronous motor is a highly coupled nonlinear system, the d, q-axis flux linkages ψ d and ψ q are mainly dependent on the permanent magnet flux linkages ψ f and d, q-axis currents i d and i q, and thus can be considered as a function of the d, q-axis currents. Taylor expansion of motor d, q axis flux linkages ψ d and ψ q can be obtained:
wherein a dd、adq、aqd、aqq is the derivative of d-axis flux linkage and q-axis flux linkage on d-axis current and q-axis current at points i d0 and i q0 respectively, which is called incremental inductance; h.o.t. represents higher order terms. The amplitude of the small-signal disturbance current excited by the disturbance voltage signal injected by the invention is smaller (preferably smaller than 5% of the rated current of the motor), and the higher-order term in the formula (3) is negligible due to the smaller amplitude of the disturbance current;
The incremental inductance in equation (3) can be expressed by equation (4):
Wherein, And/>Respectively represent d-axis increment self-inductance and q-axis increment self-inductance,/>And/>The d and q axes incremental mutual inductance is represented; based on the magnetic circuit equivalent theorem, the two incremental mutual inductances are equal, namely/>
Deriving the formula (3) to obtain:
Based on equations (2) to (5), the equation of the permanent magnet synchronous motor considering a small signal can be expressed as (6):
after the voltage and current large signal and the disturbance small signal of the fundamental frequency are separated by means of filtering and the like, equations of a large signal model (7) and a small signal model (8) can be obtained respectively.
Since the above formulas (7) and (8) take into account the effect of the injected small signal disturbance voltage on the motor state, the motor model represented by the above formulas (7) and (8) can describe the state inside the permanent magnet synchronous motor more accurately.
The invention carries out the design of the full-parameter observer based on the models shown in the formulas (7) and (8), and the specific process is as follows:
And (3) deforming the formula (7) to obtain a state equation of the permanent magnet synchronous motor, wherein the state equation is as follows:
Wherein X represents a state variable, The first derivative of the representation; y represents motor output, I represents an identity matrix; because the inductance matrix L and the inverse matrix L -1 of the inductance exist in the state equation at the same time, difficulty is brought to the design of a subsequent observer; in order to facilitate the design of the observer, the present invention further rewrites the above formula (9) to form a form that facilitates the design of the observer, specifically, define a new state variable x=lx, and may obtain a new state equation as shown in formula (10):
In the state equation shown in the formula (10), the inductance matrix L is eliminated, and only the inverse matrix L -1 of the inductance matrix exists.
Defining a variable matrix to be observed as theta= [ R s Tdd Tdq Tqq]T ], wherein theta comprises a part of resistance parameters and inductance parameters to be identified in the permanent magnet synchronous motor and can be regarded as an extended state variable; since the resistance and inductance parameters in the motor are slowly varying over time, dθ/dt≡0 can be considered. Based on this, the state equation after the extended variables can be obtained is:
Wherein, Representing the first derivative of the parameter θ,/>A first derivative representing a state variable X; f represents the state equation of state variable X, h represents the output equation of output variable Y.
Discretizing the state equation shown in the formula (11) to obtain the following discretized state equation:
where k represents the data point sequence number in the discrete sequence, T s represents the sampling time interval, and f and h are the discretizations of f and h, respectively.
According to the discretized state equation shown in (12), in one embodiment of the present invention, a kalman filter-based all-parameter observer is constructed, which as shown in fig. 1, includes two kalman filters, i.e., a parameter extended kalman filter and a state extended kalman filter;
The parameter extended kalman filter includes: the parameter prior prediction module and the parameter posterior estimation module; the state-extended kalman filter includes: the state prior prediction module and the state posterior estimation module; as shown in fig. 1, the functions of each module, that is, the connection relationship between the modules are:
The first input end of the parameter priori prediction module is connected to the output end of the parameter posterior estimation module, and the parameter priori prediction module is used for performing priori prediction on the parameters of the current period to obtain the parameter priori predicted value of the current period
The first input end of the state prior prediction module is connected to the output end of the state posterior estimation, the second input end of the state prior prediction module is connected to the output end of the parameter prior prediction module, and the state prior prediction module is used for carrying out prior prediction on the state of the current period to obtain a state prior prediction value of the current period
The first input end of the parameter posterior estimation module is connected to the output end of the parameter prior prediction module, the second input end of the parameter posterior estimation module is connected to the output end of the state prior prediction module, and the parameter posterior estimation module is used for carrying out posterior estimation on the parameters of the current period to obtain the parameter posterior estimation value of the current period
The first input end of the state posterior estimation module is connected to the output end of the state prior prediction module, the second input end of the state posterior estimation module is connected to the output end of the parameter prior prediction module, and the state posterior estimation module is used for carrying out posterior estimation on the state of the current period to obtain a state posterior estimation value of the current period
Wherein,The state posterior estimate value of the previous cycle is represented, and U k-1 represents the small signal voltage injected in the previous cycle; f * () represents the discrete state equation of the motor.
In the observer shown in fig. 1, in the same observation period, the state prior prediction depends on the result of the parameter prior prediction, the parameter posterior estimation depends on the result of the state prior prediction, and the state posterior estimation also depends on the result of the parameter prior prediction, and the extended kalman filtering on the state and the extended kalman filtering on the parameter are performed synchronously, so that the kalman filtering-based full-parameter observer provided by the embodiment is a full-parameter observer based on parallel extended kalman filtering.
In this embodiment, the calculation expression of the signal processing performed by each module is specifically as follows:
Parameter prior prediction:
Wherein, Parameter posterior estimate representing last cycle,/>The parameter prior predicted value of the period is obtained; /(I)Parameter prior prediction covariance matrix representing current period,/>The parameter posterior estimation covariance matrix of the last period is represented, and Q p represents the system noise covariance matrix in the parameter prior prediction;
state prior estimation:
Wherein, Parameter prior estimation covariance matrix representing current period,/>The state posterior estimation covariance matrix of the last period is represented, and Q x represents the system noise covariance matrix in state prior prediction; f x,k-1 represents the value of the derivative of the discrete state equation F * () on the motor state X in the last cycle;
Parameter posterior estimation:
Wherein, Representing the output of the motor in the current period, wherein h * () represents a discrete output equation of the motor; /(I)A parameter posterior estimation covariance matrix representing a current period; k θ,k represents the Kalman gain of the current period parameter, H θ,k represents the value of the derivative of the discrete output equation on the motor parameter theta in the current period; m represents a measurement noise covariance matrix of the system;
H θ,k is calculated as follows
Wherein the method comprises the steps of
Wherein,The prior prediction vector/>, respectively, of the current period parameterThe first, second, third, and fourth elements of (a) are provided. Similarly,/>Estimating vectors/>, respectively for the last cycle state posteriorFirst, second element.
It should be noted that the number of the substrates,K x,k-1 and/>It is necessary to obtain by the previous iteration that when k=0, the initial values of these three quantities can be taken as zero matrices.
State posterior estimation:
Wherein, The state posterior estimation covariance matrix of the current period is represented, K x,k represents the kalman gain of the state of the current period, and H x,k represents the value of the derivative of the discrete output equation on the state X of the motor in the current period.
In the above formulas (13) to (18), the values of the system noise covariance matrix Q p in the parameter priori prediction and the system noise covariance matrix Q x in the state priori prediction mainly affect the convergence speed of the system parameter observation, alternatively, in this embodiment, the value of Q p is 0.1 times of the parameter estimation amount, namely:
QP＝0.1·diag([Rs Tdd Tdq Tqq]) (19)
M is mainly dependent on the noise of the current samples in the actual electro-drive system.
The full-parameter observer based on Kalman filtering, which is designed in the embodiment, uses a discretized motor state model f * (), fully considers the high coupling strength nonlinearity characteristic of the permanent magnet synchronous motor and the influence of the injected signal on the motor state under the condition of injecting a high-frequency disturbance voltage signal, so that the motor state model f * () can more accurately reflect the state in the motor operation process, is not limited by the motor rotating speed, and can realize accurate observation of motor parameters under all working conditions; in addition, in the embodiment, two kalman filters are designed to observe the state and the parameters of the motor respectively, and the prior prediction results of the two kalman filters are called mutually, so that the accuracy of parameter observation can be further improved under the mutual promotion of state observation and parameter observation, and the accuracy of the subsequent motor parameter identification is further improved.
Based on the kalman filter-based full-parameter observer provided in the above embodiment, in another embodiment of the present invention, a full-parameter identification method of a permanent magnet synchronous motor is provided, where the identified parameters include: motor stator winding resistor R s and d-axis increment self-inductanceQ-axis delta self-inductance/>Incremental mutual inductance of d and q axes/>D-axis flux linkage ψ d0 and q-axis flux linkage ψ q0.
As shown in fig. 2 and 3, the present embodiment includes:
A small signal disturbance voltage injection step: in the running process of the motor, d-axis small signal disturbance voltage u dh and q-axis small signal disturbance voltage u qh with the frequency of omega h are respectively injected into d-axis command voltage and q-axis command voltage output by a current loop of the motor; to avoid odd harmonics in the ABC three phases of the motor, in this embodiment ω h≠6nωe,ωe represents the fundamental motor frequency and n is a positive integer ω e represents the fundamental motor frequency;
The full parameter identification step comprises the following steps:
(S1) sampling three-phase current in a motor running state, and transforming the three-phase current into a dq-axis rotation coordinate system with synchronous rotation speed of motor fundamental frequency omega e to obtain d-axis current i d and q-axis current i q;
(S2) extracting components with the frequency of ω h from the d-axis current i d and the q-axis current i q, respectively, to obtain a d-axis small signal disturbance current i dh and a q-axis small signal disturbance current i qh under the excitation of a d-axis small signal disturbance voltage u dh and a q-axis small signal disturbance voltage u qh;
Specifically, the d-axis small signal disturbance current i dh and the q-axis small signal disturbance current i qh can be extracted by a band-pass filter with the frequency of omega h;
(S3) inputting the d-axis small signal disturbance current i dh and the q-axis small signal disturbance current i qh into a full-parameter observer based on Kalman filtering to obtain a parameter posterior estimation value theta= [ R s Tdd TdqTqq]T output by the full-parameter observer based on Kalman filtering so as to calculate the motor stator winding resistance R s and d-axis increment self-inductance Q-axis delta self-inductance/>D and q axis incremental mutual inductance
The observer provided by the embodiment can accurately observe the parameter posterior estimated value theta of the motor under the full working condition, so that the embodiment can accurately identify the resistance parameter and the inductance parameter of the motor under the full working condition;
(S4) calculating a d-axis flux linkage ψ d0 and a q-axis flux linkage ψ q0 based on the parameters calculated in (S3);
In order to ensure accurate identification of the d-axis flux linkage ψ d0 and the q-axis flux linkage ψ q0, in this embodiment, after obtaining the resistance parameter and the inductance parameter based on the parameter identification output by the observer, these parameters are substituted into the above formula (6), and the calculation formula of the d-axis flux linkage ψ d0 and the q-axis flux linkage ψ q0 can be obtained as follows:
wherein u d0 and u q0 represent d-axis fundamental frequency signal voltage and q-axis fundamental frequency signal voltage, respectively, and i d0 and i q0 represent d-axis fundamental frequency signal current and q-axis fundamental frequency signal current, respectively;
Since the above formula (6) fully considers the influence of the injected small signal disturbance voltage, the d-axis flux linkage ψ d0 and q-axis flux linkage ψ q0 can be accurately identified in this embodiment.
In this embodiment, between steps (S1) and (S2), further includes:
Filtering out components with the frequency omega h in the d-axis current i d and the q-axis current i q to obtain d-axis direct current i d0 and q-axis direct current i q0 in the motor running state; specifically, a band-reject filter with a frequency omega h may be used to reject components with a frequency omega h from the d-axis current i d and the q-axis current i q;
D-axis direct current i d0 and q-axis direct current i q0 are input into a current loop;
In this embodiment, after the small signal disturbance voltage is injected into the current loop, the d-axis current i d and the q-axis current i q obtained by sampling and coordinate transforming the three-phase current of the motor include two parts: the small signal currents i d0 and i q0 with the frequency omega h and the small signal currents i dh and i qh with the frequency omega h are generated under the motor operation, and components with the frequency omega h in d-axis current and q-axis current are filtered out through filtering before the d-axis current and the q-axis current are input into a current loop in the embodiment, so that the output command voltage of the current loop can be prevented from generating an inhibition voltage signal which is 180 degrees different from a rotation voltage signal, the injected rotation high-frequency voltage is prevented from influencing a motor control part, the normal operation of the motor is ensured, meanwhile, the inhibition signal generated by the current loop is prevented from interfering the motor parameter identification, and the accuracy of the motor parameter identification is ensured.
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 (4)
1. The full-parameter observer based on Kalman filtering is used for carrying out parameter identification on a permanent magnet synchronous motor, and small signal voltages u dh and u qh with the frequency of omega h are respectively injected into d and q-axis command voltages output by a current loop of the permanent magnet synchronous motor in the operation process; wherein the full parameter observer comprises: a parameter extended kalman filter and a state extended kalman filter;
The parameter extended kalman filter includes: the parameter prior prediction module and the parameter posterior estimation module; the state-extended kalman filter includes: the state prior prediction module and the state posterior estimation module;
the first input end of the parameter priori prediction module is connected to the output end of the parameter posterior estimation module, and is used for performing priori prediction on the parameters of the current period to obtain the parameter priori predicted value of the current period
The state prior prediction module has a first input end connected to the output end of the state posterior estimation and a second input end connected to the output end of the parameter prior prediction module, and is used for carrying out prior prediction on the state of the current period to obtain a state prior prediction value of the current period
The first input end of the parameter posterior estimation module is connected to the output end of the parameter prior prediction module, the second input end of the parameter posterior estimation module is connected to the output end of the state prior prediction module, and the parameter posterior estimation module is used for carrying out posterior estimation on the parameters of the current period to obtain the parameter posterior estimation value of the current period
The state posterior estimation module has a first input end connected to the output end of the state prior prediction module and a second input end connected to the output end of the parameter prior prediction module, and is used for performing posterior estimation on the state of the current period to obtain a state posterior estimation value of the current period
Wherein,The state posterior estimate value of the previous cycle is represented, and U k-1 represents the small signal voltage injected in the previous cycle; t s denotes the sampling time interval, ω e denotes the motor fundamental frequency; /(I)R s represents the resistance of the motor stator winding; Expressed as an inverse of the inductance matrix,/> AndRespectively represent d-axis increment self-inductance and q-axis increment self-inductance,/>The d and q axes incremental mutual inductance is represented; θ= [ R s Tdd Tdq Tqq]T ] represents the parameter to be observed,/>F * () represents the discrete state equation of the motor;
The calculation expression of the parameter prior prediction module for signal processing is as follows:
Wherein, A parameter posterior estimate representing a previous period; /(I)Parameter prior prediction covariance matrix representing current period,/>The parameter posterior estimation covariance matrix of the last period is represented, and Q p represents the system noise covariance matrix in the parameter prior prediction;
The state prior prediction model carries out signal processing and has the following calculation expression:
Wherein, Parameter prior estimation covariance matrix representing current period,/>The state posterior estimation covariance matrix of the last period is represented, and Q x represents the system noise covariance matrix in state prior prediction; f x,k-1 represents the value of the derivative of the discrete state equation F * () on the motor state X in the last cycle;
The calculation expression of the signal processing by the parameter posterior estimation module is as follows:
Wherein, Representing the output of the motor in the current period, wherein h * () represents a discrete output equation of the motor; /(I)A parameter posterior estimation covariance matrix representing a current period; k θ,k represents the Kalman gain of the current period parameter, H θ,k represents the value of the derivative of the discrete output equation on the motor parameter theta in the current period; m represents a measurement noise covariance matrix of the system;
The state posterior estimation module performs signal processing according to the following calculation expression:
Wherein, The state posterior estimation covariance matrix of the current period is represented, K x,k represents the kalman gain of the state of the current period, and H x,k represents the value of the derivative of the discrete output equation on the state X of the motor in the current period.
2. A method for identifying all parameters of a permanent magnet synchronous motor based on the kalman filter-based all-parameter observer as claimed in claim 1, comprising:
a small signal disturbance voltage injection step: in the running process of the motor, d-axis small signal disturbance voltage u dh and q-axis small signal disturbance voltage u qh with the frequency of omega h are respectively injected into d-axis command voltage and q-axis command voltage output by a current loop of the motor;
The full parameter identification step comprises the following steps:
(S1) sampling three-phase current in a motor running state, and transforming the three-phase current into a dq-axis rotation coordinate system with synchronous rotation speed of the fundamental frequency omega e of the motor to obtain d-axis current i d and q-axis current i q;
(S2) extracting components with frequency ω h from the d-axis current i d and the q-axis current i q, respectively, to obtain a d-axis small signal disturbance current i dh and a q-axis small signal disturbance current i qh under excitation of the d-axis small signal disturbance voltage u dh and the q-axis small signal disturbance voltage u qh;
(S3) inputting the d-axis small signal disturbance current i dh and the q-axis small signal disturbance current i qh into the Kalman filtering-based full-parameter observer to obtain a parameter posterior estimation value theta= [ R s Tdd Tdq Tqq]T output by the Kalman filtering-based full-parameter observer so as to calculate the motor stator winding resistance R s and d-axis increment self-inductance Q-axis delta self-inductance/>Incremental mutual inductance of d and q axes/>
(S4) calculating d-axis flux linkage ψ d0 and q-axis flux linkage ψ q0 based on the parameters calculated in (S3).
3. The method of claim 2, wherein in the step (S4), the d-axis flux linkage ψ d0 and the q-axis flux linkage ψ q0 are calculated as:
Where u d0 and u q0 represent a d-axis fundamental frequency signal voltage and a q-axis fundamental frequency signal voltage, respectively, and i d0 and i q0 represent a d-axis fundamental frequency signal current and a q-axis fundamental frequency signal current, respectively.
4. A method of full parameter identification for a permanent magnet synchronous motor according to claim 2 or 3, further comprising, between said steps (S1) and (S2):
Filtering out components with the frequency omega h in the d-axis current i d and the q-axis current i q to obtain d-axis direct current i d0 and q-axis direct current i q0 in the motor running state;
The d-axis direct current i d0 and the q-axis direct current i q0 are input to the current loop.
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