CN108183648B - Permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation - Google Patents
Permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation Download PDFInfo
- Publication number
- CN108183648B CN108183648B CN201810070132.XA CN201810070132A CN108183648B CN 108183648 B CN108183648 B CN 108183648B CN 201810070132 A CN201810070132 A CN 201810070132A CN 108183648 B CN108183648 B CN 108183648B
- Authority
- CN
- China
- Prior art keywords
- permanent magnet
- motor
- voltage
- inverter
- identification
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Images
Classifications
-
- 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
- H02P21/16—Estimation of constants, e.g. the rotor time constant
-
- 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
- H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter 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
- H02P21/04—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation specially adapted for very low speeds
-
- 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
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Control Of Ac Motors In General (AREA)
Abstract
The invention discloses a permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation, which is characterized by firstly researching the inverter nonlinearity on the basis of the vector control of a permanent magnet synchronous motor, considering the influence of the inverter nonlinearity on the parameter identification and carrying out online dead-zone compensation aiming at the error voltage caused by nonlinearity; the parameters of the motor closed-loop control are identified off line on the basis of dead zone compensation, so that the identification precision is improved; and finally, combining a least square method and a model reference self-adaption method to realize simultaneous online identification of multiple parameters. The method can simultaneously identify the motor inductance, the permanent magnet flux linkage and the resistance value in a relatively short time under the condition of low speed, and has relatively high popularization and application values.
Description
Technical Field
The invention belongs to the technical field of parameter identification of permanent magnet synchronous motors, and particularly relates to a permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation.
Background
The Permanent Magnet Synchronous Motor (PMSM) has the advantages of stable rotation speed, fast dynamic response, strong overload capacity, high reliability, diversified structure, wide application range and the like due to simple structure, and becomes a research hotspot and is widely applied.
The commonly used control strategies for permanent magnet synchronous motors are vector control and direct torque control. The vector control method mainly comprises the following steps: i.e. idThe control method comprises 0 control, field weakening control, cos phi 1 control and maximum torque current ratio control. The vector control mainly adopts double closed-loop control, wherein the design of a current loop and a rotating speed loop adopts PI control based on a linear system theory, and the PI control is sensitive to the change of motor parameters. In practical application, the controller is required to identify required parameters to realize good control performance aiming at motors produced by different manufacturers. Meanwhile, in electric vehicle applications, reliable operation of the motor drive system has strict requirements, and early diagnosis of a fault is required. On-line monitoring of faults can be achieved by identifying motor parameters, making parameter identification one of the important methods for early fault diagnosis. HealdThe parameter identification of the PMSM can realize parameter setting and early fault diagnosis of the controller, can improve accurate and reliable operation of the controller, and has important theoretical research significance. The electromagnetic parameters that the PMSM needs to identify are: rs、Ld、LqAnd psif. The main factors influencing the motor parameters are temperature variations and magnetic circuit saturation. The online parameter identification is to identify the motor parameters by adopting different algorithms according to the input and output data of the motor, and continuously update the parameters according to new data. Compared with an off-line identification method, the on-line parameter identification can monitor the running state in real time, and meanwhile, the real-time updating of the parameters can enhance the self-adaptive control of the controller. The current identification algorithm for parameter online identification mainly comprises: recursive least squares, model reference adaptation, extended kalman filtering, intelligent algorithms, and the like.
Meanwhile, in the parameter identification process, the existence of the inverter nonlinearity has a serious negative effect on the parameter identification result, and how to inhibit the inverter nonlinearity voltage error and improve the accuracy of the identification result is also a hot spot of current research. In parameter identification, the nonlinearity of the inverter can bring more serious influence on the estimated rotor information, especially under the conditions of low speed and light load, the phase current is small, and most of the time is in a zero-current clamping area, so that more serious nonlinear voltage error is caused, and the precision of parameter identification is influenced. Therefore, the influence caused by the nonlinearity of the inverter needs to be considered while identifying the parameters of the permanent magnet synchronous motor.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the influence of the nonlinearity of the inverter on the parameter identification precision, the problem of non-unique solution of identification parameters occurs for the identification equation under-rank generated by the multi-parameter identification of the motor by adopting a single method. The invention provides a permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation, so that a permanent magnet synchronous motor can simultaneously identify motor inductance, permanent magnet flux linkage and resistance value in a short time under the condition of low speed.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation is characterized in that: the method comprises the following steps:
step 1: on the basis of vector control of the permanent magnet synchronous motor, the nonlinearity of the inverter is researched, the influence of the nonlinearity factor of the inverter on parameter identification is considered, and the online dead-zone compensation is carried out on the error voltage caused by the nonlinearity;
step 2: the parameters of the motor closed-loop control are identified off line on the basis of dead zone compensation, so that the identification precision is improved;
and step 3: and the simultaneous online identification of multiple parameters is realized by combining a least square method and a model reference self-adaption method.
As an improvement, the non-linearity factors of the inverter in step 1 include the switching dead zone of the switching devices of the driver, the tube voltage drop of the switching devices, and the switching delay.
As an improvement, the online dead zone compensation method in the step 1 comprises the following steps: and designing an error voltage caused by the dead zone effect observed by a disturbance observer as a disturbance voltage, carrying out online estimation on the disturbance voltage, then feeding forward the disturbance voltage to the input end of the inverter, and carrying out online compensation on the dead zone effect by adopting a voltage feed-forward mode.
In step 2, a direct current experiment method is adopted to identify the stator resistance in an off-line mode, firstly, a constant voltage vector in any direction is introduced into the motor, after the motor rotates to a certain position and stops, the current of the stator at the moment is recorded, and the resistance value of the stator is calculated.
As an improvement, in step 2, the method for offline identifying the d-axis and q-axis inductance values of the synchronous motor comprises the following steps: high-frequency voltage signals are injected into the d axis and the q axis respectively, and amplitude and phase information of high-frequency current response are extracted by utilizing discrete Fourier transform, so that inductance values of the d axis and the q axis are obtained.
As an improvement, in step 3, the model reference adaptation method requires an input stator voltage ud、uqAnd stator current id，iqIdentifying the parameter permanent magnet flux linkage psifAnd a motor inductance L.
As an improvement thereof,in step 3, identifying the parameters by the least square method needs to consider the motor stator voltage u on the basis of identifying the parameters in step 2d、uqAnd stator current id，iqIdentifying the parameter motor resistance Rs。
Compared with the prior art, the invention has the following main advantages:
1. compared with the prior art, the method and the device consider the influence caused by the nonlinearity of the inverter, carry out nonlinear compensation on the inverter and improve the precision.
2. The problem that the identification parameter is not unique due to the fact that a single method is adopted to carry out multi-parameter identification on the motor, and the problem that four electromagnetic parameters of the motor are identified once by combining two methods to solve the problem of multi-parameter simultaneous identification of the motor is solved.
Drawings
FIG. 1 is a schematic diagram of the overall structure of an embodiment of the present invention;
FIG. 2 is a block diagram of a vector control for a permanent magnet synchronous motor;
FIG. 3 is a schematic block diagram of a vector control with dead band compensation;
FIG. 4 is an off-line equivalent model of stator resistance identification;
FIG. 5 is a schematic diagram of d-axis and q-axis inductances measured by high frequency injection;
FIG. 6 is a block diagram of a model adaptive parameter identification system;
FIG. 7 is a waveform of a simulation of motor operation without dead band compensation;
FIG. 8 is a waveform diagram of a motor operation simulation with dead zone compensation;
FIG. 9 is a simulation waveform diagram of stator resistance identification for permanent magnet synchronous motor parameter identification;
fig. 10 is a simulation waveform diagram of permanent magnet flux linkage identification for parameter identification of a permanent magnet synchronous motor.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, the present invention comprises the steps of:
step 1: on the basis of vector control of the permanent magnet synchronous motor, the nonlinearity of the inverter is researched, the influence of the nonlinearity factor of the inverter on parameter identification is considered, and the online dead-zone compensation is carried out on the error voltage caused by the nonlinearity;
step 2: the parameters of the motor closed-loop control are identified off line on the basis of dead zone compensation, so that the identification precision is improved;
and step 3: and the simultaneous online identification of multiple parameters is realized by combining a least square method and a model reference self-adaption method. And performing online identification on the inductance and the permanent magnet flux linkage by using a model reference self-adaptive method and using the initial value of the resistance. Model reference adaptation requires input stator voltage ud、uqAnd stator current id，iqIdentifying the parameter permanent magnet flux linkage psifAnd a motor inductance L; the stator resistance of the PMSM is identified on line by adopting a least square method which has a simple identification algorithm, occupies the minimum system time and meets the parameter identification precision. In step 2, the permanent magnet flux linkage psi is identifiedfOn the basis of the L parameter of the motor inductance, the motor stator voltage u is consideredd、uqAnd stator current id，iqIdentifying the parameter motor resistance Rs。
As shown in fig. 2, the vector control of the permanent magnet synchronous motor is to control the ac motor as a dc motor, i.e. to simulate the control characteristics of the dc motor to control the permanent magnet synchronous motor. The mathematical model of the permanent magnet synchronous motor is specifically as follows:
in the two-phase synchronous rotating coordinate system, the stator voltage equation is as follows:
in equation (1): u. ofdAnd uqRepresenting the components of the stator voltage in the d-axis (direct axis) and q-axis (quadrature axis), idAnd iqRespectively representing the components of the stator current in the d-axis and the q-axis; rsStator resistance given for the machine, LdAnd LqRespectively representing a direct axis inductor and a quadrature axis inductor of the motor; omegarThe angular velocity of the motor; psifIs a permanent magnet flux linkage; p is the number of pole pairs of the motor.
As shown in fig. 3, the adopted method converts the dead zone error voltage caused by various non-ideal factors such as dead zone effect into a two-phase rotating coordinate system, and the dead zone error voltage is used as disturbance voltage, and the disturbance voltage is estimated on line and then fed to the input end of the inverter for voltage feedforward compensation.
In the two-phase synchronous rotating coordinate system, considering the influence caused by dead time, the stator voltage equation is as follows because dead time error voltage is added in the above formula:
in equation (2): u. ofdAnd uqRepresenting the components of the stator voltage in the d-axis (direct axis) and q-axis (quadrature axis), idAnd iqRespectively representing the components of the stator current in the d-axis and the q-axis; rsStator resistance given for the machine, LdAnd LqRespectively representing a direct axis inductor and a quadrature axis inductor of the motor; omegarThe angular velocity of the motor; psifIs a permanent magnet flux linkage; remember ue d rrAnd ue q rrDead zone error voltages for the d-axis and q-axis, respectively; p is the number of pole pairs of the motor.
According to the discrete mathematical model of the above expression, a discrete expression of the dead zone error voltage can be obtained:
in the above equation, k is a discrete parameter, and there is no large change in the disturbance voltage caused by the dead zone effect in one sampling period, so there are:
based on the assumption of the above equation, the disturbance voltage at the current time can be estimated from the disturbance voltage at the previous time. The estimation calculation formula is as follows:
as shown in fig. 4, when a low dc voltage is applied to the three-phase winding of the permanent magnet synchronous motor in the manner shown in fig. 4, no alternating current is generated at the stator side of the motor, no rotating magnetic field is generated, and thus the rotor of the motor does not rotate, and no back electromotive force is generated, and the generated three-phase stator current quickly reaches a stable value. Since the motor does not rotate and the current is a stable value, the third term and the second term in the above equation are 0 at the time of steady state, and then the equation can be obtained: u. ofs＝Rsis. Since the three-phase stator resistances are equal, the calculation formula of the stator resistance can be represented by:
as shown in fig. 5, in the offline parameter identification scheme of the permanent magnet synchronous motor based on composite signal injection, on one hand, the inductance value is estimated by analyzing the relationship between the injection voltage and the response current amplitude in a manner of injecting a high-frequency voltage signal into the d-axis. According to the d-q axis voltage equation, omega is obtained when the motor is in a static state r0, in the high-frequency motor model, the stator resistance value relative to reactance is very small and can be basically ignored, so that the mathematical model formula of the permanent magnet synchronous motor is simplified as follows:
from the above formula, if the amplitude of the high-frequency voltage and the response current under the injection frequency is extracted by the signal amplitude extraction method, the reactance under the frequency can be calculated, the resistance influence is ignored, the inductive reactance value is obtained, and the estimated value of the axis inductance is obtained.
As shown in fig. 6, in a high-speed operation state of the motor, a model reference adaptive method is adopted, firstly, an initial value of a resistance is obtained by utilizing off-line identification, and then, on-line identification of an inductance and a permanent magnetic flux linkage is carried out on the basis of the initial value; and finally, the stator resistance of the PMSM is identified on line by adopting a least square method which has a simple identification algorithm, occupies the minimum system time and meets the parameter identification precision.
Fig. 7 is a motor operation simulation waveform diagram without dead-zone compensation and fig. 8 is a motor operation simulation waveform diagram with dead-zone compensation, which shows that the current waveform of the motor for inverter compensation is more stable, and the accuracy of parameter identification can be improved. And (4) carrying out simulation verification on the complaint method, and identifying parameters when the permanent magnet synchronous motor runs for 0.1 s. According to the simulation oscillogram for identifying the stator resistance of the parameter identification of the permanent magnet synchronous motor shown in fig. 9 and the simulation oscillogram for identifying the permanent magnet flux linkage of the parameter identification of the permanent magnet synchronous motor shown in fig. 10, it can be seen that the required resistance value and the permanent magnet flux linkage value can be effectively identified by the method.
Claims (3)
1. A permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation is characterized in that: the method comprises the following steps:
step 1: on the basis of vector control of the permanent magnet synchronous motor, the nonlinearity of the inverter is researched, the influence of the nonlinearity factor of the inverter on parameter identification is considered, and the online dead-zone compensation is carried out on the error voltage caused by the nonlinearity;
step 2: the parameters of the motor closed-loop control are identified off line on the basis of dead zone compensation, so that the identification precision is improved; the method comprises the following steps of (1) identifying the stator resistance off line by adopting a direct current experimental method, firstly introducing a constant voltage vector in any direction to a motor, recording the current of the stator at the moment after the motor rotates to a certain position and stops, and calculating the resistance value of the stator; then injecting high-frequency voltage signals into the d axis and the q axis respectively, and extracting amplitude and phase information of high-frequency current response by utilizing discrete Fourier transform so as to obtain inductance values of the d axis and the q axis;
and step 3: the method is characterized in that a least square method and a model reference adaptive method are combined to realize multi-parameter simultaneous online identification, and a model reference adaptive method is adopted to perform online identification of inductance and permanent magnet flux linkage by using an initial value of resistance; model reference is fromAdapting the required input stator voltage ud、uqAnd stator current id，iqIdentifying the parameter permanent magnet flux linkage psifAnd a motor inductance L; the stator resistance of the PMSM is identified on line by adopting a least square method, and the permanent magnet flux linkage psi is identifiedfOn the basis of the L parameter of the motor inductance, the motor stator voltage u is consideredd、uqAnd stator current id，iqIdentifying the parameter motor resistance Rs。
2. The permanent magnet synchronous motor parameter identification method according to claim 1, characterized in that: the nonlinear factors of the inverter in the step 1 include a switch dead zone of a switch device of the driver, a tube voltage drop of the switch device, and a switch delay.
3. The permanent magnet synchronous motor parameter identification method according to claim 1, characterized in that: the online dead zone compensation method in the step 1 comprises the following steps: and designing an error voltage caused by the dead zone effect observed by a disturbance observer as a disturbance voltage, carrying out online estimation on the disturbance voltage, then feeding forward the disturbance voltage to the input end of the inverter, and carrying out online compensation on the dead zone effect by adopting a voltage feed-forward mode.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810070132.XA CN108183648B (en) | 2018-01-24 | 2018-01-24 | Permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810070132.XA CN108183648B (en) | 2018-01-24 | 2018-01-24 | Permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108183648A CN108183648A (en) | 2018-06-19 |
| CN108183648B true CN108183648B (en) | 2020-04-24 |
Family
ID=62551463
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810070132.XA Expired - Fee Related CN108183648B (en) | 2018-01-24 | 2018-01-24 | Permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108183648B (en) |
Families Citing this family (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109004669B (en) * | 2018-07-26 | 2020-07-10 | 华中科技大学 | Improved passive control method of three-phase grid-connected inverter based on interference observer compensation |
| GB201813335D0 (en) | 2018-08-15 | 2018-09-26 | Technelec Ltd | Flux linkage to current observer for synchronous machines |
| CN109060184A (en) * | 2018-08-27 | 2018-12-21 | 天津中科华盈科技有限公司 | A method of for realizing the monitoring of motor temperature redundancy |
| CN109245650A (en) * | 2018-09-30 | 2019-01-18 | 核工业理化工程研究院 | The parameter identification method of permanent magnet synchronous motor and the control system of permanent magnet synchronous motor |
| CN110112975B (en) * | 2019-05-14 | 2022-04-29 | 安徽首智新能源科技有限公司 | Motor parameter online identification method and system |
| CN110365269A (en) * | 2019-07-31 | 2019-10-22 | 苏州英威腾电力电子有限公司 | A kind of vector control method and device based on motor stator parameter off-line identification |
| CN112421997B (en) * | 2019-08-23 | 2022-07-12 | 广东美的生活电器制造有限公司 | Torque compensation device, method and system of motor, household appliance and storage medium |
| CN112415382B (en) * | 2019-08-23 | 2023-09-22 | 广东美的生活电器制造有限公司 | Motor parameter determining device, method and system, household appliance and storage medium |
| CN110824252B (en) * | 2019-09-30 | 2021-12-14 | 东南大学溧阳研究院 | Permanent magnet synchronous motor stator resistance measurement method based on dead time compensation |
| CN110758413B (en) * | 2019-10-24 | 2021-04-27 | 北京航盛新能科技有限公司 | Train speed self-adaptive control method based on system parameter identification |
| CN110932584B (en) * | 2019-12-05 | 2021-11-19 | 深圳市汇川技术股份有限公司 | Inverter nonlinear compensation method, system, device and storage medium |
| CN111030546A (en) * | 2019-12-19 | 2020-04-17 | 深圳市显控科技股份有限公司 | Permanent magnet motor offline parameter identification method and device |
| CN111030534B (en) * | 2019-12-20 | 2021-07-13 | 东南大学 | Parameter identification method for permanent magnet synchronous motor in steady-state operation mode |
| CN111130425B (en) * | 2019-12-23 | 2021-07-20 | 潍柴动力股份有限公司 | Dead zone compensation method and device, motor driver and storage medium |
| CN111551781B (en) * | 2020-05-18 | 2021-06-11 | 湖南大学 | Equivalent distortion voltage direct detection method and inverter nonlinear factor compensation method |
| CN111756287B (en) * | 2020-06-18 | 2022-08-05 | 中车永济电机有限公司 | Dead zone compensation method suitable for permanent magnet motor control based on current prediction |
| CN112152532A (en) * | 2020-09-10 | 2020-12-29 | 敬科(深圳)机器人科技有限公司 | Method, system and device for online estimation of joint motor parameters |
| CN112737451B (en) * | 2020-12-29 | 2022-04-01 | 华中科技大学 | Control-oriented permanent magnet synchronous linear motor system identification method |
| CN112953318B (en) * | 2021-02-01 | 2022-08-30 | 哈尔滨工业大学 | Nonlinear compensation method for permanent magnet synchronous motor driving system inverter |
| CN113221396B (en) * | 2021-03-22 | 2023-12-12 | 梁文毅 | Modeling method for motor concentrated parameter model |
| CN112968645B (en) * | 2021-04-01 | 2023-02-17 | 深圳市英威腾电气股份有限公司 | Motor mutual inductance identification method, system and device |
| CN113630054A (en) * | 2021-08-13 | 2021-11-09 | 东南大学 | Permanent magnet synchronous motor online parameter identification method based on inverter dead zone compensation |
| CN114006558B (en) * | 2021-08-31 | 2023-07-18 | 北京信息科技大学 | Motor parameter online identification method only for bill parameters |
| CN113852309B (en) * | 2021-09-07 | 2022-06-14 | 之江实验室 | Robot joint motor parameter identification and control parameter self-tuning method |
| CN114337434B (en) * | 2022-01-12 | 2023-07-21 | 湖南大学 | Permanent magnet motor parameter offline identification method considering inductance saturation effect |
-
2018
- 2018-01-24 CN CN201810070132.XA patent/CN108183648B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN108183648A (en) | 2018-06-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108183648B (en) | Permanent magnet synchronous motor parameter identification method based on inverter nonlinear compensation | |
| Liu et al. | A novel MTPA control strategy for IPMSM drives by space vector signal injection | |
| Wang et al. | An EMF observer for PMSM sensorless drives adaptive to stator resistance and rotor flux linkage | |
| Cupertino et al. | Sensorless position control of permanent-magnet motors with pulsating current injection and compensation of motor end effects | |
| CN109672383B (en) | Salient pole type permanent magnet synchronous motor online parameter identification method | |
| CN113300647B (en) | Static AC-DC axis inductance identification method for permanent magnet synchronous motor | |
| CN110071674B (en) | Position-sensor-free permanent magnet synchronous motor maximum torque current ratio control method | |
| Zhao et al. | A high frequency injection technique with modified current reconstruction for low-speed sensorless control of IPMSMs with a single DC-link current sensor | |
| CN113630054A (en) | Permanent magnet synchronous motor online parameter identification method based on inverter dead zone compensation | |
| Sun et al. | Self-learning direct flux vector control of interior permanent-magnet machine drives | |
| CN111585488B (en) | Permanent magnet motor speed sensorless control method and system | |
| Zhong et al. | Online-parameter-estimation-based control strategy combining MTPA and flux-weakening for variable flux memory machines | |
| Aydeniz et al. | A Luenberger-sliding mode observer with rotor time constant parameter estimation in induction motor drives | |
| Jannati et al. | Speed sensorless fault-tolerant drive system of 3-phase induction motor using switching extended kalman filter | |
| Pramod et al. | Impact of parameter estimation errors on feedforward current control of permanent magnet synchronous motors | |
| Min et al. | On-line estimation of permanent-magnet flux and temperature rise in stator winding for PMSM | |
| Zhou et al. | Inductance parameter identification method of permanent magnet synchronous motor based on the HF rotating square wave voltage injection | |
| Xiao et al. | New adaptive sliding-mode observer design for sensorless control of PMSM in electric vehicle drive system | |
| Pengcheng et al. | Offline parameter identification strategy of permanent magnet synchronous motor considering the inverter nonlinearities | |
| Popescu et al. | Estimation of the Rotor Flux in the Traction Systems with Induction Motors and Field-Oriented Control | |
| Chen et al. | Totally adaptive observer for speed sensorless induction motor drives: Simply a cost of extra energy consumption | |
| Wang et al. | An online estimation method for both stator inductance and rotor flux linkage of SPMSM without dead-time influence | |
| Li et al. | Investigation of on-line parameter estimation for interior PMSMs considering current injection and machine operating conditions | |
| Pan et al. | Online Inductance Identification of PMSM Based on High Frequency Signal Injection into Virtual Axis | |
| CN112491308A (en) | Permanent magnet synchronous motor control method adopting torque and stator flux estimator |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200424 Termination date: 20210124 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |