CN111082714A - Switched reluctance motor accurate modeling method based on small sample flux linkage characteristics - Google Patents

Switched reluctance motor accurate modeling method based on small sample flux linkage characteristics Download PDF

Info

Publication number
CN111082714A
CN111082714A CN202010034404.8A CN202010034404A CN111082714A CN 111082714 A CN111082714 A CN 111082714A CN 202010034404 A CN202010034404 A CN 202010034404A CN 111082714 A CN111082714 A CN 111082714A
Authority
CN
China
Prior art keywords
flux linkage
fuzzy
switched reluctance
rotor
reluctance motor
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.)
Pending
Application number
CN202010034404.8A
Other languages
Chinese (zh)
Inventor
李存贺
张存山
边敦新
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shandong University of Technology
Original Assignee
Shandong University of Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shandong University of Technology filed Critical Shandong University of Technology
Priority to CN202010034404.8A priority Critical patent/CN111082714A/en
Publication of CN111082714A publication Critical patent/CN111082714A/en
Pending legal-status Critical Current

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
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/34—Modelling or simulation for control purposes
    • 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
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P23/0013—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using fuzzy control
    • 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
    • H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
    • H02P23/14—Estimation or adaptation of motor parameters, e.g. rotor time constant, flux, speed, 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
    • 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
    • H02P25/08—Reluctance motors

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Fuzzy Systems (AREA)
  • Control Of Electric Motors In General (AREA)

Abstract

The invention relates to the technical field of switched reluctance motor flux linkage modeling, in particular to a switched reluctance motor accurate modeling method based on small sample flux linkage characteristics, which comprises the following steps: step 1), carrying out fuzzy space division on input and output variables of a flux linkage model according to the prior knowledge of a motor; step 2), extracting fuzzy rules through a fuzzy logic system, and establishing a fuzzy rule base; step 3), solving the flux linkage value under any current and position through a gravity center method, and completing flux linkage modeling; the method can use the prior knowledge of the motor such as the change trend of the SRM pole logarithm and flux linkage along with the rotor position and the like for the selection of the fuzzy membership function and the division of the fuzzy set, and can automatically create a fuzzy rule base based on sample data, thereby realizing the accurate modeling under the SRM small sample flux linkage characteristic.

Description

Switched reluctance motor accurate modeling method based on small sample flux linkage characteristics
Technical Field
The invention relates to the technical field of switched reluctance motor flux linkage modeling, in particular to a switched reluctance motor accurate modeling method based on small sample flux linkage characteristics.
Background
At present, the switched reluctance motor has wide application prospect in the fields of oil field pumping units, wind power generation, electric vehicles and the like due to the advantages of simple structure, large starting torque, wide speed regulation range, high reliability and efficiency and the like. Establishing an accurate mathematical model is critical to SRM performance assessment and implementing advanced control strategies. However, the doubly salient structure and the magnetic saturation characteristics of the SRM itself make it difficult to derive an accurate nonlinear mathematical model thereof through conventional electromagnetic and physical property derivation. The current nonlinear modeling method of the switched reluctance motor mainly comprises the following steps: interpolation iteration method, equivalent magnetic circuit method, function fitting method and neural network approximation method. The equivalent magnetic circuit in the equivalent magnetic circuit method is difficult to divide, the accuracy of magnetic resistance calculation depends on assumption and estimation, and the universality is poor; the function fitting method adopts an analytic expression to carry out nonlinear fitting on the flux linkage characteristic, and the precision of the function fitting method excessively depends on the form of the function analytic expression and the fitting precision of the analytic expression coefficient; both the interpolation iteration method and the neural network approximation method need a large amount of flux linkage sample data, so that the application range is not large.
Disclosure of Invention
In order to solve the deficiencies in the above technical problems, the present invention aims to: the accurate modeling method of the switched reluctance motor based on the small sample flux linkage characteristics is provided, the characteristics of less flux linkage sample data, high accuracy and good rapidity can be realized, and support is provided for performance evaluation and advanced control strategy implementation of the switched reluctance motor.
The technical scheme adopted by the invention for solving the technical problem is as follows:
the accurate modeling method of the switched reluctance motor based on the small sample flux linkage characteristic comprises the following steps:
step 1), carrying out fuzzy space division on input and output variables of a flux linkage model according to the prior knowledge of a motor;
step 2), extracting fuzzy rules through a fuzzy logic system, and establishing a fuzzy rule base;
and 3) solving the flux linkage value under any current and position by solving the fuzzy problem through a gravity center method to complete flux linkage modeling.
Preferably, the following method is specifically adopted in the step 1):obtaining enough sample data of flux linkage changing along with current through experimental measurement, comprehensively considering model complexity and precision, and dividing phase current into 21 intervals by adopting a triangular membership function to perform fuzzy set division; specifically, the jth phase current fuzzy set AjThe membership function of (d) can be described as:
Figure BDA0002365427080000011
wherein i is a current value, j is a phase current, imaxAt maximum current, a ═ imaxAnd/20 is the step size of the current membership function.
Preferably, in the step 1), the prior knowledge of the SRM is introduced into the membership function selection and fuzzy set division of the rotor position for supplement, and a curve of flux linkage changing along with the rotor position can be divided into three regions, namely a region I [ theta ] (theta)u,θ1) Region II [ theta ]1,θhr) And region III [ theta ]hr,θa]，θaThe complete alignment position of the salient poles of the stator and the rotor is as follows: calculating the alignment position theta of the leading edge of the rotor pole and the leading edge of the stator pole1And rotor pole centerline and stator pole leading edge alignment position θhrThe following formula is adopted:
Figure BDA0002365427080000021
in the formula, βsAnd βrThe pole arc widths of the stator and rotor, respectively, and satisfy the following relationship:
Figure BDA0002365427080000022
wherein, in the formula, m and NrRespectively representing the number of stator phases and the number of rotor poles of the motor, and m is 3, N is used for a three-phase 12/8-pole SRM modelrWhen formula (3) is substituted for formula (2), 8 can be obtained:
Figure BDA0002365427080000023
preferably, the change of the flux linkage along with the position of the rotor can be approximated to a linear relation in a region II, the change of the flux linkage along with the position of the rotor can be approximated to a cosine relation in regions I and III, the flux linkage-position characteristic is approximated to a linear relation in a position interval [7.5 degrees, 15 degrees ], and the linear membership function is adopted for division; the interval [0 degrees, 7.5 degrees ] and [15 degrees, 22.5 degrees ] are approximately cosine characteristics, and are divided by cosine membership functions.
Preferably, in step 1), in order to implement accurate solution of the flux linkage characteristics, a more refined fuzzy partition is adopted for fuzzy space partition of input and output variables of the flux linkage model, specifically as follows: dividing flux linkage into 201 regions by using a triangular membership function, wherein the ith flux linkage fuzzy set ClThe membership function of (d) can be described as:
Figure BDA0002365427080000024
in the formula, #maxIs the maximum value of flux linkage, c ═ ψmaxAnd 200 is the step size of the flux linkage membership function.
Preferably, the following method is specifically adopted in the step 2): a method for designing a fuzzy inference system based on input and output data of the system is adopted, and fuzzy rules are automatically extracted from sample data.
Compared with the prior art, the invention has the following beneficial effects:
the invention adopts a fuzzy logic system to solve the problem of accurate modeling when the flux linkage sample data of the switched reluctance motor is insufficient. Firstly, fuzzy space division is carried out according to motor priori knowledge; secondly, extracting fuzzy rules from small sample flux linkage data only containing a plurality of special positions through a fuzzy logic system, and forming a fuzzy rule base; and finally, solving the ambiguity by adopting a gravity center method, solving the flux linkage characteristic under any current and any rotor position, and completing the nonlinear accurate modeling of the flux linkage characteristic. Specifically, a fuzzy logic system accurate modeling method suitable for SRM small sample flux linkage characteristics is provided. The method can use the prior knowledge of the motor such as the change trend of the SRM pole logarithm and flux linkage along with the rotor position and the like for the selection of the fuzzy membership function and the division of the fuzzy set, and can automatically create a fuzzy rule base based on sample data, thereby realizing the accurate modeling under the SRM small sample flux linkage characteristic. The fuzzy modeling method not only makes full use of sample data, but also well combines inherent prior knowledge of the motor, greatly improves the modeling precision of the SRM flux linkage under the small sample data, and can provide powerful support for the analysis of the SRM operating characteristics and the verification of advanced algorithms.
Drawings
FIG. 1 is a block diagram of the present invention;
FIG. 2 is a schematic view of flux linkage characteristics at four particular positions of a rotor according to the present invention;
FIG. 3 is a schematic diagram of the input division (current) of the fuzzy logic system of the present invention;
FIG. 4 shows the flux linkage of the present invention as a function of rotor position;
FIG. 5 is a schematic diagram of the fuzzy logic system input partitioning (rotor position) of the present invention;
FIG. 6 is a schematic diagram of the input division (current) of the fuzzy logic system of the present invention;
FIG. 7 is a schematic diagram of the flux linkage modeling results of the present invention.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
example 1
As shown in fig. 1 to 7, the method for accurately modeling a switched reluctance motor based on small sample flux linkage characteristics according to the present invention includes the following steps:
step 1), carrying out fuzzy space division on input and output variables of a flux linkage model according to the prior knowledge of a motor;
step 2), extracting fuzzy rules through a fuzzy logic system, and establishing a fuzzy rule base;
and 3) solving the flux linkage value under any current and position by solving the fuzzy problem through a gravity center method to complete flux linkage modeling.
In the specific method, a torque balance position measurement method is adopted to obtain small sample flux linkage data of four special positions of the three-phase switched reluctance motor, namely 0 degree, 7.5 degrees, 15 degrees and 22.5 degrees, as shown in fig. 2.
The following method is specifically adopted in the step 1): obtaining enough sample data of flux linkage changing along with current through experimental measurement, comprehensively considering model complexity and precision, and dividing phase current into 21 intervals by adopting a triangular membership function to perform fuzzy set division; specifically, the jth phase current fuzzy set AjThe membership function of (d) can be described as:
Figure BDA0002365427080000041
wherein i is a current value, j is a phase current, imaxAt maximum current, a ═ imaxAnd/20 is the step size of the current membership function.
For the rotor position in the step 1), flux linkage information of four special positions can be measured and obtained only based on a torque balance method. Flux linkage data of limited positions brings difficulty to membership function selection and fuzzy set division of rotor positions. Therefore, the prior knowledge of the SRM is introduced into the membership function selection and fuzzy set division of the rotor position for supplement, and the approximate change trend is very similar although the numerical values of the flux linkage-position (psi-theta) characteristics have differences for any given SRM prototype. Fig. 4 shows the flux linkage as a function of rotor position at a particular current. The curve of flux linkage changing with the position of rotor can be divided into three regions, I [ theta ] respectivelyu,θ1) Region II [ theta ]1,θhr) And region III [ theta ]hr,θa]，θaThe complete alignment position of the salient poles of the stator and the rotor is as follows: calculating the alignment position theta of the leading edge of the rotor pole and the leading edge of the stator pole1And rotor pole centerline and stator pole leading edge alignment position θhrThe following formula is adopted:
Figure BDA0002365427080000042
in the formula, βsAnd βrThe pole arc widths of the stator and rotor, respectively, and satisfy the following relationship:
Figure BDA0002365427080000043
wherein, in the formula, m and NrRespectively representing the number of stator phases and the number of rotor poles of the motor, and m is 3, N is used for a three-phase 12/8-pole SRM modelrWhen formula (3) is substituted for formula (2), 8 can be obtained:
Figure BDA0002365427080000044
the change of the magnetic linkage along with the position of the rotor can be approximated to a linear relation in a region II and approximated to a cosine relation in regions I and III by combining experimental data, the magnetic linkage-position characteristic is approximated to a linear relation in a position interval [7.5 degrees, 15 degrees ], and a linear membership function is adopted for division; the interval [0 degrees, 7.5 degrees ] and [15 degrees, 22.5 degrees ] are approximately cosine characteristics, and are divided by cosine membership functions. The fuzzy set partitioning for rotor position is shown in fig. 5.
Rotor position ambiguity set B in FIG. 51And B2The membership functions of (a) can be described as:
Figure BDA0002365427080000051
Figure BDA0002365427080000052
fuzzy set B3And B4Are respectively connected with B2And B1Regarding the middle position symmetry, the principle of its membership functions is the same.
In step 1), in order to realize accurate solution of flux linkage characteristics, more precise fuzzy division is adopted for flux linkage model input and output variable fuzzy space division, and the method specifically comprises the following steps: dividing flux linkage into 201 regions by using a triangular membership function, wherein the ith flux linkage fuzzy set ClThe membership function of (d) can be described as:
Figure BDA0002365427080000053
in the formula, #maxIs the maximum value of flux linkage, c ═ ψmaxAnd 200 is the step size of the flux linkage membership function.
The following method is specifically adopted in the step 2): a method for designing a fuzzy inference system based on input and output data of the system is adopted, and fuzzy rules are automatically extracted from sample data.
The fuzzy rule is composed of a front piece and a back piece and can be expressed as follows: "If x1is Ajand x2is Bkthen y isCl". For the SRM flux linkage fuzzy logic model, the fuzzy rule corresponding to each input/output data pair (i, theta; psi) can be described as "R(s):If i(s)is
Figure BDA0002365427080000054
andθ(s)i s
Figure BDA0002365427080000055
thenψ(s)is
Figure BDA0002365427080000056
". For each sample data, selecting the corresponding maximum membership function value, and calculating the corresponding fuzzy set as follows:
Figure BDA0002365427080000057
in the formula, q1,q2,q3The fuzzy set numbers of the front piece i, theta and the back piece psi, respectively. From the preceding fuzzy set partition, q1＝4，q2＝21，q3201. Extracted fuzzy rule R(s)Confidence of (D) (R)(s)) The following can be calculated:
Figure BDA0002365427080000061
it should be noted that some sample data have the same fuzzy front part, different fuzzy back parts will generate conflicting fuzzy rules, and the solution is to select the rule with the maximum confidence as the best fuzzy rule. Based on the measured special position flux linkage sample data, the fuzzy rule extraction is completed, as shown in table 1.
TABLE 1 fuzzy rule base extracted from experimental measurement data
Tab.1The final fuzzy rule base generated from the measured sampledata.
B1 B2 B3 B4
A1 C1 C1 C1 C1
A2 C3 C4 C8 C19
A3 C5 C8 C23 C37
A4 C8 C12 C35 C55
A5 C10 C15 C45 C71
A6 C12 C19 C61 C92
A7 C15 C23 C73 C109
A8 C17 C26 C80 C123
A9 C20 C30 C94 C137
A10 C22 C34 C105 C148
A11 C24 C38 C111 C157
A12 C27 C41 C120 C166
A13 C30 C45 C126 C172
A14 C32 C48 C132 C176
A15 C35 C52 C136 C181
A16 C37 C55 C141 C185
A17 C40 C58 C145 C188
A18 C43 C62 C149 C192
A19 C45 C65 C153 C195
A20 C48 C69 C157 C198
A21 C51 C72 C161 C200
Taking row 3, column 2 in table 1 as an example, the rules can be described as: "If phase current i is A3and rotor positionθis B2then flux-linkageψis C8”。
Solving the flux linkage value under any current and position by a gravity center method through ambiguity resolution to complete flux linkage modeling, which is concretely as follows:
flux linkage output of SRM under any current and position
Figure BDA0002365427080000071
The following can be calculated:
Figure BDA0002365427080000072
wherein
Figure BDA0002365427080000073
Figure BDA0002365427080000074
In the formula (I), the compound is shown in the specification,
Figure BDA0002365427080000075
representing fuzzy sets
Figure BDA0002365427080000076
The area of (a) is,
Figure BDA0002365427080000077
is that
Figure BDA0002365427080000078
The center of gravity of (a).
The SRM full period flux linkage values were calculated according to the proposed fuzzy logic system modeling method, as shown by the dotted line in fig. 7. In order to verify the modeling accuracy of the proposed method, the measured flux linkage value (shown as a solid line in fig. 7) is compared with the measured flux linkage value of the traditional rotor locking method, and the comparison result shows that the measured flux linkage value and the measured flux linkage value have better consistency.

Claims (6)

1. A switched reluctance motor accurate modeling method based on small sample flux linkage characteristics is characterized by comprising the following steps:
step 1), carrying out fuzzy space division on input and output variables of a flux linkage model according to the prior knowledge of a motor;
step 2), extracting fuzzy rules through a fuzzy logic system, and establishing a fuzzy rule base;
and 3) solving the flux linkage value under any current and position by solving the fuzzy problem through a gravity center method to complete flux linkage modeling.
2. The accurate modeling method for the switched reluctance motor based on the small sample flux linkage characteristic according to claim 1 is characterized in that the following method is specifically adopted in the step 1): obtaining enough sample data of flux linkage changing along with current through experimental measurement, comprehensively considering model complexity and precision, and dividing phase current into 21 intervals by adopting a triangular membership function to perform fuzzy set division; specifically, the jth phase current fuzzy set AjIs subject toThe degree function can be described as:
Figure FDA0002365427070000011
wherein i is a current value, j is a phase current, imaxAt maximum current, a ═ imaxAnd/20 is the step size of the current membership function.
3. The method for accurately modeling the switched reluctance motor based on the small-sample flux linkage characteristic as claimed in claim 1 or 2, wherein the prior knowledge of the SRM is introduced into the membership function selection and fuzzy set division of the rotor position in the step 1) for supplement, and the flux linkage change curve along with the rotor position can be divided into three areas, namely an area I [ theta ]u,θ1) Region II [ theta ]1,θhr) And region III [ theta ]hr,θa]，θaThe complete alignment position of the salient poles of the stator and the rotor is as follows: calculating the alignment position theta of the leading edge of the rotor pole and the leading edge of the stator pole1And rotor pole centerline and stator pole leading edge alignment position θhrThe following formula is adopted:
Figure FDA0002365427070000012
in the formula, βsAnd βrThe pole arc widths of the stator and rotor, respectively, and satisfy the following relationship:
Figure FDA0002365427070000013
wherein, in the formula, m and NrRespectively representing the number of stator phases and the number of rotor poles of the motor, and m is 3, N is used for a three-phase 12/8-pole SRM modelrWhen formula (3) is substituted for formula (2), 8 can be obtained:
Figure FDA0002365427070000014
4. the accurate modeling method of the switched reluctance motor based on the small sample flux linkage characteristic according to claim 1 or 2, characterized in that the experimental data is combined to obtain that the flux linkage can be approximated to a linear relationship in the region II along with the change of the rotor position, approximated to a cosine relationship in the regions I and III, and the flux linkage-position characteristic is approximated to a linear relationship in the position interval [7.5 °,15 ° ] and divided by adopting a linear membership function; the interval [0 degrees, 7.5 degrees ] and [15 degrees, 22.5 degrees ] are approximately cosine characteristics, and are divided by cosine membership functions.
5. The accurate modeling method for the switched reluctance motor based on the small sample flux linkage characteristic as claimed in claim 1, wherein in step 1), in order to realize the accurate solution of the flux linkage characteristic, a more refined fuzzy partition is adopted for the fuzzy space partition of the flux linkage model input and output variables, which is specifically as follows: dividing flux linkage into 201 regions by using a triangular membership function, wherein the ith flux linkage fuzzy set ClThe membership function of (d) can be described as:
Figure FDA0002365427070000021
in the formula, #maxIs the maximum value of flux linkage, c ═ ψmaxAnd 200 is the step size of the flux linkage membership function.
6. The accurate modeling method for the switched reluctance motor based on the small sample flux linkage characteristic according to claim 1, characterized in that the following method is specifically adopted in the step 2): a method for designing a fuzzy inference system based on input and output data of the system is adopted, and fuzzy rules are automatically extracted from sample data.
CN202010034404.8A 2020-01-14 2020-01-14 Switched reluctance motor accurate modeling method based on small sample flux linkage characteristics Pending CN111082714A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010034404.8A CN111082714A (en) 2020-01-14 2020-01-14 Switched reluctance motor accurate modeling method based on small sample flux linkage characteristics

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010034404.8A CN111082714A (en) 2020-01-14 2020-01-14 Switched reluctance motor accurate modeling method based on small sample flux linkage characteristics

Publications (1)

Publication Number Publication Date
CN111082714A true CN111082714A (en) 2020-04-28

Family

ID=70323141

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010034404.8A Pending CN111082714A (en) 2020-01-14 2020-01-14 Switched reluctance motor accurate modeling method based on small sample flux linkage characteristics

Country Status (1)

Country Link
CN (1) CN111082714A (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2903154A1 (en) * 2014-02-03 2015-08-05 Samsung Electro-Mechanics Co., Ltd. Controlling apparatus for switched reluctance motor and controlling method thereof
CN109995276A (en) * 2019-05-06 2019-07-09 江苏雷利电机股份有限公司 Switched reluctance machines brake apparatus, reluctance motor and working method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2903154A1 (en) * 2014-02-03 2015-08-05 Samsung Electro-Mechanics Co., Ltd. Controlling apparatus for switched reluctance motor and controlling method thereof
CN109995276A (en) * 2019-05-06 2019-07-09 江苏雷利电机股份有限公司 Switched reluctance machines brake apparatus, reluctance motor and working method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
J. LIU ET AL.: "A New Modeling Method for Switched Reluctance Motor Based on the Fuzzy Logic System", 《2018 37TH CHINESE CONTROL CONFERENCE (CCC)》 *
李存贺: "开关磁阻电机非线性建模及先进控制策略研究", 《中国博士学位论文全文数据库工程科技Ⅱ辑》 *

Similar Documents

Publication Publication Date Title
CN109600006B (en) Solving method for electromagnetic design of surface-mounted permanent magnet motor
Li et al. Two-axis model development of cage-rotor brushless doubly-fed machines
CN106655951B (en) A kind of torque capacity current control method based on curve matching
CN113326673B (en) VBR model electromagnetic transient simulation method and system of synchronous motor
Chen et al. A new analytical model for switched reluctance motors
CN105610369A (en) Sliding-mode observer based flux linkage observation method of asynchronous motor
CN112380670A (en) Virtual rotor-based modeling method and system for sectional power supply linear induction motor
Chen et al. Fast steady-state analysis in time-stepping finite-element simulation of induction motors based on virtual blocked rotor techniques
CN110390157B (en) Doubly salient hybrid excitation generator optimization design method based on Taguchi method
CN108899930A (en) Wind-powered electricity generation station equivalent modeling method based on Principal Component Analysis Method and hierarchical clustering algorithm
CN109687792B (en) Online identification optimization method for traction motor rotor parameters facing vector control system
CN111082714A (en) Switched reluctance motor accurate modeling method based on small sample flux linkage characteristics
CN111478632B (en) Observer-free control method for improving parameter robustness of permanent magnet synchronous motor
Xie et al. The optimal speed-torque control of asynchronous motors for electric cars in the field-weakening region based on the RFR
CN113420386A (en) Vehicle driving motor robustness optimization design method based on interpolation model and multi-objective genetic algorithm
CN112904731A (en) Stability domain determination method for multi-time-lag distributed power information physical system
CN107359838A (en) A kind of ultrahigh speed permagnetic synchronous motor based on limited element analysis technique is without sensor rotation speed and location estimation method
Zhang et al. A multi-objective analytical design approach of switched reluctance machines with integrated active current profile optimization
US3500092A (en) Airgap for small asynchronous motors
CN113556069B (en) Terminal sliding mode flux linkage observation method of brushless doubly-fed motor
CN110365273B (en) Method for determining alternative voltage vector set by equally dividing flux linkage circle
CN103346710B (en) A kind of Over Electric Motor with PMSM direct Torque Control and method
CN112468032A (en) Full-speed domain efficiency MAP graph generation method of permanent magnet synchronous motor
CN113098358B (en) Switched reluctance motor position sensorless control method based on conducting phase real-time inductance positioning
CN112580209B (en) On-line torque estimation method of switched reluctance motor based on segmented analytical modeling

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200428