CN110086395A - A kind of permanent magnet synchronous motor parameter identification method - Google Patents
A kind of permanent magnet synchronous motor parameter identification method Download PDFInfo
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- CN110086395A CN110086395A CN201910378026.2A CN201910378026A CN110086395A CN 110086395 A CN110086395 A CN 110086395A CN 201910378026 A CN201910378026 A CN 201910378026A CN 110086395 A CN110086395 A CN 110086395A
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- permanent magnet
- synchronous motor
- magnet synchronous
- parameter identification
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
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- Power Engineering (AREA)
- Control Of Motors That Do Not Use Commutators (AREA)
- Control Of Ac Motors In General (AREA)
Abstract
The present invention discloses a kind of parameter of electric machine discrimination method, belongs to parameter identification field, and in particular to a kind of permanent magnet synchronous motor parameter identification method.A kind of permanent magnet synchronous motor parameter identification method, including building permanent magnet synchronous motor state equation, by permanent magnet synchronous motor state equation discretization, noise vector will be introduced and measure noise vector, the Adaptable System of parameter identification is analyzed, constructed to prediction correction, wavelet analysis method.The present invention combines least square method supporting vector machine with wavelet analysis, Kalman filtering, by the training of least square method supporting vector machine, obtains reliable permanent magnet synchronous motor parameter, the accuracy of the parameter identification improved.
Description
Technical field
The present invention relates to parameter identification field more particularly to a kind of permanent magnet synchronous motor parameter identification methods.
Background technique
Permasyn morot (PMSM) has many advantages, such as that specific power is high, energy-efficient, control is accurate, obtains in every field
It must be more and more widely used.The high performance control method of PMSM mainly has vector controlled and Direct Torque Control etc..Wherein, it is
Senseless control system at low cost, environmental suitability is strong of uniting becomes research hotspot.
Traditional permanent magnet synchronous motor parameter identification needs based on accurate mathematical model, is divided into defeated based on inputting
Out and signal processing method, based on state estimation or the procedure parameter estimation technique and statistic decision method appropriate.Conventional motors parameter
Although discrimination method can understand the essential dynamic property of electric system in depth, realize real-time diagnosis, however need with establish compared with
Premised on accurate mathematical model, especially when electric system has uncertain or nonlinear characteristic, traditional parameters identification side
Method is more difficult to handle.
Summary of the invention
The purpose of the invention patent is to solve in the prior art when electric system has uncertain or nonlinear characteristic
When the parameter of electric machine be difficult to the problem recognized or accuracy is not high.
To achieve the goals above, present invention employs following technical solutions:
A kind of permanent magnet synchronous motor parameter identification method, comprising the following steps:
Step 1: building permanent magnet synchronous motor state equation；
Step 2: the permanent magnet synchronous motor state equation according to constructed by step 1, by permanent magnet synchronous motor state equation
Discretization will introduce noise vector ω and measurement noise vector ν, such as following formula:
In formula, xk+1For the state value at k+1 moment, Ak、BkAnd CkFor constant；
Step 3: step 2 is predicted, predicts process by the Optimal Temperature value at k-1 momentGo the prediction k moment
The state value of system, as follows:
In formula, A is to update sytem matrix, and B is input matrix, and Q is process noise；
Step 4: step 3 is corrected, and correction course is as follows:
In formula, KkFor the kalman gain at k moment, P is filtering mean square error；
Step 5: the function after step 4 is corrected is analyzed with wavelet analysis method, and by after analysis function with
Least square method supporting vector machine is bonded hybrid permanent magnet parameter of synchronous machine；
Step 6: the hybrid permanent magnet parameter of synchronous machine in step 5 is compared with other models, building parameter identification
Adaptable System carry out permanent magnet synchronous motor parameter identification.
Further, permanent magnet synchronous motor state equation described in step 1 are as follows:
Wherein, ω is rotor machinery angular speed, and J is that load is converted to total rotary inertia on motor shaft, TLFor equivalent negative
Set torque, TeFor motor electromagnetic torque, B is viscous damping coefficient.
Further, the wavelet analysis extracts characteristic variable, then by decomposing to permanent magnet synchronous motor parameter
It inputs in least square method supporting vector machine and is trained again.
Compared with prior art, the present invention has the advantages that: permanent magnet synchronous motor parameter identification disclosed in this invention
Least square method supporting vector machine is combined with wavelet analysis, Kalman filtering, passes through least square method supporting vector machine by method
Training, obtain reliable permanent magnet synchronous motor parameter, the accuracy of the parameter identification improved compared with prior art, for forever
The accuracy of magnetic-synchro parameter of electric machine identification is laid a good foundation.
Specific embodiment
The following is a clear and complete description of the technical scheme in the embodiments of the invention, it is clear that described embodiment
Only a part of the embodiment of the present invention, instead of all the embodiments.
A kind of permanent magnet synchronous motor parameter identification method, comprising the following steps:
Step 1: building permanent magnet synchronous motor state equation；
Permanent magnet synchronous motor state equation described in step 1 are as follows:
Wherein, ω is rotor machinery angular speed, and J is that load is converted to total rotary inertia on motor shaft, TLFor equivalent negative
Set torque, TeFor motor electromagnetic torque, B is viscous damping coefficient.Step 2: the permanent magnet synchronous motor according to constructed by step 1
Permanent magnet synchronous motor state equation discretization will be introduced noise vector ω and measurement noise vector ν, such as following formula by state equation:
In formula, xk+1For the state value at k+1 moment, Ak、BkAnd CkFor constant；
Step 3: step 2 is predicted, predicts process by the Optimal Temperature value at k-1 momentGo the prediction k moment
The state value of system, such as following formula:
In formula, A is to update sytem matrix, and B is input matrix, and Q is process noise；
Step 4: step 3 is corrected, correction course such as following formula:
In formula, KkFor the kalman gain at k moment, P is filtering mean square error；
Step 5: the function after step 4 is corrected is analyzed with wavelet analysis method, and by after analysis function with
Least square method supporting vector machine is bonded hybrid permanent magnet parameter of synchronous machine；
The wavelet analysis extracts characteristic variable, then inputs again most by decomposing to permanent magnet synchronous motor parameter
Small two multiply in support vector machines and are trained.
Step 6: the hybrid permanent magnet parameter of synchronous machine in step 5 is compared with other models, building parameter identification
Adaptable System carry out permanent magnet synchronous motor parameter identification.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (3)
1. a kind of permanent magnet synchronous motor parameter identification method, it is characterised in that: the following steps are included:
Step 1: building permanent magnet synchronous motor state equation；
Step 2: the permanent magnet synchronous motor state equation according to constructed by step 1, permanent magnet synchronous motor state equation is discrete
Change, noise vector ω and measurement noise vector ν will be introduced, such as following formula:
In formula, xk+1For the state value at k+1 moment, Ak、BkAnd CkFor constant；
Step 3: step 2 is predicted, predicts process by the Optimal Temperature value at k-1 momentRemove to predict etching system when k
State value, as follows:
In formula, A is to update sytem matrix, and B is input matrix, and Q is process noise；
Step 4: step 3 is corrected, and correction course is as follows:
In formula, KkFor the kalman gain at k moment, P is filtering mean square error；
Step 5: the function after step 4 is corrected is analyzed with wavelet analysis method, and by function after analysis and minimum
Two, which multiply support vector machines, is bonded hybrid permanent magnet parameter of synchronous machine；
Step 6: the hybrid permanent magnet parameter of synchronous machine in step 5 is compared with other models, constructs parameter identification oneself
Adaptation system carries out permanent magnet synchronous motor parameter identification.
2. a kind of permanent magnet synchronous motor parameter identification method according to claim 1, it is characterised in that: described in step 1 forever
Magnetic-synchro motor status equation are as follows:
Wherein, ω is rotor machinery angular speed, and J is that load is converted to total rotary inertia on motor shaft, TLFor equivalent negative idling
Square, TeFor motor electromagnetic torque, B is viscous damping coefficient.
3. a kind of permanent magnet synchronous motor parameter identification method according to claim 1, it is characterised in that: the wavelet analysis
By decomposing to permanent magnet synchronous motor parameter, extract characteristic variable, then input again in least square method supporting vector machine into
Row training.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113659906A (en) * | 2021-08-31 | 2021-11-16 | 北京信息科技大学 | Online identification method for unknown motor parameters |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103414416A (en) * | 2013-07-11 | 2013-11-27 | 中国大唐集团科学技术研究院有限公司 | Permanent magnet synchronous motor sensorless vector control system based on EKF |
| CN103338003B (en) * | 2013-06-28 | 2015-08-26 | 西安交通大学 | A kind of method of electric motor load torque and inertia on-line identification simultaneously |
| CN109713971A (en) * | 2019-03-01 | 2019-05-03 | 北京理工大学 | A kind of Disturbance Rejection method of permanent magnet synchronous motor |
-
2019
- 2019-05-08 CN CN201910378026.2A patent/CN110086395A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103338003B (en) * | 2013-06-28 | 2015-08-26 | 西安交通大学 | A kind of method of electric motor load torque and inertia on-line identification simultaneously |
| CN103414416A (en) * | 2013-07-11 | 2013-11-27 | 中国大唐集团科学技术研究院有限公司 | Permanent magnet synchronous motor sensorless vector control system based on EKF |
| CN109713971A (en) * | 2019-03-01 | 2019-05-03 | 北京理工大学 | A kind of Disturbance Rejection method of permanent magnet synchronous motor |
Non-Patent Citations (1)
| Title |
|---|
| 王军: "模糊小波网络及其在永磁同步电机控制中的应用", 《中国博士学位论文全文数据库（电子期刊）•工程科技Ⅱ辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113659906A (en) * | 2021-08-31 | 2021-11-16 | 北京信息科技大学 | Online identification method for unknown motor parameters |
| CN113659906B (en) * | 2021-08-31 | 2023-07-07 | 北京信息科技大学 | Online identification method for unknown motor parameters |
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