CN-116094395-B - Method for constructing full-speed range no-speed sensor of bearingless permanent magnet synchronous motor

CN116094395BCN 116094395 BCN116094395 BCN 116094395BCN-116094395-B

Abstract

The invention discloses a method for constructing a full-speed range no-speed sensor of a bearingless permanent magnet synchronous motor, which comprises the steps of connecting a high-frequency signal injection method module and an AGA-EKF detection method module in parallel and then connecting the high-frequency signal injection method module and the AGA-EKF detection method module in series at the front end of a rotating speed switching algorithm module to form a composite method rotating speed detection module, connecting the composite method rotating speed detection module in series into a control system of the bearingless permanent magnet synchronous motor, injecting high-frequency voltage signals, detecting the rotating speed of the motor under the conditions of zero speed and low speed by using a pulse array high-frequency signal injection method, detecting the rotating speed information by using an expansion Kalman filtering method optimized by a self-adaptive genetic algorithm under the conditions of medium speed and high speed, and completing the speed detection of the motor running in the zero speed, low speed and high speed full speed range and the stable suspension running of the motor.

Inventors

ZHU HUANGQIU
DU JIANKUN
YAN YUTONG
LIU YICHEN
HUA YIZHOU

Assignees

江苏大学

Dates

Publication Date: 20260512
Application Date: 20230220

Claims (6)

1. The construction method of the full-speed range no-speed sensor of the bearingless permanent magnet synchronous motor is characterized by comprising the following steps: step 1) constructing a high-frequency signal injection method module Current in two-phase stationary coordinate system alpha-beta Obtaining the current under the rotation coordinate system d-q through Park inverse transformation Current flow Then the high-frequency current containing the rotating speed information is obtained through BPF filtering Then high-frequency current is conducted And modulating the current signal After multiplication, passing through LPF to obtain error function Adjusting said error function with a PI module The angle estimation value under the working condition of zero low speed is obtained by setting the angle estimation value to 0 And angle estimation value Differential obtaining rotation speed estimated value ; Step 2) constructing AGA-EKF detection method module Selecting state variables And output variable Taking covariance matrix And Selecting a covariance matrix Q, R by an AGA method to obtain an optimal Q, R value; Respectively is Is used for the error of the variation of (a), Respectively is Is a variation error of (2); the actual rotating speed and the actual angle of the rotor under the medium-high speed working condition are respectively, and T is matrix transposition; The AGA method selects covariance matrix Q, R by taking Initializing a population, determining a value range and population precision, binary encoding initial population parameters to obtain a first chromosome, performing cross operation on the first chromosome to obtain a second chromosome, performing mutation operation on the second chromosome to obtain a third chromosome, decoding and proper value calculation on the first chromosome, the second chromosome and the third chromosome, outputting the result to a roulette selection model to obtain a new chromosome set, judging termination conditions, continuously calculating the fitness value of the population if the conditions are not met, adaptively changing the values of the cross probability and the mutation probability through an adaptive algorithm according to the fitness value, and updating the chromosomes, and obtaining the Q and R optimal solutions if the conditions are met; based on the optimal Q, R value, obtaining the rotating speed estimated value of the rotor under the medium-high speed working condition through an extended Kalman algorithm And angle estimation value ; The extended Kalman algorithm comprises state prediction estimation, state estimation value correction, covariance estimation, error covariance matrix update and Kalman gain calculation, and the rotation speed estimation value is obtained by continuous update cycle And angle estimation value ; The state prediction estimation is as follows: the state estimate correction is: The covariance estimate is: , The error covariance matrix update is: the kalman gain calculation is: K/k-1 represents a state transition from time k-1 to time k, and subscripts k and k-1 both represent the time; is an estimate of the state variable; The state quantity at the corresponding moment respectively, the nonlinear function f is the relation between the k-1 order state and the k order state, Represents the system state quantity of B at time k-1, , Is the inductance of the stator winding, K k is a gain matrix, H k is a K moment transmission matrix; For state transition time transfer matrix, P k , 、 The error covariance matrix at the corresponding time, F k-1 is the gradient matrix at time k-1, The system state quantity at time k-1; the system output variable at the moment k; is the control variable of the system at the moment k-1; Step 3) constructing a rotating speed switching algorithm module Determining a rotation speed switching weighting coefficient, and calculating a rotor speed detection value according to the weighting coefficient And angle detection value ; Step 4), the high-frequency signal injection method module and the AGA-EKF detection method module are connected in parallel and then are connected in series at the front end of the rotation speed switching algorithm module to form a composite method rotation speed detection module, And step 5), the rotating speed detection module of the composite method is connected in series with a control system of the bearingless permanent magnet synchronous motor, and high-frequency voltage signals are injected to realize the control of the bearingless sensor.
2. The method for constructing a speed-free sensor for a speed range of a bearingless permanent magnet synchronous motor according to claim 1, wherein said high-frequency current Average inductance Half difference inductance , For the high frequency inductance component of the torque winding on the d-q axis, For the signal amplitude value, For the frequency of the injected high-frequency voltage signal, For time, estimate angle error , Is the actual angle.
3. The method for constructing a speed-free sensor for a full speed range of a bearingless permanent magnet synchronous motor according to claim 2, wherein said error function , 。
4. The method for constructing a speed-free sensor for a full speed range of a bearingless permanent magnet synchronous motor according to claim 1, wherein said variable crossover probability Probability of variation F max is the maximum fitness value of the population, f avg is the average fitness value of the population, f , is the larger fitness value of the individuals to be crossed, b 1 、b 3 is the cross probability parameter, b 2 、b 4 is the variation probability parameter, the parameters are selected according to the optimizing process, and the optimal chromosome meeting the convergence condition of the whole population can be obtained through repeated selection, crossing and variation processes, namely the Q and R noise matrixes meeting the optimal filtering condition are obtained.
5. The method for constructing a speed-free sensor for a full speed range of a bearingless permanent magnet synchronous motor according to claim 1, wherein said weighting coefficients are 。
6. The method for constructing a speed-free sensor for a full speed range of a bearingless permanent magnet synchronous motor of claim 5, wherein the method comprises the steps of And Calculating a rotor speed detection value And angle detection value 。

Description

Method for constructing full-speed range no-speed sensor of bearingless permanent magnet synchronous motor Technical Field The invention belongs to the field of control of bearingless permanent magnet synchronous motors, and particularly relates to a method for constructing a bearingless permanent magnet synchronous motor without a speed sensor. Background The bearingless permanent magnet synchronous motor is a new type of motor with both permanent magnet synchronous motor technology and magnetic bearing technology, a torque winding and a levitation force winding with the phase difference of pole pairs are embedded in a stator slot, and the stable levitation operation of the motor is realized by controlling the current of the torque winding and the current of the levitation winding. The bearingless permanent magnet synchronous motor not only has the excellent characteristics of high power density and high efficiency of the traditional permanent magnet synchronous motor, but also has the advantages of no mechanical abrasion, no lubrication, small noise, long service life and the like of a magnetic bearing, and has wide application prospects in the fields of aerospace, famous medicine, flywheel energy storage and the like. The accurate observation of the displacement and the angular position of the rotor of the bearingless permanent magnet synchronous motor is a basic condition for realizing the stable suspension operation of the rotor of the bearingless permanent magnet synchronous motor. Typically, rotor speed is observed by means of mechanical sensors such as eddy current sensors, photoelectric encoders, etc. However, the mechanical sensor has a plurality of disadvantages, for example, the measurement accuracy of the mechanical sensor is greatly affected by environmental factors, and the use of the mechanical sensor can make the bearingless permanent magnet synchronous motor system more complex and have higher cost, and meanwhile, the bearingless permanent magnet synchronous motor is unfavorable for developing in the directions of miniaturization, high speed, high precision and the like, so that the bearingless sensor technology is favorable for improving the reliability and stability of the bearingless permanent magnet synchronous motor system and reducing the cost thereof. The method for controlling the rotating speed of the sensorless ultra-high-speed permanent magnet synchronous motor based on EKF (extended Kalman) is adopted in the Chinese patent publication No. CN 106130426A to control the rotating speed of the permanent magnet synchronous motor, but the values of the covariance matrix Q, R of system noise and measurement noise are determined through experience and simulation tests, certain errors exist, the optimization cannot be achieved, the performance of the system is greatly influenced, and the method is only suitable for the rotating speed control under the medium-high speed condition. The Chinese patent publication No. CN 106020803A, named "a speed-sensor-free control method based on a sliding-mode observer" adopts a method for constructing the sliding-mode observer to control the rotating speed of the permanent magnet synchronous motor, but the method is compared with a mathematical model which depends on fundamental wave excitation of the motor, and the rotating speed detection can not be carried out under the condition of zero speed or low speed. Disclosure of Invention The invention aims to solve the defects of the existing speed-free sensor control of the bearingless permanent magnet synchronous motor, and provides a speed-free sensor construction method in a full speed range, which can accurately and rapidly detect rotor position and speed information in the full speed range of the constructed speed-free sensor, realize stable suspension operation of the motor under the condition of no speed sensor and improve the reliability and stability of a bearingless permanent magnet synchronous motor control system. In order to achieve the above purpose, the technical scheme adopted by the invention comprises the following steps: step 1) constructing a high-frequency signal injection method module The current i α、iβ under the two-phase static coordinate system alpha-beta is subjected to Park inverse transformation to obtain the current i d、iq under the rotating coordinate system d-q, and the current i d、iq is subjected to BPF filtering to obtain the high-frequency current containing the rotating speed informationAnd then high-frequency current is conductedMultiplying the modulated current signal sin omega h t, obtaining an error function f (delta theta) through an LPF, and regulating the error function f (delta theta) to be 0 through a PI module to obtain an angle estimated value under the zero low-speed working conditionAnd angle estimation valueDifferential obtaining rotation speed estimated value Step 2) constructing AGA-EKF detection method module Selecting a state variable x= [ i α、iβ、ω2、θ2]