Permanent magnet synchronous motor parameter online identification method based on NLMS algorithm

A technology of permanent magnet synchronous motor and identification method, which is applied in the direction of control of generator, motor generator control, electronic commutation motor control, etc., and can solve the problem that it is difficult to ensure the control performance of the motor

Inactive Publication Date: 2020-06-19
SOUTH CHINA UNIV OF TECH
View PDF4 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

During the operation of the motor, the stator resistance, stator inductance, rotor flux amplitude and other parameters of the permanent magnet synchronous motor will change with the temperature, load and magnetic saturation. It is difficult to guarantee the control performance of the motor if it is called parameter design controller

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Permanent magnet synchronous motor parameter online identification method based on NLMS algorithm
  • Permanent magnet synchronous motor parameter online identification method based on NLMS algorithm
  • Permanent magnet synchronous motor parameter online identification method based on NLMS algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0057] The present invention will be further described below in conjunction with specific examples.

[0058] The online identification method of permanent magnet synchronous motor parameters based on NLMS algorithm provided in this embodiment is a motor vector control system based on three-phase inverter control, using Adaline neural network plus NLMS algorithm to online identify the parameters of the motor, specifically including The following steps:

[0059] 1) Build an Adaline neural network identification system based on the NLMS algorithm

[0060] The Adaline neural network identification system is also called an adaptive linear neural network identification system, and its network structure is as follows figure 1 As shown, the relationship between its input and output is as follows:

[0061] y=WX=∑W i x i (16)

[0062] Among them, X, y, W are the input, output and weight of the adaptive linear neural network identification system respectively, and W i 、X i are th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a permanent magnet synchronous motor parameter online identification method based on an NLMS algorithm. The method comprises the steps: 1) constructing an Adaline neural network identification system, and updating the weight of the Adaline neural network identification system through the NLMS algorithm; 2) constructing a discrete domain mathematical model of a permanent magnet synchronous motor control system by considering inverter nonlinear factors, and simplifying the discrete domain mathematical model in combination with an identification principle of the Adaline neural network identification system to obtain identification equations respectively used for the iterative computation of motor stator resistance, inductance and rotor flux linkage; and 3) calculatingaccording to the identification equations of the motor stator resistance, the inductance and the rotor flux linkage to obtain each vector of the Adaline neural network identification system, and constructing a parameter identifier based on the NLMS algorithm for identifying the values of the motor stator resistance, the inductance and the rotor flux linkage. According to the method, the nonlinearfactors of the inverter are considered, an adaptive neural network and a normalized least mean square algorithm are combined, and the parameters of a permanent magnet synchronous motor can be effectively identified.

Description

technical field [0001] The invention relates to the technical field of motor control, in particular to an online identification method for parameters of a permanent magnet synchronous motor based on an NLMS algorithm. Background technique [0002] Permanent magnet synchronous motor (PMSM) has the advantages of high specific power, energy saving and high efficiency, and precise control, and has been widely used in various fields. The high-performance control methods of PMSM mainly include vector control and direct torque control. In the control system of permanent magnet synchronous motor, the parameters of the controller often need motor parameters to assist in the design (such as speed sensorless control, vector control optimal controller parameter design, etc.), so the control performance depends to a certain extent depends on the accuracy of the motor parameters. During the operation of the motor, the stator resistance, stator inductance, rotor flux amplitude and other ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): H02P6/34H02P21/00H02P21/14
CPCH02P21/0014H02P21/14H02P6/34
Inventor 游林儒梁百泉文小琴
Owner SOUTH CHINA UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products