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Asynchronous motor parameter identification method based on improved particle swarm optimization algorithm

An improved particle swarm and particle swarm algorithm technology, applied in the field of motor parameter identification, can solve problems such as no specific solutions have been proposed, and achieve the effects of improving learning ability, accurate identification and tracking, increasing convergence speed and optimization accuracy

Inactive Publication Date: 2020-01-24
FOSHAN UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] It can be seen that using particle swarms to identify asynchronous motor parameters, there are still many practical problems that need to be dealt with in practical applications (such as improving the accuracy of motor parameter identification, etc.) and there are still many specific solutions that have not been proposed.

Method used

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  • Asynchronous motor parameter identification method based on improved particle swarm optimization algorithm
  • Asynchronous motor parameter identification method based on improved particle swarm optimization algorithm
  • Asynchronous motor parameter identification method based on improved particle swarm optimization algorithm

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Embodiment 1

[0028] Particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm proposed by scientists Kennedy and Eberhart to simulate the foraging behavior of birds. The PSO algorithm regards the bird flock as a particle group, and each individual particle has its own speed and position information. The PSO algorithm first initializes the particle swarm randomly, and then in each iteration, the particles update their speed and position information by continuously tracking the individual extremum and group extremum until they find their own optimal solution. The structure of the PSO algorithm is relatively simple, with few parameters and relatively strong search ability. It has been widely used in numerical calculation, machine learning, pattern recognition and other fields.

[0029] Although the search ability of the PSO algorithm is strong, there are still some defects. In the late iteration of the PSO algorithm, the convergence speed of the algorithm tends to slow...

Embodiment 2

[0042] A method for identifying parameters of an asynchronous motor based on an improved particle swarm algorithm, comprising the following steps:

[0043] Step 1, obtain the rotational speed, rotor flux linkage and stator current of the asynchronous motor;

[0044] Step 2, through the improved particle swarm optimization algorithm, obtain the rotor time constant and excitation inductance of the motor in real time;

[0045] Among them, in step 2, through the improved particle swarm optimization algorithm, the specific method to obtain the motor rotor time constant and excitation inductance in real time is as follows:

[0046] 2a, Randomly generate NP initial population x with dimension D within a given range of [xmax,xmin];

[0047] 2b, by tracking the individual extremum p of individual particles ij and the population extremum p of the particle population gj Update the position information of the particle;

[0048] 2c, recalculate the fitness value of each particle, and r...

Embodiment 3

[0057] A method for identifying parameters of an asynchronous motor based on an improved particle swarm algorithm, comprising the following steps:

[0058] Step 1, obtain the rotational speed, rotor flux linkage and stator current of the asynchronous motor;

[0059] Step 2, through the improved particle swarm optimization algorithm, obtain the rotor time constant and excitation inductance of the motor in real time;

[0060] Among them, in step 2, through the improved particle swarm optimization algorithm, the specific method to obtain the motor rotor time constant and excitation inductance in real time is as follows:

[0061] 2a, Randomly generate NP initial population x with dimension D within a given range of [xmax,xmin];

[0062] 2b, by tracking the individual extremum p of individual particles ij and the population extremum p of the particle population gj Update the position information of the particle;

[0063] 2c, recalculate the fitness value of each particle, and r...

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Abstract

The invention provides an asynchronous motor parameter identification method based on an improved particle swarm optimization algorithm. The method comprises the following steps: 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor; 2, obtaining the time constant and the excitation inductance of the motor rotor in real time through the improved particle swarm optimization algorithm, wherein the improved particle swarm optimization algorithm specifically includes the steps: randomly generating initial populations with dimensions within a givenrange; updating the position information of particles by tracking the individual extremum of particle individuals and the population extremum of particle populations; recalculating the fitness value of each particle and then updating and assigning the individual extreme value of the particle and the population extreme value of the particle population again; and judging whether the number of iterations reaches the set maximum number of iterations, and terminating the operation if the maximum number of iterations is reached so as to realize identification and tracking of the parameters of the asynchronous motor. The improved simplified particle swarm optimization algorithm can be utilized to stably, quickly and accurately identify and track the parameters of the asynchronous motor.

Description

technical field [0001] The invention relates to the technical field of motor parameter identification, in particular to an asynchronous motor parameter identification method based on an improved particle swarm algorithm. Background technique [0002] Since the slip function of the asynchronous motor's operating characteristics is a very complex rational function, the current identification methods for asynchronous motor parameters mainly include the following: generalized Kalman filter, least square method, genetic algorithm (GA), etc. . [0003] After a large number of searches, some typical existing technologies were found. For example, the patent application number 201710163793.2 discloses an asynchronous motor parameter identification method based on an improved particle swarm optimization algorithm. The patent obtains the measured values ​​of various operating characteristics of the asynchronous motor through measurement. The improved particle swarm optimization algori...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02P21/14
CPCH02P21/0017H02P21/14H02P2207/01
Inventor 林梅金汪震宇王飞
Owner FOSHAN UNIVERSITY
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