阅读说明：本技术 一种基于改进粒子群算法的异步电机参数辨识方法 (Asynchronous motor parameter identification method based on improved particle swarm optimization ) 是由 林梅金 汪震宇 王飞 于 2019-10-17 设计创作，主要内容包括：本发明提供了一种基于改进粒子群算法的异步电机参数辨识方法,包括以下步骤：1,获取异步电机的转速、转子磁链以及定子电流；2,通过改进的粒子群算法,实时获取电机转子时间常数和励磁电感；其中,改进的粒子群算法具体为：在给定的范围内随机生成个维度为的初始种群；通过追踪粒子个体的个体极值和粒子群体的群体极值更新粒子的位置信息；重新计算每一个粒子的适应度值,然后重新对粒子的个体极值和粒子群体的群体极值进行更新赋值；断迭代次数是否达到设置的最大迭代次数,如果达到最大迭代次数则终止运行,实现异步电机参数的辨识及追踪。本发明利用改进的简化粒子群算法,可以对异步电机参数进行稳定、快速而又精确的辨识追踪。(The invention provides an asynchronous motor parameter identification method based on an improved particle swarm algorithm, which comprises the following steps: 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor; 2, acquiring a time constant and an excitation inductance of a motor rotor in real time through an improved particle swarm algorithm; the improved particle swarm algorithm specifically comprises the following steps: randomly generating an initial population with dimensions of a given range; updating the position information of the particles by tracking individual extrema of the individual particles and group extrema of the particle group; recalculating the fitness value of each particle, and then carrying out update assignment on the individual extreme value of the particle and the group extreme value of the particle group; and judging whether the iteration interruption times reach the set maximum iteration times or not, and stopping running if the iteration interruption times reach the set maximum iteration times, so as to realize the identification and tracking of the parameters of the asynchronous motor. The invention can stably, quickly and accurately identify and track the parameters of the asynchronous motor by utilizing the improved simplified particle swarm algorithm.)

1. An asynchronous motor parameter identification method based on an improved particle swarm algorithm comprises the following steps:

step 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor;

step 2, acquiring a time constant and an excitation inductance of a motor rotor in real time through an improved particle swarm algorithm;

in step 2, the specific method for acquiring the time constant and the excitation inductance of the motor rotor in real time through the improved particle swarm optimization comprises the following steps:

2a, randomly generating NP initial populations x with the dimension D in a given [ xmax, xmin ] range;

2b, updating the position information of the particles by tracking individual extremum of the individual particles and group extremum of the particle group;

2c, recalculating the fitness value of each particle, and performing update assignment on the individual extreme value of the particle and the group extreme value of the particle group again according to the calculation result;

and 2d, judging whether the iteration frequency reaches the set maximum iteration frequency, if so, stopping the operation, and realizing the identification and tracking of the parameters of the asynchronous motor, otherwise, repeating the steps 2b to 2 d.

2. The method for identifying parameters of an asynchronous motor based on the improved particle swarm optimization as claimed in claim 1, wherein in step 2a, the generation equation of the initial population x is: x ═ rand (NP, D) × (x)_max-x_min)+x_min。

3. A process as claimed in claim 2In step 2b, the position information updating equation of the particles is as follows: x is the number of_ij(t+1)＝w*x_ij(t)+c₁r₁[p_ij(t)-x_ij(t)]+c₂r₂[p_gj(t)-x_ij(t)]And

Technical Field

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 optimization.

Background

Since the operating characteristics of asynchronous motors are complex rational functions with respect to slip, the methods currently used for identifying the parameters of asynchronous motors are mainly the following: generalized Kalman filtering, least squares, Genetic Algorithms (GA), and the like.

After a lot of searches, some typical prior arts are found, for example, patent application No. 201710163793.2 discloses an asynchronous motor parameter identification method based on an improved particle swarm optimization algorithm, the patent obtains the measurement values of various working characteristics of an asynchronous motor through measurement, the improved particle swarm optimization algorithm is applied to realize the static parameter identification of the asynchronous motor, and the asynchronous motor still has higher identification accuracy under the condition of noise. For another example, the patent with application number 201410539036.7 discloses an asynchronous motor parameter tracking method based on an improved particle swarm algorithm, and the particle swarm algorithm has the capability of detecting the change of a target function and tracking the change of parameters in real time, and can identify the key state information of two asynchronous motors. For another example, patent with application number 20131048889.8 discloses a method for identifying parameters of a generator speed regulating system, which maps the parameters of the speed regulating system to be identified into 'particles' of a particle swarm algorithm, thereby improving the accuracy of the parameters and improving the simulation calculation results to enable the parameters to accurately reflect the characteristics of a power grid.

It can be seen that, the particle group is used to identify the asynchronous motor parameters, and many practical problems to be dealt with (such as improving the identification precision of the motor parameters) in practical application still do not provide a specific solution.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides an asynchronous motor parameter identification method based on an improved particle swarm algorithm, which has the following specific technical scheme:

an asynchronous motor parameter identification method based on an improved particle swarm algorithm comprises the following steps:

step 1, acquiring the rotating speed, the rotor flux linkage and the stator current of an asynchronous motor;

step 2, acquiring a time constant and an excitation inductance of a motor rotor in real time through an improved particle swarm algorithm;

in step 2, the specific method for acquiring the time constant and the excitation inductance of the motor rotor in real time through the improved particle swarm optimization comprises the following steps:

2a, randomly generating NP initial populations x with the dimension D in a given [ xmax, xmin ] range;

2b, by tracking individual extrema p of individual particles_ijPopulation extremum p of sum particle population_gjUpdating the position information of the particles;

2c, recalculating the fitness value of each particle, and performing update assignment on the individual extreme value of the particle and the group extreme value of the particle group again according to the calculation result;

and 2d, judging whether the iteration frequency reaches the set maximum iteration frequency, if so, stopping the operation, and realizing the identification and tracking of the parameters of the asynchronous motor, otherwise, repeating the steps 2b to 2 d.

Optionally, in step 2a, the generation equation of the initial population x is: x ═ rand (NP, D) × (x)_max-x_min)+x_min。

Optionally, in step 2b, the position information update equation of the particle is: x is the number of_ij(t+1)＝w*x_ij(t)+c₁r₁[p_ij(t)-x_ij(t)]+c₂r₂[p_gj(t)-x_ij(t)]And

wherein, c_1sAnd c_2sAre respectively a learning factor c₁And c₂Initial setting value of c_1fAnd c_2fAre respectively a learning factor c₁And c₂Iter represents the current number of iterations, Iter_maxRepresenting the maximum number of iterations of the algorithm.

The beneficial effects obtained by the invention comprise:

1. the learning capacity of the particles is effectively improved by introducing the asynchronous learning factor on the basis of simplifying the particle swarm optimization and dynamically changing the value of the learning factor by using the asynchronous change strategy of the learning factor;

2. and optimizing parameters of the asynchronous motor by using an improved simplified particle swarm algorithm, realizing intelligent optimization of the parameters and finally identifying the electrical parameters of the asynchronous motor.

3. The improved simplified particle swarm algorithm can effectively improve the convergence speed and the optimization precision of the algorithm, is applied to the field of asynchronous motor parameter identification, and can stably, quickly and accurately identify and track asynchronous motor parameters.

Drawings

The present invention will be further understood from the following description taken in conjunction with the accompanying drawings, the emphasis instead being placed upon illustrating the principles of the embodiments.

Fig. 1 is a schematic flow chart of an asynchronous motor parameter identification method based on an improved particle swarm optimization in one embodiment of the present invention;

FIG. 2 is a graph illustrating the convergence process of three different particle swarm algorithms under the Tablet test function in one embodiment of the present invention;

fig. 3 is a graph illustrating the convergence process of three different particle swarm algorithms under the schafer test function in one embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to embodiments thereof; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Other systems, methods, and/or features of the present embodiments will become apparent to those skilled in the art upon review of the following detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims. Additional features of the disclosed embodiments are described in, and will be apparent from, the detailed description that follows.

The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not intended to indicate or imply that the device or component referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present patent, and the specific meaning of the terms described above will be understood by those of ordinary skill in the art according to the specific circumstances.

The invention relates to an asynchronous motor parameter identification method based on an improved particle swarm algorithm, which is based on the following embodiments shown in figures 1-2: